From fba0d480f124aea90a60f338067e3f288927e6cb Mon Sep 17 00:00:00 2001 From: Kelly Ho Date: Sun, 26 Aug 2018 22:18:47 -0400 Subject: [PATCH 1/8] update analysis --- Kelly/Kaggle_house_price/EDA.ipynb | 215 +- ...rest Models (one hot yr_month sold).ipynb | 337 ++ .../Forest Models (no ordinal encode).ipynb | 337 ++ Kelly/Kaggle_house_price/Forest Models.ipynb | 234 +- ...ood feature engineering (regression).ipynb | 678 +++ ...larization Model (no ordinal encode).ipynb | 5226 +++++++++++++++++ ...zation Model (one hot yr_month sold).ipynb | 5210 ++++++++++++++++ .../Regularization Model.ipynb | 5146 +++++++++++++++- Kelly/Kaggle_house_price/Untitled.ipynb | 6 + .../__pycache__/preprocess.cpython-36.pyc | Bin 0 -> 3938 bytes .../__pycache__/preprocess1.cpython-36.pyc | Bin 0 -> 3856 bytes .../preprocess1copy.cpython-36.pyc | Bin 0 -> 3860 bytes .../preprocess2_label.cpython-36.pyc | Bin 0 -> 3479 bytes .../preprocess3_yrmonthsold.cpython-36.pyc | Bin 0 -> 3916 bytes .../preprocess_final.cpython-36.pyc | Bin 0 -> 4603 bytes .../preprocessfinal.cpython-36.pyc | Bin 0 -> 4606 bytes .../preprocessfinal1.cpython-36.pyc | Bin 0 -> 4603 bytes .../housing price data manipulation.ipynb | 2 +- Kelly/Kaggle_house_price/preprocess1.py | 214 + Kelly/Kaggle_house_price/preprocess2_label.py | 214 + .../preprocess3_yrmonthsold.py | 216 + Kelly/Kaggle_house_price/preprocessfinal.py | 241 + 22 files changed, 18066 insertions(+), 210 deletions(-) create mode 100644 Kelly/Kaggle_house_price/Forest Models (one hot yr_month sold).ipynb create mode 100644 Kelly/Kaggle_house_price/Forest Models (no ordinal encode).ipynb create mode 100644 Kelly/Kaggle_house_price/Neighborhood feature engineering (regression).ipynb create mode 100644 Kelly/Kaggle_house_price/Regularization Model (no ordinal encode).ipynb create mode 100644 Kelly/Kaggle_house_price/Regularization Model (one hot yr_month sold).ipynb create mode 100644 Kelly/Kaggle_house_price/Untitled.ipynb create mode 100644 Kelly/Kaggle_house_price/__pycache__/preprocess.cpython-36.pyc create mode 100644 Kelly/Kaggle_house_price/__pycache__/preprocess1.cpython-36.pyc create mode 100644 Kelly/Kaggle_house_price/__pycache__/preprocess1copy.cpython-36.pyc create mode 100644 Kelly/Kaggle_house_price/__pycache__/preprocess2_label.cpython-36.pyc create mode 100644 Kelly/Kaggle_house_price/__pycache__/preprocess3_yrmonthsold.cpython-36.pyc create mode 100644 Kelly/Kaggle_house_price/__pycache__/preprocess_final.cpython-36.pyc create mode 100644 Kelly/Kaggle_house_price/__pycache__/preprocessfinal.cpython-36.pyc create mode 100644 Kelly/Kaggle_house_price/__pycache__/preprocessfinal1.cpython-36.pyc create mode 100644 Kelly/Kaggle_house_price/preprocess1.py create mode 100644 Kelly/Kaggle_house_price/preprocess2_label.py create mode 100644 Kelly/Kaggle_house_price/preprocess3_yrmonthsold.py create mode 100644 Kelly/Kaggle_house_price/preprocessfinal.py diff --git a/Kelly/Kaggle_house_price/EDA.ipynb b/Kelly/Kaggle_house_price/EDA.ipynb index 3eaab74..d010328 100644 --- a/Kelly/Kaggle_house_price/EDA.ipynb +++ b/Kelly/Kaggle_house_price/EDA.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 64, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 299, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -26,16 +26,54 @@ }, { "cell_type": "code", - "execution_count": 182, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { + "image/png": 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\n", "text/plain": [ - "" + "" ] }, - "execution_count": 182, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "Index(['MeadowV', 'IDOTRR', 'BrDale', 'OldTown', 'Edwards', 'BrkSide',\n", + " 'Sawyer', 'Blueste', 'SWISU', 'NAmes', 'NPkVill', 'Mitchel', 'SawyerW',\n", + " 'Gilbert', 'NWAmes', 'Blmngtn', 'CollgCr', 'ClearCr', 'Crawfor',\n", + " 'Veenker', 'Somerst', 'Timber', 'StoneBr', 'NoRidge', 'NridgHt'],\n", + " dtype='object', name='Neighborhood')" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "orderIdx = train.groupby(\"Neighborhood\").agg({\"SalePrice\":\"median\"}).sort_values(\"SalePrice\").index\n", + "sns.boxplot( x=train[\"Neighborhood\"], y=train[\"SalePrice\"], order=orderIdx ) \n", + "plt.xticks(rotation=90)\n", + "plt.show()\n", + "orderIdx" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, @@ -43,7 +81,7 @@ "data": { "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -56,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 183, + "execution_count": 26, "metadata": { "scrolled": false }, @@ -64,18 +102,21 @@ { "data": { "text/plain": [ - "" + "(array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n", + " 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n", + " 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n", + " 51, 52, 53, 54]), )" ] }, - "execution_count": 183, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -84,12 +125,15 @@ ], "source": [ "#sns.lmplot(\"MoSold\", \"SalePrice\", train, fit_reg=False)\n", - "sns.boxplot(x=\"MoSold\", y=\"SalePrice\", hue=\"Neighborhood\", data = train)\n" + "sns.boxplot(x=\"YrMo\", y=\"SalePrice\", data = train)\n", + "plt.ylim(100000, 400000)\n", + "plt.xticks(rotation=90)\n", + "# hue=\"Neighborhood\"" ] }, { "cell_type": "code", - "execution_count": 241, + "execution_count": 5, "metadata": { "scrolled": false }, @@ -289,15 +333,15 @@ "[5 rows x 82 columns]" ] }, - "execution_count": 241, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -318,16 +362,16 @@ }, { "cell_type": "code", - "execution_count": 185, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 185, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, @@ -335,7 +379,7 @@ "data": { "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -350,7 +394,7 @@ }, { "cell_type": "code", - "execution_count": 186, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -361,7 +405,7 @@ " 34, 35]), )" ] }, - "execution_count": 186, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, @@ -369,7 +413,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -385,7 +429,7 @@ }, { "cell_type": "code", - "execution_count": 187, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -396,7 +440,7 @@ " )" ] }, - "execution_count": 187, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, @@ -404,7 +448,7 @@ "data": { "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -420,7 +464,7 @@ }, { "cell_type": "code", - "execution_count": 188, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -430,7 +474,7 @@ " )" ] }, - "execution_count": 188, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, @@ -438,7 +482,7 @@ "data": { "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -454,7 +498,7 @@ }, { "cell_type": "code", - "execution_count": 242, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -464,7 +508,7 @@ " )" ] }, - "execution_count": 242, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, @@ -472,7 +516,7 @@ "data": { "image/png": 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3zG1ZkiRJkiSN3rRNcpJfq6qPJvn9SeMAVNX75rA2SZIkSZJGaqYjyY9v358414VIkiRJktS3aZvkqvqLJHsA36+q94+oJkmSJEmSejHjZ5Kr6qE2s7VNsiSN0NjYGJs2bZr1dlu2bAEGk7fM1ooVK1izZs2st5MkSVoodnR2639uM1ufD9w3PlhVV81JVZKknXb//ff3XYIkSdJua0eb5Be27++YMFbAi3ZtOZKkcTt7RHft2rUArFu3bleWI0mStCjs6CWgjpnrQiRJkiRJ6ttjpluY5AVJvprk3iRfSPLMURUmSZIkSdKoTdskA38OrAX2A94H/PGcVyRJkiRJUk9mapIfU1WXVdUPq+rjwLJRFCVJkiRJUh9m+kzy0iS/sr37VfW3c1OWJEmSJEmjN1OT/Dngl7dzvwCbZEmSJEnSgjFtk1xVrxtVIZIkSZIk9W2mzyQDkOSAJGcmuaTdPyLJ6+e2NEmSJEmSRmuHmmTgI8ClwPJ2/1vAf5hugyRnJbk1yTUTxl6V5OtJHk6yatL6b0myMcl1SY6bMH58G9uY5M0Txg9PckWS65Ocn2SvNv64dn9jW37YDj5HSZIkSdIit6NN8v5VdQHwMEBVPQg8NMM2HwGOnzR2DfArwOcnDiY5AjgJ+Om2zQeT7JFkDwaXoToBOAI4ua0L8F7g/VW1ErgTGD+y/Xrgzqp6KvD+tp4kSZIkSTPa0Sb5viT7MZisiyRHAXdPt0FVfR64Y9LYN6vquilWPxE4r11q6l+AjcDz29fGqvp2VT0AnAecmCTAi4BPtO3PBl4+4bHObrc/ARzb1pckSZIkaVozzW497veBi4AVSf43g+slv3IX1nEQcPmE+5vbGMBNk8ZfAOwH3NWOaE9e/6DxbarqwSR3t/Vvm7zTJKcBpwEceuihu+SJaPHZsmUL99xdfOwzD8688m7mlruKbbWl7zIkSZKkkdmhJrmqrkryb4CnAwGuq6of7cI6pjrSW0x9pLumWX+6x+oOVp0BnAGwatWqKdeRJEmSJC0e0zbJSX5lO4ueloSq2lXXSd4MHDLh/sHA+OGrqcZvA5Ym2bMdTZ64/vhjbU6yJ/BkJp32Le1Ky5cv567czq8es6MnZuw+PvaZB1l64PKZV5QkSZIWiJn+qv/laZYVsKua5IuAv07yPgYzaK8EvsjgqPDKJIcD32Uwudf/U1WV5DMMTvk+DzgVuHDCY50KfKEt/3RVeZRYkiRJkjSjaZvkqnrdzj5wknOBo4H9k2wG3sbgiO6fMvhM8z8k+UpVHVdVX09yAfAN4EHgDVX1UHucNzK4/NQewFlV9fW2izcB5yV5F/Bl4Mw2fibwV0k2tv2dtLPPQZIkSZK0uOzw+aFJfonBJZp+Ynysqt6xvfWr6uTtLPq77az/buDdU4xfDFw8xfi3Gcx+PXn8B8CrtleXJPVhbGyMTZs2jWRf4/tZu3btSPa3YsUK1qxZM5J9SZIkzbUdapKTfAhYAhwD/CWD05i/OId1SdKCsmnTJr5+7dUs2W/u9/VA+4DJv2y9es73te32Od+FJEnSSO3okeQXVtWzklxdVX+U5L+z6z6PLEmLwpL94Bkv3dHL0+8erv3kw32XIEmStEvt6F9r97fv25IsZ/C54cPnpiRJkiRJkvqxo0eSP5lkKfBfgSvb2F/OTUmSJEmSJPVjpusk/yxwU1W9s91/AvA14Frg/XNfniRJkiRJozPT6dZ/ATwAkOQXgdPb2N3AGXNbmiRJkiRJozXT6dZ7VNUd7fZrgDOq6m+Av0nylbktTZIkSZKk0ZrpSPIeScYb6WOBT09YtsPXWJYkSZIkaXcwU6N7LvC5JLcxmOH6nwCSPJXBKdeSJEmSJC0Y0zbJVfXuJJ8CDgTWV1W1RY8B/t+5Lk6SJEmSpFGa8ZTpqrp8irFvzU05kiRJkiT1Z6bPJEuSJEmStGjYJEuSJEmS1NgkS5IkSZLU2CRLkiRJktTYJEuSJEmS1NgkS5IkSZLUzHgJqJ2V5CzgpcCtVXVkG9sXOB84DPgO8OqqujNJgD8BXgJsA15bVVe1bU4F/rA97Luq6uw2/jzgI8DewMXA71ZVbW8fc/U8JWlHbNmyhW3fh2s/+XDfpexS226HLT/a0ncZkqQJbrnjBj566bvmfD933vM9APZ54k/O+b5g8LyWHvDUkexLi9ucNckMGtg/A86ZMPZm4FNVdXqSN7f7bwJOAFa2rxcAY8ALWsP7NmAVUMCVSS5qTe8YcBpwOYMm+Xjgkmn2IUmSJC1oK1asGNm+br/3AQCWHvDYkexv6QFPHenz0+I1Z01yVX0+yWGThk8Ejm63zwY+y6CBPRE4p6oKuDzJ0iQHtnUvq6o7AJJcBhyf5LPAk6rqC238HODlDJrk7e1DknqzfPlyfvjY23jGSxfWp1yu/eTDLF+2vO8yJEnNmjVrRravtWvXArBu3bqR7VMahVH/tXZAVd0M0L4/pY0fBNw0Yb3NbWy68c1TjE+3j44kpyXZkGTD1q1bd/pJSZIkSZIWhvlySCNTjNVOjM9KVZ1RVauqatWyZctmu7kkSZIkaYEZdZN8SzuNmvb91ja+GThkwnoHA1tmGD94ivHp9iFJkiRJ0rRG3SRfBJzabp8KXDhh/JQMHAXc3U6VvhRYnWSfJPsAq4FL27J7khzVZsY+ZdJjTbUPSZIkSZKmNZeXgDqXwQRa+yfZzGCW6tOBC5K8HrgReFVb/WIGl3/ayOASUK8DqKo7krwT+FJb7x3jk3gBa3jkElCXtC+m2YckSZIkSdOay9mtT97OomOnWLeAN2zncc4CzppifANw5BTjt0+1D0mSJEmSZjKX10mWFo1b7io+9pkHR7KvO+8dzFG3zxOmmr9u17rlrmLpgXO+G0mSRmpsbIxNmzbNapstWwbT3yxfPvvL3q1YsWKkl2aS9OjYJEuP0qgvan97+6W+9MC53+/SA0f//CRJmo/uv//+vkuQNCI2ydKjNOp3hteuXQvAunXrRrpfSZIWip353e3vX2nxmC/XSZYkSZIkqXc2yZIkSZIkNTbJkiRJkiQ1NsmSJEmSJDU2yZIkSZIkNTbJkiRJkiQ1NsmSJEmSJDVeJ1mSRmTb7XDtJx+e8/384O7B95948pzvim23A8vmfj+SJEmjYpMsSSOwYsWKke1r0/c3AXD4shHsc9lon5skTTQ2NsamTZtGsq/x/axdu3Yk+4PBz9c1a9aMbH+SBmySJWkERvlHzvgfcOvWrRvZPiWpD5s2beIb127kifsfOuf7epC9ALjptgfmfF8A99x240j2I6nLJlmSJEm7rSfufygvOPE/913GLnfFhe/puwQtUGNjY6xfv37W223bto2qmoOKppaEJUuWzHq71atXP+qDE07cJUmSJElS45FkSZIkSVok1qxZ42fdZ9BLk5zkd4HfAAJ8uKr+OMm+wPnAYcB3gFdX1Z1JAvwJ8BJgG/DaqrqqPc6pwB+2h31XVZ3dxp8HfATYG7gY+N0a5bkBC9jucnoG7NwpGrvi9AxJkiRJu6+Rn26d5EgGDfLzgWcDL02yEngz8KmqWgl8qt0HOAFY2b5OA8ba4+wLvA14QXustyXZp20z1tYd3+74uX9mkiRJkqTdXR9Hkp8JXF5V2wCSfA54BXAicHRb52zgs8Cb2vg57Ujw5UmWJjmwrXtZVd3RHucy4PgknwWeVFVfaOPnAC8HLhnFk1voPD1DkiRJ0kLWx8Rd1wC/mGS/JEsYnEZ9CHBAVd0M0L4/pa1/EHDThO03t7HpxjdPMd6R5LQkG5Js2Lp166N+YpIkSZKk3dvIm+Sq+ibwXuAy4B+BrwIPTrNJpnqYnRifqpYzqmpVVa1atmzZtHVLkiRJkha+Xi4BVVVnVtVzq+oXgTuA64Fb2mnUtO+3ttU3MzjSPO5gYMsM4wdPMS5JkiRJ0rT6mt36KVV1a5JDgV8Bfg44HDgVOL19v7CtfhHwxiTnMZik6+6qujnJpcB7JkzWtRp4S1XdkeSeJEcBVwCnAH86sicnSZKkkdiyZQv3fP8+rrjwPX2Xssvdc9sNbHng8X2XIS1KfV0n+W+S7Af8CHhDu9TT6cAFSV4P3Ai8qq17MYPPLW9kcAmo1wG0ZvidwJfaeu8Yn8QLWMMjl4C6BCftkiRJkiTtgF6a5Kr6hSnGbgeOnWK8gDds53HOAs6aYnwDcOSjr1SSJGl+GBsbY/369bPebtu2bQz+nBqNJCxZsmTW261evXrWV9BYvnw5D+31AC848T/Pen/z3RUXvofl++/VdxnSotTLZ5IlSZIkSZqP+jrdWpIkSbOwZs2aWR9plSTNnkeSJUmSJElqbJIlSZIkSWo83VqS5qmxsTE2bdo06+3Gt1m7du2st12xYoWnc0qSpEXNJlmSFpi999677xIkSZJ2WzbJUk9GfZTQI4S7H/+9JEmSRs8mWdrNeJRQkiRJmjs2yVJPPEooaaKxsTHWr18/6+22bdtGVc1BRVNLwpIlS2a93erVq/25pzlxz203csWF75nz/Wy7+xYAljz5gDnfFwyeF/s/dST7kjTMJlmSJEm7pRUrVoxsX5vufgCAQ/bfazQ73P+pI31+kh6RUb77PJ+tWrWqNmzY0HcZkiRJmofG5wJZt25dz5Xseo92npSdaeadK0V9SHJlVa2aaT2PJEuSJEmaNedJ0UJlkyxJkiQtYh7RlYY9pu8CJEmSJEmaL2ySJUmSJElqbJIlSZIkSWpskiVJkiRJanqZuCvJ7wG/DhTwNeB1wIHAecC+wFXAv6uqB5I8DjgHeB5wO/CaqvpOe5y3AK8HHgJ+p6oubePHA38C7AH8ZVWdPrpnJ2lsbIz169fPertt27YxysvSJWHJkiWz3m716tVOciJJkrRAjfxIcpKDgN8BVlXVkQwa2ZOA9wLvr6qVwJ0Mml/a9zur6qnA+9t6JDmibffTwPHAB5PskWQP4M+BE4AjgJPbupIkSZIkTSujPGoDP26SLweeDXwf+HvgT4GPAT9ZVQ8m+Tng7VV1XJJL2+0vJNkT+B6wDHgzQFX9f+1xLwXe3nbz9qo6ro2/ZeJ627Nq1arasGHDLn2ukiRJmn/GxsbYtGnTrLYZX3/FihWz3t+KFSs8A0maB5JcWVWrZlpv5EeSq+q7wDrgRuBm4G7gSuCuqnqwrbYZOKjdPgi4qW37YFt/v4njk7bZ3nhHktOSbEiyYevWrY/+yUmSJGlB2nvvvdl77737LkPSCIz8M8lJ9gFOBA4H7gI+zuDU6MnGD3FnO8u2Nz5V4z/l4fKqOgM4AwZHkqctXJIkSQuCR3UlTaeP2a3/LfAvVbW1qn4E/C3wQmBpO50a4GBgS7u9GTgEoC1/MnDHxPFJ22xvXJIkSZKkafXRJN8IHJVkSZIAxwLfAD4DvLKtcypwYbt9UbtPW/7pGnyQ+iLgpCSPS3I4sBL4IvAlYGWSw5PsxWByr4tG8LwkSZIkSbu5kZ9uXVVXJPkEg8s8PQh8mcEpz/8AnJfkXW3szLbJmcBfJdnI4AjySe1xvp7kAgYN9oPAG6rqIYAkbwQuZTBz9llV9fVRPT9JkiRJ0u5r5LNbz1fObi1JkiRJC9e8nd1akiRJkqT5yiZZkiRJkqTGJlmSJEmSpMYmWZIkSZKkxiZZkiRJkqRm5JeAkiRpbGyM9evXz3q7bdu2McqrMiRhyZIls95u9erVrFmzZg4qkiRJc80mWZIkzUs782bKqN9IgZ17M8U3UiRp/rJJliSN3Jo1a2wQJEnSvJRRv9s6X61atao2bNjQdxmSJEmSpDmQ5MqqWjXTek7cJUmSJElSY5MsSZIkSVJjkyxJkiRJUmOTLEmSJElSY5MsSZIkSVJjkyxJkiRJUmOTLEmSJElSY5MsSZIkSVKTquq7hnkhyVbghr7rAPYHbuu7iHnGTLrMZJh5dJlJl5kMM48uM+kyk2Hm0WUmXWYybD7l8VNVtWymlWyS55kkG6pqVd91zCdm0mUmw8yjy0y6zGSYeXSZSZeZDDOPLjPpMpNhu2Menm4tSZIkSVJjkyxJkiRJUmOTPP+c0XcB85CZdJnJMPPoMpMuMxlmHl1m0mUmw8yjy0y6zGTYbpeHn0mWJEmSJKnxSLIkSZIkSY1x/zX2AAAO0UlEQVRNsiRJkiRJjU2yJEmSJEmNTbIkSZIkSY1NsiRJkiRJjU1yjzLw6iSvarePTfKBJL+dZFH+2yTZf9L9X2uZnJYkfdXVF18jXUlekWTfdntZknOSfC3J+UkO7ru+PiR5X5J/3Xcdu4Mku91lKOZaktf1XUNfkhyXZCzJRUkubLeP77uu+SLJp/uuoU/+TdLl7+CZ+f9mYbxGvARUj5J8EHgKsBfwfeBxwP8AXgLcUlW/22N5vUhyVVU9t93+Q+AXgL8GXgpsrqrf67O+UfM10pXkG1V1RLt9PnA58HHg3wK/WlUv7rO+PiTZCtwALAPOB86tqi/3W1V/xn85T7UI+GpV7Ta/pEchyY1VdWjfdYxakj8GngacA2xuwwcDpwDXL7afr0munjzEIJ/rAKrqWSMvqmf+TdLl7+Bh/r/pWiivEZvkHiX5WlX9TJLHAt8DDqyqB5LsCXy5qn6m5xJHLsmXq+pftdtXAb9QVfe1jK5abJn4GulKcl1VPb3dvrKqnjdh2Veq6jn9VdeP8f83SVYCJ7WvPYBzGTTM3+q1wBFL8hCDNw0mHumpdv+gqtqrl8J6NMUfcj9eBDytqh43ynrmgyTfqqqnTTEe4FtVtbKHsnqT5CIGb8a+C7ifwWvjn4CfB6iqG/qrrh/+TdLl7+Bh/r/pWiivkUV5uuY88iBAVf0I+FJVPdDuPwg81GdhPdo7yb9K8jxgj6q6D36c0WLMxNdI12eTvCPJ3u32ywGSHAPc3W9pvSmAqrq+qt5ZVT8NvBr4CeDiXivrx7eBo6vq8Alf/1dVHQ7c0ndxPTmAwRHSX57i6/Ye6+rTD5I8f4rxnwV+MOpi+lZVLwP+BjgDeHZVfQf4UVXdsBj/0G/8m6TL38ET+P9mSgviNbJn3wUsct9L8oSqureqfvwZqCQ/CTzQY119uhl4X7t9R5IDq+rmJPvRGsZFxtdI1xuBt9JOZQJ+L8l9DE5D/3e9VdWvzmfjqupq4GrgLaMvp3d/DOwD3DjFsv864lrmi08CT6iqr0xekOSzoy9nXngtMJbkiTxyuvUhDI4KvbanmnpVVX+XZD3wziS/zuCjPouZf5N0+Tt4Ev/fdCyI14inW89DSR4PPL6qbu27lvkiyR7A46pqW9+1zAe+RgaSPBnYs6oW65EwAMbfSOm7Dml31N50PIjBm02bq+p7PZc0LyR5NvBzVfWhvmuZb/ybZMDfwV3+vxm2O79GPJLcs/biOZ7BL+gCtgCXLubmZ5pM7uq1sJ74GumanEmSRf0aqap7/X+zY5K8uKou67uOPrTP2j6f4dfIF2uRv1vemuKhxjjJM6rq2p5K6s1UP0eSLF3MP0f82drl7+Bh/r/pWgivET+T3KMkpwBXAUcDS4DHA8cAV7Zli46ZDDOPLjPpMpNZObPvAvqQZDVwPfB2BrPj/xLwR8D1bZmGre+7gFHz50iXmXSZyTDz6FoomXi6dY+SXAe8YPK7Kkn2Aa6YatbNhc5MhplHl5l0mcmwNtvolIuAF1XV40dZz3yQ5JvACW1SmYnjhwMXV9UzeymsR0k+sL1FwKlV9aRR1tM3f450mUmXmQwzj66FkomnW/crtFlpJ3mYKSbiWSTMZJh5dJlJl5kM+wXg14DJn9MeP914MdqTRyanmui7wGNHXMt88TrgPwI/nGLZySOuZT7w50iXmXSZyTDz6FoQmdgk9+vdwFVtRryb2tihwIuBd/ZWVb/MZJh5dJlJl5kMuxzYVlWfm7ygvcO9GJ0FfCnJeQy/Rl7DIj0FHfgScE1V/fPkBUnePvpyeufPkS4z6TKTYebRtSAy8XTrnrVTD45jwsyaDD7YfmevhfXITIaZR5eZdJmJZpLkmcCJDL9GLqqqb/RaWE+S7Av8YLHPUDyRP0e6zKTLTIaZR9dCyMQmeZ5J8tKq+mTfdcwnZjLMPLrMpMtMhplHV5LnVtVVfdcxn5jJMP/fdJlJl5kMM4+u3TETm+R5JslVVfXcvuuYT8xkmHl0mUmXmQwzjy4z6TKTYebRZSZdZjLMPLp2x0y8BNT8s9t8oH2EzGSYeXSZSZeZDDOPLjPpMpNh5tFlJl1mMsw8una7TGyS55/f7LuAechMhplHl5l0mckw8+j6o74LmIfMZJj/b7rMpMtMhplH126XibNb9yjJocCtVfWDJAFeCzw3yfOAD1fVg70W2AMzGZbkZcD6qvrB+FhVfbHHknpnJl1mMsw8ppbkF4Fbquq6JD8PPDXJL1XVP/RdW1/MZFiSJwDHA4cADwLXJ3lMVT3cb2X9MZMuMxmW5Bk8MiliAVuS3FNV3+y3sv4shEz8THKPklwDPL+qtiV5L7AC+HvgRQBV9e/7rK8PZjIsyf3AfcAlwLkMZgZ8qN+q+mUmXWYyzDy6kvwxg2tE7wlcChzLIJ9/A3y5qv6gx/J6YSbDkrwa+APgq8AxwD8zOOPwZ4Bfraqv9VheL8yky0yGJXkTg+uqn8cj16I/GDgJOK+qTu+rtr4slExsknuU5BtVdUS7fSXws+PvwiX5alU9u9cCe2Amw5J8mcEbBK9k8MPlSODvgHOnugbsYmAmXWYyzDy6knydQQ57A98FDmpvRj6WQUN4ZK8F9sBMhiW5GjiqZbA/8LGqOi7Js4APVdULey5x5Myky0yGJfkW8NNV9aNJ43sBX6+qlf1U1p+FkomfSe7XTUle1G5/h8FpKyTZr7eK+mcmw6qq7qyqD1fVscCzgW8Apye5aYZtFyoz6TKTYebRVTV4V3z8dMjxd8gfZvH+LWAmwwLc327fBzwFoKquBp7UV1E9M5MuMxn2MLB8ivEDeeRny2KzIDLxM8n9+nXgnCRvB+4GvtKOgOwD/H6fhfXITIYNzQZYVd8DPgB8IMlP9VNS78yky0yGmUfXPyT5J+AngL8ELkhyOYNTiz/fa2X9MZNhFwP/mORzwAnAxwGS7MtuODPtLmImXWYy7D8An0pyPTD+JuyhwFOBN/ZWVb8WRCaebj0PJHkm8DQGb1psBr60WCc/GGcmA0mOrqrP9l3HfGImXWYyzDymluTnGBw9vTzJCuAVwI3AJxbjz1cwk8mSvAQ4AvhqVV3Wxh4DPLaqfthrcT0xky4zGdae+/MZTFIVHvm7ddHOhbEQMrFJngeSHMCE2d+q6paeS+qdmQwzjy4z6TKTYebRZSZdZjLMPLrMpMtMZpbkCVV1b991zCe7UyY2yT1K8hzgQ8CTGUwaAoPZ3+4Cfruqruqrtr6YyTDz6DKTLjMZZh5dZtJlJsPMo8tMusxkxyW5saoO7buO+WR3ysQmuUdJvgL8ZlVdMWn8KOAvFttMzmAmk5lHl5l0mckw8+gyky4zGWYeXWbSZSbDkmxvvpwAb62qfUdZz3ywUDJZjLM3ziePn/xDBqCqLgce30M984GZDDOPLjPpMpNh5tFlJl1mMsw8usyky0yGvYfB5LJPnPT1BBZvn7UgMnF2635dkuQfgHN4ZPa3Q4BTgH/srap+mckw8+gyky4zGWYeXWbSZSbDzKPLTLrMZNhVwN9X1ZWTFyT59R7qmQ8WRCaebt2zJCcAJzI8+9tFVXVxr4X1yEyGmUeXmXSZyTDz6DKTLjMZZh5dZtJlJo9I8nTgjqraOsWyAxbjhGYLJRObZEmSJEmSmt3mvPCFKMmTk5ye5JtJbm9f32xjS/uurw9mMsw8usyky0yGmUeXmXSZyTDz6DKTLjMZNiGPa81jYKFkYpPcrwuAO4Fjqmq/qtoPOIbBNPof77Wy/pjJMPPoMpMuMxlmHl1m0mUmw8yjy0y6zGTYeB5HT8rjThZnHrBAMvF06x4lua6qnj7bZQuZmQwzjy4z6TKTYebRZSZdZjLMPLrMpMtMhplH10LJxCPJ/bohyX9KcsD4QJIDkryJR2YMXGzMZJh5dJlJl5kMM48uM+kyk2Hm0WUmXWYyzDy6FkQmNsn9eg2wH/C5JHcmuQP4LLAv8Oo+C+uRmQwzjy4z6TKTYebRZSZdZjLMPLrMpMtMhplH14LIxNOte5bkGcDBwOVVde+E8eOrajFeb85MJjGPLjPpMpNh5tFlJl1mMsw8usyky0yGmUfXQsjEI8k9SvI7wIXAG4Frkpw4YfF7+qmqX2YyzDy6zKTLTIaZR5eZdJnJMPPoMpMuMxlmHl0LJZM9+y5gkfsN4HlVdW+Sw4BPJDmsqv6EwcXZFyMzGWYeXWbSZSbDzKPLTLrMZJh5dJlJl5kMM4+uBZGJTXK/9hg/BaGqvpPkaAYvpJ9iN3oR7WJmMsw8usyky0yGmUeXmXSZyTDz6DKTLjMZZh5dCyITT7fu1/eSPGf8TntBvRTYH/iZ3qrql5kMM48uM+kyk2Hm0WUmXWYyzDy6zKTLTIaZR9eCyMSJu3qU5GDgwar63hTL/nVV/e8eyuqVmQwzjy4z6TKTYebRZSZdZjLMPLrMpMtMhplH10LJxCZZkiRJkqTG060lSZIkSWpskiVJkiRJamySJUlaADLwv5KcMGHs1Un+cYp1v5PknyaNfSXJNaOoVZKk+cwmWZKkBaAGk4z8FvC+JD+R5PHAu4E3jK/TGunx3/1PTHJIG3/myAuWJGmeskmWJGmBqKprgP8BvAl4G3AO8FCSbyb5IHAVcEhb/QLgNe32ycC544/Tmuz/P8nXknw5yTEjexKSJPXM2a0lSVpA2hHkq4AHgFXAgcC3gRdW1eVtne8Aq4GPVNULk3wZ+FXggqo6Msl/BI6sqtcleQawHnhaVf1g9M9IkqTR2rPvAiRJ0q5TVfclOR+4t6p+mATghvEGeYI7gDuTnAR8E9g2YdnPA3/aHu/aJDcATwOunvMnIElSzzzdWpKkhefh9jXuvu2sdz7w50w41brJXBQlSdLuwCPJkiQtXn/H4HTsS4HlE8Y/z+D0608neRpwKHDd6MuTJGn0PJIsSdIiVVX3VNV7q+qBSYs+COyR5GsMjja/tqp+OPoKJUkaPSfukiRJkiSp8UiyJEmSJEmNTbIkSZIkSY1NsiRJkiRJjU2yJEmSJEmNTbIkSZIkSY1NsiRJkiRJjU2yJEmSJEnN/wFbcdJ7H7WjMQAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -488,16 +532,16 @@ }, { "cell_type": "code", - "execution_count": 190, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 190, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, @@ -505,7 +549,7 @@ "data": { "image/png": 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juYlMCm8cqTzjrVpcpF61esDDM9407VimrWK42FY+5ut+Mi4yVqMmYHffYGZZ4Ii7/3lC5yQyqTSZMeSO++nxRrZ1Vw8btnWzt7efxR1trF3RldhmldNFzSoIdy8SLcQzJmaWNbNnzezH4fESM/uZme02s78zs+YQnxEe7wnPv73sPb4c4r80sw+WxVeF2B4zW1cWr9iGyFjMqXKxrVq8EWzd1cMtm3fQ0zfI3NYcPX2D3LJ5B1t39aR9ag2l3jK0/8/Mvmlm/9zMLh3+qfO1nwN2lj2+A/hzd18K9AKfDPFPAr3u/g7gz8NxmNk7gY8DFwGrgP8cknoW+EtgNfBO4Ppw7GhtiJyxY8cLZxRvBMPbMLU1N2EW3WobpolXbxnaPwu368tiDlw92ovMbBHwm8BXgS9YdPXuauB3wyH3Al8B7gLWhPsAm4BvhuPXAN9z9+PAy2a2B7gsHLfH3btDW98D1pjZzlHaaBj6epicfJXB3mrxRrC3t5+5raf28FtzWfb19qd0Ro2p3jK03xjj+/8F8MecvIg3D3jL3Ye7DvuAheH+QqL6Yty9YGaHw/ELgSfL3rP8NXtHxC+v0cYpzOwm4CaA8847bwy/XjqGvx7msnbK18P1oCQcA68y5a1afKKl8WG7uKONnr5B2soWnR8YKrKooy3WdqebUYcgzOxyM3vOzI6a2RNmdmG9b2xmvwX0uPvT5eEKh3qN5yYqfnrQfaO7L3f35QsWLKh0yKSkr4fJqpZmk0i/aY3Frl3RxVDR6c8XcI9uh4rO2hVdsbY73dQaA/5L4I+IepVfJ+rR1ut9wHVm9mvge0TDAn9BtKbw8MfqIuC1cH8fsBggPD8HOFQeH/GaavGDo7TREPb29tM6YhKAvh7GJ80EnNaH7cplnay/7iI621s4PDBEZ3sL66+7SN+wJlitIYhM2bq/3zezL496dBl3/zJhDzkzWwn8kbv/npl9H/goUVK+EXggvGRzePxEeP5xd3cz2wz8rZl9HXgbsBT4OVFPd2lYKvNVogt1vxte85MqbTQEfT2cPtIci125rFMJN2a1EvBcM/vtao/d/YdjaPNLwPfM7E+BZ4F7Qvwe4L+Ei2yHiBIq7r7DzO4H/hEoAJ8JpXGY2R8CjwBZ4FvuvqNGGw1h7Youbtm8g/58gdZcloGhor4eNih92CYvyTF385HV5eVPmv31KK91d/+DiT+ldCxfvty3b9+e9mnUbfiPZF9vP4tUBRGrt6/7b1Wf+/Xtvxlr2+UXXMs/bDUcEI8J/O9d1yydWjPhPnEmLUpy9PVweli5rJP1oA/bhJSPuQO0NTfRny+wYVt3LP/N692S6Gzgz4C3ufvqMOHhSndvqK/2IpORPmyTk/SYe70z4b5NNNb6tvD4JeDzcZyQiEhaFne0MTBUPCUW55h7vQl4vrvfT9gXLkxyKI7+EhGRqSXp+ud6E/AxM5tHKH00syuAw7GckYhISpKuf653LYgvENXp/k9m9v8CC4jqbEVEGkqSY+71rgXxjJn9L8AFROUVv3T30/doERGRutXakui3qzx1vpmNdSKGyJTSlsvQP1SqGE+CVr5rXLV6wP9ilOccUAKWhjcZFuPRyneNSRMxRGrIF50MoQQoyJDMesBJTwyQZNV7EQ4z+02iXSlahmPuvr76K0Qaw4xshv7SqVWXJaAtG/8QxN7efrIG3QeOki+WaM5mmD+rWSvfNYi6/oLM7P8Gfgf4LNFFuI8B/yTG8xKZNFqrjPVWi0+kWc1ZXn1rkELRyZpRKDqvvjXIzOZs7RfLpFf3lkTufrGZPe/uf2Jm/xGN/8o0cXiwcsFPtfhEsuGdl42Ty7t4WTxGuvgXv3o/wgfCbb+ZvY1oWcgl8ZySyORSPL0AYtT4ROo7XmDh3BaaMkax5DRljIVzWzga84ag2hU5GfUm4B+b2Vzg/wKeBl4mWuxcpOGlWQWxuKONpmyGrgWzWHbObLoWzKIpm4l9PWBte5WMWnvC/VMzO8fdb3P3t4BZwAvA94m2jheRGKW1N5u2vUpGrR7wBiAPYGYrgNtD7DCwMd5TE5G09mZLelWwkbbu6uH6jU9y1R2Pc/3GJxt26KPWRbisux8K938H2OjuPwB+YGa/iPfURATSWQ84zW2vptPkk1o94GzZ7sLXAI+XPVd3DbGITC1p7oo8ncafayXR7wL/3cwOElVC/AOAmb0DLUcp0tDS2okjzZ2gk1ZrKvJXzewx4Fzg7/3kDp4ZokkZIiITKu2doJOsf65ZhubuT7r7j9z9WFnsJXd/JpYzEpFpLa3KD0i+/jmZ9fREROo0ncaflYBFZNJKYrJLuaTrn5WARWRS2bqrhy9ueo5nX+nljcMDPPtKL1/c9FwitcCTdVdkEZlm0poMccfDu+jtH8KBpmwGB3r7h7jj4V2xt712RRdHBobYvb+Pna8fZvf+Po4MDKW+K7KITCNpLsbTffAYGYOMGYaRMSNjUTwJ0dbvYcU5i3cYRAlYRE4znSZDlNuwrZs5rTmWdraz7JzZLO1sZ05rThfhRCQ5aS7Gs2ReGyWHUslxd0olp+RRPG4NcxHOzFrM7Odm9pyZ7TCzPwnxJWb2MzPbbWZ/Z2bNIT4jPN4Tnn972Xt9OcR/aWYfLIuvCrE9ZrauLF6xDRGpT5qL8axbfSFz23JYBoruWAbmtuVYt/rC2Nte3NHGwaPH6T5wlF1vHKH7wFEOHj0+JS/CHQeudvd3A+8BVpnZFcAdwJ+7+1KgF/hkOP6TQK+7v4Noqcs7AMzsncDHifajWwX8ZzPLmlkW+EtgNfBO4PpwLKO0ISJ1SHMyxMplnXzto+/mksUdnDO7hUsWd/C1j747kTrgK7vO4sDRPPliiYxBvljiwNE8V3adFUt7sS2oE6YtHw0Pc+HHgauB3w3xe4GvAHcBa8J9gE3ANy3ad2UN8D13Pw68bGZ7gMvCcXvcvRvAzL4HrDGznaO0ISJ1WLmsk/VEY6L7evtZlPCWRGmtQ/FE9yHmtDTx1sAQQw4Zg7mtOZ7oPsTNMbQX6xhw6Kn+AugBtgC/At5y9+H9VPYBC8P9hcBegPD8YWBeeXzEa6rF543Sxsjzu8nMtpvZ9gMHDoznVxVpWElPhkjT7p4++gYL5LIZWnIZctkMfYMFdvf0xdJerEtKunsReE/YzuhHQKVBnOH/v5V2GfRR4pU+PEY7vtL5bSQsLL98+fLp9HcmMqrhyRB9gwUKpRIH+47zxU3P8R8SGgpIa0PQfKEEoQQOwAxK5lE8BolUQYTtjLYCVwBzy9YYXgS8Fu7vAxYDhOfnAIfK4yNeUy1+cJQ2RKacNCZEpDkZIs0a5Fw2SrzlFRgAzdl4dqGOswpiQej5YmatwPuBncBPgI+Gw24EHgj3N4fHhOcfD+PIm4GPhyqJJcBS4OfAU8DSUPHQTHShbnN4TbU2RKaUtJJRmpMh0qxBPv/s2cyb2UxT1ii605Q15s1sZunZs2NpL84e8LnAT8zseaJkucXdfwx8CfhCuJg2D7gnHH8PMC/EvwCsA3D3HcD9wD8CDwOfcfdiGOP9Q+ARosR+fziWUdoQmVKm44SINGuQ167oorkpyzlzWrjg7HbOmdNCc1M2tuqPOKsgngcuqRDv5mQVQ3l8EPhYlff6KvDVCvEHgQfrbUNkqklrd4gl89rYc+AYVnLMwB1KDu+YH38dcJoLsidd/aF93UQmsbSS0brVF3Lzd5/haL5IKZRjzWrOJjIZIs0NQSHZEjhNRRaZxNau6OLwwBC7e/rY9cYRdvf0cTjG1bnKjSwLSqpMKM0F2ZOmHrDIJGcADu4ObhXrLCfa7Q/t5NjxIsPFVyWHY8eL3P7QzkQSYVoTMZKmBCwyiW3Y1s3s1hznzGk9EevPF9iwrTvWBLW75ygjK19LIS4TRwlYZBLb29tP1qD7wFHyxRLN2QzzZzXHfhGuWGW8oVq8kUyqXZFFJD3tM5p49a1BCiUnmzEKJefVtwaZNUN9pzhoV2QROSGaV0R0BWz4pzwek2rjzEmMP6cp6bprfYyKTGJH80U62pp489jQiXKweTNzHMsXa794HJqb4Hjh9HhSHe+01oJIuu5aPWCRSWxWc5ZDx4Y40RF2OHRsiJnN2dFfOE7ZTOX3z1SJT6Q014LQrsgicsKxfPHEha/hr/9FJ/Ye8OBQ5fevFp9IaU6/TnoheiVgkUmsp+84WYuWRYToNmtRPE6lKkPM1eITKc21IJKeBKIxYJEpoFQ2BBHTyoiTRpprQYCmIotIMKs5c1rtbdGjeKNKcz+6pDXu/0WRBjBQqPydv1q8EUyntSCUgEUmsbQuhlXbASKunSGqadyPmYgSsMgkZlY54VWLT5ShKnOOq8Un0tZdPfzRpud4dm8v+48M8uzeXv5o03OJlKElTRfhZMpIqzg/TValD5iJuW9Y7d2T6JHe/tBO3uofImtG1gwvwVv9Q4mtxJYkJWCZEoaL83NZO6U4fz003D/Kco5RKe2VGnhS8Mtv9kf70WVO7kzsJeflN+MvQwMtxiNymum4NxqcXPPBiBKRjYjLxNJiPCIVpFmcn6bhVc+cqAbYR8QbUdf8mZQcSu44TsmdkkfxuCX9Qa8ELFPC4o423jx2nO4DR9n1xhG6DxzlzWPHEyvOT0uuyr/QavFG8KVVy5g5I0u+UGJwqES+UGLmjCxfWrUs9raT/qBv3I9RaShXdp3Fz399KBobNMgXS/T05bn+n56V9qnF6s3+CkuSjRJvFLlshhm5DMWwDnIum8wnzuKONl4+eJS+wcKJBfDbW5pYMn9WLO018OeoNJInug+xYFYzzdkMJYfmbIYFs5p5ovtQ2qfWkLKZyhf5qsUn0oZt3cxpzbG0s51l58xmaWc7c1pziYz3X9l1FgeO5skXSyc+6A8czXNlVzwf9OoBy5Swt7ef+bNmsKC95UTM3Rt+DDgtzVljoMLKO0lMxEhrGyaIPug725s5MnCyBzy7tYknug9xcwztKQHLlJD2Ai3TTS6bYWBo5LacJDIUMKs5y54Dx07UAReK0TZM71gQ/0W4vb39zJs5g/mzkvmg1xCETAnTaYGWyaBamVsS5W8nZvlZ2Q/xz/4DLcguUtF0WqBlMjCz06Z6GMkkwb7jBRbObaEpYxRLTlPGWDi3haOV9kiaYEl/0GsIQqaMJNdpne5KXjpt/l1Ui3z6sMREGx5u6lpwsvKgP1+gs2z8Py4rl3WynuhC4L7efhbFPBNOCVhETlOskmcL8edf1q7o4pbNO+jPF2jNZRkYKiY63NQQC7Kb2WIz+4mZ7TSzHWb2uRA/y8y2mNnucNsR4mZmd5rZHjN73swuLXuvG8Pxu83sxrL4e83shfCaOy18P6rWhozf1l09XL/xSa6643Gu3/hkQ65QJXC8SqatFp9I02m4Kc4ecAH4P9z9GTNrB542sy3AvwYec/fbzWwdsA74ErAaWBp+LgfuAi43s7OAW4HlRN+Cnjazze7eG465CXgSeBBYBTwU3rNSGzIO03VBnOkozT3hYPoMN8XWA3b31939mXC/D9gJLATWAPeGw+4FPhzurwHu88iTwFwzOxf4ILDF3Q+FpLsFWBWem+3uT3h0afa+Ee9VqQ0Zhw3buskXirxxeJBf7u/jjcOD5AvFhl8QRyQuiYwBm9nbgUuAnwFnu/vrECVpMxv+mFsI7C172b4QGy2+r0KcUdqQcXhp/xGODBbIcLI+881jeQrFI2mfmsiUFHsZmpnNAn4AfN7dR/uXWqm+xccQP5Nzu8nMtpvZ9gMHDpzJS6el4d0QMhnDzE6s15pPYJcEkUYUaw/YzHJEyfc77v7DEN5vZueGnum5wPBVnH3A4rKXLwJeC/GVI+JbQ3xRheNHa+MU7r4R2AiwfPlyZZEampsyDOSLlNyjRbId8CguMpHufPQl7v7pyxzLF5nZnOVTVy3h5vefn/ZpTbg4qyAMuAfY6e5fL3tqMzBcyXAj8EBZ/IZQDXEFcDgMIzwCXGtmHaGa4VrgkfBcn5ldEdq6YcR7VWpDxmFpZzvz25tPKZCf397M0s72RNpXBcb0cOejL/H1R3dzZLBAseQcGSzw9Ud3c+ejL6V9ahMuzq7L+4B/BVxtZr8IPx8Cbgc+YGa7gQ9SAkVyAAAU2ElEQVSExxBVMXQDe4C/Av4NgLsfAm4Dngo/60MM4NPA3eE1vyKqgGCUNmQc1q7oIpfNcs6cFi44u51z5rSQy2YTqc9MeqcCSc83f7LnjOITLckP+tiGINz9p1QepwW4psLxDnymynt9C/hWhfh24F0V4m9WakPGJ+lZQuXKdyoAaGtuoj9fYMO27mlRrjSdVLumkMS1hqRLLTUTTs5IWvWZe3v7mduaOyU2HbYkkmQNl1q+efTUBdnj+qBXApYpQctRShKSLrXU5WuZEtau6OLIwBC79/ex8/XD7N7fx5GBIS1HKRNqqOgUS85QqcTxQomhUoliyWMb/lAPWKaMY8cLDIa1CAqlEpkElieU6cXDDswnH5+Mx0E9YJkS/v0DL9I/YoeG/qES//6BF1M6I2lEZsbIbe8yFt86yOoBy5Swt3fgjOIiY1XyU8u34lyASAlYzsjWXT1s2NbN3t5+FidYhibJMirP649/P4x02wbIZqKhB3cw47Qe8UTSEITUTZMhpo9qnb4k5utnq2S8pjgzYZALuz67D+8AEsXj2g1aCVjqluZylNX+7SXwb1ISNqel8hfz2VXiE6mzvQUvcbK7beAlWBDTdkgagpC6pbkc5Yymytukz9BCQA3n8GDl6pZq8Ynk5QtNAYRhCFVBSOrSXI6y2tfPJL6WSrIKVa56VYtPpIPH8lHyLe8BexSPg3rAUrfmpkxUi1ssnliQOWPJLEeZ5j9KmT7yhRLZrNGUOfk3XSiVyMe0F556wFK3BbNmRHcsdBBsRDxGlYYfRouLjMXwRbhSyaNJGeEDXhfhJHXR+JjRnMkwoylDcyaDmcU2PiaStPPPns3M5ixDpRKDYSryzOYsS8+eHUt7SsBSt6P5IgvnttCUNYruNGWNhXNbOJYvpn1qIhPiyq6zODJYODH5ouRwZLDAlV1nxdKeErDUbXFHG8dHjIUdL5S0Ipk0jIdefOP0YmcP8RgoAUvdruw6iwNH8+SLJTIG+WKJA0fzsfUOylX7Q9UfsEykPT1HGS4DtnCtoxTicdDfr9Ttie5DzGlpolhyjheiZfvmtDTxRPeh2i8ep2qX2nQJTiZSIVzPGJ4F5yPiE01laFK33T199A0WyGUzJ4rV+wYL7O7pS/vURCaEeZV1KGK6zqwesNQtXyiBQcYMw8iE72hx1UiKJC1XpdysWny8lIClbknXSIokrdq8nrjm+ygBS93OP3s282Y2n1KGNm9mc2w1kiJJy2SMpkxYhJ3otinDiWn3E95eLO8qDWntii6am7KcM6eFC85u55w5LTQ3ZbUvmzSMJfPaACMXJhvlMhnAQnziKQFL3VYu62T9dRfR2d7C4YEhOttbWH/dRYksyF5tlEOjHzKR1q2+kLZc5pSZcG25DOtWXxhLe6qCkDOycllnKjtgzGjKnLYn3HBcZCI157I0l6Iyy2zGaM5lY2tLCVjOSFpbElVKvqPFRcZiw7Zu5rTmOHdO64lYf77Ahm3dsfydKwFL3bbu6uGLm56jb7BAoVTiYN9xvrjpOf7DR9+tfeGkIezt7Sdr0H3gKPliieZshvmzmtnX2x9Le/r+JnW74+Fd9PYP4UBTNoMDvf1D3PHwrrRPTWRCtM9o4tW3BimE4YdCyXn1rUFmzYinr6oesNSt++AxMmEiBoStWszpPngs5TMTmRgnllYt25LolPgEUw9YRCRIesnV2BKwmX3LzHrM7MWy2FlmtsXMdofbjhA3M7vTzPaY2fNmdmnZa24Mx+82sxvL4u81sxfCa+40i7pl1dqQ8Vsyr42SnzoTruTEViMpkrTFHW00ZTN0LZjFsnNm07VgFk3ZTGxLrsbZA/42sGpEbB3wmLsvBR4LjwFWA0vDz03AXRAlU+BW4HLgMuDWsoR6Vzh2+HWrarQh47Ru9YXkMnC8GNVIHi+WyGWIrUZSJGlrV3QxVHT68wXco9uhosc22Si2BOzu24CR6xSuAe4N9+8FPlwWv88jTwJzzexc4IPAFnc/5O69wBZgVXhutrs/4dHgzH0j3qtSGzJOz+97i+MjdkA+XnSe3/dW7G1XmwmqTZEbT1uVuttq8YmU9GSjpC/Cne3urwO4++tmNvxbLQT2lh23L8RGi++rEB+tjdOY2U1EvWjOO++8sf5O08bdP30ZiJKee3QRbjh+8/vPj7Xtz1+zlK8/urtiXBpLU9ZgqEo8AUlONposF+Eq/Zf1McTPiLtvdPfl7r58wYIFZ/ryaacv7JVVCmumDt/vGyzE3vaDL7x2RnGZugpVlh6rFp/Kku4B7zezc0PP9FygJ8T3AYvLjlsEvBbiK0fEt4b4ogrHj9ZGQ0ljRtrwIuyV4nHbtb9yqVu1uIxPxiovwZjEkE++ULnioFp8Kku6B7wZGK5kuBF4oCx+Q6iGuAI4HIYRHgGuNbOOcPHtWuCR8FyfmV0Rqh9uGPFeldpoGMMz0p59pZc3Dg/w7Cu9fHHTc2zdFe9nTbV/exqGbTxJr4tbzqp8oleLT2VxlqF9F3gCuMDM9pnZJ4HbgQ+Y2W7gA+ExwINAN7AH+Cvg3wC4+yHgNuCp8LM+xAA+DdwdXvMr4KEQr9ZGw9CMNGlk0+mDPrYhCHe/vspT11Q41oHPVHmfbwHfqhDfDryrQvzNSm00krRmpBWr9H6qxUXGolRl1lm1+FSmqchTVMmdQqF4ohohY5BtwK9oMv1kMxncSyd2Jh7eHj6bmSw1AxOn8X6jaWDBzByF0qnVCIVSFBeZ6pbMa8Ps1F0pzOLblSJNSsBTUHtrMxlOVh+YRf8j21ub0zwtkQmxbvWFzG3LYRkoumMZmNuWa8gZl0rAU1Df8QKLz2qlLZcllzXaclkWn9XK0ePx1uOmuS3QdLowMxksmjPjjOITaeWyTr720XdzyeIOzpndwiWLO/hag645rTHgKWhxRxsvHzx6Sux4ocSS+bNibXdp5yx+uf/oKTNeLMTjdsWSDp54ubdivJGlVY/7px+5mM9+9xmO5YuUPGpvZnOWP/3IxfE2HKS19VXS1AOegq7sOosDR/PkiyUyBvliiQNH81zZdVas7X7ofz4XONnrtBHxWFmGWTNOXQtg1owsWPx/wrNbKvdTqsUnUlo9/5XLOvlP11/K5UvmsbijlcuXzOM/XX/ptEiKSVIPeBzS2h/tie5DdLY3c2SgcGLblNmtTTzRfYibY253ZnOGo/loHzYHZjVnYm8Xoq1i3j5v5inF+O4e21Yx5a5ZtoAf/eL1ivG4pVn6N116oWlSAh6jrbt6uGXzDnJZY25rjp6+QW7ZvIP1EPsf7d7efubNnMH8WS0nYkkkoxdffetE8h12NF/ixVfjXw1tcUcbPX2DtDWf/JMdGCrGtk5ruZ2v9502FJCxKB43o/IiJxr7bgwaghijDdu6owtgzU2YRbe5rLFhW3fsbS/uaGNg6NR58Ukko6PHK8/FrxafSGtXdHF4YIjdPX3seuMIu3v6ODwwFNs6reVefrP/tIRnIR63piqDvdXiMrUoAY/R3t5+WkesT9qayybylTjpRaOHVfvWm9T8pPxQkXyhxFDRyRdK5IeSWZylUCyd9pW/6FE8bu/onHXaP9JMiMvUpwQ8Rmn1QiH5RaOHpVkKdvtDO+kfKpHLZGgJxfn9QyVuf2hn7G2n+cGz+l3nnP4f2EJcpjyNAY/R2hVd3LJ5B/35Aq25LANDxUR6ocPSuECyqKOVvb0Dp+QDD/G4vfxmf7T+RaZs/YuSJzIMUG0JgiSWJoguuM6gb/DkBdf2lvgvuEoy1AMeo7R6oWm6bc27mNMafWYP5545rU3ctua0NZEaSntLU1j8KOqMDt9vT6AMbW9vP/NnzThlk8j5s2YkMtQl8VMPeBymW5nOymWdfON3LmHDtm729fazKMHSu675M9ndcxRzP7EwfMlh6YKZsbf9qauW8I3H95xIvMM7gXzqqiWxt51m9YfETz1gGZOkFwb80qplzJyRJV8oMThUIl8oMXNGli+tWhZ72ze//3w+d/U7aM1lKZSii62fu/odse+DB+ldcJVkqAcsdUuz9hkgl80wI5ehWHKyGSOXTa7/cPP7z08k4Y60clknH933Fnf/9GWO5YvMbM7yqauWTKtvXo1MCVjqtmFbN/lCkTePnnpBaMO27tgTwoZt3cxpzXHunJMX/PrzhUTahvRmPW7d1cOmZ15lQfsMzgsXezc98yoXL5qrJNwANAQhdXtp/xHePJanUHSyZhSKzpvH8uzefyT2ttOsux7u+ff0DZ7S8497Dz5Id8KPxE8JWOo2FGYjZDKGmZ0oCcsnsDBBmnXXaSbBND94JH5KwFK35qYMeLQdkuPRHl0e4jFL82JUmkkwzQ8eiZ8SsNRtaWc789ubacoYxZLTlDHmtzeztLM99rbTrLtOMwmqCqKx6SKc1G149t85c5qmzew/SHfW48plnayHVGqvJX7mDbjV81gsX77ct2/fnvZpTHrD1QDTLRlM199bxqyuJVKUgAMlYBGZQHUlYI0Bi4ikRAlYRCQlSsAiIilRAhYRSYkSsIhISho2AZvZKjP7pZntMbN1aZ+PiMhIDZmAzSwL/CWwGngncL2ZvTPdsxIROVVDJmDgMmCPu3e7ex74HrAm5XMSETlFoybghcDessf7QuwUZnaTmW03s+0HDhxI7ORERKBx14KoNAvltCl/7r4R2AhgZgfM7H+Msb35wMExvna80mp7Ov7O07Xt6fg7j7fth919Va2DGjUB7wMWlz1eBLw22gvcfcFYGzOz7e6+fKyvH4+02p6Ov/N0bXs6/s5Jtd2oQxBPAUvNbImZNQMfBzanfE4iIqdoyB6wuxfM7A+BR4As8C1335HyaYmInKIhEzCAuz8IPJhQcxsTamcytT0df+fp2vZ0/J0TaVvLUYqIpKRRx4BFRCY9JWARkZQoAY+DmX3LzHrM7MWE211sZj8xs51mtsPMPpdg2y1m9nMzey60/SdJtV12Dlkze9bMfpxwu782sxfM7Bdmltj2KWY218w2mdmu8P/8yoTavSD8rsM/R8zs80m0Hdr/t+Fv7EUz+66ZtSTU7udCmzvi/n01BjwOZrYCOArc5+7vSrDdc4Fz3f0ZM2sHngY+7O7/mEDbBsx096NmlgN+CnzO3Z+Mu+2yc/gCsByY7e6/lWC7vwaWu3uiEwPM7F7gH9z97lBW2ebubyV8DlngVeBydx/rhKUzaW8h0d/WO919wMzuBx5092/H3O67iJYuuAzIAw8Dn3b33XG0px7wOLj7NuBQCu2+7u7PhPt9wE4qTLWOqW1396PhYS78JPYpbmaLgN8E7k6qzTSZ2WxgBXAPgLvnk06+wTXAr5JIvmWagFYzawLaqDGZaoJcCDzp7v3uXgD+O/CRuBpTAp7izOztwCXAzxJsM2tmvwB6gC3unljbwF8AfwyUEmxzmAN/b2ZPm9lNCbXZBRwA/joMu9xtZjMTarvcx4HvJtWYu78KfA14BXgdOOzuf59A0y8CK8xsnpm1AR/i1Fm1E0oJeAozs1nAD4DPu/uRpNp196K7v4doivdl4Wtb7Mzst4Aed386ifYqeJ+7X0q0zOlnwhBU3JqAS4G73P0S4BiQ6PrWYdjjOuD7CbbZQbSC4RLgbcBMM/v9uNt1953AHcAWouGH54BCXO0pAU9RYfz1B8B33P2HaZxD+Cq8Fai56MgEeR9wXRiL/R5wtZn9TUJt4+6vhdse4EdE44Rx2wfsK/uWsYkoISdpNfCMu+9PsM33Ay+7+wF3HwJ+CPyzJBp293vc/VJ3X0E0xBjL+C8oAU9J4ULYPcBOd/96wm0vMLO54X4r0T+UXUm07e5fdvdF7v52oq/Ej7t77L0iADObGS54EoYAriX6uhord38D2GtmF4TQNUDsF1tHuJ4Ehx+CV4ArzKwt/L1fQ3StI3Zm1hluzwN+mxh/94adipwEM/susBKYb2b7gFvd/Z4Emn4f8K+AF8JYLMC/C9Ov43YucG+4Kp4B7nf3RMvBUnI28KMoF9AE/K27P5xQ258FvhOGArqBTyTULmEc9APA2qTaBHD3n5nZJuAZoiGAZ0luWvIPzGweMAR8xt1742pIZWgiIinREISISEqUgEVEUqIELCKSEiVgEZGUKAGLiKRECVimLDNbZGYPmNluM/uVmX0jlGrF2ebRcPv28lXwzOwyM9tmZr8Mq5bdHUq4xtveV8zsj8b7PjI5KQHLlBSK838I/Fd3XwqcD8wCvjrO9z3j2ngzO5tomu6X3P0CogVdHgbax3Mu0viUgGWquhoYdPe/hmh9CuDfAn9gZk+Z2UXDB5rZVjN7b5jN9q3w/LNmtiY8/6/N7Ptm9v8QLbYzy8weM7Nnwvq/a2qcy2eAe939iXAu7u6b3H2/mZ1lZv/VzJ43syfN7OLQ5lfCuWw1s24zu7nsfP/P0JN+FLigcpPSCDQTTqaqi4jWQT7B3Y+Y2SvAj4F/Cdwa1k5+m7s/bWZ/RjR9+Q/CdOqfhyQHcCVwsbsfCr3gj4T3mw88aWabvfqspXcB91Z57k+AZ939w2Z2NXAf8J7w3DLgN4h6yr80s7uAi4mmWV9C9O/zmZG/pzQO9YBlqjIqr0NsRAsEfSw8/pecXMXrWmBdmL69FWgBzgvPbXH3Q2Xv8Wdm9jzwKNFay2eP8TyvAv4LgLs/Dswzsznhuf/m7sfDAu89oY1/DvworEd7BNg8xnZlClAClqlqB9GuGCeExcsXA08Bb4av+79DtHIaRIn1f3X394Sf88LygxAt8zjs94AFwHvDspv7iZL1aOfy3irPWYXY8AfH8bJYkZPfSLU+wDShBCxT1WNAm5ndACe2zPmPwLfdvZ8o6f4xMMfdXwiveQT4bLiAh5ldUuW95xCtOzxkZr8B/JMa5/JN4EYzu3w4YGa/b2bnANuIEjpmthI4WGPt5m3AR8ysNay+9i9qtC1TmBKwTElhPPYjwMfMbDfwEjAI/LtwyCaisdT7y152G9EWSs+HErLbqrz9d4DlFm28+XvUWG4zrJP7ceBr4eLZTqKhhCPAV8J7PQ/cDtxY472eAf4O+AXRes//MNrxMrVpNTQRkZSoBywikhIlYBGRlCgBi4ikRAlYRCQlSsAiIilRAhYRSYkSsIhISv5/POlAtSJspY4AAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -518,16 +562,16 @@ }, { "cell_type": "code", - "execution_count": 191, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 191, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, @@ -535,7 +579,7 @@ "data": { "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -548,40 +592,48 @@ }, { "cell_type": "code", - "execution_count": 307, + "execution_count": 14, "metadata": {}, "outputs": [ { - "ename": "TypeError", - "evalue": "median() takes 1 positional argument but 3 were given", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mindex_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Neighborhood'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'OverallQual'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'a'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m's'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Neighborhood\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"SalePrice\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m#plt.xticks(rotation=90)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: median() takes 1 positional argument but 3 were given" - ] + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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AgMlRZgEAAJgcZRYAAIDJUWYBAACYHGUWAACAyVFmAQAAmBxlFgAAgMlRZgEAAJicQctsVZ1eVddU1Z6qOm+N9beuqjfP1l9eVfcYMg8AAADbw2BltqoOTXJBkoclOSHJ2VV1wqrNnpjkq919ryQvSvL7Q+UBAABg+xjyzOxJSfZ097XdfWOSNyU5c9U2ZyZ5zWz6rUlOq6oaMBMAAADbwGEDPvfRSa6bm9+b5IHrbdPdN1XVDUnunORL8xtV1TlJzpnNfqOqrjkA+Y5avZ8tNqY8Y8qSyLORMWVJ5FlkTHnGlCWRZyNjypLIs8iY8owpSyLPRsaUJZFnkTHlGVOW5MDlufsyGw1ZZtc6w9o/xjbp7guTXHggQv1gx1W7u3vngXzOW2JMecaUJZFnI2PKksizyJjyjClLIs9GxpQlkWeRMeUZU5ZEno2MKUsizyJjyjOmLMnm5xnyMuO9SY6dmz8myfXrbVNVhyW5U5KvDJgJAACAbWDIMntFkuOr6riqOjzJWUl2rdpmV5LfmE0/Osm7u/tHzswCAADAvMEuM579Dey5SS5NcmiSV3X31VV1fpLd3b0ryR8neV1V7cnKGdmzhsqzhgN62fIBMKY8Y8qSyLORMWVJ5FlkTHnGlCWRZyNjypLIs8iY8owpSyLPRsaUJZFnkTHlGVOWZJPzlBOhAAAATM2QlxkDAADAIJRZAAAAJmdbl9mquk9VfWTu5+tV9bRV21RVvbSq9lTVVVV1vwHzHFlVb62qT1XVJ6vqQVuVZaR5PltVH5u9VrvXWH9Qjs/YjuPZ/kYxNnP7G82xM7bXa0xjM7fPQ6vqw1X19jXW3bqq3jzLc3lV3WPgLKMZnzFlme1vbO/z0eSpqt+qqqur6uNVdVFV3WbV+s0+jseWx7G8fhZjs36WsX1+juZ9teTYnFpVN8xt85yh8sz2N45jp7sPip+s3ITqH5PcfdXyhyd5R1a+8/bkJJcPmOE1Sf7DbPrwJEduVZaR5vlskqM2WH9Qj89sn1t+HI9xbMZ27Izp9Rrj2CR5epI3Jnn7Guv+Y5KXz6bPSvLmg+XYGVOW2f7G9j4fRZ4kRyf5TJLbzuYvTvL4Vdts2nE8tjyzfTiWjc0tzbWln59jfF8tMTanZo3P1e1+7GzrM7OrnJbk0939uVXLz0zy2l5xWZIjq+quB3rnVXXHJA/Jyh2c0903dvfXtiLLGPMsyfhs8XGcjHpsNrJVebb89VrCpmapqmOS/GqSV26Q5zWz6bcmOa2qaqg8SzgoX6uxvc/Hlicr3wZx26o6LMntkly/RpbNPI7HlmeRg/nYWcTYrBjD5+dY31frjc2mGdOxczCV2bOSXLTG8qOTXDc3v3e27EC7Z5J9Sf6kVi6ve2VVHbFFWcaYJ0k6yTur6kNVdc4a6w/28Um2/jhOxjk2Yzp25o3h9Rrb2Lw4yX9K8v111v8gT3fflOSGJHceMM+YxmdMWcb2Ph9Nnu7+hyQvSPL5JF9IckN3v3O9LEMfx2PLsz9WHMvrMTbL2dLPz5G+r/Zbb2yS5EFV9dGqekdV/eyAGUZz7BwUZbaqDk9yRpK3rLV6jWVDfF/RYUnul+SPuvvEJN9Mct4WZRljniQ5pbvvl+RhSX6zqh6yhXlGNz4jOY6TEY5NxnXsrOxwPK/XaMamqv5Vki9294c22myz8syMZnxGlmVs7/PR5Kmqn8jKGYfjktwtyRFV9dityDLGPDOO5fUZmwXG8Pk50vfVorG5MiuXHv+zJP89yZ8PGGU0x85BUWaz8h+MK7v7f6+xbm+SY+fmj8mPXkZwIOxNsre7L5/NvzUrB8FWZBljnnT39bPfX0zyZ0lO2sI8oxufjOM43r+vUY3NyI6d/Ubxeo1sbE5JckZVfTbJm5L8clW9fr08s0u77pTkKwPlGdX4jClLxvc+H1Oehyb5THfv6+7vJnlbkgevl2UTjuOx5XEsb8DYLGUMn5+je1/NrDs23f317v7GbPqSJLeqqqMGyjGaY+dgKbNnZ/3T8buSPG52x62Ts3IZwRcOdIDu/sck11XVfWaLTkvyia3IMsY8VXVEVd1h/3SSf5Hk41uVZ2zjM7Plx3EyvrEZ27EzZ8tfr7GNTXf/dncf0933yMplUu/u7tX/p3tXkt+YTT96ts1QZ7RGMz5jypKM730+sjyfT3JyVd2uqmqW5ZNrZNmU43hseRzL6zM2S9vyz8+M7H01Z92xqaq7zLKmqk7KSs/78hAhRnXs9Cbd8WqrfrLyB9tfTnKnuWVPTvLk2XQluSDJp5N8LMnOAbP8fJLdSa7Kyqn/n9iqLGPLk5Vr7z86+7k6ybO38rUa4fiM5jge4diM8dgZxes1xrGZy3ZqZnddTHJ+kjNm07fJyuVTe5J8MMk9D4ZjZ0xZ5jKN5n0+tjxJfifJp7JSRF6X5NZbdRyPLY9j2djcwjyj+Pyc7Ws076slx+bc2XH10SSXJXnwwXDs1GxnAAAAMBkHy2XGAAAAbCPKLAAAAJOjzAIAADA5yiwAAACTo8wCAAAwOcosAKyhqrqq/nBu/plV9bwFjzmjqs5bsM2pVfX2ddZ9tm7Bl9xX1fOq6pk/7uM3+3kB4JZQZgFgbd9J8qibUy67e1d3/96AmdZVVYdtxX4BYKsoswCwtpuSXJjkt1avqKodVfWnVXXF7OeU2fLHV9XLZtM/XVWXzdafX1XfmHuK21fVW6vqU1X1hqqquXXPqqoPzn7uNXuuu1fVu6rqqtnvn5otf3VVvbCq3pPk92ePP6Gq3ltV11bVU+cyP72qPj77edoSy59dVddU1V8nuc8tHEsAOOCUWQBY3wVJfr2q7rRq+UuSvKi7H5DkXyd55RqPfUmSl8y2uX7VuhOTPC3JCUnumeSUuXVf7+6TkrwsyYtny16W5LXd/XNJ3pDkpXPb3zvJQ7v7GbP5+yb5l0lOSvLcqrpVVd0/yROSPDDJyUmeVFUnLlh+1izno5I8YKNBAoCt4JIkAFhHd3+9ql6b5KlJvj236qFZOQO6f/6OVXWHVQ9/UJJfm02/MckL5tZ9sLv3JklVfSTJPZL8r9m6i+Z+v2juuR41m35dkufPPddbuvt7c/N/2d3fSfKdqvpikp9M8gtJ/qy7vznb59uS/PMktc7yQ2bLvzVbvmvNAQKALaTMAsDGXpzkyiR/MrfskCQP6u75gpsfvlp4Q9+Zm/5efvjzuNeZzjrLv7nEc68XbKPA6+0bAEbBZcYAsIHu/kqSi5M8cW7xO5Ocu3+mqn5+jYdelpVLkJOVS3aX9Zi53x+YTf/d3HP8ev7/WdxlvS/Jr1XV7arqiCSPTPL+BcsfWVW3nZ1xfsTN3B8ADM6ZWQBY7A8zV16zctnxBVV1VVY+S9+X5MmrHvO0JK+vqmck+cskNyy5r1tX1eVZ+R/OZ8/t71VV9awk+7Lyd65L6+4rq+rVST44W/TK7v5wsnITqXWWvznJR5J8LisFFwBGpbpdRQQAB1pV3S7Jt7u7q+qsJGd395lbnQsAtgtnZgFgGPdP8rLZ1+58Lcm/3+I8ALCtODMLAADA5LgBFAAAAJOjzAIAADA5yiwAAACTo8wCAAAwOcosAAAAk/P/APSCLooLUM9dAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ "index_ = train.groupby(by='Neighborhood')['OverallQual'].median()\n", "sns.boxplot(x=\"Neighborhood\", y=\"SalePrice\", data=train, order=index_)\n", - "#plt.xticks(rotation=90)\n", - "index" + "#plt.xticks(rotation=90)\n" ] }, { "cell_type": "code", - "execution_count": 138, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 138, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, @@ -589,7 +641,7 @@ "data": { "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -602,16 +654,16 @@ }, { "cell_type": "code", - "execution_count": 271, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 271, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, @@ -619,7 +671,7 @@ "data": { "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -638,16 +690,16 @@ }, { "cell_type": "code", - "execution_count": 200, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 200, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, @@ -655,7 +707,7 @@ "data": { "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -668,16 +720,16 @@ }, { "cell_type": "code", - "execution_count": 199, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 199, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, @@ -685,7 +737,7 @@ "data": { "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -698,16 +750,16 @@ }, { "cell_type": "code", - "execution_count": 201, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 201, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, @@ -715,7 +767,7 @@ "data": { "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -728,7 +780,7 @@ }, { "cell_type": "code", - "execution_count": 297, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1090,7 +1142,7 @@ " dtype='period[M]', freq='M')))]" ] }, - "execution_count": 297, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1104,7 +1156,7 @@ }, { "cell_type": "code", - "execution_count": 298, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1174,7 +1226,7 @@ "Length: 82, dtype: int64" ] }, - "execution_count": 298, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -1185,7 +1237,7 @@ }, { "cell_type": "code", - "execution_count": 300, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1255,7 +1307,7 @@ "Length: 80, dtype: int64" ] }, - "execution_count": 300, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1264,6 +1316,13 @@ "test.isnull().sum(axis=0)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, diff --git a/Kelly/Kaggle_house_price/Forest Models (one hot yr_month sold).ipynb b/Kelly/Kaggle_house_price/Forest Models (one hot yr_month sold).ipynb new file mode 100644 index 0000000..92800ec --- /dev/null +++ b/Kelly/Kaggle_house_price/Forest Models (one hot yr_month sold).ipynb @@ -0,0 +1,337 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Random Forest (regression)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "## import data\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "train = pd.read_csv(\"train.csv\").set_index(\"Id\")\n", + "test = pd.read_csv(\"test.csv\").set_index(\"Id\")\n", + "combine = pd.concat([train, test])\n", + "\n", + "# process data before model fitting\n", + "from preprocess3_yrmonthsold import impute\n", + "fact, encodedDic = impute(combine, False) # process data and label encode \n", + "\n", + "# seperate factorized data into train and test\n", + "train_label = fact.iloc[0:1460,]\n", + "test_label = fact.iloc[1460:2919,].drop('SalePrice', axis = 1) # salesprice col were all NA \n", + "\n", + "# split train data frame into x var and y var for model testing\n", + "x_label = train_label.drop('SalePrice', axis=1)\n", + "y_log = np.log(train_label.SalePrice)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "GridSearchCV(cv=5, error_score='raise',\n", + " estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n", + " max_features='auto', max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n", + " oob_score=False, random_state=None, verbose=0, warm_start=False),\n", + " fit_params=None, iid=True, n_jobs=-1,\n", + " param_grid=[{'n_estimators': array([100, 200, 300]), 'min_samples_leaf': range(10, 15), 'min_samples_split': array([ 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30]), 'random_state': [42]}],\n", + " pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n", + " scoring='neg_mean_squared_error', verbose=0)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn import ensemble\n", + "from sklearn.model_selection import GridSearchCV\n", + "\n", + "randomForest = ensemble.RandomForestRegressor()\n", + "grid_para_forest = [{\n", + " \"n_estimators\": np.arange(100, 400, 100),\n", + " \"min_samples_leaf\": range(10,15),\n", + " \"min_samples_split\": np.linspace(start=2, stop=30, num=15, dtype=int),\n", + " \"random_state\": [42]}]\n", + "grid_search_forest = GridSearchCV(randomForest, grid_para_forest, scoring='neg_mean_squared_error', cv=5, n_jobs=-1)\n", + "grid_search_forest.fit(x_label, y_log)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters found: {'min_samples_leaf': 10, 'min_samples_split': 2, 'n_estimators': 300, 'random_state': 42}\n", + "Lowest RMSE found: 0.1501387751567232\n" + ] + } + ], + "source": [ + "print(\"Best parameters found: \", grid_search_forest.best_params_)\n", + "print(\"Lowest RMSE found: \", np.sqrt(np.abs(grid_search_forest.best_score_)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### RMSE for all train data using best random forest" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 4.154388487892194e-05\n" + ] + } + ], + "source": [ + "# fit best random forest to all train data \n", + "best_forest_y = grid_search_forest.best_estimator_.predict(x_label)\n", + "print(\"RMSE: \", np.sqrt(np.mean(y_log-best_forest_y)**2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Feature importance" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "pd.DataFrame(list(zip(x_label.columns, grid_search_forest.best_estimator_.feature_importances_ ))).set_index(0).sort_values(1).plot.barh()\n", + "plt.rcParams['figure.figsize'] = 4, 32" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### test prediction" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "best_forest_test_y = grid_search_forest.best_estimator_.predict(test_label) \n", + "best_forest_test_y = np.expm1(best_forest_test_y)\n", + "sub = pd.DataFrame() \n", + "sub['SalePrice'] = best_forest_test_y\n", + "sub = sub.set_index(np.arange(1461, 2920))\n", + "sub.to_csv('(14) forest_log(y)_label_submission.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Extreme Gradient Boosting Random Forest (regression)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# !pip install xgboost" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GridSearchCV(cv=5, error_score='raise',\n", + " estimator=XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n", + " colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n", + " max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n", + " n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n", + " reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n", + " silent=True, subsample=1),\n", + " fit_params=None, iid=True, n_jobs=-1,\n", + " param_grid=[{'colsample_bytree': array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]), 'max_depth': [2, 4, 6], 'n_estimators': array([500, 600, 700, 800, 900]), 'random_state': [42]}],\n", + " pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n", + " scoring='neg_mean_squared_error', verbose=0)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import xgboost as xgb\n", + "xgbforest = xgb.XGBRegressor()\n", + "grid_para_xgbforest = [{\n", + " \"colsample_bytree\": np.linspace(0.1, 0.9, 9),\n", + " 'max_depth':[2, 4, 6],\n", + " \"n_estimators\": np.arange(500, 1000, 100),\n", + " \"random_state\": [42]}]\n", + "grid_search_xgbforest = GridSearchCV(estimator = xgbforest, param_grid = grid_para_xgbforest, \n", + " scoring = 'neg_mean_squared_error', cv = 5, n_jobs=-1)\n", + "\n", + "grid_search_xgbforest.fit(x_label, y_log)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters found: {'colsample_bytree': 0.30000000000000004, 'max_depth': 4, 'n_estimators': 500, 'random_state': 42}\n", + "Lowest RMSE found: 0.12012350484358175\n" + ] + } + ], + "source": [ + "print(\"Best parameters found: \", grid_search_xgbforest.best_params_)\n", + "print(\"Lowest RMSE found: \", np.sqrt(np.abs(grid_search_xgbforest.best_score_)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### RMSE for all train data using best gradient boost" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 1.7992035825790895e-05\n" + ] + } + ], + "source": [ + "# fit best XGB to all train data \n", + "best_xgbforest_y = grid_search_xgbforest.best_estimator_.predict(x_label)\n", + "print(\"RMSE: \", np.sqrt(np.mean(y_log-best_xgbforest_y)**2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Feature importance" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pd.DataFrame(list(zip(x_label.columns, grid_search_xgbforest.best_estimator_.feature_importances_ ))).set_index(0).sort_values(1).plot.barh()\n", + "plt.rcParams['figure.figsize'] = 4, 32" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### test prediction" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "best_xgb_test_y = grid_search_xgbforest.best_estimator_.predict(test_label) \n", + "best_xgb_test_y = np.expm1(best_xgb_test_y)\n", + "sub = pd.DataFrame() \n", + "sub['SalePrice'] = best_xgb_test_y\n", + "sub = sub.set_index(np.arange(1461, 2920))\n", + "sub.to_csv('(15) xgb_log(y)_label_submission.csv')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Kelly/Kaggle_house_price/Forest Models (no ordinal encode).ipynb b/Kelly/Kaggle_house_price/Forest Models (no ordinal encode).ipynb new file mode 100644 index 0000000..449b661 --- /dev/null +++ b/Kelly/Kaggle_house_price/Forest Models (no ordinal encode).ipynb @@ -0,0 +1,337 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Random Forest (regression)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "## import data\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "train = pd.read_csv(\"train.csv\").set_index(\"Id\")\n", + "test = pd.read_csv(\"test.csv\").set_index(\"Id\")\n", + "combine = pd.concat([train, test])\n", + "\n", + "# process data before model fitting\n", + "from preprocess2_label import impute\n", + "fact, encodedDic = impute(combine, False) # process data and label encode \n", + "\n", + "# seperate factorized data into train and test\n", + "train_label = fact.iloc[0:1460,]\n", + "test_label = fact.iloc[1460:2919,].drop('SalePrice', axis = 1) # salesprice col were all NA \n", + "\n", + "# split train data frame into x var and y var for model testing\n", + "x_label = train_label.drop('SalePrice', axis=1)\n", + "y_log = np.log(train_label.SalePrice)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "GridSearchCV(cv=5, error_score='raise',\n", + " estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n", + " max_features='auto', max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n", + " oob_score=False, random_state=None, verbose=0, warm_start=False),\n", + " fit_params=None, iid=True, n_jobs=-1,\n", + " param_grid=[{'n_estimators': array([100, 200, 300]), 'min_samples_leaf': range(10, 15), 'min_samples_split': array([ 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30]), 'random_state': [42]}],\n", + " pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n", + " scoring='neg_mean_squared_error', verbose=0)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn import ensemble\n", + "from sklearn.model_selection import GridSearchCV\n", + "\n", + "randomForest = ensemble.RandomForestRegressor()\n", + "grid_para_forest = [{\n", + " \"n_estimators\": np.arange(100, 400, 100),\n", + " \"min_samples_leaf\": range(10,15),\n", + " \"min_samples_split\": np.linspace(start=2, stop=30, num=15, dtype=int),\n", + " \"random_state\": [42]}]\n", + "grid_search_forest = GridSearchCV(randomForest, grid_para_forest, scoring='neg_mean_squared_error', cv=5, n_jobs=-1)\n", + "grid_search_forest.fit(x_label, y_log)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters found: {'min_samples_leaf': 10, 'min_samples_split': 2, 'n_estimators': 300, 'random_state': 42}\n", + "Lowest RMSE found: 0.15032551004916286\n" + ] + } + ], + "source": [ + "print(\"Best parameters found: \", grid_search_forest.best_params_)\n", + "print(\"Lowest RMSE found: \", np.sqrt(np.abs(grid_search_forest.best_score_)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### RMSE for all train data using best random forest" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.0001501873512564858\n" + ] + } + ], + "source": [ + "# fit best random forest to all train data \n", + "best_forest_y = grid_search_forest.best_estimator_.predict(x_label)\n", + "print(\"RMSE: \", np.sqrt(np.mean(y_log-best_forest_y)**2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Feature importance" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "pd.DataFrame(list(zip(x_label.columns, grid_search_forest.best_estimator_.feature_importances_ ))).set_index(0).sort_values(1).plot.barh()\n", + "plt.rcParams['figure.figsize'] = 4, 32" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### test prediction" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "best_forest_test_y = grid_search_forest.best_estimator_.predict(test_label) \n", + "best_forest_test_y = np.expm1(best_forest_test_y)\n", + "sub = pd.DataFrame() \n", + "sub['SalePrice'] = best_forest_test_y\n", + "sub = sub.set_index(np.arange(1461, 2920))\n", + "sub.to_csv('(9) forest_log(y)_label_submission.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Extreme Gradient Boosting Random Forest (regression)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# !pip install xgboost" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GridSearchCV(cv=5, error_score='raise',\n", + " estimator=XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n", + " colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n", + " max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n", + " n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n", + " reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n", + " silent=True, subsample=1),\n", + " fit_params=None, iid=True, n_jobs=-1,\n", + " param_grid=[{'colsample_bytree': array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]), 'max_depth': [2, 4, 6], 'n_estimators': array([500, 600, 700, 800, 900]), 'random_state': [42]}],\n", + " pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n", + " scoring='neg_mean_squared_error', verbose=0)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import xgboost as xgb\n", + "xgbforest = xgb.XGBRegressor()\n", + "grid_para_xgbforest = [{\n", + " \"colsample_bytree\": np.linspace(0.1, 0.9, 9),\n", + " 'max_depth':[2, 4, 6],\n", + " \"n_estimators\": np.arange(500, 1000, 100),\n", + " \"random_state\": [42]}]\n", + "grid_search_xgbforest = GridSearchCV(estimator = xgbforest, param_grid = grid_para_xgbforest, \n", + " scoring = 'neg_mean_squared_error', cv = 5, n_jobs=-1)\n", + "\n", + "grid_search_xgbforest.fit(x_label, y_log)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters found: {'colsample_bytree': 0.1, 'max_depth': 2, 'n_estimators': 900, 'random_state': 42}\n", + "Lowest RMSE found: 0.12053449602685692\n" + ] + } + ], + "source": [ + "print(\"Best parameters found: \", grid_search_xgbforest.best_params_)\n", + "print(\"Lowest RMSE found: \", np.sqrt(np.abs(grid_search_xgbforest.best_score_)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### RMSE for all train data using best gradient boost" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 5.955640741391253e-06\n" + ] + } + ], + "source": [ + "# fit best XGB to all train data \n", + "best_xgbforest_y = grid_search_xgbforest.best_estimator_.predict(x_label)\n", + "print(\"RMSE: \", np.sqrt(np.mean(y_log-best_xgbforest_y)**2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Feature importance" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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eLK6t8Icz4AV332BezytD6xOJRGLB0aRD6wskGr9PQKRWeD0pVnk5qbS0ixbxPh/1FScgN+zlyLCzAXLCViISBTlun0bpRlugXuylqG+5CrCNu7czs7HIFHQ4WtKsv5n9E5V1J6IeZ3NU2t0LuNjdVwMws7cQMf4bEfpRiGzL0ILg0+PzdkImqTUQoR8W19UNRRo+UvcZpSJNNBakukw0djRKIp0PPA6cbWZ7ufsNZtYbkWIZcsVORYtjt6ao/LaK4w51dzez1dz9MzN7EhHY+ahs2wuNuZSb2duo3LtH7NPazKYgYi+naEDaDqnZrYF+ZlYV19mcYkl5MFKp7VBv9HUU5lBQnp1QwlK/OG4ASkzqy1yINHukiUQiUT9olKXd+YGZLYECGFZAJddhyPgzG7l9dwdOdfdTY//paB5zPURsY919sJnti1RhoUu+LnIAL4L6slugEZnbkWq8CZmRPqC42sunqGxriGCnIEJeE3gi9pkc701G4RDNkOu3ChHnbFRmHoziBB0RbSfgKHefw2xUipY9l/Wee1+0wM8wkfgxkIoz0Vgxv6XdJkukBUTgwcl1HLztUQn3A2Afd38ziHQkcAQy8fwV5eAuhlZbWQL1N59BwQ4d0VjNf9DIC0hFHoV6pB+jEZx+KKChHeqVro8iAQ+Oc76IeqSHo7GX7khlTkc90VuQmj0srnkGMkjd5e67m9ks4Li6RFonkGGNcePGff+HmEgkEgshmnSPdAHRn3DwFuDuX5vZR8jBu4uZnYDKvoMQUVai9UeHxD7ropVdNkIKdBQix+HEAuHIxXsFcBXqczZDaUWlWAGNqWwYx7VDyrYZivirRtm+z1NUwO/FufvG5xeU6zZm9kEcWzrOU7jHDGRI1AtScSYWdiwMRGrMOdtZuv0plFk7E6nBbqiXegcirtuR6qxBhqDPEWn2RYr1tTh+MVSGrQVGuPuQ6IMuhXJ0zyr5zGp3/wPwBzMrrD3aDPVVK4AH3X0/M7sCqeVTzKwcuX9nUMz5XQeRcWV8/pw3lxGBiUQiUS9YGIj0TeR6/QZR2i2UVfshAjwTOWcrEHHuhBTkPYisXkY9yRfQaiyHImXZGqnEr+L0y5rZGKQaZ6PIwubxGe8AA4F7Y98VEYnWICLtjOZEQQlN5Wb2Biojz0KRg6eiVWweoDg+s5eZHR/JSEAq0sRPi/xilkgUsTD0SA0R5iXh4G2Gxl6+dvcjQjm2QKaeSSj79gZEqB+icZMHUDn3AOSW/T1y4C6HxmLeB85BMYBvx+9Xx+ecB9zn7gNiUfHnKYbO/xup4GOR8/Z54IFQtB8CfWQgtsPQOqVjEZG2Rar4C0TunwODI3iicN/ZI00kEokfgEZtNgryewY4w90fjG27AEPdfcvvcb5SB28PpA7HxtvLIbI8BQUlHIJU4UsoRKEl6l0ejNTflaif+hoyBK2AFGMVGqvpgNTpEoiYTwUOdvcBcS03ICXbERHiFKRKL0VlYI9zLYnmUluY2Y6ImD0++3NgK3cfa2aFVKY1S4m0FBnIkEgkEguORk2kAGY2ABlvVkNE8yqwZd0FrBfwnM2BfwB3uPs9sSboI4hYxyA1uglSoJch1XgdGjk5DPgLWqy70t1bm9ndaPWVK1A27/VIOR4C7Am84+6HUAIz+zMi2L3i381Q2bYZiv67zN1PMLNXgdfcfW8z2yzOORtY1d2XKTnfJ3Hdi7n75yXbSwMZ1uj129I44kRiwZHl3MTChkbv2nX3NyLE/Wik3G5w9/fNbG9UWi1Hoe+HILPNMKQ2y5Ca3NDdZ8XC3legFVjqDlMOj39nuvsKZjYUKeFyFNCwGFKk26IeZiHPt6WZDUdqdioi4teRIagcuWynokAGzOwk5AD+CK0hWgi47x7HHoiIuwIp3xNQIP0QM9sJEe3XaCWZDc1sdmyroNg/razz/DKQIZFIJOoBDZZIA6cik88sYGCo1B2A9dx9tpldCeyGZjDPc/e/hcP1I9SfHBrnmeHu6wOY2a9Kzr8lUnmtzew1pCZbILVoiLD3QwRdhXqfR1Bcc/TvaF3SVshEVI4cwH9GTt01zOzd2N4v/n0VEfhYYGNE/LejUZhdgeXMbBAKcKhGZeLOiMjfjGcxE5V3eyIl+5G7F8xOxH1mRGDiR0Eq0UTi29GgidTdZ5jZbcB0d6+KEueawMhYjqc1IrVKwM3sZXRPLYBfm9lIpOy2NbOD0JqeoOXUroz3DM1+XoFiA5sDFyClV4lU5nA0erIZIsPpiORaInU5GC3+bciJuw8iuAmo11mGDEktUQ/1dOCkOK4WOBH1R1dAqrkitk9CjmCPf2+K66yJ6/kMkf9Nc3l2qUgTiUSiHtCgiTRQGz8gorrW3U8svGlmhYW9DwPWcvevzOxGFETfHBHfUKTgXkRq9XwUt/cM6nvuhJy0hRCEA5AKPA94BZWWhyN1uDIixlpEZp0Quc1EQfKXoPnOVihu8E6Kc56j4tgPUD/1FBTacDRSxmWIeAurwfREZF8DtHf3mZHA1A6Rbos47vy6Dy0VaWJBkcozkfh+aAxEWorHgDvM7OJY5aWwsHU7lF37lJktRpF8jkKqckqQ0F1obGVNNAu6RLw/E5mQhsR57kMk1Q2VaV9EJPs7ikr0YVR6PhEp0FpkFhpEcf3SSSjg4RxUml0c6BPbByBFXYvcwTei0u1gpCQdjceshXqprcxsWaRCl0XmqK7x3lYoSvAbpCJNJBKJ+kGjIlJ3f93MTgUeM7MyRGrXIDK5E63A8hUiqiVQyMK5dU+DiLDK3Vc1s13RWM2J4cJ9EfUxmyMSLF3S/VNEzn+Pz9wWKcch7j7CzE4DjkOGoo+BI9z9DjPbHcX7LY5U7L3IELUtIn2Q6n0elYafQqS6PSrpPhTHL4YU8vKopDst7vXBb3tur38yNRVp4juRijSR+H5o8ETq7qfUeX0zxV5nYeZ0KPCou/8miPaXSI1VoNLn8Ji37Ih6kWcAXSM16D7gQDNzRICguMCHEIk1d/cNzOwqVO49HpWLeyIymwkMKwl2qEBKsQq4wMy2R0aj6YjEW6Pl0Nqj8IYNkOkIVGKegUj1BTSr+nLcXzNUMq6Ke+pZ12BUiowITCQSifpBgyfS70KsHboDcJmZnYgU3ltIGR6HyGsssDQiwFnIjfsYIqStgc0RIX6EepsXotLwlyhUHpRtWxX734+U6uvAQagsXFhj9GFUnn0rznM8GlupBvaOa5kV+xVcxd0Qgf4FrVTzVRD7bqjsOwMoc/fPw2QF8IyZ1QAV7r7eXJ5LRgQ2AeQXoESi4aPREymAu3+MyqSY2SkUXb7LAaPdfat47yykAB9BJeCVEBk2RwS6BIrdWwL1Pa9Ea4uCHLjLobCGvqhs/Dha1/QJRLATUKDDs2hJts6IQIcgQp/i7v3MbB9Utl0Z9Wjbxn1cYGbnmdmSce5fIOJdF82ubhGvKxGRzwR6mdl7pSENca+pSBOJRKIe0CSI9Ftg3/H+zOiT7o/6npsjBbkOMgNdWbLvTYgkl0bP7beIdA8BeqMyrCPTTw0qIS+DgvCfQSXdNcysVZxvVdQHnRGfV/daD0K9UEPrl3YD/oj6qG2A/3P3R2I5uP9BKtLGh/yyk0g0TjR1Ir0AuMLMWqMe469Q6ME0iiVb0Gynu/srZrY+CmC4O95rZmYbITJ0ZOwZhJZXewbNhx6BiLcarVcKItblEMk+A9yG1OxA1EutQT3b/sgwtGMoVUMJSssBX7n78mGsmoCUaZ/Y54X4nAnIuTwHUpEmEolE/aDs576AnxLu/gJSiC8CI4C/u/vr7j4BlV7bmtkEYF+gRQQ4VFIcPamLMuAad++M+qknxPa1Ub+1FzIW3YjKtgCXu/u+aAm1apTdew7Q1d1XQtGGSyKVWcD+aLSmg5m9g8rP49AaqBsgIu3/Hfd+pbsPdPeBXbt2/bZdE4lEIvED0OQU6Vxcvucg4qq7365mto27dw/TTi1Sis2BUe7+WCjRiXFIb2QSus/MFkE91TYoXKEDEVsY+54WpHwj0f9EfVVDyvU54KNQoOsitXwdGq+ZjhTzioh493f3YWZ2ByLYnQmiN7OvkUP4i7r3l4EMjQNZKUgkGj+aHJF+T8wEcPdVzGwWcyr1wu9rIKJ9LbY9jkxJZyGCvC2ctl2Rc3g6GnFZzsxej2M+CRNUaT/0NmRYOhElKhX+JmXx+z/M7CJgG9SnXRklJHVBZd1u8TlzIAMZEolEon6QRBpw93aFX1GKUBlShp3QWqCVaPm0lSOm7z0UAfguGqVZCinTj2P7s4hge6MQh+fRzCpIqRZKsx0R6d6KjEtPRGpTm/jM/dGape/GPv1RMMNR7v6PWAB8MTNr5+7TC/eTirThIdVnItE00WDXI60PmNl0d28Xpd3XUbl2eRTG0BuVVtsDxyJS2gilDm2CsnJ3R73RWmT+GYRMRBvHeY5D5eAHUSLRNYiAX0YkuzHqq05B5qda4A7kzn0HEfOHKGDiWaSIp8blL0FxHdNKYKNc2DuRSCR+PDSq9UijJHqju/8mXjdHmbLPu/vgBTzXU8BZ7v5wybbDgeXc/Xel+5ao0JnuvmrsW4FIdACa47wWzZLeiNRmQVasg0IX3kORgpcgA5K5+3Azuw54JXqxrVA27ipohnU7lIBUE/+eDPwNkfSy7v6Fmd2LQvhXjOt6BxmkFkHE2h0p5idQgH3d55CKtIEhFWki0TTRIIgUzVIOMLPW7j4TzXN+8j3PdQtKBHq4ZNtuwJGlO5lZD7Rk2ZpAGzN7AK1rWotGW7q6+9OxNuhoYBcU4nAIKrGuioxIn6JUI0PqsI2ZNUMmo37xcQcDE9z93WiPPoLKs9cgAr4rEprupugEnhs+QGq1DfqicTzK7V09rvEbZI80kUgk6gcNhUhB5c9tUGlzd0SIvwAws7UQ6bVGxqB93X20mfVHbtdyZM7ZKY7/s5m1DGNPH0Raw8KFewqa29wGmXYK4e+rol5mYUWWyZEw9AkiyutQv7KAQrBCwalbWCKtGcrSnYqcuWXA/8V5C7gV+E3J6+Zm9jhSrO1LFx83s73Q4uFLoS8czZHJ6Eu0dFxH4MP4AjJXZGh9w0Aq0kSiaaJB9EjDvLMeWux6TzTzeTjqTXZHBNUNlUJnIsXYDa3DOcLdbzKz8ti+HYoLvBIlB72NepCT0SzmusgdewjqOR6JFvSuQQTbA5Vop6HVXzZEIQhliLx6ILKcQXEN0UrUSx2Pko/2QHOgXRGBFtTqu7FfV2Qw+gqNxVTHtdQi0q6N8xaOnY1MUFsCX8fzqYhjWqLghs51nmlpIMMa48aNm78/RiKRSCSARtYjBXD3UaEed0cJQADVEeG3BCqHdkRE+rm7zzKz4cDxZtYLuAuRzcFoQe7d0EjJbERAo1CPcRwiw5eQquzj7s3MbCwaZbkcPZcuKKbvdFQ2vRGte/o2MvmcgZRlC7Ro+CxkEFo0Pn9RRJ53IdIbh0i+G+q1Hg/cgwj5c5R+tB0iRmL7VKSEP4l7meHuI6OP2wYRrqEc3kXcfVrJ88yIwJ8JqTwTiYULDYZIA/ciEtqI4jqdIDIbi0w2U4ClzOxTpNq+Rn3OgxBBLY9KtEuj8rC5+8sAZjYamYhORi7dJYB1zex0ZOLZG6nRT1AI/V9jn30RgU9GSnNTVBp+Dy2ndhVaUeYWNG/aCilcQ+XmrxAJX4HGaV6Ia6iJcz6MHLo7oy8Cq6O/zRVIPT8e51zXzCbFuSbFPQwC9iwl0bjXjAhMJBKJekBDI9JrgamxgPdGJds7IMKsBm5HBDERqdePETE9h8i2Q6jY24GzEYkRebvLx3FdEEF2RkuX/RuZd2YholwNlZgBdkRGo8PjGkAEuD5SwJPjOkrRAhHyVESKIFX6uzhma6RWxyBlORmVdGuQki30Zp9AIzQdURDEDUgZVyMz01pIlX5Y90GmIv35kF9aEomFCw2KSN19PHDxXN46B4UZtEAE1Aq4E4XQ74kC3rugHuaSZrYCUod3ATVmNhOVTF9BvclKdO8VaJbzD0g9lqFQ+/uQwWcxRFoXI5ItA/4Z11GGiG0TpDIXRw7ccoqznhWo1/oJIsm+cZ5P0ZeDGxGRzkJh9vvGvTnqDY+O6x0cn/dY4FSGAAAgAElEQVRfNHc6Mu65Q1zLKWhZtm9Qd/wlkUgkEj8R3L3B/iDyeRWpsc+Q+noVEdNnwK6x31coam8ZpFCvju0j0dqkIFL8AKnBscjYNA4pwbcQub6HghYmx+tdEXEfi1RfBSK4YXHcNOSg9fj37DrbnkXkvidaJcZRqXY6MkkBDEd93DeQuWk8UsVTgL0QyU6I874a11AVP1PjmTz5bc9xjTXW8EQikUgsGICRPh9c1aAU6VxQGpTwT2CIq2y7Fuof3mtm7ZCquxKRjSGXLah8OwPA3T81s6MRKYLKtB8C17v7SWY2HinY8chdexTqd9a4+1lm9iBKJHqUImF/gEZd3gamufsxZjYAzcEOQ8TZEanbCkR8hfSj4+I6fofK0sciVb1h3MP5sc90YEvXEm9dkSmpFhHy6sCf4z7nQAYy/HzI0m4isXChoRNpKVoiYsLdXwiz0ddIMYLmTPdHpqBNwoXbEik3zOxzRFirIyLrhOY2+8UapIXzbANshkqpf4pja5BqnI6IuRzYD5mTXkAjLZPMbI04R3NUol0NeMnd1wyi/hIYEvdxctzDbkh1/gU5etsiRVyYH51Yct8TzWwwIvQbEaG2RklMc8AzkCGRSCTqBQ2dSFub2auIlHpSJMUtUO9yJOpxfolGVzoiNTcM9RXHMWfiT3t3bxskuw5wIXLK/gsR6BBETMeifud+KEnoDUTaO6BwhW7AfxDZ7Y8I9gJ3f8nMvkJKek0zeyyuqYC+aD3RtZE79zCUftQWmZd+hRYJ/wty5r4Z9/Nu3Hdnd3/PzF5GPeAL4j7uqvvgUpHWL1KFJhILLxpEIMO8UAiVj9/XRYryNdTv7ISU5TtoabG7UdnzN0hpfo1MPZWo1NsJuN3ddw8l2BXNlhbWCy0kHDnqaZ6NFF9/FOLwb0SyLZEibRbn746ItA1Sre+jEu3KKDhhSTQLug2Au/eKAIphFJVvFSLzzxCRdkIEfgLFMIeZFJdmK0dfJMpR37Szu/eZ13PM0PpEIpFYcDS6QIbvgisI/gtgC+BopDaHIgJ7FI2ygGYuQeXSDogsq5HL9RUza4HMRi2RmtsRBTYUAhRqgUfd/YHY91OkSLsBN7j7fmY2EX0J6RXqtjz2uww4F5VmP4xzzYrrOxD1Xws4B5F4dzTm0g2VcSe6+zQzOwKRZyfkDP6ru/czs9loIe9C6XcgCpiYJzIi8KdDKtFEItFoiDRGWppRDDA4HRl3VkdznPuj1VP2RCMhrZCCHIfKp5VIFR6AcmurkEv3a1Q2Lkek/Cqwm5ndiPJ/W6JUpVrkpAURXmlfchgyCS2ByO9YZIYaH9fcBhmNekUoRGs0RvNvRKTvIzLvj0LvWyOSfRKZiU4E2plZt/i8sejLQTUi1e7MSdIZyJBIJBL1hIZOpIUeKajEube71wCPmFk/NF4CxdzazZAynYYIhng9ClgJEebOaNWVnhTXHC1DztseaE6zEA24CCrRLsucq8nURQ0i7M0RYR+KwhZ2AC5F5dpCZu7ION8sRKSboXLyONQ/fdTdZ4bqXQuVnA9EJd17EZmvjcjdUMLR//wdPQMZfjLkl5JEIlGKBk2k7t7sW95+CHjA3ccAmNnraMTEkfLshUIbuiKV2Ar1LFsjchqOSPZKpDBfRGuCtkHRgOsik88WKHihEilB4rjL4/deKO7v3Dj/YSiGsAL1dL+Oc68M3OnuQ8xsJ+B+RILlqK87Pj7rj2a2ASLJmrjeSqSMh6EvAO+6+/pmti8qJ1vdh5OKNJFIJOoHDZpIvwPtgL+aWUek9Aqq8CvgZkRMjsIbjkJO2GnuPiLiBx9G5LQncuCOQS7ZJdAozaIUgxbeRiH3l5jZyciJOyjmWZsBjyFCvB0ZjapREH1tXNdESsZYSjAq9ulNsVRcHtdTgcxTK1IsRQ9F5NwRwN2vM7O/ISU7R0xgKtIFQ37RSCQS3xeNlkjd/SW09BoAZrYqGgN50903i22FedHhyAF7kJltAhyDVB6on7onWvqssB7qNFQu/hStU/prZEwyRHDVaJ70z4i8h6Ol236NSso9gH+4+/lm1hmN4mwK/MrMnkWjLxuiPmrLOOd1aLTmChQ32ByFNWwW590SjfJ0jOsroAVzBvwXnkcq0kQikagHNOjxlwWBmTVD/cNL3P2E2FZwzb6DFNt7qEfaBZmO/olWUHkahdKDFO1EVOLthMxNH6FSby0qEU9EBDYjznVDnLcvWgptD7Qo+aeIfNsiIl4BKeOOyFH8+zhuc6Rsy5CC/YQ5TVBfIXIeTeT3uvtWcY+zgKPc/aJ5PZuWPZf1nnvP8+2FGvkFI5FIzAvzO/5SVh8XUx9w9xp3b18g0UCFu7dy91XdfQd3PxIFwz/t7rfFPve6+x8RKVahcIdmiERnImJ7GhmbqsLs9CdUBj4Pkd7RccwfgNOQk3gWKjG/BTzm7h+6+4PuvjLK+a1Cynh1RI5foHJzC1ROPhDNxn6OlpWrRjOpY5FZqoBmyMA0B8zsQDMbaWYjayqm1n07kUgkEj8SGm1p90fEjPjX4ucQFOCwJSrJVqMeK4CZ2S/i9zURAfZEpqbmKEShEoVGNEdk2ApYxMzuLSHvLZAJaivkJh7o7oeY2T6xrRatVLNZ7PdoXNujaG70ETO7jmKgQ/e6N5URgYlEIlE/WNiJtDlyye6JSrmTkGGokFoEIsY7UAm2BpHkCYjA3kdGoOlIST6GSrePoN7nvXHOoSiTl+iZrkNxdZgKoNzM/oNKz82Qq3glNH7zNAprOBGN5ExBBDkblZhBgQ5zICMC5w9Z2k0kEj8UTaZHOjeURgyWbNsI+JO7DzazU9Aya+eZ2Qzklv0l6mkWRm8GA1dTNAUVZlbXQWXbxVCpdRoKhlgaker7iIDboczedxD5TkPjM4ehhcxBqtYRsb+GCLsGqV6QKv4ytg0DdkKl43LA3b3Ftz2HjAhMJBKJBUeTiwj8PqhLorHtKZSAhLufAqrXohLsU+6+W2ybDLR29wfN7HBEps2AW5GLtgKR6fNIOTZHKnEEKr9WxT7t0FJsq4Uh6lhUpn0cEeP5wPJoHnVT1Afth8h3ICLQz5BCPsPdnzaz1xCZfgK8aGZdgK+if0tcfyrSeSBVaCKR+DHRqBVpnVD7rYGLERltjYxGN0Tf8RF3//RbzvMX4Eh3L4vXKyDl1wmVTzsiNfkkUqavofSia4DtETEOR2XgrVCZeFb8tKU4NgMi3HeRAm2Dwhq+RES6WJynArl/l0bkXROfPzleT0Tl3s5IrX4IHODuw+Z2f6lIE4lEYsGxUClSM9sUpRFt4e4fUUwdAtgHhc7Pk0jR3KbVjSNEpdm3keGnGinE/5Tscyly1e6PSPD+2HdtRKKVSOk2RwpzD0Ty7eMcXZBh6PZ4XViX9GWkZP8O/Ba5hz9D/dItEJk/jJZ9A7mDR8zr5jK0fk6kIk0kEj8mGj2Rhov2KmBrd38/tj2OAuCriKhAM5uCZkEHIOXaNt7fNE71HiLFpYG73f3+KMX2Q6VbQ27az5AKnQ3sgpy601EPdHvUP70I9UALs6EgMuyLEpQWRwQ7BanS9ZEqxd0/jbLyRyg68Lj4jF/G9R4APOvue5rZEPQ33Amp2PNLnksGMiQSiUQ9oLGXdquReWcjdx8V29ZFCu9v7v6XSBI63d0fMrNyRHi7uvuLZtYeEdAZaAa0ByKr0Sh27300t7mau88ws6ORGmwBHI+I9xwUvtAXuBE5aw3F/z2F0opmx+e0RMS3Lhq7eT7O8T4Ksp+E+q/vxv5dUAm4WVz3tvHeCETuKyGlPSR6v3NFBjLMifxSkUgk5gcLS2m3GmXP7ocUIMgBW4H6ioV9as3sIWBjdM9PhGJriWIBl0FktQvF1KHnEHn1A4abWQfUl5yJllebjVy1o5F7t38c/0Ic8xBSqo7KsffHMW1QH7QlItxyVLLdHAUxDENqdUmkXr+kmKZ0J1K4zSnm9v5+biSaijSRSCTqB41dkU5H5PYYcJ+7n2lm7SjOY/4Lqb/mSG3+AcXyHYR6k1NRXu5ZwBqorLsaIrg30correPjeiDCHAKcgkZdeiCCuxdYBSnhTeIcxPk7IKfvxmi+dEvUR30JqdBKpGLPQURaG9c/ChmOzkHLrb2LSsKVwMPuvmVEIF7u7r/7tueUinRO5JeKRCIxP5hfRdroidTd20XIwTPABe5+jZmdigj2CzRu8gUKl9+YOUu7xyP3bSVSio+hIPyuFHuirRDZHo1UYBnqg7ZCSrYwmtIBkeO6qO+6Q5wbpD5/j0xIv0EGoseB/wKt3H1RMxsR51gSqdylY79TkbotR4lLmyET0ppoabYqlIw0ps6zKVWka4wbN+57PuVEIpFYOLFQZe26+5dI6Z1gZr9ChPc+yrSdhCL0VkFk1AlF7L2GiKYKPYdyYEekTkfF60+Q6edYVNJ9D60UMwmFyndHyndfRMYrov7pCDQGc427t0bjKRcici0DzkTkeCcyPYF6scsBf3H31ZARaRNUst4B2Av1RWuQ6/fiuMdhaMm4us/kSncf6O4Du3bt+n0eayKRSCTmA426R1oauODuHwN9zWx54C13HxPJRe3QCEs7FNU3GynEWYgkl0OK9UvU2xyKIveWRspyFup/3olI80S0duheqDRb4+5PRA92CWAxd3czWwroHElKS6Ay71LAx6jE+0dkciqNIjwS2DIWKW8HbOru+5nZVUBLdz82ytkPIrPTy0Abd59Z99lkIMO8kaXdRCLxY6JRE+k8ULrgd2ukKsuBXZERaDYq266CSrTT4/cyRGY3IyW7COqx7okU7hdx7ABkZloMlVoLUYLEtiXN7N3YviixUou7nwRgZk+gHuqjcV1fxLG94hprkKI9E7jQzD5ARPy1mW0Z+z6Jln3bGuhoZoe5+8WlDyFD6xOJRKJ+0OSItHTB71BvgxHxrIN6npuj0PllkKLbCvUrvwSaufvFZvYpcD0i4t8hFTnG3beNTN4WyHg0G6gws06InLuj/NyLUfpRM0S815vZqu7+KjIq3QX8CpmJjjWz/0NjL0+gcIfVkOJ8B433PIlU9eWI7E8F/u3uZ5jZxahfOwdSkc6JVKGJROKnQqM2G30XSsxIryECWguRU1dk/nkZOWJ7IQUK6mfORgr1LWT82Q8R5ZfIDGRIuf4CzXp+gXqdHyL1+Bly9PZEPdgpce7Z8VmTkLGoBcXIv/vj+vrFtX6BStDlqCf6PLAbcgAvG/dQi5y+K0WfeK7IiMBEIpFYcCwsc6TzizuRaacWkVcLYDd3v83MrkGq8dco93YAMg+dB3zk7kea2QREvPuhiMB2aNTlYWREugWR2/bAP9z9MDMbg8q0byFj0bnIzLQBWqi7J3L5Lufu3czsKWRIOhytiXo1UqLjEDnviEq+E+Ke+iEz0sWoXzvHfEsq0jmRijSRSPxUWFgUaS8Uo3cGKvUWllHbBBHXRWgW9GOkBp9EJeD/ol7lK0gVfo5IuBsyIX2CEo2gGFBfBVyBiLkHSjBqixRlL1SG7RDXcgxSnG+jfupXSPFOQWr0EOCSOHc3ZI6qQip2cpx/EaDa3Reb13NIRZpIJBILjlSkJXD38cDFZnZGnbfWQOqwPyK79ojIlkdktjsi4PEUwxsWA25AhDoauW6XQs+yGRpjORSZlK5FqvRSVJatQeM3hty/4xERroPU7hFINY8CNkTq8wPkEiauYSbq71YjUl0S9WPniYU1tD5VaCKRqA80aSKdj/VIzzWzGkSk3VFgw6MoNOF6VFY9Nn4fACzl7leb2eWISLdF8X5TkMN3HUSAjki4OerJfolIdhQi7w7IRLQIiiS8HQU+7BTXsSEi22tRT7QDKtOeg1TqeET8kyhGBs6BjAhMJBKJ+kGTJtL5xGXIDdsauA3YJxbPboPU5mVI8VUBx5nZrDiuOn56o3CGKtSL3QSR2zrx78vAPWikph8qBzsqzbZHpeRVkdnoNGQ4OgaRansUa/g1GrkpjO6siFKN3kMK9n9QOv7Ssueynoo0kUgkfhosVEQ6D4VaCexjZjtHolBhYe92iPjKUPl0LxT/t3r0XafHKW5B6nMlRHKnof7n31HYw2BU1nX0vJdHfdNmaJ3UVqj/2RaF1M9CIzIt0BJqR6F1UfeOz5sS+9yJequOSr5zIBVpIpFI1A8WKiL9DrQuWdi7LTL2rI5I8Bjkov0KEWopbkWzpoWZ0Wtjey3FwIbPkZqsjvcmxXvtkTHpaaSK/xWffSEi7WOQGv0lKgOvEMfVuvvKZrYyWnHmf7AwK9L80pBIJOoTSaQBd/8mocjMdgT2dfcq1B+9PrafBNxuZq3RHOke7j7KzGYjVfgAUqFTkRrsTNE0dF+8fhcFzy+H+qXHotGa59H86W5otnU39PfpjgxMM1C/9RNgmYgRnIl6rf+DVKSJRCJRP2gSofU/BGZWY2avlvz0QQEJ65jZu2Z2mZltGLv/zd3XdPcBqKdaIN+PkVL9F+ppvoTUYAXwJxQsXxol2AL1TA9Apd1a1Eftj3J4z0Xl3nKkdm+muJxbf6SOW6BScmvmggytTyQSifpBKlKY6e6r1tk21sx6oOSijYHbzOwYYIaZ/QktudaZ4heRMWjWtBrNlS6BiLAWxf3NRmXaM1FPc2lgsLtXmNlBsf1l4GSkZtdH5FyLZlrbA2e5+x1mdj3qyX6Myr3N49xzYGEOZEj1nUgk6hNJpHNBrNhSCG3YCHgdOB8R6ApoBGUPoEUQYQUalfkHxXjBESgv91qU53simhO9Hc2hLmpmkxBx1qAA+hbItDQVBTIciUh2INAsloirQglMg5DjtwKR9hzI0PpEIpGoHzQ6IjUzB25099/E6+aot/i8uw9ewHM9xZwmow9RmtFg5ix7D0AEuGO8NxO5Z69F5dmxJft9DgxBhqERiCRBgfTvUAx6eAUR4oYojvB4NHrzz1iofBSaU70OmZF2RiXfJxFxHuDuN5nZQODNudzbQqVIU4UmEomfC40uIjDGTsYA67n7TDPbCi3GPf57EGklWvGlRcm2EYi49kek2AXNfA5CcYKHo95kJSrlzozreRatX/oWsAUq71aifmsvZBL6NyLeWYhgWyBH7iDUM23v7rVxHePjczqhcvA4NJKzGyLpVZByfdTd9/y2+8yIwEQikVhwNPWIwAeBbdB85e5olvMXAGa2FsrObY1Ibl93H21m/ZG6K0dqcydikW8za+nuVWE0Wgy5b3+ByqigHNyLUexfP9TDvAwR5t/c/dYoAW+CyPcytNRZDbADItC1UWJSC0SkjuL/lkSLfDcDHjWzfd39I0SgFUjFdgROcvc74h7viPvrAmxjZpu4+xOlD6gpK9JUn4lEoiGhsSrS9ZCC2xOVTw+n2NNsD1S4+2wz2wz4rbvvZGZ/BUZEObSc4vJlbVFptgKViL9GPcnFEeFOQWXZtVHC0bLxc5+7b2dmy6F+6dA45n7gGURiy6DxlDZIsV6LQhZeQSS+M1KtreKzxiFy3R0tozY9rmsW+gKwDSobDwXeR33UnsAt7v6HeT2zVKSJRCKx4GjSijRmN/sgwnmgztsd0ELayyLVVyjbDgeOj5Vg7nL3MWYGUp0j0PjKVyjzdhDwKiK4aYi0lkSznyujudBtzOwNpHrboJLtccjluzZwN+plFsZUVqIYG7glCmUYgb4UHIxI/Aq06sxtFMl9OArWXxIlJvVGLt4BcV/lKMt3nmhqofWpSBOJRENCoyLSCJgnFuruikq4G6ASZwGnA0+6+w5Btk8BuPvNZvY8UnUPm9n+sf/GqFS8JsV0oi4UR0tmIAW4MSKskaisWoUI8Rw04nI6mvF8C7jN3c8zsy2R6lwblYZ3RcuibYGiAisR0d4S/d7OiHTbIxJ/BRFwZVzrn4G7KKYktURE/9xcnlUGMiQSiUQ9oFERKVJ/uPsqZjYEONXdX4/+ZAEdkLEHYJ/CRjNbCvjA3S+J31eOt1ZApPUMRfVXmNGsQSXWd0r27Y/KtFUUn18PFLzwe0TsHcyswFxjERlXI+XaBoXU34yU6CzgbLTweFtgtruvbWbvIcLdzN1fjC8Rs1DpuXNcy5couP6Lug+qqUUE5heBRCLRUNHYiLQUs1DoO4hY1o0xlkWAi8zs/5DKnGlmLyGCLTOzbqi8WlCkpyJSbIni+NoiUn0IuAC5b69Fhp9yNCvaPvbritRpW0SEfRApd0LGot4USW5cHPdpnGcfZE76FNjTzH4b+z0eIy29EPkOjnspQ2T9XFzTe0i9glRrYYQHSEWaSCQS9YXGRqStgdfN7B1kstkktvcFLnD3M8ysGdDG3afFzOmv3P1BM3sOjYysBOyLRmbaUCSjT1AvtC0qx26DepQt0MjJKFRGfRX1K9sjoptccg3jEJn2iPP/OrZNRwalI+LYaUjtzorPKzi+ahD5dYtjqhDhdgd2cve7zOw6pES/ROS6PApt+Gfpg0pFmkgkEvWD7yTSWFLsV8iR6khB3evub//E1zY3fBPnZ2brAjeY2QAUbHCtmbUA7nH3UnX2kJltikqhl7v7B2Z2MnAoIqhnUS9yH5RzOxKY5O47xOdshlToC8A1KHx+BCrXFvCBu68Q+7+LRmA2QIS5lrtPMrPT0fN+C6nb69E4zhkoMnASmiP9KFTz7Nj/fkTyj0b4xAD0BWAiIt5qioaqb1B3/CWRSCQSPw2+dfzFzI5GzthbgfGxuRdSaLe6+9k/+RXOeT3TS9cUNbMJwEru/oWZLYZU5B+Ac939hoIiRf3IGkRsHyOVuANaeeU8lGlbjZKDWqC5TuKYUcDDqKc6CBmRZqNZ0cNRD7QQvjALqeZn0BJsbYDRSDk+g3qtO8a5FkHmptFxDRfFfi8idXkGcvEej8rDE5BCXjWudWv0peYNYKK7953Xc8vxl0QikVhw/FjjL/sB/d29unSjmV2AYunqlUjrXMMKiIgmm9mSwCfufpWZtUUkdkPseie6zhpULv0XWnQbd7/AzIYCY2MGtRx9YbjX3Xcxs45xzJ5oPOYj5Ly9B5l+uiACnYBMSL9FSvcY4F5kjloXKcOT4v11gF3c/d5QjRdSVLc7o/GbTRDp/hX1Xi9AxF0b1/HreG8nRLIz5/J8mlQgQ5Z2E4lEQ8V3EWktSvoZV2d7z3ivvlGai2vA3u5eE67dI82sGhFf6eLbX6N+5hQAdx9nZi8Am5nZM6hnuXSUZK9EfcntzWwKUpfvxL/d4zxLI3fvXnHssoikP0fE/hp6Xh3QXOqk+OyJKKd3KnCemZ2GlOX5ca7PEenXINXcAq0IAzIvPYb6oW2QMu2ElHIPilm/3yBD6xOJRKJ+8F1EejhykY5B/3MHOVGXQSug/CDESMfrJZu+q1x8orufWXeju3+z+HYdzEJl3d2Am939lJL3TkZq8CE043k86pHujVzAq7v7h3Gdv0fRgCsjJViFCHp7RHYvIBPTa+5+iJndjFzAGyPF+l8UuNAfOX2rKBqZhlPszy6DSPuvKOHoFUTGZ6D+9HOIHG9GRP1LRKb/86WmKSjSVKGJRKIx4DsjAs2sDFgLmY0MlT5fdPeabz1wfj68Ts/zJ9i/CpVxT0M9ygmoHLtobF8e9VC/QMq7I1J3n7r7QDNbBJVN74j9CoQ1ExmQDkAK+AHU21wRlWGHIcIcgwhzZWRq6hz7zEIO4a+Q4egS1Pu8IrY9jErQ78bxJyPHbyekYhdD6rdv7Fsdi43PFdkjTSQSiQXHjxYRGKuRjPhRrmo+YGYdkMLbLsLmb0FZt0tTLO2+6e5DzGxPZC4qB54Hfhel3umI0FqgZcruAo4C/oZI72DUi3wbpRadgZTgKUhBLh/pSTPRnOmKiMCeQyahWfHebNQzPQtl4FYiFVoet/MbimMz3ZBjtx8ycJ0a93RF3N/VaDxnEnLm7ofMRx1Qf3Q6KvtWIBKtAf6DjE4FI9hc0VgjAlORJhKJxoCfe460tOcJcJa732ZmhwD/MLOLgU7ufhWAmR1SMv7SD5HY+u5ebWaXITK7Ac1mvoEIZwRScBPQqi0vonGSu5DLdzYyI12LZkQfoRiAXwNcihRsGSLR1dC6pYcgMtsVGYoK/dmDUEl1V0S4/4n97gV2Qar+ijjnh3HO9sDjaGSmCvVB70Mk2SaeTSUi1G2BpSgGM7SMe5sDGciQSCQS9YOfdfWXbyvVmtmVyJW6iruPr7t/kO1xFJODWqPM2lPMbDbQMtRpT2QAqkbu2PbICfsyUoLNUZn2mPj9FxSJdDoqBd+DcnVrEdG9hMZY3ovPfgWNxryPyr1vxHXNQgqzO1KwA9G4y2xE8hb3+Hicqz3FoP0J6AvBlcAaiHgLZeLWqLfcHCncD919qXk955Y9l/Wee180r7cbHJL0E4lEQ0CjXv0l+rL9EPl0Zu6lSwOud/dj5/JeZaGH6+6fmdlbiCA/QgpuZ6QUT0HjPcea2aFxzFPxQxxfBWwVi4BPRr3Sl1BYfQukNq9AanIqKhtvENc3HBHx4kh5FoxVV8a5DgWuQuahGXGuctQL7Rr77ANMcfflzOwPiIwdmOzuG5tZRZyn7jNMRZpIJBL1gAZJpCgQ4W2kOK81s3VjlrXazFrE748D/zazCyOQoTOwiLvXHdUpoAotWTYCkdxvga2QQ/ZbEbGDzSnGAe6IZlVr4+cGRG7lyHl7IkpBWh65dWvRl4IByI27Nwp/+C9y3q6O+qjNECmfFNe4Dwpr6GlmKwM3xns1qMxbwDJ1r7kxRwQm6ScSicaEn5tI6/ZIH0K9yv1RtN40M3saOAE5V68ERpnZy2E2OgF4JBRsNVp9ZV5EWlCnxyIS2xiR3b5mdlJcy4HufmUEyPctub7lUI/0S6RkF41TjkEu2/fRiM1KaExovXj/BUSgq1I0IHnsvxpSqNsi01GXeG8PirF/tyDS7Ix6t9PiHG8DV5rZ9ajMuz4a4fkGqUgTiUSifvCz9kjrE2dkEQwAACAASURBVNHvBI243IlIeyYyKx1gZq2REWlDRJrDgR7u3s7MHkVpQ2ejHuWHyJg0EJFkf1Qy/hK4CRHpSvEZNUjB1iI12g8R5mxEpKshYt0G9VqrEQFvjkxRAxBxdkNkujbqvS7j7jPMbBZwh7vvMa97bww90iT6RCLR0DC/PdKy+riYBoLW8TMS9Uqvie2rxKjLCOSCXdbdJyLSKzOzLkgxDkTl4DWQirwI2BTNjFYhV+4hiGhvj5+1ERmeiIj2l+5ehsrSb8YfqBr4l7uPiWsbhgjzdbTg+G6o5OuIyKfFuZ4tUfNzDWQws5FmNrKmYuoPe3KJRCKRmCd+7tJufaKwKPiqhQ2R19sL9Ri7IpV5Uyz8XSivfhG/d0Pl2fax/VLU6zwUjb0sAtyNiO9cigH0s1CUoFHssXYHPjSz9eJcQ6MU+z5SuNcjdToYLfn2PCLoChTAsCcqPb+PSPbWujebEYGJRCJRP1iYiBT+tye7ODDd3SvM7B9o3GQHVJJ9HJVhWwMXu/vD4f4diEj3fRQxeBUq0w5HhNwTEegrqF+6JRpleQsYZ2ajEbFuHiapmcBx7n4dgJl9BvRx933M7CzU5/wMOXlBQRN7A+u4++tm9gGan72v9EYbQ0RglnMTiURTwELXI62zDFstIsEaVC4di5KO7kMBCCNQz3Rfd78jSGtxij3LHqgv+jIKeDgRqU1HCrQaxfz1Rwq1sA2kdN9CpqcL0BjOYDMbhoj2K2RyegatGPN1bGsbn1Eb52sFvODua8/r3jMiMJFIJBYcjXqO9KdAmIam19k8C7g6guYnActHSlJrRFC9Yp/1Ud7ukojANkPE+QtkQjoN9S4PQWuJbuzuL5pZe0TId6M0olXdvcrM/gb8Dq0c83dUsi1gBCLeQ5DSPMjdpwTBHo6I/EqkaEdF+tLv695vKtJEIpGoHyw0RDofeA7YzczuRUrvCXffPIhqBTM7G6nKMtRLbYOSkRZFvdON0UxqC2BYGJgMZej+EgUurGVm/0T5uTXInVtYpLuVmf0lzvMfRL63A0+ZWSGs/sb4rG7Af8yssOZqb2RU+gbZI00kEon6wcJOpOXArma2Qfx+NHAkKs12NrPd4vdOSHkWxlZeRWp1KVS6nY7KwuvH6wFEdB/K4a1Ea5JejXJ/v0CGojuBJ4FPUWl5EBrPuRcF3t/i7v+OERcDtkAhFT0REQ9FxN7n226yoYbWpyJNJBJNAU2aSM2sOzIErYOI7HUz28Hd745dKty961yOm45iAC8Fqtx9ndheA5yJSHQ9RGbLIZJbA5Vp21JMO7oJkWXrOOZNpFhbAr9Gs6Zfo4W9/4RItgci2VrgXjO7EP2dalFQxZpxjo9Qr3Qx5Oqtew8ZyJBIJBL1gCZLpGZmKGz++kJYgZktCWw3H4e3RglD5UALM5uAnLNlKEv3DlRm3YXiguf/jX93RcS2KyLXM1Eu71toVKUw81mGMnmHlXzuP1CgfmdUOn4dRQIejOZRV0dEXYbKyhchA9ImaL3Tb9BQIwKT0BOJRFNDkyVSRC6z3P3ywobI4f2rmc1ATtvWZvYyWqO0IyLIk9FqMX1RFu/TKDS/FTIFHY/SiL5AsX5tkCLtjUq+r8TH1SDi7I5cvqPQEmsVwPZx/g8QAf4RheGPQaEQhQSFPmi8piVa4WXziExcD5V4T4z9Ote9+VSkiUQiUT9oykTaH5Hl3OAogm8GIsgjUYm1M0oj+hToHVm/1Wgc5mxUTj0jjmuGZkm7IBKejsIatkZ91H8hJToCKePpFFXpZcBR7v6cmU1BM6uFUIcPkErdFynSq1BvdT0za4lSloj37gFOR+XdOW+wgSnSJPJEItFU0ZSJdA6Y2aWolDorNl2FiK0mXt+GFGlvFKDwhKrDGFKQuyPT0UREuE8hVTkbRQS2RspxMDIX7YrKw21Q1m4FcBRKIdoFOCwWLm+HUpduR73Snoho9wGedPdrzewKZGYaEp9Tg/qkv0a938q53G8q0kQikagHNOWs3TdRTxEAd/89ysbtishtK2QA6od6oYPjdV/gQndfE0X9NUfO24GoDHs9cuaujsrAzRCxvYVGVNqgUZTnkWt3FFLGhyFVC0o+2juOOyqOqULKtgVaZHwSUs6gkvGywP/FMYb+dhX/3955x+s9n2/8fWcvWQRRQmKLndh7j1J7q1GjfqiqHbuqKGqromatqFFa1AxBCSkhCWIlIUGMRPY659y/P677m+fJcQ6Jw3OSuK/X63mdc57nOz7fT6rXc93julFedXjth3f3G9y9t7v37tLlG/VUiUQikfiBsCAr0meAC8zs/9z9univTfysQqHUixCB3YxCsJ1QX+g1oUYLQ4WmiDg3Rop1Ggqn9kek9wfUh3of6v9s6e5bhu3gMDSXtCXKl45CFoQDymwB90MGDHcAvdx9LzO7A1gtiqaGIvvBm5Aq/QSp3G3iGdav/fDzmiFDKuJEIrGgYoEiUjObVFgAurub2a7A5WZ2CgrJTka9orchwjwE9XFORwpwMNqT5qi69izgXBRWbYtCsB0RsRrwGSLkdqhYqAYR5aJm9jEyTijU5GaIyMcj04XFwne3NSLa9rGmpc1sIRROHowck9ogdfwm8gJ+K67bBOVMv/HvmIYMiUQiURksUERaG+7+KZrGMhvCEehGd/9d/D0MhW4XpmSWcBsKwfZDIdTDkeLsggwXpqCJLB8gv93jUB61Gk1oGYhI+xIUNv43cKu7L2lm/VBR02ZIiTZF5DgRkeVlsdTBqDf1tFjbM6id5gN3X9XMTkIquXess/wZU5EmEolEBbBAmdaXK9Ky93YGzkR50K9QaPQjlLd8G7kTrQK8g0wYhiPSXAER3BfAQ6gndC1EdC1QVXDRrjIRqdjCbGEEqtg9EbWwjIj3u6N860lIWRbm9jXA+UjVrhDvt0U9oiBi3dzdt47WnUdQe09nVBg12d2Xq29f0rQ+kUgk5h5pWl/CC2jkmJvZ4ai4Z0eUF3UUZl0I5TIHIeVniKRuRrZ9IBKe5O69zOw2lE/dB7gSeemOQUp0C0SwW6K2lp1QOPdS4HpkMD8JkfMIZPvXHdkLPhnv/Ru4hpJhw5qU/q0cke5GKFe7obt/o9hoXlCkqUITicRPAQsMkYYdYMsYdTYOtblcDLwP9DWzrkhJDkeKsCkwxt2nA9PNbAKqut0H9YC2Rerx4eIWlKpoC7yDSHArpB6XRYb06yOiHYsIuSki6xYoHPw6yoF+hiwBpyPi7YQI+WSkYCchAl8P6Ghmu6B/sxfj/WrgITPbw93fK19Y5kgTiUSiMlggiLTMDrDa3XvEe4Ud4G+By9z9YTPbHDg31GlfYCsz+wApu0XQ1JW1kAPRrki9rocKgqqAyWbWo7ivu08L5Xcdqsj9AKnPf6P2mntRmPgSRI6t4jogErwMhXXPQWHhIcBdaFzaCsjxaBRSujvFdVqgf7dWce7MOKZeNJZpfSrSRCLxU8ACkSM1s62QJ22vOnKkQ5E6dTRPdKS7rx2tKRujUWZrIlL6hNJUlxOR8fxrSEGuGZdsjYZtrw7sjEzpa+Lzu1B7TEdggrtvambTgJ+7+9NmNgWp1J/FevrHmrohr96XkCLugEK6q8dnINU6FRH+q6jgqRnyEj6ijj0pN2ToNXLkyLnZ0kQikfjJY05zpAsKkR6HQqy/RWRY4DJkKn8xUm3vAPu6+0JBpPsBK7n7cDMbAhzg7m+Y2TnIy/a/KPy6HerjnIjMGDqh0O9E4Bp3P9vMHkMDv6ejHOmSyGh+deBSdz8liLQKefluHGudidyMHkQqtCci5hVR3nafWEtz4AmkUj9BVoUnxDkHunvf+vanZdflvevBV8zlrjYcqUgTicT8jJ9ksZG7N4FZdoCbI4P5CahHc21Ebk3j8FuBpYtCnWgnObdsWPY0oA/KYc5w97Fx7csRea2PcqK/MLPdUK/oV0jBvgm0c/djzexZ4F4z2wHlPVtQ8sZdF4WS/4GU5gnIV7dpXGtyfDYGkelQZMQwHLjZ3W8ysypUwDQbkaZFYCKRSFQGCwqRDgX2KPv7WBSyBfWDtkNVsLsAfy47bnLxS+RPt0YFRtNRxW0rVGRUFw5CVb6G8p2HufudZrZH3KuZmZ0cxy6L+j+rkHXglkiJrohUsiNi3rPs+megvOovkHr9e6ypP/BzYIiZNUGmDP+tvbhKm9YnUScSiZ8qFhSv3WeAVmb2f/H3lojcJqJ846fuPgKp06Zm9jwimU3MbMM4ZyNgJaQIhyFiOx/4PbCfmR1vZs0QYa+MipsKw/gXgkS3QEb1U1F4dm133xwR3x+R2cOz7l6FcqW7oyKlAgWJP4LU5GWoPaY9IuFrUX60aNFpjr4MTK29IWZ2pJkNNLOB1VPG1/44kUgkEj8QFogcKUC0t1yOqmwLpXYSCrXej5yI+gG/QWHU9VGBUnt3721m2wCPofzmEORg9Gd3/5eZHYNI7VVUmbsLKkhqgdyHDJFkczT+bDkUWl7P3VeOmafHI8Ifh8hvSZR/fQdYDZlDDEBDvP+OHJmqkCFEq1hPJ+AqZG14GKriXQI4391/X9/epCFDIpFIzD3mu2IjM6tGpFTgHne/6FuOP93dL6jns+OA7mUWgOUj1N5ChNQdqdYV3L1NhHbPcfctyq5zLnIgaoKqZ3cErkBEfI+794l7rYCU7EBEwj2AkaiHdCVUkfsIsKi7/7rs+s8BawCjIke7FTJlWBeZQTRBYV9HRUi7I8OH8cARqHq4A7CKuxfVvcW1yw0Zei35f7fUt5U/CDK0m0gkFjTMj8VGU919ze8+bBZOR3nHujBbztTdjzGzRRDRDUcqb1dEVOWzPCeXEXoHFFJdHVXKHgXcjQqJRqFq2j5l5+6LwrIbuPtUADO7BE2VAVkTTjKzQajo6HmklDdEZg218R7qRR1OyUpwvfhsBApT34KU7jeQhgyJRCJRGcxLRPoNmFkHVDT0C3cfZmZ3o/DoskDrIKWh7n6AmR2IjONboBBpkTO9BIVld0YEdhhwn7vXmNlfUM50CCJIUL5xOWSqsBsyS7gMFTAdhAjuRWB85Ff3QMS7d6zt2AgF9waORu03Q+Na7yDP3p+hvOqRZnYoIvD/AVejal1QmHgMsLq7V5vZDJRXHYi+RHRHOdxVUVi59t5V1CIwFWkikfipYl4O7V7o7n0jd3ke8rQ9xN23j+NnGdSb2cqoV3R3d58ZBPk2KiDaB+U1Pwb+ilpMOiNFtxxqNemICHstVCTUhNKw7PYot9oF5TRXjePbU3Ix2hiR+whkFdgbhZHbICV5MyqAWhgVQNWgIqPRiFSL4eDDYk07A79EYd+eqAVmL6SAP0etOzMR6bcCmrp78/r2NnOkiUQiMfdYYEK77v6kme2FKlbXqOfcrZDSezUGcrcGPnf3fc1sT2BFd68GCJW6HyKwPdz9oHj/WmT1NwOR028QYR3o7v+JtpajYg3dkPNQE2AdRIpnocHcryIluRbKk05EhHce8BdUQNQGFTp1RWHii4Dt3f0ZMxsO7ODuB5vZtmiE2mBge1TBu1escTV3f8/Mfktp7NosVEKRpgpNJBKJeYtI60T0Sq6MyKgzdfvKGrLK61PHZ9MKEq3jnLrQGhHkvajS98moCL4SKck3ELG1RkVL7eP3XVHes22sc2C83wqp4j1RWLhTPMOhcZ9H0VDxw1FouDMKF5+IiLunu29pZqvFNV5AIepHwilpobqeJXOkiUQiURnM80SKCn3eRnnBm81sA3efCcw0s+bx+9NoCsrl7v65mXUGFnL3bzOYHQBcGUVI45BKbVr2eRUivGNQvnIkcBMyeGiD1OWBiNTGIDI8H+3pBEpKdCIaCP4lUs1dUPvKnajC9yBEzqvEfR34OlT4BpR6RDugEPVGKFda4+5rmtl1iJTrxY9lWp+KNJFIJOYtIi2Khwr8B+UWDwfWdfeJZtYfDek+B6mtN83stSg2OhN4IhTsTESA9RKpu39qZn1Q/tOQMlw/Pm6LQql3oF7Ug4FTkWPRaShX2iPu0xyR4wbAAyjM/Bkls/li2styKATcGfWrtkXTZbZBDkcdwmC/BXIvuiau66E8DeVtX4z7rmhmk1Ce9hvGGmkRmEgkEpXBPFFsZGYO3OHuv4y/mwGfAgPcfae5vNazqFDp8bL3jkf9okd/x7mTit/dvZ2ZjUE2gHeg4p91kDIsxpc1QS0xnRCRVqPw7jaoIKkg9DWQGl0Ttcg0J/K4cUwzRKC/RQVRe6ACo4WAL929S+ROWwBLEcPKUYi5M+pPbVnfc/2QpvVJyIlE4qeC+a3YaDKwqpm1jh7MbVBF6/fB3ain8/Gy9/ZFvrXfiSDQSWa2Egr17ohciC5HQ75rULh2f0Ri7ZAT0evAJHe/1MyWRxW4gxE5/i/W829gE2S44CikfHKcXwz0bhHXfgOFcQs/4H5oCk0bVBVcDBqfiJTrbEhFmkgkEpXBvKJIJyHru9fc/b6YwDIU2MTddzKzdZGjUGuUMzw0+kp7IlOCFkgd7oHyh+8AS7r7dDNbhtLcz82Ac1G+clVEcAfGoO8RKBw7BZHVYJSXPRxNjumGFGcVUPSndo5HGBmff47Cr1ehHOiGiECrUS71KaROT0Yq9xOkMN9Cod+PEWm3jfs0o0S4ZyHD/bEoRNwUqdnx6N9xkfr2NxVpIpFIzD3mVJHOS6b19wD7mlkr5CY0oOyzd4BN3X0t1DZSOBodBVwZbTO9kdXeV6gndPs4Zl+gr5e+MayF3IBWQXnOjcruc1r0pk5x9zXc/RF3383dlwb+haasfIqM7DuitpUq1NrycXw2FI00ex6ZJrRGavZSRIKD3P0+4H3UW/owasUBkWlzRLBFH+shqD/1bGRD+BAlH+El4vcba29mmtYnEolEZTCvhHZx9zdDPe6HCn/K0QG4LUKmjsgG4CXgDDNbEnjA3d+L94vw7kPx81dl13rF3UcBRHHTMijnCKWZnm3MrLzN5jLkmvQS6uF8D/V03oZCvSejntD7kKH9MKSAN0WKcRIiXoDlwklpuVjX/miSzHYojDsaKdwxSOUuj8LHiyLCfgqp3G1QxW+7sv0o389sf0kkEokKoFGJtMzNqKjY/Rwpt82RC1CBP6Ac4avAXcCzAO5+l5kNQGPKHjezw939GURMl5nZ2kgRHm1mhWnB9LLrVjP7HkyO69ap1E1uD22ArSMc/CxSwt2QGu2JSPcVZFR/OyLMo1BF70OoZaYICQ8AHnT3HcxsMiLQFZAxxFqxtt0oee12RYT6lLufGGsaxXfkSH9IQ4YM7SYSicTsaGxFOjV6ISfFzyWR29DgmMZSoANSarehfCgAZtYD+NDdr4rfVweecfdJQXI3oykt58Txi87JosysaT0mDqAc6kZm9jIKCzdBZL0EmtxyE8qhfglcj1TpsShEOwap2VNRqPYBYHcz6zb7LXAUFrZ4pm6oh3SOY7SpSBOJRKIyqBiR1jUmrfYx7j7KzG41s2GoapUwqn8H5QHbInXXLk65ENg5lOKXwFpm9jcUHv43Kuy5OEj1pDhnCTMrZohORw5EILI61cw2BR40s/XdfddYQx9kdg9qSXkEEegAFOqdGfd/AynnpYDFgSPDmP50pLAvQTnVQ5BzUXMUGj49rl3kSnsjcn48zPWXAqrcfaSZfQ5sbGbvIhXcpp79/kEVaSrRRCKRqAfuXpEXag2p/V416tMsXvvE+9vEZ/sC/6nrGsg28F9A8/j7L8DB8bsDe5cd+ywipyWAj1AotBmy5Nu99jnx+Ziy8x9GA8JfRKPMPkfGDE+gcPCkOO4S5FZUjaqLx6OioglodNvaaMrMBohoh6D5ou+jPOqtcd6H8ewjEDl/AJwe99gSEfd6SJ2/iCbJ1Lv3vXr18kQikUjMHYCBPgf81uihXaS8Zo1JM7P2aJKKoRaRhwFiTFqRSx2ASKgXMM3MvkDh31YxlqwauN/M9kNqrwcyoX8AkepwlMtcBo0/e6A4B8DdvzCz8Wa2nLu/j5Ti/UjpViMiax2vpkAL05DuTpQcjcaiAeCtkXJs7e6vmdmtSF13RYr4XqRWa1D+E+AXKB88hVJLzimRD/4ZIuZ+cX57VClcL34Ii8BUpIlEIlE3KkmktS0AL4yfxwK3mtmVQCd3vzFs/k5GVnvnxpi0fSjlVP+CXIZuQ8rwOHe/F2Y5G81AvZZ/QmR7H2p3+STu2RYR8R9RgRCUmdubWQ0itrciJF2FiLozCkm3j3Vfhqpzi+KhxVFv6jruXoRpMbOD4hlnxFvN0ZeICXHtGUhZbhefD0X51H+gkWrLoQHkeyFinYlU68KURsDNhjRkSCQSicqgkkT6jTFpZnaT1z0m7XeIyPZDBUO3I0JsFWTcGrWB7ElJfXZG+csC6wDPhrp05G+7GDJlqAYejPeurmOtU2INvVDodhdUOXxUrGsoIucxSOHujkLDz6LiouJLQ3MUnj0IzSl9CqnIA+K6/eJZ9gXOQN67u6Iiphbx/Lsh1ftPRJ47o7zo9ohcJ9V6bmD2YqOWXZf3VKSJRCLx46CxQ7sF4SyHiOP3ZvYn5CY03d2fD6P6HZD6bILCngPd/Rgz2we1w7xOyde2QF1j0iaj4diFpd+jwANRfNTazF5DShNkvvAblA8d5TLNNzQW7UVE/E8gRdsSEeJI4F0Uou2JCHkwMnCoQsRfE2vrjEhyNCLhL+K+NSjU3ASFih9DxVVNUb/r6YiMPy97rm84b6QiTSQSicqgYhaB0eLSro73T0TTT/6OHIA2cPeZZjYOmbHPNLNVUA/mRl5rTFrt65ZV6I4GXkaqchwyMbja3R8qP6fW79shotoYEeCqiPQ2dPcBZnYnIsP9UUh3HWQDeCBSl4MRQR+KXIiORgVNM1Bx0G9RTnQ8KkwagELTSyCCvwqFn0cgg/uFkNXgTXE94r1JKDQ+Np5rDLCMu0+ra++/r0Vgkm8ikfgpY140rW/0MWnu/tB3rLE9It2pSAUPQWHU58xsG0SS+6NK2wNRNe1tiMyaIAP5rnGtXRHRvo4MI6pQTvV3sf6bENkficj+ZhT+XQoVWW2G1O+2KJw7EeVpr0a53bviOo7UajekhoFUpIlEIlEpzBOm9Y2Jsv7WVogEt0RKcQwixzEoTHsYMr8fjibC/BoV+3SK97eI409D+dPr3P1YMzsOfTnYFlUI74wKn/ojI/obkGJtiojwF8ARyN1pV0S2TVGF8Thk5nAjUq0tkTI+2N1vr+8Ze/fu7QMHDqzv40QikUjUgXlRkc6rmFUEZWYboMKmaZQKnV5AZHUnUosgNb0yUqXvoMrZ5ZGSfQURXkvTXNU94hx399OB0yOcvIWZHYIcjNoDJ6DQ7zR3vzWcncYA/YKQp6Aq5i1RjrQzItMPKbXNzMIPYciQKjaRSCS+G6lIzapQCLdAT6QsB6MvGh1Qle/ZhCJ1dzOz1qjXc0lEcGNRXvMxVLnbCZkvDEAGE6Dw7FRUndwJGTEsGcddj0bF1bh7azN7H5HlFNSP2gLNMT0w7rMGUqqtgZHuvlx9z5iKNJFIJOYeja5I67IEdPeLvuX40939gvo+/5bz/gZc5u5vfY9lghTgmlGktDTKp76L+kUvdfcqMzseWNvdbzczoh90GCI4RybyxwYpd0CVt5ORAf3XKBR7BOqF7Ya8eI9F5HwOmjM6E+V8m5atrRkKBU8Hhrj7wKhU7oncl1ZBjlD/rGNfUpEmEolEBfBjhna/0Tf6HTid0pzROUKYyx/+Pc4pN6Qvb8H5BPWMvoTmi/4ySHMS6gUt0Bx4G7kdraTL2heIBFdE+daFECkvhAqDdkV2fxavD5GK7Y8qcBdHBg0Pl91nGnC/u/eI1pvi3m8gFfsuIuDFaj+np2l9IpFIVAQVzZGaWQeUQ5xlCYjaQ5alRGhDo0r3QDQDtAUKjx7t7tVmNglVv24HnGhm5wMnhVorLAENeMTdT437znYOpfmjuHvTOObZsut0Q3nO1cvueWi0x0xFRLkSKjjqjxToeFR8tIK7T6313LeiEO0HaM87u/vJZnYRsDXqS10HEeISZvZKXOtV4Pk4v8rM+iG/3utQXnQDpIjLlX9xz1SkiUQiUQnMiSHv93nx7Yb0LzH3hvQH+dwb0u9a1zn1rPdZFK59E5Hlr8s+Kze0n4SI89o49gZUcPRQfHYymu5yDcpl7oVIdCoi3WICzHWomKgGuRq1jPeHxn0GoPDw8sjMvgZNqvki9nZ1pGIduOLbni1N6xOJRGLuwTxgWl9naNfrtgSsja2AnYAJEdE0lG+8nTJz+Vr4E2EJCBDmCZui/GF958zKscafB7gUaRfgv2b2H3cfWc/5W6P856vIoagJCruegbx8ZyBC3wF9ceiJ1PW0uN4aSNHehr5UvI9CvcW8Vdd2+XuxBzPcvbeZLQ+8hSwOB8d9Wtezj8D3N61PRZpIJBLfjYq3v4ShwspIoXUGRtV1GDDT3esiiFnm8rWwJzJ5rwt1nlOeYy2lIGdNf3kNuRGNrHV+B5SXfBoVD1VRGuY9xd07mdnhwObufpRpturqaIbpie7+VZjYP4aI+Hl33ynWcDzQPfZoFLN76N4Wa2od+3MGGiQ+Jl61ny0NGRKJRKICaIw+0t+hQp0NgZdNA6oBmphZc3cvSKqZmS3qJUvA05CjTxszW9FLOdauqAK2BbCvmbVBBPc7NN7sYNTT2dS/Jccaa9jWzG5B6rIrcHG839bMRqAc5mhEZLcBayHrviMJD10zOwIR7ddm9qu4ThXqS93WzD6K67yDSHlLdGJT1ArTAXgP5UAHmNk6SJ13QKHkPVGx0d/Qv18TZJp/dvkm+/c0rU/CTSQSibnDj0mk32oJiKz2bgAmuvs5YVZfbgkIMDJ+OtDU3U8xs+mUjV1Dsz6vQQRzJPKsfQeRXU93n25mM9HElduJEWrufjbMpkRbIHP5YYicxiEDhv/F55OBKWlJMgAAHZNJREFUTSL063HsXmgSzKbI5H5r5FbUAZFn93j/b0jdHovMGx5DZH9nrKFz/N0KWQBui/Kq04G+cY+lEemuGM/2urtvYmYTgeXMrJWXee2mIk0kEonK4EcjUo9q2HKU9ZY+j0KUn3v0lrr7qWZ2AfCKma1Iqcr1GlTVe3IQ8wOI1G5EeUVDPZq4+11mdgMiwcWBIWbWFanIHlHVC3C2mfXyqOpFRUAPo8Keo9z9BTM7DNjUzF6MY8r3agrQzd1HmdnriPz2Rrnd+5CbUQtEyocC6yNydlRUtChqtbkDkeefkaocG3/3RJNn2iOrwCnIzOFl1DYzAVjFNEx8IsrNroAUa7H/qUgTiUSiAqh0aLfcjq8ahWL3jc8udPe+ZnYsqlJthhTljXH8sS7jhCaohaYZcJi7v2Ya9F08S1s0YWWMu/ex0jSYTxARTUbVtE+Y2a5xTmvUN/qYu89qjQnshcK25b2a5/LN0WW/R+TXF/XDjgFuQQqyF+oV3RPlS/8K9EDEeRAi4OfR9JfT4p6vIDU8GE2jeSI+H4C+YByA8q79kPKdDalIE4lEojJo0oj3Lkh1M0RkRRj4VyjH2BLlHAdFBS7RWzoC9XACvBAq9SigafRfVqPxZweY2aJI/d2LiGkCUnwDkdK7E9gEEeUVwGZmtkjkK/cDnouf02M9feN+G8XahiBCPRj1gY5DBvUvI6XcOtbbCfgyrvMxsFrct5gz6nGdvwF3o1DxINTqshYKVT+NCHe72J8bkRLuiYqShpVvrrvf4O693b13ly5d5ugfJJFIJBJzj0or0rZmVpgVtDKzu9x9/0KFluU9i8Rl8bNoHfkVUpRXIYL7HBHkGkidrYhIaSlk8fcMIt33UZjzt6gaeM3Ic/ZDZLauu4+2WmPXgNeAnyFCHAs85O4nmFkv4CJ3XzUU76PAKagKebtY190oJ/zreP0dqeJBqM+0Paoybo7ypiNRNfMliCgXRpW7G6MvAn1QGHd5pEjfRLnUDmhE3PTyjf6+hgypXBOJRGLuUFHTejNzd7f4vfZA7htQbvFalN/8O8pdtnAN956C2kU+R+pwJeSFe7LJ43Z5dx8eVbkDUTj3Z8gMfieUJx2MSOk3iKz2cPcH45wDgfMQYVeh8G13VODTDpH1x+7e08w6xT0eRYr6MHd/NUh1iTj+6/h5NCUv3C8RSS+CekFfQ9aBX6Pw76Uon3oq6lv9ArkerYnGqBUVzu1RNfDgWNdYoJe7j6hr39O0PpFIJOYe1tim9XOAoqq3CSqUGYfI7QSUJyyk0cdm9jQilPUR0Y1CIdxtzOxNFB4908xWjeutidTdRBTKvQcZIYxC+cdNEaGtZWanoX24FNgmyLg78BQyO+gBfIVIcYUwRPgQmSzsgojzFhROXhOFbheJ875GCvlTROrvohaXjij/+UGsq1DCSwAD3P2hqCb+Ou5Rg0h1N/TlYmRcb0VE+g/WJtE5UaSpPhOJRKLhqLgiRT2QBYoCo+uRMrsO9X8uEirUKSnSnRAhruDun0RLyynufnkctw8qxLkZ5T/fRiFhRwS6HCoA6ohIcIN4/x6kgPu5+81laz0dEdxnSM1+iMhrGRRedUT8LRCpD473QaHcS1B4dnukrkcj9bo4ItBbY309EZG/E9eegci+XzzPA8j28GB37xp54EURWR+CwsNd3f2z+vY9FWkikUjMPeYHRQpShK8jhfgoparXM9F4sZlEbykivxnAB6HWmiESKnA/CqHejsKkL1FyUOqEejG/RMqyc5xTFaHh15AiLUcxxPteVFz0RKyLWJchb90JqD/0M6QyP0bG+AtTCu8uHq+v4/iv4mdPVJi0KFKmLVHO95y4z2Xoy8UEoGOo4f5ImbZFHsQgUt6+fPGpSBOJRKIyqLQinS0vWvZ+E1Qh2x3Y0d3frH28mf0GWMLd+9Tx2SR3b2dmX6Lc4ZMoz/gyGtpd9K6eiaa0LIIIbggiro1QT+ehiECbIgVZKE9DqtOQI1N/ZL6/DGpraYMM81dH1bOOiHMSsg68E4WZJ1DqM/0SFRK9jcK9O6Fwcn/UN9seVeoegdTpyohoh8Y1zkEOSCshg/0b69v3VKSJRCIx95hfFGmBwjbwdOBmM9sgrAJnltkGXoFGie2M8oLNzWzpMJWvCwfHz4WRK1BT1At6A3B8/F20jAyNNayH+kFvRK0mZyGl3BGFefdAYVdQCHkhFNodjYqVCsK+ABUTtQOuRET7FAo/vxHnPYVyqochK8LpsaaPUe4TAHefZGafoZzrbYisT41jJ6P2mj5RAT25ro2oz7Q+FWkikUg0HJUm0m+1DXT3iWbWn1Jo9wZKod2piHT6oIKiZkh9lhPpf9EkFeLYalS9exMiyclI+R6K2kbeJWaAxrWGoQrYTVC49VNknrAPChffiUK8hUJtEus6BRVIgYqDDkQtM3fHM9yL1OfrKFc7KY415NzUM45tgkh16/h8kagQBhUbrRxrWgaRc/NY91igG/oyogunIUMikUhUBBUJ7ZZZAxa4p7AGrOf40939glrv1W6XqUGksijKOV6M+kSrkRLsgqpd70EEeTYinAmIRJsjFXo9cBEi4DaIWJsh8vsFUn7VSDG+hXpTz0DmCevEvb6M626NfIRnonzpQohYb0Lh25VRmHZCXLMVKrJ6OK7fCanRpxG5E2sxFJK+AX2RGB3PNxR9kXjO3WvneGehZdflvevBV3zj/STXRCKRqB9zGtr90QZ7l78oG9r9fY/nuweF70etQeHFdVAV7xBEnpujcOhBSJk68sld1ksDvqci0h2LSG6pOP9ALw36Pq3W+lZBnrjbxN9/RwT7GCK7IrQ8FbgmjhmMVORmsaZxKHzbJJ737TjuRWB6/H4kUtXXoJzpJ8BNdezXkajXdWC3bt08kUgkEnMH5nCwd6UUaXlhUKFOi/7Ra9z9JNNItGcIg/o4Zmi8PgqCGYxaQo722UeiHRWk8hbwG9eElqq4x1tIufZEZg6rIZL6PK7ZB3gI2CKu0QIpwIsQgW6OFGZzVNxzC6oMHhbrOdSVxxyBKmnbIaJtFvdpHtebhtyI1kJtN1WIeFcDhqNw7RcoVNsfhYL7ufs2ZvYEsIW7Nzez+2JNHeO6hualtqlv/1ORJhKJxNxjThVppbx2W4dnbpEfvdDdVwd2BjYK4/pO7n6ju59G+PC6+wEojLoPJW/eamTYDiKuoYjUJhI5XzNbMp5tMVTQszTw3zi/CVKWS7r7GWgY927Awq5B4q+h3OkriBA/QIr1XNTreSxQ4+4rIcV3QtlzjkP51evj75UQMY5HOdMiv3sxUrBVwFh3XwEpy12AQe6+eXzWPfx8N0bqGNS6MxFV9RZj5EbU3nAzO9LMBprZwOop4+v9h0kkEolEw1CpYqPyqS+T3L0vgLs/aWZ7oX7IcRYDu9Eg7iOQOm2NeiSbBREvBuxiZicgoiuKbHqhvOGtqJUFoIe7D4he1T3M7O045xkzWxqRcAszG4B6P8snvwxAFbejEPnth0LIy6Mh5IMIBWtme8a9iyrgdxERTnb3L6PvdUZ89hlSv8chkjUz2yQ+uxtob2ZDUSj41+7+tJlNBrqY2asov9oMEf6XiKRnDVUt4GVj1Hr37u0DU30mEonEj4LGmP4yS50GGW2CQpx/Qsb1+6IiqEKdzkRmCTWIuNqgEOh/EIH8HpFga5QXXRa1qUwB7jGzB1AR0DOooMdQf2lXpGhr3H094Hwz2w7lMIse0OdQ8dDzKAR7ACK4mvhicCMq9FkdmS2sHs/oKKQ7Okz6O8W9myPVujsKaz+BCPdaVFw0AqnY3nGf9nG9NkiJHxPP3SWeoyMKCX+DSMsV6aD3PmKZ0x6ZY+P6RCKRSMw5GqOPtFydnoiqVP8OXI7aQ64FJpX1j1ahOZ5dUYj1DERIhZPP+DimGlWztkSk1gy11bRHdoDXujxspwEPu/vLka9tG9e5G9jX3Tc1s4vRhJkVUZHRaYi8HkeFTmPinC2QRR+I8IfG79MQIfcIRTo57rMLIsirUD/qV0jlFsYLQ1GxU4Ep8bMaEfBI1GdahULWzZAKrqm9yalIE4lEojKoFJGW94+2NrOLqLt/dA9EQE9Q6h911Ff6BJp0UoVyhDPjercgg/tplGaP9kDeuA+gFpMa4FQzeyfOWTg8aw143cz2QG5B55tZS+R81AZYG4Vk/4pCqrvGOT3CLH9pYGMzWwap2NrKsLmZXYHUcnU870fxWRNkdD8eEWw31L7ztruvYmYzgH+aWWHU8AEqkHonzvkq9mIGdfw71u4jTSQSicSPg4qEdt29aRQPrYkU6Wnu/q67r+zuE+Ow0Ujx7YdCpKtHsdFM4IE4dwoKf24cfxcGC4UJw2eIPEGk0xyFej9H4eELUB50YZT/nIrCqKPc/StUYLSju/8KqeMrgFdRBe8yyBFpI0ReR6OwcjtUNTwCkeGdqABoLPoCsBBSrVOQmm0D7IjIsQuq4m2LTO6/ANqV5UyLIeWFY9FGaLbqIsBm7t4eKdJWZjYbmXoO9k4kEomKoDFCu+XqdA1EcmORz+4F7v78t7gbNYv3nwh/3tZIle1EaRj3WYhoOqEw7xBEVIVz0JcoX3k1ItqlkSq9DIVyjzaz36JQ8n5I7Z6FiO+/KE85FVn8XRbXnIFIeCxwXLTfGJoCU1TYFuHmzoj894t7TEYh3QFxj8XRF4pJwIfuPiqedWlKw8GrgefNrDlS4s2Rmv1zscmpSBOJRKIyqDiRunvT4veo4F2sjmNOKPv9VOQti5ntj1pIQOHamVHV2hfYH+URF0IDuNeIUWvnuPuVZrYV8C+gm2ss2x1o6swQ1Dt6EArfroNUX0ukcNsh8jwSGdavgEj5GKQwPwZ6u/uXtR5jZ1QYdA8iz3+jvtOHEclXIzX5FOovnYn+Pf6F+lQvAKYHWQ5C/bAvoC8Jy7v7oqZB4m+5+9F17OFsOdLanycSiUTih8G8Ylo/C2bWAam75RDxLI0Irn/ZYUPd/QAzmxa5zrYoZ3gaCrPebWbnoedrF4TTHJHjqLiHobDtA2h82yKIwE5CoeVbEak+i/pC30BFRmsjwgaFhbsC95tZYV/YCYWAR8b9RxNj29x9WLTa7BOfLYtIe0NErK/HNccj1dwGVfiuG69folxpOzO7M45dysz+GD2xiUQikagwGqP9pRyztcKY2T7uPh6ZHoBykwOQGuwGUBg1mNnKiIw2Qq0pNYgcF0Ih0iEoBHswykMaCsG2QfnS91FoedM4ri1SpUWbyjZx3rehH8qHTkKh6aPRrNDz4votELnvCOxrmie6DCLlKkSKu6HCqDUQSZ+C8qmO/n0uRWr0ryh8uzqqcN62bB2f115YefvLF1988R2PkUgkEonvi4rOI/3GzeufT1rM/qxB4c9BlNpHlkDDv3sgc4YRqFq3PVKwhYnDhqj/82OUh/wjMmqoQpWvV6Ec5tuovaYVUqxnIeW3D1KTL8daeiOLwGkoPHs4mnu6C2rJORYp5wnItagvItnfxXttKc053TjOLVR3UYH8aZz7KCoy2hSRaCtE1nsisj8FVf8uhSwWT/62fc55pIlEIjH3mNcsAucWUykRaRtUTPSH+Gwgchcqft8bqVBQLrWw5zsPVd2CTA7eR+QFeu5jUH6ysN6b5u7V5Ytw909RTnZxVCA0GVX8XhuHfI1adjaPax6AiLEKeJBSPnca8tOdhELIoC8BNXHeTKRoQa5IiyOzhU9Qjna/eIb7Uei6GSqWeg04IvKosyEVaSKRSFQGFSFSM6uuFcI97TtOaYXIaCuU+/w5UpM1iJwmo/DnCpQmuDhwHRpvVoNGlm2DyLPo79wifp+JlOLRqPWlHbIgHAz8HyWXIVDBzgyUCx0HfOLug2IN/0OkvjbKa/ZDpNoUDSkfEtfoSck0fyaq5G2LWoFWRUTZEZHpWsB2iERPiLU8iMj4aRTiHYd6WjeP619dewOz/SWRSCQqg0op0sKEvngVs0hr50gvMrMVEBE1QQTRDRXvdEYVrX9FKnIGIrNd4lqO+kmL3OLVZSb3xXO2RGHdFsjfdyLQ38yWiPe2RCHgZsAOcU7bOP+FOL7wzCWuvSgi0Zr4OZSSe1Ohokeh8O7SwIDoWZ0Zz38JKmoqJsZ8hELTyyPyLMK+k5CdYrO41hjUytMWke5sSEWaSCQSlUGjhXajcvZ9NFd0TZSr/AD4VdlhQ919UVRw1BkV6oxGedDtUXj00zh2OipOOhQR7Gnhc3sY4O4+EJHdKkjhTUZqF0Rkd7j7F6hK11B+Ekp5WhBRtzezm1FYuQ8qdGqNQrbtkBJ2M/sK9cEWa3sdhaw3jQrfJxARb48Gf3/h7iuiQqKaOPZ49G/kKJ87HRF5N0T6VXHsi7X3NxVpIpFIVAYVH6NWR3VuYVRfPkatQM+Y2HImykWujQirAwrZtkAmBVOZ3W/2LUSG3ZBiczPbNY5rhgp2LkMKEsqs/dz9TaQEdwvjCEdjz8qxEgq/XhlrqkZK9GNEcNPR3vaM48cDh8R1m6KQ7PmoOrgJCtVOjDU+E+sZBixJydChCyqeKvBe3GcK8umdDalIE4lEojJorNDurDFqqDjnWlQFOwuFrWB8NhkVD32MSOb2eK8ahXpnov5NUG/oUsCToTDbo3xloTCrgWWLNQQGAJuZ2SJm1hQpzMKWz1CIdhF3H4FCwo+4+3R3Pw/1i/aNnwuhIeFXAsPdfRpSvc8i4i36Vy9AOVdQAVFBsHuhvtamSG03iXX8IV57o7xxDVLVHWOtPzezZWvtXyrSRCKRqAAqGtqto+ioDwqFTiVMC8qOHWRmn6A+zJGIRCYAS4UXbn80zqyasBGMaw1HucPtwlj+SURgBb6tOrcf6vH8ArgQFRxNRe0mzczs/ThlupntbGYPI2K+HBFmJxR+PhnoYGbD470DkKptitTyo4R9HypIWhEpzL1Rn+iLKMwNckS6EOVi/4kMI5rFOu9FKnVKPHf5/qUiTSQSiQqg0jnS2ZQpyve9japWby5r4/ga5S3bI+OBRYCv3X1lNFFl6bARnAGyEYzP3kbPtF1cY0vkCLQemhs6q2fVzA5Fiu5vEcI9BegfVbR3oB7PDRAJHxjXLfpBQX2qRW5yJsrPvosGdk8CzkaEPBApzqOQ5eDoOKcYxfZsXLt9XOd9VJR0AArdnoEIuBqp0evj9zfi/CfRxJjZRqmlIk0kEonKoNJj1Nqa2VikIIsxaosin9x/AGdG1e7LqIioDXAXIqAnzKwz6uMcHQU7TUHGDijnuT4itCZxj36ILFsBfzSzDcvWdHWcsx1woru/UPbZeiik2pHSjNAdUF5zNUSCG6Lc7S+RUcLRqH1lHaQij0d5zVtQlfEelCpwiWvXoHD1BFSRDAr57o5C1K2QcQSIlDdw96mh1J+L63dHZhGJRCKRaARUdIxa/NkKkdK9KFT5MQpvroQUZSd338HdFwGmhHrdBKnWN4CO7r4CUn+vxjXbxjUGUaqUfRyRpKNK3TURyR1Yds5Qd1+vFomCFN+ZyJz+gljr9Fj3FnGNjYBrULvMcZTIdxX0BWB/NGbtBNTK0jE+Pwq14KyJwsYdUGj2fESMLVFIeXmkTK9AXyxGA3vEVJmiwKgvUr+zhXUhQ7uJRCJRKTSGaf0VwGvufp+Z3Y7IahPUC3k98J6ZFa0iBmBmPYFHgJ8BEyL32BxYIgZxV6MQ6hKo37NjXGsaIqorEPndCdxmZsW4se/6IvEiUp7vx3p2QOQ9FhFYG1Qg9Ky7fyGOoy8i29dQNe44VDjUDqnpNxApPh1regnlh69CXxZeQN67n8U9XkI+vP9GRvpnIoLdBYV6l0NVycu6e5FXzekviUQiUSE0Rh/pPcjAvRUqrBkQ76+MiOMId18L5RhbxmdHoVDmpYgYe7n78ijkuj0izL2Bvl4yD14RhY8fQr68G5WtYRpSh3vXKn4aZGbblR33X0SkvZA6nogIfA3UdrMa8s1tXXbOQii3uru7d0NtLzUo/DoMqc0HUA/ol8jT97m47kYopD0O5YarkNLuguwM345rVwPN3H0VFEKeWU6ikIo0kUgkKoXGmEf6ppktg9TXo/F2D6TErgCeMbPRKCRrUYD0Emp/6Qj8090HRL70CWSSQPyczcwhrn826s3sHvechlTj2e6+W11rLLMwfAup3LYonApyHuqCVOg0MxsCbGJmCyMyPB143N2fNLPtUV70FUSKC6OQ8yKouGi4u78bSvZxSi06wymZ718W73cHPnL3cWHqX5hErBD3rb3PsxSpmU00s2G1j0kA+reoPUs2IeTe1I/cm/qxIO3N0nNyUMWJNIqOFgNuBG5DpgNLASeiAqDXUUvHLSgM+iYKk26Hwpr9zWwUqso9CVW1NgFau/trZbea4e6fRovN1ch39y4UCp3Mt+MrlKv1mB+6RNwP1JayOVKrIIK9FZF9s/i8W3y2LSpE6oiI8mRUENWKMhOIOvBy3MOREr0VEekz8fk7wMlReTwgjvs2DJuTCQY/RZjZwNybupF7Uz9yb+rHT3FvKh3anRxFR+ugStnDkA/tcxE27QBc6+7noJDouGhrOQv40N0PRcb0V7t7L3fvh8K776Jca4HjCdcid78rPjvT3U8pDqhrfFsZngUONDNz95+jntV+8dkLscZPy47/TxRATUXh3o/M7LRo0TkItdWs4e53lF3jEGAZM1vO3XdCYe7n3H0Z4D9ojuoVYSqxMCLfY+L8KcB+7r4Bqvgt9/9NJBKJRAXRKF677j7K3a+s46OLgQvN7EWitSWwDzAk1OxKyNmowN0oZ3nPD7jEG5A13xtm9gYqFLp0Tk4Ms4d9gS3M7OhvOW4a8gX+R0ydqUEuTSCVuRgicJAqf7Ms/9sZuDPCyutTUsuJRCKRqDAadbD3vAIze5DSPNACp7r7442xnh8aZnZk5EwTtZB7Uz9yb+pH7k39+CnuTRJpIpFIJBINQGP0kc4zMLNrmb0tBuBKd7+lMdaTSCQSifkPjTaPdF6Aux9TayrNmvMbiZrZ9mY2zMzeL2vbKf+8pZn1jc8HROtR8VmfeH9Yrf7ZBQLfd2/MbGEz62dmk8zsmkqvuxJowN5sY2b/M7PB8XPLSq/9x0QD9mXdsl70N8yszta6+RkN+f+a+Lxb/Dd1UqXWXDG4e77m0xcqyPoA9eG2QO1Cq9Q65mjgr/H7vsi0AmRl+AYyvege12na2M80j+xNW2S0cRRwTWM/yzy2N2sBS8TvqwKjG/t55pF9aYNMUkB+2Z8Xfy8Ir4bsTdnn9yNP9ZMa+3l+6NdPWpEuAFgXeN/dP3T3GahyeZdax+yC+nUB7gO2MjlA7ALc45qrOhzZIK5boXVXAt97b9x9sst/eVrllltRNGRvXnf3T+L9oUCrsOlcENCQfZni7lXxfiu+u7d7fkND/r8GM9sVWZ8OrdB6K4ok0vkbP0Om/wVGxXt1HhP/oY9Hfalzcu78jIbszYKOH2pv9gBed/fpP9I6K40G7YuZrWdmQ4HBwFFlxLog4HvvjZm1BU5FQzwWSCSRzt+oyx2p9jfh+o6Zk3PnZzRkbxZ0NHhvTIMk/gT8+gdcV2OjQfvi7gPcvScynOlj8hNfUNCQvfk9cLm7T/rBVzWPIIl0/sYoZK9YYEk0RafOY8ysGXKPGjuH587PaMjeLOho0N6Y2ZLAg8BBXmtYwnyOH+R/M+7+NrIhXfVHW2nl0ZC9WQ+42MxGINe5083s2B97wZVEEun8jVeB5c2su5m1QAn+h2sd8zCyGwT5/j7jyvw/jKbwtDSz7mj+6SsVWncl0JC9WdDxvffGzDqikYZ93P3Fiq24MmjIvnQP8sDMlkbTp0ZUZtkVwffeG3ffxN2XcdmfXgFc4O4LVjV8Y1c75athL2BH5DX8AXBGvHce8Iv4vRWqlHsfEWWPsnPPiPOGATs09rPMY3szgtLc2VHUqlCc31/fd2/Q4IjJwKCy16KN/TzzwL78EhXSDEJDNnZt7GeZV/am1jXOZQGs2k1no0QikUgkGoAM7SYSiUQi0QAkkSYSiUQi0QAkkSYSiUQi0QAkkSYSiUQi0QAkkSYSiUQi0QAkkSYSiUQi0QAkkSYSiUQi0QAkkSYSiUQi0QD8P65s/e3gy/ftAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pd.DataFrame(list(zip(x_label.columns, grid_search_xgbforest.best_estimator_.feature_importances_ ))).set_index(0).sort_values(1).plot.barh()\n", + "plt.rcParams['figure.figsize'] = 4, 32" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### test prediction" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "best_xgb_test_y = grid_search_xgbforest.best_estimator_.predict(test_label) \n", + "best_xgb_test_y = np.expm1(best_xgb_test_y)\n", + "sub = pd.DataFrame() \n", + "sub['SalePrice'] = best_xgb_test_y\n", + "sub = sub.set_index(np.arange(1461, 2920))\n", + "sub.to_csv('(10) xgb_log(y)_label_submission.csv')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Kelly/Kaggle_house_price/Forest Models.ipynb b/Kelly/Kaggle_house_price/Forest Models.ipynb index 577e6b6..7cf9fe5 100644 --- a/Kelly/Kaggle_house_price/Forest Models.ipynb +++ b/Kelly/Kaggle_house_price/Forest Models.ipynb @@ -9,80 +9,158 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "## prepare data\n", + "## import data\n", "import pandas as pd\n", "import numpy as np\n", - "train_final = pd.read_csv('train_final.csv')\n", - "x = train_final.drop(['Id', 'SalePrice'], axis=1)\n", - "y = train_final['SalePrice']" + "\n", + "train = pd.read_csv(\"train.csv\").set_index(\"Id\")\n", + "test = pd.read_csv(\"test.csv\").set_index(\"Id\")\n", + "combine = pd.concat([train, test])\n", + "\n", + "# process data before model fitting\n", + "from preprocess1 import impute\n", + "fact, encodedDic = impute(combine, False) # process data and label encode \n", + "\n", + "# seperate factorized data into train and test\n", + "train_label = fact.iloc[0:1460,]\n", + "test_label = fact.iloc[1460:2919,].drop('SalePrice', axis = 1) # salesprice col were all NA \n", + "\n", + "# split train data frame into x var and y var for model testing\n", + "x_label = train_label.drop('SalePrice', axis=1)\n", + "y_log = np.log(train_label.SalePrice)" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 2, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "GridSearchCV(cv=5, error_score='raise',\n", + " estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n", + " max_features='auto', max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n", + " oob_score=False, random_state=None, verbose=0, warm_start=False),\n", + " fit_params=None, iid=True, n_jobs=-1,\n", + " param_grid=[{'n_estimators': array([100, 200, 300]), 'min_samples_leaf': range(10, 15), 'min_samples_split': array([ 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30]), 'random_state': [42]}],\n", + " pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n", + " scoring='neg_mean_squared_error', verbose=0)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "from sklearn import ensemble\n", "from sklearn.model_selection import GridSearchCV\n", "\n", "randomForest = ensemble.RandomForestRegressor()\n", "grid_para_forest = [{\n", - " \"n_estimators\": [250, 500],\n", + " \"n_estimators\": np.arange(100, 400, 100),\n", " \"min_samples_leaf\": range(10,15),\n", " \"min_samples_split\": np.linspace(start=2, stop=30, num=15, dtype=int),\n", " \"random_state\": [42]}]\n", "grid_search_forest = GridSearchCV(randomForest, grid_para_forest, scoring='neg_mean_squared_error', cv=5, n_jobs=-1)\n", - "grid_search_forest = grid_search_forest.fit(x, y)" + "grid_search_forest.fit(x_label, y_log)" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Best parameters found: {'min_samples_leaf': 10, 'min_samples_split': 2, 'n_estimators': 250, 'random_state': 42}\n", - "Lowest RMSE found: 32353.161815955562\n" + "Best parameters found: {'min_samples_leaf': 10, 'min_samples_split': 2, 'n_estimators': 300, 'random_state': 42}\n", + "Lowest RMSE found: 0.1501241888269882\n" ] } ], "source": [ "print(\"Best parameters found: \", grid_search_forest.best_params_)\n", - "print(\"Lowest RMSE found: \", np.sqrt(np.abs(grid_search_forest.best_score_)))\n" + "print(\"Lowest RMSE found: \", np.sqrt(np.abs(grid_search_forest.best_score_)))" ] }, { - "cell_type": "code", - "execution_count": 42, + "cell_type": "markdown", "metadata": {}, + "source": [ + "#### RMSE for all train data using best random forest" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, "outputs": [ { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.00012110195560096155\n" + ] } ], + "source": [ + "# fit best random forest to all train data \n", + "best_forest_y = grid_search_forest.best_estimator_.predict(x_label)\n", + "print(\"RMSE: \", np.sqrt(np.mean(y_log-best_forest_y)**2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Feature importance" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", - "pd.DataFrame(list(zip(x.columns, grid_search_forest.best_estimator_.feature_importances_ ))).set_index(0).sort_values(1).plot.barh()\n", + "pd.DataFrame(list(zip(x_label.columns, grid_search_forest.best_estimator_.feature_importances_ ))).set_index(0).sort_values(1).plot.barh()\n", "plt.rcParams['figure.figsize'] = 4, 32" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### test prediction" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "best_forest_test_y = grid_search_forest.best_estimator_.predict(test_label) \n", + "best_forest_test_y = np.expm1(best_forest_test_y)\n", + "sub = pd.DataFrame() \n", + "sub['SalePrice'] = best_forest_test_y\n", + "sub = sub.set_index(np.arange(1461, 2920))\n", + "sub.to_csv('(5) forest_log(y)_label_submission.csv')" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -101,34 +179,57 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "GridSearchCV(cv=5, error_score='raise',\n", + " estimator=XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n", + " colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n", + " max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n", + " n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n", + " reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n", + " silent=True, subsample=1),\n", + " fit_params=None, iid=True, n_jobs=-1,\n", + " param_grid=[{'colsample_bytree': array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]), 'max_depth': [2, 4, 6], 'n_estimators': array([500, 600, 700, 800, 900]), 'learning_rate': [0.001, 0.01, 0.1, 0.2, 0.3], 'random_state': [42]}],\n", + " pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n", + " scoring='neg_mean_squared_error', verbose=0)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ + "from sklearn.model_selection import GridSearchCV\n", "import xgboost as xgb\n", "xgbforest = xgb.XGBRegressor()\n", "grid_para_xgbforest = [{\n", " \"colsample_bytree\": np.linspace(0.1, 0.9, 9),\n", " 'max_depth':[2, 4, 6],\n", - " \"n_estimators\":[500, 800, 1000, 2000],\n", + " \"n_estimators\": np.arange(500, 1000, 100),\n", + " \"learning_rate\": [0.001, 0.01, 0.1, 0.2, 0.3],\n", " \"random_state\": [42]}]\n", "grid_search_xgbforest = GridSearchCV(estimator = xgbforest, param_grid = grid_para_xgbforest, \n", " scoring = 'neg_mean_squared_error', cv = 5, n_jobs=-1)\n", "\n", - "grid_search_xgbforest = grid_search_xgbforest.fit(x, y)" + "grid_search_xgbforest.fit(x_label, y_log)" ] }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Best parameters found: {'colsample_bytree': 0.30000000000000004, 'max_depth': 2, 'n_estimators': 1000, 'random_state': 42}\n", - "Lowest RMSE found: 25274.277318629447\n" + "Best parameters found: {'colsample_bytree': 0.1, 'learning_rate': 0.1, 'max_depth': 2, 'n_estimators': 700, 'random_state': 42}\n", + "Lowest RMSE found: 0.1200139613943591\n" ] } ], @@ -137,16 +238,49 @@ "print(\"Lowest RMSE found: \", np.sqrt(np.abs(grid_search_xgbforest.best_score_)))" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### RMSE for all train data using best gradient boost" + ] + }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 3.4734747123886844e-06\n" + ] + } + ], + "source": [ + "# fit best XGB to all train data \n", + "best_xgbforest_y = grid_search_xgbforest.best_estimator_.predict(x_label)\n", + "print(\"RMSE: \", np.sqrt(np.mean(y_log-best_xgbforest_y)**2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Feature importance" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -154,16 +288,42 @@ } ], "source": [ - "pd.DataFrame(list(zip(x.columns, grid_search_xgbforest.best_estimator_.feature_importances_ ))).set_index(0).sort_values(1).plot.barh()\n", + "import matplotlib.pyplot as plt\n", + "pd.DataFrame(list(zip(x_label.columns, grid_search_xgbforest.best_estimator_.feature_importances_ ))).set_index(0).sort_values(1).plot.barh()\n", "plt.rcParams['figure.figsize'] = 4, 32" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### test prediction" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "best_xgb_test_y = grid_search_xgbforest.best_estimator_.predict(test_label) \n", + "best_xgb_test_y = np.expm1(best_xgb_test_y)\n", + "sub = pd.DataFrame() \n", + "sub['SalePrice'] = best_xgb_test_y\n", + "sub = sub.set_index(np.arange(1461, 2920))\n", + "sub.to_csv('(4.1) xgb_log(y)_label_submission.csv')" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "#### submit original\n", + "#### change to label encoding and submit\n", + "#### change to onehot for yrsold and mosold" + ] } ], "metadata": { diff --git a/Kelly/Kaggle_house_price/Neighborhood feature engineering (regression).ipynb b/Kelly/Kaggle_house_price/Neighborhood feature engineering (regression).ipynb new file mode 100644 index 0000000..8e9056a --- /dev/null +++ b/Kelly/Kaggle_house_price/Neighborhood feature engineering (regression).ipynb @@ -0,0 +1,678 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "## import data\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "train = pd.read_csv(\"train.csv\").set_index(\"Id\")\n", + "test = pd.read_csv(\"test.csv\").set_index(\"Id\")\n", + "combine = pd.concat([train, test])\n", + "\n", + "# seperate based on neighborhoood median SalesPrice\n", + "worst_neighbor_df = combine[combine.Neighborhood.isin(['MeadowV', 'IDOTRR', 'BrDale', 'OldTown','Edwards', 'BrkSide', 'Sawyer', 'Blueste'])]\n", + "med_neighbor_df = combine[combine.Neighborhood.isin(['SWISU', 'NAmes', 'NPkVill', 'Mitchel', 'SawyerW', 'Gilbert', 'NWAmes', 'Blmngtn'])]\n", + "best_neighbor_df = combine[combine.Neighborhood.isin(['CollgCr', 'ClearCr', 'Crawfor', 'Veenker', 'Somerst', 'Timber', 'StoneBr', 'NoRidge', 'NridgHt'])]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(862, 80)\n", + "(1077, 80)\n", + "(980, 80)\n" + ] + }, + { + "data": { + "text/plain": [ + "2919" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(worst_neighbor_df.shape)\n", + "print(med_neighbor_df.shape)\n", + "print(best_neighbor_df.shape)\n", + "862+1077+980" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/pandas/core/generic.py:3643: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + " self[name] = value\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py:2540: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + " self[k1] = value[k2]\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/pandas/core/generic.py:3643: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + " self[name] = value\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py:2540: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + " self[k1] = value[k2]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(417, 229)\n", + "(445, 228)\n", + "(536, 225)\n", + "(541, 224)\n", + "(507, 217)\n", + "(473, 216)\n", + "2919\n", + "(417, 1)\n", + "(536, 1)\n", + "(507, 1)\n" + ] + } + ], + "source": [ + "# process data before model fitting\n", + "from preprocessfinal import impute\n", + "onehot_worst, encodedDic = impute(worst_neighbor_df, True) # process data and onehot encode\n", + "onehot_med, encodedDic = impute(med_neighbor_df, True) # process data and onehot encode\n", + "onehot_best, encodedDic = impute(best_neighbor_df, True) # process data and onehot encode\n", + "\n", + "# seperate onehot data into train and test\n", + "train_onehot_worst = onehot_worst.iloc[onehot_worst.index < min(test.index),]\n", + "test_onehot_worst = onehot_worst.iloc[onehot_worst.index >= min(test.index),].drop('SalePrice', axis = 1) # salesprice col were all NA \n", + "\n", + "train_onehot_med = onehot_med.iloc[onehot_med.index < min(test.index),]\n", + "test_onehot_med = onehot_med.iloc[onehot_med.index >= min(test.index),].drop('SalePrice', axis = 1) # salesprice col were all NA \n", + "\n", + "train_onehot_best = onehot_best.iloc[onehot_best.index < min(test.index),]\n", + "test_onehot_best = onehot_best.iloc[onehot_best.index >= min(test.index),].drop('SalePrice', axis = 1) # salesprice col were all NA \n", + "\n", + "print(train_onehot_worst.shape)\n", + "print(test_onehot_worst.shape)\n", + "print(train_onehot_med.shape)\n", + "print(test_onehot_med.shape)\n", + "print(train_onehot_best.shape)\n", + "print(test_onehot_best.shape)\n", + "print(417+445+536+541+507+473) \n", + "\n", + "#split train data frame into x var and y var for model testing\n", + "x_onehot_worst = train_onehot_worst.drop('SalePrice', axis=1)\n", + "x_onehot_med = train_onehot_med.drop('SalePrice', axis=1)\n", + "x_onehot_best = train_onehot_best.drop('SalePrice', axis=1)\n", + "\n", + "y_log = pd.DataFrame(np.log(train.SalePrice))\n", + "y_log_worst = y_log.iloc[y_log.index.isin(train_onehot_worst.index),] # convert y to normal distribution for regression models\n", + "y_log_med = y_log.iloc[y_log.index.isin(train_onehot_med.index),] # convert y to normal distribution for regression models\n", + "y_log_best = y_log.iloc[y_log.index.isin(train_onehot_best.index),] # convert y to normal distribution for regression models\n", + "\n", + "print(y_log_worst.shape)\n", + "print(y_log_med.shape)\n", + "print(y_log_best.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'alpha': 0.0011905772393787845}\n", + "Lowest RMSE found: 0.18540119029199814\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import GridSearchCV # search for the best lambda\n", + "from sklearn import linear_model\n", + "\n", + "lasso_worst = linear_model.Lasso(normalize=True) # create a ridge regression instance\n", + "\n", + "# find the best alpha (lambda) for lasso \n", + "grid_param = [{'alpha': np.logspace(-4.5, 2, 100)}]\n", + "para_search_lasso_worst = GridSearchCV(estimator=lasso_worst, param_grid=grid_param, scoring='neg_mean_squared_error', cv=10, return_train_score=True)\n", + "para_search_lasso_worst.fit(x_onehot_worst, y_log_worst)\n", + "\n", + "print(para_search_lasso_worst.best_params_)\n", + "print(\"Lowest RMSE found: \", np.sqrt(np.abs(para_search_lasso_worst.best_score_)))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'alpha': 0.00022570197196339215}\n", + "Lowest RMSE found: 0.09904998644870235\n" + ] + } + ], + "source": [ + "lasso_med = linear_model.Lasso(normalize=True) # create a ridge regression instance\n", + "\n", + "# find the best alpha (lambda) for lasso \n", + "grid_param = [{'alpha': np.logspace(-4.5, 2, 100)}]\n", + "para_search_lasso_med = GridSearchCV(estimator=lasso_med, param_grid=grid_param, scoring='neg_mean_squared_error', cv=10, return_train_score=True)\n", + "para_search_lasso_med.fit(x_onehot_med, y_log_med)\n", + "\n", + "print(para_search_lasso_med.best_params_)\n", + "print(\"Lowest RMSE found: \", np.sqrt(np.abs(para_search_lasso_med.best_score_)))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'alpha': 0.00022570197196339215}\n", + "Lowest RMSE found: 0.10183032494655822\n" + ] + } + ], + "source": [ + "lasso_best = linear_model.Lasso(normalize=True) # create a ridge regression instance\n", + "\n", + "# find the best alpha (lambda) for lasso \n", + "grid_param = [{'alpha': np.logspace(-4.5, 2, 100)}]\n", + "para_search_lasso_best = GridSearchCV(estimator=lasso_best, param_grid=grid_param, scoring='neg_mean_squared_error', cv=10, return_train_score=True)\n", + "para_search_lasso_best.fit(x_onehot_best, y_log_best)\n", + "\n", + "print(para_search_lasso_best.best_params_)\n", + "print(\"Lowest RMSE found: \", np.sqrt(np.abs(para_search_lasso_best.best_score_)))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.38880211110670043\n", + "RMSE: 0.16594978199486426\n", + "RMSE: 0.3355265626860251\n", + "RMSE: 0.006975697521277645\n", + "RMSE: 0.45634835784886807\n", + "RMSE: 0.007200614006508818\n" + ] + } + ], + "source": [ + "from sklearn.metrics import mean_squared_error\n", + "# fit best ridge equation to all train data \n", + "lasso_y_worst = para_search_lasso_worst.best_estimator_.predict(x_onehot_worst)\n", + "print(\"RMSE: \", np.sqrt(np.mean((y_log_worst.values-lasso_y_worst)**2)))\n", + "print(\"RMSE: \", np.sqrt(mean_squared_error(y_log_worst.values, lasso_y_worst)))\n", + "\n", + "lasso_y_med = para_search_lasso_med.best_estimator_.predict(x_onehot_med)\n", + "print(\"RMSE: \", np.sqrt(np.mean((y_log_med.values-lasso_y_med)**2)))\n", + "print(\"RMSE: \", mean_squared_error(y_log_med.values, lasso_y_med))\n", + "\n", + "lasso_y_best = para_search_lasso_best.best_estimator_.predict(x_onehot_best)\n", + "print(\"RMSE: \", np.sqrt(np.mean((y_log_best.values-lasso_y_best)**2)))\n", + "print(\"RMSE: \", mean_squared_error(y_log_best.values, lasso_y_best))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "lasso_worst_test_y = para_search_lasso_worst.best_estimator_.predict(test_onehot_worst)\n", + "lasso_med_test_y = para_search_lasso_med.best_estimator_.predict(test_onehot_med)\n", + "lasso_best_test_y = para_search_lasso_best.best_estimator_.predict(test_onehot_best)\n", + "\n", + "#convert predicted y to datafram to combine later on for submission\n", + "worst = pd.DataFrame(lasso_worst_test_y).set_index(test_onehot_worst.index)\n", + "med = pd.DataFrame(lasso_med_test_y).set_index(test_onehot_med.index)\n", + "best = pd.DataFrame(lasso_best_test_y).set_index(test_onehot_best.index)\n", + "lasso_neighbor_pred_y = pd.concat([worst,med,best])\n", + "lasso_neighbor_pred_y = np.expm1(lasso_neighbor_pred_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "lasso_neighbor_pred_y.to_csv('(16) lasso_neighbor_submission.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "plt.hist(train_onehot.SalePrice, bins=50) # original y" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 2., 2., 1., 0., 0., 0., 2., 3., 4., 3., 5.,\n", + " 1., 5., 21., 22., 23., 18., 29., 58., 56., 65., 100.,\n", + " 122., 93., 90., 82., 108., 91., 64., 55., 58., 51., 46.,\n", + " 42., 23., 29., 22., 13., 13., 13., 7., 5., 4., 1.,\n", + " 2., 2., 2., 0., 0., 2.]),\n", + " array([10.46024211, 10.52172673, 10.58321134, 10.64469596, 10.70618058,\n", + " 10.7676652 , 10.82914982, 10.89063444, 10.95211906, 11.01360367,\n", + " 11.07508829, 11.13657291, 11.19805753, 11.25954215, 11.32102677,\n", + " 11.38251138, 11.443996 , 11.50548062, 11.56696524, 11.62844986,\n", + " 11.68993448, 11.75141909, 11.81290371, 11.87438833, 11.93587295,\n", + " 11.99735757, 12.05884219, 12.12032681, 12.18181142, 12.24329604,\n", + " 12.30478066, 12.36626528, 12.4277499 , 12.48923452, 12.55071913,\n", + " 12.61220375, 12.67368837, 12.73517299, 12.79665761, 12.85814223,\n", + " 12.91962684, 12.98111146, 13.04259608, 13.1040807 , 13.16556532,\n", + " 13.22704994, 13.28853455, 13.35001917, 13.41150379, 13.47298841,\n", + " 13.53447303]),\n", + " )" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(y_log, bins=50) # log(y)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ridge regularization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Find best lambda" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'alpha': 0.43287612810830617}\n", + "Lowest RMSE found: 0.14658042639621965\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import GridSearchCV # search for the best lambda\n", + "from sklearn import linear_model\n", + "\n", + "ridge = linear_model.Ridge(normalize=True) # create a ridge regression instance\n", + "\n", + "# find the best alpha (lambda) for ridge\n", + "grid_param = [{'alpha': np.logspace(-4, 2, 100)}]\n", + "para_search_ridge = GridSearchCV(estimator=ridge, param_grid=grid_param, scoring='neg_mean_squared_error', cv=5, return_train_score=True)\n", + "para_search_ridge.fit(x_onehot, y_log)\n", + "\n", + "print(para_search_ridge.best_params_)\n", + "print(\"Lowest RMSE found: \", np.sqrt(np.abs(para_search_ridge.best_score_)))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### RMSE for all train data using best ridge" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.11860807897401618\n" + ] + } + ], + "source": [ + "# fit best ridge equation to all train data \n", + "best_ridge_y = para_search_ridge.best_estimator_.predict(x_onehot)\n", + "print(\"RMSE: \", np.sqrt(np.mean((y_log-best_ridge_y)**2)))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### RMSE across different grid search lambda" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", + " DeprecationWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", + " DeprecationWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "error = pd.DataFrame(para_search_ridge.grid_scores_)['mean_validation_score']\n", + "error = np.sqrt(np.abs(error))\n", + "alpha = pd.DataFrame(para_search_ridge.grid_scores_)['parameters'].apply(lambda x: x.get('alpha'))\n", + "plt.scatter(alpha, error)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Variable importance for best lambda" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pd.DataFrame(list(zip(x_onehot.columns, para_search_ridge.best_estimator_.coef_))).set_index(0).sort_values(1).plot.barh()\n", + "plt.rcParams['figure.figsize'] = 4, 36" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "alpha_100 = np.logspace(-4, 2, 100)\n", + "coef = []\n", + "for i in alpha_100:\n", + " ridge.set_params(alpha = i)\n", + " ridge.fit(x_onehot, y_log)\n", + " coef.append(ridge.coef_)\n", + "\n", + "df_coef = pd.DataFrame(coef, index=alpha_100, columns=x_onehot.columns)\n", + "title = 'Ridge coefficients as a function of the regularization'\n", + "axes = df_coef.plot(logx=True, title=title)\n", + "axes.set_xlabel('alpha')\n", + "axes.set_ylabel('coefficients')\n", + "plt.rcParams['figure.figsize'] = 16, 4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### test prediction" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "best_ridge_test_y = para_search_ridge.best_estimator_.predict(test_onehot)\n", + "best_ridge_test_y = np.expm1(best_ridge_test_y)\n", + "best_ridge_test_y\n", + "sub = pd.DataFrame()\n", + "sub['SalePrice'] = best_ridge_test_y\n", + "sub = sub.set_index(np.arange(1461, 2920))\n", + "sub.to_csv('(6) ridge_log(y)_onehot_submission.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lasso regularization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Find best lambda" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'alpha': 0.0001232846739442066}\n", + "Lowest RMSE found: 0.14732670392217098\n" + ] + } + ], + "source": [ + "lasso = linear_model.Lasso(normalize=True) # create a ridge regression instance\n", + "\n", + "# find the best alpha (lambda) for lasso \n", + "grid_param = [{'alpha': np.logspace(-4.5, 2, 100)}]\n", + "para_search_lasso = GridSearchCV(estimator=lasso, param_grid=grid_param, scoring='neg_mean_squared_error', cv=5, return_train_score=True)\n", + "para_search_lasso.fit(x_onehot, y_log)\n", + "\n", + "print(para_search_lasso.best_params_)\n", + "print(\"Lowest RMSE found: \", np.sqrt(np.abs(para_search_lasso.best_score_)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### RMSE for all train data using best lasso" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.12074042400970975\n" + ] + } + ], + "source": [ + "# fit best lasso equation to all train data \n", + "best_lasso_y = para_search_lasso.best_estimator_.predict(x_onehot)\n", + "print(\"RMSE: \", np.sqrt(np.mean((y_log-best_lasso_y)**2)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### RMSE across different grid search lambda" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", + " DeprecationWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", + " DeprecationWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "error = pd.DataFrame(para_search_lasso.grid_scores_)['mean_validation_score']\n", + "error = np.sqrt(np.abs(error))\n", + "alpha = pd.DataFrame(para_search_lasso.grid_scores_)['parameters'].apply(lambda x: x.get('alpha'))\n", + "plt.scatter(alpha, error)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Variable importance for best lambda" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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uMLNH4VkDw2b2RbysYgDvTNEDPA34MJ5RkWUIk3ipBNRf+P8Wz2bIzg9jwKeApwD/CTwj5kvhIDMSTorjubF9esz5eDzjwmLt6auQGQOtmWSWP4zE9RQVeqk+ECkKpMnkxnjPQapfRIoVubZBqodDm+1yN3A5Lk5ky81BaivPxHDhgZhvjCo+DCKEEEIIIYT4qflFKLuQQCJ+Xuxu08A340H2X0f7zGcCB5rZhJltxTMMwE0RP1ZKOQHPlnidmf0IFxK2mFk/XsJxKe7r8H68LOOtcf8U8F7gkXhryg14NsQ4sBIPqJfE9lpcnPg6HoyvxQ0vV+LZCVviX1JwASD3UzAYi20PnjVQ8HKMLHVomcIzMH4Yz5zAfS6yreVfAK+K/RSMUhwYonpKZCeN7GhxXbOG8Tj3pWbMUvz7t5kSKVgU/FtmWUYyFO+RY29ie1R2IYQQQgghxC8wK99wloQH8XPl/zzzIfwecv9uYI6ZbQPOAf4eD4bfgQevp5nZucBdwLPM7Co8ID+/lPIGM/s6HnAfjZdgpMnjX8b9g9TsgHW4EeUNuEHl/rH9CB5EPx0P6M/Ag/M/xcWMRbg55DAeiG/ASyYywyGzEqAG9eCZAVN44H9k8wmyfSXNuGy9eQxVEDo77t8GvBEXAtpnbKK262zNLZPe+Ja9eIbD/nE+/w9SgGtwkWMuboS5EjfR3EoVZzITIskyk9y/y8yOL6Wsasao7EIIIYQQQohdoOBe7E3sTs+Hlgk8MyHbb96IB8zvAF6EZzFcjwfilwEvMbMX4sLAn+KtMQ0PlnuA4/Hgej4enF+Ol1O8GvdmWI/7M4ALFaPAKlwIeGDM8d3YrsIzLL6Gd72Yj7fuTGEhyyTyPSZinXPxjITMXhiL41E8sM/7p+gsjyCO74znbKAzu8HiX3bmGMLFmeHm3hQwsrQjhYdcR5ZVnB7/UjA5vFmfUTuDrI79XF+WfAAMdwkPQgghhBBCCCF+RnaHKPW0pz2NCy+8kPvuu4+lS5fy2te+lmc/+9k/12fsKeIDwDuBa82sNTBcARyElzk8FDdtvAJ4A/4r/SvxwPtFuJ/Ci3Hvhf+HB+C/DXwBD8gPAr4Yc62gmiquwAWMZ+HdMzYBj8VLPR6HB+QFeAQekGfmQluyshUP/ifwDIsD8CC/xPOy+4XhAscduFlkig99zXViO4gH/TfE2gvwPWo3jBQAJug0obwEeHDspxCRJpZzcS+MJTHuOLyV6Fmx5hQ9RoADqWJHijhJvvs40GNm+5RS1jXXs+wClV0IIYQQQggxM7vD80HZFmImPvKRj9zvz9gt4oOZFeAtpZSXx6l+4GW4UPAp6i/s38a7MYzhGQBTbB9sgwsTv4p7PRxG9T54X4zfD29tmb4Hzwf+IZ7zcTxDoJ+aDZCZGGdQ/Q3W48LCHGpGQGYz7BvHg7F/a6xxCbVcY7wZf1Cz9j482+BHeMB+By6y7BPPyqyFnnjPZH3MOQfviEE8Zw2dIkayDhcfBnBx4Qz8e/5e3JPrX0b9vmtj7hLX0mgyhYh+4LIu4QFUdiGEEEIIIcQukRAg9iZ2l+HkKHBOtNJseSUeEL8LeHcp5Rw8gD6slNJfShkspcwtpfx5jP9qKSUzJa4E/hjPXtiKZxw8B/h6GFXeDtwDvKuU8mE8cL8Jz3j4azywHsTLGP4HD+5b9sWzAfZpzt0W8/wgjrN0YjHVb6HgpRuDePC/Ls5ly0qLZx6PB/PL4hukQeVQHG+N75bXNse1uXT6Qfxas5Zc1xjeqaPEe9yAtxrNEpE1zf39VF+J4Zjnm7hpZtJP/W/nPIQQQgghhBBCiJ2wu8oupj0e8E4OwHT7zc/gIsRaMzsL93h4kZndC5yKZzNsBP4KGDGzUTygvh0P7A/BA32LudtODQuAZ5rZmfi7L8EFhdfiwfQYXlbxK3hXjG3UcopJqrlkliN8Bc8cyDaUacw4J57bF+eWNffModO0MUsj7qGWYkziAf5EXM9shTtxsWEEFwNOiflTyIDaurMHLykh5npxs7Yj4vtZ3H9IzDFKzfQYpwofv0xne83We+KlwIcQQgghhBBC/FT8b8sulDkxeyilYGa7HrgbKaW7aeNPx+70fHgn8AMze2PX+YPxwPmf8DKKr+Ciw8PwwLsPbxH5JTxIfxK1Zeb8uHdzbA/CBYlkPV4S8T7gH/Fg+9/xzIFrgAvwTIhfogb12W7yBlys+CpwMfB24CkxbhwP0n8V95XopZaOgIslaTKZnTE+Bzw+9iep5RWjzb19MXeSPgxTeNvQ9HO4L74JVIHgk3jHjF6qt0R3OUYKKSlW9DbX0oOit2uOQmfGzNvZHnk+CCGEEEII8XNGYsPsZGhoiNWrV7No0aI9VoAopbB69WqGhoZ+5jnsf6te/EwPNdtUShkxs9fhwfVWYKSU8hozuwf3PUiW4C0ofxM4tZTyIjNbjQfrj8SzFlbgwf0jgfOBm4HH4GLDKO5dcCBwJvA24OH4L/y34lkE345zc/FA+9u4ILCC6qeQAsMWvPTC6MxamIt36FhMzR5I8SBFnsyeMKogUWb4dzPenrMN8rMjxoZYU2Zl5LUcm1kJLfncbtKDoh3fztVNCift9d8tpfxLO8jMngs8F2DZsmWn3HLLLTuYTgghhBBCCCH2bsbHx1m1ahXbtm3b3UvZKUNDQyxdupT+/v6O82Z2aSnl1F3dv7u7XbwNL6v4QHOuBzi9lLK1HbgDBeh43OvhQXgXjBFcGDgHFzDejHeI+DbeYnMU75TxOWBdKWW5md0H/EEp5R4zm4znL8IzCaaARwNfxrMprimlPNzMJvBg/gjgjbgw8mO8/GI9LhB8F89suB4vp1hCFQwmcR+IY+I9NgN/B7w+7juZGuCvwcWGnmZt4BkOqRyVeLeCl0pso2ZPGLUsI1uD0syRTMS1buEhRY/MfrgRN/XMcZ9HCCGEEEII8VPz05RdKOth9tLf389hhx22u5dxv7O7DCcB93gA/gNoG4h+CW+dCYCZrei+Dw/QfzP2jwamSikn4QaL/cBHYnsm8DfAx+L4KXHPLbggAW5Q+R4zu5IaqP8BcDlwVSnlazHH3c3zM4vgOrzUwnDxAdxXYmE8G+AV1OyJ7KixDRcjUggYwYWHHuA0OttaLmz2J/HvBVVISMHgxzEvuABheGbHON6yczzO5ZgUJP4Zz8LIMpMc86O4PoULKm+J/UO71vdwtifLLo5V2YUQQgghhBA/OyvfcJaEBzEr2N2ZD+DZCS9qjl8MvNPMfoCv7xvA87rueQnwYTzQH6J2pjgPL3k4GjdC3Iq31PwQHnwP4p00jgUwsyvwTIn9cU+HKeAS3G9iElhnZmfgbUB7gDlm9l/NOu4AluPZCb8a564EDsAD+GXAp6nlF5fhrTzn4iUXUD0ULgIeiJtKHtw8Y0PMlWaUT26utcaPJzTnN8UzDo3jRbjwkeaVU1QB5alUE8xXAP8vvtMRcb0PF0CeSs2+WE3NwHiPmX2+lDLWPF+tNoUQQgghhNgFEhXE3sRuyXwopYw0+3eXUuaE30MBXlVKeUop5YG44eRdMe6CUkqKFLcDD8UD3AHiPUopF+JZC+DCxIdLKR8A/jauvxZ4ATBZSjmulLICL4P4RinlRDwAPy3uPRIvL1iMCwVrYi2nUo0X943tQryEZAwvBdmfGvin4cFUrHkc94jYEuct5j4SD/rvwwWVZBAXEyZjjhub+3bkRjK8g/MT1L+5xZxtp4y3xfPaNp2ZMdFmQsxt7nlcl/AghBBCCCGEEEJ0sFvLLmZgFDjHzBbvYtwpuMfDlXhpwaVmdrOZXQx8EG/V+S7gd83su3gryckdzLUV6DWzq/Hv8X2848WtuFHlv8a4hbhHwzxqW87srmF4xsYAHqxPUlt8XkRnhkIfLiZ8kNqBYiGe2dALPJjq5TAa5w6ldp04fAfv0b7fKNUcEjq7WRD7G+M5rfhwZ3wPgJNim4aUBzb3tuJIWxYihBBCCCGE+An537baFOIXiT1NfJgA3ouXTHRgZkvM7BNm9n3cf+AFkR3xHmBlKeUw4Gn4r/6vAJ4JHF5KeSheRvEtM/t4jO8xsyui7OIg3PBxCx6MD+BlE6uBo5olrMZLIFbH8RrgXmr7zMvi/CDuJ3E3HrT/dmz7qd0sDsK7QbQdKjKbog8XCbIc4y6qQJAixhQuFLT04ILJKC4OrO2ae5TOv/fmeNbc2Ida2tG23Ezuim2uM3kZ2yPPByGEEEIIIXaByi7E3sSeJj4AvBN4hpkt6Dp/HvDWUsqDcbPJ981w73nAeTHmjq5rJ+NeEcfhwfOLouziDuDiUspD8GyGhbGGbXFtY9y/GM90WBrHB+LlFZn5cHKc7wEegfs9gAsCydYYOwcP8DM7IU0jU3QgrvXH89rOFeBCSSsGgGc+5NieWG/Sw/YtOA+I7ShueNk+E7YXH3J8d8eMB7I93wR+CPxwZGRkhstCCCGEEEIIIfYm9jjxoZSyAfgXvIyh5THAOyJb4dPAfDOb1zXmdLyzBcC/dV27uJSyqpSSmQrLm2sfie0EnpWwFfd2+EjMN4kH75fjQf9nSymGiwXZJWNljJnEsx7SBPNoqrdCGnxuw7tTZKlEbidwr4Wb6PzbZHlEChMj1LKIm+N8K2ZMdt27jeoxkc9JWn8Iwz0nrOuZBRdeknFqecZGM2tbdgohhBBCCCGEEB3sceJD8Da8/WZrbNgDnF5KWRH/Di6lbJz59hkZbfazvKE9BngtHrg/DQ/we3CDx414VsQBwNXAqWZ2NJ5pcEXc8zg8+M/yh/RFmMKD+F7caPI2POBf1Dx3Q2x78VKOFBKyZMLi3u/HuHXAhbG/nJrVcEXzrLY04kaqeDAV45Lbmv0teDlJ+12y1eaDm/P9VNHiYTMYTqrsQgghhBBCCCHENHuk+FBKWQP8By5AJF+iaclpZitmuPW7eEkGeGvIHdEHvC66axwCfMDMHoB7TRhuWHlrM9d38ID7Xjy7og/Pgvgq3mJzM94NohcP/ieopQkbqcLHQmqLzdYQ8hbgqrjn0bGmFBBy3IHAQ2K/H8+oIMZtxcWJU6miSj5/HO/Aked7YlzSGljui3f6yPv7Y/4h3A/iJ0VlF0IIIYQQQgghptkjxYfgzXT6FrwYzzj4gZldCzxvhnteArwsul4cSC196OZdwKtx0WACD5Y/hHehyFaWj8ZNHZ+FB9/Hl1Iejpd8PD/agx6FG2R+FTeZ3Bb3HkXNXhjDRYRR3ATyNlwsSB+GtXipxxFxb4oGKTz8Y8zb+iwMA0+IOQvuIZHiQmZaZLnEJVTx40pqlwuaMVA7ZKyOc/9DzcgYAj7f3HNXvNeVpZR1CCGEEEIIIYQQO2GPEh9KKSPN/t0R4L8mju8rpTyllPLAUspxpZTnxfkLSimZEXE78NAwj7weD7zBA+m1VF4CvAkPqt+MB9Gn4ZkTH4x5VwJ/hgsSC4F/j1KL5cBCM7s4/CfOwQP2U/FOGX8F/A0ezBtucHldPGtfYB9cPLgXFzf2xX0Wsg3mRMx3Txw/L+4teMBPzPv5Zt9iXujsVmF4uUTOfTvwHFx0WYOXi+ScA3FvloOcQafPw5VUEeSAGH8tM6OyCyGEEEIIIYQQ0+xR4sPPgVOAK8zsB8ALgJfH+c3ACWaWPgW/ggfiAP8OPNXMhvDODd9r5rsOeEQp5WQ8U+LvgIfi4sV50S3jPODX8QB/HXAMcE3cPxXPfm0cD+BZClN4q83fivPL8WyI9KLYBCyJ/TGqQWRmN4zhgkUaPU5RS0QK1bdhCveGSPHh8cBHcT+LObG27Aryw9jeQ/WZWI9nOUzh5RjdBplPYGZUdiGEEEIIIYQQYpq+XQ/ZszCz/YG34iLAWjwQf2Mp5b9KKd+kdoHo5gvAWcDHcUPJz+BiwyfxEo21+C/778CzEsCzBj7WiBZ34ELAUuCNZvYmXMDZHzeiPAx4Ou7rMEw1iZyLB+ybYr2b8W4cKRJY3GPUThLXAifgAkN2ysjSi0HgAXh2xVCs4eyYf4SaBbEm3sHi+aN4Wck+VF+JbK2ZJS7HUMWKBfGvAJfifhc014fMbN5PafwphBBCCCGEEGIv4xcq8yGEh6vxzIUUHj6AiwG7ojvD4RyglFIeIa7pAAAgAElEQVQOB96CCw/vjvmOiXveAXymlDIc96RYczMezE/hwkP6JnwZFwtOpAoDC+NaX4y7m1rusDyu3Qi8Pq5/vpRyPG5oeTu1jGJNXL81toPUzAdwYeXHsT8az9gHz3DY0MyzABceiHdOYWUtnR4VhSqEFODtVBEjyy22SXgQQgghhBBCCLErfmHEBzMz4GvA2lLKklLKKXhHi8XA6WZ2q5ltNLNNZnajmT0s7jsDFwFehQfoT8N9DQ4DeszsuXj2w23A7wJvAA4ws/uAI4FfM7PX4eULh+BZDMfhmQrZuQJcFHkMHpw/AM8oWIAH8EvwQP8i3C9hPrAM923YGPflXGeY2XPwgH9fvPTjs1Qfh4Njex/VHBO8G8XSuC/H9uLtM99KZ1bFFC5QDFOFk3H8v4fMhEjPiCm8nCS7bvTgfg7gwsZMyPNBCCGEEEIIIcQ0e1zZxY7KKvBf7wfxconkVtxv4bPAobhRYz9wMXC+mb0i5urFDSH/Hu8esQYPtgfxLhpX4YH8scBNeOeJbGG5BDeRTCZjXQuBlbhosBlYEdeyNGItHtwfjAsF+wG/1szzn8BvxzM+hosh4KUa78X9Fqbi/A+pLTCz5GExLk605HF/fIslce75cT5LN3pwUWLf2M6hCgpDVKGhL/4N4sIPdHbd6DWz3lLKJJ18M+cbGRk5BSGEEEIIIYQQezV7VOZDZDd8EvhGKeXwJrthKXA8np3Q8km8xOEc4Em40WN/3HM8cD5eLgGe1fB+4IIYM4y//9HAU4An4oH2FqoHwwg12M4yi97YvxvPXjA8eD847r8HF0xW4V02RvFMi/GY56547pNiuxU3ytw/rh8V27lUT4YsA8nMhVHgCjxj4ktxbQsucmTJRNumdAT4Di6SvC7OzcOzNfpi3s/S2YKzpQ/P2CgzjDkTIYQQQgghhBBiJ+xR4gPwKGCslPKePFFKuaWU8nY80+BE4DlmdpmZfQLPZhjHhYbrcdFhK+6FMISXN9yOiwXnA4/FDSEXUt/9TrwDRP56fxIuKNxOzQAAFwKm8KB7BM8E6Kf6KfwQFwXmx/OPBh4ec22iZh3sg4sTl+BiwPyY9764PhHHuZ77qOLDVDxzMN75IcBpcW0IFwh6Yk2jcX4tVcDoA/48zvfFHGMx769QhZb0eWhFiCzl2BLHOfZpbI/KLoQQQgghhBBCTLOniQ/HA5ft4NrFuNHhtXjGw6G4IeRg/LsNb5OZXgcAN+AZCVti7tfjJRdr8KB7Eg/Yz8Q9GYhzBc9qyO9TcKFgIuYfAZ4R59Kr4SF4kJ8mkAO40DAU+z0x90D8Oy3m6YtnD8Z9aQw5hGdn7E8N9Dc16+wH/oLq0TCG+0dM4eJDltQMxrl5uNCRxpfgIkOOSdGCeHZ6PuT75z2rqdwBnG1mg3SiVptCCCGEEEIIIabZ08SHDszsnWZ2pZl9H/g2bvS4DHgp8CDg2XigvBr4HB5gnxi3F7ycAVwkGMCFi8yEyBIKw/0Y5uCiRC/1u2xqxg7HHBmIT1ANF8djzFxcBIAqgGyMcSWelaJAT9yT2Q7zm7mIec6NbZpFvpxOz4X5MfdUrC2FkEOb58zBxYUUKdoOGa1oMNS8z3WxnYh1j+HlIlA7ZYCXdlxUShlFCCGEEEIIIYTYAXua+HANLioAUEp5IfBo3Djxpbgx49fx7guGZzvcgYsPZ+EZDlkq8U7gI/iv+3fg75qtK4dijs3Aa/DA/pt48G54BkDBBYf1VEFiC17OMEUtozDc52EixmyiM1Og/em/FQ6I8T3xfikWpMDRj5tl5t/oDuAR8U4TzRybY8yaZk6aMYVawmHAvc29V8a5bXH+4hi7jCqwpGDS1k/kuw0AP2B7VHYhhBBCCCGEEGKaPU18+BowZGbPb87lr/ELgBtKKU8lTBNLKWfi3SpGcOGgBw+WL8M9Hh4d94/gwsEZ+K/982K+TXini368W8W6eNZ8PAh/Ay5AJP240eRFXev+EdVjYZ/YGp0iRHaZSHFkdawpRQuacflvX6oosQV4JPAEXHDIeVN0mMv2Hg3gnhGtWHFcM+7kmL8/3vnBsZYUYTIzYgueTUHzTjlHa2yZqOxCCCGEEEIIIcQ0e5T4UEopeNeJR5rZzWZ2MfBB4JXAu4DfNbPvAg/AA3Dw4PvyUsphpZSHxLmj8F/k98dLBq4tpcyNsQfgAsAleHD/z3jJxiZq6QP4t9kX+EJzbhDvvPEwOs0oD6X6JWRGBHFuC53kN59H9YIYxY0vYccdJ44BluPixoLmGSPUTIk8N4VnbIB32mhLLdpWmgU3pJyiekPcTBVOsoTkJjwzIkWHUWo2xecQQgghhBBCCCF2Qt+uh/zfUkq5E+9aMRMPbPZfFeMvBC5szm8tpUyLCGZ2Lt4VA9x88kA84B7Eg/KL8YyIu/Bg+1t4681+4IUzrCEzCrZQszIO7xqTXg4L6BQRpvAykLnU8oo0yFyQS27OT+HiRB73xLrX4SLERbiI8kfNupK1MWYz/n3OivuvxcWS+XhGxG142QfUziFZymG4yHBkc5ziySj+38/BM3wjIYQQQgghhBBimj0q8+H/gDnA9/Hg/0EApZSz8Fabf4FnWVwZYzfhpQO3ASubOb6CB+1DdPoqZJbA/1DFB+j0eeihig4t/VQhYxIvlWhJE8oUIbK04yHAC2J9dzTz9uBZEmPxHvtRMzhW4m1JibUcTxUubga+3Kx5IK7NAa5o3mcY/4YAH2d75PkghBBCCCGEEGKavU18+ArwZjz74DpqsL6S2hkjW2b24FkSmYGQLIttGjKCCxbfw4WDthvETLTlEd0GlODBfmZu3BPbdc31rc3+O2JtC3HvhZzv3lhbPy5UnET1ZriQzgySW6glFNfE2LbFZq7zyJ2+VSfyfBBCCCGEEEIIMc0eV3ZxP/Ni4P146cJaoJjZnwP/BHwKFw4G8JKCIeov/7fH/aOllKPN7Czg01RDxkV4+UEv7keRjOKB+wCevdCPB/qFmb/9JC565JgUDLIsIteVZLeKEVygyPUspJpW9sezTsPNMl9FZ4nGsc3+U5r9Qm1HCtW74vvxvt2lJkIIIYQQQgghxIzMusyHUspI1/EFpZQXxf7KUsqjcF+IM/EsgmcATyilPBQXIRbjZpCZ2TCGZwO0XI4bOmZGxDq8VKItvwDvaJFlHFvj2mTXGGKeMerfY4jqBTHazH0rtasGuH9D7wxzXhPjp0op/bjJ5o2llAOAv6EKHykwZObD5maOD1NNNG/F25yCl6sc3twzEyq7EEIIIYQQQggxzawTH3aEme0oCv414C/N7De6zo/jmQT7UH/9HzCzbbgPRJ4fA27AfRUyywE8qB/AO2+ACxp9cS7LKNqOET+kZkpk2cM6XADYJ849gJrdMAV8Ms730fm3PCDW1mNmfxjPScHh07iYkP4RKXJAZ0bEM/FuH8S934p9A1Y1z/sA26OyCyGEEEIIIYQQ0+wV4kO38NBkR8wtpdwWbTo/VUp5DR6oP5WaTfAZ4DE5FTX4b00jn4iXIoCXc4B/28VUc8gsqQAvo7iiOX4cbvy4ARcV0ktinxibc3abV+7XnN+vubaYmtXwQqpPBXhpyUZqV4311IyH0pxvMeDMOJ9eGNvi2k0IIYQQQgghhBA7Ya8QH4K5M500s1EzGzOzbWb2ajz74HUxfhAvMzguht9EFRHy2w3QWa7QmlNui/lSCJiKcxN4x4ipuJYeENkas7+Za4wqYEAVIC6NebbgbUIzm6LNnugBTgQ+ARxkZo8FnkA1jzS8O0dmPKSwkt01kiV4FsR4rLU31g9eWtKNyi6EEEIIIYQQQkyzN4kPHZjZr+PB/bXAN/BAfAEuJhzYDL0GeHXsH04N1LMd5hSdwsYqqqgwiHeTIO7LDhSTwGFxLkWCEs9u6xQs7hlge5ZTRYAlVIFikCpQjOJix3q81OI/gVfEGlIkuQX4Y7yzRppKZjvQu6jCyCvjfM6dfhCZpdGisgshhBBCCCGEENPsteID7mEwjosAj8AD65PxQDuD84KXRPTPcH+WNkBna8qDm3sN93yYwjMasntG+4xxOn0eWkPJSWDODta/L/736848mGq2a3Ax4mi83GJOPGdb87xn4JkRG+gUUQzYvxn34uYdS3P+RztYnxBCCCGEEEIIAezd4sNSPDBfB9wc506I7Tj+bTLAbsWIpDVsbLk2xub5CWqGwgY622z2xNzt32EoxmykZhckW5p15PoW02kUmZkIPVTx4GzgscB3gR/j2Rn5Lgvi+Ii4J7tf5DtkF42z6SwpUUqDEEIIIYQQQoifiL1ZfHg7HoAvpWY2LI5teh+MUgPxDNbHqf4N4GaQKQoYnj2xCfhCXM+5r23myHILqMaNyWSMm0c1sSx4ecRmOoWNXOOW5rjttvGN2P/v+HcScCjeDSP/9huAe6lCh9GZfdEDXIJniMwkuJzB9sjzQQghhBBCCCHENLNGfOjuaGFm55rZO7rOrcp/uOliZhBkaUO2vizA9XhGRAoPWaqwFc8GyCyBu/FshQzI5+ElEY9vHn0P8EA8ywA6g/ePx3Y9NbifpJo+ptiQnS+2NefBhYy2NCPLMHqowkBPrHOQahiZQsildHa/APg89b+Nvlh7PmsSuL0Z+8tsjzwfhBBCCCGEEEJMM2vEB3ABokuEOMbMvoJ7GbQlE2/BW1Bm6UJ6HWRAb8AD8EC7j9rdYjTGDlDLEW6Ma3mc95cYP4a3wUzBoc2aAHhybBdQswr647kF921IkYQ4PwVcHsdtyQW48AFefrG2Ob8s5hiKd8j1/Grcc2m8rwG/1ax1LJ55a5zrwUsu8v6lCCGEEEIIIYQQO2FWiQ9dvKfZ/xc8WD4QL634E9zj4GtxPQPp7PIwigfex+AlFADfppo49lDLL7I0IgP9zXh2hOGZBjlnkgH9Tc1xobN7BtQsiJ6Ye5RqWnk9bmRZmudm6UX+TXtxUaE1oITtyzwmcCHjg1QhozWVHIg5D411t2INwBfZHpVdCCGEEEIIIYSYZjaJD8P5z8yuoHofDAC/iYsIk3G8H3AOLiRMAt+PsZupmQdj1JIGAx6MiwlZDjEc57Mk4YDYTuGZAu+hChdtmUU/nn1wKLCSmm0wjxr0QxUT5uPB/qJYy1TM24cLD1nKMdQ8o+1GMdF13G2Q2Y+bUb44rrXdN9pSjP7412ZhgHtBdKOyCyGEEEIIIYQQ08wm8WErtTziaDxwfhTwcFxwuAH3VfhTPCA/CfgNXGAYwjMC0mgyOz6AB+P3NXMfSKdIkJ0rcvw8PEh/VowZj7kz0wGqGHBIbKfiHqNmH6Q/Qzt3dsp4MLWrxSdi29OMt7g+Rs286O7c0dILrIhxeW286741zdwtAwghhBBCCCGEEDthNokPyeZSyjA18IdqmjgCvDPOGVU4OJUqLoAH43fE/iCeXZClBq3w0EsVLDJLIOefQxUi+oG3AtfEmDFqV4v1uFDQ/i0KtUtGXzzjnjhehYsZvbGm38D9GnINU819vbGe25q5J5tnpdiQ3hSZhUHz/BQ52rafpdlegBBCCCGEEEIIsRNmo/iQjOG/1m/C21SO47/S/4haVrE1xhrVyyGPz6X6MWRWQjJFzVBIEaEHD9Sfw/amkr24+PAAqhdEigMHUrMHsuNFd2bC7VT/h3vw7hQnA9fh/g9tG892nffi5RSHNe+ygU5jzZ5mPd1tNFuB5vA43kA16gQ4je2R54MQQgghhBBCiGlmo/jQej4sopYjDMb+QTGuF/dMyNaT+1HLGwC+QTWWvJ0qVGSGxLq4dx3wvRiXXSqyXeZEM9+9sW3NHLvHTcVzshVmrmd13F9wr4h+4CJcEDgGeDrVr+F1uCBxJ244+QTgO1TBYGHzTVpzy7yehpSjeMZEihqpIrTCw2bgK2yPPB+EEEIIIYQQQkwza8SHUkpGuVtLKStww8Yv4+UPt+CB8l3Ujg3zcYFiMx5w91BNHqEG2wPAwTEu6cEDe4ttdtMAFw7Su+H1VIHjAGoWQStKbKZmUexDp3Fkjj851rAJFwXuwFt8ZtvPvL8feDXe0WMk7r8AOKV57jguakzGe0/h2RQphmR5ySDeRjPf632xndOsby7V8FIIIYQQQgghhJiRWSM+BJsbEQLgajzg/g9cAPgvvERhHR50L8CD/cwwOCq2hRpkr47jfZp5b6GWOhhwPh7oT+BZB+Df9s/jvtW4cJDjs+xjEs90yAyEtdSsh1FcYEjRo+AeEhN4J40FVJ+JD3R9h4UxNtcxGs+6BnglXrbR16w5BYQbgQ8386SHRMFbkyZt1sRM3S5UdiGEEEIIIYQQYprZJj5MEyLEwbjHwyPxd30icCTevWIUD6IngFvxYPp/4nbDvRL6gc9RSzOSEWBZ7Lf+Dr24rwPNsVEzEVo/hQGqEJFMxFyGt/9cSmcmAvHc86n+EdvoNIC8m2pA2a55Q2SEfJ0qpPQ0618NXEUVYMCFj1G8lGNZc0/bbvNrbI/KLoQQQgghhBBCTDNrxYeGiVLKU4Hj8WyG+XiLywvxYH8A/5W+hxqUb8ED/DXA4+Ja6wexkNoKswfPWIDObhNtV4nkvtim70NPrGdjnF8Uc23FswfaNpbdJpQpZAwCz2jObwX2b47X4cLHvmY2josPKVak2eQQXj7y4K41z6WKI4ub823bzYsRQgghhBBCCCF2wqwSH7pKLsDLEdbHtZXAh/DMgH48G2IYL3+4AQ/IF8Z9aQR5Ni4MTFG/1W24aLGOar64X/PM7paZKVoYsKS5dlkzfgQvy0gviRQaVuGZB+ACQbcAkff3xXtuoZaTJNnpIks8MhsD4I3Am3AfiNFSyiHx/BRDpgBKKa9p5pzARRKAbaWUu2ZYk8ouhBBCCCGEEEJMM6vEhxn4GjBkZs+P4zm4UJBtMUfx1pgH4QH53TFuOP69H+8u0XaBOAQP4OdTO2BkJgHU1pttiQUzjDuWKmoYLi5kNkT6TRxCzX7oo2ZMjOKiSbINL5tYE+ue31zbTM3cmEMt3wDPbHhhXM/nHBZzQBU2oBphtuJFWzLSorILIYQQQgghhBDTzGrxoZRScJ+HR5rZzcAz8cB5PR6MDwL/SA3ssw1nigTzqK0zUxCYwjMFUjCAztaV26glF9n1Ik0bJ3DRoJ0jhYot1L9HCgvZleKmuCcD/0FcLMh1fgovtTgg3mG8+QytaLCh69ofxfnNQK+ZfYfaLnQs1ouZXdI87/PUtqGY2fEIIYQQQgghhBA7YVaLDwCllDvD8+HZeNCcosMCPKCfoHacaDMTUiCATuHgPjxAz2B8jM5Mh2xxSXPvZFzvowoIWcYwjv8d5jX3DMQ9vXHPYcBn6WwFCnBnbF8M/DFV6JgpI6GX2iEjmcTFiX3i/OnNtRQhwEUNYswJ8Y55/M4ZniWEEEIIIYQQQkwz68WHhhNw8SEzEu7GhYM1eKA9jmcGJBPUoHsDHogP4L4N41TBIbtWEOe3UUUNqCaU+S/vbe8HuIsqfkziHhDJNtzYcbg5d3Os5apSympcONhAk5XQrCm5EXhpc9yaaOa6k1akaOdc2qzjI6WUM9geeT4IIYQQQgghhJhmbxIfoLatnMDFhB48mB7AsyEWNWO3Ubs6ZEtKcNGil5l9HXrxTIIeOttc9sczc57NzbUUAObSKRS0a91AZ0eLKWB5zLcVoJTynFLKvsB7qGUbm4Grm/uOojNT4b7m+YPU8pHJmGN9XDu2uWcTNQPjycyMPB+EEEIIIYQQQkyzN4kP11BbUG6j+ib0UX0dMvPA8G+TpRG9VGFgEjd87MGD8LZUo50jMx+y00RmSKQ4YdQsCOgs15ii01ByHbCMTrEj/SROCU+GlsymmAs8qOtau94DqSJJwb+LUTMyFlA7e+R9C6iZDzsynBRCCCGEEEIIIabZm8SHr+GmkZOxvxkPstfhQfcmPKshSyLaEoS7qIH/flTzyTSuzMA823IWXFhIw8i2bGGCGrSP40LGNjr5T+D6mKsPF03azIgevLvF5cC6UsqpZvY+Mzuua56ru95jtJmjPZ/f5KpY+03UFp8GvLV5/9YbY8rMWn+LRGUXQgghhBBCCCGm2ZvEhzOpng9n4JkP2WqzF8882BcvccisgswKOJyaTdB+s4V49kNmPAw014fi/n6qMSR4oH9H7A/jIkgG8HnvbwEnN8cj1HIIcAFgfVzfx8yuAE4F/g14XvOsI3FBBFxkubJ5j1ZM6AEeg2dJGG5+eXtc7wVeTRUcftDcW3Cjy25UdiGEEEIIIYQQYpq9SXw4Afg2LhbcgosPAK/HMw/uA1YC8+N8678wRjWjnKDTJ2F+M+7G2GbrzOTIrrWkkeUUnknRR/VXyOd9qDlOs8qh5tzBuHiR9x0FnEQtLSHGXxf7+wDH0GmEmSaThgsxt8a1/XHBJbml2T+UKj70llLejBBCCCGEEEIIsRP2JvEB4Dbgx7E/iQf984Hv4WUXE9SOFNmaM3/xzxKGK9m+5SV4QH5Mc7ySTpPKLOe4CLgszvdQxYBbqB0uBqiGk+DZEaub454YMz+2R+JCw2jzfgDXlFIe2hzPofNvblRTzX7gEGrJSMsgtbykFVsKM6OyCyGEEEIIIYQQ08xa8cHMJs3sCjO70swuwwPlU4An4G0qN+PvPwh8Ffg41UhxPdULAlykWIEH6yezvUcDdHohgAffmSGwlGrk+EigFQSytOMgvLwin3d27Bfg+3jmQh5ntsIBuKBwB971og/PTMj1HGtmKZSsBv4ZFznG4voA7i9xX3yLPlxc+QGdvhd3xTrTiLM15pwJlV0IIYQQQgghhJhm1ooPwNZSyopSyknAq3AfhUHgrFLKk/GyhR/j2QKvBh6PB+Fb8ED+n/EOE0nBg/bPAotneJ5RTSwBroi5u1trti04J4HvxP5+zXOyzCJ5BPVvNdl1rQcvuZgTc6enw6nx3HzeQuCZ8d63xjon4r3nUbtdHEsVTvKZJ1B9LVYzc+aHEEIIIYQQQggxI7NZfGiZD6wFngT8upltxQPopXggPgYch2c3rKNmBUD1eLgKFy/Opn63zACYxIPzfaklCyfjpQwFFwDarIgstejFA/32WooPbXbBAFVEyHKRblLoSD5DZ/tOYj291Ladt+KGlG25Rz+1m0cyj9qG8/N0dvgQQgghhBBCCCF2ymwWH4aj7OI64H3A35RS7gC+DvxtKWUYWAD8aiklSxqeWEpZhrewvAbPHHg8HsCnAeMU1XCy7fqQ9HbtZyeNtkShv9nfp+talj/kc3LuTbGmF3StIcmyjuSAOF7dXG9bfBY8C+ITeMZHwUWR1XSKKuDCTWY7PAsXcwzAzNK4s0WeD0IIIYQQQgghppnN4kOWXRwD/BrwL2Y2ibeifLWZ3Qn8Tillo5ktxwPu/457r8JFimHgk3igvSSubQAu6HpWm12Q3/SHsS3UoH+iGbcRz7jozqLIzIehuJ4tNq8FlgP/FPdkNsVY3DdAp/iQ55fQyXrcADO5L77RjTHHGF52Au4lcRcuNrQqwsXUzhivZnvk+SCEEEIIIYQQYprZLD5Mm04C7wEeAIyWUo7Cf5nvB95pZs+K4aWUUszsQjxrYBQPwi+J7cNj3DDunbAjtuKiwPE5L/D+2G9LJ+bGM7Y056CWTpR41t/FuNPinu6/WQob3eaPbVvO8WbeUVwEGcXFhn8ws1Fq+89zqWLKIdTWnQubdZ0Y18BLWYQQQgghhBBCiB0yq8UHIvsBeAoe5A+a2an4r/UL8CD7tcBhzT09wKOAl+EeD8MApZSL4voQ25c8rG/2r8C7RaSo0AM8rmv8KC4WjOACwCTVlDJbXaZ3xEOAX6fTtDIZx7tV5H2TzOzH0EP9W7etOXuBO3EDzXynL+PdQKaoPg+Gf7PssnElsCrGt9kcicouhBBCCCGEEEJMM5vFh2HC9wH4KPAu/H2/jZdh9OGlAS/FW232mNlaPMNgAfAWPDh/CC5afCbm/QGwkhrgb6Gz7OIkPEhf1VxfFPuTcTwHD+Ivj7nSF4KY69p4bgF+Bfhg3DNBFQbAhYQTYj/LNbIlZutH0f6dWyFiCi/l+BTeghM8c+MBVO8JYq43Nvedhpt1gpendKOyCyGEEEIIIYQQ08xa8aGU0ho/DuJGjaPA9/Bf/m/Cg/fD8eB6Evc2GMCFi6dS20tuAc6KuR4E/AM1uB/Cg/j0SZgEHggc0XUd3E/huubeE3HDSXAvieRk4LJmXHanGAUOpbbbHKNmbXyB7bMjxuK+f6N27ZhPFU7y738mVcTob57b14xrBZYUNwrwaYQQQgghhBBCiJ0wa8WHoNt0cgC4NK/h5QaPiWMDXoWXMgzjXgcZpD8F+GJz33lUQeFh+HccjuPs/tDTbLfiJQzDwEHUEofN1G4U+8Z2C3A9VQDI7QQuImTXihQj1sV836a2B52Kf38e852DiwdX4SUiKbZ8ms6WnoXtvSMAbqcaWLatQA3PBBFCCCGEEEIIIXbIbBcfpgnPBqOKBAN4iUIG1T3AG6htLr+HB+gW5x8d43rxbIYM1v8n5koxwqilD2N4RsO+VG+HJfGMbG2ZmQ/JMF4KsS2XHtuL8dKIfO6GGLsWuBp4LNV8Mksr3hRjco5DqeJIL3A2tWvG1ljPTB4OBzXnp7q2h84wXp4PQgghhBBCCCGm2WvEBzM7JnZ/B/g83q3hd/EAPMlf8zcDv0kVIg7AA/sS5/qowsQQnlGwJf7d28x1Ne4nYXjwnoICcW4BnmEximdcjOKCxSAuVmQrzYJnaZwb947hRpG9eAnJicAKvH1nPju3U7hfBLjQMdJc6zam7KdmT7Smmimo0GyTB7E98nwQQgghhBBCCDHNbBcfhs3sisZ08gnANcC/ADeWUj5HZ5nBGF5G0E9nacGiZkx2lWjNGG/HBYv1wH7NuAfgnSqIOYfZPngHFxvabhQbqBkJA7hwcSDVR2IQOBq4MbGYcokAACAASURBVI57Yu25pqub92kFhh9RvSVaU8oUUaaoZSC5zoKLIt3/rfQCk6WU98zwPkIIIYQQQgghxDSzWnwopfSG58OKUspJITY8Cf+1ftjMrum6pR8XDNI8MjMW7mvGGDUrIVmDl1PsQ/2mBsylCgKZaTBGzSoYaK71UM0oB6nlFatj/Fa2F0quxcsuwEs78lnzm/k3Nvc8ABcZwAUFgLfHO+Ya1jbjU3z5B1xggdpuM6/PhMouhBBCCCGEEEJMM6vFBwAzm8zsh8iAGCilPLmUckQp5fju4cAmXDTIb9NPzWZoGadmQZxCNZ3soQbtbXC+FXg3Lgisj3OtT4ThJRGjdJY4LMbFiG5viD68Dee34/je5r6D8UwMw4URqJ4NKVDcHduzmnsLcAfVsDL5fbxEZCzWd0Nc7zWzfrZHZRdCCCGEEEIIIaaZVeJDt9BgZsv56d7R8AyCFA3W0Gm02GYeZHA/hhtEggsS7Taf/VVcmHheHGdni2/g3g3JYbgI0Eun5wLAzc1zJ2PMMPDgOH8ANZOiDxdQjFqq0UdnqUWKGcN41kZ2wEgRon3XhcAVMcdgM6fhnhVCCCGEEEIIIcQOmVXiA7W1Zv5bCZDHwEnA35jZkJl9wMyuivs24QH3XXR2xNgXzxBYy/bfahu1VCED9fNjOxDXU0A4GLiVTo8FgF+imk2Cl0ikSHBzPLvE9cOaubNkYoIa/K/FDS/bzhbtu13ePHs9VagYx8tKJnCB5XRcgLgHL+sg5n1FXO9p/hVqGUeLyi6EEEIIIYQQQkwz28SHGTGz1WaWPg4PBV4OPI4aoGcgvh/bd3k4iJqpkEzhJpTdYsLLYnsbsI7axvMYPDsij7vnb1tkggf1h+PCguG+D3ltktq9YpRaHtED/Fcz97diuzrmaFtijjf3rcc9Ivpw0SFLQvYHsixlMfC0Zg1ZlmHAC9gelV0IIYQQQgghhJhmtokPw03JxX91XbseD9z3A/4Q/2X/jrjWhwf899FZBgGd3geZoTBG/eV/ELglxmXgfmAcb6CT4a7jbOWZbKAaU7Yml3Op3Tfubc4PU80x9wGeEfuTeLYFeEkFwDw8m+KGWG8KHm3ZxCQuRJTmOFlB/e9lvLm2FSGEEEIIIYQQYifMNvGhLbt4UnP+t6PsYhsefO+P/zK/KM6lALASWBr7V+JB+FZcqAAP2Mfxzg9ZHgHe9nIVtctEZhAs7Frfuthm4P4t3MOBeM6BMeeNePlDGlpm94ucM8+voXahuDve5WPAc5q1pcHlNmBZrOG9VOPJkWZdA3SKIRfh5RgTsb68NhjfotDZhlQIIYQQQgghhNiO2SY+/CSkL8LpuA/DH1C/w/XUjIPlVIPGtiXm+VT/BZrzB1OD8ym8O0SKDJfENsWJFAb+Cfjd2B9q1rEeLw8xXMjIYN+AS5tnLMK9FcZxQWUYeBTwuuYZfbGOvmau32yuD1G7eZQYk9ceTvW1+FKzvjThBBc1upHngxBCCCGEEEKIafYW8SHLEQz/9f+buFhwHPC3eDC9GRcJepqx4L/4L2jm+m2qd0NmLWSAnuezRWZmF+Q2r+cz3olnO+TzUqw4FfjX5pm34qLJD3EfhhQJshRjE+7/UOK5B9IpEOxDNYY8EXh0c81ibnCDyUk6sx+yfecPmjlXUbNBjmF75PkghBBCCCGEEGKavUF82Az0RmeLIeBcvOXlJXjAfiDeZeIyXCT4atz3FTwo/088GB/DzRvvoAbyc2ObJRhpBEmMTdHhxNheH9vSbN8d+xPUQD+vpZ/Csph7BZ1Gl1vwkok+OstA8jiZiPm2xDu35pWt0HAoXnrS/neRnS2+SO2ysQwv/2hbeQohhBBCCCGEEDMyq8SHUsp2P7OXUkZKKeeWUk7EPSH+p5RyYynltFLKPsBjge+WUs7AA/YHxq3n4IH9aXiQvRXvejGP6rnwo3xMbEep2Q0HUDMZeuP+4+L4i7GdRxUceuj0nlhHNahMkWGQmulQ8FKM36PTGHIC+Dbwl3G8JuadwEWT0oztxduL3h3HY3SWlKRh5l3Ap6nGnLlegKeyPSq7EEIIIYQQQggxzawSH3bFDsSJC0spT4j9N5VSUiDYXEo5OIwqwTMoCl72YPi3S+HgfbHtxz0jsmNFlmVAp6fDGe0SqGUbef0gOrMo5uClE+DCw9pYwxDw0a6xfcDD8HKSXBO4+JElFtaMvxP4ROzvS2cmRK5vIV4Kkn4Q7Vq7O3iAyi6EEEIIIYQQQjTsUnwws2PM7JVmdr6ZnRf7x/5fLG43srnreCvuG9GWMhhweOz/QWx7cOHA8JaYc5vxWfaQZClED96xYqI5fzee5dCNxRqWUQWENKPs6RqX3IMbWL485u7uwDEEvIIqOqxo9udTDTc3Ut+/zZ547AzrFEIIIYQQQgghptmp+GBmrwT+HQ9ALwa+H/sfMbM/u/+Xt3uIDIlhM7vCzK7Af93fn05RIssfwH0gwM0or4j9JXR6OGwAfiP2s3wiRYIDqYH9vXiLy/Y52SrzHuAbcX5jXLs9tmvj/BXU0g9wgWR/vLNGWzaRYsffUTtigJdXZOvNdVQhYnOMmaR6P0BtTdqisgshhBBCCCGEENP07eL6s4HjSynj7UkzewtwDfCG+2thuwszmwSuak79O3Ak8Hw6xZopPJNhHjVw/zCdXSZaJoD3xv4mXExYEvO0mQqLcBEjxYnbcKHhWLyTxdExbjiuZ5vMfWObZSLbqMaY+fxeqrdE/u0/iAseaSC5GDfffBS1xALgBuBBdJaPwMz/DX0z1svIyMgpM1wXQgghhBBCCLEXsauyiym8jKCbA9neG2C2sDV8HraWUlaUUt6AB/qn0ulv0ItnFAD8OLb9eKnFPdRvm99pCy5igAsWi2K/9U+A6hmRgsQ1eFbDKly0WB7nM+jPTItWwAD3cmjPb2uu9dBphrmkuTYMnMn23Tu+SBUzvtm817CZtRkeQgghhBBCCCFEB7sSH14CfNXMvmBm741//423o/zj+395P1/M/j975x2uZ1Wl73udkpOTnIQkEHoCUgQp0hXUsYIdxzq2scyoiA11LMPIT2WYsXcFx8GODQsWRkfUURkbIKJUAUVI6C0hPTnt278/1lpn7/PlkDgzmsTkua/rXN9b9rvf/b5f4LrW8631LCtm9rlmv8/M7jKzb8f+TsB0M7sMmGFm/xlD+6mlCEnre5CtNPN9ZjZCpznW+jS0Y7uZ1TVud+BoYAHuwZBkB4tDqf4RyWK8bWbLIC5U5LgUDJbiZRr3xDzTmrWlwDGMiyDJQ5sxDwWOuZdnEUIIIYQQQgghNlx2UUo5z8zuCzwA2A0Pim8GLi6ljG/o2i2U1cBBZjZYSlkLHId7JiSn4UF1Zh480swWxbnduuY6ADgBOArPDsnsgVFcrEgzyWQY933IcRnwL6G24IRaxrEI2JvaXeMeXHwYxYWENtugu3RjQcyfmQp3ANvh5pB3xvx5/Q54VsRA3GNO3CMNL7Ol59difxy4Htg39r9cSvlZ17tJzwfk+SCEEEIIIYQQYqPdLkopnVLKhaWUc0opX4vtv0ThIfku8ITYfjbwpebcLsBwlFtYKWV6KWVP4MNMziwAbyX5WuAheHbCONVL4WZcDCjU8oQ+XHjIeRbjwsPCrnkLLgzsRRUwDPdkKKzf2nJlfLZlMEuBG2L8OF7iMR0XHizmzxafS2ItM3HxoZ8qZFwd1/dQxZceYCJ7BHgg66NWm0IIIYQQQgghJtio+LAVcjbwLDObDtwfuKg5dwZedvFjMzvFzNLv4j2s76kAHsxDDebzfWY7y9awsac5Bp45sD3VV+H7VLGiu4wiWcvkbhVjMZ91jd8BzzzIcyO4INGOyXPb4yaWPdRshmnxeT9cNBnFsyeS0+Kz4FkQQgghhBBCCCHEvbLNiQ+llMtx08ZnA//Zde57eGnGx4F/ARab2ZVxuoOXTmQAX6imjovxjhEpBixupr0b+BkuALQtKsEzDdbG9qPjMztSjMQ92k4jM6jiRV+My64XWUaRGRCZnTKKiwnzmmvHWd/DIo+PUbMpcnw/1SCzzXqxONeNWm0KIYQQQgghhJhgmxMfgnOB9zK55CIZBN6IB/5rgN/hQkAPHmivoAoQ6dWwABcWVsa5zCAYwb0WFsS10/DgPgWC++LlDuAtNZMeaktLowb8fTEnwAXUThljzfEkxYhpcV0rmqzBvR2S1c19+5jc5SJJEaaPyQLE3CnGquxCCCGEEEIIIcQE26r48CngtFLKFe1BM3skMCtbbQK3Au+gliH0ULMP2l/9LcbMwYP7sTjeH9csoAoJq5hcipGiwGwmZyZ0qMaQrblkzn10c++bgdubOZOCl1uAixtpTHlJMw9UsSE9IvJ+mZXRwQWLZB1VQDkUIYQQQgghhBBiA2yT4kMp5eZSyoemOHUE8CszuxzPSJiNmyumSHAVLkgsjv3FwI14sN5HzWrILiLLqF4LGayfHZ9ZVpGCwwBViBiP+/Qy2ecBqlCQ2QcFFzfSZ6JDNZPs4EIBuLFlbxx7eHPftc1cWfJBPMvdzX1XNPPPyHGllHmsj8ouhBBCCCGEEEJMsE2JD6WU9WoASinnl1KeGNvvKaUcUEq5P7C6lLJbKWV/qgBwMLXlpFG7XFyAmzwCfJoqVizFsyE6wDVx/onx2e2VMBBzDuPCRJZjtGaSw1QR483N+U4zPrMbUly4mOrlALAcbzGagsMtVLGkA5wf233UDhs9wCObta7KNZnZHqyPyi6EEEIIIYQQQkywTYkP/wc6eBbAfDyov5TaEnOXOH4bHti/HRcJDG+X2YcLDRmk797MeydVBGgzIIbwrIvcz+8pvRtGgH9t5umjZjy0JRq9wOFULwdwj4bzmjlzXZ04djRV4JhJFS0GmnXObNZ7JEIIIYQQQgghxAaQ+PDHMYh3e/g+8EzgCXjGwGh8gptXDuJB+mLcfPIOvPSiA1wb4y8A/iPGz6L6KtwbK/B2lmNU/4dVwHepJpB5bgw4h5op0cGFjxQQfg58NuZM8SC7XvTgQsh/4waU2ZljXRzPEoxOc814KeWcjaxfCCGEEEIIIcQ2Tt/Gh2y9mNkpwHOoBo8vLaVcFKcHzezS2L4Sb6X5sRj3UTzj4RpcZHgVcCbeoSLLK7L7xWW4BwK4Z8ThVAPLEnPvhwf8HbzUoQfPpNgV91cYB94KvAjPpphHLd+Aye03j2/2l1JbdY4Ax+C+Fn/Asxd6mdyt4gK85ec49d/GrNjPspJRXLzIkoypSM8H5PkghBBCCCGEEGKbzXwws2PwAP7w8Hg4FrjJzE4xs6twoQBckDgE7ygBnv2wDg/w98Xf4cy4/j3UbIQleHnGITH2EOD+ce4OapnDfnj2Q/o8tBkJPbhQsT3wNuA+cS7bambwn+0vO9QshxQMdqW2CM12nntTRYlrm/muiOuyM0fSC6QoY7ioksenQp4PQgghhBBCCCEm2GbFBzxzYQ7w6+hu8V/A44ATcTGgB89ayID9IcDRpZRVwA/i2DTgAbiHwvnAPwAHNvOnf0IP1aRxANiJGvxPA7aLY9Ob9a3GO14UXIhYhLe7/C2ehZEtPb+Miwf5Xc6kmkSmB8SVeNnEQ0opg0wuzTgQF0oAHoELGb145kZbEnJYfA7g2R3DgJlZu2YhhBBCCCGEEGI9tuWyi2V4+8lbcTHhPNw48qpSymMBzOw3wAVmNgDsCLzIzPbFOz/sCXwc2AcvhTiIagaZ5ozT4m8c94PIgN+opRfDuMCxd+yniDALL73YERdJ1sUcu+OlGAXPQHga9XtcHdcZ1chyJfAW4IPAb83sLlzoyJadtzXX7x+fBc+WaMWp7M4xipduTAMopaxjfVR2IYQQQgghhBBigq028yHLJ8zscjO71Mwe2DVkDvAj4CXAg3Ajxj7ggWbWMbNxvFTiTOAAPPPgatwTYSj2j8O7RWR3ix68gwV48H8JcDtVDFje3D+FiP/EhYfMVkj2wIP8LG3YOea5OY714EJAH54BUXAhhdgfi/F3AWfjxpjDwO9jbHbH2Inq55C+E/ksucZCzQD5EZ7V0Qoc3ajsQgghhBBCCCHEBFu0+BACwh1mts7M1prZ76YQEXLsZ8zsx2b29Hvzc4hxrzezO/FuE0/Asx52BL4HPB0P6J8JfC2mPh4vR1iAiwFJDzVAb4PwbKXZh7eh3CX2p1G9FVoWxjyfbI4V4J9wIWC0OX5P7KcfQ4oG2YJz3zif4kQPbij5EuDzeFbEA/ASjA7eRcOoQsKvmPxvYhVeemF4pgd4+UmWY2zR/36EEEIIIYQQQmwZbLHBYwgIzwJuwD0R3o+v99P3ksnQ8jagv5QyDFBKubuUcquZnQv8C15y8G3gQ7gR4w7A3+BCwSAuBDwJD7qPAj4X9z4LzyKAKiT0M3X5ShpW9lEzCXahZinku98TFwIyE4HYfi/VcDLFjbFSyqHNPZY028dSvR+MWiYxnVp2kf4SC/FyjO2ae/biGR4d3FuC+Mx51sXYC5q1t6aUQgghhBBCCCHElGx28WED5RG74AHvXXh7ykcCvwAux0sFzjezpWZ2ppl1B8G7AIebWYkSisVmtkvMcw9eTnFfPCPgLvwX/tvj2BBu2ngPXiZhuCDRg7e6fB4uILSCQ3o8rKSKDgfgIoc1fzlmDHhX7G8fc78ED/xh/aC+xLvY0cz2b47f3Wy/P9bZSxUKiOfZGxcRcv4eXHjYgcntMjNjIv0gsnwDasvNGbiQsSHS8+F+8nwQQgghhBBCCLFZxYcNlUfgLS0HcF+F/8IzBObjxpAHRteGG/HsiEXAMXHdY3ADyEXAk4Gf4CUTvwd2w70TvokHx2fjwf8a3AOiDc53wQN08GAeXBToxYWH1XE8xQTwAD2NI8HFi4K3sMy5Z8b1JzTjxuKe6e+QvgvE9X3U7+q/43MUeDBVPBiMYwX4GbVTRq4t139vWQsl3kFbQrIDVVhZHes4Bs+k2BDyfBBCCCGEEEIIMcHm7naxC3B3Wx4BYGZvwb0WenBPhuXAs4HH4hkF/2Jm8/EygkLNWliI/+reg4sVZ8eY4fibSRUQAP4aFwx2YLKXQjIec7XtJAsuVsyK/YGua1bhQgDUzIeDqQJDlmHMizGjzXMkw3F8ToxtxYgdY+wvcIEm2ZcqLBxKzWrobcZ0mmNr8KyIvCaftX0HA1O8A4trZwKY2QGllN8ihBBCCCGEEELcC5u77OL7wIIwkvyomT0sjp9eSjmqlHIQ3hLzy7j4sBI3dNwNbxF5Lv6L/E5x3e3UwH0Z7tewmpqBAB6An4U/+wjwVmonihUxpgCXxnZbLpHMnOJZRvFMiDbTINtpjlFLIe6Iz1zPXVPMNYMqYBQmZyzk/kPxMhKLYxc35/+DahS5upl3SXPfNLj8fLN+gH9u1nwjVby4vZlnsNl+wRTrV9mFEEIIIYQQQogJNqv4UEpZhXeQOAEPwr9sZi8EHmFml5nZ9XjZxYHA/XGxYAwPqBfgmQszqGURPdRf9+fgJRrpgZBjCh5gj+CixRtjTJvdYLgvRHazGMHFjDw38QjU4L8/5pg+xbgOnmUAnpEwRi3D2K0Zk8dGgV8286TnQ2ZA5GdvM+bIZvuFzToyM6MAX23W9PL4fE58pq/Fq2O/D88kSc5ptnNN4AJINyq7EEIIIYQQQggxwebOfKCUMl5KOb+U8lbglcBzgY/irSZX4p4M/4oHxWvxlpj9eODdwQ0eP4kHwx0882E5/kv9HFyc2BX4NLWTQ3aR6MWFgNG4ri19uDk+x+N+KT60YzJbIEWKFD0mPWKzbXhQ34v7MtB17u5YXx+TS2J68GyLnma/e+5cwxju95DeDTmPAS9r9q+Izz9QsyM6zXU593hc++jm+ArU6UIIIYQQQgghxB/J5jac3M/M9m0OHQpcG9s/xg0Vr8XFhStwn4VDgO/iLTjPwoPy7JAxiIsN2wEnl1KGgK8AS/EsiR482P4KHmhfg4sOKQh8tlnLAjzAXoEbWI4250bwIHwVNSvhd3ignkH7BfGZBpCdZr8HL5voxFz3AD/ERY4sadgfFxIW46UaHSZnR7TCA9SOGn242NJmRSRZ4lGoGQv3oWZH9OCmnu11+W9kdnM8t6F2xmhR2YUQQgghhBBCiAk2d+bDEPBZM/utmV2Om0meCnwcFxu+iXsZ3FZKeVDsv66U8hTgqXhnidV4B4uRUsrOuInlbcB7zOxK4BHAudFNYx2wB/AwPBD/APD8OP4DXKQAD/B3xzMtVuJixzSqZ8I0ahBusb8P/j7XxBy/j88VuNAALjQ8mSpc/J4qOJwC/APu19DBBZQ+PONiXhxbHvMMM3W3imUxbqw53929gjiXx5fi4keJYyuo/ha34VkUAJ+jCh5X4SIOrC+CgMouhBBCCCGEEEI0WClTxY5/eZjZqsh0wMwOA76FixPHAy8FHo8H70vwTIkHxfFH40H2KJ758OaYshNj5wKnxfExamZCBw/AM3Mju1mk78TyuHYMz0LIYH851bNiGPdmWAtcj3ss5PjpuKiRpSStyWOSnhPJmhiX2RX55S7FhZ7TgLfF/bMTxzpccJjPZMEivTNuxIWV23GRJ581yze+V0p5bLsoMzuBaCW6cOHCIxYvXjzF0oUQQgghhBBC/KVjZpeUUo7c2LjNnfnwZ6GU8hvgMuBZwDeAy2P/BuCNpZTbm+MdXCQ4pZTylmaaVfiv/quA/wfchAfcGdB/l8leD2uoJRGXANfFdh8uOGSLze2pRo7nx7WDuFDSy9SeEVdR/Rw61K4crfAwigsE6dFQcNHi2rjnALWTxUjzNx1v35nX3BX36MfFjxQc3hTvglhnbmcGhBBCCCGEEEIIMSVbjfiQWQ/N/vGllM8V5w2llINKKQeXUr4c50sp5Q141sEHqN0wxvCg/KellEOBd+OB9gq8rCED/sfiXhBQW2Gmz8KheBeP9IC4I47n+Xvimr2bJXeo2QizqZkFmQUx3Bxr23N+IT5X4SUlfbG9Kq7bmyqYLGdyhkZ/nPsVtYPGnOY+w9S2opc020bt3pFlGS3yfBBCCCGEEEIIMcFWIz78HzkbeJaZTcffSR/wV2Z2DXAy8DiqmDCGCxYfw0sSRmKOXzTnfod7N6QHA3hmwlice3ccm44bSoIH9D+ndpx4GLW9Js19WtFiGM9MGMWzJ3LsSqo40N88Z2Y7zI7jKaTsFdtjzfhxPHsj1YMLmJxpkYLGwayPPB+EEEIIIYQQQkwg8QEopVwO7Ak8Gw+6M/Nhf9yQ8r+AW6i//E/HfSJm4OUMY8A7cAGiA+wG/CjGpK8Dsf94qinlAtzYElw8OAb/TpYDp8d1BXh43GsUFzySNXhHkD5ccMiMhV2aMc8opfTgQsjseLYUEbJrx9xY98rmfJZejOMiyAyq4JAtSwEuRQghhBBCCCGE2ADbhPhgZuNmdmnzd/IUw84F3ouXOQzgmQ+XAv+Kv6eVuDhwOx58nwT8O57J0Ae8D88guBAvXTgZN6Oc1i4FuL6U8uHmWNs1ogcXK+Y2x3rxrhjpGTHYnLPmeJZs5LlFsf1+M/sRLpDMpHpKFGqWg8V9+ppjI7H+OVTxJDtp/KEZ1wodicouhBBCCCGEEEJMsE2ID8DaUsqhzd87m3MzQ2R4Jl4ycQoejKfnQ4oN9wNejJczrAPOBGaVUvbDMxIuwA0sf4aLBfOAO6nZAlfggfyVcb/11hif6+L++1M7ZOQcfXj2AjHmhubcZbhwAf69LojtQbwE5GV4tkLBPSeuxjMxOtRyjRQy8rrMjEjvikupbTpviXHbTfEsKrsQQgghhBBCCDHBVi0+ZMYDMNid8WBm25nZtQAhMlwO/JKaGfA4M1uKl2PMwgPxk+L8CuBvgZPM7E48C+AZwKOAp+E+CQO4kNALfAU4FQ/a96d2wgA3iQTPXFiHB+7gQkAHFyV+24xvsxAOpAoHD6RmUXSoJpezgZfjosGvYr6ZwAHArnGf/HdwG1XMKHjWhlE7YQzG2H2o2RkphgghhBBCCCGEEFOyVYsPRMZDs/+sECGeWUpZDrwS6JjZs/Bg+tGllJOB1bhAcAzwJDzwfgXwntgexf0d0hvhBjxwvwe4Hjgoxp2LB/yPwj0hjsXfeWYNAHyYKiBMBx6EixDZPWM6LhR0kyJBGlH24qIAMd8wcHesca941u1j3CJchMjWmpkRsX0z9yrce+LW5tjcuN89TPax6EZlF0IIIYQQQgghJrBSysZH/YViZqtKKUP5GcfGcfHgBjxAPwQP1pcCO+DdJ3bDjSE/insazASWADcD98d9Dz4AvBEP0n+NCwYPwEst7htLGAbOB47GyxOW4AH+CmrGQPoojMZ9bop79lCzDsaoZpJJwUUSqJ0t0gcit5fhAsIOeNbFbCZ3rJiKu2M8uECRmRL9sab8B5Pz7FtKaTM5MLMTgBMAFi5ceMTixYsRQgghhBBCCLH1YWaXlFKO3Ni4rT3zYbCr7CJ9HY7Hyx+yxGIc+AKwupSyJy4a3IyXHWSHi7l4BoLhgfgluHAwAy95eAieDbAbHqCnEDCXWgLxpbimt1ljmkb+ttmH+t20gkJLmk/+iJo5YbiIkttz8W4ag7jw8E1qJkP6Q4w228NUnwni+efG+tpWn61ipbILIYQQQgghhBAbZGsXH7qf7z4ApZQf4NkE8+PzScCJ1M4U04C34EJCO9fbqYH6m/BAfEmM78U7YoxSMxauw7MT9oprXhbzDOAGkcS4HjwDA+DnTA70Dbiy6zmuoIoY1+NZF+CiwL8140bwEouc5zhqd4re5jOfu58qyOS57ITx5K53keyDEEIIIYQQQgixAbZ28YFuzwe8u8Ud+K/5M4BOKeU84NtAv5nlr/p/g7+fNhPgZFw4WAucgQfm8/GgfwAP3ttMgP2Bh1EzF9IAIdtagpdpdIAvxv79gRfE2E7cfx4ukiQHUL+7E+M5GqgQIAAAIABJREFUMkPidTF2DBcV9mzGzmieJ++fmQx5fbbQfCPeNjQ5Jz5zTclzWR95PgghhBBCCCGEmGCrFB+aLhfE5ynZZhPPRMiA+zpgWvhAHNdMsRr3PsgshuQePPgeBD5GLT/I93gz67/TJc32tJh7jMjCwP0ieoDHxP4CXICYgbf5vA4vneilZjhk1sINuFhQYr3gIkgfVVxohYLukomkJ54rjS4L8CrcqyKFibPjsyf+MjvjFVPMp1abQgghhBBCCCEm2CrFByZ3udgPeKuZlRAZZuKGiga8GS9LGMe9DTIwX4lnG1zbNe8sapA/TPVaSBZ07RtexpDveTouKiylCgg7xmdmNqwCTsGFj11j/bmuT8Vnmma+u5lnPm4umQaVKVQMdK2nn8m0HhO9zbFdm20DnslkISZLNV6NEEIIIYQQQgixAbZW8SFZXUoZLKUMxv51wJpSSv66/wngP/Gg+6YY08EzDfqAg6lZAQUXDtKA8ut4WUIHzwIYi/PJGC4S9FOFhctxg8iXNveb3lzTwdterga+2hzPzhdPif30YnhvrOsLce2suNcKvJ1nZi3k/XMfakZEnrsLz25I7u7aXtJcO/FZSnkD66OyCyGEEEIIIYQQE2yt4sNUXS4AXgnMMLNFePA+gHsmjOIBe5IZDQUXFrJcwajiwzOAo/B3mK0wf9PMkRkDULMNDsANIr9JLbv4ScydJQ8LY56/7XqmDnBms64+XLgwvH1nejH04VkcrQ9ErqO7a0bOA54Ncmxzbsdmex3w77gwAvXfjZnZ8ayPyi6EEEIIIYQQQkyw1YgP6fNgZlfiQfXDifKLUsqXY9i5eIC+R+wviv0BqhmkUUsWwM0lsyNFy9VUUWEAb2G5OvbvxAWNLGP4XXz2Ai9stgH+PuY/Ey/lGAMeHNe39FB9KQx4fTPvQ3ARIdczBuzUrLkXfyf5LHnMqFkdPbgA0WZGfD62dwfeShVeUoQAN+YUQgghhBBCCCHula1GfKAKDQfhAfQrqJkPl8aYDwGX4sH5MaWUfYHT49wRwOdiO8s0no2/ozbYTqPF6cBtsX1tzPvKuPdsPLhfHveaFeMuB/7QzLUI70YxArwcD/LTtyGFhOH4G8ezIpL34tkTK6nZC6tx4SQFmMzgGIs58rkWxWfpWs89VHGiD8++yLKNRzfzTaeKFJchhBBCCCGEEEJsgC1SfAhzyPc1+683s1M3clmvmZ0c2+PAbqWUXuAw4Adx/CV41sMjgdPjHo/O2wA/ZnKWw7vwoP1qPGtgdSllABcxdsFNHsHNKR+Ht8hcS+02MRjzXU4tzUijxnFgTinlNuDGWNftMWf7vQzEXy+eHVGAw2PsdGCImrUxM47dn5pZQWynzwV4h41kAVVcmYt7P6Q3xEVxvA/PGsnykwcCF8e5F5hZ978jeT4IIYQQQgghhJhgixAfusUGPFD/ezPbYQPXPKkRGwDGSynvNLNePFh/jJldh2cDPD3GzMMD7Otw/4WnAftSSxw+AXyXGqRvH9cfiAsJM8zsKuARwDW40AAewF8NvAwP/u/APST6qJkEK3Ch4PzYvwGYY2bXANvhWQXD+SzxmfvEOrKt5QW44JCeDvObcR1qmUWWTdwZz5xreUR8Gp51kZkT62KuLMeY3Vzzb801vwQeEPvnl1K6u37I80EIIYQQQgghxARbhPiAB9lPbcSGDnAJ8NrugWY238zOwdtkPs3MHhynZpjZXbhwcVf8ZbnBns0Uvbg/wyDeTnIaLgikX8Nj8QB7DS4k9AKLYy7Df9G/Ly5aZND+LuB7eCBfgJ3jcxn+jh+DB/J7AH8X12QJxX3wdpyjwM9wH4cUP+7ChYZhvNvETrgw0QvsRRUG0mxyRXNsFHhWbM9mcovNuc32jOY5WqVgmMkmnG+kll20xpVPMbNBhBBCCCGEEEKIe2GTiA9/RBnFGF5S0IoN9wAvN7O9gOcDLzGzi/G2kh8AzsAD8vPMbO+4poMH3fOBg4B/ZLIfwjge2P8U90oowFK8a0U/1XuhE9fdiPs5/BrPHPh+nPsannkwLfafgosS0/FWmmO4oNCPB/Cd+MxMhXHgI3imwUrcN2E68DBc2Gg7UByNZ3KkYNBHLaNILotjs2N/JO6ZY8bx7AzwrIyLmmvXMtkbIoWPGcCRsX0zkz0k1lL5z1JKuw8quxBCCCGEEEII0dC38SF/EjKz4R2llLvvZcwZwOVm9u7YvwpP3f8G3o7ybuBTeKvKHfESitn4M5yOB9hfpZYmDOIB+fTYX001XDw4tvtwkWMtLlbcQi0v6MdLIx6KCxoF2C3m3DHulz4Kd+DZDdfE+PG4ZhwvqRimtr4k5ngdtdXlwXjgfx5wPFU4GMANKKcBXwSeg2cnZOZBihm7xTPksxLX5rjf4aJGibWnKNGJcffE8VXxXlNkyOdb0zxTf9d9WhPM5Ke4+MDQ0NARU5wXQgghhBBCCLENsanKLqbKbAC8jAIPZn+IB7bL8ID8UNzU8WC8VOFNeHDfA+wDvAU3iLwIODauOa6ZuuClAvmMGXSP4FkJKbzsgXs6EPebWBrewrIfFw56qK0wH4WXbPTgQsIteBbDPngw3x9/q2N8fxxvu2asxsUNo7bJfFKz3ttxQWFO3P/5uACzFs/C+D1VXLg1nms87td2rSDe4a3Nc6VHRE/cN7Mq8nOM6juxJp61n9pqs527FSKEEEIIIYQQQoj12JSeD2cAzzWz7bqOfwgYKaUchQe5ua5p+K/nJbbPLaXMAH7FZP+CB+K/yL+YWnaQpFiQlLj2pti+Bw/8W6PHFbjpZJZw5PU9eGZFttdcSTWqfDQehGdbzqw1+Bnw7bjXnbhAQcz5JdzQsi2PWET1ntg5nrs0Y/bAhYi5cS1xbgHwflwISb+KFA/yuedQu1isY3IZRX5ej4sb06jiycX4u13dzHdz8yxXsj4quxBCCCGEEEIIMcEmEx9KKSuAs4CTuk4dCwyYWek6fiSe0n83LhD8NrIkRvDA+N/x9pbDeBbAa2IseGDdiwfBGcAnPcDusT0rPvuaz9l4xsMVeLZCBt0DuMiwLuaY3lzXjwf9vXFsu7jnEfF8Y7hZ5Fxc3OjFfSKGqB0xlsf56dTMgpx/HM+EuAUXCuZSsxRG8IyEU+I6w0WV3ub8KC4q5LwDuHgCVaxIgeLTzbsaxD0nPkzNcCi4EWa26zx+CsNJdbsQQgghhBBCCDHBpu528UHgRdT0/VzDWlxEKNRsgzlx7rd48P10vHTgoBh3Jh5YL8IzDeYzuWwif+WHGnRb17FWdBhuxs0CDqGWXCS74aUVFsdzfJuJsYgqAuwdc03HBYF7qJ4N86ieCuCCxRyqaDDWfI7GHDNjnT3UdpjgwkWuIYWKpCeu2xn3xsi1zY5zHVyIMDyb4uXNPBfF/efjXTcyW+Iw4EEx7u+mMJwUQgghhBBCCCEm2KTiQyllKfAVXIBIvg/8MzXINvwX/j/ggfoo3upyxzg3GJ8vic/d8YD8+9RWkTlPa6wILnKMNvs051OY6DA5U+JnuXw8YyDLGW5vxrbvcReqcNB6Iwzi5QptB4osp8hWmeCZFatxQSTLRJYBn8SFjFxbdu+4ONaVGJ5tkaSYMQ14adezQRV/uo/34t1EzsG7fhxDfad34tkP4C1HhRBCCCGEEEKIe2VTZz4AvA9vIZmchJdYDOKBdh/wZTwYHopjmQnRi//Kb3hg/PEYNxt4Jh5gw+RWklCFhR/h2RJp/JgixEo8kG+7OuT5L8V2ZmW0bTBbcSGD9+lMzkK4HhdHpuEZAzl2WTxPBw/+e2L87+N5s10m8XwnxvgscZgZ8+wd76YTf3cz+Xt9NW5seTlV2BnDxZ1k1+YdtSLE8cAL8PKXfG6LZ0/TygNZH3k+CCGEEEIIIYSYYJOID6WUoWb7jlLKjFLKqbF/dynlmXgAvobaonJnvMzicKpHQ3bCWAfsH/uXxWcHL2sAz4LIzg/pUZAspXo9ZMCdxpQzceGg9Yk4s2ssU+xnKUN7PDMi7hPz5fkUPjKY7+CCxWiM3xcXG/pxsSH9K9LjYVWMvT3GXI77SCyL63dicleND8caDqZmiuQ6wcWczzZrbp/rh/i7brtfEPfL/W6/B5DngxBCCCGEEEKIhs2R+bAxRoDn4F4P2WayFw+6UyRYDFwKPKLr2sx4eDLV/LGDB+OGt+/clRo4p2gwiypcQA3EP0fNPjBqF4skvRfymvzsMLnUI6+/He+0AS4wdKiGk2lauawZn50pLJ67J9ZqVDHisXEsu1+MU8tPkvYZiHvuGdtD1HKSvGeaUT4yzq3CMyby+YaauTIDQgghhBBCCCGEmJItTnyILImv4MHxIPA24OvAB4D/pv7i/wP8F3uAa/CSiu/hgfb2TP6lPj0NhnFRIksr0pshW1SuAn6Mt8f8NvAoapB9Rfy12Q65vY4qfNzT3HsZNahfA/wNNesCquHkihizlmoEWXAhJoP/ZXgmyG24eNAfz3ozVSzJEg6YLIa074JYy1ubNTyDKnB0ixejuBlma4DZS/23M8z6qOxCCCGEEEIIIcQEW5L4MBNvnwnuC7Ej8PMozzgJOAAPaPuAH5RSTsQzGWaUUl6Jt9r827i+LQVYi3sWpLnjGDWbID0XllI7cBwZ93kCXvpheAB+ILV0gbhuJLb78C4c4FkD18e95lDFhn7gE3hZRFumMY4H97nuQaqA0I+XRczBMx0G470Q8/fiZRSZgdEDXIcLMyk+pGFn3uuueAf/FMeuwctCeqmlHj1U085psb9Xs6Z18QfrZ5+Ayi6EEEIIIYQQQjT0bXzInxczK8D78V/Qn2pmy/Fg903EL/CllLtxQ8m85klmdnIp5TXNVLcA/wCcH3OtxrMI/ht4aIyZh4sD82I/TSQX4EJCtq/cG8+wMFyAgFrykCJG216zQxUv9mZyRkQG9oZ7L/Ti2RFfBF7BZCECXByY0xxPH4bW92EqCi4q7IeLJCkspUBR8O97e2qHC5hcdtEbc2QZSPt8V+MCzHjMk/N/egNrEkIIIYQQQgghtojMh2HgqXjQeyZVKJjAzOab2TlmdrGZXQwsKaW808xeaGanx7DjgfOoZQDZfvLr1PacM3FzR/AgPzMgrorPYbyrxLlxfYoU++EeFK1xZIfqjZAmmcSca6ltQXtxYeMeatvLubjwAHAHXkqRHTbmx30ysyLLNvI+6d+Qn2PNOahlFimA3BnrbLMjbqWaSX6LKnR08BKPvpgjn6nN8nh+PN/1sZ8Gli0quxBCCCGEEEIIMcGWID6k6DANOAPvbjEAvAd4gpmdg5cGPATv7PA04KuN6DDbzC7ESzYuwwP8d1CD7xdQf6n/NDVovy3u048LC3fG/juBJ8U1GdBfC+wT21mGMUIt7xjDW38Sc8yI7YK3A20zDojrsyPFXGAXJrfnJMZOo5Y9JMvjM00t2wwHo2YytH4WabyZAsMg/p6nAy+POfLvtua6FEuWN896Mp4Bsnfs/xfro7ILIYQQQgghhBAT/NnFBzMrZva+Zv/1ZnZq17Az8OD7bcAluN9CwQPkT1F/yX8BnpUwCOwBvAV4IvChGPN5PLD/p2buH1FLDH6KGzSCl2OkkPBI4Jtx/KI4vpwawIMLEAX4DtUfYSnVkDHfZc6ZPImaddBmK6RB5QAuvuS9hplsFrkitjvAOXhJBtQuGT3N+bH46wB3UztTDFGFhB5c8MguFWPUDhtr8NKQJNfbj2eVEOfHm3PteCGEEEIIIYQQYj02ReZDejnscG8DSikZYP8t8CuqQSTAJ/Hyh3E8OP4rXHQ4HPdjmIP7PczDRYeZeGCdz/YiajDfoQoD++LBe3aOOC6OPyA+F+Ciwnicv1+MP45q9rg9tczhx3HdKLX7RKF6QbRCwQBuPEkcewnVfyPLQvKaNKwcwbMNUqS4KZ5nXfN8ffFX8GyKtvSDGJuiSmZa9FAFj14mm3VmucdM6ntLISPXcRhCCCGEEEIIIcQG2BTiQ5ZVvLb7hJnNB6aHjwN44Pxg3I8hO1GM4b/+5y/1y4BX410dsgQhuztMaz6nxbmdqAH1B4D9Y3uHGNuDB/wL4niOzWC7p+uzzWrIdpjgnhNQO0ak2NDfNT5FlZ2pokHbwhNq0D+GiwzggsURVD+GvEcv7hvRTxUwMstirGveXlzMaLMz+qgdOAaa9RXgd1RR4sVxfLwZC/59dSPPByGEEEIIIYQQE2wqz4czgOea2XZdxz8EjJRSjor9UeBBwA2xvxQPclfj7SKzfGEv4BA8aC5xPv0OoPomjANfpQbzO1CzABYzuZtE6bo2RYMUOHKONjMg5yrUYHwMuLGZJwWRlVSxw/BOHDnnumY75+zgpSEPo5Y5fCHmH8czGzIDY6QZk94To/FeoH7P/c01bblGku+CWO/8Zj/FnBRb8vi+rI88H4QQQgghhBBCTLBJxIcoqzgLOKnr1LHAgJldigfBmf6/IPY/iwfpuwN7xnpTJPg5bhT5ReD3cexqvDSj/bX/OmppBNSg+aBmf5zqC9GKC+N4y832uvHmfHbQ6KV6SYzhokiKCMtw/4Vu48hxaunCA5o1Z3Dfg2cVHEPNdHgOLiBMi/WOx7ghXMAALwXpxcWQ7Zp1gxt3ptjQekDksXwXHfy9t2LRYPPZChbvRQghhBBCCCGE2ACbstvFB3H/hZnNsR5gqJRyKLC2lLILHrhnicUavPXjWlwE+FBsn41nF4zhv+6/NMYfFvP3U4P7g5gsRqSJYwbT62IdVzf3BBcNsuNEXpdlFtc32yXus2OMS8+GFBamUdtntmt4KfCZZm3fjflGqC01p8WYzJ54GFV06KeKLfPwLJGpvs8UJQpuDplixO1xj/HmuhL3vjH2szRjDDfczLlaQWPPKe6psgshhBBCCCGEEBNsMvGhlLIU+AouQCTfB16ZO2Z2aIz7ZbO2C3FPhJ2o2QpnAvvF33Oo7R4LXiaRwfEA8JTYTzFgBBcoMipeHtftFvspjmwHLMK9GYg5DG+jmffI8gXDRYHW0DL9F3K+1isiu3cc0xx7HC4yDMacI/GZQozhmSCtl8SBVBFlt2auu5v7LY3PtjQEquHlbcBH8feTBpV7xLn0gFgK3Ep9Z2mq2WGyQWaisgshhBBCCCGEEBNsyswHgPfhvgvJScCRZnY5HtSfGMfPo4oFr8ED+ZXAUXhA/CzgNPzX+LPw1pFQg/oxqmHlbXh2QPofDODPvUvsz232f8Hklpn3iXW1rS+hdni4tTl+I1UU6DZ6TBPM3O7BMxp2bY59IbZ74286k8sbwAWGfqq4kr4VHVwYyMyE25ox2aki12PNZw9e0nIiVSxpfR8+Qc3qeDj1Pef12cpTCCGEEEIIIYS4V6yUsvFRmxEzm4GXZBQzexbw7FLKX3eNWYUH2RnUH4hnSUwDrsRLAwZi/y480J6NB+xp/Phx/Bf/R8e0y/A2nh28TGMO8Hbg9VQhIb0YRqndJwruXdF6R7SkZ8SFuLHmY/GyiWvxTI4e4PI4tiuTBaKXA++itt9saUWDDpO9HIaBO/HsjOz2sZrJJTDJNc2Y66li0UdwD4qHN88NcHkp5ZB2AjP7HPBUgHnz5s1YsmQJQgghhBBCCCG2PszsklLKkRsb17exAVsARwCnm5nhgsDf38u4tcCluOHi9sDL8ID5erw7RgoGQ8BPcJEhDRXHgcdTfRtozg3j2Q8Ar6CaMvYA34vrwDMz0nyyW9FJMWAdLlTMwrt6HBX7xuQWmPvimQ8pJqSwcAbVlLMVGvJ+rYnkSIydhX/Pu1I7eKxrninv3xfz7NWsN7NHZgDPw99rzr+ua40tP8U9HxgaGjpiivNCCCGEEEIIIbYhNnXZxf+YUspPSymHlFLuX0p5aCnlunsZOoAHzzviv96fBbwAL5F4NdVIcjmwD/7L/yj1V/wRPDsgSTPG9HoouCCR7Sonlticy+yHblFnZXz2U4N+wwWRmdSuGRZjRvCWmd2lEoX1PRYMuIUqPGTJRx/u1XBznHs1tSxjgMnlF+nfQFyXz7cwnmsa8PRm7TTrWIkQQgghhBBCCLEBtnjx4Y8kSxz2xH0bVuFZCg/ByxlSIAAvn9gBD9JvpxozLmRy5sMqPCC/iypOwOSshsfhAkXbyWIdVQhIBqhCRp4b7xozp5l/AC+76G7vmV0w2myDMWpGQj5nmkfOjvXfhJeLLIrzv2uu/y9cdOnBs0fSG6ODtylNhpr7Xt+1JiGEEEIIIYQQ4l7ZWsSHtfG3Bx7c/wIPtnvxQPuZzdh+PKtgNl6SkNkOq5gc1B+AB+srqYaRtwPfoZYqgGcKrIjjmZmQQXtmE0xjsv9CKaX04eUfl+EBf5vlMB3/brK7RA9VlMiyj6SXany5NO6TLUDX4ULLLOBtcW8Dfh/PMAYcHO+CGP/L5p7tuj/W3HNBc7w1oUzUalMIIYQQQgghxARbi/gAQCllOZ7t8DLctLEAx+G/2md2RP7Kvyz2s5Xmj/AAPfkdnkmxBy5AjOIZBo/N21GzIYaAh1JFgt3j+Do882KU2qViHDAze2Ds7xhrye4ey3AzytKsOa9bRu1usTbOrcE9IgDOwQ03B+P6XeL55gL/GNeuA55I9X/4VDPX1XGPFEIW4OLGOPCG5t1cSf23szPro1abQgghhBBCCCEm2CLFBzMrZva+Zv/1ZnbqRi57jpmdjP+634dnIrwGbzu5bzNuJR5U/5bqd1CAw6nZDPdQswzeg3tHgAfhI3hGwAi1hWUP7tmQgkR6K0zDBYABXDD4LlVQ+CmeITAfFzWydGMOcDS1g0bSi2cojMfz5TyXUsWCE4CXAt9qruuL59gXuH+shVhnHy5KpBBxFHB8jB8Gjo3jFutqW43m2uaZ2d4IIYQQQgghhBD3whYpPuCB71PNbIeNjgRKKUOllHNLKe/EMwj+Bdi9eB/RbwMfxQWBDpCB8kNwv4Pf4MH1AuBpuFHlH2Ke1cBbgHfgYsDVwN1Un4e7Yq611MyFPqrh5DrgDmrQfha1ZKKP2jXjeiZ7SYzF/G3NwjhezjHW3Au8a4YxWfR4CvW77eBCy0tjnUuA0/A2n524TxpJXoOLGeAixQ+oHhJZltIBftysd1kp5Q9MRmUXQgghhBBCCCEmMI/PtyzMbBVeAjFUSjnFzF4f26ea2Xzcf2BhDH9NKeXnZvZC4EjghcCDce+Cq/Bg+2HUThSX4R4JR+PlEplxUKhiwBjwTTyIz+B7MK7/Nu6jcDQukmSXjeV4ZkJ2x8guGtk1ox8vkViE+0nkPVufiQ5e9jCHKh5kycYYNdtgOd6FIkWHaTHXaGwvw8WNoWbeVXF8QbyPc3EviCyryLlzTffg2SDXxblF8c67BasbSyl7tAfM7AQ8C4OFCxcesXjxYoQQQgghhBBCbH2Y2SWllCM3Nm5LzXwAOAN4rplt13X8Q8AHSilH4ZkKn5ji2rfhIsE38EDdgJ/F5/1xL4ebmGyo2LaevBN4MjXwzxKHAjweOCT2R4E3xfFZ+Pscpho7puljPx7Mz6AKD51mzO3N/PNinlxXfzN2GBcbsi1n97rb0o0spUjxZAbVi+In1NagHTw7I70eljZz/IEqSvRTsx1ybVCzLYQQQgghhBBCiCnZYsWHUsoKvEzhpK5TxwKnm9ml+K/3s81sVteYY4ATgRfhxpGGlwJ08OyDUeBsPCgfxYP6lcBH4vp9qYaPmclwI3AJLiL8JMbNiP2CixnZ7WKgue68WOfKfLRmnSMxJjMUVscaV1AzMK6Ncx1qlsUdeLbCUmqJx69w/4YkhQiLea+KNWS2xM3UEpC2ree8Zo41zZjXMjkbgym2hRBCCCGEEBtgz5O/M/EnxLZE38aHbFY+CPwa+HRzrAc4ppSyth1o1lYvAC4KLKS23Cwx10zg58CzqSUHl+BlFC+OcedRTSIND8Lnxt8Q8Mi4x4W4IWUv3hUjMxnG428AeGqMXd2sLQWIDO6nx/6srs8OsF9cO42a3ZAZDGkmSRx7ZXOPRcB9YnsYeB6eEfJw/B3uj3tN7AhchJeqtC+xUEtVANraiTbzoW3BmaTnA/J8EEIIIYQQorLonU/Y3EsQYrOwxWY+AJRSlgJfwTMYku/TBNlmdugUl16Il2SspQb6BhwE7IMLAtPj2AAuPPTEfg/uiZDXgGdAzMA9HXqoLTU7uJgBtV2l4UH99VQRgrh+TYwz3HdiOp7FkOLIcIzPNQ8398iyh2zvCVW0KLgw8sPm3J7N9o7Af+AeDjNivpVxPLNCUojq4BkXPfHc+W/k4OZ9tNkbR7A+arUphBBCCCGEEGKCLT3zAeB9TP5F/yTgDDO7HF//T/ASC8A7X5jZvsDn8eD8NOCtVGEhOz4MNHPeiQf32zfHsrxhh7hPL7VEI8sxxqgB+bTm2hnNNXfFXDvG8RQPct6F1IyJ6Uymu5wE3IfhPjHXm4FTYz1tlkVLGkjuzuTWnSkq3IP7YuzZHG8zG3KO9LlYzWTR6pYp7imEEEIIIYSADZZXKAtCbEtskd0u/q+Y2Qxq+8ub8JII8KyJ+1KNFhfihonrcIPF9Wo3gCvwjIkUCKC2nMyWmFCD/A7ejnMGXqKRpRvdc48Dfwd8KrbXxBrako3MmsisjMy2yDlX48aRxFo+QRViTsfLKc7CxZUf4n4Oj4nz63DRYmk8w8ZSFG7E31d2BMnn+etSyrntQDP7HCGEzJs3b8aSJUs2MrUQQgghhBBbHxvzdZD4ILYG/thuF38JmQ//G47Ag2/woHkBHiw/FDdw/AbugZBB/mJcLBjommcF7l2QosJFsT+Ev7s22+FyPDuggwf2O8bxzBIYp2Zf5P57qKUffbgAMY3awjOzKzJjwWI7yzTW4D4WD45jh1MFipfj4obhWRZ/w2TRILthjFIzLlZTy0iSFFU+i2da9HadX836/JTwfBgaGpqqLEMIIYQQQoitHokLQlS2SvGhlPJ90Tg1AAAW50lEQVRT4BAzW1VKeaiZpevhauDReBB+GLBXHN8PeBUuWKzFMwIKsAr4DF7q0YO36ZxJDfDb8oMsS+ildqVoPRK6g/Z+anvOMfy7GMSFixnULIte3J9hZtf9svXmg6kiyAPicxQ4BS9ZyTna9UD97lNsKGy4c8VFzbh2nnlTjBVCCCGEEGKb43/awULihNiW2KINJ/8nmNm4mV1qZpeZ2a/N7EFdQwZxT4fv42aQg/iv82O44LC6GbcW+AWeEfA4vF0neIkCePC9vJl7DHgELlYso3pCjFKD9RQskk8C51NLMsbj/Hwmd9kYwzMtRuJ4GlOCl5OsjGNjwG1xfAB4e7O2XHO7hg7V3DLLOwZje10zbjTex7JmntbIcwHrk90u7qduF0IIIYQQYltArTOF2DBbU+bD2lLKoQBm9hjgHcCgmV2KB9VXAm/CA+eP4x0tfoSbT74ab+uZZBeKm4GHADvH8V3xwL+fyR4JfTFXlmekMLEK93FI0i8BvINHCgLL8W4VI7iBY2YyzGzG9DFZLMrMi7kxpi/WleJBYf0yEuJ5t8eNMOcBXwcehAsZ6+LZs61nelp04nwKKW35xlSo7EIIIYQQQmxTKItBiA2zNYkPLbOBe0opvWaWAf/BeLvJ5bgPxDRgJ9xQ8gQmlxwYbpjYwcWKDPrTbyH9G1rSgPKXeDA/jAsD4EH7Smr3ik7sbxfzZNvPPqqoUXCjyJ3i3EicH6WKCm3pxyrg93jb0Na3of2Ol+Hixnxqm83nN89suIfEA7uuzbKQfOZW1NgFIYQQQgghhBBiA2xS8cHMCvD+UsrrYv/1wFAp5dQNXPMk4IBSyjs3MObhwMzIcpiOB8SPzNPA20spp5jZ43Bfh1/h/g03l1IOM7NvULtGjOAZBAW4EDgSN54cxAWDuXg3izSUnFgGLkwcQS2jaI0jZ1GzCcZwgSRLLaZTSycy62Etkw0tp8f9V+OZGAW4BPeu6MVFjzVUYSQzFFqPBqOWSbTHfoCLFr14d5B0Kh3F/42sxstPpuIP93JcCCGEEEKIrY4/ZXmFsiXEtsSmznwYBp5qZu8opdz9x1wQbRzP3ehAGG/KLo4BzjKzg+Lcs81sFA+ib8NbZ6ahJMDV1OB6OV6WcD4ehPdQjSHnxXU7TXV//H2mkDCACwgDwK14YL9TzJNjeoGTgdNwcSMzG7Ls4WZq9kSuI9t3pmnmiljXfjF/H5MFhywT6cUFj7Hm+vSQeCRVeBiillWkSeYcoP0/Y3pH9OJdND7W9S7S8wF5PgghhBBCiK0JCQZC/O/Y1IaTY8CZwGu7T5jZfDM7x8wujr8Hx/EXmtnpsb23mV0Y509ruljEafuamV0DvBJvLzmfmnXwJuDdwMXU4P5+ZrYWeGPsr6AKDQ/BfSJ6cZ+ENGAcZrLp48T9cQFiNdWQMksodqUKFunhkJ/vpra6XEEVhKZTv591wA1Uk8e2DGQxLljsGtd2cFHlVzFmgCoi9OClHHl9elR8JuaYjXth5H06eDaFARc0x/PeAEeYWbe3xE9xQefqoaEhhBBCCCGEEEJs22wOz4e3AyvM7N2x/yAzOxW4L/CBUsrPzGwh8D3i13NgTzM7GRcEPlRK+ZKZndg1bw/+C/0BeDnCAC4apFnjrrjJ4kl4lsDv41w/k40fd4/5xqnlB+nNYHiGQt5vDd4WM7MAjNoSMzMWujkP2BfYh8m+EaW5Jksn8vn7mVwmQbN9WGxniUdmSOQ6Hwr8JM5PB3Zrrr8Jz/JYF/ccoooKKTRk28/jup4zeXsppVuIEUIIIYQQYotmS+hOoSwKsS2xOcSHYTyIPRkXB5JjgQPMJuLa2WaWBo2LSinvNLMlwJPj2BeB93bNfRRumLg78IlSynjMN4R3o8hyg5EYvwQvw9gHD7KHqOaKn8f9Gw7Hg/9VePDeH9dmKQNUA8k+qtnjWHNddq1YB+wPLIxr0jxyBLgOF05W4dkMB1LLIt4M/B3VdDLve2Mc2yfm7qV+pwvj84XUDIdP4+LDsXFuQZx7OTUb4268jKPtaFHwNqM7xPZvcM+MPrxlaDcquxBCCCGEEFskW4LoIMS2yOYQH8aAj+AZCB/Af1FPn4Hjge/gv8bfige4APtn6QWwl5n9Bx7QT4vSiyfi5pDDePC8B/BYM9s/rrkD+C4e0IMHz+CmkVmaUahtJQ14HrXUYhU1g6L1U1gb6+jDSxbWNedSpGjrDvrxDIwUD9JQsjTbt+AeF/s2x/4FOATvzNFmHSykZihs1xxfHusBFy0KLq7shws03eU2pTm2Q9fxgpeD5FoMF2SSTwJ/3TWfWm0KIYQQQogtEmUbCLF52NSeD8l78SD2xXiAfz/gv/Gg+yfAx/Ff+z/Rdd2FwJeAU3GTxk7X+cOA1wBfwEsY5sZ9dsZbSqaJYxuo34WLCKtwESH9C/rxzIhfx/ZsaulFkhkUFwC/Y3J5RNKusZfJbTIzuF9L7UKxN/B4Jmcd9FKFhzEmZ26sZX2Gmuu/QW0juh/wiynGr2vu1fo6ZMeMETyzYyq6vwMhhBBCCCGEEGISmyPzgVLKCjP7BPAqasvIxXhHhYPwoHcZHjSfhAfkB8f254G34aLBUlxI+CieBXAn7mNwLZ7tMI/qy7B93OtZwM+a5ewQY67GxYvMbOiJc+fg4sX0uFeH2oozswuOAP4RLyXZnsmdIrJcATzIT3PJ6/Csibl4YH8J8Cg8c2MIFxX6qFkZo/Fse+L+DAD/Djw21p1+DOBGmYfE9gI8a2E47nUwkxkFPhxrT1+IfAeL4/peaqeP9hms610mKrsQQgghhBATqNRhapSFIbYlrJSy8VH/m4nNCvD+UsrrYv/1eFD9+lLKkJnNw7MKdgHeDzwbD3B/DlxUSjnVzL6ICwv7AA8Hjo7xN+DiwhjwGLxE4jbg+7gwcCnuh3Ac8FK8NGBtnPsCnnEBLnB8HXgaLgYcjosa0/DAe2+8hGHXGD+Cd4O4cIpH/hYunmTHicwqeSVeZpIiQrbkHI/5BvESk7xHJ/5SGFpONbHM40tjO70ZxmPdO8c1o7hg0IOLBdPiXfXE8VVx38yOuBkvVbkp5t8ljt+Bd+lYigsXma3R0zzfN0spT2lfhJmdAJwAsHDhwiMWL148xesSQgghhBBCCPGXjpldUko5cmPj/pxlF8PAU81sh6lOllKWAl/BBYFh4CxcVBhrhj0WOB0vsXgCnlXQjxszHoSXEPQCX43xee138UD8elyAWEUVBI6gli304H4F2+HdNiy2e2ItM3GBpADn40H8N5v1nd9sPxEP3EepJowFN79MhWcJNWvBYr6CCw9tycO3mnlnN9tL4/p51JIS4h3MjOdKcSM7UKyhejakZ8UQLtb0xl8KH7vhPhhZSjEbF3EyGyINLVvTzksRQgghhBBCCCE2wJ+z7GIMOBN4LXBKe8LM5gMfA/bCA+gFwBuAV+DB+gPM7Lm4EDCGG0Q+rpQyLY5/AhciXnMv9z4e9ze4HjetHMOD9b443o8H6TdRswUGqa03B6hdNQ7Ag+2jY78VUxZTyzqy5OFualcIw8sZ8nhmIgxQjS1bb4fsMHEYtbzBqN/Tjs1Yw4WOafEs2Rkk/R2yNGIeVfCguWd6TOQzg2eHDMY6SmwvaOYseIZIa4b5EYQQQgghxF88Ko3Y9KjsQmxL/Lk9H84ALjezd+eBKLn4IvCBUsrPzGw18KBSylIzuwj3C/gIcCTu4fBN3BPhUWZ2KB7EF+BsvMzis8CbSimnmdkaYLCUcrCZ3YaXD5yBl2zsiwscPXg2RT8eXG+HB+dL4z5D+HvZPpZ8InBebF8f9zst9p8S84zgGRJHA3OAR1NFhRV4BsFcXKDIQN6A/we8neoxAS6UzMIFijSjvBMXAXrwUoidY1y251yGiwyGZ3nsH+MKXo6xXdy7xHXXABcDfx/XrMYzJ2ZQy0Ms3ksvLrDcjGeb/BZ4Bm6yOQh8DXgkk5HngxBCCCHE/wEJAUKIrY0/q/gQxpJn4UaRbVeGY4EDzAz8F/rZZjYLD/KPizHH4IH1h/EWmTNwIeAqPDA+HvdkOA94vpk9kRpgA3wPeAHeHeMVwD1x/HN4N40xXChIc8gdYz8zEtKfIQP0VXimRitPzqQaUGZmxDRqFsSNwI+AF1ID+dYT4h3N8bzvGLXUIrMesnRjHPdgII4fENspPIALAm/AhZRZ1FaiULMc5gEPbt7VBfh3kt4Ot8Y9r417DMW7BngE/n1mtsQVrI9abQohhBBC/B/QL+JCiK2NTdHt4oN4VsCnm2M9wDGllEltIs1sBfDvYTb5KmBJKeWZZjYbuLWUcqKZPRz4YSnliXHNx4Cvl1I+Y2Z3Uf0KTgWeXkq5IkSOq2LMcXiQPY/acrPgJRxpknE4Lnj04GKD4eLHrbgvQmYGpGfCGPVd3kH1UBgHntqMz2C/LbcYi+N5/fR4hszcyPKGDtU0Mq8difN3UrtsLAVeRM2kKM34dwH/hGdK7N4c3yPGzY3P3eL4dtSykvSQmI2XmySnIoTYotCvZUIIIcRfBhKZxLbEn118iHKKr+AB8afi8PfxLhDvATCzQ0sp3caFF+JdKL6Mt8e8N+YDp5nZP+C/9Ke/wUOAH8f2lVRB4MV4ScMivD3nF+M+v8P9GVqPhYL7VQzigXsaOWaGwpK4ZwoE4K0nn4YH7enFYM3fKNXPobf5aw0nl+C+EXmf8VjzeNyrg4sc02N/IK6zuN90qviwDM/o6MFbc74UF1YWx/PMwEUHqEaSee2usd8BbsFFiZlxrgN0SimZUdLyF1N2oSBNCCGEEEIIIf78bIrMB4D34WJDchJwhpldHmv4CV5S0fIa4PNm9jrgO3jLyam4DXhLZDUsAj4d3hE9ePtOgOcCn4r73YV7FzwZD8T/A3haKeU8MzsTuA8edD84rv0n4ElUc8e1eEDej2cBZNZCwf0o7htrXYsLAQUXG1bhQsnd1BaZa6nGjqN4BsMcPMDPrIQeXIBYQQ38wX0asgRjdoy/Ku59ON5+9CvxvLNwseDduHnngTHnDLw05PF4+YTh3SsOi+czvFxlx3j2G+LdZQlIZkNMopTyvLg/ZnaXmanX5h/PDvi/EbFtou9/20Xf/baNvv9tG33/2zD2Ln3/2zhby/e/xx8zyEopGx+1GTCzGcDaUkoxs2cBzy6l/PWfcP6TgPuUUl5rZqtKKUNx/AN4IP5hvOXlE2I/MwqW4h00BvAA/jO48eQc4At4cH4wns2QWQcrcIFiFm7cuGPsLwduxw0ie3FDS8MzDJbiXgu9uPdCL+45MRSPkCUYPbi/wyieSfJ6YLyUspOZ3Y53HDkFFwtGcKFjJOYbj7U9EM8OmRFr2DPmvTHWuQMu8kyjdgfpiet3LaXc+T96+eJeMbNf/TE9csXWib7/bRd999s2+v63bfT9b9vo+9+22da+/56ND9lsHAFcGtkKLwde9yee/6q4Byk8BJlxQCnluXhmxHvwTIzr8F//F+NlG4uBS3BxYKyU8iLgbXhHiC8B/wh8HvhFTNmDiygzcBFibcwzAlwNHIKLAkvxzIYh4O/wMoadcTEmu1P8Ag/+R2PNc3GRoQDfiPvdFNevK6VMwzMk3lBKGcTNLm/HMxu+iWddnI0LIim47BB/K2OuXjxrpSeeebmEByGEEEIIIYQQG2OLFR9KKT8tpRxSSrl/KeWhpZTr/sS3+BEwYGYvyQNmdhQejD8T2M7M5gMPBX7ZrOu5pZQD8W4a78TFhfvgwgF4qcftpZQXxv7ayNj4PF768G9mdimeETEOnEst47gIFwHeVUrZF8+c+Oc4/nlc1ICaHXElnqnQE9f1AO8vpZwYaz0KeBkw3cyujvG7xBzfwkWFw/ESkzW4yHIWXibTj6cA9eIiSgcXVl4V68h5hBBCCCGEEEKIDbKpPB+2OKKc4ynAB83sZNyXYRGe4TAEXIZnEbyxlHK7me1/L/OsM7MTgO+Y2d244eRBUww9G3hUzPl04M3At0spXzOz64H34t/HlcDH4poVwF+VUu42syOJTA08q+JpuI/EOuDqUsqRZvZk4Bwze0vXvUdLKffrWvevzewzVGHlE6WU35jZjtTOGqvxjiG/MrPnAxeUUj4CYGaX4OKF+NNy5uZegNis6PvfdtF3v22j73/bRt//to2+/22bber732I9H8SfjjDgHOg6/LxSyhWbYz1CCCGEEEIIIbYtJD5sg5jZKcAzug5/tZTyts2xHiGEEEIIIYQQWzdbrOeD+J9hZtub2aVT/G3fPbaU8rZSyqFdfxIeNgNmNs/MfmBmv4/Pufcybrz5Ts/d1OsUfx7+2O8/xs42s1vM7PRNuUbx5+GP+e7NbA8zuyT+u7/KzP5/e/cSIkcRx3H8+1PZVRA1+IjRJETRgznIihpQSS4ajBfjIb4Q3YC34EkUAnrSS0TBkwdBD1EQNAEfqKi40eDBBIPghigxaxB32SVCfIAohsDfQ9fiMO6jF7uqM72/z2W6p4vhB/+pnqG6u6p/SWobUDXrPyLpy1T7cUn3t5HVmreE3/6PJP0m6f3SGa15krZIOippIj3y3X98WNKb6fhBSevKp7QcatR+k6SvJZ2WtK2NjKV48KEjIuLkHAMKIxFxsu1stqCdwFiaYHQs7c/lr56a3l0unmVWt/4AzwL7i6SyEurUfga4NSJGqJZE3inpioIZLZ869f8TeCRNcr2Fao6qiwpmtHzqnvufBx4ulsqykXQ28BJwF7AeeFDS+r5mjwK/RsQ1wIvAc2VTWg41a/8TsB14o2y68jz4YNaurcDutL0buKfFLFZerfpLuhFYCXxSKJflt2jtI+JURPyddofxb3aX1Kn/9xFxLG1PU61qdWmxhJZTrXN/RIzhyb27YgMwERHHI+IU1UT0W/va9H4v9gK3S1LBjJbHorWPiB8jYpxq0v9O8x8Zs3atjIgZgPR62TztzpV0SNKBtKqJdcOi9Zd0FtXyt08WzmZ51er7ktZIGgcmqZZhni6Y0fKpe+4HQNIGYAj4oUA2y29J9bdOuJLqPD5rKr03Z5uIOA38Dvzn8WkbOHVqv2ws26U2zUqR9Clw+RyHnlrCx6yNiGlJVwP7JB2OCP8JHQAN1H8H8GFETPoCyGBpou9HxCRwfXrc4h1JeyPiRFMZLZ+Gzv1IWgW8DoxGROevinVFU/W3zpjrB7x/1v86bWzwuK49PPhglllE3DHfMUknJK2KiJn0B/PneT5jOr0el/Q5cAO+AjYQGqj/LcBGSTuA84EhSX9ExELzQ9gZoIm+3/NZ05KOABupbse1M1wT9Zd0AfAB8HREHMgU1TJosv9bJ0wBa3r2VwP9d7LNtpmSdA5wIfBLmXiWUZ3aLxt+7MKsXe8Bo2l7FHi3v4GkFZKG0/YlwG3At8USWk6L1j8iHoqItRGxDngCeM0DD51Qp++vlnRe2l5B1fePFktoOdWp/xDwNlWf31Mwm+W3aP2tc74CrpV0VerbD1B9D3r1fi+2AfsiYtleIe+QOrVfNjz4YNauXcBmSceAzWkfSTdJeiW1uQ44JOkb4DNgV0R48KEb6tTfuqlu3z+Y+v5+4IWIONxKWmtanfrfB2wCtuvfpZZH2olrDat17pf0BbCHauLBKUl3tpLW/rc0h8NjwMfAd8BbEXFE0jOSZlcxexW4WNIE8DgLr4BlA6JO7SXdLGkKuBd4Od3p2EnygJqZmZmZmZmZ5eQ7H8zMzMzMzMwsKw8+mJmZmZmZmVlWHnwwMzMzMzMzs6w8+GBmZmZmZmZmWXnwwczMzMzMzMyy8uCDmZmZmZmZmWXlwQczMzMzMzMzy8qDD2ZmZmZmZmaW1T9gosq9BrddcwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pd.DataFrame(list(zip(x_onehot.columns, para_search_lasso.best_estimator_.coef_))).set_index(0).sort_values(1).plot.barh()\n", + "plt.rcParams['figure.figsize'] = 4, 36" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "alpha_100 = np.logspace(-4.5, 2, 100)\n", + "coef = []\n", + "for i in alpha_100:\n", + " lasso.set_params(alpha = i)\n", + " lasso.fit(x_onehot, y_log)\n", + " coef.append(lasso.coef_)\n", + "\n", + "df_coef = pd.DataFrame(coef, index=alpha_100, columns=x_onehot.columns)\n", + "title = 'Lasso coefficients as a function of the regularization'\n", + "axes = df_coef.plot(logx=True, title=title)\n", + "axes.set_xlabel('alpha')\n", + "axes.set_ylabel('coefficients')\n", + "plt.rcParams['figure.figsize'] = 16, 4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### test prediction" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "best_lasso_test_y = para_search_lasso.best_estimator_.predict(test_onehot)\n", + "best_lasso_test_y = np.expm1(best_lasso_test_y)\n", + "sub = pd.DataFrame()\n", + "sub['SalePrice'] = best_lasso_test_y\n", + "sub = sub.set_index(np.arange(1461, 2920))\n", + "sub.to_csv('(7) lasso_log(y)_onehot_submission.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Elastic net regularization" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'alpha': 0.0002915053062825182, 'l1_ratio': 0.4}\n", + "Lowest RMSE found: 0.1447146344909162\n" + ] + } + ], + "source": [ + "elasticnet = linear_model.ElasticNet(normalize=True) # create a ridge regression instance\n", + "\n", + "# find the best alpha (lambda) for lasso \n", + "grid_param = [{'alpha': np.logspace(-10, 6, 100), 'l1_ratio': np.arange(0, 1.1, 0.1)}]\n", + "para_search_elas = GridSearchCV(estimator=elasticnet, param_grid=grid_param, scoring='neg_mean_squared_error', cv=5, return_train_score=True)\n", + "para_search_elas.fit(x_onehot, y_log)\n", + "\n", + "print(para_search_elas.best_params_)\n", + "print(\"Lowest RMSE found: \", np.sqrt(np.abs(para_search_elas.best_score_)))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### RMSE for all train data using best lasso" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 9.818629927369878e-16\n" + ] + } + ], + "source": [ + "# fit best elastic net equation to all train data \n", + "best_elas_y = para_search_elas.best_estimator_.predict(x_onehot)\n", + "print(\"RMSE: \", np.sqrt(np.mean(y_log-best_elas_y)**2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### RMSE across different grid search lambda" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", + " DeprecationWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", + " DeprecationWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "error = pd.DataFrame(para_search_elas.grid_scores_)['mean_validation_score']\n", + "error = np.sqrt(np.abs(error))\n", + "alpha = pd.DataFrame(para_search_elas.grid_scores_)['parameters'].apply(lambda x: x.get('alpha'))\n", + "plt.scatter(alpha, error)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "alphas_elastic = np.logspace(-10, 6, 100)\n", + "coef_elastic = []\n", + "for i in alphas_elastic:\n", + " elastic = linear_model.ElasticNet(l1_ratio =0.6)\n", + " elastic.set_params(alpha = i)\n", + " elastic.fit(x_onehot, y_log)\n", + " coef_elastic.append(elastic.coef_)\n", + "\n", + "df_coef = pd.DataFrame(coef_elastic, index=alphas_elastic, columns=x_onehot.columns)\n", + "title = 'ElasticNet coefficients as a function of the regularization'\n", + "df_coef.plot(logx=True, title=title)\n", + "plt.xlabel('alpha')\n", + "plt.ylabel('coefficients')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### test prediction" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "best_elas_test_y = para_search_elas.best_estimator_.predict(test_onehot)\n", + "best_elas_test_y = np.expm1(best_elas_test_y)\n", + "sub = pd.DataFrame()\n", + "sub['SalePrice'] = best_elas_test_y\n", + "sub = sub.set_index(np.arange(1461, 2920))\n", + "sub.to_csv('(8) elas_log(y)_onehot_submission.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Kelly/Kaggle_house_price/Regularization Model (one hot yr_month sold).ipynb b/Kelly/Kaggle_house_price/Regularization Model (one hot yr_month sold).ipynb new file mode 100644 index 0000000..260abfd --- /dev/null +++ b/Kelly/Kaggle_house_price/Regularization Model (one hot yr_month sold).ipynb @@ -0,0 +1,5210 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multiple linear regression " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "## import data\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "train = pd.read_csv(\"train.csv\").set_index(\"Id\")\n", + "test = pd.read_csv(\"test.csv\").set_index(\"Id\")\n", + "combine = pd.concat([train, test])\n", + "\n", + "# process data before model fitting\n", + "from preprocess3_yrmonthsold import impute\n", + "onehot, encodedDic = impute(combine, True) # process data and onehot encode\n", + "\n", + "# seperate onehot data into train and test\n", + "train_onehot = onehot.iloc[0:1460,]\n", + "test_onehot = onehot.iloc[1460:2919,].drop('SalePrice', axis = 1) # salesprice col were all NA \n", + "\n", + "# split train data frame into x var and y var for model testing\n", + "x_onehot = train_onehot.drop('SalePrice', axis=1)\n", + "y_log = np.log(train_onehot.SalePrice) # convert y to normal distribution for regression models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### original y vs log(y) distirbution" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 5., 11., 13., 61., 58., 126., 165., 180., 122., 130., 121.,\n", + " 78., 61., 64., 49., 36., 36., 25., 13., 25., 16., 11.,\n", + " 4., 11., 9., 5., 4., 4., 4., 2., 1., 1., 1.,\n", + " 0., 1., 0., 2., 0., 1., 0., 2., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 2.]),\n", + " array([ 34900., 49302., 63704., 78106., 92508., 106910., 121312.,\n", + " 135714., 150116., 164518., 178920., 193322., 207724., 222126.,\n", + " 236528., 250930., 265332., 279734., 294136., 308538., 322940.,\n", + " 337342., 351744., 366146., 380548., 394950., 409352., 423754.,\n", + " 438156., 452558., 466960., 481362., 495764., 510166., 524568.,\n", + " 538970., 553372., 567774., 582176., 596578., 610980., 625382.,\n", + " 639784., 654186., 668588., 682990., 697392., 711794., 726196.,\n", + " 740598., 755000.]),\n", + " )" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "plt.hist(train_onehot.SalePrice, bins=50) # original y" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 2., 2., 1., 0., 0., 0., 2., 3., 4., 3., 5.,\n", + " 1., 5., 21., 22., 23., 18., 29., 58., 56., 65., 100.,\n", + " 122., 93., 90., 82., 108., 91., 64., 55., 58., 51., 46.,\n", + " 42., 23., 29., 22., 13., 13., 13., 7., 5., 4., 1.,\n", + " 2., 2., 2., 0., 0., 2.]),\n", + " array([10.46024211, 10.52172673, 10.58321134, 10.64469596, 10.70618058,\n", + " 10.7676652 , 10.82914982, 10.89063444, 10.95211906, 11.01360367,\n", + " 11.07508829, 11.13657291, 11.19805753, 11.25954215, 11.32102677,\n", + " 11.38251138, 11.443996 , 11.50548062, 11.56696524, 11.62844986,\n", + " 11.68993448, 11.75141909, 11.81290371, 11.87438833, 11.93587295,\n", + " 11.99735757, 12.05884219, 12.12032681, 12.18181142, 12.24329604,\n", + " 12.30478066, 12.36626528, 12.4277499 , 12.48923452, 12.55071913,\n", + " 12.61220375, 12.67368837, 12.73517299, 12.79665761, 12.85814223,\n", + " 12.91962684, 12.98111146, 13.04259608, 13.1040807 , 13.16556532,\n", + " 13.22704994, 13.28853455, 13.35001917, 13.41150379, 13.47298841,\n", + " 13.53447303]),\n", + " )" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(y_log, bins=50) # log(y)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ridge regularization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Find best lambda" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'alpha': 0.49770235643321137}\n", + "Lowest RMSE found: 0.14614522543308797\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import GridSearchCV # search for the best lambda\n", + "from sklearn import linear_model\n", + "\n", + "ridge = linear_model.Ridge(normalize=True) # create a ridge regression instance\n", + "\n", + "# find the best alpha (lambda) for ridge\n", + "grid_param = [{'alpha': np.logspace(-4, 2, 100)}]\n", + "para_search_ridge = GridSearchCV(estimator=ridge, param_grid=grid_param, scoring='neg_mean_squared_error', cv=5, return_train_score=True)\n", + "para_search_ridge.fit(x_onehot, y_log)\n", + "\n", + "print(para_search_ridge.best_params_)\n", + "print(\"Lowest RMSE found: \", np.sqrt(np.abs(para_search_ridge.best_score_)))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### RMSE for all train data using best ridge" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.1209304793183424\n" + ] + } + ], + "source": [ + "# fit best ridge equation to all train data \n", + "best_ridge_y = para_search_ridge.best_estimator_.predict(x_onehot)\n", + "print(\"RMSE: \", np.sqrt(np.mean((y_log-best_ridge_y)**2)))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### RMSE across different grid search lambda" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", + " DeprecationWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", + " DeprecationWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "error = pd.DataFrame(para_search_ridge.grid_scores_)['mean_validation_score']\n", + "error = np.sqrt(np.abs(error))\n", + "alpha = pd.DataFrame(para_search_ridge.grid_scores_)['parameters'].apply(lambda x: x.get('alpha'))\n", + "plt.scatter(alpha, error)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Variable importance for best lambda" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pd.DataFrame(list(zip(x_onehot.columns, para_search_ridge.best_estimator_.coef_))).set_index(0).sort_values(1).plot.barh()\n", + "plt.rcParams['figure.figsize'] = 4, 36" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "alpha_100 = np.logspace(-4, 2, 100)\n", + "coef = []\n", + "for i in alpha_100:\n", + " ridge.set_params(alpha = i)\n", + " ridge.fit(x_onehot, y_log)\n", + " coef.append(ridge.coef_)\n", + "\n", + "df_coef = pd.DataFrame(coef, index=alpha_100, columns=x_onehot.columns)\n", + "title = 'Ridge coefficients as a function of the regularization'\n", + "axes = df_coef.plot(logx=True, title=title)\n", + "axes.set_xlabel('alpha')\n", + "axes.set_ylabel('coefficients')\n", + "plt.rcParams['figure.figsize'] = 16, 4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### test prediction" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "best_ridge_test_y = para_search_ridge.best_estimator_.predict(test_onehot)\n", + "best_ridge_test_y = np.expm1(best_ridge_test_y)\n", + "best_ridge_test_y\n", + "sub = pd.DataFrame()\n", + "sub['SalePrice'] = best_ridge_test_y\n", + "sub = sub.set_index(np.arange(1461, 2920))\n", + "sub.to_csv('(11) ridge_log(y)_onehot_submission.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lasso regularization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Find best lambda" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'alpha': 0.0001232846739442066}\n", + "Lowest RMSE found: 0.14563024917025766\n" + ] + } + ], + "source": [ + "lasso = linear_model.Lasso(normalize=True) # create a ridge regression instance\n", + "\n", + "# find the best alpha (lambda) for lasso \n", + "grid_param = [{'alpha': np.logspace(-4.5, 2, 100)}]\n", + "para_search_lasso = GridSearchCV(estimator=lasso, param_grid=grid_param, scoring='neg_mean_squared_error', cv=5, return_train_score=True)\n", + "para_search_lasso.fit(x_onehot, y_log)\n", + "\n", + "print(para_search_lasso.best_params_)\n", + "print(\"Lowest RMSE found: \", np.sqrt(np.abs(para_search_lasso.best_score_)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### RMSE for all train data using best lasso" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.12104384332174772\n" + ] + } + ], + "source": [ + "# fit best lasso equation to all train data \n", + "best_lasso_y = para_search_lasso.best_estimator_.predict(x_onehot)\n", + "print(\"RMSE: \", np.sqrt(np.mean((y_log-best_lasso_y)**2)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### RMSE across different grid search lambda" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", + " DeprecationWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", + " DeprecationWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "error = pd.DataFrame(para_search_lasso.grid_scores_)['mean_validation_score']\n", + "error = np.sqrt(np.abs(error))\n", + "alpha = pd.DataFrame(para_search_lasso.grid_scores_)['parameters'].apply(lambda x: x.get('alpha'))\n", + "plt.scatter(alpha, error)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Variable importance for best lambda" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "alpha_100 = np.logspace(-4.5, 2, 100)\n", + "coef = []\n", + "for i in alpha_100:\n", + " lasso.set_params(alpha = i)\n", + " lasso.fit(x_onehot, y_log)\n", + " coef.append(lasso.coef_)\n", + "\n", + "df_coef = pd.DataFrame(coef, index=alpha_100, columns=x_onehot.columns)\n", + "title = 'Lasso coefficients as a function of the regularization'\n", + "axes = df_coef.plot(logx=True, title=title)\n", + "axes.set_xlabel('alpha')\n", + "axes.set_ylabel('coefficients')\n", + "plt.rcParams['figure.figsize'] = 16, 4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### test prediction" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "best_lasso_test_y = para_search_lasso.best_estimator_.predict(test_onehot)\n", + "best_lasso_test_y = np.expm1(best_lasso_test_y)\n", + "sub = pd.DataFrame()\n", + "sub['SalePrice'] = best_lasso_test_y\n", + "sub = sub.set_index(np.arange(1461, 2920))\n", + "sub.to_csv('(12) lasso_log(y)_onehot_submission.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Elastic net regularization" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'alpha': 0.000200923300256505, 'l1_ratio': 0.6000000000000001}\n", + "Lowest RMSE found: 0.14299733584858237\n" + ] + } + ], + "source": [ + "elasticnet = linear_model.ElasticNet(normalize=True) # create a ridge regression instance\n", + "\n", + "# find the best alpha (lambda) for lasso \n", + "grid_param = [{'alpha': np.logspace(-10, 6, 100), 'l1_ratio': np.arange(0, 1.1, 0.1)}]\n", + "para_search_elas = GridSearchCV(estimator=elasticnet, param_grid=grid_param, scoring='neg_mean_squared_error', cv=5, return_train_score=True)\n", + "para_search_elas.fit(x_onehot, y_log)\n", + "\n", + "print(para_search_elas.best_params_)\n", + "print(\"Lowest RMSE found: \", np.sqrt(np.abs(para_search_elas.best_score_)))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### RMSE for all train data using best lasso" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 8.395111090316252e-16\n" + ] + } + ], + "source": [ + "# fit best elastic net equation to all train data \n", + "best_elas_y = para_search_elas.best_estimator_.predict(x_onehot)\n", + "print(\"RMSE: \", np.sqrt(np.mean(y_log-best_elas_y)**2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### RMSE across different grid search lambda" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", + " DeprecationWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", + " DeprecationWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "error = pd.DataFrame(para_search_elas.grid_scores_)['mean_validation_score']\n", + "error = np.sqrt(np.abs(error))\n", + "alpha = pd.DataFrame(para_search_elas.grid_scores_)['parameters'].apply(lambda x: x.get('alpha'))\n", + "plt.scatter(alpha, error)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "data": { + "image/png": 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yMo2fY2JibFJSUsyJiM6cOWOZm5trZvyurq6OU1lZya6oqOCMHz/e/dq1a3wWi2XQarWs9uw1atSoKg6HQ76+vpry8nITIqLTp09bvPHGG5UcDodcXV11AwYMqCUi0uv1xGKxDPfXMBj+MARPGcIuAAAAAAC02591YJ8Fg8FAKSkpVywsLP4nYb7zzjuugwcPrj169Gh+dna2aXBwsGd76vH5/Ht1jKH1QeHVy8ur6ebNm7zKykq2tbX1ve5uamqqYPz48ZUajYaVmJho3ZHrgseDX2MGAAAAAIDnWmBgYM2aNWvsjZ/Pnj1rRkRUU1PDcXZ2biYiio2NtTV+b2lp2VJXV/dIWSgoKKguMTHRuqWlhYqKirjnz58X/reWfuzYsWVRUVEuOp2OiIg2bdpkw+Px9MOGDasbOXJkbXNzM2vdunX39j958qTg4MGDFo910fCnEHYBAAAAAOC59tVXXxVdunTJnGEYRe/evb02bdpkR0S0ePHikk8++cS5X79+spaWlnvzw8LCanNycsxkMpni66+/blfXdcqUKZUODg7NDMN4TZ061U2lUtVbWVm1EBF98cUXxXw+X9+rVy+lvb19n02bNol//vnnPDabTWw2m5KSkvJ/+eUXSxcXF6WHh4fXxx9/7Ojq6qp9IjcD7mHhWXIAAAAAAHgYtVp9TaVSlT3rczxr1dXV7B49euhLSko4L730kvzMmTNZrq6uutZzrl+/zh0+fDjzzjvv3F6wYMELf88el1qttlWpVJKOrMU7uwAAAAAAAO0wbNgwaU1NDUer1bIWLlx46/6gS0Tk6uqqa/1DWvDsIOwCAAAAAAC0w4ULF7Kf9Rmg/fDOLgAAAAAAAHQ7CLsAAAAAAADQ7SDsAgAAAAAAQLeDsAsAAAAAAADdDsIuAAAAAAB0eQKBwMf49127dvVwc3NT5ubmmq5du9Zu06ZNNkREMTExNteuXTN5WJ2YmBibyMhI1848W0hISO++ffvKWo+Fh4dLtm3b1q7/w9doz549lt7e3nJ3d3cvmUymePXVV3vl5uaaduZZXyT4NWYAAAAAAHhu7Nu3T7hgwQKX5OTkXKlU2rxo0aI7xu927Nhh27dv30aJRKJ9WucpKyvjZGRkmAsEgpasrCxTmUzW3JE6v//+O3/+/PmuP/30U16/fv00REQJCQk98vLyTKVS6f/U1Gq1ZGLy0EwPhM4uAAAAAAA8J5KTky1mzJghSUpKyvPy8moiIpo3b57jsmXLxNu2bbNOT08XREZG9pLJZIq6ujrWyZMnBT4+PjJPT0+Ft7e3vLKykk1EVFJSYhIUFCR1c3NTTps2zdlYf+/evZZ9+/aVKRQKeVhYWK/q6mo2EZGTk5P33LlzHRUKhZxhGMXly5f5xjXx8fHWoaGhVa+//nrFd999J2p93qNHjwp9fX09JRKJcufOnT2IiPr06SNLSUm5t97f39/z9OnTgpUrVzrMmzfvljHoEhFNmjSpOiwsrM44b+bMmU4vvfSS52effSZ+Mne4e0FnFwAAAAAA2u2XuCsuFcV1gs6sKXKyaAiJlBc9bE5zczNr/PjxHkeOHMn28fHR3P/91KlTK7ds2WL/+eefFw0aNKhBo9GwJk2a1DshISF/8ODBDRUVFWwLCws9EVFmZqZArVZnmpmZ6T08PJQLFiwoNTc3N6xatcrh1KlTOZaWlvolS5b0/PTTT8Wff/75LSIiW1tbXWZm5pXo6Gi76Oho8a5duwqJiH788UfRsmXLbjo6OmrHjh3be/Xq1SXGMxUVFfEuXLiQnZmZyQsNDfUcPXp0Wnh4eEVCQoLIz8/vZmFhocnt27dNgoKCGv72t7/xFy9eXHL/dbVWVVXF+f333/F//bYTOrsAAAAAANDlmZiYGPr161e3detW2/bMT01N5dvb22sHDx7cQEQkEon0xkd/AwMDa2xsbFoEAoHBw8NDk5+fz/v111/N8/Pz+f7+/jKZTKb44YcfbK5fv37vfdmIiIhKIiJ/f/+GoqIiHhFRUVERt7CwkDd8+PC6Pn36NHG5XMPvv/9+r2sbHh5eweFwyNvbu8nFxaXpP//5Dz8yMrIyKSnJmogoLi7OeuTIkZX3n72kpIQjk8kUEolEuWzZsntd3IkTJ1Z06Oa9oNDZBQAAAACAdvuzDuyTwmKxKCkp6eqgQYOY999/v2d0dPRDu6AGg4FYLJahre9MTU3vjXM4HINWq2UZDAYKDAys2b9/f0Fba/h8voGIiMvlGnQ6HYuI6LvvvhPV1NRwXFxcvImI6urqOPHx8aKXXnrppvHM91+Du7u71srKSnf+/HmzvXv3imJjYwuJiBiG0Vy4cEEQEBDQ2LNnz5asrKzMZcuWievq6jjG9UKhUN+OWwX/hc4uAAAAAAA8F4RCoT45OTl3z549Nhs2bPhDh9fCwqKlurqaQ0SkUqk0paWlpidPnhQQEVVWVrK12gf/btWQIUPqU1JSLNLT03lERLW1tezU1FTew86zZ88e0U8//ZRbXFycVlxcnHb+/PnMxMTEe+/t7t2717qlpYUyMjJ4RUVFPJVKpSEiGjt2bMWqVat61tbWcvz9/RuJiD788MOSdevWOVy6dOleZ7ihoQF57TGgswsAAAAAAM8NsVjckpycnDN48GCZnZ2drvV3kZGRZbNmzXJbuHChPiUl5UpCQkL+e++956rRaNh8Pl9/6tSpnAfVdXR01MXGxl6bMGFCr+bmZhYR0ccff1zcp0+fprbmZ2dnm968edM0ODi43jgmk8maLSwsWo4fP25OROTh4dHk7+/vWV5ebrJx48ZCgUBgICKaPHly5UcffeQ6e/bsm8a1/v7+jWvXri2KjIx0r6+vZ1tbW7c4OTk1rVy58uYfd4f2YBkMbXb2AQAAAAAAiIhIrVZfU6lUZc/6HPDiUavVtiqVStKRtWiLAwAAAAAAQLeDsAsAAAAAAADdDsIuAAAAAAAAdDsIuwAAAAAAANDtIOwCAAAAAABAt4OwCwAAAAAAAN0Owi4AAAAAAHR5AoHAx/j3Xbt29XBzc1Pm5uaarl271m7Tpk02REQxMTE2165dM3lYnZiYGJvIyEjXzjpXfHy8FcMwCnd3dy+pVOq1bds2647Wys7ONpVKpV6ddbYXHfdZHwAAAAAAAKC99u3bJ1ywYIFLcnJyrlQqbV60aNEd43c7duyw7du3b6NEItE+jbOcO3fObMmSJc5HjhzJkclkzVlZWabDhg1jPDw8moKCghqexhngwdDZBQAAAACA50JycrLFjBkzJElJSXleXl5NRETz5s1zXLZsmXjbtm3W6enpgsjIyF4ymUxRV1fHOnnypMDHx0fm6emp8Pb2lldWVrKJiEpKSkyCgoKkbm5uymnTpjkb6+/du9eyb9++MoVCIQ8LC+tVXV3NJiJycnLynjt3rqNCoZAzDKO4fPkyn4hozZo1PefNm3dLJpM1ExHJZLLmefPmlaxdu1ZMROTv7+956tQpARHRrVu3uE5OTt5Edzu4vr6+ngqFQq5QKORHjx41f5r38UWBzi4AAAAAALTbz1s2upQVFQo6s6ati1vDy1Fzih42p7m5mTV+/HiPI0eOZPv4+Gju/37q1KmVW7Zssf/888+LBg0a1KDRaFiTJk3qnZCQkD948OCGiooKtoWFhZ6IKDMzU6BWqzPNzMz0Hh4eygULFpSam5sbVq1a5XDq1KkcS0tL/ZIlS3p++umn4s8///wWEZGtra0uMzPzSnR0tF10dLR4165dhTk5OfzFixeXtD7HgAED6mNjY+0fdi2Ojo6606dP5wgEAkNaWhpv4sSJvdLT0688+p2Dh0HYBQAAAACALs/ExMTQr1+/uq1bt9r279//ocGYiCg1NZVvb2+vHTx4cAMRkUgk0hu/CwwMrLGxsWkhIvLw8NDk5+fzKioqOPn5+Xx/f38ZEZFWq2X5+vrWGddERERUEhH5+/s3JCUlWRMRGQwGFpv9vw/LGgyGP72W5uZm1ttvv+2WmZlpxmazqbCwkNeeewCPBmEXAAAAAADa7c86sE8Ki8WipKSkq4MGDWLef//9ntHR0SUPm28wGIjFYrWZPE1NTe+Nczgcg1arZRkMBgoMDKzZv39/QVtr+Hy+gYiIy+UadDodi4iIYZjGc+fOCfr3799onHfhwgWBSqWqN85taWkhIqKGhgaWcc7KlSvF9vb22n/9618Fer2ezMzMfNt9I6Dd8M4uAAAAAAA8F4RCoT45OTl3z549Nhs2bLC9/3sLC4uW6upqDhGRSqXSlJaWmp48eVJARFRZWcnWah/8u1VDhkQkW8IAACAASURBVAypT0lJsUhPT+cREdXW1rJTU1Mf2nFdvHhxyYYNGxyys7NNie6+i7t582bxhx9+WEJE5OLi0nThwgVzIqKEhIR7v9JcXV3NcXBw0HI4HNq8ebONMRBD50JnFwAAAAAAnhtisbglOTk5Z/DgwTI7Oztd6+8iIyPLZs2a5bZw4UJ9SkrKlYSEhPz33nvPVaPRsPl8vv7UqVM5D6rr6Oioi42NvTZhwoRezc3NLCKijz/+uLhPnz5ND1ozcODAxhUrVtwYOXKkR3NzM7u4uNj04MGD2SqVqomI6P333y8dP358rx9++MEmKCioxrhuzpw5t8PDw3snJiZaBwYG1pqZmekftAd0HKs9z5QDAAAAAMCLS61WX1OpVGXP+hxd3fTp050uXrxofvLkyVzjY8/weNRqta1KpZJ0ZC06uwAAAAAAAJ1g8+bNxc/6DPD/4Z1dAAAAAAAA6HYQdgEAAAAAAKDbQdgFAAAAAACAbgdhFwAAAAAAALodhF0AAAAAAADodhB2AQAAAACgyxMIBD7tnRsfH2918eJFfusxrVZL1tbWqhkzZjh1/umgK0LYBQAAAACAbiUxMdEqNTXVrPXY3r17e7i7uzclJSVZ6/X6NtfpdLqncj54OhB2AQAAAADguZSTk2MaEBDAMAyjCAgIYHJzc02PHj1qfuzYMaulS5c6y2QyRUZGBo+IaOfOnaLp06eXOjo6Nh8/ftzcWMPJycl7wYIFDr6+vp7ffvutdUZGBi8oKEjq5eUl9/X19bx8+TKfiOj777/v0adPH5lcLlcMHDiQKSoq4j6r64b2wT8QAAAAAAC0W8WeHBdtSb2gM2ua9DRvEI1lih513bRp01wjIiLKZ82aVb5x40abqKgol2PHjuWHhoZWvfbaa9VTp06tJCKqq6tjnT17VhgfH19YVVXF2bFjhyg0NLTeWIfP5+svXryYTUQUEBDAfPXVV4Xe3t5Nx48fN4+KinL997//nTNs2LC6CRMmZLHZbFq/fr3tihUren799dc3Ou8uQGdD2AUAAAAAgOfS5cuXzQ8fPpxPRBQVFVWxfPly57bm7d6922rAgAG1QqFQP3ny5Mq+ffs66nS6Ii73bhyKjIysJCKqrq5mX7582WLcuHG9jWubm5tZREQFBQWmY8aMcb5z545Jc3Mz28XFpemJXyA8FoRdAAAAAABot450YJ+1H374QXTx4kULJycnbyKi6upqzoEDB4RjxoypJSISCoV6IqKWlhYSCoW6rKyszPtrzJw503X27NklkyZNqj5w4IBwxYoVjk/3KuBR4Z1dAAAAAAB4Lvn4+NR/88031kREsbGxIj8/vzoiIgsLi5aamho2EVFFRQU7JSXF4saNG6nFxcVpxcXFadHR0de///570f31RCKR3tnZufnbb7+1JiLS6/V07tw5MyKi2tpajqurq5aIaPv27TZP6xqh4xB2AQAAAACgy9NoNGyxWNzH+OeTTz4Rb9my5Xp8fLwtwzCKnTt32mzevLmIiGjSpEkVMTExPeVyueK7774TDRw4sNbMzMxgrDVhwoSqo0ePWjU2NrLu32fnzp1Xt23bZuvp6amQSqVe//rXv6yIiJYsWXJz4sSJvX19fT1tbGzws83PAZbBYPjzWQAAAAAA8MJSq9XXVCpV2bM+B7x41Gq1rUqlknRkLTq7AAAAAAAA0O0g7AIAAAAAAEC3g7ALAAAAAAAA3Q7CLgAAAAAAAHQ7CLsAAAAAAADQ7SDsAgAAAAAAQLeDsAsAAAAAAF2eQCDwae/c+Ph4q4sXL/KNn8PDwyVOTk7eMplMIZPJFJ999pl9Z5zpwIEDwqNHj5p3Ri3ofNxnfQAAAAAAAIDOlJiYaKXT6ap9fX01xrHPPvvsxtSpUysftEan0xGX+2jx6Pjx40ILC4uWYcOG1T/GceEJQWcXAAAAAACeSzk5OaYBAQEMwzCKgIAAJjc31/To0aPmx44ds1q6dKmzTCZTZGRk8B60XiAQ+MyZM8exT58+sl9++cVi3759QrlcrmAYRjFu3DhJY2Mji4jIycnJe+7cuY4KhULOMIzi8uXL/OzsbNO4uDi7rVu3imUymSI5Odni+++/79GnTx+ZXC5XDBw4kCkqKuISEd28eZM7cOBAqUKhkEdERLg5Ojp637p1i0tEtHnzZpG3t7dcJpMpIiIi3HQ63dO5eS8AdHYBAAAAAKDdEhMTXW7fvi3ozJr29vYNY8aMKXrUddOmTXONiIgonzVrVvnGjRttoqKiXI4dO5YfGhpa9dprr1W37uQuXbrUec2aNQ5ERHFxcQX+/v6NjY2NbKVS2bhx48abDQ0NrF69enkfOXIku0+fPk2vv/665O9//7vdsmXLbhMR2dra6jIzM69ER0fbRUdHi3ft2lUYGRl5x8LComXFihWlRER37tzhTJgwIYvNZtP69ettV6xY0fPrr7++8f777zsOHjy4dvXq1SV79uyx3Llzpy0R0aVLl/h79uwRpaSkZPF4PMPkyZNdt27dajNz5szyzrmzLzaEXQAAAAAAeC5dvnzZ/PDhw/lERFFRURXLly93ftDcth5j5nA49NZbb1USEanVar6zs3NTnz59moiI3nrrrfIvv/zSnohuExFFRERUEhH5+/s3JCUlWbe1R0FBgemYMWOc79y5Y9Lc3Mx2cXFpIiK6cOGCRWJiYh4R0dixY2ssLS1biIiSk5OF6enpApVKJSci0mg0bHt7e7R2OwnCLgAAAAAAtFtHOrBdlampqd74nq7BYHjoXD6fbyAi4nK5Bp1Ox2przsyZM11nz55dMmnSpOoDBw4IV6xY4fiw2gaDgTVu3LjyL7/8svgxLgMeAO/sAgAAAADAc8nHx6f+m2++sSYiio2NFfn5+dUREVlYWLTU1NQ8Utbp27evpri42DQ9PZ1HRBQXF2cTFBRU+7A1QqGwpba2lmP8XFtby3F1ddUSEW3fvt3GOO7v718XHx8vIiLau3evZU1NDYeIaMSIETUHDhywLi4u5hIRlZaWcnJyckwf5dzwYAi7AAAAAADQ5Wk0GrZYLO5j/PPJJ5+It2zZcj0+Pt6WYRjFzp07bTZv3lxERDRp0qSKmJiYnnK5/KE/UNWaQCAwbN269dq4ceN6MwyjYLPZtGDBgjsPWxMeHl518OBBK+MPVC1ZsuTmxIkTe/v6+nra2Njcexw5Ojr65vHjxy0VCoX84MGDPezs7LRWVlYtvr6+mqVLlxaHhIQwDMMogoODmaKiIpPHu1NgxPqzdj0AAAAAALzY1Gr1NZVKVfasz/G8amxsZHG5XIOJiQkdO3bMfObMmW5ZWVmZz/pczwO1Wm2rUqkkHVmLd3YBAAAAAACeoLy8PNO//vWvvfV6PZmYmBhiY2OvPeszvQgQdgEAAAAAAJ4gb2/vpitXrqCT+5ThnV0AAAAAAADodhB2AQAAAAAAoNtB2AUAAAAAAIBuB2EXAAAAAAAAuh2EXQAAAAAA6PIEAoFPZ9aLj4+3YhhG4e7u7iWVSr22bdtm3dFa2dnZplKp1IuI6MCBA0KhUNhXJpMpZDKZYuDAgQwR0dq1a+02bdpk87A6tbW17FGjRrkzDKOQSqVevr6+ntXV1WwiIg6H42usKZPJFNnZ2aYdPe+LAr/GDAAAAAAAL5Rz586ZLVmyxPnIkSM5MpmsOSsry3TYsGGMh4dHU1BQUMPj1vfz86s7ceJEXuuxRYsW3fmzdatWrbK3t7fXJiUlFRARqdVqnqmpqYGIiMfj6fF/8z4adHYBAAAAAOC5lJOTYxoQEMAwDKMICAhgcnNzTXU6HTk7O3vr9XoqKyvjsNls38OHD1sQEfn6+nqmp6fz1qxZ03PevHm3ZDJZMxGRTCZrnjdvXsnatWvFRET+/v6ep06dEhAR3bp1i+vk5ORNdLeD6+vr66lQKOQKhUJ+9OhR8/aedd68eY7Lli27Vz8qKsrJ29tbLpFIlMnJyRb/3cvEyclJa1yjUqmazMzMDJ11v1406OwCAAAAAEC7ZV5Z7FJflyPozJrmFkyDQr6m6FHXTZs2zTUiIqJ81qxZ5Rs3brSJiopyOXbsWL67u7vm0qVL/NzcXJ5CoWj49ddfLYYMGVJfUlJiqlQqm3JycviLFy8uaV1rwIAB9bGxsfYP28/R0VF3+vTpHIFAYEhLS+NNnDixV3p6+pX756WkpFjIZDIFEdHo0aMr1qxZU3L/HJ1Ox0pLS7uya9euHitWrHAcMWJEzrvvvlv22muvMfv27bMeNGhQzf/93/+Ve3t7NxERNTU1sY01XVxcmo4ePZr/qPfrRYOwCwAAAAAAz6XLly+bHz58OJ+IKCoqqmL58uXOREQDBw6s/eWXX4QFBQW8hQsX3vrnP/9pd+rUqTqVSlVPRGQwGFhs9v8+5Gow/HkDtbm5mfX222+7ZWZmmrHZbCosLOS1Na+tx5jvN27cuMr/nrV+4cKFpv/9e2NBQUFaYmKi5dGjRy0HDhwoP3nyZFa/fv00eIz50SHsAgAAAABAu3WkA/u0DRkypG7z5s12paWlpuvXry/esGFDz19++UUYGBhYS0TEMEzjuXPnBP379280rrlw4YLAGIa5XK6hpaWFiIgaGhpYxjkrV64U29vba//1r38V6PV6MjMz8+3oGfl8vuG/e1FLS8u9PXr06KGfMmVK1ZQpU6oiIyNp3759Pfr166fp6D4vMryzCwAAAAAAzyUfH5/6b775xpqIKDY2VuTn51dHRDRkyJD6S5cuWbDZbINAIDB4eXk1xMXF2Q0dOrSOiGjx4sUlGzZscDD+onF2drbp5s2bxR9++GEJ0d3HhC9cuGBORJSQkHDvV5qrq6s5Dg4OWg6HQ5s3b7YxBuLOcuTIEfM7d+5wiIg0Gg0rJyeHL5FImjt1kxcIOrsAAAAAANDlaTQatlgs7mP8HBUVVbply5brU6ZMkfzjH//oaWNjo4uLi7tGRGRmZmbo2bNns5+fXz0RUVBQUF1SUpLI39+/keju48IrVqy4MXLkSI/m5mZ2cXGx6cGDB7NVKlUTEdH7779fOn78+F4//PCDTVBQUI1xzzlz5twODw/vnZiYaB0YGFhrZmam78xrzMnJ4c+cOdONiEiv17NCQ0Orp0yZUtmZe7xIWO15Nh0AAAAAAF5carX6mkqlKnvW53hSpk+f7nTx4kXzkydP5hofL4auQa1W26pUKklH1qKzCwAAAAAAL7TNmzcXP+szQOfDO7sAAAAAAADQ7SDsAgAAAAAAQLeDsAsAAAAAAADdDsIuAAAAAAAAdDsIuwAAAAAAANDtIOwCAAAAAECXx2KxfMeMGeNu/KzVasna2lo1dOhQDyKihISEHh9++GHPR6k5b948xxkzZji1Hjt79qxZr169vB62zt/f3/PUqVOCR9kLnj6EXQAAAAAA6PLMzMz02dnZZnV1dSwiop9++slSLBZrjd9PmjSpetWqVSWPUnPKlCnl+/btE7Ue27Fjhyg8PLyic04NzxLCLgAAAAAAPBdCQkKqf/zxRysiop07d/5PKI2JibGJjIx0JSL69ttvraVSqZenp6fCz8/Pk4hIp9PRu+++68wwjIJhGMXKlSvtVSpVk6Wlpe748ePmxjpJSUmiyMjICiKiSZMmuSqVSrmHh4fX3LlzHZ/u1cLj4j7rAwAAAAAAwPNjzpXrLln1mk59hFdmzm/YKHct+rN5b775ZsXHH3/sMH78+KorV64I3n777fKzZ89a3D8vOjra4ciRIznu7u7asrIyDhHRunXr7AoLC3kZGRmZJiYmVFpayiEiCg8Pr0hISBAFBwfX//LLL+ZWVlY6b2/vJiKi9evXF4vF4hadTkcDBw70PH/+vFn//v0bO/Pa4clBZxcAAAAAAJ4L/fv3b7xx4wbv66+/FoWGhlY/aJ6fn1/dpEmTJOvWrbPV6XRERHT8+HHLadOm3TExMSEiIrFY3EJENGXKlIqDBw9at7S0UEJCgmjs2LH3usXfffedSKFQyBUKhSI3N5evVqv5T/YKoTOhswsAAAAAAO3Wng7skzRixIiqjz/+2OXIkSPZt2/fbjPPfP/999ePHz9unpSU1KNv375e//nPfzIMBgOxWCzD/XM9PDy0Tk5OTYcOHRIeOnTI+syZM1eIiLKyskw3bdokvnjx4hU7O7uW8PBwiUajQbPwOYJ/LAAAAAAAeG5ERUWVzZ8//6a/v/8DHyfOyMjgBQcH12/cuPGmtbW17urVq6ahoaE1W7dutdNq7/6mlfExZiKicePGVSxcuNDF1dW1qXfv3loiosrKSo6ZmZleJBK1FBUVcX/99dceT/zioFMh7AIAAAAAwHOjd+/e2o8++uj2w+bMnTvXmWEYhVQq9RowYEDtgAEDGufOnXvH2dm5WSaTeXl6eir++c9/3vsV5sjIyMq8vDx+60eYAwICGpVKZYNUKvV68803Jb6+vnVP8rqg87EMhj908gEAAAAAAO5Rq9XXVCpV2bM+B7x41Gq1rUqlknRkLTq7AAAAAAAA0O0g7AIAAAAAAEC3g7ALAAAAAAAA3Q7CLgAAAAAAAHQ7CLsAAAAAAADQ7SDsAgAAAAAAQLeDsAsAAAAAAF1efn6+SUhISG83Nzeli4uLcurUqS4ajYbVmXvMmzfP0d7evo9MJlNIpVKvhISEHp1RVyAQ+LQ1rlaref7+/p4ymUzRq1cvr4kTJ7oRER04cEAoFAr7ymQyhUwmUwwcOJDpjHO8aBB2AQAAAACgS9Pr9TRmzBiPUaNGVRUWFqYXFBSk19fXs2fPnu3U2XtNmzatNCsrK3PXrl35M2fOlLS0tLRrnVarfeS9ZsyY4free++VZmVlZV69ejVj7ty5t43f+fn51WVlZWVmZWVlnj17NueRiwPCLgAAAAAAdG379+8X8ng8/ezZs8uJiLhcLm3durVo165dttHR0XYhISG9g4KCpBKJRDl//nwH47rNmzeLvL295TKZTBEREeGm0+mI6G6nddasWU6enp4KlUolKyoq4t6/Z79+/TQcDodKSkq4OTk5pgEBAQzDMIqAgAAmNzfXlIgoPDxc8s477zj379+fmT59unN1dTV77NixEoZhFAzDKLZv325lrNfWfrdv3zZxc3NrNs7x9/dvfGI38QX0h39UAAAAAACAB1m4R+2SU1Ir6MyaTE9hw9/Hqooe9H1aWpqZSqVqaD0mEon0Dg4OzTqdjpWammqelpaWYWFhoffx8VGMHj262sLCQr9nzx5RSkpKFo/HM0yePNl169atNjNnzixvbGxkBwQE1H3xxRfF06ZNc/7iiy/s1q5de6t1/ePHj5uz2WyDg4ODLjQ01CMiIqJ81qxZ5Rs3brSJiopyOXbsWD4RUX5+Pv/MmTM5XC6XoqKinCwtLVtycnIyiYju3LnDISJ60H4zZswofeWVVxgfH5/6kJCQ6hkzZpTb2tq2EBGlpKRYyGQyBRHR6NGjK9asWVPSmff8RYCwCwAAAAAAXZrBYCAWi2V4wDgFBgbW9OzZs4WI6NVXX6389ddfLbhcriE9PV2gUqnkREQajYZtb2+vIyIyMTExTJgwoZqIyNfXt/7YsWOWxppbt24V796928bc3LwlLi7uKpvNpsuXL5sfPnw4n4goKiqqYvny5c7G+W+88UYll3s3Vp06dcryhx9+uGr8zs7OruVh+82ePbt89OjRNYmJiZb79++32r59u11mZmYm0d3HmE+cOJHXqTfyBYOwCwAAAAAA7fawDuyT4u3t3bhv3z7r1mMVFRXskpISUw6HY2Cx/vd3qlgsFhkMBta4cePKv/zyy+L763G5XAObzTb+nXQ63b0C06ZNK12xYkVpe89mYWGhN/7dGL4fZT+JRKKdM2dO+Zw5c8qlUqlXSkqKWXv3hofDO7sAAAAAANCljRo1qlaj0bA3bdpkQ0Sk0+lo+vTpLuPGjSsTCAT63377zbK0tJRTV1fHOnTokNXgwYPrRowYUXPgwAHr4uJiLhFRaWkpJycnx7Qj+/v4+NR/88031kREsbGxIj8/v7q25g0ZMqRm/fr19sbPxseYH2TPnj2WTU1NLCKi69evc6uqqjit3+GFx4OwCwAAAAAAXRqbzabExMS8vXv3Wru5uSnd3d2VPB5PHxMTU0x095Hf8ePHuyuVSq+RI0dWDho0qMHX11ezdOnS4pCQEIZhGEVwcDBTVFRk0pH9t2zZcj0+Pt6WYRjFzp07bTZv3txmd3v16tW3qqqqOFKp1MvT01Nx6NAh4cPqJicnW3p6enp5enoqhg0bxixfvvyGq6urriNnhD9iGQx/ePQdAAAAAADgHrVafU2lUpU963O0JSYmxiYlJcU8Li7u+rM+C3Q+tVptq1KpJB1Zi84uAAAAAAAAdDv4gSoAAAAAAHhuvffee+VEVP6szwFdDzq7AAAAAAAA0O0g7AIAAAAAAEC3g7ALAAAAAAAA3Q7CLgAAAAAAAHQ7CLsAAAAAANDl5efnm4SEhPR2c3NTuri4KKdOneqi0WhYT3JPgUDgQ0SUnZ1tKpVKvYzjJ06cEPj5+XlKJBKlu7u71/jx491qa2sfO1vNmzfPcdmyZeLHrQN3IewCAAAAAECXptfracyYMR6jRo2qKiwsTC8oKEivr69nz5492+lx6mq12kdeU1RUxJ00aVLv6OjoG9euXUvPz8/PGDFiRE1VVRWyVReDfxAAAAAAAOjS9u/fL+TxePrZs2eXExFxuVzaunVr0a5du2yVSqU8JSWFb5zr7+/vefr0aUFNTQ173LhxEqVSKZfL5YodO3ZYERHFxMTYhIWF9QoODvYICgpiqqur2QEBAYxCoZAzDHNv3oOsW7fO/q9//Wt5aGhoPRERm82mqVOnVrq4uOhKS0s5oaGhvRmGUahUKtn58+fNiO52bMeNGyfx9/f3dHZ29v7ss8/sjfUWL17cUyKRKAcOHMjk5ubynsT9e1Hh/9kFAAAAAID2S5zhQrczBZ1a017RQGO+LHrQ12lpaWYqlaqh9ZhIJNI7ODg0v/zyy1UJCQkiPz+/m4WFhSa3b982CQoKapg5c6bT0KFDa3788cdrZWVlHD8/P/moUaNqiIguXbpkkZqamiEWi1u0Wi0dPHgwTyQS6W/dusXt37+/LCIioorNbrsvmJmZaRYZGdnm/+u7aNEiR5VK1XDs2LH8pKQk4ZQpU9yzsrIyiYjy8vL4Z8+eza6qquLI5XLlwoUL71y4cMHsp59+EqWlpWVqtVrq27evwsfHp6Gt2vDo0NkFAAAAAIAuzWAwEIvFMrQ1HhwcXJuUlGRNRBQXF2c9cuTISiKiX3/91XLDhg0OMplMERgY6NnU1MTKy8szJSIKCgqqEYvFLUREer2eNWfOHGeGYRRDhw5lbt++bXrjxo0ONQUvXLggfPvtt8uJiEaNGlVbVVXFLS8v5xARDR8+vMrMzMzg4OCgE4lE2hs3bnBPnDhh8corr1QJhUK9SCTSDx8+vKpjdwjags4uAAAAAAC030M6sE+Kt7d34759+6xbj1VUVLBLSkpMBw0a1GBlZaU7f/682d69e0WxsbGFRHeD8J49e/JUKlVT63W//fabuUAg0Bs/x8bGisrLy7lpaWlXeDyewcnJybuxsfGBTUG5XN6YkpIimDx58h+CqcHwhzx+L6TzeLx7X3I4HNLpdKz/ft/u+wCPBp1dAAAAAADo0kaNGlWr0WjYmzZtsiEi0ul0NH36dJdx48aVCYVC/dixYytWrVrVs7a2luPv799IRDR06NCadevWifX6u7n2zJkzZm3Vrq6u5tja2mp5PJ5h//79wps3b5o+7CwLFiy4vXv3bpvjx4+bG8c2b94sun79OnfAgAG127ZtsyEiOnDggNDa2lonEon0D6oVHBxcd/DgQau6ujpWZWUl++jRow99XxgeDcIuAAAAAAB0aWw2mxITE/P27t1r7ebmpnR3d1fyeDx9TExMMRHR5MmTKw8ePCgaPXp0hXFNdHT0TZ1Ox5LJZAqpVOq1dOnSNn+5+Z133qlQq9XmSqVSvmPHDpG7u7vmYWdxcXHRxcXFXV24cKGzRCJR9urVy+u3334TWltb69esWXPz0qVLAoZhFEuWLHHavn17wcNqBQYGNrz++usVSqXS67XXXuvt7+9f15H7A21jtdVqBwAAAAAAMFKr1ddUKlXZsz4HvHjUarWtSqWSdGQtOrsAAAAAAADQ7SDsAgAAAAAAQLeDsAsAAAAAAADdDsIuAAAAAAAAdDsIuwAAAAAAANDtIOwCAAAAAABAt4OwCwAAAAAAXV5+fr5JSEhIbzc3N6WLi4ty6tSpLhqNhvUk9xQIBD5ERNnZ2aZSqdTLOP7zzz9beHt7y93d3b0kEoly9erVdp2xD3QuhF0AAAAAAOjS9Ho9jRkzxmPUqFFVhYWF6QUFBen19fXs2bNnOz1OXa1W+8hrrl+/zn3rrbfct2zZUlhQUJBx7ty5rB07dtjGxcVZPc5ZoPMh7AIAAAAAQJe2f/9+IY/H08+ePbuciIjL5dLWrVuLdu3aZatUKuUpKSl841x/f3/P06dPC2pqatjjxo2TKJVKuVwuV+zYscOKiCgmJsYmLCysV3BwsEdQUBBTXV3NDggIYBQKhZxhmHvzHmTdunX248ePLw8MDGwgInJwcNCtWrXqxoYNG3oSEYWHh0u2bdtmbZxv7No+6j7w+LjP+gAAAAAAAPD8+OjMRy55lXmCzqzpYe3R8OlfPi160PdpaWlmKpWqofWYSCTSOzg4NL/88stVCQkJIj8/v5uFhYUmt2/fNgkKCmqYOXOm09ChQ2t+/PHHa2VlZRw/Pz/5qFGjaoiILl26ZJGampohFotbtFotHTx4ME8kEulv3brF7d+/vywiIqKKzW67L3jlyhWzyMjI8tZjgYGBDXl5efw2F/yXQCDQP8o+8PhwZwEAAAAAoEszGAzEYrEMbY0HBwfXJiUlTVPXvgAAIABJREFUWRMRxcXFWY8cObKSiOjXX3+13LBhg4NMJlMEBgZ6NjU1sfLy8kyJiIKCgmrEYnELEZFer2fNmTPHmWEYxdChQ5nbt2+b3rhx44FNwQed5c886j7w+HBzAQAAAACg3R7WgX1SvL29G/ft22fdeqyiooJdUlJiOmjQoAYrKyvd+fPnzfbu3SuKjY0tJLobSvfs2ZOnUqmaWq/77bffzAUCgd74OTY2VlReXs5NS0u7wuPxDE5OTt6NjY0PbArK5fLG33//3XzSpEnVxrEzZ84IvL29G4iIuFyuoaWlhYjuvmus1WpZHdkHHh9uLgAAAAAAdGmjRo2q1Wg07E2bNtkQEel0Opo+fbrLuHHjyoRCoX7s2LEVq1at6llbW8vx9/dvJCIaOnRozbp168R6/d1ce+bMGbO2aldXV3NsbW21PB7PsH//fuHNmzdNH3aW+fPn39m1a5fN2bNnzYiISkpKOMuWLXNasmTJTSIiNze35osXLwqIiBISEqx0Oh2rI/vA40PYBQAAAACALo3NZlNiYmLe3r17rd3c3JTu7u5KHo+nj4mJKSYimjx5cuXBgwdFo0ePrjCuiY6OvqnT6VgymUwhlUq9li5d2uYvN7/zzjsVarXaXKlUynfs2CFyd3fXPOwsbm5u2m+//bbgb3/7m0QikShdXV1VUVFRt1999dU6IqJZs2bdOXv2rNDb21v+73//29zMzEzfkX3g8bEMhkd+3BwAAAAAAF4garX6mkqlKnvW5+iKVq9ebbdt2za7M2fOZNvZ2bU86/N0N2q12lalUkk6shadXQAAAAAAgA764IMP7uTk5GQi6HY9CLsAAAAAAADQ7SDsAgAAAAAAQLeDsAsAAAAAAADdDsIuAAAAAAAAdDsIuwAAAAAAANDtIOwCAAAAAECXx+FwfI3/Z25YWFiv2traDmUZgUDg0/rz8uXL7Xk8Xr/y8nJO55wUugqEXQAAAAAA6PJ4PJ4+KysrMzc3N8PExMSwbt06u86ou2fPHhulUlmfkJBg1db3Wq22M7aBZwBhFwAAAAAAniuBgYF1eXl5PCKiTz75RCyVSr2kUqnXihUr7I1zHjTeWkZGBq+hoYG9YsWK4t27d4uM4zExMTZhYWG9goODPYKCghgioo8++kisVCrlDMMo5s6d62icGxoa2tvLy0vu4eHh9fnnn9s+uauGR8V91gcAAAAAAIDnx80Pl7g05eYKOrMmTyptcFy1sqg9c7VaLf3888+Ww4cPrzl9+rTg+++/t7l48eIVg8FAvr6+8pCQkFq9Xs9qa/wvf/lLY+ta3333neiNN96oGDFiRN27777LLy4u5jo5OemIiC5dumSRmpqaIRaLW/bu3WuZl5fHT01NvWIwGCg0NNTj8OHDFmFhYXUJCQnXxGJxS11dHcvHx0cxefLkyp49e7Z05v2BjkFnFwAAAAAAurympia2TCZTeHt7K5ydnZtnz55d9uuvv1q88sorVZaWlvoePXroX3311coTJ04IHzR+f82ffvpJFBkZWcHhcCgsLKwyLi7O2vhdUFBQjVgsbiEiSk5Otjx16pSlQqFQeHl5KfLz8/lZWVl8IqI1a9aIPT09Fb6+vvKSkhKTjIwM/tO7K/Aw6OwCAAAAAEC7tbcD29mM7+y2HjMYDG3OfdB4a+fPnzcrLCzkjRgxgiEi0mq1LBcXl6YPPvjgDhGRQCDQt643Z86cWwsXLixrXePAgQPCkydPClNSUrKEQqHe39/fs7GxEQ3FLgL/EAAAAAAA8FwKDg6uO3TokFVtbS27pqaGfejQIeuhQ4fWPmi89dq4uDjR/PnzbxYXF6cVFxen3b59O7WkpMQ0JyfH9P59wsLCauLj422rq6vZREQFBQUmxcXF3KqqKk6PHj1ahEKh/vLly3y1Wm3+tK4d/hw6uwAAAAAA8FwKDAxsiIiIKO/Xr5+ciOjNN9+8Y3wv90HjRomJiaIDBw7kth4LCwur/O6770Risfh/foL5jTfeqMnIyOC/9NJLMqK7Xd+EhISC8PDw6q+++sqOYRhF7969NSqVqv5JXi88GlZ7WvwAAAAAAPDiUqvV11QqVdmfzwToXGq12lalUkk6shaPMQMAAAAAAEC3g7ALAAAAAAAA3Q7CLgAAAAAAAHQ7CLsAAAAAAADQ7SDsAgAAAAAAQLeDsAsAAAAAAADdDsIuAAAAAAB0eRwOx1cmkymkUqlXWFhYr9ra2g5lGYFA4GP8e0pKCn/AgAGMRCJRuri4KOfOnevY0tJyb+7u3bstlUqlvFevXl7u7u5e7777rnMnXAo8JQi7AAAAAADQ5fF4PH1WVlZmbm5uhomJiWHdunV2j1Ovrq6O9frrr3ssWrSo5Nq1a+mZmZmZFy9eNP/ss8/siYh+//13/vz5813j4+MLrl69mpGTk5PRq1evps65GngaEHYBAAAAAOC5EhgYWJeXl8cjIvrkk0/EUqnUSyqVeq1YscLeOOdB40Zff/21jZ+fX90bb7xRQ0QkFAr1W7Zsuf7ll1/2JCJatWpVz/nz59/y8fHREBGZmJjQ+++/f+fpXCF0Bu6zPgAAAAAAADw/fom74lJRXCfozJoiJ4uGkEh5UXvmarVa+vnnny2HDx9ec/r0acH3339vc/HixSsGg4F8fX3lISEhtXq9ntXW+F/+8pdGY52MjAx+v379GlrX9vLyatJoNOyysjJOdna22aJFi0o78zrh6ULYBQAAAACALq+pqYktk8kURET9+/evnT17dtnf//53u1deeaXK0tJST0T06quvVp44cUJoMBiorfHWYddgMLBYLNYf9jEYDE/piuBJQ9gFAAAAAIB2a28HtrMZ39ltPfagYNqewOrl5dV4+vRpi9ZjmZmZptbW1jpbW9sWhmE058+fFwQEBDQ+qAZ0bXhnFwAAAAAAnkvBwcF1hw4dsqqtrWXX1NSwDx06ZD106NDaB423Xvvuu++W//7778LExEQh0d0frJoxY4brBx98cJOI6IMPPihZv369Q2pqKo+IqKWlhT755BPx079K6Ch0dgEAAAAA4LkUGBjYEBERUd6vXz85EdGbb755x/io8oPGjSwsLAw//fRT7syZM13nzJnjVlpaajJnzpxbUVFRFURE/fv3b1yzZk3RxIkTezU2NrJZLBaFhoZWP+1rhI5j4Zl0AAAAAAB4GLVafU2lUpU963M8SfHx8VYffPCBy/Hjx7MZhml+1ueBu9Rqta1KpZJ0ZC0eYwYAAAAAgBfem2++WXXjxo00BN3uA2EXAAAAAAAAuh2EXQAAAAAAAOh2EHYBAAAAAACg20HYBQAAAAAAgG4HYRcAAAAAAAC6HYRdAAAAAADo8hYvXtzTw8PDi2EYhUwmUxw/ftz8aZ9h3rx5jvb29n1kMplCKpV6JSQk9OiMugKBwKcz6sD/4j7rAwAAAAAAADzMsWPHzH/++WertLS0TDMzM8OtW7e4TU1NrD9bp9VqycTEpFPPMm3atNIVK1aUXrp0iR8SEuI5YcIENYfD+dN1T+Is8HDo7AIAAAAAQJdWXFxsIhKJdGZmZgYiIgcHB51EItGePHlS4OPjI/P09FR4e3vLKysr2TExMTZhYWG9goODPYKCghgioo8++kisVCrlDMMo5s6d62isu3nzZpG3t7dcJpMpIiIi3HQ6HRHd7bTOmjXLydPTU6FSqWRFRUV/aBL269dPw+FwqKSkhJuTk2MaEBDAMAyjCAgIYHJzc02JiMLDwyXvvPOOc//+/Znp06c7V1dXs8eOHSthGEbBMIxi+/btVsZ6f7YfPDrcRAAAAAAAaLeft2x0KSsqFHRmTVsXt4aXo+YUPej7MWPG1KxevdpRIpEoAwMDayZOnFgREhJSP2nSpN4JCQn5gwcPbqioqGBbWFjoiYguXbpkkZqamiEWi1v27t1rmZeXx09NTb1iMBgoNDTU4/DhwxZisVi3Z88eUUpKShaPxzNMnjzZdevWrTYzZ84sb2xsZAcEBNR98cUXxdOmTXP+4osv7NauXXur9ZmOHz9uzmazDQ4ODrrQ0FCPiIiI8lmzZpVv3LjRJioqyuXYsWP5RET5+fn8M2fO5HC5XIqKinKytLRsycnJySQiunPnDoeIqD37waND2AUAAAAAgC6tR48e+vT09Mzk5GThL7/8IpwyZUrvuXPn3rK3t9cOHjy4gYhIJBLpjfODgoJqxGJxCxFRcnKy5alTpywVCoWCiKihoYGdlZXFv3z5Mis9PV2gUqnkREQajYZtb2+vIyIyMTExTJgwoZqIyNfXt/7YsWOWxtpbt24V796928bc3LwlLi7uKpvNpsuXL5sfPnw4n4goKiqqYvny5c7G+W+88UYll3s3dp06dcryhx9+uGr8zs7OruXP9oOOQ9gFAAAAAIB2e1gH9knicrn02muv1b722mu1ffr0ady6dasdi8UytDVXIBDcC74Gg4HmzJlza+HChWWt56xcudJ+3Lhx5V9++WVxG3sZ2Gz2vX11Ot2994ON7+y299zGbrPxLCzWH181fth+0HF4ZxcAAAAAALo0tVrNS0tL4xk/X7582UwqlWpKS0tNT548KSAiqqysZGu12j+sDQsLq4mPj7etrq5mExEVFBSYFBcXc0eMGFFz4MAB6+LiYi4RUWlpKScnJ8e0I+fz8fGp/+abb6yJiGJjY0V+fn51bc0bMmRIzfr16+2Nn42PMcOTgc4uAAAAAAB0aTU1NZz33nvPtaamhsPhcAwSiaTpu+++K8zJySl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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "alphas_elastic = np.logspace(-10, 6, 100)\n", + "coef_elastic = []\n", + "for i in alphas_elastic:\n", + " elastic = linear_model.ElasticNet(l1_ratio =0.6)\n", + " elastic.set_params(alpha = i)\n", + " elastic.fit(x_onehot, y_log)\n", + " coef_elastic.append(elastic.coef_)\n", + "\n", + "df_coef = pd.DataFrame(coef_elastic, index=alphas_elastic, columns=x_onehot.columns)\n", + "title = 'ElasticNet coefficients as a function of the regularization'\n", + "df_coef.plot(logx=True, title=title)\n", + "plt.xlabel('alpha')\n", + "plt.ylabel('coefficients')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### test prediction" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "best_elas_test_y = para_search_elas.best_estimator_.predict(test_onehot)\n", + "best_elas_test_y = np.expm1(best_elas_test_y)\n", + "sub = pd.DataFrame()\n", + "sub['SalePrice'] = best_elas_test_y\n", + "sub = sub.set_index(np.arange(1461, 2920))\n", + "sub.to_csv('(13) elas_log(y)_onehot_submission.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Kelly/Kaggle_house_price/Regularization Model.ipynb b/Kelly/Kaggle_house_price/Regularization Model.ipynb index 6c361f0..ce0f996 100644 --- a/Kelly/Kaggle_house_price/Regularization Model.ipynb +++ b/Kelly/Kaggle_house_price/Regularization Model.ipynb @@ -9,21 +9,41 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "## prepare data\n", + "## import data\n", "import pandas as pd\n", "import numpy as np\n", - "train_final = pd.read_csv('train_final.csv')\n", - "x = train_final.drop(['Id', 'SalePrice'], axis=1)\n", - "y = train_final['SalePrice']\n" + "\n", + "train = pd.read_csv(\"train.csv\").set_index(\"Id\")\n", + "test = pd.read_csv(\"test.csv\").set_index(\"Id\")\n", + "combine = pd.concat([train, test])\n", + "\n", + "# process data before model fitting\n", + "from preprocess1 import impute\n", + "onehot, encodedDic = impute(combine, True) # process data and onehot encode\n", + "\n", + "# seperate onehot data into train and test\n", + "train_onehot = onehot.iloc[0:1460,]\n", + "test_onehot = onehot.iloc[1460:2919,].drop('SalePrice', axis = 1) # salesprice col were all NA \n", + "\n", + "# split train data frame into x var and y var for model testing\n", + "x_onehot = train_onehot.drop('SalePrice', axis=1)\n", + "y_log = np.log(train_onehot.SalePrice) # convert y to normal distribution for regression models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### original y vs log(y) distirbution" ] }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -45,15 +65,15 @@ " )" ] }, - "execution_count": 109, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -61,54 +81,226 @@ } ], "source": [ - "import matplotlib\n", - "import matplotlib.pyplot as plt\n", - "plt.hist(y, bins=50)" + "from matplotlib import pyplot as plt\n", + "plt.hist(train_onehot.SalePrice, bins=50) # original y" ] }, { "cell_type": "code", - "execution_count": 107, - "metadata": {}, + "execution_count": 30, + "metadata": { + "scrolled": false + }, "outputs": [ { - "ename": "TypeError", - "evalue": "cannot convert the series to ", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 110\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mconverter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 111\u001b[0m raise TypeError(\"cannot convert the series to \"\n\u001b[0;32m--> 112\u001b[0;31m \"{0}\".format(str(converter)))\n\u001b[0m\u001b[1;32m 113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: cannot convert the series to " - ] + "data": { + "text/plain": [ + "(array([ 2., 2., 1., 0., 0., 0., 2., 3., 4., 3., 5.,\n", + " 1., 5., 21., 22., 23., 18., 29., 58., 56., 65., 100.,\n", + " 122., 93., 90., 82., 108., 91., 64., 55., 58., 51., 46.,\n", + " 42., 23., 29., 22., 13., 13., 13., 7., 5., 4., 1.,\n", + " 2., 2., 2., 0., 0., 2.]),\n", + " array([10.46024211, 10.52172673, 10.58321134, 10.64469596, 10.70618058,\n", + " 10.7676652 , 10.82914982, 10.89063444, 10.95211906, 11.01360367,\n", + " 11.07508829, 11.13657291, 11.19805753, 11.25954215, 11.32102677,\n", + " 11.38251138, 11.443996 , 11.50548062, 11.56696524, 11.62844986,\n", + " 11.68993448, 11.75141909, 11.81290371, 11.87438833, 11.93587295,\n", + " 11.99735757, 12.05884219, 12.12032681, 12.18181142, 12.24329604,\n", + " 12.30478066, 12.36626528, 12.4277499 , 12.48923452, 12.55071913,\n", + " 12.61220375, 12.67368837, 12.73517299, 12.79665761, 12.85814223,\n", + " 12.91962684, 12.98111146, 13.04259608, 13.1040807 , 13.16556532,\n", + " 13.22704994, 13.28853455, 13.35001917, 13.41150379, 13.47298841,\n", + " 13.53447303]),\n", + " )" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "import math\n", - "y = math.log(y)\n", - "x = math.log(x)\n", - "plt.hist(y, bins=50)" + "plt.hist(y_log, bins=50) # log(y)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ridge regularization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Ridge regularization" + "#### Find best lambda" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Id\n", + "1 61\n", + "2 0\n", + "3 42\n", + "4 307\n", + "5 84\n", + "6 350\n", + "7 57\n", + "8 432\n", + "9 205\n", + "10 4\n", + "11 0\n", + "12 21\n", + "13 176\n", + "14 33\n", + "15 389\n", + "16 112\n", + "17 0\n", + "18 0\n", + "19 102\n", + "20 0\n", + "21 154\n", + "22 205\n", + "23 159\n", + "24 110\n", + "25 90\n", + "26 56\n", + "27 32\n", + "28 50\n", + "29 258\n", + "30 87\n", + " ... \n", + "1431 40\n", + 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\n", 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "plt.scatter(train_onehot.SalePrice,train_onehot.TotalProchSF)" ] }, { "cell_type": "code", - "execution_count": 193, + "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'alpha': 0.4229242874389499}\n", - "Lowest RMSE found: 36189.27422835055\n" + "{'alpha': 0.49770235643321137}\n", + "Lowest RMSE found: 0.14572897725974193\n" ] } ], @@ -119,24 +311,109 @@ "ridge = linear_model.Ridge(normalize=True) # create a ridge regression instance\n", "\n", "# find the best alpha (lambda) for ridge\n", - "grid_param = [{'alpha': np.logspace(-2, 5,100)}]\n", + "grid_param = [{'alpha': np.logspace(-4, 2, 100)}]\n", "para_search_ridge = GridSearchCV(estimator=ridge, param_grid=grid_param, scoring='neg_mean_squared_error', cv=5, return_train_score=True)\n", - "para_search_ridge = para_search_ridge.fit(x, y)\n", + "para_search_ridge.fit(x_onehot, y_log)\n", "\n", "print(para_search_ridge.best_params_)\n", "print(\"Lowest RMSE found: \", np.sqrt(np.abs(para_search_ridge.best_score_)))\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### RMSE for all train data using best ridge" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.1212332775621472\n" + ] + } + ], + "source": [ + "# fit best ridge equation to all train data \n", + "best_ridge_y = para_search_ridge.best_estimator_.predict(x_onehot)\n", + "print(\"RMSE: \", np.sqrt(np.mean((y_log-best_ridge_y)**2)))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### RMSE across different grid search lambda" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", + " DeprecationWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", + " DeprecationWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "error = pd.DataFrame(para_search_ridge.grid_scores_)['mean_validation_score']\n", + "error = np.sqrt(np.abs(error))\n", + "alpha = pd.DataFrame(para_search_ridge.grid_scores_)['parameters'].apply(lambda x: x.get('alpha'))\n", + "plt.scatter(alpha, error)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Variable importance for best lambda" + ] + }, { "cell_type": "code", - "execution_count": 168, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -144,20 +421,20 @@ } ], "source": [ - "pd.DataFrame(list(zip(x.columns, para_search_ridge.best_estimator_.coef_))).set_index(0).sort_values(1).plot.barh()\n", - "plt.rcParams['figure.figsize'] = 4, 32" + "pd.DataFrame(list(zip(x_onehot.columns, para_search_ridge.best_estimator_.coef_))).set_index(0).sort_values(1).plot.barh()\n", + "plt.rcParams['figure.figsize'] = 4, 36" ] }, { "cell_type": "code", - "execution_count": 175, + "execution_count": 35, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -165,14 +442,14 @@ } ], "source": [ - "alpha_100 = np.logspace(-2, 5, 100)\n", + "alpha_100 = np.logspace(-4, 2, 100)\n", "coef = []\n", "for i in alpha_100:\n", " ridge.set_params(alpha = i)\n", - " ridge.fit(x, y)\n", + " ridge.fit(x_onehot, y_log)\n", " coef.append(ridge.coef_)\n", "\n", - "df_coef = pd.DataFrame(coef, index=alpha_100, columns=x.columns)\n", + "df_coef = pd.DataFrame(coef, index=alpha_100, columns=x_onehot.columns)\n", "title = 'Ridge coefficients as a function of the regularization'\n", "axes = df_coef.plot(logx=True, title=title)\n", "axes.set_xlabel('alpha')\n", @@ -180,76 +457,163 @@ "plt.rcParams['figure.figsize'] = 16, 4" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### test prediction" + ] + }, { "cell_type": "code", - "execution_count": 210, + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "best_ridge_test_y = para_search_ridge.best_estimator_.predict(test_onehot)\n", + "best_ridge_test_y = np.expm1(best_ridge_test_y)\n", + "best_ridge_test_y\n", + "sub = pd.DataFrame()\n", + "sub['SalePrice'] = best_ridge_test_y\n", + "sub = sub.set_index(np.arange(1461, 2920))\n", + "sub.to_csv('(1) ridge_log(y)_onehot_submission.csv')" + ] + }, + { + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "predict() got an unexpected keyword argument 'alpha'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mridge_pred_y\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpara_search_ridge\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m 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"metadata": {}, "source": [ - "### Lasso regularization" + "#### Find best lambda" ] }, { "cell_type": "code", - "execution_count": 192, + "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'alpha': 47.50810162102793}\n", - "Lowest RMSE found: 36824.83481246612\n" + "{'alpha': 0.0001232846739442066}\n", + "Lowest RMSE found: 0.14547505715137055\n" ] } ], "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore')\n", - "\n", "lasso = linear_model.Lasso(normalize=True) # create a ridge regression instance\n", "\n", "# find the best alpha (lambda) for lasso \n", - "grid_param = [{'alpha': np.logspace(-2, 5, 100)}]\n", + "grid_param = [{'alpha': np.logspace(-4.5, 2, 100)}]\n", "para_search_lasso = GridSearchCV(estimator=lasso, param_grid=grid_param, scoring='neg_mean_squared_error', cv=5, return_train_score=True)\n", - "para_search_lasso = para_search_lasso.fit(x, y)\n", + "para_search_lasso.fit(x_onehot, y_log)\n", "\n", "print(para_search_lasso.best_params_)\n", "print(\"Lowest RMSE found: \", np.sqrt(np.abs(para_search_lasso.best_score_)))" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### RMSE for all train data using best lasso" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.12111931813978087\n" + ] + } + ], + "source": [ + "# fit best lasso equation to all train data \n", + "best_lasso_y = para_search_lasso.best_estimator_.predict(x_onehot)\n", + "print(\"RMSE: \", np.sqrt(np.mean((y_log-best_lasso_y)**2)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### RMSE across different grid search lambda" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", + " DeprecationWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", + " DeprecationWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "error = pd.DataFrame(para_search_lasso.grid_scores_)['mean_validation_score']\n", + "error = np.sqrt(np.abs(error))\n", + "alpha = pd.DataFrame(para_search_lasso.grid_scores_)['parameters'].apply(lambda x: x.get('alpha'))\n", + "plt.scatter(alpha, error)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Variable importance for best lambda" + ] + }, { "cell_type": "code", - "execution_count": 173, + "execution_count": 41, "metadata": { "scrolled": false }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -257,20 +621,20 @@ } ], "source": [ - "pd.DataFrame(list(zip(x.columns, para_search_lasso.best_estimator_.coef_))).set_index(0).sort_values(1).plot.barh()\n", - "plt.rcParams['figure.figsize'] = 4, 32" + "pd.DataFrame(list(zip(x_onehot.columns, para_search_lasso.best_estimator_.coef_))).set_index(0).sort_values(1).plot.barh()\n", + "plt.rcParams['figure.figsize'] = 4, 36" ] }, { "cell_type": "code", - "execution_count": 182, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -278,14 +642,14 @@ } ], "source": [ - "alpha_100 = np.logspace(-2, 5, 100)\n", + "alpha_100 = np.logspace(-4.5, 2, 100)\n", "coef = []\n", "for i in alpha_100:\n", " lasso.set_params(alpha = i)\n", - " lasso.fit(x, y)\n", + " lasso.fit(x_onehot, y_log)\n", " coef.append(lasso.coef_)\n", "\n", - "df_coef = pd.DataFrame(coef, index=alpha_100, columns=x.columns)\n", + "df_coef = pd.DataFrame(coef, index=alpha_100, columns=x_onehot.columns)\n", "title = 'Lasso coefficients as a function of the regularization'\n", "axes = df_coef.plot(logx=True, title=title)\n", "axes.set_xlabel('alpha')\n", @@ -297,20 +661,4391 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Elastic net regularization" + "#### test prediction" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "best_lasso_test_y = para_search_lasso.best_estimator_.predict(test_onehot)\n", + "best_lasso_test_y = np.expm1(best_lasso_test_y)\n", + "sub = pd.DataFrame()\n", + "sub['SalePrice'] = best_lasso_test_y\n", + "sub = sub.set_index(np.arange(1461, 2920))\n", + "sub.to_csv('(2) lasso_log(y)_onehot_submission.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Elastic net regularization" ] }, { "cell_type": "code", - "execution_count": 198, + "execution_count": 16, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "{'alpha': 43.287612810830616, 'l1_ratio': 1.0}\n", - "Lowest RMSE found: 36826.45930992512\n" + "{'alpha': 0.000200923300256505, 'l1_ratio': 0.6000000000000001}\n", + "Lowest RMSE found: 0.14284287262599676\n" ] } ], @@ -318,24 +5053,226 @@ "elasticnet = linear_model.ElasticNet(normalize=True) # create a ridge regression instance\n", "\n", "# find the best alpha (lambda) for lasso \n", - "grid_param = [{'alpha': np.logspace(-2, 4, 100), 'l1_ratio': np.arange(0, 1.1, 0.1)}]\n", + "grid_param = [{'alpha': np.logspace(-10, 6, 100), 'l1_ratio': np.arange(0, 1.1, 0.1)}]\n", "para_search_elas = GridSearchCV(estimator=elasticnet, param_grid=grid_param, scoring='neg_mean_squared_error', cv=5, return_train_score=True)\n", - "para_search_elas = para_search_elas.fit(x, y)\n", + "para_search_elas.fit(x_onehot, y_log)\n", "\n", "print(para_search_elas.best_params_)\n", "print(\"Lowest RMSE found: \", np.sqrt(np.abs(para_search_elas.best_score_)))\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### RMSE for all train data using best lasso" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 1.361468015951288e-15\n" + ] + } + ], + "source": [ + "# fit best elastic net equation to all train data \n", + "best_elas_y = para_search_elas.best_estimator_.predict(x_onehot)\n", + "print(\"RMSE: \", np.sqrt(np.mean(y_log-best_elas_y)**2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### RMSE across different grid search lambda" + ] + }, { "cell_type": "code", - "execution_count": 199, + "execution_count": 18, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", + " DeprecationWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", + " DeprecationWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "error = pd.DataFrame(para_search_elas.grid_scores_)['mean_validation_score']\n", + "error = np.sqrt(np.abs(error))\n", + "alpha = pd.DataFrame(para_search_elas.grid_scores_)['parameters'].apply(lambda x: x.get('alpha'))\n", + "plt.scatter(alpha, error)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, { "data": { - "image/png": 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\n", 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MwWDgS6VSOcuyPQUFBVczMzMlhw4dcu7r6+PNnTu3Kycn55pQKCSRSDRn48aNzadPn7bft29f/a5du9z2799fFxwc3J2VlSVJS0ubynEcb9GiRfpDhw5piegnMUuXLu0cKL+8vDzJ/v376xISEmZcvXrVysvLq89oNFJMTIznxYsXJ/B4PC42Nlb3yiuv3CAiOnr0qMN//dd/Te/s7BQcOXLk6sKFC7ubmpqslixZ0maZc968eT2W911dXYKIiIgZly5dsg0ICOg+ceLEVT6fT25ubgHPPPNMy6effjrJaDTyjh07dmXOnDm9DQ0NwqefftpLr9cLZ8+e3X3mzBl7pVJZ7eLiYhz5rzb2YRszAAAAAACMeZbi1fJ64403xA4ODqb09PTrCQkJXkeOHBHr9XphUlKSLjMzU2vpBBcUFFw9f/68TX5+vuTcuXNqtVpdxefzucOHDzsQEfX09PD9/f17Ll68qL6zaK2trbXavXu325kzZzRVVVWVFy5cmJCbmzt5qJg7Xb582Uqn01ktXLiwOyoqqjU7O1tCRPTVV1+JGhsbrb777rtKjUZT9etf/7rFEtPd3c2/cOGCOiMj49rGjRu9iIh+/etf3/h//+//ec6bN499+eWXp9bW1lpZxldXV9sePHiw7vLly5XXr19nioqKJlruOTo6Gquqqqqfe+6571NTU52JiJKTk11DQkI6qqqqqleuXNna2NhofW9/pbEFnV0AAAAAABi20XRg74XBtjGvWLGi/fjx4+Lt27d7KJXKyoFiT506ZVdRUSFSKBQyIqLe3l6+k5OTkYhIIBDQunXrWvvHlJaWTpg/f36Hq6urkYgoJibmZnFx8cT4+Hj9YDF3ys7OlkRFRbUSEcXHx99cv3695+7du5ulUqmhrq6OSUhIcI+MjGxbsWJFuyVmzZo1N4mIli1b1tnZ2cnX6XSC6Ojo9scff7z8o48+mnTq1KlJgYGB8vLy8koiooCAgC5vb+8+IiI/P7/umpoa6zvmaiUiCgoK6i4oKBATEX3zzTcTT5w4cZmI6Omnn263t7c3DfUdHnQodgEAAAAA4IFlMplIo9HYMAxj1ul0QkvxdyeO43irVq1qOXjwoLb/PWtra/NAZ245jht0zcFi7vTBBx9IdDqd1YcffighIrpx44ZVeXk5ExAQYKioqKj66KOP7DMzM52OHTsmycvLqyUi4vF4P5rD8tnZ2dm0adOmm5s2bbq5cOHCmX//+98nOjo6mhiG+SFJgUBARqPxhwlsbGw4IiKhUMhZrg/1ncYjbGMGAAAAAIAH1p49e5xZlu3Nzs6+sn79ek+DwcAjul3kWd5HRES0FxYWirVarZCIqLm5WaDRaIbcwhscHNxVVlZm19jYKDQajZSXlycJDQ0dcMtyfyqViunu7hbcuHHjolarLddqteVbtmxpysnJkTQ2NgpNJhOtW7dO//rrr2vLy8t/OP/8/vvvi4mIPv3004l2dnYmBwcHU0FBgV1HRwefiKi1tZV/7do1xsvL69Zo/lZBQUGdubm5EiKiDz/80L69vV0wmnkeFOjsAgAAAADAmGc5s2v5HBYW1rZp0yZdbm6uo1KprBaLxeb8/PyO5ORkl/T09IbY2NjvZTKZ3N/fv7ugoODqzp07teHh4azZbCYrKysuIyPjOsuygxaNHh4efSkpKdqQkBCW4zheeHh4W1xcnH44uWZnZzssX778R9ucV69e3bpmzZoZK1eu1K9fv97TbDbziIj27NlTbxkjFotNc+bMkVoeUEVE9I9//EP04osvThcIBBzHcbz4+HhdSEhId2Fhod1I/4apqakNTz/99Ay5XC7+5S9/2TllypS+yZMnj9utzLyHrZUNAAAAAAAjo1KpahUKxaj+6xwYO3p6enhCoZCzsrKizz77bMKWLVs8RvLfOd0PKpXKUaFQeI4mFp1dAAAAAACAh8Dly5etn3nmGW9LdzsrK6v2fuf074RiFwAAAAAAYJQWL17sXVdXx9x5be/evfXR0dHtg8XcLwEBAYbq6uox3cm9l1DsAgAAAAAAjFJRUVHN/c4BBoanMQMAAAAAAMC4g2IXAAAAAAAAxh0UuwAAAAAAADDuoNgFAAAAAIAxTyAQBEqlUrnltWPHjqlDjU9OTh7y/mBiYmI8lEqlzeiy/LGGhgahUCicu2/fPsfRzvGb3/zGNSUlxXmgey+//PLUmTNn+rEsK5dKpfLTp09PICJyc3MLaGxsfOifz/TQ/wEAAAAAAGDsYxjGPJL/EzYjI8MlNTW1aSRrGI1GOnbs2LWRxgiFA5dVOTk5YoVC0ZWXl+fw0ksv3dP/p/izzz6b8Omnn04uLy+vsrW15RobG4UGg4F3L9d40KGzCwAAAAAAD6SWlhaBp6env0qlYoiIIiMjvdLS0hwTExPdDAYDXyqVyqOioryIiDIzMyUBAQEyqVQqX7NmjYfRaCQiIpFINGfbtm2us2bNkn7++ecTg4KCfEtKSkRERFlZWRKWZeU+Pj5+mzdvdrOs2z9msPzy8vIk+/fvr2tqarK6evWqFdHt4jg6OtrTx8fHj2VZ+auvvupERBQUFOT73HPPuc+ZM0fq4+Pj98UXX4gs81RXV9sGBQX5Tps2LeD11193IiLSarVWEonEaGtryxERubi4GD09PfssMX/84x+d5HK5jGVZ+YULF2yIbneJV61a5dl/rvEKnV0AAAAAABi2EydOuN+4cUN095HD5+Tk1P3UU0/VDTXGUrxaPiclJTVu2LChNT09/XpCQoJXYmJis16vFyYlJemIiI4ePepk6QSfP3/eJj8/X3Lu3Dk1wzBcXFzc9MOHDzts2bKlpaenh+/v799z4MCBBiKiXbt2ERFRbW2t1e7du92USmX1lClTjAsWLGBzc3Mnx8fH6/vHDOTy5ctWOp3OauHChd1RUVGt2dnZkt27dzd/9dVXosbGRqvvvvuukohIp9MJLDHd3d38CxcuqP/2t79N3Lhxo5dlzOXLl22+/PLLS3q9XiCTyfxfeuml75966qn2//7v/3b19PT0f/zxx9ufffbZm0888USnZS5HR0djVVVVdWpq6pTU1FRnS8d6oLkYhuFG/KM9ANDZBQAAAACAMc+yjdny2rBhQysR0YoVK9plMlnP9u3bPY4ePVo7UOypU6fsKioqRAqFQiaVSuWlpaX2V65cYYiIBAIBrVu3rrV/TGlp6YT58+d3uLq6Gq2srCgmJuZmcXHxxKFi7pSdnS2JiopqJSKKj4+/mZ+fLyEikkqlhrq6OiYhIcE9Pz/fXiwWmywxa9asuUlEtGzZss7Ozk6+pRBesmSJ3tbWlnNxcTFKJJK++vp64aRJk8wVFRVVf/7zn69NmTLFmJCQ4J2RkeFwx1ytRERBQUHddXV1jOX6QHMN6wd4AI3bLwYAAAAAAPfe3Tqw/2kmk4k0Go0NwzBmnU4n9Pb27us/huM43qpVq1oOHjyo7X/P2traPNCZW44bvNk5WMydPvjgA4lOp7P68MMPJUREN27csCovL2cCAgIMFRUVVR999JF9Zmam07FjxyR5eXm1REQ83o+P3Fo+39l5FQgEZDQaeUREQqGQnnzyyY4nn3yyY9asWT25ubkOW7dubSEisrGx4f45hrOMH2qu8QidXQAAAAAAeGDt2bPHmWXZ3uzs7Cvr16/3tDykSSgUcpb3ERER7YWFhWKtViskImpubhZoNBrroeYNDg7uKisrs2tsbBQajUbKy8uThIaGdg4VY6FSqZju7m7BjRs3Lmq12nKtVlu+ZcuWppycHEljY6PQZDLRunXr9K+//rq2vLz8hy3h77//vpiI6NNPP51oZ2dncnBwMA21Rnl5+Q8d2wsXLthOmzbt1nDye1igswsAAAAAAGNe/zO7YWFhbZs2bdLl5uY6KpXKarFYbM7Pz+9ITk52SU9Pb4iNjf1eJpPJ/f39uwsKCq7u3LlTGx4ezprNZrKysuIyMjKusyw7aHHo4eHRl5KSog0JCWE5juOFh4e3xcXF6YeTa3Z2tsPy5ct/tM159erVrWvWrJmxcuVK/fr16z3NZjOPiGjPnj31ljFisdg0Z84caWdnp+DIkSNXh1qjvb1dsHXr1unt7e0CgUDAeXp6GrKzs0f0JOnxjjdUex4AAAAAAEClUtUqFIp7+l/nwI8FBQX57t+/vy44OLj7fucylqhUKkeFQuE5mlhsYwYAAAAAAIBxB9uYAQAAAAAARmnx4sXedz7tmIho79699dHR0e0jmeebb765dG8zAxS7AAAAAAAAo1RUVFRzv3OAgWEbMwAAALhlhEEAACAASURBVAAAAIw7KHYBAAAAAABg3EGxCwAAAAAAAOMOil0AAAAAAAAYd1DsAgAAAADAmCcQCAKlUqnc8tqxY8fUocYnJycPeX8wMTExHkql0mZ0Wf5LUFCQr6enp79UKpXPmDHDb//+/Y6DjXVzcwtobGz8ycODr1+/LnzyySdnuLu7+3t7e/uFhITMvHjxIjPQHPBTeBozAAAAAACMeQzDmNVqddVwx2dkZLikpqY2jWQNo9FIx44duzbSGKFw4LIqJyfnSnBwcHdzc7PAx8cnYMuWLS02NjZc//iBmM1mioqKmrlmzZqWwsLCK0REX375pW1DQ4PVrFmzDCPJ8WGFzi4AAAAAADyQWlpaBJ6env4qlYohIoqMjPRKS0tzTExMdDMYDHypVCqPioryIiLKzMyUBAQEyKRSqXzNmjUeliJTJBLN2bZtm+usWbOkn3/++cSgoCDfkpISERFRVlaWhGVZuY+Pj9/mzZvdLOv2j7lbnu3t7QJbW1uzUCjkhorv7OzkLViwwCctLc2xsLDQTigUctu3b//ecv/RRx/tiYiI6DSbzfTCCy9M8/Hx8WNZVv7GG2+IiYgKCwvtHnnkEd/ly5fP8PT09E9MTHQ7dOiQJCAgQMayrLyyspIhIoqOjvaMjY2dPm/ePHbatGkBn3zyycRVq1Z5zpgxwy86Otrz5/8yYwM6uwAAAAAAMGxV1S+7d3VqRPdyzgkT2W657A91Q42xFK+Wz0lJSY0bNmxoTU9Pv56QkOCVmJjYrNfrhUlJSToioqNHjzpZOsHnz5+3yc/Pl5w7d07NMAwXFxc3/fDhww5btmxp6enp4fv7+/ccOHCggYho165dRERUW1trtXv3bjelUlk9ZcoU44IFC9jc3NzJ8fHx+v4xg1m7du0Ma2tr8/Xr121ee+2165YO8EDx7e3t/Ojo6Blr1qxp2bJlS8vrr7/upFAougeaNycnZ3J5ebltdXV1ZWNjozAoKEi2ZMmSTiIitVptm5+ff8XJycno4eERwDCMrry8vPq1115zSktLc3r77bfriIja2tqEX331lea9996bHBMT43P69Gl1YGBgz6xZs2Rffvml7aOPPtpzt99trEOxCwAAAAAAY95g25hXrFjRfvz4cfH27ds9lEpl5UCxp06dsquoqBApFAoZEVFvby/fycnJSEQkEAho3bp1rf1jSktLJ8yfP7/D1dXVSEQUExNzs7i4eGJ8fLx+sJj+LNuYGxoahL/85S+lv/rVr9pZlr01UHxUVNTMbdu2NW3evPnm3eY9e/as3TPPPHNTKBSSu7u7cd68eZ2lpaWiSZMmmQMCAro8PDz6iIimT59uWLZsWRsRkUKh6CkuLrazzPHEE0/o+Xw+zZ07t9vBwaEvKCioh4iIZdmempoaBsUuAAAAAAA8VO7Wgf1PM5lMpNFobBiGMet0OqG3t3df/zEcx/FWrVrVcvDgQW3/e9bW1uaBztxyHPeTa3eLGYyrq6vR39+/u6SkZALLsrcGin/kkUc6T506NemFF164yefzKSAgoOfEiRPigeYbKjeGYX64yefzyXJGmM/nk8lk4lnuWa4LBAKytrb+UYzRaOTROIAzuwAAAAAA8MDas2ePM8uyvdnZ2VfWr1/vaTAYeEREQqGQs7yPiIhoLywsFGu1WiERUXNzs0Cj0VgPNW9wcHBXWVmZXWNjo9BoNFJeXp4kNDS0czQ5dnR08CsrK0W+vr6DPlhq3759DRKJxBgfHz+diCgyMrLj1q1bvLS0tB+e4lxcXCz65JNPJoaEhHTk5+dLjEYjNTQ0CL/55puJCxYs6BpNbuMZil0AAAAAABjzLGd2La/ExES3ixcvMrm5uY6ZmZl1ERERnfPnz+9ITk52ISKKjY39XiaTyaOiorwCAwN7d+7cqQ0PD2dZlpWHhYWxdXV1VkOt5+Hh0ZeSkqINCQlhZTKZ36xZs7rj4uL0I8l57dq1M6RSqVyhUMhWr16tW7BgwYBncC3eeuutOoPBwN+0adM0Pp9PBQUFNZ9//rm9u7u7/8yZM/1eeeUV1+nTp/fFx8fr/fz8emQymV9oaCj76quv1k+fPn3gxzo/xHhDtcABAAAAAABUKlWtQqHQ3e884OGjUqkcFQqF52hi0dkFAAAAAACAcQcPqAIAAAAAABilxYsXe9fV1TF3Xtu7d299dHR0+/3KCW5DsQsAAAAAADBKRUVFNfc7BxgYtjEDAAAAAADAuINiFwAAAAAAAMYdFLsAAAAAAAAw7qDYBQAAAAAAgHEHxS4AAAAAAIx5AoEgUCqVyi2vHTt2TB1qfHJy8pD3BxMTE+OhVCptRpflvwQFBfl6enr6S6VS+YwZM/z279/vONhYNze3gMbGxp88PLj/d7506ZL1z83rYYKnMQMAAAAAwJjHMIxZrVZXDXd8RkaGS2pqatNI1jAajXTs2LFrI40RCgcuq3Jycq4EBwd3Nzc3C3x8fAK2bNnSYmNjw/WPH8xIvzP8GDq7AAAAAADwQGppaRF4enr6q1QqhogoMjLSKy0tzTExMdHNYDDwpVKpPCoqyouIKDMzUxIQECCTSqXyNWvWeFiKTJFINGfbtm2us2bNkn7++ecTg4KCfEtKSkRERFlZWRKWZeU+Pj5+mzdvdrOs2z/mbnm2t7cLbG1tzUKhkBsqvrOzk7dgwQKftLS0QbvAly5dsg4MDPSVy+UyuVwuKyoqmmC5t3PnTmeWZeW+vr7yxMRENyKiyspKZsGCBT5+fn6ywMBA3wsXLvzsrvWDAp1dAAAAAAAYtm3V193VXb2iezmndIJN9wHZ9LqhxliKV8vnpKSkxg0bNrSmp6dfT0hI8EpMTGzW6/XCpKQkHRHR0aNHnSxd0fPnz9vk5+dLzp07p2YYhouLi5t++PBhhy1btrT09PTw/f39ew4cONBARLRr1y4iIqqtrbXavXu3m1KprJ4yZYpxwYIFbG5u7uT4+Hh9/5jBrF27doa1tbX5+vXrNq+99tp1Swd4oPj29nZ+dHT0jDVr1rRs2bKlpf93dnd3NxQVFdW4uroaz549qxGJRFx5eTnz7LPPzqioqKg+fvy4/SeffCJWKpVqOzs7c3Nzs4CI6Pnnn/c4cuTItYCAAMPp06cnbN68efrXX3+tGelv9CBCsQsAAAAAAGPeYFt6V6xY0X78+HHx9u3bPZRKZeVAsadOnbKrqKgQKRQKGRFRb28v38nJyUhEJBAIaN26da39Y0pLSyfMnz+/w9XV1UhEFBMTc7O4uHhifHy8frCY/izbmBsaGoS//OUvpb/61a/aWZa9NVB8VFTUzG3btjVt3rz55lDf+datW7z169d7VFVV2fL5fLp27RpDRFRUVGQfFxens7OzMxMROTs7m9ra2vgXLlyYuGrVKu874++W93iBYhcAAAAAAIbtbh3Y/zSTyUQajcaGYRizTqcTent79/Ufw3Ecb9WqVS0HDx7U9r9nbW1tHujMLcdxP7l2t5jBuLq6Gv39/btLSkomsCx7a6D4Rx55pPPUqVOTXnjhhZt8/uCnTffu3evs5OTU98EHH1w1m81ka2sbaMmXx/txHWsymcjOzs74sJ77xZldAAAAAAB4YO3Zs8eZZdne7OzsK+vXr/c0GAw8IiKhUMhZ3kdERLQXFhaKtVqtkIioublZoNFohnyycXBwcFdZWZldY2Oj0Gg0Ul5eniQ0NLRzNDl2dHTwKysrRb6+vobBxuzbt69BIpEY4+Pjpw81V1tbm8DFxaVPIBBQZmamg8lkIqLb3zE3N9exo6ODT3T7O0okEvO0adNuvf3222IiIrPZTF999ZXtaL7DgwjFLgAAAAAAjHmW86uWV2JiotvFixeZ3Nxcx8zMzLqIiIjO+fPndyQnJ7sQEcXGxn4vk8nkUVFRXoGBgb07d+7UhoeHsyzLysPCwti6ujqrodbz8PDoS0lJ0YaEhLAymcxv1qxZ3XFxcfqR5Lx27doZUqlUrlAoZKtXr9YtWLCge6jxb731Vp3BYOBv2rRp2mBjtm3bduP99993UCgUUo1GY2Nra2smInr66afbly1bpp89e7ZMKpXKX3vttalERO+///6V//3f/3X09fWV+/j4+H3wwQeTR/IdHmS8odrzAAAAAAAAKpWqVqFQ6O53HvDwUalUjgqFwnM0sejsAgAAAAAAwLiDB1QBAAAAAACM0uLFi73r6uqYO6/t3bu3Pjo6uv1+5QS3odgFAAAAAAAYpaKiopr7nQMMDNuYAQAAAAAAYNxBsQsAAAAAAADjDopdAAAAAAAAGHdQ7AIAAAAAAMC4g2IXAAAAAADGPIFAECiVSuWW144dO6YONT45OXnI+4OJiYnxUCqVNqPL8l8MBgMvMTHRzcPDw9/Hx8cvICBAdvz4cXsiIjc3twCWZeVSqVTOsqz8nXfemWyJE4lEc0azXlBQkG9JSYno5+Y9nuBpzAAAAAAAMOYxDGNWq9VVwx2fkZHhkpqa2jSSNYxGIx07duzaSGOEwp+WVS+++KJrU1OTlVqtrrS1teXq6uqEn376qZ3lfnFxscbFxcWoUqmYZcuWsXFxcfqRrAt3h84uAAAAAAA8kFpaWgSenp7+KpWKISKKjIz0SktLc0xMTHQzGAx8qVQqj4qK8iIiyszMlAQEBMikUql8zZo1HkajkYhud1K3bdvmOmvWLOnnn38+8c4OaVZWloRlWbmPj4/f5s2b3Szr9o/pn1dHRwf/vffem/Lmm29et7W15YiI3N3djc8//3xr/7F6vV5gb29v6n/dbDbTCy+8MM3Hx8ePZVn5G2+8Ibbc27lzpzPLsnJfX195YmKi251xJpOJVq5c6bl161bXUf5Zxw10dgEAAAAAYNheyle5a5o67ul2WXaqXfe+pxV1Q42xFK+Wz0lJSY0bNmxoTU9Pv56QkOCVmJjYrNfrhUlJSToioqNHjzpZOsHnz5+3yc/Pl5w7d07NMAwXFxc3/fDhww5btmxp6enp4fv7+/ccOHCggYho165dRERUW1trtXv3bjelUlk9ZcoU44IFC9jc3NzJ8fHx+v4x/VVVVTEuLi63JBKJebDvExISwnIcx6uvr7d+++23r/S/n5OTM7m8vNy2urq6srGxURgUFCRbsmRJZ1lZme0nn3wiViqVajs7O3Nzc7PAEtPX18d76qmnvORyec8f/vCHEXW1xyMUuwAAAAAAMOYNto15xYoV7cePHxdv377dQ6lUVg4Ue+rUKbuKigqRQqGQERH19vbynZycjEREAoGA1q1b95OOa2lp6YT58+d3uLq6GomIYmJibhYXF0+Mj4/XDxYzEpZtzJWVlcySJUvY5cuXV06aNOmH4vjs2bN2zzzzzE2hUEju7u7GefPmdZaWlorOnDljFxcXp7OzszMTETk7O//QFU5MTPR46qmnbqLQvQ3FLgAAAAAADNvdOrD/aSaTiTQajQ3DMGadTif09vbu6z+G4zjeqlWrWg4ePKjtf8/a2to80JlbjuMGXXOwGAu5XG5obGy0bm1t5YvF4kG7u0REfn5+BgcHh77z58/bLFy4sPtu63McRzweb8B7v/jFLzrPnj1r393d3SwSiQb/Ag8JnNkFAAAAAIAH1p49e5xZlu3Nzs6+sn79ek+DwcAjIhIKhZzlfURERHthYaFYq9UKiYiam5sFGo3Geqh5g4ODu8rKyuwaGxuFRqOR8vLyJKGhoZ3DycnOzs68evVq3YYNG6b39vbyiIiuXbtmlZmZKek/VqvVCuvr65mZM2feuvN6SEhIR35+vsRoNFJDQ4Pwm2++mbhgwYKuiIiI9tzcXMeOjg6+5btYYl544QXdkiVL2p588knvvr6f1PwPHXR2AQAAAABgzOt/ZjcsLKxt06ZNutzcXEelUlktFovN+fn5HcnJyS7p6ekNsbGx38tkMrm/v393QUHB1Z07d2rDw8NZs9lMVlZWXEZGxnWWZW8Ntp6Hh0dfSkqK1nK2Njw8vG0kT0w+cOCAdtu2bW4sy/oxDMPZ2tqaXnnllR/O+IaEhLB8Pp+MRiMvJSWl3t3d3XhnfHx8vP7LL7+cKJPJ/Hg8Hvfqq6/WT58+3Th9+vT28+fPi2bPni2zsrLiFi1a1PbnP//5h4717t27m1988UXBypUrvU6cOHFVIBDQw4o3VHseAAAAAABApVLVKhQK3f3OAx4+KpXKUaFQeI4mFtuYAQAAAAAAYNzBNmYAAAAAAIBRWrx4sXddXR1z57W9e/fWR0dHt9+vnOA2FLsAAAAAAACjVFRUVHO/c4CBYRszAAAAAAAAjDsodgEAAAAAAGDcQbELAAAAAAAA4w6KXQAAAAAAABh3UOwCAAAAAMCYJxAIAqVSqdzy2rFjx9ShxicnJw95fzAxMTEeSqXSZnRZ/ovBYOAlJia6eXh4+Pv4+PgFBATIjh8/bv9z5x3Knj17nDo6On6o8dzc3AJYlpVLpVI5y7Lyd955Z/JAcb/5zW9cU1JSnP+dud0PeBozAAAAAACMeQzDmNVqddVwx2dkZLikpqY2jWQNo9FIx44duzbSGKHwp2XViy++6NrU1GSlVqsrbW1tubq6OuGnn35qN5K5RyorK8t5w4YNN+3s7MyWa8XFxRoXFxejSqVili1bxsbFxen/nTmMJejsAgAAAADAA6mlpUXg6enpr1KpGCKiyMhIr7S0NMfExEQ3g8HAl0ql8qioKC8ioszMTElAQIBMKpXK16xZ42E0GomISCQSzdm2bZvrrFmzpJ9//vnEoKAg35KSEhERUVZWloRlWbmPj4/f5s2b3Szr9o/pn1dHRwf/vffem/Lmm29et7W15YiI3N3djc8//3wrEdGHH35oP3v2bKlcLpctW7ZsRltbG5/odid2y5YtbrNnz5b6+/vLSktLRY8//riPu7u7/x//+McpRESFhYV2QUFBvhERETO8vLz8oqKivMxmM73++utON27csAoJCWHnzZvH9s9Jr9cL7O3tTZbPL7/88lRPT0//Rx99lP3uu++Y/uPHA3R2AQAAAABg+E782p1uVInu6ZxO8m566mDdUEMsxavlc1JSUuOGDRta09PTryckJHglJiY26/V6YVJSko6I6OjRo06WTvD58+dt8vPzJefOnVMzDMPFxcVNP3z4sMOWLVtaenp6+P7+/j0HDhxoICLatWsXERHV1tZa7d69202pVFZPmTLFuGDBAjY3N3dyfHy8vn9Mf1VVVYyLi8stiURi7n+vsbFR+Pvf/96lpKREY29vb/7d73439bXXXnPev39/IxGRu7v7rW+//Va9fv169+eee86zrKxM/c/1/LZv3/49EVF1dbXtt99+e8XT07MvMDBQWlRUNHHnzp03Dh065Gzp5FrWCwkJYTmO49XX11u//fbbV4iIzp49K/roo48k5eXlVX19fTR79mz5nDlzukf0mz0AUOwCAAAAAMCYN9g25hUrVrQfP35cvH37dg+lUlk5UOypU6fsKioqRAqFQkZE1Nvby3dycjISEQkEAlq3bl1r/5jS0tIJ8+fP73B1dTUSEcXExNwsLi6eGB8frx8sZjjOnDkzoaamxiYoKEhKRNTX18cLDAzstNx/5pln9EREAQEB3V1dXXyxWGwWi8VmhmHMOp1O8M97Xd7e3n1ERH5+ft01NTXWg61nKX4rKyuZJUuWsMuXL6/84osvJi5fvlxv2e68ZMmScbm1GcUuAAAAAAAM3106sP9pJpOJNBqNzT+LQaGlCLwTx3G8VatWtRw8eFDb/561tbV5oDO3HMcNuuZgMRZyudzQ2Nho3drayheLxT/q7nIcR48//nj7xx9/fHWgWBsbG46IiM/nk7W19Q9J8Pl86uvr4xERMQzzw3WBQEBGo5E3aDL/5OfnZ3BwcOg7f/68DRERj3fXkAcezuwCAAAAAMADa8+ePc4sy/ZmZ2dfWb9+vafBYOAREQmFQs7yPiIior2wsFCs1WqFRETNzc0CjUYzaDeUiCg4OLirrKzMrrGxUWg0GikvL08SGhraOVSMhZ2dnXn16tW6DRs2TO/t7eUREV27ds0qMzNTEhoa2nXu3LmJFRUVDNHt870XL168J2dmJ0yYYLKc/+1Pq9UK6+vrmZkzZ94KCwvr/OSTTyZ3dnbyWltb+UVFRQM+pflBh84uAAAAAACMef3P7IaFhbVt2rRJl5ub66hUKqvFYrE5Pz+/Izk52SU9Pb0hNjb2e5lMJvf39+8uKCi4unPnTm14eDhrNpvJysqKy8jIuM6y7K3B1vPw8OhLSUnRWs68hoeHt43kScYHDhzQbtu2zY1lWT+GYThbW1vTK6+80uDq6mrMysqqXb169Yxbt27xiIheeeUV7axZsww/7y9ElJCQoFu2bJmPk5NTX1lZmYbo9pldPp9PRqORl5KSUu/u7m50d3c3rlix4qa/v7+fm5ubISgoaFhF/IOGN1R7HgAAAAAAQKVS1SoUCt39zgMePiqVylGhUHiOJhbbmAEAAAAAAGDcwTZmAAAAAACAUVq8eLF3XV3dj87c7t27tz46Orr9fuUEt6HYBQAAAAAAGKWioqKa+50DDAzbmAEAAAAAAGDcQbELAAAAAAAA4w6KXQAAAAAAABh3UOwCAAAAAADAuINiFwAAAAAAxjyBQBAolUrllteOHTumDjU+OTl5yPuDiYmJ8VAqlTajy/JfgoKCfEtKSkSWz5cuXbL28fHxu9tYNze3AJZl5VKpVM6yrPydd96Z/HNzGUhhYaHdwoULZ/475h4r8DRmAAAAAAAY8xiGMavV6qrhjs/IyHBJTU1tGskaRqORjh07dm2kMULhvS2riouLNS4uLkaVSsUsW7aMjYuL0//cOf8deY51D9e3BQAAAACAn2XX/+1yv9x6WXT3kcM3Uzyz+7XHXqsbaVxLS4sgMDBQdvLkye8UCoUhMjLSKzQ0tKOmpoYxGAz8f3ZHewoKCq5mZmZKDh065NzX18ebO3duV05OzjWhUEgikWjOxo0bm0+fPm2/b9+++l27drnt37+/Ljg4uDsrK0uSlpY2leM43qJFi/SHDh3SEtFPYpYuXdo5krw7Ozt5q1ev9tJoNDY+Pj69vb29vIHG6fV6gb29vcnyeffu3c7vvvuuIxFRfHz89ykpKTeIiBYtWuTd2NhobTAY+Js2bWr+7W9/qxsoz46ODv5LL73kLpFIjAEBAd0j/Xs/aFDsAgAAAADAmGcpXi2fk5KSGjds2NCanp5+PSEhwSsxMbFZr9cLk5KSdERER48edbJ0gs+fP2+Tn58vOXfunJphGC4uLm764cOHHbZs2dLS09PD9/f37zlw4EADEdGuXbuIiKi2ttZq9+7dbkqlsnrKlCnGBQsWsLm5uZPj4+P1/WMGs3bt2hk2NjZmIqK+vj4en3/7FOn+/fudbG1tzRqNpqqsrMz2sccek98ZFxISwnIcx6uvr7d+++23rxARnT17VvTee+85KJXKao7jKDAwUBYeHt7x2GOP9bz77ru1zs7Ops7OTt6cOXPkcXFxrVOnTjXdmWd3dzdvxowZAUVFRZf8/PwMTz755Ix79duMVSh2AQAAAABg2EbTgb0XBtvGvGLFivbjx4+Lt2/f7qFUKisHij116pRdRUWFSKFQyIiIent7+U5OTkYiIoFAQOvWrWvtH1NaWjph/vz5Ha6urkYiopiYmJvFxcUT4+Pj9YPF9JeTk3MlODi4m+j2md0nn3zS559zT9y6desNIqJ58+b1sCz7oy6rZRtzZWUls2TJEnb58uWVZ86cmbh8+XK9vb29mYjoiSeeaP3iiy/sHnvssZ4//OEPzp988slkIqKmpiaryspKm6lTp3bdmee3335rM23aNENAQICBiCg2NrblzTffnHK37/AgQ7ELAAAAAAAPLJPJRBqNxoZhGLNOpxN6e3v39R/DcRxv1apVLQcPHtT2v2dtbW0e6Cwrx3GDrjlYzEjweAPuXP4RPz8/g4ODQ9/58+dtBsunsLDQrri42O7cuXNqOzs7c1BQkG9PTw9/oDyHs+Z4gqcxAwAAAADAA2vPnj3OLMv2ZmdnX1m/fr2nwWDgEREJhULO8j4iIqK9sLBQrNVqhUREzc3NAo1GYz3UvMHBwV1lZWV2jY2NQqPRSHl5eZLQ0NARnc0dzOOPP975zjvvSIiI/vGPf9hoNJoBz0BrtVphfX09M3PmzFthYWGdf/3rXyd3dHTw29vb+X/961/FCxcu7NDr9YJJkyaZ7OzszBcuXLBRqVQTBppr9uzZvfX19daVlZUMEdFf/vIXyb34LmMZOrsAAAAAADDm9T+zGxYW1rZp0yZdbm6uo1KprBaLxeb8/PyO5ORkl/T09IbY2NjvZTKZ3N/fv7ugoODqzp07teHh4azZbCYrKysuIyPjOsuytwZbz8PDoy8lJUVrOT8bHh7edi+eikxE9Nvf/vbG6tWrvViWlfv5+XUHBAR03Xk/JCSE5fP5ZDQaeSkpKfXu7u5Gd3d345o1a1rmzp0rI7r9gKrHHnusZ+7cub1HjhyZwrKs3Nvbu1ehUHQNtKZIJOL+9Kc/XXvyySdnSiQS47x58zqrq6tt78X3Gat4Q7XnAQAAAAAAVCpVrUKh0N3vPODho1KpHBUKhedoYrGNGQAAAAAAAMYdbGMGAAAAAAAYpcWLF3vX1dUxd17bu3dvfXR0dPv9ygluQ7ELAAAAAAAwSkVFRTX3OwcYGLYxAwAAAAAAwLiDYhc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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -343,15 +5280,15 @@ } ], "source": [ - "alphas_elastic = np.logspace(-2, 4, 100)\n", + "alphas_elastic = np.logspace(-10, 6, 100)\n", "coef_elastic = []\n", "for i in alphas_elastic:\n", - " elastic = linear_model.ElasticNet(l1_ratio =0.5)\n", + " elastic = linear_model.ElasticNet(l1_ratio =0.6)\n", " elastic.set_params(alpha = i)\n", - " elastic.fit(x, y)\n", + " elastic.fit(x_onehot, y_log)\n", " coef_elastic.append(elastic.coef_)\n", "\n", - "df_coef = pd.DataFrame(coef_elastic, index=alphas_elastic, columns=x.columns)\n", + "df_coef = pd.DataFrame(coef_elastic, index=alphas_elastic, columns=x_onehot.columns)\n", "title = 'ElasticNet coefficients as a function of the regularization'\n", "df_coef.plot(logx=True, title=title)\n", "plt.xlabel('alpha')\n", @@ -359,6 +5296,27 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### test prediction" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "best_elas_test_y = para_search_elas.best_estimator_.predict(test_onehot)\n", + "best_elas_test_y = np.expm1(best_elas_test_y)\n", + "sub = pd.DataFrame()\n", + "sub['SalePrice'] = best_elas_test_y\n", + "sub = sub.set_index(np.arange(1461, 2920))\n", + "sub.to_csv('(3) elas_log(y)_onehot_submission.csv')" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/Kelly/Kaggle_house_price/Untitled.ipynb b/Kelly/Kaggle_house_price/Untitled.ipynb new file mode 100644 index 0000000..2fd6442 --- /dev/null +++ b/Kelly/Kaggle_house_price/Untitled.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Kelly/Kaggle_house_price/__pycache__/preprocess.cpython-36.pyc b/Kelly/Kaggle_house_price/__pycache__/preprocess.cpython-36.pyc new file mode 100644 index 0000000000000000000000000000000000000000..eddc08b2f3fdd625cfdc38b2359cff97c0322986 GIT binary patch literal 3938 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LabelCountEncoder(object): + def __init__(self): + self.count_dict = {} + self.rev_count_dict = {} + def fit(self, column): + # We want to rank the key by its value and use the rank as the new value + count = column.value_counts() + self.count_dict = dict( list( zip (count.index, reversed(range(len(count)+1 ) ) ) ) ) + self.rev_count_dict = dict( list( zip ( reversed(range(len(count)+1 ) ) , count.index ) ) ) + def transform(self, column): + # If a category only appears in the test set, we will assign the value to zero. + missing = 0 + return column.map(lambda x: self.count_dict.get(x, missing)) + def fit_transform(self, column): + self.fit(column) + return self.transform(column) + + + +import numpy as np +import pandas as pd +import math +import re + +def impute( inputDF, onehot = False): + + #input: pd.dataframe + #one-hot or label encoding for the categorical field + #return: + # Imputed pd.DataFrame + # label-encoded dictionary + + + encodedDic = {} + + inputDF.Exterior1st = inputDF.Exterior1st.str.replace("Brk Cmn", "BrkComm") + inputDF.Exterior2nd = inputDF.Exterior2nd.str.replace("Brk Cmn", "BrkComm") + inputDF.Exterior1st = inputDF.Exterior1st.str.replace("CmentBd", "CemntBd") + inputDF.Exterior2nd = inputDF.Exterior2nd.str.replace("CmentBd", "CemntBd") + inputDF.Exterior1st = inputDF.Exterior1st.str.replace("Wd Shng", "WdShing") + inputDF.Exterior2nd = inputDF.Exterior2nd.str.replace("Wd Shng", "WdShing") + + ############################### purposelyEncodeData ######################################## + ## Start to purposely encode the information based on our best understanding. + ## Combine Exterior1st and Exterior2nd to Exterior + ## Add TotalFlrSF = 1stFlrSF + 2ndFlrSF + TotalBsmtSF + ## BsmtFinType1 and BsmtFinType2 to Bsmt -Replace each type to it's actually square feet BsmtFinSF1, BsmtFinSF2 -For Unf in type 1 and type2, replace it with the BsmtUnfSF + ## Combine BsmtFullBath, BsmtHalfBath to BsmtBath + ## Add all different PorchSF to TotalProchSF + ## Dummy MasVnrType to MasVnr and replace the value with MasVnrArea + ############################################################################################### + + preProcessCatField = ["MasVnrType", "Exterior1st", "Exterior2nd", "BsmtFinType1", "BsmtFinType2"] + preProcessNumFiled = ["1stFlrSF", "2ndFlrSF", "MasVnrArea", "BsmtFinSF1", "BsmtFinSF2", "BsmtUnfSF", "BsmtFullBath", "BsmtHalfBath"] + inputDF[preProcessCatField] = inputDF[preProcessCatField].fillna("Unknown") + inputDF[preProcessNumFiled] = inputDF[preProcessNumFiled].fillna(-1) + + # Exterior1st, Exterior2nd (Exterior covering on house) + var1_dummy_columns = pd.get_dummies(inputDF['Exterior1st'], prefix= "Exterior") + var2_dummy_columns = pd.get_dummies(inputDF['Exterior2nd'], prefix= "Exterior") + var_dummy_columns = pd.concat([var1_dummy_columns,var2_dummy_columns]).groupby(level=0).sum() + var_dummy_columns = var_dummy_columns.replace(2, 1) + + inputDF = pd.concat([inputDF, var_dummy_columns], axis=1) + inputDF = inputDF.drop( columns=['Exterior1st', 'Exterior2nd'] ) + + #TotalBsmtFinSF = "BsmtFinSF1"+ "BsmtFinSF2" ( + "TotalBsmtSF" ) + inputDF["TotalFlrSF"] = inputDF["1stFlrSF"].fillna(0) + inputDF["2ndFlrSF"].fillna(0) + inputDF["TotalBsmtSF"].fillna(0) + inputDF["TotalFlrSF"].replace(0, math.nan) + # BsmtFinType1, BsmtFinType2, BsmtFinSF1 (Type 1 finished square feet), BsmtFinSF2 (Type 1 finished square feet), BsmtUnfSF: Unfinished square feet of basement area + var1_dummy_columns = pd.get_dummies(inputDF['BsmtFinType1'], prefix= "Bsmt") + var1_dummy_columns = var1_dummy_columns.mul( inputDF['BsmtFinSF1'] , axis=0) + tmp = var1_dummy_columns['Bsmt_Unf'] + tmp [ inputDF.loc[inputDF['BsmtFinType1'] == "Unf"].index ] = 1 + tmp = tmp.mul( inputDF['BsmtUnfSF'] , axis=0) + var1_dummy_columns['Bsmt_Unf'] = tmp + + var2_dummy_columns = pd.get_dummies(inputDF['BsmtFinType2'], prefix= "Bsmt") + var2_dummy_columns = var2_dummy_columns.mul( inputDF['BsmtFinSF2'] , axis=0) + tmp = var2_dummy_columns['Bsmt_Unf'] + tmp [ inputDF.loc[inputDF['BsmtFinType2'] == "Unf"].index ] = 1 + tmp = tmp.mul( inputDF['BsmtUnfSF'] , axis=0) + var2_dummy_columns['Bsmt_Unf'] = tmp + + var_dummy_columns = pd.concat([var1_dummy_columns,var2_dummy_columns]).groupby(level=0).sum() + inputDF = pd.concat([inputDF, var_dummy_columns], axis=1) + inputDF = inputDF.drop( columns=['BsmtFinType1', 'BsmtFinType1', 'BsmtFinType2', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF'] ) + + #BsmtFullBath, BsmtHalfBath (number of type of bathroom in the basement) + inputDF['BsmtBath'] = inputDF["BsmtFullBath"] + 0.5* inputDF["BsmtHalfBath"] + inputDF = inputDF.drop( columns=['BsmtFullBath', 'BsmtHalfBath'] ) + + inputDF["TotalProchSF"] = inputDF["OpenPorchSF"] + inputDF["EnclosedPorch"] + inputDF["3SsnPorch"] + inputDF["ScreenPorch"] + + #MasVnrType, MasVnrArea + var_dummy_columns = pd.get_dummies(inputDF['MasVnrType'], prefix= "MasVnr") + var_dummy_columns = var_dummy_columns.mul( inputDF['MasVnrArea'] , axis=0) + inputDF = pd.concat([inputDF, var_dummy_columns], axis=1) + inputDF = inputDF.drop( columns=['MasVnrType', 'MasVnrArea'] ) + + ############################### Ordinal Features Label encoding ####################### + + ord_cols = ['ExterQual', 'ExterCond','BsmtCond','HeatingQC', 'KitchenQual', + 'FireplaceQu', 'GarageQual', 'GarageCond', 'PoolQC', 'BsmtQual'] + ord_dic = {'Ex': 5, 'Gd': 4, 'TA': 3, 'Fa':2, 'Po':1} + + for col in ord_cols: + inputDF[col] = inputDF[col].map(lambda x: ord_dic.get(x, 0)) + + + ############################### Median Impute ####################### + impute_cols = ['GarageArea', 'GarageCars','GarageYrBlt','LotFrontage', 'TotalBsmtSF', 'TotalFlrSF'] + for i, c in enumerate ( impute_cols ): + if inputDF[c].isnull().any(): + inputDF[c] = inputDF[c].fillna( inputDF[c].median() ) + + ############################### Label frequency encoding ####################### + inputDF.MSSubClass = inputDF.MSSubClass.astype('object') + if onehot: + + object_feats = inputDF.dtypes[inputDF.dtypes == "object"].index + numeric_feats = inputDF.dtypes[inputDF.dtypes != "object"].index + objEnc = pd.get_dummies(inputDF[object_feats], drop_first=True, dummy_na=True) + numEnc = inputDF[numeric_feats] + inputDF = pd.concat( [objEnc,numEnc], axis=1) + + else: + for i, c in enumerate ( inputDF.columns ): + if inputDF[c].dtype == 'object': + lce = LabelCountEncoder() + inputDF[c] = lce.fit_transform(inputDF[c]) + encodedDic[inputDF.columns[i]] = lce.rev_count_dict #add reversed dic back to the global variable + + return inputDF, encodedDic + + +##################################This is from Zeyu's original file. ######################## +###########???? Should we also include those ???????????????????????????????################## +# # LotFrontage : Since the area of each street connected to the house property most likely have a similar area to other houses in its neighborhood , we can fill in missing values by the median LotFrontage of the neighborhood. +# # Group by neighborhood and fill in missing value by the median LotFrontage of all the neighborhood +# all_data["LotFrontage"] = all_data.groupby("Neighborhood")["LotFrontage"].transform(lambda x: x.fillna(x.median())) +# +# # GarageYrBlt, GarageArea and GarageCars : Replacing missing data with 0 (Since No garage = no cars in such garage.) +# for col in ('GarageYrBlt', 'GarageArea', 'GarageCars'): +# all_data[col] = all_data[col].fillna(0) +# +# # Functional : data description says NA means typical +# all_data["Functional"] = all_data["Functional"].fillna("Typ") +# +# # Electrical : It has one NA value. Since this feature has mostly 'SBrkr', we can set that for the missing value. +# all_data['Electrical'] = all_data['Electrical'].fillna(all_data['Electrical'].mode()[0]) +# +# # KitchenQual: Only one NA value, and same as Electrical, we set 'TA' (which is the most frequent) for the missing value in KitchenQual. +# all_data['KitchenQual'] = all_data['KitchenQual'].fillna(all_data['KitchenQual'].mode()[0]) +# +# # SaleType : Fill in again with most frequent which is "WD" +# all_data['SaleType'] = all_data['SaleType'].fillna(all_data['SaleType'].mode()[0]) +# +# # Transforming some numerical variables that are really categorical +# #Year and month sold are transformed into categorical features. +# all_data['YrSold'] = all_data['YrSold'].astype(str) +# all_data['MoSold'] = all_data['MoSold'].astype(str) +# +################################################################################################ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/Kelly/Kaggle_house_price/preprocess2_label.py b/Kelly/Kaggle_house_price/preprocess2_label.py new file mode 100644 index 0000000..f7d7a1f --- /dev/null +++ b/Kelly/Kaggle_house_price/preprocess2_label.py @@ -0,0 +1,214 @@ +class LabelCountEncoder(object): + def __init__(self): + self.count_dict = {} + self.rev_count_dict = {} + def fit(self, column): + # We want to rank the key by its value and use the rank as the new value + count = column.value_counts() + self.count_dict = dict( list( zip (count.index, reversed(range(len(count)+1 ) ) ) ) ) + self.rev_count_dict = dict( list( zip ( reversed(range(len(count)+1 ) ) , count.index ) ) ) + def transform(self, column): + # If a category only appears in the test set, we will assign the value to zero. + missing = 0 + return column.map(lambda x: self.count_dict.get(x, missing)) + def fit_transform(self, column): + self.fit(column) + return self.transform(column) + + + +import numpy as np +import pandas as pd +import math +import re + +def impute( inputDF, onehot = False): + + #input: pd.dataframe + #one-hot or label encoding for the categorical field + #return: + # Imputed pd.DataFrame + # label-encoded dictionary + + + encodedDic = {} + + inputDF.Exterior1st = inputDF.Exterior1st.str.replace("Brk Cmn", "BrkComm") + inputDF.Exterior2nd = inputDF.Exterior2nd.str.replace("Brk Cmn", "BrkComm") + inputDF.Exterior1st = inputDF.Exterior1st.str.replace("CmentBd", "CemntBd") + inputDF.Exterior2nd = inputDF.Exterior2nd.str.replace("CmentBd", "CemntBd") + inputDF.Exterior1st = inputDF.Exterior1st.str.replace("Wd Shng", "WdShing") + inputDF.Exterior2nd = inputDF.Exterior2nd.str.replace("Wd Shng", "WdShing") + + ############################### purposelyEncodeData ######################################## + ## Start to purposely encode the information based on our best understanding. + ## Combine Exterior1st and Exterior2nd to Exterior + ## Add TotalFlrSF = 1stFlrSF + 2ndFlrSF + TotalBsmtSF + ## BsmtFinType1 and BsmtFinType2 to Bsmt -Replace each type to it's actually square feet BsmtFinSF1, BsmtFinSF2 -For Unf in type 1 and type2, replace it with the BsmtUnfSF + ## Combine BsmtFullBath, BsmtHalfBath to BsmtBath + ## Add all different PorchSF to TotalProchSF + ## Dummy MasVnrType to MasVnr and replace the value with MasVnrArea + ############################################################################################### + + preProcessCatField = ["MasVnrType", "Exterior1st", "Exterior2nd", "BsmtFinType1", "BsmtFinType2"] + preProcessNumFiled = ["1stFlrSF", "2ndFlrSF", "MasVnrArea", "BsmtFinSF1", "BsmtFinSF2", "BsmtUnfSF", "BsmtFullBath", "BsmtHalfBath"] + inputDF[preProcessCatField] = inputDF[preProcessCatField].fillna("Unknown") + inputDF[preProcessNumFiled] = inputDF[preProcessNumFiled].fillna(-1) + + # Exterior1st, Exterior2nd (Exterior covering on house) + var1_dummy_columns = pd.get_dummies(inputDF['Exterior1st'], prefix= "Exterior") + var2_dummy_columns = pd.get_dummies(inputDF['Exterior2nd'], prefix= "Exterior") + var_dummy_columns = pd.concat([var1_dummy_columns,var2_dummy_columns]).groupby(level=0).sum() + var_dummy_columns = var_dummy_columns.replace(2, 1) + + inputDF = pd.concat([inputDF, var_dummy_columns], axis=1) + inputDF = inputDF.drop( columns=['Exterior1st', 'Exterior2nd'] ) + + #TotalBsmtFinSF = "BsmtFinSF1"+ "BsmtFinSF2" ( + "TotalBsmtSF" ) + inputDF["TotalFlrSF"] = inputDF["1stFlrSF"].fillna(0) + inputDF["2ndFlrSF"].fillna(0) + inputDF["TotalBsmtSF"].fillna(0) + inputDF["TotalFlrSF"].replace(0, math.nan) + # BsmtFinType1, BsmtFinType2, BsmtFinSF1 (Type 1 finished square feet), BsmtFinSF2 (Type 1 finished square feet), BsmtUnfSF: Unfinished square feet of basement area + var1_dummy_columns = pd.get_dummies(inputDF['BsmtFinType1'], prefix= "Bsmt") + var1_dummy_columns = var1_dummy_columns.mul( inputDF['BsmtFinSF1'] , axis=0) + tmp = var1_dummy_columns['Bsmt_Unf'] + tmp [ inputDF.loc[inputDF['BsmtFinType1'] == "Unf"].index ] = 1 + tmp = tmp.mul( inputDF['BsmtUnfSF'] , axis=0) + var1_dummy_columns['Bsmt_Unf'] = tmp + + var2_dummy_columns = pd.get_dummies(inputDF['BsmtFinType2'], prefix= "Bsmt") + var2_dummy_columns = var2_dummy_columns.mul( inputDF['BsmtFinSF2'] , axis=0) + tmp = var2_dummy_columns['Bsmt_Unf'] + tmp [ inputDF.loc[inputDF['BsmtFinType2'] == "Unf"].index ] = 1 + tmp = tmp.mul( inputDF['BsmtUnfSF'] , axis=0) + var2_dummy_columns['Bsmt_Unf'] = tmp + + var_dummy_columns = pd.concat([var1_dummy_columns,var2_dummy_columns]).groupby(level=0).sum() + inputDF = pd.concat([inputDF, var_dummy_columns], axis=1) + inputDF = inputDF.drop( columns=['BsmtFinType1', 'BsmtFinType1', 'BsmtFinType2', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF'] ) + + #BsmtFullBath, BsmtHalfBath (number of type of bathroom in the basement) + inputDF['BsmtBath'] = inputDF["BsmtFullBath"] + 0.5* inputDF["BsmtHalfBath"] + inputDF = inputDF.drop( columns=['BsmtFullBath', 'BsmtHalfBath'] ) + + inputDF["TotalProchSF"] = inputDF["OpenPorchSF"] + inputDF["EnclosedPorch"] + inputDF["3SsnPorch"] + inputDF["ScreenPorch"] + + #MasVnrType, MasVnrArea + var_dummy_columns = pd.get_dummies(inputDF['MasVnrType'], prefix= "MasVnr") + var_dummy_columns = var_dummy_columns.mul( inputDF['MasVnrArea'] , axis=0) + inputDF = pd.concat([inputDF, var_dummy_columns], axis=1) + inputDF = inputDF.drop( columns=['MasVnrType', 'MasVnrArea'] ) + + ############################### Ordinal Features Label encoding ####################### + + #ord_cols = ['ExterQual', 'ExterCond','BsmtCond','HeatingQC', 'KitchenQual', + # 'FireplaceQu', 'GarageQual', 'GarageCond', 'PoolQC', 'BsmtQual'] + #ord_dic = {'Ex': 5, 'Gd': 4, 'TA': 3, 'Fa':2, 'Po':1} + + #for col in ord_cols: + # inputDF[col] = inputDF[col].map(lambda x: ord_dic.get(x, 0)) + + + ############################### Median Impute ####################### + impute_cols = ['GarageArea', 'GarageCars','GarageYrBlt','LotFrontage', 'TotalBsmtSF', 'TotalFlrSF'] + for i, c in enumerate ( impute_cols ): + if inputDF[c].isnull().any(): + inputDF[c] = inputDF[c].fillna( inputDF[c].median() ) + + ############################### Label frequency encoding ####################### + inputDF.MSSubClass = inputDF.MSSubClass.astype('object') + if onehot: + + object_feats = inputDF.dtypes[inputDF.dtypes == "object"].index + numeric_feats = inputDF.dtypes[inputDF.dtypes != "object"].index + objEnc = pd.get_dummies(inputDF[object_feats], drop_first=True, dummy_na=True) + numEnc = inputDF[numeric_feats] + inputDF = pd.concat( [objEnc,numEnc], axis=1) + + else: + for i, c in enumerate ( inputDF.columns ): + if inputDF[c].dtype == 'object': + lce = LabelCountEncoder() + inputDF[c] = lce.fit_transform(inputDF[c]) + encodedDic[inputDF.columns[i]] = lce.rev_count_dict #add reversed dic back to the global variable + + return inputDF, encodedDic + + +##################################This is from Zeyu's original file. ######################## +###########???? Should we also include those ???????????????????????????????################## +# # LotFrontage : Since the area of each street connected to the house property most likely have a similar area to other houses in its neighborhood , we can fill in missing values by the median LotFrontage of the neighborhood. +# # Group by neighborhood and fill in missing value by the median LotFrontage of all the neighborhood +# all_data["LotFrontage"] = all_data.groupby("Neighborhood")["LotFrontage"].transform(lambda x: x.fillna(x.median())) +# +# # GarageYrBlt, GarageArea and GarageCars : Replacing missing data with 0 (Since No garage = no cars in such garage.) +# for col in ('GarageYrBlt', 'GarageArea', 'GarageCars'): +# all_data[col] = all_data[col].fillna(0) +# +# # Functional : data description says NA means typical +# all_data["Functional"] = all_data["Functional"].fillna("Typ") +# +# # Electrical : It has one NA value. Since this feature has mostly 'SBrkr', we can set that for the missing value. +# all_data['Electrical'] = all_data['Electrical'].fillna(all_data['Electrical'].mode()[0]) +# +# # KitchenQual: Only one NA value, and same as Electrical, we set 'TA' (which is the most frequent) for the missing value in KitchenQual. +# all_data['KitchenQual'] = all_data['KitchenQual'].fillna(all_data['KitchenQual'].mode()[0]) +# +# # SaleType : Fill in again with most frequent which is "WD" +# all_data['SaleType'] = all_data['SaleType'].fillna(all_data['SaleType'].mode()[0]) +# +# # Transforming some numerical variables that are really categorical +# #Year and month sold are transformed into categorical features. +# all_data['YrSold'] = all_data['YrSold'].astype(str) +# all_data['MoSold'] = all_data['MoSold'].astype(str) +# +################################################################################################ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/Kelly/Kaggle_house_price/preprocess3_yrmonthsold.py b/Kelly/Kaggle_house_price/preprocess3_yrmonthsold.py new file mode 100644 index 0000000..e9cd554 --- /dev/null +++ b/Kelly/Kaggle_house_price/preprocess3_yrmonthsold.py @@ -0,0 +1,216 @@ +class LabelCountEncoder(object): + def __init__(self): + self.count_dict = {} + self.rev_count_dict = {} + def fit(self, column): + # We want to rank the key by its value and use the rank as the new value + count = column.value_counts() + self.count_dict = dict( list( zip (count.index, reversed(range(len(count)+1 ) ) ) ) ) + self.rev_count_dict = dict( list( zip ( reversed(range(len(count)+1 ) ) , count.index ) ) ) + def transform(self, column): + # If a category only appears in the test set, we will assign the value to zero. + missing = 0 + return column.map(lambda x: self.count_dict.get(x, missing)) + def fit_transform(self, column): + self.fit(column) + return self.transform(column) + + + +import numpy as np +import pandas as pd +import math +import re + +def impute( inputDF, onehot = False): + + #input: pd.dataframe + #one-hot or label encoding for the categorical field + #return: + # Imputed pd.DataFrame + # label-encoded dictionary + + + encodedDic = {} + + inputDF.Exterior1st = inputDF.Exterior1st.str.replace("Brk Cmn", "BrkComm") + inputDF.Exterior2nd = inputDF.Exterior2nd.str.replace("Brk Cmn", "BrkComm") + inputDF.Exterior1st = inputDF.Exterior1st.str.replace("CmentBd", "CemntBd") + inputDF.Exterior2nd = inputDF.Exterior2nd.str.replace("CmentBd", "CemntBd") + inputDF.Exterior1st = inputDF.Exterior1st.str.replace("Wd Shng", "WdShing") + inputDF.Exterior2nd = inputDF.Exterior2nd.str.replace("Wd Shng", "WdShing") + + ############################### purposelyEncodeData ######################################## + ## Start to purposely encode the information based on our best understanding. + ## Combine Exterior1st and Exterior2nd to Exterior + ## Add TotalFlrSF = 1stFlrSF + 2ndFlrSF + TotalBsmtSF + ## BsmtFinType1 and BsmtFinType2 to Bsmt -Replace each type to it's actually square feet BsmtFinSF1, BsmtFinSF2 -For Unf in type 1 and type2, replace it with the BsmtUnfSF + ## Combine BsmtFullBath, BsmtHalfBath to BsmtBath + ## Add all different PorchSF to TotalProchSF + ## Dummy MasVnrType to MasVnr and replace the value with MasVnrArea + ############################################################################################### + + preProcessCatField = ["MasVnrType", "Exterior1st", "Exterior2nd", "BsmtFinType1", "BsmtFinType2"] + preProcessNumFiled = ["1stFlrSF", "2ndFlrSF", "MasVnrArea", "BsmtFinSF1", "BsmtFinSF2", "BsmtUnfSF", "BsmtFullBath", "BsmtHalfBath"] + inputDF[preProcessCatField] = inputDF[preProcessCatField].fillna("Unknown") + inputDF[preProcessNumFiled] = inputDF[preProcessNumFiled].fillna(-1) + + # Exterior1st, Exterior2nd (Exterior covering on house) + var1_dummy_columns = pd.get_dummies(inputDF['Exterior1st'], prefix= "Exterior") + var2_dummy_columns = pd.get_dummies(inputDF['Exterior2nd'], prefix= "Exterior") + var_dummy_columns = pd.concat([var1_dummy_columns,var2_dummy_columns]).groupby(level=0).sum() + var_dummy_columns = var_dummy_columns.replace(2, 1) + + inputDF = pd.concat([inputDF, var_dummy_columns], axis=1) + inputDF = inputDF.drop( columns=['Exterior1st', 'Exterior2nd'] ) + + #TotalBsmtFinSF = "BsmtFinSF1"+ "BsmtFinSF2" ( + "TotalBsmtSF" ) + inputDF["TotalFlrSF"] = inputDF["1stFlrSF"].fillna(0) + inputDF["2ndFlrSF"].fillna(0) + inputDF["TotalBsmtSF"].fillna(0) + inputDF["TotalFlrSF"].replace(0, math.nan) + # BsmtFinType1, BsmtFinType2, BsmtFinSF1 (Type 1 finished square feet), BsmtFinSF2 (Type 1 finished square feet), BsmtUnfSF: Unfinished square feet of basement area + var1_dummy_columns = pd.get_dummies(inputDF['BsmtFinType1'], prefix= "Bsmt") + var1_dummy_columns = var1_dummy_columns.mul( inputDF['BsmtFinSF1'] , axis=0) + tmp = var1_dummy_columns['Bsmt_Unf'] + tmp [ inputDF.loc[inputDF['BsmtFinType1'] == "Unf"].index ] = 1 + tmp = tmp.mul( inputDF['BsmtUnfSF'] , axis=0) + var1_dummy_columns['Bsmt_Unf'] = tmp + + var2_dummy_columns = pd.get_dummies(inputDF['BsmtFinType2'], prefix= "Bsmt") + var2_dummy_columns = var2_dummy_columns.mul( inputDF['BsmtFinSF2'] , axis=0) + tmp = var2_dummy_columns['Bsmt_Unf'] + tmp [ inputDF.loc[inputDF['BsmtFinType2'] == "Unf"].index ] = 1 + tmp = tmp.mul( inputDF['BsmtUnfSF'] , axis=0) + var2_dummy_columns['Bsmt_Unf'] = tmp + + var_dummy_columns = pd.concat([var1_dummy_columns,var2_dummy_columns]).groupby(level=0).sum() + inputDF = pd.concat([inputDF, var_dummy_columns], axis=1) + inputDF = inputDF.drop( columns=['BsmtFinType1', 'BsmtFinType1', 'BsmtFinType2', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF'] ) + + #BsmtFullBath, BsmtHalfBath (number of type of bathroom in the basement) + inputDF['BsmtBath'] = inputDF["BsmtFullBath"] + 0.5* inputDF["BsmtHalfBath"] + inputDF = inputDF.drop( columns=['BsmtFullBath', 'BsmtHalfBath'] ) + + inputDF["TotalProchSF"] = inputDF["OpenPorchSF"] + inputDF["EnclosedPorch"] + inputDF["3SsnPorch"] + inputDF["ScreenPorch"] + + #MasVnrType, MasVnrArea + var_dummy_columns = pd.get_dummies(inputDF['MasVnrType'], prefix= "MasVnr") + var_dummy_columns = var_dummy_columns.mul( inputDF['MasVnrArea'] , axis=0) + inputDF = pd.concat([inputDF, var_dummy_columns], axis=1) + inputDF = inputDF.drop( columns=['MasVnrType', 'MasVnrArea'] ) + + ############################### Ordinal Features Label encoding ####################### + + ord_cols = ['ExterQual', 'ExterCond','BsmtCond','HeatingQC', 'KitchenQual', + 'FireplaceQu', 'GarageQual', 'GarageCond', 'PoolQC', 'BsmtQual'] + ord_dic = {'Ex': 5, 'Gd': 4, 'TA': 3, 'Fa':2, 'Po':1} + + for col in ord_cols: + inputDF[col] = inputDF[col].map(lambda x: ord_dic.get(x, 0)) + + + ############################### Median Impute ####################### + impute_cols = ['GarageArea', 'GarageCars','GarageYrBlt','LotFrontage', 'TotalBsmtSF', 'TotalFlrSF'] + for i, c in enumerate ( impute_cols ): + if inputDF[c].isnull().any(): + inputDF[c] = inputDF[c].fillna( inputDF[c].median() ) + + ############################### Label frequency encoding ####################### + inputDF.MSSubClass = inputDF.MSSubClass.astype('object') + inputDF.YrSold = inputDF.YrSold.astype('object') + inputDF.MoSold = inputDF.MoSold.astype('object') + if onehot: + + object_feats = inputDF.dtypes[inputDF.dtypes == "object"].index + numeric_feats = inputDF.dtypes[inputDF.dtypes != "object"].index + objEnc = pd.get_dummies(inputDF[object_feats], drop_first=True, dummy_na=True) + numEnc = inputDF[numeric_feats] + inputDF = pd.concat( [objEnc,numEnc], axis=1) + + else: + for i, c in enumerate ( inputDF.columns ): + if inputDF[c].dtype == 'object': + lce = LabelCountEncoder() + inputDF[c] = lce.fit_transform(inputDF[c]) + encodedDic[inputDF.columns[i]] = lce.rev_count_dict #add reversed dic back to the global variable + + return inputDF, encodedDic + + +##################################This is from Zeyu's original file. ######################## +###########???? Should we also include those ???????????????????????????????################## +# # LotFrontage : Since the area of each street connected to the house property most likely have a similar area to other houses in its neighborhood , we can fill in missing values by the median LotFrontage of the neighborhood. +# # Group by neighborhood and fill in missing value by the median LotFrontage of all the neighborhood +# all_data["LotFrontage"] = all_data.groupby("Neighborhood")["LotFrontage"].transform(lambda x: x.fillna(x.median())) +# +# # GarageYrBlt, GarageArea and GarageCars : Replacing missing data with 0 (Since No garage = no cars in such garage.) +# for col in ('GarageYrBlt', 'GarageArea', 'GarageCars'): +# all_data[col] = all_data[col].fillna(0) +# +# # Functional : data description says NA means typical +# all_data["Functional"] = all_data["Functional"].fillna("Typ") +# +# # Electrical : It has one NA value. Since this feature has mostly 'SBrkr', we can set that for the missing value. +# all_data['Electrical'] = all_data['Electrical'].fillna(all_data['Electrical'].mode()[0]) +# +# # KitchenQual: Only one NA value, and same as Electrical, we set 'TA' (which is the most frequent) for the missing value in KitchenQual. +# all_data['KitchenQual'] = all_data['KitchenQual'].fillna(all_data['KitchenQual'].mode()[0]) +# +# # SaleType : Fill in again with most frequent which is "WD" +# all_data['SaleType'] = all_data['SaleType'].fillna(all_data['SaleType'].mode()[0]) +# +# # Transforming some numerical variables that are really categorical +# #Year and month sold are transformed into categorical features. +# all_data['YrSold'] = all_data['YrSold'].astype(str) +# all_data['MoSold'] = all_data['MoSold'].astype(str) +# +################################################################################################ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/Kelly/Kaggle_house_price/preprocessfinal.py b/Kelly/Kaggle_house_price/preprocessfinal.py new file mode 100644 index 0000000..3d4bb1f --- /dev/null +++ b/Kelly/Kaggle_house_price/preprocessfinal.py @@ -0,0 +1,241 @@ +class LabelCountEncoder(object): + def __init__(self): + self.count_dict = {} + self.rev_count_dict = {} + def fit(self, column): + # We want to rank the key by its value and use the rank as the new value + count = column.value_counts() + self.count_dict = dict( list( zip (count.index, reversed(range(len(count)+1 ) ) ) ) ) + self.rev_count_dict = dict( list( zip ( reversed(range(len(count)+1 ) ) , count.index ) ) ) + def transform(self, column): + # If a category only appears in the test set, we will assign the value to zero. + missing = 0 + return column.map(lambda x: self.count_dict.get(x, missing)) + def fit_transform(self, column): + self.fit(column) + return self.transform(column) + + + +import numpy as np +import pandas as pd +import math +import re +from scipy.stats import kurtosis, skew +from scipy.special import boxcox1p + + +def impute( inputDF, onehot = False): + + #input: pd.dataframe + #one-hot or label encoding for the categorical field + #return: + # Imputed pd.DataFrame + # label-encoded dictionary + + + encodedDic = {} + + inputDF.Exterior1st = inputDF.Exterior1st.str.replace("Brk Cmn", "BrkComm") + inputDF.Exterior2nd = inputDF.Exterior2nd.str.replace("Brk Cmn", "BrkComm") + inputDF.Exterior1st = inputDF.Exterior1st.str.replace("CmentBd", "CemntBd") + inputDF.Exterior2nd = inputDF.Exterior2nd.str.replace("CmentBd", "CemntBd") + inputDF.Exterior1st = inputDF.Exterior1st.str.replace("Wd Shng", "WdShing") + inputDF.Exterior2nd = inputDF.Exterior2nd.str.replace("Wd Shng", "WdShing") + + ############################### purposelyEncodeData ######################################## + ## Start to purposely encode the information based on our best understanding. + ## Combine Exterior1st and Exterior2nd to Exterior + ## Add TotalFlrSF = 1stFlrSF + 2ndFlrSF + TotalBsmtSF + ## BsmtFinType1 and BsmtFinType2 to Bsmt -Replace each type to it's actually square feet BsmtFinSF1, BsmtFinSF2 -For Unf in type 1 and type2, replace it with the BsmtUnfSF + ## Combine BsmtFullBath, BsmtHalfBath to BsmtBath + ## Add all different PorchSF to TotalProchSF + ## Dummy MasVnrType to MasVnr and replace the value with MasVnrArea + ############################################################################################### + + preProcessCatField = ["MasVnrType", "Exterior1st", "Exterior2nd", "BsmtFinType1", "BsmtFinType2", "Condition1", "Condition2"] + preProcessNumFiled = ["1stFlrSF", "2ndFlrSF", "MasVnrArea", "BsmtFinSF1", "BsmtFinSF2", "BsmtUnfSF", "BsmtFullBath", "BsmtHalfBath"] + inputDF[preProcessCatField] = inputDF[preProcessCatField].fillna("Unknown") + inputDF[preProcessNumFiled] = inputDF[preProcessNumFiled].fillna(0) + + # Exterior1st, Exterior2nd (Exterior covering on house) + var1_dummy_columns = pd.get_dummies(inputDF['Exterior1st'], prefix= "Exterior") + var2_dummy_columns = pd.get_dummies(inputDF['Exterior2nd'], prefix= "Exterior") + var_dummy_columns = pd.concat([var1_dummy_columns,var2_dummy_columns]).groupby(level=0).sum() + var_dummy_columns = var_dummy_columns.replace(2, 1) + + inputDF = pd.concat([inputDF, var_dummy_columns], axis=1) + inputDF = inputDF.drop( columns=['Exterior1st', 'Exterior2nd'] ) + + #TotalBsmtFinSF = "BsmtFinSF1"+ "BsmtFinSF2" ( + "TotalBsmtSF" ) + inputDF["TotalBsmtSF"] = inputDF["TotalBsmtSF"].fillna(0) + inputDF["TotalFlrSF"] = inputDF["1stFlrSF"] + inputDF["2ndFlrSF"] + inputDF["TotalBsmtSF"] + + # BsmtFinType1, BsmtFinType2, BsmtFinSF1 (Type 1 finished square feet), BsmtFinSF2 (Type 1 finished square feet), BsmtUnfSF: Unfinished square feet of basement area + var1_dummy_columns = pd.get_dummies(inputDF['BsmtFinType1'], prefix= "Bsmt") + var1_dummy_columns = var1_dummy_columns.mul( inputDF['BsmtFinSF1'] , axis=0) + tmp = var1_dummy_columns['Bsmt_Unf'] + tmp [ inputDF.loc[inputDF['BsmtFinType1'] == "Unf"].index ] = 1 + tmp = tmp.mul( inputDF['BsmtUnfSF'] , axis=0) + var1_dummy_columns['Bsmt_Unf'] = tmp + + var2_dummy_columns = pd.get_dummies(inputDF['BsmtFinType2'], prefix= "Bsmt") + var2_dummy_columns = var2_dummy_columns.mul( inputDF['BsmtFinSF2'] , axis=0) + tmp = var2_dummy_columns['Bsmt_Unf'] + tmp [ inputDF.loc[inputDF['BsmtFinType2'] == "Unf"].index ] = 1 + tmp = tmp.mul( inputDF['BsmtUnfSF'] , axis=0) + var2_dummy_columns['Bsmt_Unf'] = tmp + + var_dummy_columns = pd.concat([var1_dummy_columns,var2_dummy_columns]).groupby(level=0).sum() + inputDF = pd.concat([inputDF, var_dummy_columns], axis=1) + inputDF = inputDF.drop( columns=['BsmtFinType1', 'BsmtFinType1', 'BsmtFinType2', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF'] ) + + #BsmtFullBath, BsmtHalfBath (number of type of bathroom in the basement) + inputDF['BsmtBath'] = inputDF["BsmtFullBath"] + 0.5* inputDF["BsmtHalfBath"] + inputDF = inputDF.drop( columns=['BsmtFullBath', 'BsmtHalfBath'] ) + + inputDF["TotalPorchSF"] = inputDF["OpenPorchSF"] + inputDF["EnclosedPorch"] + inputDF["3SsnPorch"] + inputDF["ScreenPorch"] + + #MasVnrType, MasVnrArea + var_dummy_columns = pd.get_dummies(inputDF['MasVnrType'], prefix= "MasVnr") + var_dummy_columns = var_dummy_columns.mul( inputDF['MasVnrArea'] , axis=0) + inputDF = pd.concat([inputDF, var_dummy_columns], axis=1) + inputDF = inputDF.drop( columns=['MasVnrType', 'MasVnrArea'] ) + + #Condition1, Condition2: Proximity to various conditions + var1_dummy_columns = pd.get_dummies(inputDF['Condition1'], prefix= "Cond") + var2_dummy_columns = pd.get_dummies(inputDF['Condition2'], prefix= "Cond") + var_dummy_columns = pd.concat([var1_dummy_columns,var2_dummy_columns]).groupby(level=0).sum() + var_dummy_columns = var_dummy_columns.replace(2, 1) + inputDF = pd.concat([inputDF, var_dummy_columns], axis=1) + inputDF = inputDF.drop( columns=['Condition1', 'Condition2'] ) + + + ############################### Median Impute ####################### + ## Some NA existed in these numerical fields +# impute_cols = ['GarageArea', 'GarageCars','GarageYrBlt','LotFrontage'] +# for i, c in enumerate ( impute_cols ): +# if inputDF[c].isnull().any(): +# inputDF[c] = inputDF[c].fillna( inputDF[c].median() ) + + + # LotFrontage : Since the area of each street connected to the house property most likely have a similar area to other houses in its neighborhood , we can fill in missing values by the median LotFrontage of the neighborhood. + # Group by neighborhood and fill in missing value by the median LotFrontage of all the neighborhood + inputDF["LotFrontage"] = inputDF.groupby("Neighborhood")["LotFrontage"].transform(lambda x: x.fillna(x.median())) + + # GarageYrBlt, GarageArea and GarageCars : Replacing missing data with 0 (Since No garage = no cars in such garage.) + for col in ('GarageYrBlt', 'GarageArea', 'GarageCars'): + inputDF[col] = inputDF[col].fillna(0) + + ## Some NA existed in these categorical fields. + # Functional : data description says NA means typical + inputDF["Functional"] = inputDF["Functional"].fillna("Typ") + + # Electrical : It has one NA value. Since this feature has mostly 'SBrkr', we can set that for the missing value. + inputDF['Electrical'] = inputDF['Electrical'].fillna(inputDF['Electrical'].mode()[0]) + + # KitchenQual: Only one NA value, and same as Electrical, we set 'TA' (which is the most frequent) for the missing value in KitchenQual. + inputDF['KitchenQual'] = inputDF['KitchenQual'].fillna(inputDF['KitchenQual'].mode()[0]) + + # SaleType : Fill in again with most frequent which is "WD" + inputDF['SaleType'] = inputDF['SaleType'].fillna(inputDF['SaleType'].mode()[0]) + + + + ############################### Ordinal Features Label encoding ####################### + + ord_cols = ['ExterQual', 'ExterCond','BsmtCond','HeatingQC', 'KitchenQual', + 'FireplaceQu', 'GarageQual', 'GarageCond', 'PoolQC', 'BsmtQual'] + ord_dic = {'Ex': 5, 'Gd': 4, 'TA': 3, 'Fa':2, 'Po':1} + + for col in ord_cols: + inputDF[col] = inputDF[col].map(lambda x: ord_dic.get(x, 0)) + + + ############################### Transform numerical data to categorical ########## + + inputDF.MSSubClass = inputDF.MSSubClass.astype('str') + inputDF.YrSold = inputDF.YrSold.astype('str') + inputDF.MoSold = inputDF.MoSold.astype('str') + + ############################### Label frequency or onehot encoding ############## + + ##Get all numerical feature transformed + + numeric_feats = inputDF.dtypes[inputDF.dtypes != "object"].index + skewed_data = inputDF[numeric_feats].apply(lambda x: skew(x)) + skewed = skewed_data[abs(skewed_data) > 0.75] + for i in skewed.index: + inputDF[i] = boxcox1p(inputDF[i], 0.15) + + ##Onehot or label encoding + if onehot: + + numeric_feats = inputDF.dtypes[inputDF.dtypes != "object"].index + object_feats = inputDF.dtypes[inputDF.dtypes == "object"].index + objEnc = pd.get_dummies(inputDF[object_feats], drop_first=True, dummy_na=True) + numEnc = inputDF[numeric_feats] + inputDF = pd.concat( [objEnc,numEnc] , axis=1) + + else: + for i, c in enumerate ( inputDF.columns ): + if inputDF[c].dtype == 'object': + lce = LabelCountEncoder() + inputDF[c] = lce.fit_transform(inputDF[c]) + encodedDic[inputDF.columns[i]] = lce.rev_count_dict #add reversed dic back to the global variable + + + return [inputDF, encodedDic] + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + From 43f96063c87b7a480c60b40192bdc1174dd016b6 Mon Sep 17 00:00:00 2001 From: kellyho15 <40176918+kellyho15@users.noreply.github.com> Date: Thu, 27 Sep 2018 20:31:15 -0400 Subject: [PATCH 2/8] Delete Untitled.ipynb --- Kelly/Kaggle_house_price/Untitled.ipynb | 6 ------ 1 file changed, 6 deletions(-) delete mode 100644 Kelly/Kaggle_house_price/Untitled.ipynb diff --git a/Kelly/Kaggle_house_price/Untitled.ipynb b/Kelly/Kaggle_house_price/Untitled.ipynb deleted file mode 100644 index 2fd6442..0000000 --- a/Kelly/Kaggle_house_price/Untitled.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 2 -} From bd7339b8a33278d6db24153e41fb3306bce529d0 Mon Sep 17 00:00:00 2001 From: kellyho15 <40176918+kellyho15@users.noreply.github.com> Date: Fri, 28 Sep 2018 00:16:02 -0400 Subject: [PATCH 3/8] Update README.md --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index ec5f19d..a9e3eba 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,5 @@ # 4sigma ML project +The goal of this project was aimed to utilize supervised machine learning techniques to predict the price of houses located in Ames, Iowa. This dataset was provided by Kaggle. This dataset provided around 80 different features, including multiple aspects of the house that would help or may not help predict the fluctuation of the house prices. + +https://nycdatascience.com/blog/student-works/predictive-modeling-on-house-prices-ames-iowa/ From 3e14721eecd5a434f6daa2130ee1dfa7d2ff7aea Mon Sep 17 00:00:00 2001 From: kellyho15 <40176918+kellyho15@users.noreply.github.com> Date: Fri, 28 Sep 2018 01:16:50 -0400 Subject: [PATCH 4/8] Delete ML_lab_Kelly.ipynb --- Kelly/Machine_Learning_Lab/ML_lab_Kelly.ipynb | 3004 ----------------- 1 file changed, 3004 deletions(-) delete mode 100644 Kelly/Machine_Learning_Lab/ML_lab_Kelly.ipynb diff --git a/Kelly/Machine_Learning_Lab/ML_lab_Kelly.ipynb b/Kelly/Machine_Learning_Lab/ML_lab_Kelly.ipynb deleted file mode 100644 index 1b3bff9..0000000 --- a/Kelly/Machine_Learning_Lab/ML_lab_Kelly.ipynb +++ /dev/null @@ -1,3004 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd \n", - "import numpy as np\n", - "orders = pd.read_csv(\"./data/Orders.csv\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Part I: Preprocessing and EDA" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Problem 1: Dataset Import & Cleaning\n", - "Check **\"Profit\"** and **\"Sales\"** in the dataset, convert these two columns to numeric type." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "orders.dtypes\n", - "\n", - "# convert sales and profit to numeric\n", - "orders.Sales = orders.Sales.str.replace('$', '').str.replace(',' , '').astype(float).round(2)\n", - "orders.Profit = orders.Profit.str.replace('$', '').str.replace(',', '').astype(float).round(2)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Problem 2: Inventory Management\n", - "- Retailers that depend on seasonal shoppers have a particularly challenging job when it comes to inventory management. Your manager is making plans for next year's inventory.\n", - "- He wants you to answer the following questions:\n", - " 1. Is there any seasonal trend of inventory in the company?\n", - " 2. Is the seasonal trend the same for different categories?\n", - "\n", - "- ***Hint:*** For each order, it has an attribute called `Quantity` that indicates the number of product in the order. If an order contains more than one product, there will be multiple observations of the same order." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import datetime\n", - "import seaborn as sns\n", - "from matplotlib import pyplot as plt\n", - "\n", - "# convert order date/ship date to datetime object\n", - "orders['Order.Date.mod'] = pd.to_datetime(orders['Order.Date']).dt.to_period('M')\n", - "orders['Ship.Date.mod'] = pd.to_datetime(orders['Ship.Date']).dt.to_period('M')\n", - "\n", - "# 1. Is there any seasonal trend of inventory in the company?\n", - "season = orders[['Quantity', 'Order.Date.mod']].groupby('Order.Date.mod').sum().reset_index()\n", - "sns.barplot(x='Order.Date.mod', y='Quantity', data=season)\n", - "plt.xticks(rotation=90)\n", - "plt.rcParams['figure.figsize']=16,4\n", - "\n", - "# sales seems to be better during winter months and June" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n", - " 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n", - " 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47]),\n", - " )" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# 2. Is the seasonal trend the same for different categories?\n", - "\n", - "catseason = orders[['Quantity', 'Category', 'Order.Date.mod']].groupby(['Order.Date.mod','Category']).sum().reset_index()\n", - "sns.barplot(x='Order.Date.mod', y='Quantity',hue='Category',data=catseason)\n", - "plt.xticks(rotation=90)\n", - "\n", - "# Sale of office supplies increased. Sales of furniture and technology items decreased." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Problem 3: Why did customers make returns?\n", - "- Your manager required you to give a brief report (**Plots + Interpretations**) on returned orders.\n", - "\n", - "\t1. How much profit did we lose due to returns each year?\n", - "\n", - "\n", - "\t2. How many customer returned more than once? more than 5 times?\n", - "\n", - "\n", - "\t3. Which regions are more likely to return orders?\n", - "\n", - "\n", - "\t4. Which categories (sub-categories) of products are more likely to be returned?\n", - "\n", - "- ***Hint:*** Merge the **Returns** dataframe with the **Orders** dataframe using `Order.ID`." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "returns = pd.read_csv(\"./data/Returns.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

" - ], - "text/plain": [ - " Profit\n", - "Order.Date.year \n", - "2012 17477.26\n", - "2013 9269.89\n", - "2014 17510.63\n", - "2015 17112.97" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "combine = pd.merge(orders, returns, left_on='Order.ID', right_on='Order ID', how='outer')\n", - "\n", - "# 1. How much profit did we lose due to returns each year?\n", - "# new df that drop all NaN in returns\n", - "combine['Order.Date.year'] = pd.to_datetime(combine['Order.Date']).dt.year\n", - "returninfo = combine[combine['Returned']=='Yes']\n", - "returninfo[['Order.Date.year','Profit']].groupby('Order.Date.year').sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(547, 2)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 2. How many customer returned more than once? more than 5 times?\n", - "\n", - "returnnum = returninfo[['Returned','Customer.ID']].groupby('Customer.ID').count().reset_index()\n", - "returnnum[returnnum['Returned']>5].shape\n", - "returnnum[returnnum['Returned']>1].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Region_yReturned
2Central America248
22Western Europe233
23Western US180
12Oceania154
14Southeastern Asia140
9Eastern US134
13South America133
6Eastern Asia131
17Southern Europe112
16Southern Asia111
20Western Asia108
18Southern US83
11Northern Europe76
4Central US71
0Caribbean69
19Western Africa60
10North Africa51
8Eastern Europe42
15Southern Africa25
5Eastern Africa18
1Central Africa17
7Eastern Canada10
3Central Asia9
21Western Canada5
\n", - "
" - ], - "text/plain": [ - " Region_y Returned\n", - "2 Central America 248\n", - "22 Western Europe 233\n", - "23 Western US 180\n", - "12 Oceania 154\n", - "14 Southeastern Asia 140\n", - "9 Eastern US 134\n", - "13 South America 133\n", - "6 Eastern Asia 131\n", - "17 Southern Europe 112\n", - "16 Southern Asia 111\n", - "20 Western Asia 108\n", - "18 Southern US 83\n", - "11 Northern Europe 76\n", - "4 Central US 71\n", - "0 Caribbean 69\n", - "19 Western Africa 60\n", - "10 North Africa 51\n", - "8 Eastern Europe 42\n", - "15 Southern Africa 25\n", - "5 Eastern Africa 18\n", - "1 Central Africa 17\n", - "7 Eastern Canada 10\n", - "3 Central Asia 9\n", - "21 Western Canada 5" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 3. Which regions are more likely to return orders?\n", - "returninfo[['Returned','Region_y']].groupby('Region_y').count().reset_index().sort_values('Returned', ascending=False)\n", - "# Central America" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Sub.CategoryReturned
3Binders269
2Art217
14Storage212
12Paper150
5Chairs147
13Phones145
0Accessories138
10Labels137
9Furnishings135
4Bookcases104
15Supplies103
8Fasteners102
7Envelopes99
6Copiers99
11Machines63
1Appliances59
16Tables41
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" - ], - "text/plain": [ - " Sub.Category Returned\n", - "3 Binders 269\n", - "2 Art 217\n", - "14 Storage 212\n", - "12 Paper 150\n", - "5 Chairs 147\n", - "13 Phones 145\n", - "0 Accessories 138\n", - "10 Labels 137\n", - "9 Furnishings 135\n", - "4 Bookcases 104\n", - "15 Supplies 103\n", - "8 Fasteners 102\n", - "7 Envelopes 99\n", - "6 Copiers 99\n", - "11 Machines 63\n", - "1 Appliances 59\n", - "16 Tables 41" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 4. Which categories (sub-categories) of products are more likely to be returned?\n", - "returninfo[['Returned','Sub.Category']].groupby('Sub.Category').count().reset_index().sort_values('Returned', ascending=False)\n", - "# Binders\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Part II: Machine Learning and Business Use Case" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Problem 4: Feature Engineering" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# step 1 & step 2\n", - "combine.Returned = pd.get_dummies(combine.Returned)\n", - "combine['Order.Month'] = pd.to_datetime(combine['Order.Date']).dt.month\n", - "combine['Process.Time'] = pd.to_datetime(combine['Ship.Date']) - pd.to_datetime(combine['Order.Date'])\n", - "combine['Process.Time'] = combine['Process.Time'].dt.days" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# step 3 Let us generate a feature indictes how many times the product has been returned before\n", - "combine ['Return.Freq'] = combine.groupby('Product.ID')['Returned'].transform('sum')" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Row.IDOrder.IDOrder.DateShip.DateShip.ModeCustomer.IDCustomer.NameSegmentPostal.CodeCity...Order.PriorityOrder.Date.modShip.Date.modReturnedOrder IDRegion_yOrder.Date.yearOrder.MonthProcess.TimeReturn.Freq
040098CA-2014-AB10015140-4195411/11/1411/13/14First ClassAB-100151402Aaron BergmanConsumer73120.0Oklahoma City...High2014-112014-110NaNNaN20141120
140099CA-2014-AB10015140-4195411/11/1411/13/14First ClassAB-100151402Aaron BergmanConsumer73120.0Oklahoma City...High2014-112014-110NaNNaN20141120
226341IN-2014-JR162107-416752/5/142/7/14Second ClassJR-162107Justin RitterCorporateNaNWollongong...Critical2014-022014-020NaNNaN2014222
326339IN-2014-JR162107-416752/5/142/7/14Second ClassJR-162107Justin RitterCorporateNaNWollongong...Critical2014-022014-020NaNNaN2014221
426340IN-2014-JR162107-416752/5/142/7/14Second ClassJR-162107Justin RitterCorporateNaNWollongong...Critical2014-022014-020NaNNaN2014220
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" - ], - "text/plain": [ - " Row.ID Order.ID Order.Date Ship.Date Ship.Mode \\\n", - "0 40098 CA-2014-AB10015140-41954 11/11/14 11/13/14 First Class \n", - "1 40099 CA-2014-AB10015140-41954 11/11/14 11/13/14 First Class \n", - "2 26341 IN-2014-JR162107-41675 2/5/14 2/7/14 Second Class \n", - "3 26339 IN-2014-JR162107-41675 2/5/14 2/7/14 Second Class \n", - "4 26340 IN-2014-JR162107-41675 2/5/14 2/7/14 Second Class \n", - "\n", - " Customer.ID Customer.Name Segment Postal.Code City \\\n", - "0 AB-100151402 Aaron Bergman Consumer 73120.0 Oklahoma City \n", - "1 AB-100151402 Aaron Bergman Consumer 73120.0 Oklahoma City \n", - "2 JR-162107 Justin Ritter Corporate NaN Wollongong \n", - "3 JR-162107 Justin Ritter Corporate NaN Wollongong \n", - "4 JR-162107 Justin Ritter Corporate NaN Wollongong \n", - "\n", - " ... Order.Priority Order.Date.mod Ship.Date.mod Returned Order ID \\\n", - "0 ... High 2014-11 2014-11 0 NaN \n", - "1 ... High 2014-11 2014-11 0 NaN \n", - "2 ... Critical 2014-02 2014-02 0 NaN \n", - "3 ... Critical 2014-02 2014-02 0 NaN \n", - "4 ... Critical 2014-02 2014-02 0 NaN \n", - "\n", - " Region_y Order.Date.year Order.Month Process.Time Return.Freq \n", - "0 NaN 2014 11 2 0 \n", - "1 NaN 2014 11 2 0 \n", - "2 NaN 2014 2 2 2 \n", - "3 NaN 2014 2 2 1 \n", - "4 NaN 2014 2 2 0 \n", - "\n", - "[5 rows x 33 columns]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "combine.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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261207.5640.0020278.340120.0000...0000100000
271061.0480.0021162.5101253.0400...0000100000
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5128961.3830.00401.00251.8000...0001001000
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51290 rows × 45 columns

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" - ], - "text/plain": [ - " Sales Quantity Discount Process.Time Return.Freq Shipping.Cost \\\n", - "0 221.98 2 0.00 2 0 40.770 \n", - "1 341.96 2 0.00 2 0 25.270 \n", - "2 3709.40 9 0.10 2 2 923.630 \n", - "3 344.68 2 0.10 2 1 65.350 \n", - "4 133.92 5 0.10 2 0 41.640 \n", - "5 70.79 2 0.10 2 0 10.480 \n", - "6 5175.17 9 0.10 1 2 915.490 \n", - "7 333.15 3 0.10 1 1 71.020 \n", - "8 64.15 6 0.10 1 2 5.980 \n", - "9 16.04 2 0.10 1 3 2.250 \n", - "10 27.27 1 0.10 1 2 1.620 \n", - "11 2892.51 5 0.10 2 0 910.160 \n", - "12 2832.96 8 0.00 1 0 903.040 \n", - "13 2862.68 5 0.10 3 1 897.350 \n", - "14 687.85 4 0.10 3 1 166.980 \n", - "15 233.77 2 0.10 3 0 55.480 \n", - "16 90.83 2 0.10 3 2 17.500 \n", - "17 26.73 3 0.10 3 0 8.740 \n", - "18 1822.08 4 0.00 2 5 894.770 \n", - "19 53.16 4 0.00 2 1 10.060 \n", - "20 5244.84 6 0.00 4 0 878.380 \n", - "21 48.71 1 0.20 1 1 11.130 \n", - "22 17.94 3 0.00 1 0 4.290 \n", - "23 242.94 3 0.00 1 0 1.280 \n", - "24 4626.15 5 0.00 3 0 835.570 \n", - "25 2616.96 4 0.00 2 0 832.410 \n", - "26 1207.56 4 0.00 2 0 278.340 \n", - "27 1061.04 8 0.00 2 1 162.510 \n", - "28 54.00 4 0.00 2 1 11.080 \n", - "29 25.29 1 0.00 2 1 10.030 \n", - "... ... ... ... ... ... ... \n", - "51260 43.92 3 0.00 5 0 2.780 \n", - "51261 4.02 1 0.60 6 2 1.060 \n", - "51262 15.28 2 0.00 5 0 2.030 \n", - "51263 8.73 1 0.00 5 0 1.570 \n", - "51264 5.68 2 0.00 5 0 1.190 \n", - "51265 6.90 1 0.50 4 1 1.050 \n", - "51266 48.93 3 0.50 2 2 1.050 \n", - "51267 2.78 1 0.70 5 1 1.050 \n", - "51268 25.83 1 0.00 4 2 1.050 \n", - "51269 4.51 3 0.20 6 12 1.440 \n", - "51270 12.54 1 0.20 6 0 1.380 \n", - "51271 1.08 2 0.70 6 0 1.060 \n", - "51272 36.68 7 0.00 2 1 1.042 \n", - "51273 4.57 4 0.70 6 0 1.280 \n", - "51274 823.96 5 0.20 0 0 69.660 \n", - "51275 15.98 2 0.20 0 0 2.010 \n", - "51276 213.48 3 0.20 5 0 14.750 \n", - "51277 22.72 4 0.20 5 1 3.210 \n", - "51278 8.56 2 0.00 5 0 1.580 \n", - "51279 47.14 2 0.10 5 1 1.030 \n", - "51280 259.96 4 0.00 6 0 11.840 \n", - "51281 71.12 4 0.00 6 0 7.300 \n", - "51282 49.30 3 0.45 1 0 1.030 \n", - "51283 61.44 3 0.00 3 1 8.470 \n", - "51284 9.61 2 0.70 5 0 1.020 \n", - "51285 84.00 5 0.00 2 2 1.019 \n", - "51286 58.05 5 0.10 5 1 1.010 \n", - "51287 26.94 2 0.00 0 0 1.010 \n", - "51288 16.72 5 0.20 4 0 1.930 \n", - "51289 61.38 3 0.00 4 0 1.002 \n", - "\n", - " Order.Month Profit Segment_Corporate Segment_Home Office \\\n", - "0 11 62.15 0 0 \n", - "1 11 54.71 0 0 \n", - "2 2 -288.77 1 0 \n", - "3 2 34.42 1 0 \n", - "4 2 -6.03 1 0 \n", - "5 2 25.13 1 0 \n", - "6 10 919.97 0 0 \n", - "7 10 88.80 0 0 \n", - "8 10 22.03 0 0 \n", - "9 10 -1.30 0 0 \n", - "10 10 9.09 0 0 \n", - "11 1 -96.54 0 1 \n", - "12 11 311.52 0 0 \n", - "13 6 763.28 1 0 \n", - "14 6 213.97 1 0 \n", - "15 6 20.77 1 0 \n", - "16 6 35.27 1 0 \n", - "17 6 -2.97 1 0 \n", - "18 11 564.84 0 0 \n", - "19 11 26.52 0 0 \n", - "20 4 996.48 0 0 \n", - "21 3 5.48 0 0 \n", - "22 3 4.66 0 0 \n", - "23 3 4.86 0 0 \n", - "24 4 647.55 1 0 \n", - "25 12 1151.40 0 0 \n", - "26 12 0.00 0 0 \n", - "27 12 53.04 0 0 \n", - "28 12 17.28 0 0 \n", - "29 12 1.26 0 0 \n", - "... ... ... ... ... \n", - "51260 5 12.74 0 0 \n", - "51261 12 -1.11 0 0 \n", - "51262 11 7.49 0 0 \n", - "51263 11 2.97 0 0 \n", - "51264 11 1.76 0 0 \n", - "51265 11 -0.84 0 0 \n", - "51266 12 -2.97 1 0 \n", - "51267 12 -3.25 0 0 \n", - "51268 7 9.03 0 0 \n", - "51269 9 0.85 0 0 \n", - "51270 9 4.23 0 0 \n", - "51271 9 -0.79 0 0 \n", - "51272 12 16.80 1 0 \n", - "51273 6 -3.81 0 0 \n", - "51274 7 51.50 0 0 \n", - "51275 7 5.00 0 0 \n", - "51276 8 16.01 0 0 \n", - "51277 8 7.38 0 0 \n", - "51278 8 2.48 0 0 \n", - "51279 11 8.86 1 0 \n", - "51280 4 124.78 0 0 \n", - "51281 4 22.05 0 0 \n", - "51282 8 -18.83 0 1 \n", - "51283 6 16.59 0 0 \n", - "51284 3 -21.17 1 0 \n", - "51285 6 9.20 0 0 \n", - "51286 8 19.95 0 1 \n", - "51287 5 1.86 1 0 \n", - "51288 5 3.34 0 0 \n", - "51289 5 1.80 0 0 \n", - "\n", - " ... 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"x = pd.get_dummies(combine[use_columns], drop_first=True, dummy_na=True)\n", - "y = combine['Returned']\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Problem 5: Fitting Models" - ] - }, - { - "cell_type": "code", - "execution_count": 372, - "metadata": {}, - "outputs": [], - "source": [ - "# factorized categorical col\n", - "class LabelCountEncoder(object):\n", - " def __init__(self):\n", - " self.count_dict = {}\n", - " \n", - " def fit(self, column):\n", - " # This gives you a dictionary with level as the key and counts as the value\n", - " count = column.value_counts().to_dict()\n", - " # We want to rank the key by its value and use the rank as the new value\n", - " # Your code here\n", - " self.count_dict = dict([(key[0], rank+1) for rank, key in enumerate(sorted(count.items(), key = lambda x: x[1]))])\n", - " \n", - " def transform(self, column):\n", - " # If a category only appears in the test set, we will assign the value to zero.\n", - " missing = 0\n", - " # Your code here\n", - " return column.map(lambda x: self.count_dict.get(x, missing)) # if the key:value pair is not found in the dictionary, fill the value with missing(0)\n", - " \n", - " def fit_transform(self, column):\n", - " self.fit(column)\n", - " return self.transform(column)" - ] - }, - { - "cell_type": "code", - "execution_count": 373, - "metadata": {}, - "outputs": [], - "source": [ - "# factorized categorical col with obj dtype\n", - "\n", - "label_count_df = x.copy()\n", - "\n", - "key_val_rank ={}\n", - "for c in label_count_df.columns:\n", - " if label_count_df[c].dtype == 'object':\n", - " lce = LabelCountEncoder() # objects are instances of a class. We create an instance by calling the class.\n", - " label_count_df[c] = lce.fit_transform(label_count_df[c])\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 375, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[array([[-2.51553479e-04, -8.25943771e-03, 2.54445203e-01,\n", - " -1.97819035e-02, 8.48438718e-01, 4.26019393e-04,\n", - " 1.08786757e-02, 5.07857175e-04, -8.04051138e-02,\n", - " -1.01120425e-01, 0.00000000e+00, -6.28253534e-02,\n", - " 5.00434738e-02, 1.93926159e-01, 0.00000000e+00,\n", - " 0.00000000e+00, -3.49255920e-01, 1.78376185e-01,\n", - " -4.40778573e-01, 2.58725020e-01, -3.81088213e-01,\n", - " 2.21462348e-01, -3.31578097e-01, 7.77755606e-01,\n", - " -1.58219367e-01, -1.24900481e-02, -9.08547805e-02,\n", - " 7.44040174e-02, -6.34059968e-02, 1.15770466e-01,\n", - " 1.26441257e-01, 8.92452176e-02, 8.27573649e-01,\n", - " -7.67702384e-03, -2.55500207e-02, -4.06010057e-01,\n", - " 9.58363378e-01, 0.00000000e+00, -2.32181129e-01,\n", - 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"0.7249819617865267\n" - ] - } - ], - "source": [ - "np.random.seed(0)\n", - "from sklearn.model_selection import StratifiedKFold\n", - "from sklearn import linear_model\n", - "from sklearn.metrics import confusion_matrix, roc_auc_score\n", - "\n", - "skf = StratifiedKFold(n_splits=5)\n", - "\n", - "LR = linear_model.LogisticRegression(class_weight='balanced')\n", - "LR.set_params(penalty='l1')\n", - "\n", - "\n", - "for train_index, test_index in skf.split(label_count_df, y):\n", - " x_train, x_test = label_count_df.iloc[train_index], label_count_df.iloc[test_index]\n", - " y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n", - " \n", - " LR.fit(x_train, y_train)\n", - " y_predict = LR.predict(x_test)\n", - " \n", - " print([LR.coef_, LR.intercept_])\n", - " print(LR.score(x_train, y_train))\n", - " print(confusion_matrix(y_test, y_predict))\n", - " print(roc_auc_score(y_test, y_predict))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Problem 6: Evaluating Models" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 27794cabd05be8a635d3ef741181c112e656863e Mon Sep 17 00:00:00 2001 From: kellyho15 <40176918+kellyho15@users.noreply.github.com> Date: Fri, 28 Sep 2018 01:17:06 -0400 Subject: [PATCH 5/8] Delete Machine_Learning_Lab.md --- .../Machine_Learning_Lab.md | 106 ------------------ 1 file changed, 106 deletions(-) delete mode 100644 Kelly/Machine_Learning_Lab/Machine_Learning_Lab.md diff --git a/Kelly/Machine_Learning_Lab/Machine_Learning_Lab.md b/Kelly/Machine_Learning_Lab/Machine_Learning_Lab.md deleted file mode 100644 index 0167a60..0000000 --- a/Kelly/Machine_Learning_Lab/Machine_Learning_Lab.md +++ /dev/null @@ -1,106 +0,0 @@ -# Machine Learning Lab - -This lab is aimed to walk you through the complete workflow of a machine learning project; from data wrangling, exploratory data analysis (EDA), model training and model evaluation/comparison. - -You will work with your machine project teamates for this lab and your team needs to decide whether to use either R or Python as the main programming language. **Each team memeber needs to work on his/her own submission.** - -We will use Github for team collaboration. There is a [TL;DR](https://gist.github.com/Chaser324/ce0505fbed06b947d962) of how do programmers work together on Github or we can break it down into following steps: - -1. The team leader creates a public Github repository under his/her account first. - -2. All the other team members fork the repo so you will have a COPY of the repo under your account - -3. Git clone YOUR OWN repo otherwise you won't be able to push later. - -4. Create a subfolder under your name and finish your code. Push the changes to Github. - -5. Go to the Github page of YOUR OWN repository and click the "Pull Request" tab. You can find the details [here](https://help.github.com/articles/creating-a-pull-request-from-a-fork/) - -6. Submit the pull request so you can see it under team leader's repository. - -7. Pair review each other's code before merging it to the master branch. - - -**Homework** -To understand fork, pull request and branch better, review [this video](https://youtu.be/_NrSWLQsDL4) in 1.25X speed. - - -## Part I: Preprocessing and EDA - -- The data comes from a global e-retailer company, including orders from 2012 to 2015. Import the **Orders** dataset and do some basic EDA. -- For problem 1 to 3, we mainly focus on data cleaning and data visualizations. You can use all the packages that you are familiar with to conduct some plots and also provide **brief interpretations** about your findings. - -### Problem 1: Dataset Import & Cleaning -Check **"Profit"** and **"Sales"** in the dataset, convert these two columns to numeric type. - - -### Problem 2: Inventory Management -- Retailers that depend on seasonal shoppers have a particularly challenging job when it comes to inventory management. Your manager is making plans for next year's inventory. -- He wants you to answer the following questions: - 1. Is there any seasonal trend of inventory in the company? - 2. Is the seasonal trend the same for different categories? - -- ***Hint:*** For each order, it has an attribute called `Quantity` that indicates the number of product in the order. If an order contains more than one product, there will be multiple observations of the same order. - - -### Problem 3: Why did customers make returns? -- Your manager required you to give a brief report (**Plots + Interpretations**) on returned orders. - - 1. How much profit did we lose due to returns each year? - - - 2. How many customer returned more than once? more than 5 times? - - - 3. Which regions are more likely to return orders? - - - 4. Which categories (sub-categories) of products are more likely to be returned? - -- ***Hint:*** Merge the **Returns** dataframe with the **Orders** dataframe using `Order.ID`. - - -## Part II: Machine Learning and Business Use Case - -Now your manager has a basic understanding of why customers returned orders. Next, he wants you to use machine learning to predict which orders are most likely to be returned. In this part, you will generate several features based on our previous findings and your manager's requirements. - -### Problem 4: Feature Engineering -#### Step 1: Create the dependent variable -- First of all, we need to generate a categorical variable which indicates whether an order has been returned or not. -- ***Hint:*** the returned orders’ IDs are contained in the dataset “returns” - - -#### Step 2: -- Your manager believes that **how long it took the order to ship** would affect whether the customer would return it or not. -- He wants you to generate a feature which can measure how long it takes the company to process each order. -- ***Hint:*** Process.Time = Ship.Date - Order.Date - - -#### Step 3: - -- If a product has been returned before, it may be returned again. -- Let us generate a feature indictes how many times the product has been returned before. -- If it never got returned, we just impute using 0. -- ***Hint:*** Group by different Product.ID - - -### Problem 5: Fitting Models - -- You can use any binary classification method you have learned so far. -- Use 80/20 training and test splits to build your model. -- Double check the column types before you fit the model. -- Only include useful features. i.e all the `ID`s should be excluded from your training set. -- Not that there are only less than 5% of the orders have been returned, so you should consider using the `createDataPartition` function from `caret` package that does a **stratified** random split of the data. Scikit-learn also has a [StratifiedKfold](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedKFold.html#sklearn-model-selection-stratifiedkfold) function that does similar thing. -- Do forget to `set.seed()` before the spilt to make your result reproducible. -- **Note:** We are not looking for the best tuned model in the lab so don't spend too much time on grid search. Focus on model evaluation and the business use case of each model. - - -### Problem 6: Evaluating Models -- What is the best metric to evaluate your model. Is accuracy good for this case? -- Now you have multiple models, which one would you pick? -- Can you get any clue from the confusion matrix? What is the meaning of precision and recall in this case? Which one do you care the most? How will your model help the manager make decisions? -- **Note:** The last question is open-ended. Your answer could be completely different depending on your understanding of this business problem. - -### Problem 7: Feature Engineering Revisit -- Is there anything wrong with the new feature we generated? How should we fix it? -- ***Hint***: For the real test set, we do not know it will get returned or not. From 0d6d1baf13b572fdf4603ad69136c1b04157697d Mon Sep 17 00:00:00 2001 From: Kelly Ho Date: Fri, 28 Sep 2018 07:55:50 -0400 Subject: [PATCH 6/8] organize files --- Kelly/{Kaggle_house_price => }/EDA.ipynb | 306 +- ...rest Models (one hot yr_month sold).ipynb | 337 -- .../Forest Models (no ordinal encode).ipynb | 337 -- Kelly/Kaggle_house_price/Forest Models.ipynb | 350 -- ...larization Model (no ordinal encode).ipynb | 5226 ---------------- ...zation Model (one hot yr_month sold).ipynb | 5210 ---------------- .../Regularization Model.ipynb | 5349 ----------------- Kelly/Kaggle_house_price/Untitled.ipynb | 6 - .../__pycache__/preprocess.cpython-36.pyc | Bin 3938 -> 0 bytes .../__pycache__/preprocess1.cpython-36.pyc | Bin 3856 -> 0 bytes .../preprocess1copy.cpython-36.pyc | Bin 3860 -> 0 bytes .../preprocess2_label.cpython-36.pyc | Bin 3479 -> 0 bytes .../preprocess3_yrmonthsold.cpython-36.pyc | Bin 3916 -> 0 bytes .../preprocess_final.cpython-36.pyc | Bin 4603 -> 0 bytes .../preprocessfinal1.cpython-36.pyc | Bin 4603 -> 0 bytes Kelly/Kaggle_house_price/preprocess1.py | 214 - Kelly/Kaggle_house_price/preprocess2_label.py | 214 - .../preprocess3_yrmonthsold.py | 216 - Kelly/Machine_Learning_Lab/ML_lab_Kelly.ipynb | 3004 --------- .../Machine_Learning_Lab.md | 106 - .../preprocessfinal.cpython-36.pyc | Bin 4606 -> 4587 bytes ...nb => housing_price_data_preprocess.ipynb} | 0 Kelly/linreg_regularization_models.ipynb | 697 +++ ...ghborhood_feature_engineering_lasso.ipynb} | 14 +- .../preprocessfinal.py | 0 Kelly/tree_based_models.ipynb | 358 ++ 26 files changed, 1206 insertions(+), 20738 deletions(-) rename Kelly/{Kaggle_house_price => }/EDA.ipynb (91%) delete mode 100644 Kelly/Kaggle_house_price/Forest Models (one hot yr_month sold).ipynb delete mode 100644 Kelly/Kaggle_house_price/Forest Models (no ordinal encode).ipynb delete mode 100644 Kelly/Kaggle_house_price/Forest Models.ipynb delete mode 100644 Kelly/Kaggle_house_price/Regularization Model (no ordinal encode).ipynb delete mode 100644 Kelly/Kaggle_house_price/Regularization Model (one hot yr_month sold).ipynb delete mode 100644 Kelly/Kaggle_house_price/Regularization Model.ipynb delete mode 100644 Kelly/Kaggle_house_price/Untitled.ipynb delete mode 100644 Kelly/Kaggle_house_price/__pycache__/preprocess.cpython-36.pyc delete mode 100644 Kelly/Kaggle_house_price/__pycache__/preprocess1.cpython-36.pyc delete mode 100644 Kelly/Kaggle_house_price/__pycache__/preprocess1copy.cpython-36.pyc delete mode 100644 Kelly/Kaggle_house_price/__pycache__/preprocess2_label.cpython-36.pyc delete mode 100644 Kelly/Kaggle_house_price/__pycache__/preprocess3_yrmonthsold.cpython-36.pyc delete mode 100644 Kelly/Kaggle_house_price/__pycache__/preprocess_final.cpython-36.pyc delete mode 100644 Kelly/Kaggle_house_price/__pycache__/preprocessfinal1.cpython-36.pyc delete mode 100644 Kelly/Kaggle_house_price/preprocess1.py delete mode 100644 Kelly/Kaggle_house_price/preprocess2_label.py delete mode 100644 Kelly/Kaggle_house_price/preprocess3_yrmonthsold.py delete mode 100644 Kelly/Machine_Learning_Lab/ML_lab_Kelly.ipynb delete mode 100644 Kelly/Machine_Learning_Lab/Machine_Learning_Lab.md rename Kelly/{Kaggle_house_price => }/__pycache__/preprocessfinal.cpython-36.pyc (93%) rename Kelly/{Kaggle_house_price/housing price data manipulation.ipynb => housing_price_data_preprocess.ipynb} (100%) create mode 100644 Kelly/linreg_regularization_models.ipynb rename Kelly/{Kaggle_house_price/Neighborhood feature engineering (regression).ipynb => neighborhood_feature_engineering_lasso.ipynb} (98%) rename Kelly/{Kaggle_house_price => }/preprocessfinal.py (100%) create mode 100644 Kelly/tree_based_models.ipynb diff --git a/Kelly/Kaggle_house_price/EDA.ipynb b/Kelly/EDA.ipynb similarity index 91% rename from Kelly/Kaggle_house_price/EDA.ipynb rename to Kelly/EDA.ipynb index d010328..82be6c1 100644 --- a/Kelly/Kaggle_house_price/EDA.ipynb +++ b/Kelly/EDA.ipynb @@ -2,138 +2,52 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import datetime as dt\n", - "from matplotlib import pyplot as plt\n", - "import seaborn as sns\n", - "import datetime" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "train = pd.read_csv(\"train.csv\")\n", - "test = pd.read_csv(\"test.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "" + "1.7182818284590453" ] }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "Index(['MeadowV', 'IDOTRR', 'BrDale', 'OldTown', 'Edwards', 'BrkSide',\n", - " 'Sawyer', 'Blueste', 'SWISU', 'NAmes', 'NPkVill', 'Mitchel', 'SawyerW',\n", - " 'Gilbert', 'NWAmes', 'Blmngtn', 'CollgCr', 'ClearCr', 'Crawfor',\n", - " 'Veenker', 'Somerst', 'Timber', 'StoneBr', 'NoRidge', 'NridgHt'],\n", - " dtype='object', name='Neighborhood')" - ] - }, - "execution_count": 3, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "orderIdx = train.groupby(\"Neighborhood\").agg({\"SalePrice\":\"median\"}).sort_values(\"SalePrice\").index\n", - "sns.boxplot( x=train[\"Neighborhood\"], y=train[\"SalePrice\"], order=orderIdx ) \n", - "plt.xticks(rotation=90)\n", - "plt.show()\n", - "orderIdx" + "import numpy as np\n", + "np.expm1(1)" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 32, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "sns.lmplot(\"YrSold\", \"SalePrice\", train, fit_reg=False)" + "import numpy as np\n", + "import pandas as pd\n", + "import datetime as dt\n", + "from matplotlib import pyplot as plt\n", + "import seaborn as sns\n", + "import datetime" ] }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n", - " 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n", - " 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n", - " 51, 52, 53, 54]),
)" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": 5, + "metadata": {}, + "outputs": [], "source": [ - "#sns.lmplot(\"MoSold\", \"SalePrice\", train, fit_reg=False)\n", - "sns.boxplot(x=\"YrMo\", y=\"SalePrice\", data = train)\n", - "plt.ylim(100000, 400000)\n", - "plt.xticks(rotation=90)\n", - "# hue=\"Neighborhood\"" + "train = pd.read_csv(\"train.csv\")\n", + "test = pd.read_csv(\"test.csv\")" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 10, "metadata": { "scrolled": false }, @@ -333,7 +247,7 @@ "[5 rows x 82 columns]" ] }, - "execution_count": 5, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, @@ -341,7 +255,7 @@ "data": { "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -362,16 +276,16 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 6, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, @@ -379,7 +293,7 @@ "data": { "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -394,7 +308,75 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "Index(['MeadowV', 'IDOTRR', 'BrDale', 'OldTown', 'Edwards', 'BrkSide',\n", + " 'Sawyer', 'Blueste', 'SWISU', 'NAmes', 'NPkVill', 'Mitchel', 'SawyerW',\n", + " 'Gilbert', 'NWAmes', 'Blmngtn', 'CollgCr', 'ClearCr', 'Crawfor',\n", + " 'Veenker', 'Somerst', 'Timber', 'StoneBr', 'NoRidge', 'NridgHt'],\n", + " dtype='object', name='Neighborhood')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "orderIdx = train.groupby(\"Neighborhood\").agg({\"SalePrice\":\"median\"}).sort_values(\"SalePrice\").index\n", + "sns.boxplot( x=train[\"Neighborhood\"], y=train[\"SalePrice\"], order=orderIdx ) \n", + "plt.xticks(rotation=90)\n", + "plt.show()\n", + "orderIdx" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.lmplot('YrSold', \"SalePrice\", data=train, fit_reg=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -405,7 +387,7 @@ " 34, 35]), )" ] }, - "execution_count": 7, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, @@ -413,7 +395,7 @@ "data": { "image/png": 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Osx148yT3/xDwoQmmXwlcOcH0CTOkqXhMk6ZjeHiY2+9cxbwFnefbUR5W898Preo437ZNFS2YJEmSutaxSY6IUykGtjoRuA341cxc3Y8Fk+rC45m0N+YtgGeeMdH4gXvvziscT1Cqi3a7ze4tj/D4FV+spN7ujQ/RfuKxSmpJkqo11ZbkTwG/B3ybYvCuPwNe2+uFkmaKxzRJkiRJs9tUTfKczBwdg/vLEfHeXi+QJElS3bRaLTbtv5mnnfHzldR7/Iov0lp0RCW1JEnVmqpJPjwi3jTZ75lZzQkJJUldabfbPLEFNlxWza7YT2yE9hOeJU+SJGnUVE3yt4CfmeT3BGySJUmSJEmN0bFJzsxz+rUgGixDQ0OsXLly3LRt27aR2d3WrYhg3rx546YtX77c0aKlKbRaLbbsv4Ejz6xmcLANlyWtRa1KakmSJDVBV6eAiojFwIeBVma+LiJ+FPiJzPT8wxpYntNWkiRJ0p66apKBzwOfA/6o/P17wJcAm+RZasWKFQPfDA4PD3PX6lUcPb/zFrk5u4qt4yPt2zrO98CIp+uRJEmqUrvdZuuWrXzsho9UUu/+LfdxcPvgSmqpubptko/MzEtHR7fOzJ0RsauHyyX1xdHzg7Nf3u3ToLOLvrWzkjqSJEmSZk633cHWiFhIMVgXEXEaMNKzpZIawl26peZqt9uwZYSdl19XTcGNI7SfqOZYc0lqilarxfbdO3j3qdWcifZjN3yEA1sHVFJLzdVtk/wu4HLgxIj4P8Ai4Od6tlRSQ4zu0r3o8M7zxe7i56YfrOo43/qHf3iag6hJkiRJ1emqSc7MWyLi5cCPAAHclZlP9HTJpIZYdDi85ZX7VVLr0m94lINUF61Wiw37J3Pf8NJK6u28/DpHGpckqQY6NskR8aZJbjolIshMz5MszbAmDKImSZIk1cVUW5J/psNtCdgkS7PURMdbr1u3DoAlS5aMmz5bjqNut9tsG4E7r6hmpPNtG6G9s11JLUmSJHWnY5Ocmef0a0Gkfmu32zzycFY2KvUDDydbaWZDM1FD3G63eeyxx8ZNG/19+/btPzTvnvefLY2zJEmSBkvX576JiJ8Gng0cODotM9/fi4WSVC/XXXcdGzduYG6Xrxg7dmz9od9HRjY8+fvOnUXj3LQmudVq8fjcDTzzjGpGKL7ziqR1lMeoSrOFAzFKUj109ZE3Iv4amAe8Evg0xcjW/9nD5ZK6On3SdE6d1Gq1GGFjpedJnt9qbkMzdy4ccUQ1tTZvrqbO3vB0XJIkSepGt93BizPzxyJiVWb+aUR8jAYej+w3uPUyPDzM9+5YRWv+5Fvl9ttVPDaPrrutY632SDXHiM5WrVZr3JbgyTzySPHz0EO7q9lPw8PD3LF6FYcu6DzfzvJf5f4HO5+O65FNFS2YJJUciFGS6qHbJnn0wMNtEdECNgHP6M0iSU9pzQ9W/OT0T/g+dO2OCpZm9jrhhBN+aNpExyQ//njx+5w5B42bftBBB41rio8+euKavXboAvjx11VT68arqqkjSZKkeum2Sb4iIg4H/l/g5nLap3uzSDPHb3CliU30vHB0a0nSTPDwGUm9NtV5kn8cuD8zP1D+fghwG3An8IneL56kuvLDxMx5YiNsuKzzIQQ7R4qfc+dPXYtF1SyXJPXD8PAwd9x5D4ctXNpxvp1Z7Im2dn3nvcm2bFzzQ9M8BE+a3abakvw3wE8BRMTLgPOB3wGeB1xIMYCXNLAeGJn6FFCbHi3eEBcc0nnE4gdGkvnNHbdLNdHtbur3bim2oJy46MTOMy6amV3fparlxvXsuKLzcCk58jAAMf/wKWuxaGFly6bqHbZwKaee+d5Kat1w2UcqqTMo3BNMmtpUTfJ+mTk6PM1ZwIWZ+Q/AP0TEd3q7aFJvddsYbCh315rf6txszG/ZbKj3uv2wMrp74QUXXNDLxZFqofsvj4om+cSpGuBFC309n+Vm2yF427dvn+lFkGplyiY5IuZm5k7g1cC5e3FfqdZsNiSpGXw9l7o30fPF54Y03lSN7heBb0XEBooRrq8FiIiTgJEeL5skSZIkSX3VsUnOzA9FxNeBY4Br8qnRCuZQHJusCnhsiCRJkqSq2Wfsmyl3mc7M6yeY9r3eLI5GeWyIJEmSpKrZZ0zN44prwGNDJEmSJFXNPmPf2CQ30ES7VbTbbR577LGu7n/QQQfRao0/l9Fs2f1ior/dveXo1qMvKKNmy99EUgcbR9h5+XWd5xnZWvycf/CUtVi0pPM8klRz7t6rJrBJbqDrrruOTRs28LS5+z057Yldu9n95CHlnT2+bRtbH9781O87d9Fut2fti9iBBx4404ugAbJtE9x5Refn2vYtxc8DD5u6FkdVs1yq3t6fs3qKBnjREk87JKmR3L1Xg8YmuaGeNnc/nj5/XiW17hvZVkmdQTBbvwhQNbpumh4pmqZnHNX53Nsc5bm366wfpx0aGhpi5cqV46Zt27aN7PJLz4hg3rzx7wXLly/3tU5Sz7h7b7Pt+b7U1Pckm+QGarVa7MjHee9Ln11JvY9cdzsH7LH7tbrTbrfZMgKXfmNXJfUeehi2Z7uSWqqe52qVJEkafDbJfTbRcRoTmew42Il4PIckNdOKFSt8fZck1cZseV+ySe6z4eFh7ll9B0vnz+843wG7ii2PO9rrOs63ZmSksmVT9VqtFgfGBt7yyv2mnrkLl35jFwuOcau+JEmamhtnpH3TsyY5Ig4Evg08rcz5Sma+LyKeAVwCLABuAX45M3dExNOAi4EXAhuBszLz+2Wt9wJvB3YB78zMq8vppwOfBPYDPp2Z55fTJ8zo1brujXa7TTe77S8+ZIpRUEuZRU1JktRbuzc+xONXfLHzPCPFwJdz5h8xZS0WdZ5Hmq7h4WHuWn0PRx+xtON8c3YfAMDIA50/Lj+weU1lyybVWS+3JD8OvCozH42I/YHrIuIq4F3AJzLzkoj4a4rmd6j8uTkzT4qItwIfBc6KiB8F3go8G2gB/xoRp5QZnwKWA2uBGyPi8sy8o7zvRBmSpG5s2MHurz7YeZ6RncXP+VO8lWzYAUdWs1jSTOl+NPNNAJw4VQO86AgH5lNfHH3EUs5e/seV1Lpo5QcrqSPVXc+a5CyGOXu0/HX/8pLAq4BfKKdfBPwJRQN7Znkd4CvAX0ZElNMvyczHgf+OiHuAF5Xz3ZOZwwARcQlwZkSs7pAx41qtFjtI/vgnX1pJvQ9ee11jB9Vqt9s8OpIMXTv9nQDaI8kh4RZ3qRtdNwMj5amNjpxilO4jHaVbg8+B+SRp9ujpMckRsR9wM3ASxVbfe4GHM7Pc/MBaYPTEkUuA+wEyc2dEjAALy+nXjyk79j737zH91PI+k2XsuXznAucCLF3aeTcUSZotbAYkSdJs1tMmOTN3Ac+LiMOBfwKeNdFs5c+Y5LbJps/Zy/knWr4LgQsBli1b1t0JvtQ3rVaLR3MjK37ygGnXGrp2B4c0dIu7JEmSpOr0ZXTrzHw4Ir4JnAYcHhFzyy29xwKj+8CuBY4D1kbEXGA+sGnM9FFj7zPR9A0dMiRJkiSpcRzNvDq9HN16EfBE2SAfBPwUxYBa3wB+jmL06bOBy8q7XF7+/h/l7f+WmRkRlwNfiIiPUwzcdTLwnxRbjE8uR7JeRzG41y+U95ksQ9Is1W63eWQEbryqmnqPbIL2Lr9/kyRJ9TA8PMw9d9zF0sOO7jjfATuLHXJ3rO18Ktk1Wx6obNkGTS+3JB8DXFQelzwHuDQzr4iIO4BLIuKDwK3AZ8r5PwP873Jgrk0UTS+ZeXtEXArcAewE3lHuxk1E/DZwNcUpoD6bmbeXtd4zSYYkSZIkNdLSw47mD087p5JaH77+c5XUGUS9HN16FfD8CaYP89To1GOnbwfePEmtDwEfmmD6lcCV3WZImr1arRa79tvAj7+umno3XgWtxR7nLkmS1DR9OSZZkvbWRMfVrFu3DoAlS8YPWD9bj5dpEh9vqTmGhoZYuXLlk79v27aN4sygU4sI5s2bN27a8uXLxz3n2+02W7Zs5YbLPlLJ8m7ZeB/tJw6upJakZrBJljQwtm/fPtOLoD7y8ZYkNcWeXx5B9V8gqTo2yZJqaaIX/UE8L+9EW0gnG1VyNm8hbcrjLal4PvfytazVarF7/x2ceuZ7K6l3w2UfobVo+qebrKNi0MqtXLTyg5XUe2DzfWzd7VZ3NZ9N8gxYMzLCB6+9ruM8Dz66FYDFh3R+IVozMsJJrSUd55FULwceeOBML4IkSeqjXn95pGrZJPfZCSec0NV8O8otTQdM0QCf1FrSdU1J/ecb4uzSlD0HmnCMeFMeC2k6Wq0WI3N2cPbyP66k3kUrP8j8o8dvde/m3Lyel1eDxia5z7p90ruboSQ1Q1P2HGjCMeJNeSykOhkeHuZ7q++hNX/ppPPst6torB9t7+hYqz2yptJlk/aVTbIkqe+auuWhDstQhSYcI96Ux0IaBK35S1nx8j+adp2hb/3QGV8BuH/LGj52Q+fRzB/a+iAARx28uON8929Zw8mctG8LqFnDJlmS1HfDw8OsuvN2OHJeh7mKLQ6rNvx352IbtlW3YJKkWun2sMIn7i3eMw48tvMgbCdzkocqako2yZJqoalbFtXBkfPY78xnTbvMrstWV7AwkqQ68lBFzQSbZEm1MDw8zOrVq5h/xOTz7Npd/Gw/sKpjrZHNFS6YJEnSAGi322zd8ggfvv5zldS7b8sDHNzeWkmtQWOTrFprjyRD104+yMOGrcUJ2I88OKasc4pnyqq9+UfAy14z/Trfvmb6NSRJkjQ72SQ31JqRbXzkuts7zvPg1mKk0sUHdx7tc83INk6agQazm+NFHix3vz1kyYkd5ztlSffHtFRt/cNw6Td2dZzn4UeLn4cfMnWtBcdUtGCSRHeHOoCHO0hS3bVaLXbsHuEPTzunknofvv5zHNCaX0mtQWOTXANVn8txr8/FvOQZHec7aYYazG4+YNX9+JNu/26by8diwTGdm/0Fx8xcsy9Vqd1uw5Zt1RxPvGEb7R3t6deZpYpB1FYTCxd0nC+z2HPntvUPdp5v46bKlk2SpJlgk1xT0zmXowMc1IePhTQzhoaGWLly5bhp27Zte7LRm0pEMG/e+JG3ly9f3tito7FwAXPPeG0ltXZecfWU80z05fC6desAWLJk/K5LbpWWJPWbTXIN+OYvabZptVpsOODxyka3bh3ZqmCpNJO2b98+04sgSRJgkyxJA6/qQzaaYMWKFbNiPQfVRI+Ne9RIkurCJlmSGmg6h2xIkiTNZjbJkmqh3W7z8Eg1p296eDOwe/YM5OQWU0mSpOrYJEuSNIu1221yy0hXA251Izduov1E59PeSZJUZzbJkmqh1WrBnA287DXTr/Xta6B1tAM5SZIkae/ZJEuaNR7ZBDde1XmebY8UP+cdOnUtFleyWNKMarVabNx/v0pPAdVaNP7JMdHgcnuabLC5icyWAegkSTPDJlnSrHDCCSd0Nd+9jxYf1I9bfGLnGRd3X1OT2LCNXZetnvz2kfKUQPOnGIRswzY4srrFUvWGh4dZdeedxMKFk84zeg7r29av71grN26sdNkkSdqTTbKkWaHbrU6ehqY/uvmC4d6R4guLE498RucZj/QLi0EQCxcy94wzpl1n5xVXVLA00uzxwOY1XLTygx3n2fTIgwAsOLTzLlIPbF7D/KNPGjet3W7z6MNbGfrWh6a3oED74fs4hIOnXWc2W7PlAT58/ec6zvPg1k0ALD54wZS1TmJ+Zcs2SGySJUl9182XFn5hIUnT0+0XiBu27gBg/tEHdJxv/tEnTVjz8V2P0374PgCe2PUEu3N3V7lzYg7777f/uDo2yfuu28d7x70bADjg2M4N8EnMn7VfQtskS5IkaaBs2biGGy77SMd5to4UW0cPnt956+iWjWtg0Ukd5xlU/diL6qUvfem4MQfa7TaPPfZYV/c96KCDioE7x5itTVkV3GuuOjbJkmpjZHPn8yQ/Wg6qdcgUg2qNbIbW0dUtl9R0uXHTlKeAypHiCRjzOz8Bc+MmWOSoduqdrseY2FJsHT12UeetoyyaeOuouuMgemoim2RJtdDVMapbi2NUW0d3HlSrdbTfREvd6r7heBREhEtGAAAaBklEQVSAE6dqgBct/qGaxbmYt1RyPHFu3Ej7iSemXacKE43aPdko3Y7IXR23lknqNZtkSbXgMarSzLDhqNaBB04xGrskqfZskjUw/MZekgZTcS7m/Ssb3bq1aFEFSzV9vs9IUjPZJGug+Y29JEmSpCrZJGtg+I291FzuKSJJkurCJlmSVEvuKSJJkmaCTbIkaca5ZbheerFlPzdu7Di6dY6MABDz509Zh5ockyxJaiabZKnP3K20O/6dpHqZzpb9rk7xtmULACdO1QAvWuQp3iRJPdWzJjkijgMuBo4GdgMXZuYnI2IB8CXgeOD7wFsyc3NEBPBJ4PXANuBXMvOWstbZwB+XpT+YmReV018IfB44CLgS+N3MzMkyerWu0nS5W2l3/DtJ/VH1l06e4k2SNEh6uSV5J/DuzLwlIg4Fbo6IlcCvAF/PzPMj4g+APwDeA7wOOLm8nAoMAaeWDe/7gGVAlnUuL5veIeBc4HqKJvl04Kqy5kQZs5Zb5erDv213/DvVi68hkiQNHt+/982cXhXOzB+MbgnOzEeA1cAS4EzgonK2i4A3ltfPBC7OwvXA4RFxDPBaYGVmbiob45XA6eVth2Xmf2RmUmy1HltrogyNceCBB7plTtI+8zVEkqTB4/v31PpyTHJEHA88H7gBWJyZP4CikY6Io8rZlgD3j7nb2nJap+lrJ5hOh4w9l+tcii3RLF26dB/XbjD4rZCk6fA1RJKkweP7977peZMcEYcA/wD835m5pTj0eOJZJ5iW+zC9a5l5IXAhwLJly/bqvpIkSdK+GhoaYuXKleOmbdu2jWIHyalFBPPmzRs3bfny5TZFUgV62iRHxP4UDfLfZ+Y/lpMfjIhjyi28xwAPldPXAseNufuxQLuc/oo9pn+znH7sBPN3ypAkSTPMY+QkSXXWy9GtA/gMsDozPz7mpsuBs4Hzy5+XjZn+2xFxCcXAXSNlk3s18OGIOKKc7zXAezNzU0Q8EhGnUezG/TbgL6bIkCRJNeTxcZptVqxY4RdAUk31ckvyS4BfBm6LiO+U0/6QonG9NCLeDqwB3lzediXF6Z/uoTgF1DkAZTP8AeDGcr73Z+am8voKnjoF1FXlhQ4ZkiRphtkYSJLqrGdNcmZex8THDQO8eoL5E3jHJLU+C3x2guk3Ac+ZYPrGiTIkSZIkSeqkL6NbS5IkSZp5jgkgTc0mWZIkSZrFHBNAGs8mWZIkSZol3DIsTW3OTC+AJEmSJEl14ZZkSZIkSQPD46rVazbJkiRJGmg2TfK4alXJJlmSJEmNY9PUXFV/yTE0NMTKlSvHTdu2bRvFGWqnFhHMmzdv3LTly5f7ZcwAs0mWJEnSQLMZkVQlm2RJkiRJs9aKFSv8okXjOLq1JEmSJEklm2RJkiRJkko2yZIkSZIklWySJUmSJEkqOXCXpFnL82pKkiRpTzbJkjSG59WUJEma3WySJc1abhmWJEnSnjwmWZIkSZKkkk2yJEmSJEklm2RJkiRJkko2yZIkSZIklWySJUmSJEkq2SRLkiRJklSySZYkSZIkqWSTLEmSJElSKTJzppehFiJiPXDfXtzlSGBDjxbHjHrVN6NeGU1YBzPqU9+MemU0YR3MqE99M+qV0YR1MKM+9fc14+mZuWiqmWyS91FE3JSZy8yY+YwmrIMZ9alvRr0ymrAOZtSnvhn1ymjCOphRn/pm1Ctj0NfB3a0lSZIkSSrZJEuSJEmSVLJJ3ncXmlGbjCasgxn1qW9GvTKasA5m1Ke+GfXKaMI6mFGf+mbUK2Og18FjkiVJkiRJKrklWZIkSZKkkk2yJEmSJEklm2RJkiRJkko2yZIkSZIklWySJUlSLUTExTO9DJIk2SR3KSJeGxFvj4jj95j+qxXVj4h4S0S8ubz+6oj484j4rYjo2eMUEf9WYa2fjYgF5fVFEXFxRNwWEV+KiGMryvh4RLykilodMhZExP+KiF8rH4s/iogrIuKCiDiiooy5EfEbEfG1iFgVEf8VEVdFxG9GxP4VZexXZnxgz79ZRPxxFRkdsv9XhbUG/rkXEUfu8fsvlRnnRkRUlNHT519Tnntlzisj4i8j4rKI+IeIOD8iTqqwfs+f31PkT/uUGP14/YiIy/e4/DPwptHfK8o4NSIOK68fFBF/GhH/HBEfjYj5FWW8MyKOq6LWFDknRsTvRcQnI+Jj5f9TJetQ1n9tRAyVf//LyuunV1V/iuxK3jP68Xo+SW5ln6XKej19z+j1+0VZtx/vGQP/WttFRlXPjZ5+lpoks+rnRc//b8fleQqoqUXEh4GXArcAPwP8WWb+RXnbLZn5ggoy/go4CjgA2AI8Dfhn4PXAg5n5uxVkrNpzEnAKcBdAZv7YNOvfkZk/Wl7/EnA98GXgp4BfzMzl06lf1l0P3AcsAr4EfDEzb51u3T0yrgRuAw4DnlVevxRYDjw3M8+sIOOLwMPARcDacvKxwNnAgsw8q4KMTwPzgP8Efhn4Vma+q7ytkv/bDtlrMnNpBXWa8tx7clnLBuMngS8AZwBrM/N/VpDR0+dfg5575wOLga8DbwT+G/ge8FvAhzPzyxVk9OP5vWCym4D/ysxpfWDox+tHRNwC3AF8GkiKZf8i8FaAzPxWBRm3U/zv7Cw/0G4DvgK8upz+pgoyRoCtwL0Uy//lzFw/3bp7ZLyT4jXwWxSvTd8BNgM/C/xWZn5zmvX/jOLzwMWM/599G3B3Fa+DU+RX9Z4x8J+lyoyevmc06PPawL/WdpE/7edGnz5L9eN50fP/23Ey08sUF4oPanPL64cDVwKfKH+/taqM8uf+wEbggPL3uaO3VZBxOfB3wDOBpwPHA/eX159eQf27xly/eY/bvlPROtxa/jwZ+H+A24E7gfcBp1SU8Z3yZwDrerQed3W47XsVZawac30uxQnX/5HiQ8O0/28pPoBMdHkE2FnROjTluXfrmOu3AAePyawqo6fPvwY9924bc30u8H/K60cA3636sZjgtqqe37uAYYomf/Qy+vuOCur39PWjrDsH+J/ASuB55bThKmqPyVg95votPfqfurVcl9cAnwHWA1+j+KB+aEUZtwH7ldfnAd8sry+t6PV8wv/L8rl4d0Xr0Jf3jPLnwH6WGv2fGnO98veMXr9fjF2HHr9nDPxrbZnR0+cG/fks1Y/nRc//b8de3N26O3MzcydAZj5M8S3MYRHxZYpvK6swWv8J4MbM3FH+vpPiCTptmfkG4B8oPuw8NzO/DzyRmfdl5n0VRHwzIt4fEQeV198Ixa6NwEgF9aHY2kBm3p2ZH8jMZwNvAQ6keNJXYU4Uu3YeBxwyumtKRCykusd7c7k72JPPwYiYExFnUWwdqMKTy5qZOzPzXIqtD/8GHFJB/YeBkzPzsD0uhwI/qKA+NOS5BxwUEc+PiBdSfNDdOiazqoxeP/+a8tzbPWbLQAvYDyAzN1M0BFXox/N7GHhFZj5jzOWEzHwG8GAF9Xv9+kFm7s7MTwDnAH8UEZ+iaGaq9N2IOKe8/l8RsQwgIk4BnqgoI8t1uSYz307xf/VXwOkUj1NVRv82TwMOLYPXUDRO07U9Il40wfQfB7ZXUB/6857RhM9S0Pv3jKZ8XmvCay30/rnR889SfXpe9OP/9ilVd91NvABXAC+fYPoHgd0VZVwFHDLB9KOB/6x4fQ4GPk7xrc/aCuvuD/wJsKa87Kb4FuwLwNKKMir5xmuKjJ+neOF7EPi/gH8tL+uAcyvKOJ5i96P1FLt6fg94qJz2jIoy/g44fYLpv0bxwjXd+h8EXjTJbR+taB0a8dwDvrHH5Zhy+kLgpooyevr8a9Bz7yyKXQCvKf9OP11OXwR8oaKMfjy/30HxQWSi236ngvo9ff2YJPOnKXZ5r7LmfODzFLtC30DRGA9T7LY84d9vHzImfW4AB1WU8bvAKooPoHcC55TTFwHfrqD+C8q/zx3lc+MaYHU57YUVrUM/3jMG/rNUWbun7xm9fr8oM/rxnjHwr7VlnZ4+N+jDZ6kxNXv5vOj5/+3Yi8ckd6H8xoLMfGyC25Zk5roeZh9MsZvNQz2o/VzgJzLzr3tQez7FN1cbK657SGY+WmXNSXL2ozhmf2dEzAWeR7H7Z1Xfdo/NWlhmbai69qBr6nNvTMZ+wNMyc1vFdSt//jXpuVduST4BuCeLb9V7xud3ZxERwIuAJRRbntoUzUylH04i4lCKx3wuxQe3qrYAERGnZOb3qqrXIefZFMfqfzcz7+xRxtEUj0VQ/J0e6EVOvw3qZ6kJsip/zxj0z2tj8nytncRMfJbq9fOiV/+34zJskrtTPhinM/6N/OoqP2A1IaMJ69CvjA7ZyzNz5SBkNOWxMKMe9ZuU0SF7YJ7fva4fEa+h2C35boq9BaAYdOckisGorpluRpnT80a8KRmT5D6zV015LzKa8hri6/m0swfqtdbHuz4ZozwmuQsR8TaKgRNeQTFgxsHAK4Gby9vM6EP9JmVM4TODkNGUx8KMetRvUsYUBuL53af6nwR+KjNfl5m/Vl5OpxjN/JNVBJSN+N0Uu+i9nmKX7j8F7i5vM2NqlXxZ0Y+MpryG+HpeiYF5rfXxrk/GuDy3JE8tIu4CTt3zW4ooBpi5ITNPMaMZ69DHjMnOARrAqzLz4LpnNOixMKMG9RuW0YTndz/W4W7gWVkOKDNm+gHAHZk57XNXR8Rq4HVZDCIzdvozgCsz81lmQET8+WQ3AWdn5mHTqd/HjKa8hvh63l3GwL/Wlhk+3jXJGKvqUSSbKihH6dvD7vI2M/pTv0kZPwn8ErDn8Tqju9MNQkZTHgsz6lG/SRlNeH73Yx0+C9wYEZdQnCoEilManUV1W4Hm8tT5U8daRzWjQjcl4xzg3cDjE9z28xXU71dGU15DfD3vThNea0dr+XjXI+NJNsnd+RBwS0Rcw/g38uXAB8zoW/0mZVwPbMvMb+15Q/lN2SBkNOWxMKMe9ZuU0YTnd8/XITM/EhFfBc4EfoLiQ85a4Bcz844qMuhPI96EjBspBgT79z1viIg/qaB+vzKa8hri63l3mvBaCz7edcp4krtbd6nclP9axoz6SHGgeFXnYWtERhPWoV8ZTdCUx8KMetRvUob2TUS8IDNvqbjmsyga8bGP9+UVNuIDnxHFiO/bs+KR9vudUeY04jXE1/PZxce7PhlPyorPKTVbLsAZZsx8fTPqldGEdTCjPvXNqFdGn9bhlj5kvMCMma/fx4yBf+71I6MJ62BGfeo3IcMtyfsoIm7JzBeYMbP1zahXRhPWwYz61DejXhl9WodbM/P5Pc4Y+MeiHxlNWAcz6lPfjHplNGEdep3hKaD2XeUHiDc0ownrYEZ96ptRr4wmrIMZ9akPxWmNeq0Jj0U/MpqwDmbUp74Z9cpowjr0NMMmed/9hhm1qG9GvTKasA5m1Ke+GfXKqLx+RLwsIn6kvP5S4KSI+Omqc/bQj0a8CRlNWAdoxnOvHxlNWAcz6lN/4DPc3boLEbEUeCgzt0dEAL8CvAC4A/jb3OMcj7M1ownrYMZe1X8DcE1mbp/usppR/4wmrIMZ9alfZvwZxSlU5gJXA68GrgJeDtyamedVlPMy4MHMvKtsxE8DVmfmv1RRvykZTViHMuMQ4HTgOGAncDfF//JuM/pX34y9zngmTw3Ml0CbYmC+1YNQv0kZT2bZJE8tIr4LvCgzt0XER4ETga8CrwLIzF81oxnrYMZe1X8M2ErxofaLFKML7preUptR14wmrIMZ9alfZtwOPAc4iOJ8v0vK16v9KZrk51SQ0fNGvAkZTViHMuMtwHnAfwGvBP6dYq/J/0FxarHbzGjGOjQs4z0U5wq/hKfOh34s8Fbgksw8v871m5QxTvZ41LEmXIA7xly/GZgz5vf/MqM562DGXtW/FTgC+HXg68CDwF8DL69i+c2oV0YT1sGM+tQvM75b/jwQ2AwcVP6+39jXr2lm3E5xzNq8MmNeOX3/0XwzmrEOZa1VY+oeSfHlDsCPAf9uRnPWoWEZ3wP2n2D6AcDdda/fpIyxF49J7s79EfGq8vr3KXa3ICIWmtHX+mbUKyMzc3Nm/m1mvhp4LsWu3OdHxP1T3NeMwctowjqYUZ/6AP8SEdcC1wKfBi6NiD+i2Lr47YoyMotPUaO7RY7uPreb6sZlaUJGE9YBiib8sfL6VuCoMngVcJgZfatvxt7ZDbQmmH4MTz1f6ly/SRlPcnfrLkTEccDFFN9ujwAv5alv2X8vM79uRjPWwYy9qj/pqVoi4umZed906ptRr4wmrIMZ9ak/ptZPUDRP10fEicDPAmuAr2QFx/uVh5q8mGJr9TeBZwLXU+ziO5yZv2lGM9ZhTMbzgG8BrwOuyswPR8QC4NrMfLYZzViHhmWcDvwlxbHOo19CLgVOAn47M79W5/pNyhiXZ5PcvYh4FnAKxfE0a4Ebq3gTb1pGE9bBjK7qviIzvzndOmYMRkYT1sGM+tTfI2sxYwZhycwHK67f00a8KRlNWIcy4/XAj1IcVrSynDaHYjfNx83oT30z9jpjDsUx+0sotl6Pfl6rZCyIXtdvUsaTWTbJ3ev1G3lTMpqwDmbUp74Z9cpowjqYUY/6EfE8iuOc51MM3AXFICwPA7+VmbdUmDXwj0U/MpqwDmbUp74ZleQekpmPDmr9Qc6wSe5CP97Im5DRhHUwoz71zahXRhPWwYz61C8zvgP8RmbesMf004C/ycznVpAx8I9FPzKasA5m1Ke+GdWJiDWZuXRQ6w90RlY8ElgTL8B3gFMnmH4a1Y1EPPAZTVgHM+pT34x6ZTRhHcyoT/2y1qSjkQL3DMLfqSkZTVgHM+pT34y9znjXJJd3A5vqXr9JGWMvjm7dnYNzj2+6ATLzeuBgM/pW34x6ZTRhHcyoT30z6pXRj3W4KiL+JSLOiogXl5ezIuJfgKoGYGnCY9GPjCasgxn1qW/G3vkwxaCqh+5xOYRqRn7vdf0mZTxpbtUFG+qq8k37Yp4aTe044G1U90behIwmrIMZ9alvRr0ymrAOZtSnPpn5zoh4HXAm4wdh+VRmXllFBs14LPqR0YR1MKM+9c3YO7cAX83Mm/e8ISJ+bQDqNynjqZrl5mtNYZI38ssrfCNvREYT1sGM+tQ3o14ZTVgHM+pTv1+a8Fj0I6MJ62BGfeqbsVf1f4Rid+H1E9y2OKc5SFiv6zcpY1xNm2RJktRvETEfeC/Fh8+jyskPAZcB52fmwzO1bJKk2c1jkrsQEfMj4vyIWB0RG8vL6nLa4WY0Zx3MqE99M+qV0YR1MKM+9UuXApuBV2bmwsxcCLySYtTYL1cR0ITHoh8ZTVgHM+pT34x9zrizx493T+o3KWMsm+Tu9PyNvCEZTVgHM+pT34x6ZTRhHcyoT32A4zPzo5n5wOiEzHwgM88HqjqVRxMei35kNGEdzKhPfTP2LeMVe2Rsriij1/WblPGUrHi47CZegLv25bbZltGEdTCjPvXNqFdGE9bBjPrUL+tcA/w+sHjMtMXAe4B/HYS/U1MymrAOZtSnvhn1ymjCOvQrY+zFLcnduS8ifj8iFo9OiIjFEfEenhrpzoxmrIMZ9alvRr0ymrAOZtSnPsBZwELgWxGxOSI2Ad8EFgBvqSijCY9FPzKasA5m1Ke+GfXKaMI69CvjSTbJ3enHG3kTMpqwDmbUp74Z9cpowjqYUZ/6ZOZm4HPAbwPHZeaCzHxWZr4HeFEVGTTjsehHRhPWwYz61DejXhlNWId+ZTyl6k3TTb0AzwR+Cjhkj+mnm9GsdTCjPvXNqFdGE9bBjFrVfydwF/BV4PvAmWNuu2VQ/k5NyWjCOphRn/pm1CujCevQr4wna1ZdsImXfryRNyGjCetgRn3qm1GvjCasgxn1qV/WuY3ygw5wPHAT8Lvl77cOwt+pKRlNWAcz6lPfjHplNGEd+pUxLq/qgk280J838oHPaMI6mFGf+mbUK6MJ62BGfeqXde7Y4/dDgK8BHwe+Mwh/p6ZkNGEdzKhPfTPqldGEdehXxtjLXNSN/TLzUYDM/H5EvAL4SkQ8HQgz+lbfjHplNGEdzKhPfTPqldGPdXggIp6Xmd8pcx6NiDOAzwL/o6KMJjwW/chowjqYUZ/6ZtQrownr0K+MJzlwV3ceiIjnjf5SPkBnAEdS3Rt5EzKasA5m1Ke+GfXKaMI6mFGf+gBvAx4YOyEzd2bm24CXVZTRhMeiHxlNWAcz6lPfjHplNGEd+pXxlCo3Szf1AhwLHD3JbS8xoznrYEZ96ptRr4wmrIMZ9anfr0sTHoumPN5m1CejCetgRn3qNylj7CXKwpIkSZIkzXrubi1JkiRJUskmWZIkSZKkkk2yJEkNEIXrIuJ1Y6a9JSK+NsG834+Ia/eY9p2I+G4/llWSpDqzSZYkqQGyGGTkN4GPR8SBEXEw8CHgHaPzlI306Hv/oRFxXDn9WX1fYEmSasomWZKkhsjM7wL/DLwHeB9wMbArIlZHxF8BtwDHlbNfCpxVXv954Iujdcom+3MRcVtE3BoRr+zbSkiSNMMc3VqSpAYptyDfAuwAlgHHAMPAizPz+nKe7wOvAT6fmS+OiFuBXwQuzcznRMS7gedk5jkR8UzgGuCUzNze/zWSJKm/5s70AkiSpOpk5taI+BLwaGY+HhEA9402yGNsAjZHxFuB1cC2Mbe9FPiLst6dEXEfcAqwqucrIEnSDHN3a0mSmmd3eRm1dZL5vgR8ijG7WpeiFwslSdIgcEuyJEmz1z9R7I59NdAaM/3bFLtf/1tEnAIsBe7q/+JJktR/bkmWJGmWysxHMvOjmbljj5v+CtgvIm6j2Nr8K5n5eP+XUJKk/nPgLkmSJEmSSm5JliRJkiSpZJMsSZIkSVLJJlmSJEmSpJJNsiRJkiRJJZtkSZIkSZJKNsmSJEmSJJV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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -429,7 +411,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -440,7 +422,7 @@ " )" ] }, - "execution_count": 8, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, @@ -448,7 +430,7 @@ "data": { "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -464,7 +446,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -474,7 +456,7 @@ " )" ] }, - "execution_count": 9, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, @@ -482,7 +464,7 @@ "data": { "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -498,7 +480,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -508,7 +490,7 @@ " )" ] }, - "execution_count": 10, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, @@ -516,7 +498,7 @@ "data": { "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -532,16 +514,16 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 11, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, @@ -549,7 +531,7 @@ "data": { "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -562,16 +544,16 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 12, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, @@ -579,7 +561,7 @@ "data": { "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -592,16 +574,16 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 14, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, @@ -609,7 +591,7 @@ "data": { "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -624,16 +606,16 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 15, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, @@ -641,7 +623,7 @@ "data": { "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -654,16 +636,16 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 16, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, @@ -671,7 +653,7 @@ "data": { "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -690,16 +672,16 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 17, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, @@ -707,7 +689,7 @@ "data": { "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -720,16 +702,16 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 18, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, @@ -737,7 +719,7 @@ "data": { "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -750,16 +732,16 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 19, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, @@ -767,7 +749,7 @@ "data": { "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -780,7 +762,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -1142,7 +1124,7 @@ " dtype='period[M]', freq='M')))]" ] }, - "execution_count": 20, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -1156,7 +1138,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -1226,7 +1208,7 @@ "Length: 82, dtype: int64" ] }, - "execution_count": 21, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -1237,7 +1219,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -1307,7 +1289,7 @@ "Length: 80, dtype: int64" ] }, - "execution_count": 22, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -1347,7 +1329,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.6.3" } }, "nbformat": 4, diff --git a/Kelly/Kaggle_house_price/Forest Models (one hot yr_month sold).ipynb b/Kelly/Kaggle_house_price/Forest Models (one hot yr_month sold).ipynb deleted file mode 100644 index 92800ec..0000000 --- a/Kelly/Kaggle_house_price/Forest Models (one hot yr_month sold).ipynb +++ /dev/null @@ -1,337 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Random Forest (regression)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "## import data\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "train = pd.read_csv(\"train.csv\").set_index(\"Id\")\n", - "test = pd.read_csv(\"test.csv\").set_index(\"Id\")\n", - "combine = pd.concat([train, test])\n", - "\n", - "# process data before model fitting\n", - "from preprocess3_yrmonthsold import impute\n", - "fact, encodedDic = impute(combine, False) # process data and label encode \n", - "\n", - "# seperate factorized data into train and test\n", - "train_label = fact.iloc[0:1460,]\n", - "test_label = fact.iloc[1460:2919,].drop('SalePrice', axis = 1) # salesprice col were all NA \n", - "\n", - "# split train data frame into x var and y var for model testing\n", - "x_label = train_label.drop('SalePrice', axis=1)\n", - "y_log = np.log(train_label.SalePrice)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "GridSearchCV(cv=5, error_score='raise',\n", - " estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n", - " max_features='auto', max_leaf_nodes=None,\n", - " min_impurity_decrease=0.0, min_impurity_split=None,\n", - " min_samples_leaf=1, min_samples_split=2,\n", - " min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n", - " oob_score=False, random_state=None, verbose=0, warm_start=False),\n", - " fit_params=None, iid=True, n_jobs=-1,\n", - " param_grid=[{'n_estimators': array([100, 200, 300]), 'min_samples_leaf': range(10, 15), 'min_samples_split': array([ 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30]), 'random_state': [42]}],\n", - " pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n", - " scoring='neg_mean_squared_error', verbose=0)" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn import ensemble\n", - "from sklearn.model_selection import GridSearchCV\n", - "\n", - "randomForest = ensemble.RandomForestRegressor()\n", - "grid_para_forest = [{\n", - " \"n_estimators\": np.arange(100, 400, 100),\n", - " \"min_samples_leaf\": range(10,15),\n", - " \"min_samples_split\": np.linspace(start=2, stop=30, num=15, dtype=int),\n", - " \"random_state\": [42]}]\n", - "grid_search_forest = GridSearchCV(randomForest, grid_para_forest, scoring='neg_mean_squared_error', cv=5, n_jobs=-1)\n", - "grid_search_forest.fit(x_label, y_log)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best parameters found: {'min_samples_leaf': 10, 'min_samples_split': 2, 'n_estimators': 300, 'random_state': 42}\n", - "Lowest RMSE found: 0.1501387751567232\n" - ] - } - ], - "source": [ - "print(\"Best parameters found: \", grid_search_forest.best_params_)\n", - "print(\"Lowest RMSE found: \", np.sqrt(np.abs(grid_search_forest.best_score_)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### RMSE for all train data using best random forest" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 4.154388487892194e-05\n" - ] - } - ], - "source": [ - "# fit best random forest to all train data \n", - "best_forest_y = grid_search_forest.best_estimator_.predict(x_label)\n", - "print(\"RMSE: \", np.sqrt(np.mean(y_log-best_forest_y)**2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Feature importance" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "pd.DataFrame(list(zip(x_label.columns, grid_search_forest.best_estimator_.feature_importances_ ))).set_index(0).sort_values(1).plot.barh()\n", - "plt.rcParams['figure.figsize'] = 4, 32" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### test prediction" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "best_forest_test_y = grid_search_forest.best_estimator_.predict(test_label) \n", - "best_forest_test_y = np.expm1(best_forest_test_y)\n", - "sub = pd.DataFrame() \n", - "sub['SalePrice'] = best_forest_test_y\n", - "sub = sub.set_index(np.arange(1461, 2920))\n", - "sub.to_csv('(14) forest_log(y)_label_submission.csv')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Extreme Gradient Boosting Random Forest (regression)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# !pip install xgboost" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "GridSearchCV(cv=5, error_score='raise',\n", - " estimator=XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n", - " colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n", - " max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n", - " n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n", - " reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n", - " silent=True, subsample=1),\n", - " fit_params=None, iid=True, n_jobs=-1,\n", - " param_grid=[{'colsample_bytree': array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]), 'max_depth': [2, 4, 6], 'n_estimators': array([500, 600, 700, 800, 900]), 'random_state': [42]}],\n", - " pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n", - " scoring='neg_mean_squared_error', verbose=0)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import xgboost as xgb\n", - "xgbforest = xgb.XGBRegressor()\n", - "grid_para_xgbforest = [{\n", - " \"colsample_bytree\": np.linspace(0.1, 0.9, 9),\n", - " 'max_depth':[2, 4, 6],\n", - " \"n_estimators\": np.arange(500, 1000, 100),\n", - " \"random_state\": [42]}]\n", - "grid_search_xgbforest = GridSearchCV(estimator = xgbforest, param_grid = grid_para_xgbforest, \n", - " scoring = 'neg_mean_squared_error', cv = 5, n_jobs=-1)\n", - "\n", - "grid_search_xgbforest.fit(x_label, y_log)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best parameters found: {'colsample_bytree': 0.30000000000000004, 'max_depth': 4, 'n_estimators': 500, 'random_state': 42}\n", - "Lowest RMSE found: 0.12012350484358175\n" - ] - } - ], - "source": [ - "print(\"Best parameters found: \", grid_search_xgbforest.best_params_)\n", - "print(\"Lowest RMSE found: \", np.sqrt(np.abs(grid_search_xgbforest.best_score_)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### RMSE for all train data using best gradient boost" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 1.7992035825790895e-05\n" - ] - } - ], - "source": [ - "# fit best XGB to all train data \n", - "best_xgbforest_y = grid_search_xgbforest.best_estimator_.predict(x_label)\n", - "print(\"RMSE: \", np.sqrt(np.mean(y_log-best_xgbforest_y)**2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Feature importance" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pd.DataFrame(list(zip(x_label.columns, grid_search_xgbforest.best_estimator_.feature_importances_ ))).set_index(0).sort_values(1).plot.barh()\n", - "plt.rcParams['figure.figsize'] = 4, 32" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### test prediction" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "best_xgb_test_y = grid_search_xgbforest.best_estimator_.predict(test_label) \n", - "best_xgb_test_y = np.expm1(best_xgb_test_y)\n", - "sub = pd.DataFrame() \n", - "sub['SalePrice'] = best_xgb_test_y\n", - "sub = sub.set_index(np.arange(1461, 2920))\n", - "sub.to_csv('(15) xgb_log(y)_label_submission.csv')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Kelly/Kaggle_house_price/Forest Models (no ordinal encode).ipynb b/Kelly/Kaggle_house_price/Forest Models (no ordinal encode).ipynb deleted file mode 100644 index 449b661..0000000 --- a/Kelly/Kaggle_house_price/Forest Models (no ordinal encode).ipynb +++ /dev/null @@ -1,337 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Random Forest (regression)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "## import data\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "train = pd.read_csv(\"train.csv\").set_index(\"Id\")\n", - "test = pd.read_csv(\"test.csv\").set_index(\"Id\")\n", - "combine = pd.concat([train, test])\n", - "\n", - "# process data before model fitting\n", - "from preprocess2_label import impute\n", - "fact, encodedDic = impute(combine, False) # process data and label encode \n", - "\n", - "# seperate factorized data into train and test\n", - "train_label = fact.iloc[0:1460,]\n", - "test_label = fact.iloc[1460:2919,].drop('SalePrice', axis = 1) # salesprice col were all NA \n", - "\n", - "# split train data frame into x var and y var for model testing\n", - "x_label = train_label.drop('SalePrice', axis=1)\n", - "y_log = np.log(train_label.SalePrice)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "GridSearchCV(cv=5, error_score='raise',\n", - " estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n", - " max_features='auto', max_leaf_nodes=None,\n", - " min_impurity_decrease=0.0, min_impurity_split=None,\n", - " min_samples_leaf=1, min_samples_split=2,\n", - " min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n", - " oob_score=False, random_state=None, verbose=0, warm_start=False),\n", - " fit_params=None, iid=True, n_jobs=-1,\n", - " param_grid=[{'n_estimators': array([100, 200, 300]), 'min_samples_leaf': range(10, 15), 'min_samples_split': array([ 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30]), 'random_state': [42]}],\n", - " pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n", - " scoring='neg_mean_squared_error', verbose=0)" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn import ensemble\n", - "from sklearn.model_selection import GridSearchCV\n", - "\n", - "randomForest = ensemble.RandomForestRegressor()\n", - "grid_para_forest = [{\n", - " \"n_estimators\": np.arange(100, 400, 100),\n", - " \"min_samples_leaf\": range(10,15),\n", - " \"min_samples_split\": np.linspace(start=2, stop=30, num=15, dtype=int),\n", - " \"random_state\": [42]}]\n", - "grid_search_forest = GridSearchCV(randomForest, grid_para_forest, scoring='neg_mean_squared_error', cv=5, n_jobs=-1)\n", - "grid_search_forest.fit(x_label, y_log)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best parameters found: {'min_samples_leaf': 10, 'min_samples_split': 2, 'n_estimators': 300, 'random_state': 42}\n", - "Lowest RMSE found: 0.15032551004916286\n" - ] - } - ], - "source": [ - "print(\"Best parameters found: \", grid_search_forest.best_params_)\n", - "print(\"Lowest RMSE found: \", np.sqrt(np.abs(grid_search_forest.best_score_)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### RMSE for all train data using best random forest" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.0001501873512564858\n" - ] - } - ], - "source": [ - "# fit best random forest to all train data \n", - "best_forest_y = grid_search_forest.best_estimator_.predict(x_label)\n", - "print(\"RMSE: \", np.sqrt(np.mean(y_log-best_forest_y)**2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Feature importance" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "pd.DataFrame(list(zip(x_label.columns, grid_search_forest.best_estimator_.feature_importances_ ))).set_index(0).sort_values(1).plot.barh()\n", - "plt.rcParams['figure.figsize'] = 4, 32" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### test prediction" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "best_forest_test_y = grid_search_forest.best_estimator_.predict(test_label) \n", - "best_forest_test_y = np.expm1(best_forest_test_y)\n", - "sub = pd.DataFrame() \n", - "sub['SalePrice'] = best_forest_test_y\n", - "sub = sub.set_index(np.arange(1461, 2920))\n", - "sub.to_csv('(9) forest_log(y)_label_submission.csv')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Extreme Gradient Boosting Random Forest (regression)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# !pip install xgboost" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "GridSearchCV(cv=5, error_score='raise',\n", - " estimator=XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n", - " colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n", - " max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n", - " n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n", - " reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n", - " silent=True, subsample=1),\n", - " fit_params=None, iid=True, n_jobs=-1,\n", - " param_grid=[{'colsample_bytree': array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]), 'max_depth': [2, 4, 6], 'n_estimators': array([500, 600, 700, 800, 900]), 'random_state': [42]}],\n", - " pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n", - " scoring='neg_mean_squared_error', verbose=0)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import xgboost as xgb\n", - "xgbforest = xgb.XGBRegressor()\n", - "grid_para_xgbforest = [{\n", - " \"colsample_bytree\": np.linspace(0.1, 0.9, 9),\n", - " 'max_depth':[2, 4, 6],\n", - " \"n_estimators\": np.arange(500, 1000, 100),\n", - " \"random_state\": [42]}]\n", - "grid_search_xgbforest = GridSearchCV(estimator = xgbforest, param_grid = grid_para_xgbforest, \n", - " scoring = 'neg_mean_squared_error', cv = 5, n_jobs=-1)\n", - "\n", - "grid_search_xgbforest.fit(x_label, y_log)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best parameters found: {'colsample_bytree': 0.1, 'max_depth': 2, 'n_estimators': 900, 'random_state': 42}\n", - "Lowest RMSE found: 0.12053449602685692\n" - ] - } - ], - "source": [ - "print(\"Best parameters found: \", grid_search_xgbforest.best_params_)\n", - "print(\"Lowest RMSE found: \", np.sqrt(np.abs(grid_search_xgbforest.best_score_)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### RMSE for all train data using best gradient boost" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 5.955640741391253e-06\n" - ] - } - ], - "source": [ - "# fit best XGB to all train data \n", - "best_xgbforest_y = grid_search_xgbforest.best_estimator_.predict(x_label)\n", - "print(\"RMSE: \", np.sqrt(np.mean(y_log-best_xgbforest_y)**2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Feature importance" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pd.DataFrame(list(zip(x_label.columns, grid_search_xgbforest.best_estimator_.feature_importances_ ))).set_index(0).sort_values(1).plot.barh()\n", - "plt.rcParams['figure.figsize'] = 4, 32" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### test prediction" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "best_xgb_test_y = grid_search_xgbforest.best_estimator_.predict(test_label) \n", - "best_xgb_test_y = np.expm1(best_xgb_test_y)\n", - "sub = pd.DataFrame() \n", - "sub['SalePrice'] = best_xgb_test_y\n", - "sub = sub.set_index(np.arange(1461, 2920))\n", - "sub.to_csv('(10) xgb_log(y)_label_submission.csv')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Kelly/Kaggle_house_price/Forest Models.ipynb b/Kelly/Kaggle_house_price/Forest Models.ipynb deleted file mode 100644 index 7cf9fe5..0000000 --- a/Kelly/Kaggle_house_price/Forest Models.ipynb +++ /dev/null @@ -1,350 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Random Forest (regression)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "## import data\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "train = pd.read_csv(\"train.csv\").set_index(\"Id\")\n", - "test = pd.read_csv(\"test.csv\").set_index(\"Id\")\n", - "combine = pd.concat([train, test])\n", - "\n", - "# process data before model fitting\n", - "from preprocess1 import impute\n", - "fact, encodedDic = impute(combine, False) # process data and label encode \n", - "\n", - "# seperate factorized data into train and test\n", - "train_label = fact.iloc[0:1460,]\n", - "test_label = fact.iloc[1460:2919,].drop('SalePrice', axis = 1) # salesprice col were all NA \n", - "\n", - "# split train data frame into x var and y var for model testing\n", - "x_label = train_label.drop('SalePrice', axis=1)\n", - "y_log = np.log(train_label.SalePrice)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "GridSearchCV(cv=5, error_score='raise',\n", - " estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n", - " max_features='auto', max_leaf_nodes=None,\n", - " min_impurity_decrease=0.0, min_impurity_split=None,\n", - " min_samples_leaf=1, min_samples_split=2,\n", - " min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n", - " oob_score=False, random_state=None, verbose=0, warm_start=False),\n", - " fit_params=None, iid=True, n_jobs=-1,\n", - " param_grid=[{'n_estimators': array([100, 200, 300]), 'min_samples_leaf': range(10, 15), 'min_samples_split': array([ 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30]), 'random_state': [42]}],\n", - " pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n", - " scoring='neg_mean_squared_error', verbose=0)" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.model_selection import GridSearchCV\n", - "\n", - "randomForest = ensemble.RandomForestRegressor()\n", - "grid_para_forest = [{\n", - " \"n_estimators\": np.arange(100, 400, 100),\n", - " \"min_samples_leaf\": range(10,15),\n", - " \"min_samples_split\": np.linspace(start=2, stop=30, num=15, dtype=int),\n", - " \"random_state\": [42]}]\n", - "grid_search_forest = GridSearchCV(randomForest, grid_para_forest, scoring='neg_mean_squared_error', cv=5, n_jobs=-1)\n", - "grid_search_forest.fit(x_label, y_log)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best parameters found: {'min_samples_leaf': 10, 'min_samples_split': 2, 'n_estimators': 300, 'random_state': 42}\n", - "Lowest RMSE found: 0.1501241888269882\n" - ] - } - ], - "source": [ - "print(\"Best parameters found: \", grid_search_forest.best_params_)\n", - "print(\"Lowest RMSE found: \", np.sqrt(np.abs(grid_search_forest.best_score_)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### RMSE for all train data using best random forest" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.00012110195560096155\n" - ] - } - ], - "source": [ - "# fit best random forest to all train data \n", - "best_forest_y = grid_search_forest.best_estimator_.predict(x_label)\n", - "print(\"RMSE: \", np.sqrt(np.mean(y_log-best_forest_y)**2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Feature importance" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "pd.DataFrame(list(zip(x_label.columns, grid_search_forest.best_estimator_.feature_importances_ ))).set_index(0).sort_values(1).plot.barh()\n", - "plt.rcParams['figure.figsize'] = 4, 32" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### test prediction" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "best_forest_test_y = grid_search_forest.best_estimator_.predict(test_label) \n", - "best_forest_test_y = np.expm1(best_forest_test_y)\n", - "sub = pd.DataFrame() \n", - "sub['SalePrice'] = best_forest_test_y\n", - "sub = sub.set_index(np.arange(1461, 2920))\n", - "sub.to_csv('(5) forest_log(y)_label_submission.csv')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Extreme Gradient Boosting Random Forest (regression)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# !pip install xgboost" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "GridSearchCV(cv=5, error_score='raise',\n", - " estimator=XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n", - " colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n", - " max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n", - " n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n", - " reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n", - " silent=True, subsample=1),\n", - " fit_params=None, iid=True, n_jobs=-1,\n", - " param_grid=[{'colsample_bytree': array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]), 'max_depth': [2, 4, 6], 'n_estimators': array([500, 600, 700, 800, 900]), 'learning_rate': [0.001, 0.01, 0.1, 0.2, 0.3], 'random_state': [42]}],\n", - " pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n", - " scoring='neg_mean_squared_error', verbose=0)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.model_selection import GridSearchCV\n", - "import xgboost as xgb\n", - "xgbforest = xgb.XGBRegressor()\n", - "grid_para_xgbforest = [{\n", - " \"colsample_bytree\": np.linspace(0.1, 0.9, 9),\n", - " 'max_depth':[2, 4, 6],\n", - " \"n_estimators\": np.arange(500, 1000, 100),\n", - " \"learning_rate\": [0.001, 0.01, 0.1, 0.2, 0.3],\n", - " \"random_state\": [42]}]\n", - "grid_search_xgbforest = GridSearchCV(estimator = xgbforest, param_grid = grid_para_xgbforest, \n", - " scoring = 'neg_mean_squared_error', cv = 5, n_jobs=-1)\n", - "\n", - "grid_search_xgbforest.fit(x_label, y_log)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best parameters found: {'colsample_bytree': 0.1, 'learning_rate': 0.1, 'max_depth': 2, 'n_estimators': 700, 'random_state': 42}\n", - "Lowest RMSE found: 0.1200139613943591\n" - ] - } - ], - "source": [ - "print(\"Best parameters found: \", grid_search_xgbforest.best_params_)\n", - "print(\"Lowest RMSE found: \", np.sqrt(np.abs(grid_search_xgbforest.best_score_)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### RMSE for all train data using best gradient boost" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 3.4734747123886844e-06\n" - ] - } - ], - "source": [ - "# fit best XGB to all train data \n", - "best_xgbforest_y = grid_search_xgbforest.best_estimator_.predict(x_label)\n", - "print(\"RMSE: \", np.sqrt(np.mean(y_log-best_xgbforest_y)**2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Feature importance" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "pd.DataFrame(list(zip(x_label.columns, grid_search_xgbforest.best_estimator_.feature_importances_ ))).set_index(0).sort_values(1).plot.barh()\n", - "plt.rcParams['figure.figsize'] = 4, 32" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### test prediction" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "best_xgb_test_y = grid_search_xgbforest.best_estimator_.predict(test_label) \n", - "best_xgb_test_y = np.expm1(best_xgb_test_y)\n", - "sub = pd.DataFrame() \n", - "sub['SalePrice'] = best_xgb_test_y\n", - "sub = sub.set_index(np.arange(1461, 2920))\n", - "sub.to_csv('(4.1) xgb_log(y)_label_submission.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#### submit original\n", - "#### change to label encoding and submit\n", - "#### change to onehot for yrsold and mosold" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Kelly/Kaggle_house_price/Regularization Model (no ordinal encode).ipynb b/Kelly/Kaggle_house_price/Regularization Model (no ordinal encode).ipynb deleted file mode 100644 index 181a051..0000000 --- a/Kelly/Kaggle_house_price/Regularization Model (no ordinal encode).ipynb +++ /dev/null @@ -1,5226 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Multiple linear regression " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "## import data\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "train = pd.read_csv(\"train.csv\").set_index(\"Id\")\n", - "test = pd.read_csv(\"test.csv\").set_index(\"Id\")\n", - "combine = pd.concat([train, test])\n", - "\n", - "# process data before model fitting\n", - "from preprocess2_label import impute\n", - "onehot, encodedDic = impute(combine, True) # process data and onehot encode\n", - "\n", - "# seperate onehot data into train and test\n", - "train_onehot = onehot.iloc[0:1460,]\n", - "test_onehot = onehot.iloc[1460:2919,].drop('SalePrice', axis = 1) # salesprice col were all NA \n", - "\n", - "# split train data frame into x var and y var for model testing\n", - "x_onehot = train_onehot.drop('SalePrice', axis=1)\n", - "y_log = np.log(train_onehot.SalePrice) # convert y to normal distribution for regression models" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### original y vs log(y) distirbution" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([ 5., 11., 13., 61., 58., 126., 165., 180., 122., 130., 121.,\n", - " 78., 61., 64., 49., 36., 36., 25., 13., 25., 16., 11.,\n", - " 4., 11., 9., 5., 4., 4., 4., 2., 1., 1., 1.,\n", - " 0., 1., 0., 2., 0., 1., 0., 2., 0., 0., 0.,\n", - " 0., 0., 0., 0., 0., 2.]),\n", - " array([ 34900., 49302., 63704., 78106., 92508., 106910., 121312.,\n", - " 135714., 150116., 164518., 178920., 193322., 207724., 222126.,\n", - " 236528., 250930., 265332., 279734., 294136., 308538., 322940.,\n", - " 337342., 351744., 366146., 380548., 394950., 409352., 423754.,\n", - " 438156., 452558., 466960., 481362., 495764., 510166., 524568.,\n", - " 538970., 553372., 567774., 582176., 596578., 610980., 625382.,\n", - " 639784., 654186., 668588., 682990., 697392., 711794., 726196.,\n", - " 740598., 755000.]),\n", - " )" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from matplotlib import pyplot as plt\n", - "plt.hist(train_onehot.SalePrice, bins=50) # original y" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([ 2., 2., 1., 0., 0., 0., 2., 3., 4., 3., 5.,\n", - " 1., 5., 21., 22., 23., 18., 29., 58., 56., 65., 100.,\n", - " 122., 93., 90., 82., 108., 91., 64., 55., 58., 51., 46.,\n", - " 42., 23., 29., 22., 13., 13., 13., 7., 5., 4., 1.,\n", - " 2., 2., 2., 0., 0., 2.]),\n", - " array([10.46024211, 10.52172673, 10.58321134, 10.64469596, 10.70618058,\n", - " 10.7676652 , 10.82914982, 10.89063444, 10.95211906, 11.01360367,\n", - " 11.07508829, 11.13657291, 11.19805753, 11.25954215, 11.32102677,\n", - " 11.38251138, 11.443996 , 11.50548062, 11.56696524, 11.62844986,\n", - " 11.68993448, 11.75141909, 11.81290371, 11.87438833, 11.93587295,\n", - " 11.99735757, 12.05884219, 12.12032681, 12.18181142, 12.24329604,\n", - " 12.30478066, 12.36626528, 12.4277499 , 12.48923452, 12.55071913,\n", - " 12.61220375, 12.67368837, 12.73517299, 12.79665761, 12.85814223,\n", - " 12.91962684, 12.98111146, 13.04259608, 13.1040807 , 13.16556532,\n", - " 13.22704994, 13.28853455, 13.35001917, 13.41150379, 13.47298841,\n", - " 13.53447303]),\n", - " )" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist(y_log, bins=50) # log(y)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Ridge regularization" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find best lambda" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'alpha': 0.43287612810830617}\n", - "Lowest RMSE found: 0.14658042639621965\n" - ] - } - ], - "source": [ - "from sklearn.model_selection import GridSearchCV # search for the best lambda\n", - "from sklearn import linear_model\n", - "\n", - "ridge = linear_model.Ridge(normalize=True) # create a ridge regression instance\n", - "\n", - "# find the best alpha (lambda) for ridge\n", - "grid_param = [{'alpha': np.logspace(-4, 2, 100)}]\n", - "para_search_ridge = GridSearchCV(estimator=ridge, param_grid=grid_param, scoring='neg_mean_squared_error', cv=5, return_train_score=True)\n", - "para_search_ridge.fit(x_onehot, y_log)\n", - "\n", - "print(para_search_ridge.best_params_)\n", - "print(\"Lowest RMSE found: \", np.sqrt(np.abs(para_search_ridge.best_score_)))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### RMSE for all train data using best ridge" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.11860807897401618\n" - ] - } - ], - "source": [ - "# fit best ridge equation to all train data \n", - "best_ridge_y = para_search_ridge.best_estimator_.predict(x_onehot)\n", - "print(\"RMSE: \", np.sqrt(np.mean((y_log-best_ridge_y)**2)))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### RMSE across different grid search lambda" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", - " DeprecationWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", - " DeprecationWarning)\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "error = pd.DataFrame(para_search_ridge.grid_scores_)['mean_validation_score']\n", - "error = np.sqrt(np.abs(error))\n", - "alpha = pd.DataFrame(para_search_ridge.grid_scores_)['parameters'].apply(lambda x: x.get('alpha'))\n", - "plt.scatter(alpha, error)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Variable importance for best lambda" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "alpha_100 = np.logspace(-4, 2, 100)\n", - "coef = []\n", - "for i in alpha_100:\n", - " ridge.set_params(alpha = i)\n", - " ridge.fit(x_onehot, y_log)\n", - " coef.append(ridge.coef_)\n", - "\n", - "df_coef = pd.DataFrame(coef, index=alpha_100, columns=x_onehot.columns)\n", - "title = 'Ridge coefficients as a function of the regularization'\n", - "axes = df_coef.plot(logx=True, title=title)\n", - "axes.set_xlabel('alpha')\n", - "axes.set_ylabel('coefficients')\n", - "plt.rcParams['figure.figsize'] = 16, 4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### test prediction" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "best_ridge_test_y = para_search_ridge.best_estimator_.predict(test_onehot)\n", - "best_ridge_test_y = np.expm1(best_ridge_test_y)\n", - "best_ridge_test_y\n", - "sub = pd.DataFrame()\n", - "sub['SalePrice'] = best_ridge_test_y\n", - "sub = sub.set_index(np.arange(1461, 2920))\n", - "sub.to_csv('(6) ridge_log(y)_onehot_submission.csv')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Lasso regularization" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find best lambda" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'alpha': 0.0001232846739442066}\n", - "Lowest RMSE found: 0.14732670392217098\n" - ] - } - ], - "source": [ - "lasso = linear_model.Lasso(normalize=True) # create a ridge regression instance\n", - "\n", - "# find the best alpha (lambda) for lasso \n", - "grid_param = [{'alpha': np.logspace(-4.5, 2, 100)}]\n", - "para_search_lasso = GridSearchCV(estimator=lasso, param_grid=grid_param, scoring='neg_mean_squared_error', cv=5, return_train_score=True)\n", - "para_search_lasso.fit(x_onehot, y_log)\n", - "\n", - "print(para_search_lasso.best_params_)\n", - "print(\"Lowest RMSE found: \", np.sqrt(np.abs(para_search_lasso.best_score_)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### RMSE for all train data using best lasso" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.12074042400970975\n" - ] - } - ], - "source": [ - "# fit best lasso equation to all train data \n", - "best_lasso_y = para_search_lasso.best_estimator_.predict(x_onehot)\n", - "print(\"RMSE: \", np.sqrt(np.mean((y_log-best_lasso_y)**2)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### RMSE across different grid search lambda" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", - " DeprecationWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", - " DeprecationWarning)\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "error = pd.DataFrame(para_search_lasso.grid_scores_)['mean_validation_score']\n", - "error = np.sqrt(np.abs(error))\n", - "alpha = pd.DataFrame(para_search_lasso.grid_scores_)['parameters'].apply(lambda x: x.get('alpha'))\n", - "plt.scatter(alpha, error)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Variable importance for best lambda" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "alpha_100 = np.logspace(-4.5, 2, 100)\n", - "coef = []\n", - "for i in alpha_100:\n", - " lasso.set_params(alpha = i)\n", - " lasso.fit(x_onehot, y_log)\n", - " coef.append(lasso.coef_)\n", - "\n", - "df_coef = pd.DataFrame(coef, index=alpha_100, columns=x_onehot.columns)\n", - "title = 'Lasso coefficients as a function of the regularization'\n", - "axes = df_coef.plot(logx=True, title=title)\n", - "axes.set_xlabel('alpha')\n", - "axes.set_ylabel('coefficients')\n", - "plt.rcParams['figure.figsize'] = 16, 4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### test prediction" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "best_lasso_test_y = para_search_lasso.best_estimator_.predict(test_onehot)\n", - "best_lasso_test_y = np.expm1(best_lasso_test_y)\n", - "sub = pd.DataFrame()\n", - "sub['SalePrice'] = best_lasso_test_y\n", - "sub = sub.set_index(np.arange(1461, 2920))\n", - "sub.to_csv('(7) lasso_log(y)_onehot_submission.csv')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Elastic net regularization" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'alpha': 0.0002915053062825182, 'l1_ratio': 0.4}\n", - "Lowest RMSE found: 0.1447146344909162\n" - ] - } - ], - "source": [ - "elasticnet = linear_model.ElasticNet(normalize=True) # create a ridge regression instance\n", - "\n", - "# find the best alpha (lambda) for lasso \n", - "grid_param = [{'alpha': np.logspace(-10, 6, 100), 'l1_ratio': np.arange(0, 1.1, 0.1)}]\n", - "para_search_elas = GridSearchCV(estimator=elasticnet, param_grid=grid_param, scoring='neg_mean_squared_error', cv=5, return_train_score=True)\n", - "para_search_elas.fit(x_onehot, y_log)\n", - "\n", - "print(para_search_elas.best_params_)\n", - "print(\"Lowest RMSE found: \", np.sqrt(np.abs(para_search_elas.best_score_)))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### RMSE for all train data using best lasso" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 9.818629927369878e-16\n" - ] - } - ], - "source": [ - "# fit best elastic net equation to all train data \n", - "best_elas_y = para_search_elas.best_estimator_.predict(x_onehot)\n", - "print(\"RMSE: \", np.sqrt(np.mean(y_log-best_elas_y)**2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### RMSE across different grid search lambda" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", - " DeprecationWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", - " DeprecationWarning)\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "error = pd.DataFrame(para_search_elas.grid_scores_)['mean_validation_score']\n", - "error = np.sqrt(np.abs(error))\n", - "alpha = pd.DataFrame(para_search_elas.grid_scores_)['parameters'].apply(lambda x: x.get('alpha'))\n", - "plt.scatter(alpha, error)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "data": { - "image/png": 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Kc2O7wmLut5rHWM1tVmXkaUVhNfdblcKmwGYuW32W7Upht5jzapb9LIoAiwWr/IESQgghhBC/IQ0W7CqlAoBvAX+zHvO11s9VSeMPfAh0B7KB4Vrrfee4qmfUziIn6/KKGroaddLo0zv+9A4/Ob96bKtaZvk56Ir1yul0lXTl+3zTa3Sl9BWTPnGk9jnOa6bXVF/nhmRT4G+x4G9RleYOi4VAq4Ug64m5sWytWA6zWU9MdlvFssMqnUOEEEIIIcT5qSFbdkuAQVrrQqWUHfhOKfWl1nqdT5o/ALla6zZKqRHAS8DwhqjsmTKtQ3xDV0GcQ1obAbFXG4GwMT8RRHu0xgt4tLHNo8GrNR7MuZnGbaZza43Ha6x7ALdXU2buLzW3l2qN22vMy8x5iddLiVfjMucV6x4vTq8Xp8fL4ZIyij1eijxeir0eijxePHVE7P4WRZjNSrSfjcZ+9op5Yz8b0T7rzQL8CJTAWAghhBCnaeHChdxyyy3s3LmTxMRE9u3bx9ChQ9m2bRurVq1i6tSpLFmy5IyU9f777/P4448TFxdHaWkpf/rTn7j33nvPSN7i3GiwYFdrrYFCc9VuTlX/tb4RmGguzwemK6WUeawQ573ybs2W8v7PFxBtBs4Fbi/H3R7y3G5jXuYh3+0xt3nIK3NzrNTN0VI3aUUujpa6KavmRzTGz0a8w58WAX4kOPyJd/gRH+BHvMOfxn42eQ5ICCGEEHWaM2cOffv2Ze7cuUycOPGslzd8+HCmT5/O0aNH6dSpEzfccAMxMTFnvVxxZjToM7tKKSuwEWgDvKW1/qFKkjggA0Br7VZK5QNRQFaVfO4D7gNo0aLF2a62EL8JSin8lcLfz0IjPxvGEwd101qT5/ZwtNTNsdIyjpSUke4qZb+zlP2uEtbmFbLgSG6lb7YibFY6BTvoFOKgS7CDTsEO2gYGYLNIACyEEEKcb57/fDs7Mo+f0Tw7Ng3lues71ZqmsLCQ77//npUrV3LDDTfUGuwWFRXx8MMPs3XrVtxuNxMnTuTGG2+kX79+TJs2jeTkZAD69OnD22+/TVJSUq1lN27cmNatW7N//37279/P+PHjcTqdOBwOZs2aRfv27bnsssuYOXMmnToZ5zFgwABeffVVEhMTq62LOPsaNNjVWnuAZKVUOLBQKdVZa73NJ0l1/+me1GSktX4XeBegR48e53Wr78+HCzjuKmvoapwRpz2AVZ0ZVE5QrwGqqiTyHWjKWK88YJVv3r77fNOXD0B14rgT2yoGpFLGoFMnji9v0TUGpVLm3GIOPuW7brWYA1JVLF/YAZ5Sigi7jQi7jfZBAdWmKfF6OeAqZZ+zlH3OElKLXGwtcPLBwSxcXuNHOMCiSAxy0DnYQbewQPpHhBAX4HcuT0UIIYQQ55FFixZx9dVX065dOyIjI9m0aRORkZHVpp08eTKDBg1i5syZ5OXl0bNnTwYPHszYsWN5//33ef3110lLS6OkpKTOQBdgz5497NmzhzZt2mCz2fj222+x2WysWLGCp59+mgULFjBixAjmzZvH888/z6FDh8jMzKR79+48/fTT1dYlKCjoTF8iUcV5MRqz1jpPKbUKuBrwDXYPAM2BA0opGxAG5Jz7Gp45k5fu5Nu0Yw1dDXGes1qMkZYtFrBZLMa6OdmqzO1WC3arBZvVWParsuxns+BfPtmtJ5ZtVvztFgLsVoL8bAT6WQn0sxLkb8PhZ27zN+bWM9zC6m+x0DowgNaBlYNht1fzi9PFtgInWwudbC9wsuRYHh8dygagtcOfKyJD6B8RQp+IYEJs1jNaLyGEEELUra4W2LNlzpw5jB8/HoARI0YwZ84cHnzwwWrTLlu2jMWLFzN16lTAeE9weno6w4YN44UXXuCVV15h5syZjB49utYyP/nkE7777jv8/f155513iIyMJCMjg7vvvptdu3ahlKKszGjIuv322xkyZAjPP/888+bNY9iwYbXWpUOHDmfisohaNORozNFAmRnoOoDBGANQ+VoM3A2sBW4DvrnQn9d94qr23NevVUNX47Sd7dGaTx5p+eQDTm7ir7qqK5V10ijM+sRozSeyrzL6sq6cj/Y9Tp8YaKpiNGZzoXy711z2auM4r9dcNvPxmOveimUjjUcb2z1eXTG5vV6fZXPu0ZR5vMZAVR4vZR4vzjIPbpeXUs+Jba4yDyVuLyVlXlxuz68eLTvY30aYw06ow06Yw0ZogJ0whzGFB9qJDvE3puAAGof6ExXkh+0UBqSyma25iUEObvP5nFKLXHybW8DqnALmHsph1sEsrAq6hQTRLzKYwZGhdA0NvOBbxYUQQghRvezsbL755hu2bduGUgqPx4NSinHjxlWbXmvNggULaN++/Un7hgwZwmeffca8efPYsGFDreWWP7Pr69lnn2XgwIEsXLiQffv2MWDAAADi4uKIiopiy5YtfPLJJ7zzzjt11kWcXQ3ZshsLfGA+t2sB5mmtlyilJgEbtNaLgRnAv5VSv2C06I5ouOqeGZ3jwhq6CuI3TpujNbvKPLjKjEDYWeahqMRNcanHnIzlohI3hSVu8p1lHHeWz8vYn11MvrOMfGcZzjLPSWUoBZGBfhVBcPPIQOIjA2kRGUiLqEDio4II9q/frx+lFB2CHXQIdvDH5o0p9XrZkF9cEfy+vu8Ir+07QiuHP8OaRHBbk0iaS3dnIYQQ4qIyf/58Ro0aVRFAAvTv358DBw5Um/6qq65i2rRpTJs2DaUUmzdvpmvXrgCMHTuW66+/nn79+tXYDbo2+fn5xMXFAcaIzb5GjBjByy+/TH5+Pl26dKmzLuLsasjRmLcAJ33KWuu/+iy7gGHnsl5CXOyUUtjNbs4h1T9S+6u4yjxkFZZwrKCEowXG3Hf5aIGLbVsPkVtc+Vn1qCA/I/CNDKRtTAid48Lo1DSURsG1D4TlZ7HQOyKY3hHBPNUqlrwyN19l5TPvcC4v7T3MS3sP0zs8mNubRDA0Opxg6eoshBBCXPDmzJnDU089VWnbrbfeypQpU6pN/+yzzzJ+/HiSkpLQWpOQkFDxSqLu3bsTGhrKmDFjTqkuTzzxBHfffTevvfYagwYNqrTvtttu49FHH+XZZ5+tV13E2aUu8F7BJ+nRo4euqzuCEOLcy3eWkZFTzP7sYvbnFJ1Yzi7mYJ6zIl2T0AA6x4XSsWkYnZuG0jkujNiwgHp1UU53lrDgSC7zDuew11mKw2LhuugwhjeJpG9EsHRzFkIIIU7Rzp07L5pnTDMzMxkwYACpqalYLL/+sStxblV37ymlNmqte9R17HkxQJUQ4uIX5rATFhdWbVf+fGcZOzKPsz0zn20H89meeZxvUo9iDsxMTKg//dpG069tI/q2aURUDa2/LRz+/CmhCePjY9hwvJh5h3P47Ggu84/k0ik4gIdaxHB9dLi80kgIIYT4jfrwww+ZMGECr732mgS6vwHSsiuEOC85Sz3sPHycbQfz+WFvDt//kkWe2RW6c1xoRfDbPT4C/1q6Krs8XhYdzeWt9KPsKi6hRYAf9zePZkRsFIGnMIiWEEII8Vt0MbXsVjVr1izeeOONStv69OnDW2+91UA1Er5Op2VXgl0hxAXB49VsO5jP/3Yd49tdWWzan4vbq3HYrfRp04hbu8Xxuw4x+NmqD2C9WrMs6zjT0o+w8XgxUXYbY5s1YkxcI8Lt0slFCCGEqM3FHOyK85sEuz4k2BXit6GwxM263dl8u+sYX28/zJHjJUQE2rkxOY7bujejU9PQap/R1VqzLr+I6fuP8t+c4wRZLYxsGsWj8TFEStArhBBCVEuCXdFQJNj1cb4HuxuW7iUro7Chq1E/Z/2xxtoLqNdYQqqanGo58KRdFcerynmpEwcon7QnltWJ9fI0Puvly0opsPgsK2OuFJWWlUUZy5bybT7LFoXFYqaxKCzmXFnAYrFgsSgsVt/JUmnZalNYbZaKyWJTWO3GcRfLgE0er+Z/u44xf+MBlu04QqnbS2KTEIb1aM6NyU1rHOF5R6GTt9KPsuhoLuE2G5PbxnFj4/CL5roIIYQQZ4oEu6KhSLDr43wPdld+uJXDewoauhp1avDbQmvqCoarq2Kl+7lSFooa73Xtk5eucu5mPbRPIu17jJlY6/JtPum0WZ9KczON19jWoNdZgdVqweZnwWa3YPO3YvOzYvezYPPzWfa34uew4Rdgw99hw89hxS/AdmJboI2AYDv+DhvqPBj4Kb+4jMVbMpm/IYOfDuRjsygGJTbmvita0SOh+nfp7Sx08v9SM9hcUMzgqFBebNeMZvKuXiGEEKKCBLuiochozBeQgeo54L8NXY26NXTM0tDl14vCp4m2hrnFZ+LEss8+jQVtsaGVBY0draxoZTPm2NEWK15lN/fZjGVlQ2MsV0z4+cxteDGWPcqGB3882PFoPzzY8WobHnNye83J4zUmt4UylxtXAbjLNGUlHkpcHtwlntqvhkUREGzHEWzHEWLHEexnLIf6ERzhT3BkACERAQRH+mOzn71334YF2vl9r3h+3yuetCMFLNh4gHkbMli24wg9EyIZN7A1/dtFV2q97RDsYEn3tsw4cIy/7zlM//WpPN0qltFxjbBKK68QQgghxAVJWnbPsZKfPoD8/Q1djbqdifuitiChzvyrtOxW29KrT1qtVGRFGb7H+ixrbaT3bdZFVW3a9SlbG0eW7zebeFXVJl+fNKoirddn24llvF4U5rrXY6Yz517fuceYe93mNveJdW8ZeMrnZcY2T9mJdU+pMZ0qmwMCwvD6h1Fqi6bU1ohSawSllghKVRglhOLSYTg9ITjLHDhL7bicFoqLPLgKyygpdp+UpSPETkhkQEUAHB7jILJpEJGxwQQE20+9rjUoLnUzd30G7/1vD4fyXXRqGsoDA1pzTedYrFVao9OdJTyZdoCVOQX0CA1kamJzEoMcZ7xOQgghxIXkfGnZXbhwIbfccgs7d+4kMTGRffv2MXToULZt28aqVauYOnUqS5YsaehqijNIWnYvIJ8WLObg8a0NXY0LouH0VzyyW/P+6p7prXS89lmuPl+lqt/nO6/0zG6VfRZAWXRFuopJnVi2KgtKWbAoCwoLVmU+i4sVq7JgtVixKqvP3AZYsSgHSoWgLDaUMiaLslesG8t2LMqOFStWbcOqLVi8CivmXFuwecHi0Vi9YPV4sXg8WNweLB43FncpltISLCUFBLiOE1CSAUXbwZUPJcfB7ar+4vo5oHk0HkdjigLaUmBrTYGKo9AdRUFJAIXFitxDRaRvy8Zd5q04zBHqR2RskBn8GvNGccH4OU7911Wgn417+rZkZK94FqUc5F+rd/PQ7M20bJTG/f1bcXPXZhWjOLdw+DM7qRWfHsnl2V8OMuTHNB6Jb8wj8TH4y/v4hBBCiAY1Z84c+vbty9y5c5k4cWJDV0ec5yTYPce2by3j4IEz33LVEPRZjpjr07ZcVx3K91fkVWW96vHVpT+RVlVKp3326/JtqvL+8m0Vk7nNa6bzmtu9lhPbvQq8Fi8a8Fo85jp4LCfSK6WwoLAohVUprApsSmGFinWrwlzX2JXGhsaKF5vyYsOLTWlsCuwK/JTGX4GfxVj2K99mAX+lCXBAUIidIFswfvZQbLZm2GwdsNlCsKtAHJ4A/D1W/Mss+JV5sJWWYXO5sLoKsRZlEZq/kdC8ReB2Vr7gAWHoji0oDL6EHHsSOZ4EcoqDycl2k7rmEGVm12mlIKpZMLFtwmnaJpzYNmEEhVU/6FRt/GwWbu/RnFu7NePr7Yf556pfeHLBVv6xfBdPX9eB65NiKwYIu7VJJP0jQ5n4y0Fe3XeEb3MK+SCppYzYLIQQQnz5FBw+w403TbrANS/WmqSwsJDvv/+elStXcsMNN9Qa7BYVFfHwww+zdetW3G43EydO5MYbb6Rfv35MmzaN5ORkwHif7ttvv01SUtJJeUycOJH09HT27NlDeno648eP55FHHgHgpptuIiMjA5fLxaOPPsp9990HQHBwMI8++ihLlizB4XDw2WefERMTc4oXRZwu+a/tHLt7fTjutd66EwpRC69FgUXhtVrQVmPutYLXYsFjVXit4LEqPFaF2wJlNiizQqkNyiyaUquXEoumxOrBafXitHkosSuO28Eyu2+8AAAgAElEQVRlh9JKc0WxPxQHgNPfic3fTaAlD4dV4VAah/ISqEoJsXoItmhCrJoQcx4cogmJDMLRrikB/l0IUhEElQbgKIUAVyn2oiKsBUcJOfYtIXkfEV9+gn7B6E6JFIR1J8fShSMlCRw6amXn95lsXXkAgNBoB01bhxHbJpy49uGERQfW+/pZLYpru8RyTecm/G9XFq8u+5lH5mxmcUomk2/uTExoAACN/GxM7xjPkEahPLIznes37mL2Ja2Id/z6QFsIIYQQp2fRokVcffXVtGvXjsjISDZt2kRkZPWDT06ePJlBgwYxc+ZM8vLy6NmzJ4MHD2bs2LG8//77vP7666SlpVFSUlJtoFsuNTWVlStXUlBQQPv27XnggQew2+3MnDmTyMhInE4nl156KbfeeitRUVEUFRXRq1cvJk+ezBNPPMF7773HM888c7YuiaiDBLvnWPzEF/AWFTV0NU7baT/rXecju/Vq1639GF35+Vld6VlbnwVd/bx8BOUT2yoNs3xif8VQzBrt9ZYPuWxs83qNdN7yoZi9Zjpz2es1lr1etNdjpPN6jHw85jbfucdtzo1neLXbg3a70e4ycLvRZW5z3dimy8qgrAxvaSm6tAxdUoIuLa2YvKWlxjaX++TrWQOPTVPqsFPisOL0VxQFWMkN8ONYQCn5gZARCMcDIT9QcTwIioIgKDibKHs24aqUCKuLSKsmyqaJdHgJDbYT1CaBEL8+RJSGEOwEx/ECbDkZhO77D6HOd0gAsDnwdLucrNDBZLq7cCjLwb5t2aSuOwxAVFwQrbo2pnXXaCKbBtXr9UFKKa5oF02fNo2Y+d1eXl3+M4NfW82Eazsw/NLmFXnc2DiCGD87o7fu5bqNu/goqRXJofUProUQQoiLSh0tsGfLnDlzGD9+PAAjRoxgzpw5PPjgg9WmXbZsGYsXL2bq1KkAuFwu0tPTGTZsGC+88AKvvPIKM2fOZPTo0bWWed111+Hv74+/vz+NGzfmyJEjNGvWjDfffJOFCxcCkJGRwa5du4iKisLPz4+hQ4cC0L17d5YvX36Gzl6cCgl2zzG/+Pi6EwlxjmmvF+1y4XU68TpdaGcxXpcLb7ETb3ER3sJCPAUFeI8X4C0swFNQiLegAE9hAd7847hzc/Hsya7hixwPJQ5NXriNw6GQGWzlWJgiLQyyQhV5EVaCI3Jp6pdFjKWApnYvTR1eQhP8CerQmTBbMyKL/QnLKcSeuYOYfU8TA3S1BaA7XUpu5GAyyrqze6+NH7/Yy49L9hIeE0irrtG07hpNdIuQOgNfq0Vx7xWtGNIxhqc+3cJTn25l8U+ZvHhLEi2ijKC2V3gwi7u15c4tu7l58y+81zmBwVGhZ/7DEEIIIcRJsrOz+eabb9i2bRtKKTweD0opxo0bV216rTULFiygffv2J+0bMmQIn332GfPmzaOugW39/U/05rJarbjdblatWsWKFStYu3YtgYGBDBgwAJfLGMPEbrdX/N9Rnl40HAl2hRAoiwUVGIgl8PRaK70lJXhycnBn5+DJyTbm2VmUZR4iKjOTFpmZlKYeRFcKij24Ar1kRCv2RFnZFW3jv40VBbEOGoXn0MSaSVNrAW0CPYR1DCaq+/XEuCIIzS3C/1AakZsnEonmkpCmFF11O3vVVeze48/mZels+mo/IVEBtO4aTYc+TYmMDaq1/gmNgpg9thdzf8xgytKdXPX6t/z5qvaM7p2A1aJoFxTAF93a8fste7h76x5eateckU2jTuuaCSGEEKJu8+fPZ9SoUbzzzjsV2/r378+BAweqTX/VVVcxbdo0pk2bhlKKzZs307VrVwDGjh3L9ddfT79+/WrsBl2b/Px8IiIiCAwMJDU1lXXr1p3aSYmzToJdIcQZY/H3xxIbiz02tsY0Wmu8x49TlplpTAcPUrJ7DxFpabRLS0NvKg+EC8kLt7MvysMvTWx80tyOs20Q8SG/0NKaQ6sgD8GJAUQmDyG2KJzIQ0cI2vpPOntep3N4C5zX3s5edSV79gawZeUBUlZkENsmjE794mjdLbrGd/1aLIo7L2vBwMRoJizcxgtLdrBkSyav3Z5My0ZBxPjb+bRrG+7dvo8//5zBQVcpT7RsUq9u00IIIYQ4NXPmzOGpp56qtO3WW29lypQp1aZ/9tlnGT9+PElJSWitSUhIqHglUffu3QkNDWXMmDGnVJerr76af/3rXyQlJdG+fXt69ep1SvmIs0/esyuEOG9orXEfOoQrLY2StF2U7NqFK+1nSnbtQnk1HotiT6xie3NNagsLrnbhtAiDltajtPH3EOHfknhncyIzD2Pbv95453Bka4rbDCe17Bp2bCgi/5gT/yAbib1i6dSvKRFNam7t1Vqz+KdMnlu8HQXMGH0p3VpEAFDm1TyRlsGcQznc3iSCV9u3wG6RgFcIIcTF6Xx5z+6ZkJmZyYABA0hNTcUirxU8753Oe3YbLNhVSjUHPgSaAF7gXa31G1XSDAA+A/aamz7VWk+qLV8JdoW4+HgKC3Fu3kzx+h8p+vFHnNu2otzGa5n2xSi2JsC2ToFEJwbS2XaIeD8v4X4JxBc1JSLzELaMjQDo9tdxIGYs23dFsTclC69X07RtOJ36NaV198ZYrdX/wdufXcSomes5ctzFW3d243cdjFcIaK15dd8Rpu47zFWNQpnZuSVWaeEVQghxEbpYgt0PP/yQCRMm8NprrzFs2LCGro6ohws12I0FYrXWm5RSIcBG4Cat9Q6fNAOAP2uth9Y3Xwl2hbj4eZ1OnCkpFP+4gYL1P+BKSUG5PRwNU6ztAGmXhNKknYNOtgM0sXuJsLSgdU4Eobs2opx50LgjxZ3/SGpBH7avPcbxLBfhMYFcflNrWiY3qrZLclZhCfe8/yPbDuYz5eYujOjZomLf/x04xjO7DjK2WSP+1rbZubwUQgghxDlxsQS71Zk1axZvvFGpzY0+ffrw1ltvNVCNhK8LMtitSin1GTBda73cZ9sAJNgVQtTBk59PwX+/IfeLz3Gu+wHl8XIkHNZ0UGR0a0RsGyuX2NIJt/jTxtWOJvsPYj2aBv5h6OSR7Au9k7XLC8k9XEyTVmH0vqU1sW3CTyqnqMTNuI83sTrtGH8a3I5HftemIjD+666DvHvgGJPbxvGHZtHn+hIIIYQQZ9XFHOyK89sFH+wqpRKAb4HOWuvjPtsHAAuAA0AmRuC7vZrj7wPuA2jRokX3/fv3n/1KCyHOS+7cXAr/+1+yvlhM6Q8bUF5NZiSs7GrDPbAZSaEZxFiLaeqOJ+GIlYB9m1FeD96kkaSGPsQPy7Mozi+l5SWNuPzm1ic901vm8fLkgi18uukgd/RswQs3dsJmteDRmnu27WV51nE+TGolryUSQghxUZFgVzSUCzrYVUoFA6uByVrrT6vsCwW8WutCpdS1wBta67a15Sctu0KIcu7cXAqWL+fI/LnoLTsptcF3HRXpA+No266MeDII9gbRPqcxET+noGz+lPV+gp8Kr2PTioO4S7107BPLpUNbEhR24j17Wmte+fpn/rlqN4M7xDDtjq44/KwUuT3ctPkX9jhLWNytLZ2CHQ149kIIIcSZI8GuaCgXbLCrlLIDS4Cvtdav1SP9PqCH1jqrpjQS7AohquNKTeXwv9+ncMkXWEvcpDWFDb0jiL4ihnbWHYSXQJfMcIIOpEJEAsV9J7NhV1u2f5uJxabodWNrkgY1q/Q87wdr9jHx8+10bR7OjLsvJSLIj0MlpVy7cRcK+LJ7O2L87Q130kIIIcQZIsGuaCinE+w22FjbyviPcQaws6ZAVynVxEyHUqonRn2zz10thRAXi4DERBImv0iH/31Po6eeIEFFc+f8XC57MpXtswNZV9qW71tmsa1rPGWUEvj5XVxR8mfueCiSuHYRfPefXXz17jZKnO6KPO/uncBbd3ZjW+ZxRs74geJSN7H+fvy7S0vy3B5+v3UPRR5PA561EEIIIcRvV0O+WKoP8HtgkFIqxZyuVUrdr5S630xzG7BNKfUT8CYwQjd0v2shxAXNGhpK9OgxJC1fRfP/+z+Cuvfg6jUu+j23my1LGvNTUFP+17mEfR3b4D20mfD/DOC6+PfpfUNT9v6UxbwpP3IsvaAiv2u7xPLOyO7sPHSc8XNT8Ho1nUMC+VfHeLYVOHloRzoe+bUlhBBCnDar1UpycjKXXHIJ3bp1Y82aNQDs27ePzp07V3vMgAEDqK3X5+TJk0lOTiY5Obki/+TkZN58882zcg7lvvrqK7p160aXLl3o3r07q1atqtg3d+5cOnTowODBg+udn9aal19+mfbt29O5c2eSk5P5+OOPz0LNLywN/szumSbdmIUQv5brl1/YOekpAtZv53A4pNzainaX5hJccoiORxvRaM9uVFgchy6fxdcLS3AVltH39rZ06te0olvzrO/38vznO7i/f2ueuiYROPFKogeaR/Ncm7iGPEUhhBDitJwP3ZiDg4MpLCwE4Ouvv2bKlCmsXr2affv2MXToULZt23bSMQMGDGDq1Kn06FFnj9dK+Z9tmzZtIjY2ltjYWH766SeGDh1KRkYGAIMHD+a5556jX79+9c5v+vTpfPHFF8ybN4+QkBDy8vJYvHgxo0aNOluncM6cTjdm21mrlRBCXCAC2rSh64fzyVn9XwonPcvVM/aQusLKrlG9cTffQmhoOF1/zqPJ8usYftM0lq9vx+rZP3Polzz639kevwAbo3snsPtYIf9avZtW0UHc3qM5Y5tFs6e4hLczjtHS4c+ouEYNfapCCCHEaXtp/Uuk5qSe0TwTIxN5sueT9U5//PhxIiIiTtrudDoZM2YMO3bsoEOHDjidzop9M2bM4KWXXqJp06a0bdsWf39/pk+fXm3+eXl5dOvWjbS0NGw2G3l5eXTt2pVffvmF/v3706NHD3744QcKCwuZNWsWPXr0oLCwkIceeogdO3ZQVlbGpEmTuP7666vNv1u3bhXLXbp0obCwkLKyMl544QXWrVvH2LFjufnmm2nTpg1Lly6ltLSU7du388QTT1BYWMjs2bNxOBwsXbqU8PBwpkyZwtq1awkJCQEgPDy8ItBdvnw5jz/+OB6Ph169evHWW2/h5+dHs2bNGDt2LJ999hkej4f58+fTrl27en8GF4KG7MYshBDnlcj+v6PH19/i/8TDxGdbuPyFTRx8z0G6Xy/WJNkoDAvG8dW9DG0zl55D49n14xHmv7iB7MxClFI8d30n+rVtxISFW1m3xxheYFKbOAZFhjBh10G2FzrrqIEQQgghauJ0OklOTiYxMZGxY8fy7LPPnpTm7bffJjAwkC1btjBhwgQ2btwIQGZmZkUguXz5clJTaw/Ww8PD6dOnD1999RUAs2fP5vbbb8dqtQJQUlLC2rVreeONNxg7diwAkyZN4uqrr2b9+vV88803PPbYY7hcrjrPa968eVx22WXY7XYmTZpEcnIyn3zyCS+++CIA27dv55NPPmHdunU8+eSTREREsHnzZrp3785HH31Ebm4uZWVlxMfHn5R3cXEx99xzDwsWLGDr1q0UFxfz7rvvVuyPiYlh8+bNjB07ltdeq3O84AuOtOwKIYQPZbPR6p5xeG4byeZXnqHrp8txb9lMys3tKem3j04ZjWi6/m0ujd9K7H3TWTb7APNf3MCgUR1o2yOG6Xd245Z/fs/9H21k0bg+JDQKYnrHeAasT+WhHfv5qkc7/C3yPaMQQogL169pgT2THA4HKSkpAKxdu5ZRo0ad1HX522+/5ZFHHgEgKSmJpKQkANavX0///v2JjIwEYNiwYaSlpdVa3tixY3nzzTcZOnQos2bN4t///nfFvjvuuAOAQYMGcfToUQoLC1m2bBlffvllRZDqcrlIT0+vtbV069atPPPMMyxfvrzGNIMGDSIoKIigoCCCg4MrWou7dOlCWloatT2WunPnTtq2bUvr1q0BGDVqFDNmzOChhx4C4JZbbgGge/fuLF26tNbrcSGS/7iEEKIa1tBQerzwJi0/X0xBYjN6zdtP7vsRbGvRhO2J4egDPxC38jqG/yGA6BYhLJuxndS1hwhz2Jk5+lIUcM8HP5JfXEak3car7Zuzs8jF1L2HG/rUhBBCiAve5ZdfTlZWFseOHTtpn+9rAsudyjhF/fv3Jy0tjZUrV2K320lMTKyxDKUUWmsWLVpESkoKKSkpdQa66enp3HLLLXz00Ue0bNmyxnT+/v4VyxaLpWLdYrHgdruJjIzEbreTnp5+0rF1nXd5XlarFbfbXWvaC5EEu0IIUYvgVm3p98nX5Iy8kk4b87D8rYB0R1fWXxJEmTufwP9cxw1X7KB5YgT//XAnO9dkEh8VxDu/70FGTjHjZm+kzONlSKMw7oqN5K30o/yYX9TQpyWEEEJc0FJTU/F4PERFRVXafsUVV1SMQrxt2za2bNkCQM+ePVm9ejW5ubm43W4WLFhQr3JGjhzJXXfdxZgxYypt/+STTwBYtWoVMTExBAUFcdVVV1UaxXnz5s015pubm8t1113H1KlT6dWrV73qUpunnnqKcePGUVBgvDEiLy+P9957j44dO7Jr1y727NkDwEcffUT//v1Pu7wLhQS7QghRB2Wx0OeZN9Cv/IWIPDfRz/3MsSNXsO4Sf46HB2Bb+hDXtllA88QIvvl3Kju+z6Rny0j+fksS3/+SzV8/247WmufbxBEX4MfDO/fL+3eFEEKIX6n8md3k5GSGDx/OBx98UPEMbbkHHniAwsJCkpKSePnll+nZsycAcXFxPP3001x22WUMHjyYjh07EhYWVmeZd911F/n5+QwfPrzS9tDQUHr37s3DDz/Me++9B8Bzzz1HcXExXbp0oVOnTkycOLHGfN944w327t3Lc889V3FO2dnZv/KKnPDwww/Tp08funfvTufOnRk4cCDBwcEEBgYyY8YMbrnlFrp06YK/vz/33nvvKZdzoZFXDwkhxK9wKC2F1PvvoXGmk903JRI6ZC9t9mTR7GAB7l7/jy/TbiR9Rw4DRybSsW9TXvoqlbdX7ebZoR35Q9+WrMkt5NaUX7g7rhEvtmvW0KcjhBBC1Mv58Oqh01VYWEhwcDBut5ubb76Ze+65h5tvvrnWY+bOncvXX3/NrFmzKrb17duX6dOnk5ycfLarLJBXDwkhxDkT2y6ZsM/+y38fGU7bRamk74pi37hY8G6m2brXuKZ/EF+qwaz8KBWtNY9f2Z49xwqZsnQnPRMi6d0sjPuaR/NOxjGuaRRG/8iQhj4lIYQQ4jdh4sSJrFixApfLxZVXXslNN91Ua/oHHniAFStWVIzILC480rIrhBCnwOv18uU/HqXFjBUcD7dT8kgTOuZsoclRJ+7BL/Hl5stJ355N/zvb07xHY658fTVhDjufP9wXrRRDNvxMocfLqkvbE2aX7x2FEEKc3y6Glt2GsHTpUp5++ulK29q0acP8+fMbqEYXntNp2ZVgVwghTsP3X8+CZ14hoAzyn2hCl5ztRGe58Fw3nS/XJ7F/qxHwZsXYGT3rR+7v35qnrkkk5Xgx121K4+bGEUzvePJ78YQQQojziQS7oqGcTrArA1QJIcRp6HPVGMJn/ZNifwh59Sg7ozuSHemP5YuHuabPLhK6RLF69s80OlzKiEub8+63u9m4P5fk0EDGx8cw/0guXxzLa+jTEEIIIYS46EiwK4QQp6lT5wH4v/UipcqDY2oWqU07kB/mh2XxfVw9INMIeOemcXdCY2LDHPz5Pz/hLPUwPr4JSSEOHv85g2OlZQ19GkIIIYQQFxUJdoUQ4gzo2eMGyv4xAbfHjf2VPFLj21MQZMXy6e+5cnAO0c1D+N+/f2bSgHbszSri5a9TsVsU0zrEU+Tx8vjPGaf0wnshhBBCCFE9CXaFEOIMGdBvJNkvPgQlpVimFrKzZVuKAsD2nxFce4Mbu5+VzCXp3N29ObO+38e6Pdm0DwrgLy1j+SrrOEuz8hv6FIQQQgghLhoS7AohxBk09MoH2f38SCxFLiyvl7KjdRucdg9Bnw/jmrsaUZRXSlJ6GQmRDh6f/xNFJW7GNoumQ1AAE3/JxOnxNvQpCCGEEOclq9VKcnIyl1xyCd26dWPNmjUA7Nu3j86dO1d7zIABA6ht8NrJkyeTnJxMcnJyRf7Jycm8+eabZ+Ucyq1YsYKwsDC6du1Ku3bt6N+/P0uXLj2tPJs1a0ZenowD4kvedyGEEGfYiBueZkZxPt3+/jne6WHseDCB5J27abxmDAPv+IQV//6FB7s14om9GUxZupPJN3fhb23juDVlN29nHOX/JTRp6FMQQgghanR4yhRKdqae0Tz9OyTSpMoreqpyOBykpKQA8PXXX/OXv/yF1atXn1a5EyZMYMKECQAEBwdX5H8uDBw4kEWLFgGwadMmbr75Zj788EP69+9/zupwsZOWXSGEOMOUUvxhxEv8b3x/rMfyUe8EsDO+EZYjO2ibM5WuQ1pwdFMW45rF8PEP6fxv1zH6RIRwfXQ40/Yf4aCrtKFPQQghhDivHT9+nIiIiJO2O51ORowYQVJSEsOHD8fpdFbsmzFjBu3atWPAgAHce++9PPTQQzXmn5eXR6tWrXC73RXrLVu2xOPx0LdvX8aPH8/ll19Oly5dKlqOCwsLGT16ND179qRr1658/vnn9T6fbt26MWHCBKZPnw7AyJEjKwJhMAJxMFqEBw4cyE033UTHjh158MEHqx3z44MPPqBnz54kJyczbtw4vF4ve/bsoW3btuTk5ODxeOjduzfffPNNvet4IWqwll2lVHPgQ6AJ4AXe1Vq/USWNAt4ArgWKgdFa603nuq5CCPFrKaV4cPR0prru5pq3NlE6J4a91xfQcuMH9LqxDzldEknfns1lsYE8MX8LX//pCv7apinLs/OZtDuTdzolNPQpCCGEENWqqwX2bHE6nSQnJ+NyuTh06FC1gdrbb79NYGAgW7ZsYcuWLXTr1g2AzMxMXnjhBTZt2kRISAiDBg3ikksuqbGs8PBw+vTpw1dffcXQoUOZPXs2t99+O1arFYCSkhLWrl3LN998w9ixY0lJSWHSpElcffXVvP/+++Tm5nLZZZcxZMgQAgIC6nV+3bp1Y9q0aXWm++GHH9ixYwfNmzdnyJAhfPbZZ9x0000V+7dt28bChQtZs2YNNpuN++67j7lz53LnnXfy2GOPMW7cOC655BK6du3KoEGD6lW3C1VDtuy6gce01h2AXsCDSqmOVdJcA7Q1p/uAt89tFYUQ4tTZLDbG3zeDz8e0I3BPNtkpSeSE2uCLh7jyehvhjQMZlGPBlVvCC5/voHmAHw+1iOGzo3msyS1s6OoLIYQQ55Xybsypqal89dVXjBo16qRWzW+//ZaRI0cCkJSURFJSEgDr16+nf//+REZGYrfbGTZsWJ3ljR07llmzZgEwa9YsxowZU7HvjjvuAGDQoEEcPXqUwsJCli1bVvEM8MCBA3G5XKSnp9f7/Or7VoZevXqRkJCA1WplxIgRfPfdd5X2r1ixgh9//JEePXqQnJzM6tWr2b17NwD3338/x44dY9asWbz88sv1rtuFqsGCXa31ofJWWq11AbATiKuS7EbgQ21YB4QrpWLPcVWFEOKUBdgCePCh91k6IJig1RnsdyVTptxYF47g2rGtsSrFPTqYzzYcYM3uLMa1aEycv51ndh3A7ZVXEQkhhBDVufzyy8nKyuLYsWMn7TM6h1Z2Kq/369+/P2lpaaxcuRK73U5iYmKNZSil0FqzaNEiUlJSSElJIT09nXbt2tW7vM2bN9OhQwcAbDYbXq8xaKXH46noTl1T2b601txzzz0V9fj555959tlnAaOr9aFDh/B4PBQWXvxfrJ8Xz+wqpRKArsAPVXbFARk+6wc4OSBGKXWfUmqDUmpDdTe8EEI0pIiACAb89Z+ktFLYZx8iLbwFltx0QtaM5+p7O2MtcnNbqYNJi7fjB0xsE8eOIhcfHcpu6KoLIYQQ56XU1FQ8Hg9RUVGVtl9xxRV8/PHHgNGdd8uWLQD07NmT1atXk5ubi9vtZsGCBfUqZ+TIkdx1112VWnUBPvnkEwBWrVpFTEwMQUFBXHXVVZVGcd68eXO9zyclJYUpU6bw4IMPApCQkMDGjRsBWLhwIR6PpyLtunXrSE9Px+PxMG/ePPr27Vspr8GDBzNv3jyysrIAyM7Ormhhfvzxxxk9ejR//etf+eMf/1jv+l2oGjzYVUoFAwuA8Vrr41V3V3PISV/LaK3f1Vr30Fr3iI6OPhvVFEKI09K96aU4n7mf7CCNd7Yfu2NCse5YQtPj8+g7rB1xTghMdzL3xwyGRofROzyYl/YcIrfMXXfmQgghxG9A+TO7ycnJDB8+nA8++KDiGdpyDzzwAIWFhSQlJfHyyy/Ts2dPAOLi4nj66ae57LLLGDx4MB07diQsLKzOMu+66y7y8/MZPnx4pe2hoaH07t2bhx9+mPfeew+A5557juLiYrp06UKnTp2YOHFirXmvXLmSrl270r59+//P3p2HVVXtDRz/rnM4TAKKI+KIAyoyHBFRFJxNMysxlczZtLK02226qQ1ee7vvzbpd8/Vmk5qWZffaYJZpzkNqDoA4oGapqKCIEyDjOWe9fwDnaiKCHETl93me/Zw9rL3Wb+N5hN9ee63N008/zXvvvWefifnxxx9n9erVhIeHEx8fj4uLi/28zp0789xzzxEUFIS/vz8PPPDAVfUGBQXx2muv0bt3b4KDg7nnnns4c+YMa9euZc+ePTz33HOMHj0am83Gp59+esOfwZ1M3UyXvsMaV8oEfA+s0lq/U8zxD4ANWusvCrcPAd211inXqzMsLEyX9C4tIYSoLDZt49X5IxjyThyqbUN8OsdRJ90KY1fz/TInju4/z1d1LHz3Ug+SbRZ67TzE6Aa1+V//hpUduhBCiCouMTHR/ojtnSozMxMPDw8sFgvR0dGMGzeO6OjoEs9ZsmQJq1atsqRUAAEAACAASURBVI/dBYiMjGTOnDmYzeaKDvkaa9asYc6cOVfN1Hy3K+67p5TarbUOu9G5ldazWzjT8jwgsbhEt9B3wChVoBNwqaREVwghbmcGZeCZR2bz5f1emBJOkprSiRwT2L6MoedD9XFxMRJ5TjFrzSHaeLgxukFtFp5K40Bm9o0rF0IIIUSJpk+fjtlsJjAwED8/v6tmMC7OxIkTeeWVV3j55ZdvUYTC0SqtZ1cpFQlsBvZS8OohgKlAYwCt9fuFCfEcoB8Frx4aq7UusdtWenaFELe7X1J+Ycefx9I7XqMfq0XrjL3YmnbmN/95rFl4kE1u+fzPS52pXdONztsTaePhxlfm5sVOuCGEEELcCndDz25lWLFiBVP/8KqmFi1asHTp0kqK6M5Tnp7dSn2MuSJIsiuEuBPM3fEuDV98H790F1zGZdEy7QyWfv/LD7s6c2zfOfYHuvPBU51YlHyOvxw+yYdtm/JA3RqVHbYQQogqSpJdUVnuyMeYhRCiKnssbBJrHw8lS+eR970v56qZUGteo0+0N04uRuomXmbtgTOM8K1FWw9X/nrkFFlW240rFkIIIYQQgCS7QghRKYwGI1Mf/Cfzh1SH42dIPRCMtubhtGYCvUe0pr7VwFeLD2C1av6nZUNO5ebz8Ul5tZoQQgghRGlJsiuEEJWkrntdRo5+my+jDLAthWM5DXA++gstXNbj1ao6bc5rFvxwiIgaHvSq6cV7SamkW6w3rlgIIYQQQkiyK4QQlalLgy7UnPAohxpAzmoXLhhM6BXPMmREI2wmA6dXJ3P6QjYvNvPhosXKhyekd1cIIYQQojQk2RVCiEr2VOhkVjzshy0nh5TDARhys1DrnqTj0BbUtijmfxhPiKc7/WtX54MTqVzIt1R2yEIIIcQtZzQaMZvNhISEEBoaytatWwE4duwYgYGBxZ7TvXt3Spq89o033sBsNmM2m+31m81mZs+eXSHXAAUzNBe14+HhQatWrTCbzYwdO7Zc9fr4+JCZmemgKO8OTpUdgBBCVHUmo4nJA9/k37uH8ciGsxxv5INf4mrC2+1le8MaeB7NYuuOZF4I8OHHtEvMTUplanPfyg5bCCFEFbX534dJO+HYpKp2Iw+ihvqXWMbNzY34+HgAVq1axZQpU9i4cWO52p02bRrTpk0DwMPDw15/Rerfvz/9+/cHIDIykjlz5mA2myu83apIenaFEOI2EFQnCK8xI/jNBy6v8yBdOWFd9jgjJ7Qk2wBbFh+ihbOJgXVr8NHJNM7m5Vd2yEIIIUSlSU9Px9vb+5r92dnZPPzwwwQHBxMTE0N2drb92Lx58/D396d79+5MmDCBSZMmXbf+ixcv0qxZMywWi33bz88Pq9VKZGQkzzzzDBEREQQFBdl7jjMzMxkzZgzh4eG0a9eO5cuXl/m6evbsyeHDhwFo06YNM2fOBOCFF17gs88+Y+XKlfTp04fo6GhatWp1TW/wP/7xD9q1a0dISAhHjhwBYPXq1YSEhNh7xHNycsoc151KenaFEOI28VT7p3lqyE88968Ukn9vS2u/PXhsewGf3n8h46cU/r3oAM894s+y1IvMOZ7KX1s2qOyQhRBCVEE36oGtKNnZ2ZjNZnJyckhJSWHdunXXlJk7dy7u7u4kJCSQkJBAaGgoAMnJybz++uvExsbi6elJz549CQkJuW5bNWrUoEuXLqxcuZIBAwbw+eefM3ToUIxGIwC5ubls27aNdevWMX78eOLj45kxYwb9+vXjk08+4cKFC3Ts2JE+ffrg6upa6mvs2rUrmzdvxtvbGw8PD7Zs2cKLL77Ili1bmDRpEomJicTGxrJ//37q1KlDhw4d2LVrF2FhBa+c9fX1JS4ujnfeeYdZs2YxZ84cZs6cyfz582nfvj0ZGRk4OzuX5cd+R5OeXSGEuE24m9x5LPoNvu6s0L+cJSm3Di7x3zAsNI0Tnopzu9OomW5liE9NFiankZKbV9khCyGEELdM0WPMBw8eZOXKlYwaNQqt9VVlNm3axIgRIwAIDg4mODgYgB07dtCtWzdq1qyJyWRiyJAhN2xv/PjxLFiwAIAFCxZc1Ys6bNgwoKAnNjU1lczMTH766Sf7GOAePXqQk5NDUlJSma4xKiqKTZs2sXnzZh566CFSU1O5fPkyZ8+epUmTJgB07twZHx8fjEYjISEhHDt2zH7+oEGDAGjfvr19f5cuXZg8eTJz5swhMzMTg6HqpIBV50qFEOIOEOEbgXVkNEl1FBkbvcnCiO2bMXR6sBEWrflm3j7+3KQuFq1593hqZYcrhBBCVIqIiAjS0tI4e/batxQopa7Z98ekuDS6devG4cOHWb9+PSaTidatW1+3DaUUWmu+/fZb4uPjiY+PJykpCX//svWCR0REsH37djZv3kzXrl0JDAzk448/plOnTvYyLi4u9nWj0Wh/1PrKY1funz59OnPnziU9PZ2wsDB+//33MsV0J5NkVwghbjPPdnqRxdHecCGTU0ltcbmUxj2Z73K4roGcE5fJT7zEI/VrsTj5HCdypHdXCCFE1XPw4EGsViu1atW6an/Xrl1ZvHgxAPv27SMhIQGA8PBwNm7cyIULF7BYLHz11VelamfEiBEMHz78mrGxX375JQAbNmygXr16VKtWjb59+141i3NcXFyZr8vd3R1vb29WrFhBWFgYUVFRvP3220RFRZW5riK//fYbISEhTJ06laCgIPuY4KpAkl0hhLjNVHepzohBr/FdR4V1axqncmvh+sunPNxLc8ZoY83nh5jkUxuDgn8eO01aWhpr165l/fr17N27l5SUFPLyJAkWQghxdykas2s2m4mJiWHhwoX2MbRFJk6cSGZmJsHBwcycOZPw8HAAGjRowNSpU+nYsSO9e/cmICCA6tWr37DN4cOHc+nSJWJiYq7a7+XlRefOnZk8eTIfffQRAK+99hpZWVkEBQXRtm1bpk+fflPXGRUVRYMGDXB2diYqKoqTJ0+WK9l98803CQwMJDg4mLp169KrV6+brutOo8rapa+U8gYaaa0TKiak8gkLC9MlvUtLCCHuBFprnls1mfteX0d9oxfNexzC4tuU8ednEXncSlCvhixplMO3WVaG7lyDd07WNY9oVa9endq1a9uX5s2bU7NmzUq6IiGEEHeyxMRE2rRpU9lhlEtmZiYeHh5YLBaio6MZN24c0dHRJZ6zZMkSVq1aZR+7C/K6oFutuO+eUmq31jrsRueWajZmpdQG4IHC8vHAWaXURq31s2UPVwghxI0opXip66s8f/8OXlp4iZPHW9BcHeapdvF8mtIGvfYE3j57MURFcTK8G6+HtcHV1ZXz58+TlpZ21RIbG0t+fj5OTk707NmTTp06VanJKYQQQggoGLu6Zs0acnJyuOeeexg4cGCJ5SdOnMiaNWtYuXLlLYpQOFqpenaVUnFa63ZKqfEU9Oq+ppRK0FoHV3yIZSM9u0KIu8nSw0s5NuNV7tulqdv/As61rdx3aTYPptelnq8Lv8c0Ye6pc2wIb02rasW/2sBms3HhwgV++uknDh06ROPGjRk4cKD08gohhCi1u6FntzKsWLGCqVOnXrWvRYsWLF26tJIiuvOUp2e3tLf2nZRS9YGhwPdlD1EIIcTNeNDvQbb3bk6aJxzfWQePnDxmBW5ko2s+l07lcd9ZA+5GA28dTbluHQaDgVq1avHwww8zcOBAzpw5w9y5c9mxYwc2m+0WXo0QQghRtfTv398+O3PRIonurVPaZPevwCrgiNZ6p1KqGfBrxYUlhBAiIyODRYsW0fRkKxb3MuF21kJyam2Cj39JjaZ5pDnDnm9/57HaNfn+7CX2ZWSVWJ9SCrPZzJNPPknjxo1ZsWIFn332GRcvXrxFVySEEEIIceuUNtlN0VoHa62fBNBa/w68U97GlVLzlVKpSql91zneXSl1SSkVX7i8Wt42hRDiTpCUlMQHH3zA6dOnGTNwDG2GjGNfY8XFXZ7oLM1rrnP4wTmH7Mx82sdnUt3JyDvHzpSq7urVqzNixAgGDBjAiRMnmDt3LrGxsTf1DkIhhBBCiNtVaZPd/yvlvrL6BOh3gzKbtdbmwmWGA9oUQojbltaaHTt28Mknn+Ds7Mz48eMJDAxkfPAElt1fCy7nciKpCc1SfiGy4WkOeWgOb07mUWM1fky7xK+Xc0rVjlKKsLAwnnzySXx8fPjuu+9YsmSJvLJICCGEEHeNEpNdpVSEUuo5oI5S6tkrlumAsaRzS0NrvQk4X956hBDibpCfn8+yZctYsWIFzZs3Z8KECdSrVw+AaqZqDLn3eVaFKnJ255OZ6cxfbG+xSmWDi5FGG8/houC9E6llatPb25vRo0fTt29fDh8+zMKFC8nKKvlxaCGEEEKIO8GNenadAQ8KXjnkecWSDgyu2NDsIpRSe5RSPyql2hZXQCn1mFJql1Jq19mzZ29RWEII4TgXL15k/vz5xMfH061bN4YNG4abm9tVZe5vfj97H2zLZTdFcmJT6l5KZny9nWyuls/54xk8es7E0tMXSM4pW++swWAgIiKCoUOHcvr0aebPn8+lS5cceXlCCCFEuRmNRsxmMyEhIYSGhrJ161YAjh07RmBgYLHndO/enZLe1PLGG29gNpsxm832+s1mM7Nnz66Qa4CCGZqL2vHw8KBVq1aYzWbGjh1brnp9fHzIzMwsVdmDBw/i7u5uj8NsNvPll1+Wq/3bUYnv2dVabwQ2KqU+0Vofv0UxXSkWaKK1zlRK9Qe+BVr+sZDW+kPgQyh49dCtDVEIIconKSmJJUuWYLVaGTZsGK1atSq2nEEZ+FP3aSzcOpzHVmZytokXE10+oL3FTNc6dam//TxO93jwwcmz/LVFgzLH0aZNG0aOHMkXX3zBvHnzGDFiBHXr1i3v5QkhhLjLrP/kQ1KP/+7QOus2aUaPMY+VWMbNzY34+HgAVq1axZQpU9i4cWO52p02bRrTpk0DwMPDw15/Rerfvz/9+/cHIDIykjlz5mA2myu83T8KCAgo8UbA3aC0Y3ZdlFIfKqV+UkqtK1oqNDJAa52utc4sXF8BmJRStSu6XSGEuFUOHTrEokWLcHNzY8KECddNdIuY65pxjR7AsXqKc/F1cc/K4bWay1hGNrkZ+Yw+AZ8mn+N8vuWm4mnatCljx47FZrOxYMECTpw4cVP1CCGEEBUpPT0db2/va/ZnZ2fz8MMPExwcTExMDNnZ2fZj8+bNw9/fn+7duzNhwgQmTZp03fovXrxIs2bNsFgs9m0/Pz+sViuRkZE888wzREREEBQUZE8YMzMzGTNmDOHh4bRr147ly5eX+bp69uzJ4cOHgYKb0DNnzgTghRde4LPPPmPlypX06dOH6OhoWrVqdU1v8D/+8Q/atWtHSEgIR44cAWD16tWEhITYe8Rzcko3v8fdoMSe3Sv8B3gf+BiwVlw4V1NK+QBntNZaKRVOQXJ+7la1L4QQFSkuLo7vvvuO+vXrM3z4cKpVq1aq8/4U9mee67ealxdmcepkfYY0W8bs/J4YmrSiXtwljPW8WHAyjef8fG4qLh8fHx599FE+/fRTFi1axNChQ2nZ8pqHaoQQQlRRN+qBrSjZ2dmYzWZycnJISUlh3bpr+97mzp2Lu7s7CQkJJCQkEBoaCkBycjKvv/46sbGxeHp60rNnT0JCQq7bVo0aNejSpQsrV65kwIABfP755wwdOhSjsWDaotzcXLZt28a6desYP3488fHxzJgxg379+vHJJ59w4cIFOnbsSJ8+fXB1dS31NXbt2pXNmzfj7e2Nh4cHW7Zs4cUXX2TLli1MmjSJxMREYmNj2b9/P3Xq1KFDhw7s2rWLsLAwAHx9fYmLi+Odd95h1qxZzJkzh5kzZzJ//nzat29PRkYGzs7OABw4cOCqHuUPPviAjh07ljrWO0Fpe3YtWuu5WusdWuvdRUt5G1dKfQFsA1oppU4qpR5VSj2hlHqisMhgYJ9Sag8wG3hYy7sxhBB3gZ9//plly5bh5+fH6NGjS53oAvhU8yGq/2NsDlBk7DZiyVD83XMRX+VngtY88quFeafOctl68/cmvb29GTduHLVq1eKLL74gISHhpusSQgghHKHoMeaDBw+ycuVKRo0adc1r8zZt2sSIESMACA4OJjg4GIAdO3bQrVs3atasiclkYsiQITdsb/z48SxYsACABQsWXNWLOmzYMKCgJzY1NZXMzEx++ukn+xjgHj16kJOTQ1JSUpmuMSoqik2bNrF582YeeughUlNTuXz5MmfPnqVJkyYAdO7cGR8fH4xGIyEhIRw7dsx+/qBBgwBo3769fX+XLl2YPHkyc+bMITMzE4OhIAUMCAggPj7evtxtiS6UPtldrpR6UilVXylVs2gpb+Na62Fa6/paa5PWuqHWep7W+n2t9fuFx+dordtqrUO01p201lvL26YQQlQmm83GTz/9xOrVqwkMDOSRRx7BxcWlzPWMaTuG1QN8yFOa5CN+dM/fiVd2Aq5tqlP3UCam1Fy+SCnfZPceHh6MGTOGJk2a8PXXX7Nt27Zy1SeEEEI4SkREBGlpaRQ3Oa1S6pp9N9Nf1q1bNw4fPsz69esxmUy0bt36um0opdBa8+2339qTx6SkJPz9/cvUZkREBNu3b2fz5s107dqVwMBAPv74Yzp16mQvc+XfDUaj0f6o9ZXHrtw/ffp05s6dS3p6OmFhYfz+u2PHW9/OSpvsjgZeALYCuwuXu3s0sxBCOJjVamXZsmVs3bqV8PBwBg0ahJNTaUeTXM3VyZVHe77IVxGQl5jFhTR3prsu4t+XL+Li7sRD+3OZe/wM+bbyPQzj6urK8OHDCQgIYNWqVfZxREIIIURlOnjwIFarlVq1al21v2vXrixevBiAffv22Z9MCg8PZ+PGjVy4cAGLxcJXX31VqnZGjBjB8OHDrxkbWzRz8YYNG6hXrx7VqlWjb9++V83iHBcXV+brcnd3x9vbmxUrVhAWFkZUVBRvv/02UVFRZa6ryG+//UZISAhTp04lKCioSv0uL1Wyq7X2K2ZpVtHBCSHE3SIvL48lS5awZ88eevTowb333mt/jOhm9W3Sl1MDQkmtaSR1ry9Blt+pf/FnqrevTa1Tubgez+Kb1Avljt3JyYlBgwZRp04dvv/++yo1sYUQQojbR9GYXbPZTExMDAsXLrSPoS0yceJEMjMzCQ4OZubMmYSHhwPQoEEDpk6dSseOHenduzcBAQFUr179hm0OHz6cS5cuERMTc9V+Ly8vOnfuzOTJk/noo48AeO2118jKyiIoKIi2bdsyffr0m7rOqKgoGjRogLOzM1FRUZw8ebJcye6bb75JYGAgwcHB1K1bl169egH/HbNbtLz//vs33cbtSpWmS18p5Q48CzTWWj+mlGoJtNJaf1/RAZZVWFiYvtun0BZC3Fmys7P5/PPPOXnyJPfdd599EglHSDyXyJv/HMJfllrxDs8is5k7U2vP5YEzLpzIz+fHB2uzoVMbDMU80lVWJ06cYN68eXTo0IH77rvPAdELIYS4UyQmJtKmTZvKDqNcMjMz8fDwwGKxEB0dzbhx44iOji7xnCVLlrBq1Sr72F2o3NcFVUXFffeUUru11jf8g6q03QoLgDygc+H2SeB/yhKkEEJURefPn2fevHkkJyczZMgQhya6AG1qtaHpvQ+R4Gfg/P7q+OWl0CB5JXUj6+F10UK1/en8lJbukLYaNWpEp06d2LlzJ8ePV8ar14UQQoibN336dMxmM4GBgfj5+TFw4MASy0+cOJFXXnmFl19++RZFKByttD27u7TWYUqpOK11u8J9e7TW15+vu5JIz64Q4naRlJTEkiVL0FoTExND06ZNK6SdtOw0nnzvXv76USae5nxoZeP1JvO555wnR09lsDHGh+86tSp2wo6yysvL47333sNoNPLEE09gMpkccAVCCCFud3dDz25lWLFiBVOnTr1qX4sWLVi6dGklRXTnuRU9u3lKKTdAF1beHMgta6BCCFFV7N27l4ULF+Lq6sr48eMrLNEFqO1Wm169x7O5rSJ9vyv1si/g89vXNO7pi1uODfed59l28bJD2nJ2dmbAgAGcO3eOTZs2OaROIYQQ4m7Vv3//q17vEx8fL4nuLVTaZPc1YCXQSCm1GFgLvFhhUQkhxB1Ka82mTZv46quvaNCgAePHj79mpsiKMDJgJCv71MJqs3HqVx+edvqarw8ewK99HSIO5fDBgVMOa6tFixaEhITw888/c/r0aYfVK4QQQgjhSKWdjXk1MAgYA3wBhGmtN1RcWEIIceexWCx8++23rFu3juDgYEaNGoW7u/stadvd5M6Q7k+yMhSyDio80rOpl7gY/94NcULhvOUs+zKyHNZe3759cXNzY9myZVitVofVK4QQQgjhKCUmu0qp1oWfoUATIAVIBhoX7hNCCAFkZWXx2WefsWfPHrp37050dPRNv0P3Zg1uOZhf+jQk10Vx4pAvjxp/4KtdP9O2ewNCjubx8e4TDmvL3d2de++9l5SUFLZv3+6weoUQQgghHOVGPbvPFn7+o5jl7QqMSwgh7hhFMy6fOHGC6Ohounfv7pDJoMrKZDQxNvJPfN0R8n+3oM7aqLd3EW17NkS7GjBuOMPJnDyHtde2bVtatWrF+vXrOXfunMPqFUIIIYRwhBKTXa31Y4WfPYpZet6aEIUQ4vZks9nYsWMH77//PllZWYwaNYqQkMqdpP5ev3v57Z7WXPQycuqgLw+zlq83LiOob2OanbawaKvjXhmklOK+++7DaDSyfPlySjO7vxBCCHGzjEYjZrOZkJAQQkND2bp1KwDHjh0jMDCw2HO6d+9OSW9qeeONNzCbzZjNZnv9ZrOZ2bNnV8g1iFurVM/YKaWeAhZrrS8WbnsDw7TW71VkcEIIcbtKS0vju+++IykpiWbNmnH//ffj7e1d2WFhUAae7PRnvoh8nIkr8sg65YKP+pSwFwYSt+YEWetSyIzyw8PkmEesvby86NOnD99//z2xsbG0b9/eIfUKIYS4fV1c/ht5yY6Z5b+Is281atzfvMQybm5uxMfHA7Bq1SqmTJnCxo0by9XutGnTmDZtGgAeHh72+sXdobSzMU8oSnQBtNYXgAkVE5IQQty+rFYrmzdvZu7cuaSmpvLggw8ycuTI2yLRLdLFtwuZvTqQXMfI6cQ6DLBt5fs182nVtzH10yx8sdlxvbsAoaGhNGnShJ9++omMjAyH1i2EEEIUJz09vdjfvdnZ2Tz88MMEBwcTExNDdna2/di8efPw9/ene/fuTJgwgUmTJl23/osXL9KsWTMsFot928/PD6vVSmRkJM888wwREREEBQXZe44zMzMZM2YM4eHhtGvXjuXLl1+3/o8//pjBgwfTt29fWrZsyZQpU+zHHnvsMcLCwmjbti0zZsyw72/YsCHTp0+nXbt2BAcHc/jw4dL/wKqo0t7aNyillC58Rk0pZQScKy4sIYS4/aSkpLBs2TJOnz5NmzZt6N+/P56enpUd1jWUUvwp/M/8s9sj/GVpHmlHvfAxLqHTS2OYveYEF1afwtrND6OxtPc7S2YwGLj//vv517/+xS+//ELv3r0dUq8QQojb0416YCtKdnY2ZrOZnJwcUlJSWLdu3TVl5s6di7u7OwkJCSQkJBAaWjCnbnJyMq+//jqxsbF4enrSs2fPEoce1ahRgy5durBy5UoGDBjA559/ztChQzEajQDk5uaybds21q1bx/jx44mPj2fGjBn069ePTz75hAsXLtCxY0f69OmDq6trsW3s2bOH2NhYnJyc8Pf3Z/Lkyfj6+vL3v/+dmjVrYrFY6NGjB4MHDyYgIACAevXqERcXx+zZs3nnnXd4//33y/tjvauV9i+dVcC/lVK9lFI9KXj90MqKC0sIIW4f+fn5rF27lg8//JCMjAyGDh1KTEzMbZnoFgmpE0KNnr053MjIucQadM3fy9qVb9PwngZ4X7DwzQbH9u7Wrl0bf39/YmNj7XfBhRBCCEcqeoz54MGDrFy5klGjRl0zX8SmTZsYMWIEAMHBwQQHBwOwY8cOunXrRs2aNTGZTAwZMuSG7Y0fP54FCxYAsGDBAsaOHWs/NmzYMAB69uxJamoqmZmZ/PTTT/YxwD169CAnJ4ekpKTr1t+7d288PT1xc3OjdevW9rJffPEFoaGhhIaGkpiYyIEDB+znDBo0CID27dtz7NixG15DVVfaZPcvwDpgIvAUsBZ4saKCEkKI24HFYiE2Npb33nuPzZs3ExISwqRJk+x3V293T4f+iU97KAyX8zl5qCa+CcsYGFmL895OHF11AqvV5tD2wsPDycrKYv/+/Q6tVwghhPijiIgI0tLSOHv27DXHinsjws1MotitWzcOHz7M+vXrMZlMtG7d+rptKKXQWvPtt98SHx9PfHw8SUlJ+Pv7X7d+FxcX+7rRaMRisfDrr7/y7rvvsm7dOhISEujXrx85OTnXnFNUXpSsVMmu1tqmtZ6rtR6stX5Ia/2B1tpa0cEJIURlKHo06d133+W7777D1dWVkSNHMnDgQNzc3Co7vFJrVqMZAd2i2dHKQMZhd4KyjhK/5nW8e/ninm5h3brr322+GX5+ftSqVYudO3c6tF4hhBDijw4ePIjVaqVWrVpX7e/atSuLFy8GYN++fSQkJAAFN2Q3btzIhQsXsFgsfPXVV6VqZ8SIEQwfPvyqXl2AL7/8EoANGzZQr149qlWrRt++fa+axTkuLq7M15Weno6npydeXl6kpKSwatWqMtch/qvEMbtKqX9rrYcqpfYC19wO0VoHl6dxpdR8YACQqrW+Zr5wVXDL5F2gP5AFjNFax5anTSGEuJ6srCx++eUXduzYQXZ2Nk2bNuXBBx+kefPmlfLeXEd40vwk43ssJ+zXXE7sq0UNj1UMeeYF3t3ghGXlcXp0a4STs9EhbRkMBjp06MDKlStJTk7G19fXIfUKIYQQYl8SdgAAIABJREFU8N8xu1DQU7tw4UL7GNoiEydOZOzYsQQHB2M2mwkPDwegQYMGTJ06lY4dO+Lr60tAQADVq1e/YZvDhw9nxowZxMTEXLXfy8uLzp07k5GRYX/U+bXXXuOZZ54hKCgIm81GixYtWLZsWZmuMTQ0lICAAAIDA2nWrBldunQp0/niaqqkLn2llK/WOlkp1aS441rrcg36Ukp1BTKBRddJdvsDkylIdjsC72qtO5ZUZ1hYmC7pXVpCCPFH6enpbN26ld27d5Ofn0+rVq2IjIykUaNGlR2aQ7y9821c/rGAngka//tOc7DPwyw1PkPd/5wk+EE/ou71c1hb2dnZvPPOOwQGBvLggw86rF4hhBCVKzExkTZt2lR2GOWSmZmJh4cHFouF6Ohoxo0bR3R0dInnLFmyhFWrVtkTWoDIyEjmzJljT7xFxSruu6eU2q21DrvRuTeajfl7IBT4H631yJsPsXha601KqaYlFHmQgkRYA9uVUjWUUvW11imOjkUIUfWkpKSwfft29u7di9aaoKAgunTpQr169So7NIcaHzSeR7r/h+77MkjaVwu3Gmt4eOJLfLTZhF6VRHj3Rri4Oea9u25ubgQFBZGQkECfPn1wd3d3SL1CCCFEeU2fPp01a9aQk5PDPffcw8CBA0ssP3HiRNasWcPKlTIv753qRn/dOCulRgOdlVKD/nhQa/11xYRl1wA4ccX2ycJ9VyW7SqnHgMcAGjduXMEhCSHuZDabjSNHjrBt2zaOHj2KyWQiLCyMiIiI2+pduY5Uw7UGD0SMZdWW2dy724kWbc5wIvYVsiJfQi1NYfdPx+n8oONeIxEeHk5sbCzx8fF07tzZYfUKIYQQ5fH222+XqfzcuXOL3b9ly5ZS17FixQqmTp161b4WLVqwdOnSMsUibs6Nkt0ngOFADeD+PxzTQEUnu8UNkitu7PCHwIdQ8BhzBcckhLgD5efns2fPHrZv305aWhqenp707t2b9u3b31GTTt2sEW1GMKT7IvrsucSJfbUx1dnE4NEv801DE6w9QbuejXDzdMzr0318fGjUqBE7d+6kU6dOGAyOeZ+vEEIIcafp378//fv3r+wwqqwbJbv1tdYTlVJxhQnlrXYSuHLQXEMguRLiEELcoS5evEhsbCw7d+4kOzub+vXrM2jQINq2bXvNpBZ3Mw9nDx7qOI4fwt5h4DYjDc+cw7r/ZU6Gv0ybb86ye+UxIodc//UIZRUeHs5XX33Fb7/9RsuWLR1WrxBCCCFEad0o2Z0C/IeCHt7KSHa/AyYppZZQMEHVJRmvK4S4kdzcXBITE9mzZw9Hjx4FoFWrVkRERNCkSZM7dmbl8nqk9SM81HUh98ae58Te2njU/5lBQxVbmjpj2HiKkF6N8azp6pC22rRpQ7Vq1dixY4cku0IIIYSoFDdKds8ppdYDfkqp7/54UGv9QHkaV0p9AXQHaiulTgKvAabCut8HVlAwE/MRCl49NLb4moQQVZ3NZuP48ePEx8dz4MAB8vPz8fb2pnv37oSEhNy143HLwt3kzsPh41kWPpOYzQZqnMoh4teXed/8V4KPn2PnD0fpOdIxM206OTnRvn17Nm3axIULF+TnL4QQQohb7kbJ7n0UzMb8KfAPRzeutR52g+MaeMrR7Qoh7g5aa1JTU9m/fz979uzh0qVLODs7ExgYiNlspnHjxlW2F/d6hrYayqCo+dy36xwnEmpRs+F27r9fs7O5C8ZtKYT2bUKNuo6ZQbl9+/Zs3ryZnTt3cs899zikTiGEEFWX0WgkKCgIrTVGo5E5c+aUeyLE+Ph4kpOTrxpX++OPP/LKK69w+fJltNYMGDCgzJNbFWfMmDEMGDCAwYMHF3vcYrHw6quv8p///Idq1aoBMGTIEKZNm3ZN2enTp+Ph4cHzzz9f7rjuZiUmu1rrPApe+dNZa31WKVVNa335FsUmhBDXyMnJ4ejRo/z6668cOXKE9PR0AJo1a0avXr1o3bo1zs6OmWjpbuTm5MaIDo/xTae/MXK9AffjVgYce5XP2v6VDr/nsWvFMXqPCXBIW9WrV6d169bExcXRo0cPTCaTQ+oVQghRNbm5uREfHw/AqlWrmDJlChs3bixXnfHx8ezatcue7O7bt49Jkybxww8/0Lp1aywWCx9+eGtGc7788sucPn2avXv34urqSkZGBv/4h8P7G6uU0r5YsYVSaiPgATRWSoUAj2utn6y40IQQ4r+9t0XJbVJSEjabDWdnZ5o3b063bt1o2bIlXl5elR3qHWOw/2A+i5zHAztSOZFQk0ZNttH9HiuxLVww/nKasHubUqOeY3p3O3ToQGJiIvv27aNdu3YOqVMIIUTl+vHHHzl9+rRD6/Tx8eHee+8tdfn09HT7EJmUlBRiYmJIT0/HYrEwd+5coqKi8PDw4KmnnmLNmjV4e3vzt7/9jRdffJGkpCRmzZpFv379ePXVV8nOzmbLli1MmTKFH374gWnTptG6dWugYFjOk08WpDzHjx9n3LhxnD17ljp16rBgwQIaN27MmDFj8PLyYteuXZw+fZqZM2cyePBgtNZMnjyZdevW4efnR8FDq8XLysrio48+4tixY7i6Fsyf4enpyfTp0+1l3njjDRYtWkSjRo2oU6cO7du3L+uPucopbbI7C+hLwYRRaK33KKW6VlhUQogqKzMzk+TkZE6dOkVycjLJyclcvlzwQEm9evWIiIigZcuWNGrUqErNpuxILkYXxoY9wdLOf+XR1QZMvyseOfE/jG79KmG/5bJzxVH6jG3rkLb8/PyoXbs2O3fulGRXCCFEuWRnZ2M2m8nJySElJYV169YB8Pnnn9O3b1+mTZuG1WolKysLgMuXL9O9e3fefPNNoqOjefnll1m9ejUHDhxg9OjRPPDAA8yYMYNdu3YxZ84cAN58802ee+65YtufNGkSo0aNYvTo0cyfP5+nn36ab7/9FihIuLds2cLBgwd54IEHGDx4MN988w2HDh1i7969nDlzhoCAAMaNG1ds3UeOHKFx48Z4enoWe3z37t0sWbKEuLg4LBYLoaGhkuyWQmmTXbTWJ/4w9s3q+HCEEFWFzWbj0qVLnDt3jpSUFHuCW/RYslKK2rVr2xPbFi1aUL169UqO+u4R3SKaT7p8TNovKRgSahLUbDNtulnZ18oNteMMYfc2xdunWrnbUUrRoUMHfvzxR06ePEnDhg0dEL0QQojKVJYeWEe68jHmbdu2MWrUKPbt20eHDh0YN24c+fn5DBw4ELPZDICzszP9+vUDICgoCBcXF0wmE0FBQRw7dqzM7W/bto2vv/4agJEjR/Liiy/ajw0cOBCDwUBAQABnzpwBYNOmTQwbNgyj0Yivry89e/YsdVsLFizg3Xff5dy5c2zdupXNmzcTHR2Nu3vBk1cPPFCueYKrjNImuyeUUp0BrZRyBp4GEisuLCHE3cBqtZKZmcn58+c5d+6c/fPcuXNcuHABq/W/98y8vb1p3Lgxvr6+NGjQAB8fH1xcXCox+rubyWhifPsn+E/kK0xcoTD8auRx/7f5U4u/EPRrDjt/OMY9jzqmdzckJIS1a9eyc+dOSXaFEEI4REREBGlpaZw9e5auXbuyadMmfvjhB0aOHMkLL7zAqFGjMJlM9okqDQaD/e8Kg8GAxWIptt62bduye/duQkJCbhjDlR2BV/7NcuXjyqWdKLNFixYkJSWRkZGBp6cnY8eOZezYsQQGBtr/XpJJN8uutMnuE8C7QAPgFLAKmSVZiCpJa01OTg7Z2dlkZ2eTlZVFRkbGVUt6ejoZGRn2WQyLGI1GatasSe3atWnVqhU1a9akVq1a1K1b136nUtw69ze/n/kRH3F660kMe2vSteUaqkc8z/EAD/SuM7S/twm1fD3K3Y6rqyvBwcHExcVxzz332GeYFEIIIW7WwYMHsVqt1KpVi+PHj9OgQQMmTJjA5cuXiY2NZdSoUaWqx9PTk4yMDPv2Cy+8wKBBg4iMjMTf3x+bzcasWbN49tln6dy5M0uWLGHkyJEsXryYyMjIEuvu2rUrH3zwAaNGjSI1NZX169fzyCOPFFvW3d2dRx99lEmTJvHBBx/g6uqK1WolLy/PXteYMWN46aWXsFgsLF++nMcff7yUP62qq1TJrtY6DRhewbEIISqA1WrFYrGQn5+PxWK5aj0/P5/8/Hzy8vLIzc0lLy/vqvXc3Fxyc3PtiW3Rcr0JFtzd3fH09MTT0xMfHx/7elFS6+XlhcFguMU/AXE9JoOJx0In8u+oKTy9XKEOOvF0y/eZ0fQp/pJoYNcPx+g7IdAhbYWHh7Nr1y7i4uJu+MeBEEIIUZyiMbtQcPN94cKFGI1GNmzYwFtvvYXJZMLDw4NFixaVus4ePXrw97//HbPZzJQpU4iJiWHWrFkMGzaMrKwslFLcd999AMyePZtx48bx1ltv2SeoKkl0dDTr1q0jKCgIf39/unXrVmL5N954g1deeYXAwEA8PT1xc3Nj9OjR+Pr60rRpU2JiYjCbzTRp0oSoqKhSX2NVpkqaFcxeSKmGwP8BXQANbAH+pLU+WbHhlV1YWJjetWtXZYdxXb///juZmZmVHcYNleZ7cSvrL6n89Y79cX9J2zdaL81nSevXW2w2W7Gff9xXtFit1qu2i/YVt1gsFqxW6039W5pMJpydnXFxccHZ2Rl3d3fc3Nxwc3O7ar1o29PTEw8PD5ycSj0NgLhNWGwWor8eyKR/Hqdpfj51Bp/D3GEVTxxVVN95gYdfDqdWg/L37gLMmzePrKwsJk2aJI9iCSHEHSYxMZE2bdpUdhiiCiruu6eU2q21DrvRuaX9y3QB8DkwpHB7ROG+PmWIUwA///wzv/32W2WHIW4hpdQNF4PBcNXnletGoxGDwXDNUjQOxcnJCaPReN3FyckJJycnTCbTVZ9F6yaTyZ7UFi3S+1p1OBmcmBj6JP/u9gLPf60w7DfyVMvFfNJwBM8kGNn5/VH6PR7kkLZCQ0NZtmwZJ06coHHjxg6pUwghhBDiekqb7NbRWl/ZT/+JUuqZigjobvfggw+Sn59f2WHcEuXtuSnN+cWVKc2+kraLWy/N543Whbhd9fPrx9zQ9zm6+SjGAzUYHfQF/wwdgbV9TX77+SxpJzOo3bD4VyGURUBAACtWrCAuLk6SXSGEEFVWdHQ0R48evWrfm2++Sd++fSsportXaZPdNKXUCOCLwu1hwLmKCenu5uXlVdkhCCHEVQzKwOSwSXze7c/8ZanCdY/mkRYr+LzRA4x2c2LH8qP0nxhc7nZcXFwIDAxk//799OvXT2bbFkIIUSV98803lR1ClVHaZxXHAUOB00AKMBgYW1FBCSGEuLV6N+nNmeAWHKnnROoBLyYfmccRbcUjog5H96RxNinjxpWUQrt27cjLy+PAgQMOqU8IIYQQ4npKm+y+DozWWtfRWtelIPmdXmFRCSGEuKUMysBzHSfzZTcb1ssGasRn0/vSVpY1NuDi7sSO74/euJJSaNSoEbVq1SIuLs4h9QkhhBBCXE9pk91grfWFog2t9XmgXcWEJIQQojL0atyL022ac9DXxNkDXkz97V9syM6mQdf6HEtII/V4ernbUErRrl07kpKSSEtLc0DUQgghhBDFK22ya1BKeRdtKKVqUvrxvkIIIe4ASime7/g0/+5mxZplwGf3BcLS97KxmRMu1RzXuxsSEoJSivj4eIfUJ4QQQghRnNImu/8AtiqlXldKzQC2AjMrLiwhhBCVoa9fL5KbN2N/QxNpiZ689ttslpy/QJueDTm+9xynf79U7jY8PT1p2bIl8fHxWK1WB0QthBCiKjAajZjNZkJCQggNDWXr1q3lrjM+Pp4VK1Zcte/HH38kLCyMNm3a0Lp1a55//vlytwMwZswYli5det3j3bt3p1WrVoSEhNClSxcOHTrkkHarslIlu1rrRcBDwBngLDBIa/1pRQYmhBDi1lNK8aewyfy7mw1rtoFmO07hl3GUhFYuuHma2P7tb2ity91Ou3btyMzMlPeOCyGEKDU3Nzfi4+PZs2cP//u//8uUKVPKXecfk919+/YxadIkPvvsMxITE9m3bx/NmjUrdzultXjxYvbs2cPo0aN54YUXblm7d6tSP4qstT4AOHT6TKVUP+BdwAh8rLX++x+OjwHeAk4V7pqjtf7YkTEIIYS4WnSrPrzVuBkJTY5hTvTktV9n85x3I+bd25Sf//0rJxLP0zigVrna8Pf3p1q1asTFxeHv7++gyIUQQtwKhw+/TkZmokPr9PRog7//K6Uun56ejrd3wSjLlJQUYmJiSE9Px2KxMHfuXKKiovDw8OCpp55izZo1eHt787e//Y0XX3yRpKQkZs2aRb9+/Xj11VfJzs5my5YtTJkyhR9++IFp06bRunVrAJycnHjyyScBOH78OOPGjePs2bPUqVOHBQsW0LhxY8aMGYOXlxe7du3i9OnTzJw5k8GDB6O1ZvLkyaxbtw4/P78y3Szu2rUrs2bNAmDt2rU8//zzWCwWOnTowNy5c+X1faVU2seYHU4pZQT+BdwLBADDlFIBxRT9UmttLlwk0RVCiAqmlOLRtk/wZVcbtlwDgduP4H4xmZTWLnjWdGX7t7+Xu3fXaDQSHBzMoUOHuHz5soMiF0IIcTfLzs7GbDbTunVrxo8fzyuvFCTHn3/+OX379rX3+prNZgAuX75M9+7d2b17N56enrz88susXr2ab775hldffRVnZ2dmzJhBTEwM8fHxxMTEsG/fPtq3b19s+5MmTWLUqFEkJCQwfPhwnn76afuxlJQUtmzZwvfff89LL70EFLxP99ChQ+zdu5ePPvqoTI9dL1++nKCgIHJychgzZgxffvkle/futSfzonQqc5KpcOCI1vp3AKXUEuBBHNx7LIQQouzGhfZj7p65xDc7TuhBD/7y60e8X7sWb97fgrULE/kt9iwt2tctVxvt2rVj27Zt7Nmzh86dOzsociGEEBWtLD2wjlT0GDPAtm3bGDVqFPv27aNDhw6MGzeO/Px8Bg4caE92nZ2d6devHwBBQUG4uLhgMpkICgri2LFjZW5/27ZtfP311wCMHDmSF1980X5s4MCBGAwGAgICOHPmDACbNm1i2LBhGI1GfH196dmz5w3bGD58OG5ubjRt2pT/+7//49ChQ/j5+dmfgho9ejT/+te/eOaZZ8ocf1VUaT27QAPgxBXbJwv3/dFDSqkEpdRSpVSj4ipSSj2mlNqllNp19uzZiohVCCGqFIPBwOBmj7Kkqw1bnoHwn/dw5vwlrG1c8a5fjV+++x2b1VauNurWrUuDBg2Ii4tzyDhgIYQQVUdERARpaWmcPXuWrl27smnTJho0aMDIkSNZtGgRACaTCaUUUPB7rejRX4PBgMViKbbetm3bsnv37lLFUFQ3cNVjxVf+TruyTGksXryY+Ph4vv32Wxo1aiS/H8upMnt2i/uX/+O/5nLgC611rlLqCWAhcM0tEa31h8CHAGFhYbf1N2Lx+rmcPHemssMQpXSj/56UKvvX7Y91/vf/QF1smav/j9TFHr+yTFFM6ortktYV6r/r9k+u2mcobMOALtivFAal7duGom0DGAvLG5S2L04KDAYbTkYwKjAoG0aDKozzygtQV2wXrqMKL0ahMIAq/ARQhsLyhquPXbPfUHidRlBGDIWfShlQFHwWbBcuhfuUcrIfMygjSpkK9xlRBhOGonVlwmAw2T8NBmeUcr5i3VjCN+L29UyXAYQfnsfulkl0OFSNZw9/wjt1XJj2gJkfP9jLwe2nCejiW642QkNDWb58OadOnaJhw4YOilwIIcTd7uDBg1itVmrVqsXx48dp0KABEyZM4PLly8TGxjJq1KhS1ePp6UlGRoZ9+4UXXmDQoEFERkbi7++PzWZj1qxZPPvss3Tu3JklS5YwcuRIFi9eTGRkZIl1d+3alQ8++IBRo0aRmprK+vXreeSRR8p0na1bt+bYsWMcOXKEFi1a8Omnn9KtW7cy1VGVVWayexK4sqe2IZB8ZQGt9bkrNj8C3rwFcVWo93fW5cT5xpUdhhC3gYJEWF2RFBuUrTBptmEsWlc2jAYbTgZrwaeyYDRacVJWnAxWnAwWTAYLzsZ8nI15OBtzcDbm4WLIxcWYi4tTDq7GLFyMOZgMeTgpK0ZlwYCtcL2gLoXtihgK1ylYV6ognjLenL2CAYPBGYPBFaPBBYPRpXDdFYPRFYPBBaPBFaPR3b4YjG6F6272fU5GT5xMXpicvHBy8sTJyRODoeImqHBzdqKXz0iWRM2g/a8Gum3+hbdbjcUYaaBuUy92fn8U//B6OJluPplv27YtP/74I3FxcZLsCiGEKFHRmF0o6D1duHAhRqORDRs28NZbb2EymfDw8LD37JZGjx49+Pvf/47ZbGbKlCnExMQwa9Yshg0bRlZWFkop7rvvPgBmz57NuHHjeOutt+wTVJUkOjqadevWERQUhL+//00lqa6urixYsIAhQ4bYJ6h64oknylxPVaUqq2tcKeUEHAZ6UTDb8k7gEa31/ivK1NdapxSuRwN/0Vp3KqnesLAwvWvXrooLvJyC3llPRmpWZYchhLgZRR3NSqEMhesGVdATbgCDgYJebQMYDWAwaAwGjVFpjEaNk8GKyWjDZLBgMlpwNuTjbMjD2ZCLsyEXF8NlXFUmroYMXFU61QyXcTNmUc14GRdDHq5Oebg65WAy/PfRK4PBtTDx9cJkqoGzqSYmkzcm51r2dWfnmphMNXFxqYuzc52C3upSOnkhi3uWxPDSqiTCj+Swe3w7YiMf5hmPrnw3K57IIS0J6VXsCJNS++abbzh48CDPPfcczs7O5apLCCFExUhMTKRNmzaVHYaogor77imldmutw250bqX17GqtLUqpScAqCl49NF9rvV8pNQPYpbX+DnhaKfUAYAHOA2MqK15HWTioIRlZMvPo3aD094mu3x1Y3ltNNvQfn3O+pgF9g/b/GMMfH5S2adtV2xquarPg56DQGrQq/EShC2OzaW0/bqOoTME+W+H5NlRBnEphKypXuN+mFDbbf8tYNVgxoCk8brORa7GRl28jN99Kbr6NfEvBZ16+tWC/xUpeno2cPCu5+Vby86zkWQqO5+fbsFhs1/33NBoVTiYDRicDTiYjRpPC4GTAYDQUPpNNQXw2jbVwsVk1FpvGZtPY8jW6cF1bbQUXZ9Ng1WDT3MST8KDA6KQwmsDJSePsZMXZyYqrKRd3pyyqmdLxMJ7HyykJb9NFapnO4206j5dzJp7Oebi6+uDq6oura31cXerj4uqLq6svbq6NcHNrjMFgsjfV0Nsds0cMX0T+nbBDBnpt3Mp7rR7GLcBCw9be7PrxGG261MfZ9eZ/nbRr1449e/aQmJhISEjITdcjhBBCCHGlynyMGa31CmDFH/a9esX6FKD8b4u+jYRu/gv8traywxDiLlLYrXrd5YrjhoKxujgZwaTA3QgGIxoj6cqdNJsX57QH56wepGlP0qzVOGerRprVnRSLB6dyPEmzuF3VupOy4euSQxPXHJpXy6GFZy7NPS208LJS282AcnIGgwmcnMHJFavRlTyjM7kGZ3KNLmRjIlObuGRz4pLVxCXtzEWrkUv5iox8K5dzLWTlW7mcV7B+OddCVq6V3HwLuXk28vKspOfbsOZa0ZluqHwvyK93/VscBnB20bi55uPhchkv5/PUcjlEPZdNNHI9SaNqqdSv4Y2HRwuqVWtONfcWPBYawMSNzdje9jidEzUTD37Je3UzefzBB1j65i72rD3x/+y9eZydZXnw/73v+1nOPnsmmSxkIwkDIQubRBYBFUREgrSAfUn44M/2/VXtz9cWWqTY1rZWhLbUUv1praCtBauCorhUDItIqiYQIISELGRPJjOZfc72PM99v388zzlzZjLZQ4bl+fK5ufflOefMZK5zXfd1cc77ZxzzO3jKKafQ0NDA888/Hwu7MTExMTFveZYuXcprr702ou2uu+7i8ssvH6cTvXUZV2H37Uj/WcvQp757vI/x1uHYL1GeFMRx627DVQ6KMWO8BMMNwhxc8yui+bX9oUOoUbpdM/IIYkQuojOIcK+oqbKOQISaS0FVgylGLGci1fBR5saA0VFdR6m2PLoehLkOorIZLusAYQLqdECd0czSPpj9oDuiMdE87YP2KPiCXeU0O70MO/0cu/w6dvr1bB1o4r96W8mTqD5dHYPMFruYLV9jjtjBQrmZ08VWksJjpMg8BtICOwV2MkopcDLgZiGZhfoMuLmwHqWSU8eQW0e3lWOvTrPdS7GrBJ2DZfbny3QOlugaKNE3WGYg79EzkEIUGxHBrJGfDWVIpTzqkz1MTL3MtOSPSfWfy4Pv3MJ5LwsufeJZ/u20a/jY7N3MXNjC8z/fzhkXTyaZOTYTZCEEixYtYsWKFXR3d9PY2HhM68TExMTExLwZeOSRR8b7CG8bYmH3JHPbrp/yq92/Gu9jxMSMO6F3ZIFERt6d5XCbkCPaK31KKKSUYR61SxHWLWlhCQslo3JUr5RtaWPLJLayo7I9ouwqF1e5JKxEmKsEjnKq9ZSVImWnSNtpTrFSzFb2Ac+ktWFPf5FNHQNs3jfApn39bNrXxuOdeb6dD+/ZWhLamy0WTlAsbNIsbPSYkSoi/CL4BfAK4BXBy0flPPhFKA9BeRCKvdC3E0oDYSoPe5B0o9QIzK40WklINUKyETItUN8G0yZDro1iZhJdiUlsppFXhizW7x9kU+cQu7sL9PQXGRhKsbOzldXmTADyU17giTNe4bKXk9zy8vf5G55lcWOAV1rKU9/9KUuunU4m046UR/9Py4IFC3jiiSdYs2bNEcUhjImJiYmJiYk5HLGwe5K5qf0mLp8emyicCMwJ0Zq+vhyvA7jDPePo/kPtV9tXmVc7f6z+0X3VecYcMLf6X01ftX10jkEbPaJdE9VrytpoAhMAEJggrOsw12j4g9K9AAAgAElEQVS0DvsDExDoAM94BDrA1z4lv0Te5PG1j6e9au4FXphHqRyUj+mzZEubtJ2uCsFZJ0vOyVHn1pFzcuRyOU5vzrFkUVjXXpZdXTZb9mle3jXEd1/t45vl8NnqklkWTp3KeTMbOX9mE2dMrsNWR+hESutQCC4NhIJwvhsK3aPy3rA82AH7XoGBvYAhQegGfwpwsZ2CuqnQNBumzcI0zaa3fiYb3Sn8esDmhT0DPPmrK/nuBet518uCS55YyX+2/yW/ftVjgR3g/0+af+v8BXNbv8Q503MsmrmQ5sZ3kkqdckSPUVdXx+zZs1m9ejUXXnghtn3glwkxMTExMTExMUdDLOyeZGb9RHLKlubxPsZbh9fbivlo1z/S8QcE2z1EfazyiLi6B+kXNQPEyDZRrY/qi+piVFvoeTgaK0XVC3EUfDcsSzGcq9AlcbVuCVAKqQTCVqAUQgmEUghLIW2JcGyEZSEdhbAlwpJhbkuEHY5/vfC1TzkoUwyKlPxSmAclin6Yl4ISeT9PwSsw5A2R9/Nh7uWr5cHyIPvy+9jYs5H+cj+D3uBB97MSFhNObyARTIfSdIpDE3lhTyNPvdoJQMKG06ckOX9mM5fMmcyZUxoOLvxKCYlcmOomH9kDB14o+Pbvhv5dUb4berbC/k2w6eeIoEwDcC5wbqIOmk7l/2+8gn/0z+YXC3/De59PcOPan7Pm3Gk8/upcTlvr0bD7DP5j/xz+Yx04VplTG77DgpYOzpvRxDmzF9LSvATbzh30WEuWLOEb3/gGL774ImedddaRPUtMTExMTExMzEGIhd2TTHfjCrygb7yP8RbhOIUfc3zzxej9D7neQfrMsARae3N25FpR2VT2rOkztfXofmxlTNQnascZgUBWXCqDkQhTM9bI4bnVsZX2mrKR1bHCqOG6UQithstGIoxCmOP/VaOlh7FKaKuMscoYu4yxPIztY2wP43iQ8DFuAIkAXINIGkiASIJyXJRKIqPYtkomo7obxbJNk7AypJ0slmpAShdxHHfCPe0xUB6gv9RPX7mPvlIfvaVeeoo9o/LN9JZWEzR34w15BPmZBPmZPL9rJqtfK3DfL3YgZJlcXSdzpxR5d3sz505uZ07DHFJ26tgOp2yomxKmMV/sAHq3h4Jv18Yw37+RD/f8B//S/0c88o7nuPSFgEtWPMujZ5zPU3O/zf/o96FeSSEvmcLjQwXyXQVe6jmDlztP4z/XgS2LzK7/N94xbZAPLm7njJmXY1mZEdtOnz6dSZMm8eyzz7Jo0SKkPPIQSTExMTExMTExoxm3OLuvF2/0OLuDQxvx/f7xPkbMkXASfjYOaz57wBkOXh+xVnXeKHPkqqOnSp8Zo73SYmrWMhh0NDzqixxEDc81mGpbZazGmDARGExgINCYgLDuh4mAMPcNeGACEZUFxgd8iSgL8GSUFMKzEJ4Cz0J4DrLsIrQ66EsZWEP4iW58txcv0YPvRikqe8kutD0cA1sIC6XSWFYGS2Ww7Dpsux7bqsd2GqNyQ5g7DTh2M647EaXcg57hcHiBR1ehi85CJ535TrZ0d/HCjiE27oYdHWnyhQwQoNKbcXJrmdU2yJkTZ3Fa02m0N7XT3tRO0jqs66vj4u9+8BwPbLqfj7zwC676rea3yxfjnraHD6zazHf3f4GiqePS035J4oxmnsrO58HiRF7YOQg9JazuEgyGd5dn1W/jkllFrjl7Ie2nXFS95/vSSy/xve99jxtuuIF58+a9rs8SExMTE3PkvBHi7CqlmD9/PsYYlFLcd999LFmy5LjWXLNmDbt37+bKK6+stv3kJz/hzjvvZGhoCGMMV111Fffcc8/xHp+bb76Zq666iuuuu+6AvoqH5sHBQTo7O5kxI4xy8KUvfYklS5bQ2dlJW1sb9913H3/wB39w3Gd5M/GmjLP7duVnX/s+e17bMt7HeNtw3M6aRy1wgDb3iPYTB3SO9NIsDtAgiorJMDV6XyGGvSWLaExF1ytEdY6ojK2kaG8pBCLSlAkpo/5wrpSREygRmh/LqK3SJ6REKlUtCymjctgmlUIqK8oVUtlIJcM2S6EsO0oWyo5yy0YlwrLlOFiui+W42I6Lsu2j1qoaYzCeRud9dN4L80KUD3oEA2WCviJ+XxHdV0YPBgcu4mjI+ehsEZ0eIkj346d68JNdFMVu8t5reF4vnteDMf6Y57DtRlx3Igl3Im5iYhjH1g3j2qZSs3Cc5oM+m61sJmUmMSkzCYDLTgEWDT/fuj39/NfqTfzkJYt9e+bw8l7N5uw2Hk7/Aiv7z9i2R3tTO2e1nsXZrWezcMJCcs7BzYaPhVsuaef+VZfw4/N/w+XP9/OuFf/DZxb+Pu9Z1sbvdAT85EcD/Pe6d3POtof4vcxn+V9uGm/KuWyZcRY/n38G/9I/if69ZbZ2zORrqwO+trrA7PqvcdkcwYfOPZf29jN5/PHHefbZZ2NhNyYmJiZmBMlkkjVr1gDws5/9jNtvv52nnnrquNZcs2YNq1atqgq7a9eu5eMf/ziPPfYY8+bNw/d9vvrVrx732Q9HxUPzk08+yT333MOPfvSjEf3f+c53eMc73sGDDz74thN2j4dY2D3J7A0EXfWt432MmLcbFSWuPlhnEKVDrVGjEY40vMJU6jUJE4YhGtUnqqGAKuWwPrKsQYdtSkqUUlhKYdkWruPiuA6JRDJM6STJZBo3lSKRzpDIZklmc9WUmJBFWQf/FWcCTTDgEfSXCPpKBD0l/J4iQXcRv6dEsLOI5U2ioqetA2TGxmpMoBoSyAYJOR+TLRJkBii7nZRLeymV9lIs7aVY2kNf//N4Xs+IfS0rRyo1i3RqJun0rLCcnkUiMfWQXoyFEJzeVsdftZ3FX15leHl3Pz98cTePvZhm554ZqA6Y1DbAoPU/fLPr37l/7f0IBHMa5rC4dTFntZ7F4gmLaUm1HPp9PgytuQRLF8zm0a3v5kfnPMK1z7pc9vxL/KrhFT7wzvu55jx48j/X89uVN9A98Voum7MCe9cvmbvlbuYCH0s2sHX65fzH1Iv4amk2Zl+JHR1T+MpvBF/5zW7OaPktH5xaz/Z129i5cydTphzE1DomJiYmZty4c+NO1g4WTuiaZ2SS/PWpR/47v7+/n4aGBgD27NnD9ddfT39/P77v8+Uvf5kLL7yQTCbDxz72MR5//HEaGhr43Oc+x2233cb27du59957ueKKK/jMZz5DoVDgmWee4fbbb+exxx7jjjvuqH7halkWf/iHfwjAtm3buOWWW+js7KSlpYX777+fadOmcfPNN5PL5Vi1ahV79+7lC1/4Atdddx3GGD7xiU+wYsUKZsyYcVyOSx988EH+/u//ng9/+MPs2rWLyZOP0E/H25zYjPkk8/AjN+M4G8b7GG8fDnmP9kg++4fTLo7sH3vFMdYY81wjos+OPd+ISmjbUeuKaJ4Yvo9bqVf6jYhC5orhcQJMdPe2OtaIEfNMzb3g4bGj+8Kyie76aiNq5NzhNqMFWoPRMgp1K9BaoANBoAWBlmitMFGujcRohdaKILCq+49ABwgdIIIAEfiIwIeobCuJa9skEgly2Rz1TY00TWilrnkC2aZmss3NpOsbkPJA82djDHrIw+8uEvQU8btLoSDcXQjbeksj3ijhKpyp2eE0LYvKOARBkVJpL4XCDvL5zQzltzA0tIl8fgvlcmd1vpQOmczp5HJnUpdbSC63gGRy2mE13MYYXtjZxw/W7OK7q3cyUPQ5Y3KWS84QuHVreaHrOV7ofIGCH/5RMjkzmcUTFrNwwkIWTVjErPpZSHF0d2M37Rvk3f/4C2bNvJe779tLY0uZu//kej6z+AJaJ1yJMYY1j+/g2Yc30Twlw5X/75lkE3nY+gy88ihs+AmUBwlSzbw05d18ue5CfqRPxd1XxN7eS1ASXJ9YQ11Ljv/zBx/DtmLPzDExMTHjTa0p6XgJuxUz5mKxyJ49e1ixYgVnnXUWf//3f0+xWOSOO+4gCALy+TzZbBYhBD/+8Y953/vex9KlSxkaGuKxxx5j3bp1LF++nDVr1vDAAw+watUq7rvvPgAWL17M/fffz4IFCw7Y/wMf+ADXXXcdy5cv5+tf/zqPPvoo3//+97n55psZGhri29/+NuvXr+fqq69m06ZNPPzww3z5y1/mpz/9KR0dHbS3t/O1r31tTDPmCmNpdnfs2MGll17Kxo0b+fSnP01zczOf+tSnjvGVfvMRmzG/idi1+nxEKY4h+ebh+L8MOoyocgwLHG5OJIqKA9tG1MWBfUKYUWPG6qtt15HpdNhWyYfLlfVNOF8MlwWghB4eJzSiWo/WEBqhDEiNECUqd4SNEFSUx5VcA8YojFYYJMaIUGDWkoJWDOZh+8AA/uY8BBsh8BBBOcwFSNvCTqZI1NWRa2mlvm0ijRPbaGxuIjMtQ93cBupdqyp8mkAT9Jbwu4v4+4t4ewYp7xhg4KkdVQ26akxUhd/srMU0Tb1wxLvgef3k81sYym9iaPBV+vtfZPfu/2Lnzm8AYNsN5HJnkssuIFe3gPq6sw9w6iSEYOHUehZOredP3juXh5/byf3PbuWffzZES/ZUfu+8y/jsVW10lrfw/L7neX7f8/xq96/44ZYfApB1sixoWcCiCYs4q/Uszmw5E1seWricPSHDe06bzK/3Xc6j5/471//S4fxVW1hTt5b3NL8XKS0WvWcaDRNT/Pe/vcx3Pr+KK//3fCa2Xw3tV4fxgzf+N2rtwyx89Qd8xX+IUrqVZyddyucXX8KGgcls2tzE3H2dXPY33+JD59XxkUuuIJN4fe8jx8TExMQcGUejgT2R1Joxr1y5kmXLlrF27VrOOeccbrnlFjzP45prrmHhwoUAOI7DFVdcAcD8+fNxXRfbtpk/fz5bt2496v1XrlzJww8/DMBNN93EbbfdVu275pprkFLS3t5OR0cHAE8//TQ33ngjSina2tqOOY78Qw89xO/+7u8CcMMNN/CRj3zkbSXsHg+xsHuSMYUJ6GKspYg5URzmDvGxrnVQefpw+432UD3WnNq7y6JmLzEiP9z96CMl8iPNYX/qSsAgmE7o2wR9lNjKZmAjYeRfTYDGFxAoiXZshOtgOQrbVbgJi3TGJtfeRD2Q8wzOQBmxvhv53D5sAenpddRfPJnkaU0IKbDtHHV1C6mrW1g9htY+Q0Mb6e9fQ3//i/T3v8Br+38JaISwqatbTFPjhTQ2XkA2ezqiRiubdi1uOn86v3feKfxyUxf3/+o17n18I196YjNXLZjERy9cyvLTl2OMYcfAjqrw+/y+53lm1zPhGnaa8yaexzsnv5MlbUuYkh37D5r/ffEsfv7lPax65yzev2oj5/9iFV8950omvfz/0X7aXVhWhunzm7nutrN57Esv8P1/eJ5L/tdc5r5jEthJaP9gmEqDsPFnuGsf5pKND3NJ8CDrWt/BZ+d+CLNeMEP18Y9PtfCvK3/E7yyUfOw976U5mz32D0RMTExMzFuC888/n66uLjo7O7nooot4+umneeyxx7jpppu49dZbWbZsGXaNDxApJa7rVsu+P7bvjdNPP53Vq1ePqdkdTa31VWVtYIS58vFEdqjw4IMP0tHRwbe+9S0Adu/ezcaNGzn11FOPe+23OrGwe5KZfVqRjn37x/sYMSeM49bbHnKNIzaLPqK1a82kK2bJpipUjvTfXGvGTI3pdM3cUeNHtosaX8815sw1JtA6GlyjI67uoatzojI1GlxtQpPoqEFjMNpEY8NJlf+GvU3XnEhECwld86QVLbRAROGVRDVU03AIJmkUQofhlKTnIbwi5SjskjCSbsJ5oqKsrhHZBSBe6sJ6qQtLgEpaWDmHdNYhl3Ooyzmksg6prEsi00wyeyWTGj/IzOkO0iowMPAi3d2/Yn/3L9m85R42b7kH226ksfGCSPi9ENcN7+NKKbh4TgsXndrMpo4evvk/2/jec3t45LldfHBBA594VwsTc5LLJp7Gpa1zMOZDDJT6eKV7HS90vciL+9bwQMfPeWA1TExP4syWBSycsIjTm88gaaVASOY1KxZPTbNj/3t55B2buekJm0W/3seuC15icPCDnH76P5BJz6F+osN1f3o2P/vXtTz+wCu8snIv0+c3ccoZTdS3phBuBs74UJgKvbD6ftpXfomHOm7l353fRXiG7FlpcjsHuP83KR587uf8/pI0H3/ve3CsODRRTExMzNuV9evXEwQBTU1NbNu2jcmTJ/PRj36UoaEhnnvuOZYtW3ZE62SzWQYGBqr1W2+9lWuvvZYLLriAOXPmoLXm3nvv5VOf+hRLlizhoYce4qabbuJb3/oWF1xwwSHXvuiii/jKV77CsmXL2LdvH0888QQf/vCHj+o5N2zYwNDQELt27aq2/cVf/AUPPfQQd95551Gt9XYkFnZPMv/6aoLn5Yn1jhoT87Zn3GUezUG8fx2aQpT2DTdJNEpopNAoYcK6DJBCY8nJKPG7WDJAyQAlPJT0UGIbSm5BSY0lTNQXIIWHEmH5nAmC7f1T+MELAY++2Mmc+s2c2fIyaTuPLX1s5eFIj4xV4hKlSWQzuKqEa+3CKWwl2P4DXtw+8vjvbDqDf17z+7xwdoarf9PP4sdX8zfnfITf435Wrbp2xNjsApsW50q6tp/Nrg0T+dV3N+FkuqmbuoX6KVvJte3FsiUi5SDffT5yoJOZO9ew+dXJ3L7viwyd6rG1/RTk/jwbttdz6wM/5cozT2XxjNkolY5SCsvKYNuNKJU4+vcjJiYmJuYNTaFQqJooG2P4xje+gVKKJ598krvvvhvbtslkMnzzm9884jUvueQSPv/5z7Nw4UJuv/12rr/+eu69915uvPFG8vk8Qgje//73A/DFL36RW265hbvvvrvqoOpQLF26lBUrVjB//nzmzJnDxRdffNTP/OCDD7J06dIRbR/60Ie44YYbYmH3CIgdVJ1kPv7599Dtj/tf5jEniDf/T484tA+vI19lRL12TWPEAcro0VuOuCksagyYRe0ZTc3Y4TEmisZkACRRcKYoSJOoBm6K1hBVy2ojZLXdIKI7ybVhokRFPRtqrUW0uBDhuhVNsBjW38qqPjesSROFgTKV3ui/qmMvGR1AglFgFMbYoVMuY4XOuozCGAsdWBhjYUzksMtIAlPJJdpIAiSayLe2EWgkARaBsQiMwtcSLzh4LOKDY7CUh5IllBrCtoawrQIpu8zennlIa4DT+37J+9YM0jKjn39735XcWP91JqhBdvspdgbp6Ok1SoAaqkd1zEF2zIOuWRA4oMqolo3Yreuxmzfj1O3EkpptL7+TwkATC89+GMsKsI/w16ePRRkXXyQJZBIt0xiZQVh1JBJtZFOzaMrOoy41gbSdJmWlSFrJE2JuFhMTE/NW5I0QZzfm7UnsoOpNxBmbmxEivm8WcxS8zhL1aEH1mBi1xCHXNIcZd6h+M6ptjPoB82rbascbwrBHNX0j9h2jfmCuI9PrSBCPhHJTcxXZRM68tIjk2ihpMbJNCxG2SYGWw/3VOgfOr+xTaas61K4e3Iyoh4bcBoPABC6BNwEdpEF4CKcb1FCoo468ZwdGokXoGTtAEWgLD4eSSVAySUomxSTTjS9ddohL+ZcFFr5UlH9tcYf8LG5iiEnJHmy7j516AM/qR9rdSKcLmfwVYuaTqFNs2vpnM623nVN62sntPZ0iUFJ59mZfoz+5lymlRv51Qztb6jcjMNgCXAGuNGEuDK4M84SEjDRklCEji2RUIaxLyCiDJYAh0PuhE9gSQJcv6fIF+33B/kDRGSQpyHoaE01MSE2gLdPG9Nx0JqUn0ZxspiXVQkuyBTWGF++YmJiYmJiYNw6xsHuS8VQT2YG+8T5GzIngza/W5Ygf4nVUdtUqVI+Zo1gj1OxGN3VFtQHk2AtUl67MGb1hVcAUIzxgm4pmmIO1idD6evScikV0VXVd03/YZ6zR1Nccxhz0xRmMElAGQaoaI1mY0KO20AaBjzDegX1RzGQZBKhAI3WA5ftRCqL2ABloRBDFUNYGEWiEtpHGQkmQcitSbsNSP0EJAUYRaEUQKAKjAMF7BCinDal8lOlDiQL9Tob9biMmCMj7CZDQmEtiJW2EYyEchS8FgRT4wlCSkJce2iminQLCzSPdAhOSBaYmi7iJItgeIlWgLHvpKuyhs9dhX9Hml0ULHYr8COEhRZms7dDi5JjkNtLiZGmwUtSrJHVWgjqVwNIBBGXwS2FeLXugvSj3h/PasoluqxsdfQYq5ejueW3c6+obXfsNjQRlg7Si3AZlgXKisg2WGyblHli2k+Bmo5QL80RdTTkHdmq02/eYmJiYmCNg6dKlvPbaayPa7rrrLi6//PJxOtFbl1jYPclcVvgFLaX+8T5GTMzJ5ShtpY/odsXoMSOUsaNVnCPnmFH16pzafjPGHDO87sgxYqTsYWrG14w1VVVsRWYRNbKLOEh7TV0TxTMG9PCY1/XbiHHiSP5xaqCfBg7z+1QYUKHsZyQIZZC2QVoG5WikrbFtjbI1VlRXjka5w8lyNOIolbg+goKQBEphpI2wHKSVxLJT2E4KIZ1hYdROhgKotEKBVFqR2XwUV1qImjJRXmPPPULgrHxGAwj8A4XqoByW/SIU+yIBvAh+lAc1+eGwkpBuDlOqGdItkG6K8hbIToL6aZCbDHZ8hzomJiamwiOPPDLeR3jbMK7CrhDiCuCfAAV8zRjz+VH9LvBN4CxgP3C9MWbryT7nicTM6qFJn9gg3DExx0p8e/ytgTah9jb0Ui0IjKjWtRHo2rKR4X1eIwgQaF1blwRaRPeAD8x9I/F1lIzE0xJPW3g6vC8MlWvNw+bhVdksOmvVH7cYqW82VGIni8iztogEexGVRz5j9ZlgeEyNAbsZ/eXFkWogPRBlgxgwyEiLLUd8+yIwQhJIhS8tysqhaLkM2UkG7BQFlaQoXUrKxZM2ZelQjnJP2njCjuoWJpKgpQlDU4RhsgRSCCwpsJXCVgJHSmwlcZXAkhIlBa4lcSyJo8LcVmFyLFEtW3a4jiUlthJYSkbrRutZEteWuJYKy5bEtcNyQkGaAkkzRDIYwvIGoTQApf4wFfsgvx+GuqLUCftegXxXKCyPJj0B6qdC3VSomxIKwU2zYdKCUFiOiYmJiYl5HRg3YVcIoYB/Ad4D7AR+K4R41BizrmbYR4AeY8xsIcQNwF3A9Sf/tCeOH3RexA8HDj8u5u3CW8IW+tAcRk0roiBBtfXqVA4MnjSirSJUjdqj1oC3ti5qQg+JEWOHBbDa9UIXUuEcMeo+cCisGWRtGyCMDhVxkaAnIxFseHzYrkSlneH2SrkytrKOGO6XUf/wmCjVlENvzmFbpaxq2i2hsaTGFaFGs9KnIg/Qtjx6z9KBEXhaUdI2xcCiZCxKgU3ZWJS0TSmwKWmbgrbJBy4FHaXAxlS0lHKkyXjFQVjoUKxiei5Cf16RozAtoqBSMhJ2K/eXTWVepVzzyaq5U137+RRopDZI7VdNsFUQgB8gtQ5NtYMAGfjIwGB8EzkBC88RCEkgJfogJvGj8YWFpxzKyqGsXMqWS8lyKSmHopWkEAnPBTvBkErRpRKUVBJjLJQGSxtkYJDahJG0jEEb8AEfEzoq45j8hB+AY0lSjiLtNJJ0Wkg7imzCJpe0qEvZ5BptckmbnKtotD0aRR8tuotGv4NscS/2wE7o2wEda+HVn44UiLNtMOlMmDgfJp4ZlutPiU2kY2JiYmKOm/HU7J4LbDLGbAEQQjwEfBCoFXY/CPxlVP4ucJ8QQpg3sQvplbPfS6fJjPcxYmJiYg6OMdj4OPi4xsPBwzEeCVMmSZmUKZI0JVKmSIoSSVMkFdUzpkDW5MmYAhmTp9kUyJhu0oyh7YvoFWl6RI4ekSUvXHyh8FEEyDAXYTn0Nl35uuA4OOrpKkoniBE28OUoDR75fFFzJPtYD3GMr6EGilE6iAV55Sb49gN6JDAN5DRwappLwNYybF0NrD62c8XExLzuzLr0k3TvOeZfOjFvMso4TJw0bbyPcdyMp7A7GdhRU98JnHewMcYYXwjRBzQBXbWDhBC/D/w+wLRpb+w3ZavXyECvO97HiImJiTmpKAKy5GlkgCb6aRL9NNNHs+gL6/TTLPqoZwBFgB05g7LQI+rqhOgpY2JiYmKOlp2mTE4PjfcxYk4S3fKt4dppPJ9irK+VR2tsj2QMxpivAl+FMM7u8R/t9eO/ep6nybSO9zFiYkZw3Jqyw3KI9Q/7EztWgKLR642sH/g8Y+0/0kD6AOdWI9pHGz3X1g+4jVozZrRH5NFp9LhKWY6qCzAyGhUaTosoD8sCcSI1j68bDUBkZishwKDRaONXvREbE+CZgJLRUV+ANmFuTIAmINAaTdgeVPNwTIChjIdnPMrGw9cenvHxAx8lBVmrDtcoFBKlTWh2rn2E0RjtE3gFhF9EekWkV8LyS0ivhPI9pO+hAn/EHd6KGb6RQMW83IT+oWpvxdd+JkNTbINKGFQCVEIjkwY7EaASAVYiQFoaoQxKGaQyCKnRjkXZzVBKZAiS9Zh0EyI9AZVtxa5rI5GbSrJuGla6GSUlUoSfCikEUoBCoARxLOGYmJhjYvcrr2BNHt84u0op5s+fjzEGpRT33XcfS5YsOa4116xZw+7du7nyyiurbT/5yU+48847GRoawhjDVVddxT333HO8x+fmm2/mqquu4rrrrjvutV5vJoz3AU4Q4yns7gSm1tSnALsPMmanEMIC6oDuk3O814c/qXuc7a1HYa4WExPzhkLoSLw0YVmiUFqhtEQZC6kVlrZQWiJN1FfJtYVlJFJbWGZ4jjKqZp6NZeywbGyktsMx2sbSLkq7KBPm0rhI4yCMg6jReI4QnU0RaQoo8kidx9L9qKAfYwbpTwh6My49mQZ6sg101Teyt7GVfDIFgDSa5t79TOnazdSuPbR17WVSzx4aCv2RAAZSRSc4yA0AACAASURBVPeXlUEoEK4PiTLGKdIlG9nCTHbRRiAkjbqT5vI+MoUimQGDPSCxCmXsUjEUIA2RIGnCm7DahM9VCXFkNMpobBO6qRboavgjZQQKETp70hqMRpgTqwXWRPGQpYVRNjgpRDKFSKSQiQQ4DoGw8YVDSTj4RqN1ANLHdss4ThnXzeMmSqiMi5tO4mSSuNkkiWwKN5NEWHYYHijVGDp1ykwIPRtnJoCTie+xxsTEvK1JJpOsWbMGgJ/97GfcfvvtPPXUU8e15po1a1i1alVV2F27di0f//jHeeyxx5g3bx6+7/PVr371uM8eMz6Mp7D7W+BUIcQMYBdwA/DhUWMeBZYDK4HrgBVv5vu6AB987h2gYs+TbxUO92fnUUbcOaYTiEP+RJyIAxxmDTHWGQ6lST2M1lWIUW2j542tVR2eN7pfHNgvRvYZIUeVZeTQqBLuJQwDY4QMXUkJFY4Tr68/a6F9pPaQxkNqHxUUsYIiKhjACvah/BIqKGD5JZRfxPEGccoD2KV+PMunP2nRU1dHV10D++sa2NvUwq4JE9ndvIB9jc1oOXz+dCHPlI49tL/8a2bv3MbsnVuZtWs7yVLpmM+fZRcz2HUiXooRGCuBcFLgpJFOBmFnkMqJJG9JQIBvfDwTanUdt55MciJFXWJbfhNFqfGVRaBsjLFJ5gexA81Qcir5zHQC1Yi20uDYSMcBywnFb23wPX1IawRlSZJZm3S9W02Z0eUGF9t5M2jiY2JiYsbmr374Mut2n9hQmu1tOf7iA6cf8fj+/n4aGkKLoT179nD99dfT39+P7/t8+ctf5sILLySTyfCxj32Mxx9/nIaGBj73uc9x2223sX37du69916uuOIKPvOZz1AoFHjmmWe4/fbbeeyxx7jjjjuYN28eAJZl8Yd/+IcAbNu2jVtuuYXOzk5aWlq4//77mTZtGjfffDO5XI5Vq1axd+9evvCFL3DddddhjOETn/gEK1asYMaMGRxOjJk+fTrLly/nhz/8IZ7n8Z3vfId58+bxm9/8hk9+8pMUCgWSyST3338/c+fO5YEHHuDRRx8ln8+zefNmli5dyhe+8IVjfAfemoybsBvdwf048DNCNxtfN8a8LIT4LLDKGPMo8G/AvwshNhFqdG8Yr/OeKPzEbIw99fADY94aVDzBRrw+39RU3QQfsvuwYw4p05pDVmsbxioNbz3yxTjAeLjqBbniFTkcKDAjyiPn1tYrGr9ovAg9K4cazqjPaKKAtaGtqYmC1kSawANT2Cd1gNQ+QgisRD0q1YRKtyCdVHj2Uh+mZyumZwuiPAg6iLwGG4yEQAp0JVeCQAl8JdFWmAdKYqQGAozQ4Twh8JQi7zoUHZe8myDvJsknEhTcBHmnnoKToOC6FG2XoUSSQTfJkJskUAcKUwmvRH2hn4ZiHzN376Sh2EtjuZdGr4eUziOFhixwGuw9rZm9oqXyqlbfwUBb6MAiCCzwFTKw8AOH3bkWNk5oY13rVHxlMadjBxeve4VJm9eSD3qxjEujnoojmkDYaKEIpBWGTRKRxlQMfxSTtkvCTpCyU6TsJEnlkrYc0spCRtpNzxj6gprkGwZqZFELWJxWNNqS7WXNC/kAzbsPeF1GYzkSN2XjpqwwJS2c5HDupCwSaZtEOhwT5jaJtIUVC7ExMTExrxuFQoGFCxdSLBbZs2cPK1asAOA///M/ufzyy7njjjsIgoB8Pg/A0NAQ73rXu7jrrrtYunQpf/7nf87Pf/5z1q1bx/Lly7n66qv57Gc/y6pVq7jvvvsAuOuuu/jjP/7jMff/+Mc/zrJly1i+fDlf//rX+aM/+iO+//3vA6HA/cwzz7B+/XquvvpqrrvuOh555BE2bNjASy+9REdHB+3t7dxyyy2HfMbm5maee+45vvSlL3HPPffwta99jXnz5vH0009jWRaPP/44n/70p/ne974HhJrp559/Htd1mTt3Lp/4xCeYOjWWNSqM681jY8yPgR+PavtMTbkI/M7JPtfryfkvfQWr6I/3MWJOJq+zdvdwAvSRbl8RasKQLsOxSTXDoV5CYURihCAQteFfKsJKlGQUEkaIULgTMsrDeiAlQTQukAJfhMKeLwW+lIc21TQGO9A4gcbSBksLbCNJGEXSWKSNQxqXhFY4RqAx+EFA4A+h/V6M6Q/vV0bPF8X7CTW5MnoeKdGWha8kRikCqQgci8Cy8O1QI+gLG11wwW+EzGRE6ymYGedQEueQ9/fSX9rOoLeXkgBPSsrKoiQtSkpRtmzKlkPZDnMvqhedBCUnQdF2KdkuJcfFtw7t+dLSPtlygbRXpqFUZvL+IRJ5DzfvkC0EZIqaXHGInN6Da/Uhk5pkfYaW5jrSk2ZiJ1NYToqyp9iyppfOrUMkcy6nLm4lmXXQviHwDIEPgafRRfB6+thfGmBN2uWFliyvtqbxLYFb1py5rcyiTb00d6zDL/6GIcBKXYBMnEWfUCA0LpqEgKSApISElCSlICklKSlJSjniXqmPoSgEBQmdxqPLz9PhDzAoStiOTa6xnoa6Ribn6lCWwnIUCR3Q9Eo3quBTOr2Z1vZG3p+wsByJZStKa56j667PYyVsJv/d35I5sx1lS6QS8Z3WmJiYmMNwNBrYE0mtGfPKlStZtmwZa9eu5ZxzzuGWW27B8zyuueYaFi5cCIDjOFxxxRUAzJ8/H9d1sW2b+fPns3Xr1qPef+XKlTz88MMA3HTTTdx2223VvmuuuQYpJe3t7XR0dADw9NNPc+ONN6KUoq2tjUsvvfSwe1x77bUAnHXWWdW9+vr6WL58ORs3bkQIged51fGXXXYZdXV1ALS3t7Nt27ZY2K3hreFm603EP33y07waxH9IvRl4/U2Qj4RDH+Lwvp0O/xCHdU41qvug48Uo3e3oekW3K8aqj1wz1OKG5sNCKBAKpKoK4lScAgkiJ0YGHWlCQ1NkcUC5EpMVEQrfte1GyEg4lxh5IkyTZ0ZpbCzfxw4CrCDA9n1cz8PxPRJBmVwhT2qwh1RQIhWUSfkBqSAg4YPrKVTJRRWTUEojimnsQEWvXuSsyhlCJQdxMymymQzJpjSCDL43Ab9s8Msar9fQ0RFQLgZ4xQBhykhACpccLrIbdj4+GLrKEiAjd1j7M5J1bYIXJ6fZ1NSIEYLGgsfFO3o4a28/p+7tppjfwd6hlynrPE3uFGblFpO16rFVHkclcGUSycjX2EiQGQurIYHdmII6i0FVorPcy67BDrbv20XX/mEn/FNnTWX+3LnMnTuX5ubmA4TTwiv76X5oA8ISNP4/80nMqh/eyxh6vvlN9t/1BernzWPKl7+E3Ro7DYyJiYl5s3H++efT1dVFZ2cnF110EU8//TSPPfYYN910E7feeivLli3Dtu3qvxFSSlzXrZZ9f2zl0+mnn87q1atZsGDBYc9Q++9PZW1ghLny0X6BWllHKVU945133skll1zCI488wtatW3nXu9415r61c2JCYmH3ZNMwlyAOnRFzFByXzG0OP/9I1j/glu0o02zBsImwMKbGQZAJcx2ZDeswyRovwgiJRCIjb0cSgZKhcKWEQNY4gwIwJiDQ3rCXXiy0tNHCwpgAtBd59i1H5TAJXQbtI3S5apocnjU0e5YapFAIY6GwkNhILJRwkMZCagdlEkhjIwxIDUobpAGlQWqDpcHWBqXDGGmTLclkCSkjCHxNZzFMhtBD7rAwKZG4SOFWfTFLMexzWVaUz7VJADYIu8aHs0igSCI9UD2gejWKIaQwKAxKVNaOnDk5AuWOrTnOK1iXU6ytV7xcJ1lbp+hMhELqqQMBH9lS5uJ9PvP6NV4QsLfQwWuDL7G3sJWc3cT5k69mYtMMZMJGpR1UykEmLFTWQdU5qJyLqnMpKI/OgS469u2jo+M19uzZQ9f6ruofCtlslra2NuafOZ9JkyYxefJk0un0mGc22jDwxA76H9+GPSlN003tWA2Jar8ul+n428/R++1vk33Pu2m76y5kKnUEPwExMTExMW801q9fTxAENDU1sW3bNiZPnsxHP/pRhoaGeO6551i2bNkRrZPNZhkYGKjWb731Vq699louuOAC5syZg9aae++9l0996lMsWbKEhx56iJtuuolvfetbXHDBBYdc+6KLLuIrX/kKy5YtY9++fTzxxBN8+MOjXRQdnr6+PiZPngzAAw88cNTz387Ewu5J5lPP9SD6429cDquTPCluyE7MJiMFQXOYpUfffR3rXutBFjAVnaoZ0TbWuDH3NaPqh9gqPEvFmVNtedi5k6jWKyanAhE5bRquRyOFqgbKkQf9hlOjTUWIDWrCzlS0tpHrX2khpTV8JgA8BF50bkMo0rkInJrnCR/UaI0XFCkGeUrBEOUgTzEoUPKHKAV5ikElz1MK8gecUgmLtFVHyspV84xVT9Zuos5pQoqaO5tVK6PwK4EJjoITeadTjLRAMBJQYFSYV5KRZjgJjR/4dO/PM9CbxyiN22yxrx42OoYNKYcN2Rxbc43oyAFXW7nAIjzOJMVlmRSzJufIT+tl2/oX+NXa1ezeth6jNclsjnfd9BEWXvEBlDXynxfP8+jq6qKjoyNMr4b50NBwzMZsNsvEiRNpb2+nra2NtrY2stnsEb0UXscQPY9sory1n9SiCTRcOxthD7/WxQ0b2H3bn1LasIGmj36Ulv/zScQJ0eLHxMTExJwsKnd2IdSefuMb30ApxZNPPsndd9+NbdtkMhm++c1vHvGal1xyCZ///OdZuHAht99+O9dffz333nsvN954I/l8HiEE73//+wH44he/yC233MLdd99ddVB1KJYuXcqKFSuYP38+c+bM4eKLLz6m577ttttYvnw5//AP/3BEptAxw4g3uXPjAzj77LPNqlWrxvsYB2XdJ3+Ia2fG+xhvEd4QdsaHYfiMx3YN8MBJ4uBdh2o+pA/kqv/i47yraIypGBhXywaDqYmZaowOo6sagyEUZiEANEKYKCbocKqcyIw6sTmgPXJRZSplUe031T5RU5dRmyRyUYU2MsyRYEAbTVnn8fQQpaCfkt9PKeij5PdTDsJyYIo1p1I4VhO204yyJ2BZTWStOjLGJzXQhZGQybXS5DahhKLXG2RPrsSTrStZz8v4IsAXPo2pJtrqJjOt/hSm1U9jesMMptfPIJOIQs/IY3uvjDb8+tkdrPjVKvan+ultc3ktleNVpxU/Ch5fFwyxSPSzqL6OxVNOZVFDA022ojg4wL6tW3jt+d+y5bnf0rMnjBTXPG06s846l5mLz2Hi7Dn4flA1K9u3bx+dnZ10dnbS09NTPYdlWbS0tDBx4kRaW1urKXUMWlZdDhhYsZ2Bp3chE4q6980gdXZr9fUxQUD3/ffT+U9fRNbVMelv/ppsjflXTExMTMyR8corr3DaaeMbZzfm7clYnz0hxGpjzNmHmxtrdk8yvpUkK2NvnUfCyfga5kj3MAcvHHSd0W1jTR0W2MwBY8KyHjHGVPvMiLZQsKzUTehoOGrRkeCpRwiepkboDGq0qQFa+wT4aONjTIA2Hr4uEZgSgS4RmGKUlwmMT2B8fO3hmzLaBDVPbIFIIGQCIdIg0wiZRohMmMsMiErboZ0wQVWJPOxTSghkpC6WUiClQEiBtKJcRW1Khv2WQlmRAyIp0OUAU/Ch6CPyPpZvkAK8hKI/pdiXEHRjGPQtBr003aUGeso+ZQEeBk9AWRgSKmBKosgs3cUpva9h9+2hlH8Fj3UA7I9MySsaWFGYQN3UxdS31jOjO8tphSZa91xE3/wLaTj/FGZOmEXKPn7T2kBrtnTt4uWOHazr6+XFwYBXyNDhNsC54X3i1vJ+Tg+6uMwM0J5tYHq6lXovzUDnAL0bXqXvqaf4Scde+vbtpZQPNbDKspg0t51TzruA9ORplIxgf28vm579Nb0//hm9vb3VM0gpaWpqYtKkSSxYsIDm5mZaW1tpbGxEjeEt+mgprO+m9webCHpKpM5qpe5901GZYW1+eedOdv/Zn1FYtZrse9/LxL/6S6woTEVMTExMTEzMW59Y2D3JbJ/6Er/ZvH68j3H8HKdFwOGnH8H65jACpxkWCMceVBMk5xDmwLV9I8ZVtJiRBGrMqHJ1mI4i4ujqGiYKg1PVgFbr44eybJRto2wHy3bCshOW7UQS202Qcuuw3ASOm8ByE9V2N50hkc7gprMkMhncVBrLdhEifP21NgRE13Yhqpsw7Iwx+ITCWaANvgbfaLQJcy8wBBrKQUDZN5QDTdnXeDV5wdcUygFFL6AQpZKnKXhhmyp4TBoqMm3QMMuDU40kEWl+u9CsdQJWOj7P4tODQZUE9dKmPmXTWOfQkHBZTIGpfonWch9NQ71kB7pxujspde5loKuL7lKJrmwD/UkHr1Z2FwojHITQGF3C6H30bvspu/dpXpvYwrlTLuWU4kwaV4FYO4D3zk6CJW2o9OG/AMAYSgOd7Nm/k519+9jQ18e6guZlk2aD3UpBuUAKSzvM8nZw9uAGTrPhnFMmMa91Nn5+IjvXd7PzhZfZ9eorbMkPmxQLKXGyOVQqizVxMsJyKEuLfhS9WsCGLWECMpkMDQ0NTJ06lUWLFtHS0kJLS8sJE2pH4/eV6Ht0M4WX92NNSNLy+/NxZ450QtX38MN0/O3nQEra7vo8uauvjr0sx8TExMS8IVi6dCmvvfbaiLa77rqLyy+/fJxO9NYlFnZPMi91lyh4R/BH7NuCo/MCfCyDRnsCHnHN84CxY3QcwmbYRPdZR84VI+bU7m/EyDHV2KVi2OS2Wq56Mo7miWHz23CuHG6LvAuHe8jQszBA1eOwRCMxQoUhdUSYDBWvxIpAqGi9isxd0Q5Hz2cMxiNMA2G/NgBFtCli6EFrU50fapIrgv1Yr/aYN52PCwuYrFymCoeZ2EzColXbTAgUKSMBSYBhf9LwWkbTnxMMNUhEyiJdLnPt0ADLBnpJ9vdg93TDtk6K+7so9PUyVCoz5KbIuwl22TZbbImnNL7wwtM3p4AU4CCtCTjJSbhNLXitCToaOtngvMR67yUsD84O5rEwmEVbr2Jgz25++8x3+a0xNDoTaW98J5N/EdD7iy3saeyla3oZU2/h25p+r0iXV6YjgL047JVp9tj1dDqNhHeUJwITaZD9zC3u5QO9a2jcP0Cm1yflJUg11aFEkaGO3Tz/+EpW9fWADr+ACZwEQSqDzjWjHRftuBjLQUhJKpUinU6TSadJp9PU19ePSHV1ddj2yfmdZgLD4Mrd9P/3Now25C4/heyFUxDW8N1bf/9+9tz5GQZXrCB13nm0/d3nsNvaTsr5YmJiYmJijoRHHnlkvI/wtiEWdk8yXn09UhQPPzAmZgSVKLgVgoMNPLplTiSvp68fA0kcUsYlHaWUcUkT5lmTJGsSSD18iKIuMBTsp9vrZXdpP4XBnQT9O7CKeVS5TF3gk9MBvrQpukn2OgmKjk3JEpSVJhAeOAZa0kDF+68CmcKoOgI7Q2ClCFyXkmMzkDbsbehjd24P+1MvEUiFETaSBFl/Ak1yKa2ynpJyWCkFQU7izzgFTyqKWBSkw4CVZsgxDLk5ytYoc1s7TK5XpCHfQ8NgH7MKm1hUHCRTHCJRLJHqGyI5OIj0PaTvIXwP43vkA49CZR0hsHINZGbNIztpMg1Tp5NtbCKRSJBKparCbTqdJpFIIN8gTpxKW3rpfXQL3t4h3DkNNHxwFlZTstpvjKH/R4/R8Xd/hx4cZMKf/SmNy5bFTqhiYmJiYmLexsTC7kmm4aoreTVfGu9jxJwA3gi+3Q53BHOoEabG3Hr4MvDwrEhNW71NHKl6TU252l8xw641ya6YaYe2yugo7BA6bDOaqBztpQE97EgqvORacSSlqndeTU3SJrxbrLWHHxTx8PGNj2cCtAAtLbSYgBatBPL0MC6vjDTi1bwSY1dhpEJHuZESIwVaSoyAQAqCSCt+tFRusa6LcmV8UkExTLpIUpfJGI8ppoc63UVdyZAeckh2N9LYm6POMwTl3fQPvYSX34tfLOKXwnSgCb7ASmTINTWSa24m09hIpqGRVH0DjW1TaDt1HnYiwZsFv7dI349fo/BiF6repfH35pE8Y2Rs3eKrr9Lx139D/re/JXHGGbT93edwTz11HE8dExMTExMT80YgFnZPMiufeZbnmyeP9zFiThAn/wbgGDuOEHYO4b35SNcew3RbHPD/sBTKG2JUT6UuR/SL2pkCUKHDqZFrVgRwXRWcDRoiZ1nG6MhplgYTUBHQxf9l797joqr2//G/1p4bMwx35OKogQoOg4gXJDMvmZGQaZoWaWpyzI9HPb/yeElRs7RPGR3LfmrS5Rzz6AfL84n0ZJKGJw0pP9qAFxQRuwDKRQW5zMAw1/X9YwBRQVBBRnk/Hw8es2fvtdd671mCvFlrrw1e9+xcDnAGBjEYtz8eiXGAQYCE25+LK7JyiGCFwDgEWMAEK8DMsAhGmEUm1IpqUcs5DDZe90xsGxi3QQwrJNwKZ5sZLjYLXLkVbjYOd3C4g8FPEKGHIIWzWAKZWAKZWAYniRQyiQxOUhlkEgUUchconFwgk7uCyf0BJzdA6nzLpbotlUbo0y6i+hgDZ/5wivSCpY8nfi/Q49fMS6gq1YMxM3wDnRAU0Q2aYb0gaebZufcTbrZCl1YI3aEL4BxwfaIHlCO6QWj06CarTofSTZtw9X+SIFIq4bd6NdwnTwJrh/uECSGEEHL/oWT3Hlt8ksPn/hlUIQ6n5bHc9qsbsHJL3crNtmurN3Nrw6rMVlv9trXuy75t4WZYuAUWm/3LDKt9m1thtllh4RYYbVbYwMHqntMrCAIEVreSsiBALAgQiwQIYqHucbsMTAwwMQCxDVYZh1VmhVlmgVFqgkFsgF5kgF5Ug0qhGldRi3JrLa5aDTDxm6eBMzB4SV3Q28kLfjJv+Cq6wE/hCz/nrvB19oefS3d4u6ggkchv/mDakdhNBtenAmHo6Y6yA/lwPl0GyekyOFls6O7nDN+YAPTs7wMn5f2f4AL26ci1Z8pQsfd3WMuNkId5w+2pQIg9nK4rU/XNN7j0t3WwlpXB/fnn0WXBq7TSMiGEEEKuQ8nuPabXdEHplbKODqPzaGlY8xbH+U2Hm97T6vYaHbu2WNW1Ohv23FAHvzZgelNdvH6b1S+YZV/qqn67oR3GAaHRs2mFa3VzxsGFugWxBA5bfVnBPkXZxjhs4rqyIm6Pp+F8DpsAcMHWsOCVjYlgY8z+7FomwAorrEyAxT6eCgsEWJgZFnBYYIMFHCZYYEQtTDYTTDYTzFYzjFYjTFb7e5PVhFpLLWqtrb/fXSpIoZQq4SJ1gYvEHd5OHghy8oCXkxc8nDzg6eTZ8Orp5Iku8i6QiBwjYTTVWnDp9yoU/VaB4l8rcSmvChajFYKI4aE+HujjJoVvQRV8Sg2QnroC7uUErva871cbNl+qRsWe32H8tQJiXwW8Z4fBqZf7dWVqc3JQ8tZ/w5CRAafwfuiemAh5WN8OipgQQsi9JBKJEBYWBs45RCIRNm3ahKFDh95VnSdOnEBRURGeeuopAMDWrVuxZMkSqFTXZmLu2LEDGo3mrtrpKOfPn8df//pXnD17Fu7u7nB1dcXq1asxYsSIm8oGBARAq9XC29u7AyJtH5Ts3mM7Ar/CT7KfOjoMQlpma7R9h+thAYCIiSBiIogF8XVfEkECsSCGiIkgFUkhFaSQiCRwFjtDKpNCKpJCIkggFUkhE8mgECvgJHayf4mcIBfLIRfL4SS2byulSrhIXKCUKqGUKCEVSVsOzgFYzFZUXjHgalE1in+rRPGvFSi7qAfn9tnNXt2UCBnqD/9ebuih8YRMYU/IudmKau0l6NIuouyf2ZD4KaAc0Q2Kfl2uW534fsCtNugOXUTVDwVgUhHcn+kF50h/MNG15N2q1+PKhg0o/58kiNzc4P/fb8Ht2WdpASpCCOlE5HI5Tpw4AQDYv38/4uPj8eOPP95VnSdOnIBWq21IdgEgNjYWmzZtuqt624PFYoFY3Pr0rba2FmPHjsW6deswfvx4AMDp06eh1WqbTHYfRJTs3mOzwmZhfK/xHR3GfaEtRqlYi0O7LVZwy/paU3/9dVx3z2rj89jN+66/v5Vduyv2hrrqj7FGjzxiYNftrz9HYML1xxqVEQT7scZlBAgNZQQm3PTVUJ4xiJkYgiA0JLYiJrpW7j4fbWwLFpMV1ZVGVFwyoOJyDSov1aDicg0qLhugu1rbMEVALBXgG+iGQU8FoGsvd/gGukIqb/rHNJOIoHykK5wj/VBz8gp0hy6i/F+5qPzuDyiHdIXzw34QKR0/4TcV6VH+VS7MRdWQ9/OG+/he18XNOYfuu+9wae27sJSWwv2FWPi8+ipE7u63qJUQQki7+m4ZUJLVtnX6hQEx77a6eFVVFTzqbl8pLi5GbGwsqqqqYLFYkJiYiOHDh0OpVGL+/Pk4cOAAPDw88M477+C1115DQUEBPvzwQ0RHR2PVqlUwGAxIT09HfHx8s+3t2rULH330EVJTU1FSUoKRI0ciLS0N+/btw65du2A0GvHHH39g6tSpeOONNwAAH3zwAbZs2QIAePnll7FgwQJUV1fj+eefx8WLF2G1WvH6668jNjb2ulFVrVaLxYsX49ChQ3jzzTdRVFSEvLw8eHt7Y/v27Vi2bBkOHToEo9GI+fPnY86cOU3GnJSUhEceeaQh0QWAvn37om9f+4yosrIyTJkyBVeuXEFkZGQTi17e/yjZvccG+w3u6BAIIW2Acw59uREVJTUov1QNfbkRxmozamssqNWbYawxo7baAmO1GRaz7bpzpXIx3H3k8OvpBvUQP7j7KuDh5wxPlTNEotsbqWQiAc4DfaEY4APj+QrofypEVWo+qg4WQNHfBy7DVJD4Obdc0T3GLTZUHbwA3cELEBRieE0Lgbzv9dOmTHl5KHnrv1H9009wCg1Ft80fQR4W1kERE0II6WgGY/RWIgAAIABJREFUgwH9+/dHbW0tiouL8cMPPwCwTzMeM2YMVqxYAavVipqaGgBAdXU1HnvsMSQkJGDixIlYuXIlUlNTkZ2djZdeegnjx4/HmjVroNVqG0Zyt27dip07dyI9Pb2h3SNHjmDixIlITk7GRx99hH379mH16tXw8/MDABw7dgynT5+GQqHA4MGDMXbsWDDG8Pnnn+Po0aPgnOPhhx/GyJEj8fvvv6Nr167Yu3cvAKCysrLF687IyEB6ejrkcjk+/fRTuLm54ZdffoHRaMSjjz6KJ598EoGBgTedd+bMGQwcOLDZelevXo1hw4Zh1apV2Lt3Lz799NNW9sT9g5JdQghphs3GUas3o7rSiMrLBlRcqsbV4hpUXKpB+aUaWIzX5ncLYgYnZ0nDl6u3HD4P2bdlzmIoXKVw91HAzUcBuYukzUe8GWNwCvaAU7AHzJdroP+pEDWZl1GjvQRZb3coh6ngFOwBJnT8SLupUI/y/82FuaQaiv5d4DauF0TO1+6XthmNKPv0M5R99hmYVArflSvhMeUFWmWZEEIcxW2MwLalxtOYjxw5ghkzZuD06dMYPHgw/vSnP8FsNmPChAno378/AEAqlSI6OhoAEBYWBplMBolEgrCwMOTl5TXbTnPTmDdu3Ii+fftiyJAhmDJlSsP+qKgoeHl5AQCeffZZpKengzGGiRMnwtnZuWH/4cOHER0djcWLF2Pp0qV4+umnMXz48Bave/z48ZDL7Qtkfv/99zh16hS++uorAPZk+fz5800muzeaOHEizp8/j+DgYHz99ddIS0vD119/DQAYO3Zsw0j5g4SSXUJIp2SzcejKalFxyZ68VlcaYagyoabKhOq611qd6abnKbt4OsHDTwH/3v7w8HOGh599VLY9Etg7JfFRwGNiENzGBEB/rATVPxehbOsZiNxlkPfrAkU/b0hUynseL7fYUPWfAuh+vADBWQqvGRrINV7XldGn/4SSt9bAnF8A17Fj4bP0NUh8fO5pnIQQQhzfI488gtLSUly5cgUjRoxAWloa9u7di+nTp2PJkiWYMWMGJJJr/zcLggCZTNawbbFYbrvNwsJCCIKAS5cuwWazQahbN+LG/08ZY81OCQ4ODkZGRgZSUlIQHx+PJ598EqtWrYJYLIbNZp8JVlt7/YKc9QkzYJ9ZtnHjRowZM6bFeENDQ5GWltbwfteuXQ1TpBvH+iDrkGSXMeYJYCeAAAB5AJ7nnJc3Uc4KoP6GgALOOd3sSghpNc45jNUWVJUZUF5SNyJbUoOKS9WouGSA1XJterEgZlC4SqFwlcHF0wm+Aa517+1frl3kcPdRQCK7f0YXBYUEro91h8twFQyny1Bz/DL06YXQp12E2MvJnviGd2n3ac6cc9Rml6Fyfx4slw1QDPSB+9M9ISiujebWZmfjyubN0B/4D6QPPYQeW/4B57tcYZMQQsiDKycnB1arFV5eXsjPz4dKpcLs2bNRXV2NzMxMzJgxo1X1uLi4QKfTtVjOYrEgLi4OO3bswLZt2/DBBx80JI2pqam4evUq5HI5du/ejS1btkAQBMycORPLli0D5xy7du3C9u3bUVRUBE9PT0ybNg1KpRJbt24FYF8JOSMjAzExMUhOTm42jjFjxiAxMRGPP/44JBIJcnNzoVKprkuI602dOhVr167FN99803Dfbv0UbwAYMWIEkpKSsHLlSnz33XcoL78pHbvvddTI7jIA/+Gcv8sYW1b3fmkT5Qyc8/73NjRCyP3AZuMwVptRo7OPwuqv1kJ31Qh9ee217au1190vywQGV28nePg5o7vGyz4q66uAu68CTkrHGZlta0wkQBFuT2xtNWZ74nvqCnSH7PfMin0UUPTzhjzMG2IfRZt+DrW/lqNyfz7MF3QQe8vhNTMUcrVnw/Ga48dR+vHHqP4xDYJSiS6vvgLPP/0JQt1f3wkhhJB69ffsAvY/pP7zn/+ESCTCoUOH8Le//Q0SiQRKpRLbtm1rdZ2jRo3Cu+++i/79+zcsUHXjPbubN2/GgQMHMHz4cAwfPhz9+/dvuDcXAIYNG4bp06fj119/xdSpUxEREQEAmDlzJiIjIwHYF6gaMGAA9u/fjyVLlkAQBEgkEiQmJgIA3njjDcyaNQvvvPMOHn744Wbjffnll5GXl4eBAweCc44uXbpg9+7dTZaVy+X49ttvsXDhQixYsAC+vr5wcXHBypUrG9qcMmUKBg4ciJEjR6JHjx6t/tzuF6wjVt1ijJ0D8BjnvJgx5g/gEOe8TxPl9Jxz5e3UHRERwbVabVuFSghpR5xzWMw2mAwWmAwWGOteTQbrde9rq80w6Mww6Eww6M2o1dtfb37QMaBwk8LF0wlKDxmUnk5w8XCCi6cT3P0UcOsih+g+eyxPe7LqTDCcLkXNySsw5VUBAESuUsh6u0PW2x1Ovd0hcr2zpNNYUIWq/Xkw/lYJkZsMrk/0gGKgL5jIPrWr5uhRlCZ+jJqjRyFyd4fnzJnweHEqRC4ubXmJhBBC2sjZs2cREhLS0WE4nK1bt163wBVpe03922OMZXDOI1o6t6NGdn0558UAUJfwNndDlhNjTAvAAuBdznmTf7ZgjP0XgP8C8ED+RYI8uDjnAK/L2Ti/lrtx1O1vfLyJ8vxaPdx247b9OLfxhn3XvdZt22wc3MZvfrXaz7dZbbBZed1X420Oq9UGm4XDarHBarbZXxttW8w2WC0cFpMVFpMVZqMVFpMN5rr3FtP1qxQ3iQEyhRhypRRyFwk8/BSQu7hDrpRA7iKB3EUKhYsUSk8nKN1lEEkomW0tkYsUyke6QvlIV1gqjajNuQrjbxWozbmKmszLAACxjxxOvT0g6+UOWaArmFx8y5Ffc0k1Kr/PR212GQRnCdye7gnlw/5gEsH+GKFDh1D28ScwnDgBcZcu8Fm6FB6xz0NQKO7VZRNCCCGkk2i3kV3G2AEAfk0cWgHgn5xz90ZlyznnNy3/xRjryjkvYoz1BPADgNGc899u1a6jj+x+93EWLuRc7egw2t89mDDQYhMt/dvmt3zbxPFbnMCb3l0fA2/y4INFJBEgEgt1rwwisQBx3T6xVASxVASJzL4tkYoglokglgqQSEWQOokglYsbvmSNXiUykUOsINyZcBuHubjanvj+WgHTH5Xg9dPBBYDJxBCcRBBkYjAnEQQn+3ubyYbas2VgUhFcRnaD8lEVBJkIposXUfXdd6ja8y2MubkQd/WH9+zZcHv2WZquTAgh9wka2XVcWVlZmD59+nX7ZDIZjh492kERtS2HHNnlnD/R3DHG2CXGmH+jacyXm6mjqO71d8bYIQADANwy2XV03TWecPFy6ugw7o17kJ+02EQL9x7edPSGHTeffmOBZo403n9jJeyGIow1tGN/vVbg2n527fXG/cz+yur3N2w3Oibc8MoYBKHuuMAgiBgExsBEDIIAMEEAEwBBYGACg0gk2MuIGITrtu1JrSBiD+z9rp0RExikKiWkKiVcRnQDt9hgKqiC6aIeNoMFtloLeK0VNqMVvNYCa5UR5stWwGKDy4hucBnZDdbKUpQnbUPVd9+hNsu+zqBTeD/4v/023MaPA5NIWoiCEEIIIa0RFhbW8Egmcr2Omsb8DYCXALxb9/rvGwswxjwA1HDOjYwxbwCPAnjvnkbZDvqOUHV0CIQQcluYWICspztkPd1vWc58+TJ0+79HwcvfwZCZCQBw0mjgs3gRXKJjIO1GP/8IIYQQcu90VLL7LoB/McZmASgA8BwAMMYiAPyZc/4ygBAAnzDGbAAE2O/Zze6geAkhhNyAm80wnDgBffpPqE5PR+2ZMwAAWXAwuix4Fa7R0ZAGBHRskIQQQgjptDok2eWclwEY3cR+LYCX67Z/BhB2j0MjhBByC6aLF1Gdng59ejpqjvwfbNXVgEgEef/+6PLqK3B58knIevXq6DAJIYQQQjpsZJcQQoiD41YrjL/+BsOpkzCcPAmDNgOmvDwAgKRrV7iOHQvnYY/C+ZFH6JFBhBBCCHE4lOwSQggBAFhKS2E4dQqGE/bktjYrC7aaGgCAyM0N8v794TF1CpyHDYM0MJAWJSOEEHJPiUQihIWFgXMOkUiETZs2YejQoXdV54kTJ1BUVISnnnoKgP25uUuWLIFKdW2diR07dkCj0dxVOx0hLy8PgYGBWLlyJd566y0AQGlpKfz9/TFnzpxO8WxgSnYJIaQTspSVofbMGdSeOQPDmTOoPZMNS3Gx/aBYDKc+feA2YQLk/cMh79cPkoceouSWEEJIh5LL5Q2rDu/fvx/x8fH48ccf76rOEydOQKvVNiS7ABAbG+uQiaDFYoFYfHvpW8+ePfHtt982JLv/+7//i9DQ0PYIzyFRsksIIQ8obrPBUloKS0kJzMUlMP56HrXZZ1F75gwsJSUN5aQBAVAMGACn6dMh7x8OJ40GglMneUQaIYSQ25ZwLAE5V3PatE61pxpLI5e2unxVVRU8PDwAAMXFxYiNjUVVVRUsFgsSExMxfPhwKJVKzJ8/HwcOHICHhwfeeecdvPbaaygoKMCHH36I6OhorFq1CgaDAenp6YiPj2+2vV27duGjjz5CamoqSkpKMHLkSKSlpWHfvn3YtWsXjEYj/vjjD0ydOhVvvPEGAOCDDz7Ali1bAAAvv/wyFixYgOrqajz//PO4ePEirFYrXn/9dcTGxiIgIABarRbe3t7QarVYvHgxDh06hDfffBNFRUXIy8uDt7c3tm/fjmXLluHQoUMwGo2YP38+5syZ02zccrkcISEh0Gq1iIiIwM6dO/H888+jqKgIADBz5ky4urpCq9WipKQE7733HiZPntzqfnB0lOwSQsh9hFutsFZVwVZZCWv9V0UFrBUVsFy+DHNxCcyXSmApLoH58mXAbL52MmOQBgZCMXgwnDQaOIVq4KTRQKRUdtwFEUIIIa1kMBjQv39/1NbWori4GD/88AMA+zTjMWPGYMWKFbBaraipuwWnuroajz32GBISEjBx4kSsXLkSqampyM7OxksvvYTx48djzZo10Gq1DSO5W7duxc6dO5Gent7Q7pEjRzBx4kQkJyfjo48+wr59+7B69Wr4+fkBAI4dO4bTp09DoVBg8ODBGDt2LBhj+Pzzz3H06FFwzvHwww9j5MiR+P3339G1a1fs3bsXAFBZWdnidWdkZCA9PR1yuRyffvop3Nzc8Msvv8BoNOLRRx/Fk08+icDAwGbPf+GFF/Dll1/Cz88PIpEIXbt2bUh2AfsfC9LT05GTk4Px48dTsksIIeT2cM7BjUbYamrsX9U1sNVUN3pfDVuVDlZdFWw6vf21SgerTgebTgdrVZU9ya2qAjhvsg0mkUDs5weJnx/kgwbC1dcPYn8/SPz8IfH3g6R7D4iUzvf4ygkhhDxobmcEti01nsZ85MgRzJgxA6dPn8bgwYPxpz/9CWazGRMmTED//v0BAFKpFNHR0QCAsLAwyGQySCQShIWFIa9uwcWmNDeNeePGjejbty+GDBmCKVOmNOyPioqCl5cXAODZZ59Feno6GGOYOHEinJ2dG/YfPnwY0dHRWLx4MZYuXYqnn34aw4cPb/G6x48fD7lcDgD4/vvvcerUKXz11VcA7Mny+fPnb5nsRkdH4/XXX4evry9iY2NvOj5hwgQIggCNRoNLly61GM/9hJLde+zCvPmoPnKko8Mg5JpmEqd7Vn97tX8795c2jqHRdlORNdRaX39z7dxwXdxiAWy2VoUjKBQQXF0hclFCcHGF2Nsb0sBAiNzcIHJ3r3t1u+694GZ/zwShVW0QQggh97NHHnkEpaWluHLlCkaMGIG0tDTs3bsX06dPx5IlSzBjxgxIJJKG9SYEQYBMJmvYtlgst91mYWEhBEHApUuXYLPZINT9n3vjmhaMMfBmfr8JDg5GRkYGUlJSEB8fjyeffBKrVq2CWCyGre73hNra2uvOqU+YAfsfzzdu3IgxY8a0Om6pVIpBgwbh/fffx5kzZ7Bnz57rjtd/LvX1P0go2b3HlKMegzQgoKPDIOR67bzuUIsLG7X1wkc3Jpqc32YMrOn9DfXy6943W3/jfSIRBIWzPZF1rntVKCA4K65tu7hA5OICdpuLTxBCCCGdTU5ODqxWK7y8vJCfnw+VSoXZs2ejuroamZmZmDFjRqvqcXFxgU6na7GcxWJBXFwcduzYgW3btuGDDz7A4sWLAQCpqam4evUq5HI5du/ejS1btkAQBMycORPLli0D5xy7du3C9u3bUVRUBE9PT0ybNg1KpRJbt24FAAQEBCAjIwMxMTFITk5uNo4xY8YgMTERjz/+OCQSCXJzc6FSqa5LiJuyaNEijBw5smEEurOg36juMY/nnuvoEAghhBBCCLnv1N+zC9j/0PzPf/4TIpEIhw4dwt/+9jdIJBIolUps27at1XWOGjUK7777Lvr379+wQNWN9+xu3rwZBw4cwPDhwzF8+HD079+/4d5cABg2bBimT5+OX3/9FVOnTkVERAQA++JPkZGRAOwLVA0YMAD79+/HkiVLIAgCJBIJEhMTAQBvvPEGZs2ahXfeeQcPP/xws/G+/PLLyMvLw8CBA8E5R5cuXbB79+4WrzM0NLRTrcJcjz1oQ9URERFcq9V2dBiEEEIIIYQ8MM6ePYuQkJCODsPhbN269boFrkjba+rfHmMsg3Me0dK5dHMXIYQQQgghhJAHDk1jJoQQQgghhJA7MHPmTMycObNDY8jKysL06dOv2yeTyXD06NEOishxULJLCCGEEEIIIfepsLCwhkcykevRNGZCCCGEEEIIIQ8cSnYJIYQQQgghhDxwKNklhBBCCCGEEPLAoWSXEEIIIYQQQsgD54FboCojI6OUMZbf0XF0It4ASjs6CHId6hPHRP3ieKhPHBP1i+OhPnE897xPUlNTw6xWq+Vetnmj8PBwRe/evW2ccwiCgOXLl5sGDhxou5s6z549K1y+fJmNHDnSCgBff/21eP369VIfH5+GehMSEoy9e/fmLdVltVrFIpGoQz+jxi5evMhiYmLkr732mmn69OkWAHjrrbekoaGhtmeffdZh4mxJSUmJWKPRZN2w+6HWnPvAJbuc8y4dHUNnwhjTtuaBzuTeoT5xTNQvjof6xDFRvzge6hPH0xF9cvLkyby+fft26B89ZDLZgHPnzh0HgOTkZNd3333X/5dffjl3N3X+8MMPXlqt1nn+/PkF9e/HjRvnvG3btoLbrev06dMhffv2PXs38dyK2WyGRCJpdXmJRCL19PQM+eKLL/jq1atznJycuEwm6+Hu7l7dt2/fsvaKs61ZrVbvO/33/sAlu4QQQgghhJD2U7R8RXfj+fOKtqxTFhRU0/Wdty+0tnxlZaXIzc3NAgD5+fmSSZMm9dTr9SKr1co2btyYHx0drVcoFANeeumly2lpaa5ubm7Wt99+++LSpUu7FxUVSRMSEgomTZpUtXbt2q61tbWCWq1WLlq0qLi59rZt2+b+8ccf+6Snp+deuHBBMnLkyD5paWk5u3fvdvv3v//tbjKZhIKCAvnkyZP933///WIAePPNN32TkpK8AWD69OlXVq1adbmqqkoYP358z+LiYqnNZmOvvfZa0ezZs8tVKlWYVqs96+/vb0lLS1MsXry4+7Fjx84tXLiwa3FxsaSgoEDq6elp2bVr1x/z58/v9tNPP7mYTCY2e/bsy0uWLGn2jxCenp6WwYMH6z/66COvRYsWXVfu559/ls+dO/chg8EgPPTQQ8YdO3bkdenSxdraPrgfULJLCCGEEEIIcXhGo1FQq9Uao9HISktLJSkpKbkAsGXLFs/Ro0dXJiQklFgsFuh0OgEADAaDMGrUKF1iYmJhVFRUr5UrV6oOHz6cm5mZ6RQXFxf44osvVsbHxxdptdqGkdwNGzZ47dmzx0OtVivr29VqtWdnzJhRkZyc7PHuu+92SU1NdYuPjy/q0aOHBQBOnTrlnJWVdebixYvBU6ZM8XzmmWcqGWPYsWOHV0ZGxlnOOQYNGhQyevRo3fnz52V+fn7mQ4cO/QoAZWVlopau+9SpU4qjR4/mKJVKvm7dOm83Nzfr6dOnzxoMBjZ48GD1uHHjqtRqtam581etWlUcExMT9Oqrr16X7M6cOTNw/fr1BWPHjtUvWLCg69KlS7tu2bKl1X9wuB9Qskvu1qcdHQC5CfWJY6J+cTzUJ46J+sXxUJ84ng7tk9sZgW1LMpnMlpOTkw0ABw4ccI6LiwvMzc09M2TIkOo5c+YEmM1mYfLkyeVDhw41AIBEIuGTJ0+uAoDQ0FCDTCazyWQyHhkZaSgsLJQ21864cePKm5rG/Pe//70gNDQ0dMCAAdVz5sy5Wr9/2LBhVX5+flYAV8aOHSs9dOiQkjGGp556qsLV1dUGAGPHji0/ePCgy/jx4ytXrFjRfe7cuapnnnmmMjo6Wt/SdUdHR1colUped92uOTk5im+++cYDAHQ6nSg7O9vpVsmuWq029e/fv/qTTz7xrN9XVlYm0ul0orFjx+oBYPbs2WXPPfdcz5Ziud/QaszkrnDO6T9AB0N94pioXxwP9Yljon5xPNQnjof6BHjiiSeqy8vLxcXFxeKYmBh9WlraOZVKZZo5c2bgpk2bvABALBZzQbCnO4IgQCaTcQAQiUSwWq3sdtvMy8uTCIKA0tJSsdV6bbYvY/aq/Pz8Suvfc970elb9+vUzZmZmZoeFhRlWrFihWrx4sX9dTNxms6+JZTAYrsvRnJ2dGxbL4pyz999/vyAnJyc7Jycnu7CwMOvZZ5+tain2VatWlXz44Yf+9W10FpTsEkIIIYQQQu4rx48fd7LZbPD19bXk5uZKVSqVedGiRaXTpk0rzczMbPX9xK6urla9Xt9iTmQ2mxEXFxe4devW34OCgmpXr17tW38sPT3d9dKlSyK9Xs9SUlLcR44cqX/88cf1KSkp7jqdTqiqqhJSUlI8Ro0apcvLy5O4uLjY5s2bd3XBggWXTpw4oQCAbt26mX766ScFAPzrX//yaC6OqKioysTExC5Go5EBwKlTp2RVVVUtxj9gwIDaoKAgw3/+8x83APDy8rK6urpa9+3bpwSAf/zjH16PPPJIi6PM9xuaxkwIIYQQQghxePX37AIA5xyJiYl5YrEY+/fvd9mwYYOfWCzmCoXCmpSU9Edr64yJidGtW7fOX61Wa+oXqLrxnt2NGzfm79+/33XIkCG66Oho/cMPP1wzcODAkAkTJlQCQEREhD42NjYwLy/PadKkSWUjRoyoAYCpU6eWDRw4MASwL1D16KOPGpKTk13j4+O7CYIAsVjMN2/enA8Aq1atKvrzn/8ckJCQYB40aFB1c/H+9a9/Lc3Ly5OFhYWFcM6Zp6enOSUl5bfWXOvrr79e/Oijj2rq33/++ed/zJ0796FXXnlF6NGjh/GLL77Ia+3ndr9gzQ2xE0IIIYQQQghgf/RQeHg4PW/5Bhs2bPBqvMAVaXsnT570Dg8PD7iTc2kaM2kXjDENY+xfjLFExtjkjo6H2DHGhjPGPmaM/Z0x9nNHx0MAxthjjLHDdf3yWEfHQ+wYYyF1ffIVY2xuR8dDAMZYT8bYPxhjX3V0LJ0d9YXjoZ9ZjqmiosIlOzu7z++//96joqLCpaPj6Ywo2SU3YYxtYYxdZoydvmF/NGPsHGPsV8bYshaqiQGwkXM+F8CMdgu2E2mLfuGcH+ac/xnAtwD+2Z7xdgZt9L3CAegBOAG42F6xdiZt9L1ytu575XkAd/Qge3JNG/XJ75zzWe0baed1O31EfXFv3Gaf0M+se+S3334LOH78eHhWVlYoALzyyitl27ZtK7h69arrqVOn+p46darvxYsX/eqKc0EQbDabTZDJZM2ulny3jh07Jler1ZrGX/369VO3V3v3E5rGTG7CGBsB+y/f2zjnfev2iQDkAoiC/RfyXwBMASACsPaGKv5U9/oGgBoAQznnj96D0B9obdEvnPPLdef9C8DLnPMWV+8jzWuj75VSzrmNMeYL4APO+Yv3Kv4HVVt9rzDGxgNYBmAT53zHvYr/QdTGP7++4pzTjKE2djt9xDnPrjtOfdGObrdP2vtnFk1jtqusrFQKgmDLy8sLDAsLOwPY7x/OysrqGxQUlCuTyczZ2dkhgYGBvysUilrGGEwmk7igoKB77969W30vMbnmbqYx0wJV5Cac8zTGWMANuyMB/Mo5/x0AGGNfAniGc74WwNPNVDW/7ofy1+0Va2fSVv3CGOsBoJIS3bvXht8rAFAOQNYecXY2bdUvnPNvAHzDGNsLgJLdu9DG3yukHdxOHwHIvrfRdU632yf0M+vecHNz09fW1l73jF6dTucslUqNcrncBADu7u5Xy8vL3Z2dnUsAQCwWW202220/6ojcPUp2SWupADR+gPhFAA83V7juh/NyAM4A/taegXVyt9UvdWYB+LzdIiK3+73yLIAxANwBbGrf0Dq12+2XxwA8C/sfIFLaNbLO63b7xAvA2wAGMMbi65Ji0r6a7CPqiw7VXJ88BvqZ1WFMJpNUIpE0TFOWSqWm6upqZWlpqXtVVZWb1WoV+fj4XOnIGDsrSnZJazX116hm58BzzvMA/Fe7RUPq3Va/AADn/I12ioXY3e73yteg2Q/3wu32yyEAh9orGALg9vukDMCf2y8c0oQm+4j6okM11yeHQD+zHA339vau8Pb2rujoQDozWqCKtNZFAN0bve8GoKiDYiHXUL84HuoTx0T94nioTxwf9ZHjoT5xQFKp1GQ2mxumNteN9Jo7MiZiR8kuaa1fAAQxxgIZY1IALwD4poNjItQvjoj6xDFRvzge6hPHR33keDp1n4hEokFqtVrTp08fjUajCUlNTXW+2zp//vln+c6dO93q32/YsMHLw8MjvPHKxhkZGU63qkOpVFYbjUYng8EgtdlsrKKiwtPDw4NGdB0ATWMmN2GMfQHgMQDejLGLAN7gnP+DMfYXAPthXylzC+dUzyOiAAAgAElEQVT8TAeG2elQvzge6hPHRP3ieKhPHB/1keNx5D75z7az3a8W6hVtWaenSlkzekbIhVuVkclktpycnGwASE5Odl2+fHm3qKioc3fTrlarVWi1WufY2NjK+n3jxo0r37ZtW0FT5c+fPx9YXV3tYrVaxSdOnOjn7+9f5OvrW9q9e/eC8+fPBwOAp6dnqbOzc+3dxNUUs9kMiUTS1tU+0CjZJTfhnE9pZn8KaNGDDkP94nioTxwT9YvjoT5xfNRHjof65NYqKytFbm5uFgDIz8+XTJo0qaderxdZrVa2cePG/OjoaL1CoRjw0ksvXU5LS3N1c3Ozvv322xeXLl3avaioSJqQkFAwadKkqrVr13atra0V1Gq1ctGiRcXNtbdt2zb3jz/+2Cc9PT33woULkpEjR/ZJS0vL2blzp9u///3vXiaTSbhw4QImTZpU9v7775cAwJtvvumblJTkDQDTp0+/smrVqstVVVXC+PHjexYXF0ttNht77bXXimbPnl2uUqnCtFrtWX9/f0taWppi8eLF3Y8dO3Zu4cKFXYuLiyUFBQVST09Py65du/6YP39+t59++snFZDKx2bNnX16yZEmTj4T69ttvXdasWdPV09PTfO7cOXlYWFjN7t27/xAEAYsXL/bft2+fu9FoFCIiIvRJSUn5giAgMjKyz6BBg/Tp6emuOp1O9PHHH+dFR0fr26MP7wVKdgkhhBBCCCGt1tIIbHsxGo2CWq3WGI1GVlpaKklJSckFgC1btniOHj26MiEhocRisUCn0wkAYDAYhFGjRukSExMLo6Kieq1cuVJ1+PDh3MzMTKe4uLjAF198sTI+Pr5Iq9U614/kbtiwwWvPnj0earVaWd+uVqs9O2PGjIrk5GSPd999t0tqaqpbfHx8UY8ePSwAcOrUKeesrKwzSqXSNmDAAM0zzzxTyRjDjh07vDIyMs5yzjFo0KCQ0aNH686fPy/z8/MzHzp06FcAKCsrE7V03adOnVIcPXo0R6lU8nXr1nm7ublZT58+fdZgMLDBgwerx40bV6VWq01NnXv27Fn5iRMnfg8ICDAPGjRInZqaqhwzZox+yZIll9etW1cMABMmTAj88ssv3aZOnVoJABaLhWVlZZ3duXOn25o1a7pGR0fn3l3PdRxKdgkhhBBCCCEOr/E05gMHDjjHxcUF5ubmnhkyZEj1nDlzAsxmszB58uTyoUOHGgBAIpHwyZMnVwFAaGioQSaT2WQyGY+MjDQUFhZKm2unuWnMf//73wtCQ0NDBwwYUD1nzpyr9fuHDRtW5efnZwWAsWPHlh86dEjJGMNTTz1V4erqaqvff/DgQZfx48dXrlixovvcuXNVzzzzTGVrRk2jo6MrlEolr7tu15ycHMU333zjAQA6nU6UnZ3t1FyyGxYWVt2rVy9z3WdQ89tvv0kB4LvvvnP54IMP/Gpra4WKigqxRqMxAKgEgOeee64cAIYOHVq9ZMmSZj+n+wEtUEUIIYQQQgi5rzzxxBPV5eXl4uLiYnFMTIw+LS3tnEqlMs2cOTNw06ZNXgAgFou5INjTHUEQIJPJOACIRCJYrdamHuN0S3l5eRJBEFBaWiq2Wq0N+xm7virGGDhv+klq/fr1M2ZmZmaHhYUZVqxYoVq8eLF/XUzcZrMBsI9INz7H2dnZVr/NOWfvv/9+QU5OTnZOTk52YWFh1rPPPlvVXMz111zXBiwWC6upqWGLFi166Ouvv/4tNzc3e9q0aaW1tbUNbTo5OXEAEIvFd/Q5ORJKdgkhhBBCCCH3lePHjzvZbDb4+vpacnNzpSqVyrxo0aLSadOmlWZmZrZ68SxXV1erXq9vMScym82Ii4sL3Lp16+9BQUG1q1ev9q0/lp6e7nrp0iWRXq9nKSkp7iNHjtQ//vjj+pSUFHedTidUVVUJKSkpHqNGjdLl5eVJXFxcbPPmzbu6YMGCSydOnFAAQLdu3Uw//fSTAgD+9a9/eTQXR1RUVGViYmIXo9HIAODUqVOyqqqq28rpampqBADw8/OzVFZWCnv27Gm2vfsdTWMmhBBCCCGEOLz6e3YBgHOOxMTEPLFYjP3797ts2LDBTywWc4VCYU1KSvqjtXXGxMTo1q1b569WqzX1C1TdeM/uxo0b8/fv3+86ZMgQXXR0tP7hhx+uGThwYMiECRMqASAiIkIfGxsbmJeX5zRp0qSyESNG1ADA1KlTywYOHBgC2BeoevTRRw3Jycmu8fHx3QRBgFgs5ps3b84HgFWrVhX9+c9/DkhISDAPGjSourl4//rXv5bm5eXJwsLCQjjnzNPT05ySkvLb7XyO3t7e1hdffPGKRqMJ7datmyk8PLzZ9u53rLkhdkIIIYQQQggBgJMnT+aFh4c3uepvZ7ZhwwavxgtckbZ38uRJ7/Dw8IA7OZemMRNCCCH3AcZYHmPM+27LEEIIIZ0FTWMmhBBCCCGEkDvwyiuvlAEo68gYjh07Jp8xY0Zg431SqdR26tSpnI6KyVFQsksIIYQ4GMbYbgDdATgB+P855582OhYAYB+AowAGAMgFMINzXlNX5P9jjI0DIAHwHOc8hzEWCeBDAHIABgBxnPNz9+hyCCGEtKPIyEhD/SOZyPVoGjMhhBDieP7EOR8EIALAK4wxrxuO9wHwKee8H4AqAPMaHSvlnA8EkAhgcd2+HAAjOOcDAKwC8E67Rk8IIYQ4AEp2CSGEEMfzCmPsJID/g32EN+iG4xc45z/Vbf8PgGGNjn1d95oBIKBu2w3A/zLGTgNYDyC0PYImhBBCHAklu4QQQogDYYw9BuAJAI9wzsMBHId9OnNjNz5KofF7Y92rFdduV3oLwEHOeV8A45qojxBCCHngULJLCCGEOBY3AOWc8xrGmBrAkCbK9GCMPVK3PQVAeivqLKzbntkmURJCCCEOjpJdQgghxLHsAyBmjJ2CfUT2/5oocxbAS3VlPGG/P/dW3gOwljH2EwBRWwZLCCH3ikgkGqRWqzV9+vTRaDSakNTUVOe7rfPnn3+W79y5063xvu3bt7sHBwdrAgMDQ4OCgkI///xzjzut/9y5c9KgoKBmbx359ttvXVxcXPqr1WqNWq3WDB06NPhO2yI3o9WYCSGEEAfCOTcCiGniUAAAMMaUAGyc8z83cW5Ao20tgMfqto8AaPwL1OttFjAhpNPZn/hh99IL+Yq2rNO7+0M1Y+YuuHCrMjKZzFa/6nBycrLr8uXLu0VFRd3VyvJarVah1WqdY2NjKwHgyJEj8hUrVnT7/vvvc9VqtSknJ0caFRUV3Lt3b+Pw4cNrWqrvTkREROgPHjz4a3vU3dnRyC4hhBBCCCHkvlJZWSlyc3OzAEB+fr4kIiKij1qt1gQFBYXu27dPCQAKhWLA3LlzVaGhoSFDhw4NPnjwoCIyMrJPt27dwpKSktxqa2vZ2rVru+7Zs8dDrVZrPvvsM4+EhAS/hQsXFqvVahMAqNVq08KFC0vee+89XwCIjIzsk5aWpgCA4uJisUqlCgPsI7iDBg3qo9FoQtpi1HnHjh1u/fr1U4eEhGiGDh0afOHCBRqkvAP0oRFCCCH3Ec55HoC+HR0HIaTzamkEtr0YjUZBrVZrjEYjKy0tlaSkpOQCwJYtWzxHjx5dmZCQUGKxWKDT6QQAMBgMwqhRo3SJiYmFUVFRvVauXKk6fPhwbmZmplNcXFzgiy++WBkfH1+k1Wqdt23bVgAA69ev91u6dGlJ43aHDBlS/cknn/jcKrauXbtaDh8+nKtQKHhWVpZsypQpPU+fPn22Ndel1WqVarVaAwDPPPPM1YSEhJKoqCj9Cy+8kCMIAj744APvNWvW+H322WcX7+Rz68wo2SWEEEIIIYQ4vMbTmA8cOOAcFxcXmJube2bIkCHVc+bMCTCbzcLkyZPLhw4dagAAiUTCJ0+eXAUAoaGhBplMZpPJZDwyMtJQWFgobaoNzjkTBOHGfS3GZjKZ2KxZsx7Kzs6WC4KA/Px8WWuvq6lpzH/88Yd0woQJ3a5cuSIxmUxC9+7djc2dT5pH05gJIYQQQggh95Unnniiury8XFxcXCyOiYnRp6WlnVOpVKaZM2cGbtq0yQsAxGIxr09cBUGATCbjACASiWC1WllT9QYHBxuOHDly3f3Ix44dU4SHh1fX12m1WgEANTU1DXW8/fbbvj4+PuazZ89mZ2VlZZvN5rvKs/7yl7/0mDdv3uXc3NzsTZs25RuNRsrb7gB9aIQQQgghhJD7yvHjx51sNht8fX0tubm5UpVKZV60aFHptGnTSjMzM1u9eJarq6tVr9c35ERLly4tWb9+vf+5c+ekgP1e3M2bN/suX768BAC6d+9uPHbsmDMAJCUlNazSXFlZKfL39zeLRCJs3rzZqz4hvlM6nU7Uo0cPMwBs3brV664q68RoGjMhhBBCCCHE4dXfswvYpxYnJibmicVi7N+/32XDhg1+YrGYKxQKa1JS0h+trTMmJka3bt06f7VarVm0aFHx7Nmzy9esWXNx3LhxvU0mk1BYWCjdu3fvufDwcCMALFu27FJsbGzPL7/80mv48OFV9fUsWLDg8qRJk3rt3r3bY9iwYTq5XG67m2tdsWJF0ZQpU3r5+vqaIiIiqgsKClo9LZpcw1ozB50QQgghhBDSeZ08eTIvPDy8tKPjuNfmzZunysjIcP7xxx/POzk5UeLUAU6ePOkdHh4ecCfn0sguIYQQQgghhDRh8+bNhR0dA7lzlOwSQgghhBBCSDtKTk52XbFiRbfG+7p3725MTU39raNi6gwo2SWEEEIIIYSQdjRp0qSqSZMmZXd0HJ0NrcZMCCGEEEIIIeSBQ8kuIYQQQgghhJAHDiW7hBBCCCGEEEIeOJTsEkIIIYQQQgh54FCySwghhBBCCHF4IpFokFqt1vTp00ej0WhCUlNTne+2zp9//lm+c+dOt8b7tm/f7h4cHKwJDAwMDQoKCv3888897rT+c+fOSYOCgkJvVebgwYOKyMjIPg899FBfjUYT8thjj/U+duyYvKmyCoViwJ3G0hnRasyEEEIIIYSQVrv6VW53c0m1oi3rlPg513hODr5wqzIymcyWk5OTDdgf5bN8+fJuUVFR5+6mXa1Wq9Bqtc6xsbGVAHDkyBH5ihUrun3//fe5arXalJOTI42Kigru3bu3cfjw4TV301ZTLly4IJ42bVqvrVu3/h4VFVUNAPv371eeO3dOFhkZaWjr9jobGtklhBBCCCGE3FcqKytFbm5uFgDIz8+XRERE9FGr1ZqgoKDQffv2KQH7KOjcuXNVoaGhIUOHDg2uH0Ht1q1bWFJSklttbS1bu3Zt1z179nio1WrNZ5995pGQkOC3cOHCYrVabQIAtVptWrhwYcl7773nCwCRkZF90tLSFABQXFwsVqlUYYB9BHfQoEF9NBpNyO2MOq9bt87n+eefL6tPdAFgzJgx+unTp1cAQE5OjrR///7qvn37hrz66qtd2/Iz7AxoZJcQQgghhBDSai2NwLYXo9EoqNVqjdFoZKWlpZKUlJRcANiyZYvn6NGjKxMSEkosFgt0Op0AAAaDQRg1apQuMTGxMCoqqtfKlStVhw8fzs3MzHSKi4sLfPHFFyvj4+OLtFqt87Zt2woAYP369X5Lly4tadzukCFDqj/55BOfW8XWtWtXy+HDh3MVCgXPysqSTZkypefp06fPtnRNZ8+elc+YMaOsuePz5s3r8fLLL1/5y1/+UrZ27dourfmcyDWU7BJCCCGEEEIcXuNpzAcOHHCOi4sLzM3NPTNkyJDqOXPmBJjNZmHy5MnlQ4cONQCARCLhkydPrgKA0NBQg0wms8lkMh4ZGWkoLCyUNtUG55wJgnDjvhZjM5lMbNasWQ9lZ2fLBUFAfn6+7E6usV+/fmq9Xi8aOXJk1eeff34hMzNT+d133/0GAHPmzCl76623ut1JvZ0VTWMmhBBCCCGE3FeeeOKJ6vLycnFxcbE4JiZGn5aWdk6lUplmzpwZuGnTJi8AEIvFvD5xFQQBMpmMA4BIJILVamVN1RscHGw4cuTIdfcjHzt2TBEeHl5dX6fVagUA1NTUNNTx9ttv+/r4+JjPnj2bnZWVlW02m1uVZ4WEhBgyMjIa2jt16lTO66+/XlRVVSWq3ycIQsvZNmkSJbuEEEIIIYSQ+8rx48edbDYbfH19Lbm5uVKVSmVetGhR6bRp00ozMzNbvXiWq6urVa/XN+RES5cuLVm/fr3/uXPnpID9XtzNmzf7Ll++vAQAunfvbjx27JgzACQlJTWs0lxZWSny9/c3i0QibN682as+IW7JokWLLu/cudOr8T2+1dXVDfEMHDhQ/9lnn3kCwGeffebV2usidjSNmRBCCCGEEOLw6u/ZBexTixMTE/PEYjH279/vsmHDBj+xWMwVCoU1KSnpj9bWGRMTo1u3bp2/Wq3WLFq0qHj27Nnla9asuThu3LjeJpNJKCwslO7du/dceHi4EQCWLVt2KTY2tueXX37pNXz48Kr6ehYsWHB50qRJvXbv3u0xbNgwnVwut7Wm/R49eli2b9/++7Jly7rNmjVL4uXlZfHw8LC8+eabRQCwefPmghdeeKHn5s2bfcePH19+e58YYa2Zg04IIYQQQgjpvE6ePJkXHh5e2tFx3Gvz5s1TZWRkOP/444/nnZycKHHqACdPnvQODw8PuJNzaWSXEEIIIYQQQpqwefPmwo6Ogdw5SnYJIYQQQgghpB0lJye7rlix4rqVlLt3725MTU39raNi6gwo2SWEEEIIIYSQdjRp0qSqSZMmZXd0HJ0NrcZMCCGEEEIIIeSBQ8kuIYQQQgghhJAHDiW7hBBCCCGEEEIeOJTsEkIIIYQQQgh54FCySwghhBBCCHF4IpFokFqt1vTp00ej0WhCUlNTne+2zp9//lm+c+dOt8b7tm/f7h4cHKwJDAwMDQoKCv3888897rT+c+fOSYOCgkKbOpacnOyqVqs1arVao1AoBgQEBPRVq9WaiRMnBtSXiYuL6+7j49PParXeaQidGq3GTAghhBBCCGm13bt3d798+bKiLev08fGpmTBhwoVblZHJZLacnJxswJ4oLl++vFtUVNS5u2lXq9UqtFqtc2xsbCUAHDlyRL5ixYpu33//fa5arTbl5ORIo6Kignv37m0cPnx4zd20daPGKzRHRkb2Wbdu3YURI0Y0tGG1WrFv3z53f39/03fffefy9NNP69qy/c6ARnYJIYQQQggh95XKykqRm5ubBQDy8/MlERERfdRqtSYoKCh03759SgBQKBQD5s6dqwoNDQ0ZOnRo8MGDBxWRkZF9unXrFpaUlORWW1vL1q5d23XPnj0earVa89lnn3kkJCT4LVy4sFitVpsAQK1WmxYuXFjy3nvv+QL2pDQtLU0BAMXFxWKVShUG2EdwBw0a1Eej0YS01ajzt99+6xIcHGx4+eWXr+zYscPzbuvrjGhklxBCCCGEENJqLY3Athej0Sio1WqN0WhkpaWlkpSUlFwA2LJli+fo0aMrExISSiwWC3Q6nQAABoNBGDVqlC4xMbEwKiqq18qVK1WHDx/OzczMdIqLiwt88cUXK+Pj44u0Wq3ztm3bCgBg/fr1fkuXLi1p3O6QIUOqP/nkE59bxda1a1fL4cOHcxUKBc/KypJNmTKl5+nTp8/ezfXu2LHD8/nnn786ZcqUirfeektlNBqZTCbjd1NnZ0PJLiGEEEIIIcThNZ7GfODAAee4uLjA3NzcM0OGDKmeM2dOgNlsFiZPnlw+dOhQAwBIJBI+efLkKgAIDQ01yGQym0wm45GRkYbCwkJpU21wzpkgCDfuazE2k8nEZs2a9VB2drZcEATk5+fL7uZaa2tr2cGDB90+/vjjCx4eHrb+/ftX79q1y/WFF16ovJt6OxuaxkwIIYQQQgi5rzzxxBPV5eXl4uLiYnFMTIw+LS3tnEqlMs2cOTNw06ZNXgAgFot5feIqCALqR0VFIhGsVitrqt7g4GDDkSNHrrsf+dixY4rw8PDq+jrrF4uqqalpqOPtt9/29fHxMZ89ezY7Kysr22w231WelZyc7KrT6UR9+/YNValUYVqtVvnFF1/QVObbRMkuIYQQQggh5L5y/PhxJ5vNBl9fX0tubq5UpVKZFy1aVDpt2rTSzMzMVi+e5erqatXr9Q050dKlS0vWr1/vf+7cOSlgvxd38+bNvsuXLy8BgO7duxuPHTvmDABJSUkNqzRXVlaK/P39zSKRCJs3b/a629WTv/zyS88PP/wwv7CwMKuwsDArLy8v6/Dhw671U7RJ69CHRQghhBBCCHF49ffsqtVqzQsvvNAzMTExTywWY//+/S4ajSY0JCRE8+9//9vjtddeu9TaOmNiYnS5ubny+gWqhg4dalizZs3FcePG9Q4ICOjbt2/fvps2bcoPDw83AsCyZcsu/eMf/+gyYMAAdWlpacMtoQsWLLj8xRdfeIWHh6tzc3Od5HK57U6vU6fTCWlpaW7PPfdcRf0+V1dXW0REhP7LL790u9W55HqsNXPQCSGEEEIIIZ3XyZMn88LDw0s7Oo57bd68eaqMjAznH3/88byTkxMlTh3g5MmT3uH/j717D2v6PP8HfucACUg4BBQ0xIKcQkIISg3OShGnrraRX60DV2gdtrNM6zbrPFVbrG5t8Uv9qjg7W3u1K1YrbWyRpa0Wz1rXOagmQBIQKgYCVjkfEgIfkt8fW/w6PCG1lcb367p6XeY53M/9CX/dfZ7PE4UiZChzcUEVAAAAAADADbz55pvme50DDB2KXQAAAAAAgB/Qvn37vNeuXRt8bZtYLLYVFxfX3Kuc7gcodgEAAAAAAH5Ac+fO7Zg7d67+Xudxv8EFVQAAAAAAAOByUOwCAAAAAACAy0GxCwAAAAAAAC4HxS4AAAAAAAx7HA4nXiKRSKOioqRSqTS6uLh4xPeNefr0aY+CgoL/+u3aXbt2+UZGRkpDQ0NlERERsvfee89vqPErKyvdIyIiZN83TxgaXFAFAAAAAADDHo/HsxuNRj3Rv283XrNmTfCMGTMqv0/MkpISz5KSkhHz5s1rJyL6xz/+4bF27drgL7/8skoikfQajUb3GTNmRIaHh9sSExMtd+M54MeDYhcAAAAAAAZNb1gl7u6q8rybMUd4RVqk0RvrBju+vb2d4+PjwxARXbx40W3u3Lnjurq6OP39/axt27ZdfOSRR7o8PT3H//rXv7584sQJbx8fn/5XX321ftWqVeKGhgb3jRs3mubOndvx+uuvj+np6WFLJBKvP/7xj42fffaZ77JlyxolEkkvEZFEIuldtmzZpf/5n/8JTExMvKBUKqPeeOONuocfftjS2NjIffDBB6PNZnNZZWWle3p6eqjVamUTEW3dutU0Y8aM7ts9R15enr9Go/G1Wq1sk8nEmzVrVtuOHTvqiYgyMjLGarXaET09PezZs2e3bt68uYGISCQSydPS0poPHjzowzAMq6Cg4Nvx48f3DOV7d3U4xgwAAAAAAMOezWZjSyQSaWhoqOwPf/jDA+vWrWskInr33XeFP//5z9uNRqPeYDBUJCQkWIiIrFYrOzk5ubOiosIwYsSI/pdeekl08uTJqo8//rj6T3/6k4jP5ztefPHFhtmzZ7cajUb9woULW6uqqvjO+U6TJk3qPn/+vMetchszZgxz8uTJKr1ebygoKPj2hRdeGDvY59Lr9Z6FhYXfGgyGiqKiIr/q6mo3IqL//d//NZeXlxuMRmPFV199JfjnP/95NYeAgABGr9cbnnnmmSs5OTmBd/I93k+wswsAAAAAAIN2Jzuwd9O1x5gPHTo0YsGCBaFVVVUVkyZN6s7Kygrp6+tj//KXv2ydPHmylYjIzc3N8ctf/rKDiEgmk1l5PJ6dx+M5lEql1Ww2u99oDYfDwWKz2QPbbptbb28v69lnn31Ar9d7sNlsunjxIm+wzzVlypQOf3//fiKi8PDwnpqaGl54eHjf+++/L/zb3/4WwDAM68qVK25arZafkJBgJSJKT09vJSJSKpWWoqKiIb9T7OqwswsAAAAAAD8p06dP725tbeU2NjZyZ82a1XXixIlKkUjUm5mZGfqXv/zFn4iIy+U6nIUrm80mHo/nICLicDjU39/PulHcyMhI6z/+8Y//OqJ95swZT4VC0e2M2d/fT0REFovlaoxXX301cNSoUX0Gg0FfVlam7+vrG3Sd5e7ufrWa5nA4jr6+PpbRaHT/y1/+Enj8+PGqqqoq/bRp09p7enquxuTz+Q5nPgzD3PBZAMUuAAAAAAD8xJw9e5Zvt9spMDCQqaqqcheJRH1//OMfm5566qmmb775ZtDvE3t7e/d3dXVdrYlWrVp1afPmzaMrKyvdif59m/Kbb74ZuGbNmktERGKx2HbmzJkRRES7d+++uqPa3t7OGT16dB+Hw6E333zT31kQD1VrayvHw8PDLhQK++vq6rjHjh3zuf0sGAjHmAEAAAAAYNhzvrNL9O+jxX/9619ruVwuHTx4UJCXlxfE5XIdnp6e/bt3774w2JizZs3qfOONN0ZLJBLpH//4x8aFCxe2btiwoX727Nnhvb29bLPZ7P7ZZ59VKhQKGxHR6tWrv5s3b964vXv3+icmJnY44yxduvTy3LlzwwoLC/2mTJnS6eHhYf8+z/qzn/3MGhMTY4mIiJCNHTvWFh8f3/V94t2vWIM5gw4AAAAAAPcvrVZbq1Aomu51Hj+2xYsXi0pLS0ccP378vPPoMPy4tFptgEKhCBnKXOzsAgAAAAAA3MCbb75pvtc5wNCh2AUAAAAAAPgB7du3z3vt2rXB17aJxWJbcXFxzb3K6X6AYhcAAAAAAOAHNHfu3I65c+fq73Ue9xvcxgwAAAAAAAAuB8UuAAAAAAAAuBwUuwAAAAAAAOByUOwCAAAAAACAy0GxCwAAAAAAw57JZOKqVKpxYrE4JiwsTJaUlBSu0+l4dxonLy/Pv7a21u1O50ykCj4AACAASURBVC1btmxMdnZ2oPNzX18f+fn5KZ5//nnRtePmzZv3QGlpKX8wMZcsWSJatGjR1flVVVXuwcHB8qamJs6d5gfXQ7ELAAAAAADDmt1up5SUlPCHH364s66urrympqbi9ddfNzc0NNxx0frBBx8EmEymG85jGGbQcT755BOf0NBQW1FRkZ/dbr/aXlBQcDE+Pr5nMLFzcnIaDhw44PvNN9/wiYgWL14sXrt2rTkgIKB/0InATeGnhwAAAAAAYNCWGkxiY3eP592MKRnBt2yJHlt3s36NRiPgcrmOlStXXnG2TZ482UpE9PLLLwd++umnwt7eXtZjjz3Wtnnz5obKykr3WbNmRSiVyq6SkhKvwMDA3oMHD1Z//PHHvuXl5Z7z588fx+fz7SUlJYaoqKiYJ598suno0aPeWVlZlzs7OznvvffeyL6+PlZISIhNrVZfEAgE9oE5ffjhh8LFixd/t3PnzpFHjhwZMX369G4iIqVSGfXGG2/UPfzwwxZPT8/xzz333HdHjhzxzs3Nrf/FL37RdW0MLy8vR05OTv2iRYvG/vGPf7zU3d3NWbRoUcvd+2bvb9jZBQAAAACAYU2n03koFArLwPZPPvnEu7q6mq/T6QwGg0F/7tw5zy+++MKLiMhkMvF///vfX66urq7w8fHpz8/P91uwYEFrTEyMJT8//1uj0aj38vJyEBHx+Xx7aWlp5XPPPdeakZHRWl5ebqisrNRHRUVZ8/LyAgau29XVxTp9+rRg3rx57ampqS0ffPCB8EZ5W61WdkxMjFWn0xkHFrpO8+bNa/f19e3PysoK3bFjx8Xv903BtbCzCwAAAAAAg3arHdgf24EDB7xPnDjhLZVKpUREFouFbTQa+ePGjesViUQ25+7v+PHjLbW1tTd9v3f+/Pmtzn+XlpZ6ZGdnizo7Oznd3d2cpKSk9oHjP/roI99JkyZ1CgQC+1NPPdUaFxc3hmGYOi73v8srDodDmZmZrQPnD/T8889f7unpYSkUCtsdPD7cBopdAAAAAAAY1uRyubWwsNBvYLvD4aClS5c2rlixouna9srKSnd3d3eH8zOHw3FYrdabnmq99pjyc889F6pWq6t/9rOfWfPy8vyPHz8uGDh+7969wtLSUi+RSCQnImpvb+doNBrB448/3nntOHd3d/vAAvhG2Gw2sdk4dHu34RsFAAAAAIBhbfbs2Z29vb2sTZs2XT1SfPz4cU9vb+/+Xbt2BbS3t7OJiC5cuOBmNptvWV16eXn1t7e33/S2Y4vFwh47dmyfzWZj7d2797rjyS0tLeySkhKv+vp6ndlsLjObzWU5OTmmPXv23PAoM9w7KHYBAAAAAGBYY7PZVFRUVHP48GFvsVgcEx4eLlu3bt2YzMzMltTU1JaJEydKIiMjpXPmzAlra2u75c/2zJ8/v+l3v/vdAxKJRNrV1cUa2L969eoGpVIZnZiYGBkREXHdrcoffPCB3+TJkzs9PDyu7hz/6le/aisuLva1Wq3XxYN7h+VwOG4/CgAAAAAA7ltarbZWoVA03X4kwN2l1WoDFApFyFDmYmcXAAAAAAAAXA4uqAIAAAAAAPiBzZgxI6yuru6/boR+9dVX6+fOndtxr3JydSh2AQAAAAAAfmDFxcU19zqH+w2OMQMAAAAAAIDLQbELAAAAAAAALgfFLgAAAAAAALgcFLsAAAAAAADgclDsAgAAAADAsGcymbgqlWqcWCyOCQsLkyUlJYXrdDre7Wf+t7y8PP/a2lq3O523bNmyMdnZ2YHOz319feTn56d4/vnnRdeOmzdv3gOlpaX8O40Pdx+KXQAAAAAAGNbsdjulpKSEP/zww511dXXlNTU1Fa+//rq5oaHhjovWDz74IMBkMt1wHsMwg47zySef+ISGhtqKior87Hb71faCgoKL8fHxPd8nNtwd+OkhAAAAAAAYtBVqrbjqUqfn3YwZGSSw5P5SUXezfo1GI+ByuY6VK1decbZNnjzZSkT08ssvB3766afC3t5e1mOPPda2efPmhsrKSvdZs2ZFKJXKrpKSEq/AwMDegwcPVn/88ce+5eXlnvPnzx/H5/PtJSUlhqioqJgnn3yy6ejRo95ZWVmXOzs7Oe+9997Ivr4+VkhIiE2tVl8QCAT2gTl9+OGHwsWLF3+3c+fOkUeOHBkxffr0biIipVIZ9cYbb9Q9/PDDFk9Pz/HPPffcd0eOHPHOzc2t/8UvftE1MI5IJJKnpaU1Hzx40IdhGFZBQcG348eP7zl69KjnsmXLxvb09LD5fL79b3/72wWFQmHLy8vz12g0vlarlW0ymXizZs1q27FjR/3d+Uu4FuzsAgAAAADAsKbT6TwUCoVlYPsnn3ziXV1dzdfpdAaDwaA/d+6c5xdffOFFRGQymfi///3vL1dXV1f4+Pj05+fn+y1YsKA1JibGkp+f/63RaNR7eXk5iIj4fL69tLS08rnnnmvNyMhoLS8vN1RWVuqjoqKseXl5AQPX7erqYp0+fVowb9689tTU1JYPPvhAeKO8rVYrOyYmxqrT6Yw3KnSdAgICGL1eb3jmmWeu5OTkBBIRKRSKnjNnzhgNBoN+3bp15pUrVwY7x+v1es/CwsJvDQZDRVFRkV91dfUd73DfD7CzCwAAAAAAg3arHdgf24EDB7xPnDjhLZVKpUREFouFbTQa+ePGjesViUQ25+7v+PHjLbW1tTd9v3f+/Pmtzn+XlpZ6ZGdnizo7Oznd3d2cpKSk9oHjP/roI99JkyZ1CgQC+1NPPdUaFxc3hmGYOi73v8srDodDmZmZrQPnD5Sent5KRKRUKi1FRUV+REQtLS2cefPmhdbW1vJZLJajr6+P5Rw/ZcqUDn9//34iovDw8J6amhpeeHh43+3Wud9gZxcAAAAAAIY1uVxu1Wq11x2ddjgctHTp0kaj0ag3Go16k8lU/sILLzQREbm7uzuc4zgcjoNhGNbA+U7XHlN+7rnnQv/yl7+Yqqqq9KtWrWqw2WzX1Ux79+4VfvXVV94ikUgeHx8vbW9v52g0GsHAce7u7vaBBfCN8Pl8BxERl8u9mueqVatESUlJnefPn6/4+9//Xt3b23s1j4HPdm0hDP8HxS4AAAAAAAxrs2fP7uzt7WVt2rTp6pHi48ePe3p7e/fv2rUroL29nU1EdOHCBTez2XzL6tLLy6u/vb2dc7N+i8XCHjt2bJ/NZmPt3bv3uuPJLS0t7JKSEq/6+nqd2WwuM5vNZTk5OaY9e/bc8CjzUHV0dHCCg4N7iYjeeuut645Sw+2h2AUAAAAAgGGNzWZTUVFRzeHDh73FYnFMeHi4bN26dWMyMzNbUlNTWyZOnCiJjIyUzpkzJ6ytre2mhSwR0fz585t+97vfPSCRSKRdXV3X7YiuXr26QalURicmJkZGRERcd6vyBx984Dd58uRODw+Pq7urv/rVr9qKi4t9rVbrXdthXbVq1aVXXnkleMKECZL+/v67Ffa+wnI4HLcfBQAAAAAA9y2tVlurUCia7nUecP/RarUBCoUiZChzsbMLAAAAAAAALge3MQMAAAAAAPzAZsyYEVZXV/dfN0K/+uqr9XPnzu24Vzm5OhS7AAAAAAAAP7Di4uKae53D/QbHmAEAAAAAAMDloNgFAAAAAAAAl4NiFwAAAAAAAFwOil0AAAAAAABwOSh2AQAAAABg2DOZTFyVSjVOLBbHhIWFyZKSksJ1Oh3v9jNvTqPRCJKTk8OJiHbv3u2zZs2aICKiXbt2+ZaWlvKd45YuXTqmsLBQMJQ1zp49y4+Li5O4u7tPyM7ODrzdeA6HEy+RSKTO/yorK92Hsu61li1bNmYwa7sa3MYMAAAAAADDmt1up5SUlPD09PRmjUbzLRHR6dOnPRoaGtxiY2Ntd2ONjIyMdiJqJyIqLCz0ZRimPT4+voeIaMuWLQ1DjTtq1Chm69atJrVa7TeY8Twez240GvVDXe9O9PX1kZub24+x1D2BYhcAAAAAAAav8HkxXdZ73tWYo6QWenx73c26NRqNgMvlOlauXHnF2TZ58mSr3W6nrKys4CNHjviwWCzHihUrGhcuXNiq0WgEGzZsGCMUCvsqKys95HK5pbCw8AKbzSa1Wu29YsUKsVAoZORyucUZLy8vz7+kpGTE008/3Xzo0CHfr7/+WrBx48bR+/btq8nOzh6tUqnaFyxY0Lp//37B6tWrxf39/aRQKCz5+fkXPTw8HCKRSJ6WltZ88OBBH4ZhWAUFBd+OHz++RyQSMSKRiNm/f7/vUL8ehmHo+eefD/7qq68Evb29rIULF15esWJFExHRyy+/HPjpp58Ke3t7WY899ljb5s2bG4iIVq1aFVRQUBAwZsyYXn9//77x48dbiIiUSmWUUqns+uc//+n16KOPtq1fv/67oeY13KHYBQAAAACAYU2n03koFArLwPb8/HzfsrIyD4PBUNHY2MhVKpXRM2fO7CIiMhgMHufOnfs2JCSkLz4+XlJcXOyVmJjYvWTJkpDi4uJKmUxmU6lU4wbGnDFjRvf06dPbnMXttX0Wi4WVlZUV+uWXX1bGxsba5syZE5KbmzsyOzv7MhFRQEAAo9frDTk5OSNzcnICCwoKLt7ps9psNrZEIpESEYnFYltxcXHNli1bAnx8fPrLy8sNVquVNXHiRMns2bM79Ho9v7q6mq/T6QwOh4OmT58e/sUXX3h5eXnZP/30U2FZWZm+r6+P4uLipM5il4iora2N869//avyTnP7qUGxCwAAAAAAg3eLHdgf28mTJwVpaWktXC6XxGIxk5CQ0HXq1ClPHx8fu1wu7w4LC+sjIpLJZJaamhp3gUDQHxwcbJPL5TYiooyMjOZ33nln5GDX02q1/ODgYJvz6HRmZmbz9u3bRxHRZSKi9PT0ViIipVJpKSoqGtSx5YFudIz50KFD3kaj0dMZs7Ozk6PX6/kHDhzwPnHihLdUKpUSEVksFrbRaOR3dnayH3300TaBQGAnIpo5c2bbtfGefPLJlqHk9lODYhcAAAAAAIY1uVxuLSwsvK54dDgcN53D4/GudnI4HGIYhkVExGKxhpzHrdYjIuLz+Q4iIi6X63Cudzc4HA7Wpk2bTHPnzu24tv2LL77wXrp0aaPzSLPThg0bRt3qOZ1FsKvDbcwAAAAAADCszZ49u7O3t5e1adOmAGfb8ePHPf38/Bi1Wi1kGIYaGhq4Z86c8UpMTOy+WZy4uLie+vp694qKCh4R0d69e4U3Gufl5dXf0dFxXa0UFxfXYzab3cvLy3lERPn5+f6JiYmd3/8Jb23GjBntf/3rX0fabDYWEZFOp+N1dHSwZ82a1bFr166A9vZ2NhHRhQsX3MxmM3fatGldn332mW9XVxertbWVXVxcPOT3hX/KsLMLAAAAAADDGpvNpqKioprFixeLt2zZEsTj8RzBwcG2bdu21XV1dXGio6NlLBbLsX79+vqxY8cyOp3uhnE8PT0d27Ztu6hSqcKFQiGTkJDQZTAYPAaOy8jIaFm0aFHIjh07AtVqdc2183fs2FGbmpoa5rygavny5VcGzr+WyWTiTpw4Udrd3c1hsViOt956K9BgMJQLhcJB766+8MILTbW1tTy5XB7tcDhYQqGw7/PPP6954oknOioqKvgTJ06U/Cc/++7duy9MmTLFMmfOnJaYmBiZSCSyKZXKrsGu5UpYt9uKBwAAAACA+5tWq61VKBRNtx8JcHdptdoAhUIRMpS5OMYMAAAAAAAALgfHmAEAAAAAAH5Ely5d4kydOjVqYPuxY8cqg4KC+u9FTq4IxS4AAAAAAMCPKCgoqH/gzwvB3YdjzAAAAAAAAOByUOwCAAAAAACAy0GxCwAAAAAAAC4HxS4AAAAAAAC4HBS7AAAAAAAw7JlMJq5KpRonFotjwsLCZElJSeE6nY73fWJqNBpBcnJyOBHR7t27fdasWRNERLRr1y7f0tJSvnPc0qVLxxQWFgqGssbZs2f5cXFxEnd39wnZ2dmBtxvPYrHiFy5cGOz8nJ2dHbhs2bIxQ1n7fofbmAEAAAAAYFiz2+2UkpISnp6e3qzRaL4lIjp9+rRHQ0ODW2xsrO1urJGRkdFORO1ERIWFhb4Mw7THx8f3EBFt2bKlYahxR40axWzdutWkVqv9BjPe3d3d8fnnn/s1NjZeGj16NHOn6/X19ZGbm9udJ+qCUOwCAAAAAMCgvfzVy+Lq1mrPuxkz3C/c8qeH/lR3s36NRiPgcrmOlStXXnG2TZ482Wq32ykrKyv4yJEjPiwWy7FixYrGhQsXtmo0GsGGDRvGCIXCvsrKSg+5XG4pLCy8wGazSa1We69YsUIsFAoZuVxuccbLy8vzLykpGfH00083Hzp0yPfrr78WbNy4cfS+fftqsrOzR6tUqvYFCxa07t+/X7B69Wpxf38/KRQKS35+/kUPDw+HSCSSp6WlNR88eNCHYRhWQUHBt+PHj+8RiUSMSCRi9u/f7zuY74LD4Tjmz59/5bXXXgvctm2b+dq+qqoq91//+tchzc3NXH9/fyY/P782IiKid+7cuSF+fn5MWVmZZ2xsrEUgENhra2vdv/vuO7fa2lr+a6+9VvePf/zD68iRI96BgYF9hw4dqubxeI6h/K1+SnCMGQAAAAAAhjWdTuehUCgsA9vz8/N9y8rKPAwGQ8Xhw4ersrOzgy9evOhGRGQwGDy2b99eV11dXWEymXjFxcVeFouFtWTJkpCioqLqf/3rX5WXL1++bgt0xowZ3dOnT2/785//XG80GvUymezqzrHFYmFlZWWFFhQU1FRVVekZhqHc3NyRzv6AgABGr9cbnnnmmSs5OTm3PbJ8MytWrLj8ySefCJubmznXtv/2t78dm56e3lxVVaWfN29e86JFi8TOvpqaGv5XX31VtXPnznoioosXL/KOHDlSrVarq3/729+GTps2raOqqkrP5/PtH330kc9Qc/spwc4uAAAAAAAM2q12YH9sJ0+eFKSlpbVwuVwSi8VMQkJC16lTpzx9fHzscrm8OywsrI+ISCaTWWpqatwFAkF/cHCwTS6X24iIMjIymt95552Rt17l/2i1Wn5wcLDNeXQ6MzOzefv27aOI6DIRUXp6eisRkVKptBQVFQ3q2PKNCIVCe2pqanNOTs4oDw8Pu7P97NmzI7744osaIqJFixa1rF+//uq7vU888UQrl/t/5d306dPbeTyeQ6lUWvv7+1m//OUvO/7zXVgvXLjgPtTcfkqwswsAAAAAAMOaXC63arXa645OOxw3P4l77TFdDodDDMOwiIhYLNaQ87jVekREfD7fQUTE5XIdzvWG6sUXX/xuz549Ad3d3YOq2by8vOzXfnY+P4fDIS6X62Cz/x2GzWbT983tpwLFLgAAAAAADGuzZ8/u7O3tZW3atCnA2Xb8+HFPPz8/Rq1WCxmGoYaGBu6ZM2e8EhMTu28WJy4urqe+vt69oqKCR0S0d+9e4Y3GeXl59Xd0dFxXK8XFxfWYzWb38vJyHhFRfn6+f2JiYuf3f8LrBQYG9s+ePbt1z549V595/Pjx3e+8844fEdFbb70lfPDBB7t+iLVdBYpdAAAAAAAY1thsNhUVFdUcPnzYWywWx4SHh8vWrVs3JjMzs0Umk1mjo6NlU6dOjVy/fn392LFjb3qDsaenp2Pbtm0XVSpVeHx8fJRYLO690biMjIyWvLy8oOjoaKmzMHbO37FjR21qampYZGSklM1m0/Lly6/cKIaTyWTiBgYGxr799tuBmzdvHh0YGBjb0tIyqDps7dq1l9ra2q6eTf7rX/9q2rVrV0BkZKT0ww8/9H/zzTeHzZHy4Yh1u614AAAAAAC4v2m12lqFQtF0r/OA+49Wqw1QKBQhQ5mLnV0AAAAAAABwObiNGQAAAAAA4Ed06dIlztSpU6MGth87dqwyKCio/17k5IpQ7AIAAAAAAPyIgoKC+o1Go/5e5+HqcIwZAAAAAAAAXA6KXQAAAAAAAHA5KHYBAAAAAADA5aDYBQAAAAAAAJeDYhcAAAAAAIY9k8nEValU48RicUxYWJgsKSkpXKfT8b5PTI1GI0hOTg4nItq9e7fPmjVrgoiIdu3a5VtaWsp3jlu6dOmYwsJCwVDWOHv2LD8uLk7i7u4+ITs7O/B24zkcTrxEIpFGRETIZs2aNa6zs3NINVtDQwOXy+VOyM3NDRjKfFeAYhcAAAAAAIY1u91OKSkp4Q8//HBnXV1deU1NTcXrr79ubmhocLtba2RkZLS/9tprl4iICgsLfXU6nYezb8uWLQ2PP/5451Dijho1itm6daspKyvru8GM5/F4dqPRqD9//nyFm5ubY9OmTSOHsm5+fr6fQqHo/vjjj/2HMt8V4KeHAAAAAABg0BrWrBXbzp/3vJsxeRERljGvvVp3s36NRiPgcrmOlStXXnG2TZ482Wq32ykrKyv4yJEjPiwWy7FixYrGhQsXtmo0GsGGDRvGCIXCvsrKSg+5XG4pLCy8wGazSa1We69YsUIsFAoZuVxuccbLy8vzLykpGfH00083Hzp0yPfrr78WbNy4cfS+fftqsrOzR6tUqvYFCxa07t+/X7B69Wpxf38/KRQKS35+/kUPDw+HSCSSp6WlNR88eNCHYRhWQUHBt+PHj+8RiUSMSCRi9u/f73un38uUKVO6nEX3K6+8Erh79+4AIqKnn376SnZ29uWOjg52SkrKuMbGRne73c5auXJlw8KFC1uJiD7++GPhG2+8UffrX/963IULF9xCQ0P77nT9nzrs7AIAAAAAwLCm0+k8FAqFZWB7fn6+b1lZmYfBYKg4fPhwVXZ2dvDFixfdiIgMBoPH9u3b66qrqytMJhOvuLjYy2KxsJYsWRJSVFRU/a9//avy8uXL1+0Mz5gxo3v69Oltf/7zn+uNRqNeJpPZnH0Wi4WVlZUVWlBQUFNVVaVnGIZyc3Ov7rwGBAQwer3e8Mwzz1zJycm57ZHlW+nr66ODBw96y+Vy68mTJz337NnjX1paaigpKTHk5+eP/Oqrrzw++eQT76CgoL7Kykr9+fPnK5544okOIqLq6mq3pqYmt+TkZEtKSkrr+++/L/w+ufxUYWcXAAAAAAAG7VY7sD+2kydPCtLS0lq4XC6JxWImISGh69SpU54+Pj52uVzeHRYW1kdEJJPJLDU1Ne4CgaA/ODjYJpfLbUREGRkZze+8886gjwlrtVp+cHCwLTY21kZElJmZ2bx9+/ZRRHSZiCg9Pb2ViEipVFqKior8hvJMNpuNLZFIpERECQkJnX/4wx+acnNzRz766KNt3t7ediKixx57rPXo0aOClJSU9rVr14oXLVok+n//7/+1P/LII11ERO+//74wJSWllYjo6aefbnn22WdDXnnllUEdo3YlKHYBAAAAAGBYk8vl1sLCwuuKR4fDcdM5PB7vaieHwyGGYVhERCwWa8h53Go9IiI+n+8gIuJyuQ7nenfK+c7uYNaNjY21ffPNN/p9+/b5rF27VnTo0KGON954o3Hfvn3CpqYmt08++URIRHT58mW3srIynrPIv1/gGDMAAAAAAAxrs2fP7uzt7WVt2rTp6s3Cx48f9/Tz82PUarWQYRhqaGjgnjlzxisxMbH7ZnHi4uJ66uvr3SsqKnhERHv37r3h8V4vL6/+jo6O62qluLi4HrPZ7F5eXs4jIsrPz/dPTEwc0sVVd2LatGldn3/+uW9nZye7o6OD/fnnn/slJyd31tbWugkEAvvixYtbli5d+t25c+c8tVotz2KxcC5fvqwzm81lZrO5bMmSJZfy8/Pvu6PM2NkFAAAAAIBhjc1mU1FRUc3ixYvFW7ZsCeLxeI7g4GDbtm3b6rq6ujjR0dEyFovlWL9+ff3YsWMZnU53wzienp6Obdu2XVSpVOFCoZBJSEjoMhgMHgPHZWRktCxatChkx44dgWq1uuba+Tt27KhNTU0Nc15QtXz58isD51/LZDJxJ06cKO3u7uawWCzHW2+9FWgwGMqFQqF9sM8/ZcoUS3p6evOECROiif59QdVDDz1k3bdvn/eLL74YzGazicvlOt58882L77//vv+jjz7aeu38X/3qV63p6enjcnNzGwe7pitg3W4rHgAAAAAA7m9arbZWoVA03es84P6j1WoDFApFyFDm4hgzAAAAAAAAuBwcYwYAAAAAAPgRXbp0iTN16tSoge3Hjh2rDAoK6r8XObkiFLsAAAAAAAA/oqCgoP6BNy7D3YdjzAAAAAAAAOByUOwCAAAAAACAy0GxCwAAAAAAAC4HxS4AAAAAAAC4HBS7AAAAAAAw7JlMJq5KpRonFotjwsLCZElJSeE6nY73fWJqNBpBcnJyOBHR7t27fdasWRNERLRr1y7f0tJSvnPc0qVLxxQWFgqGssbZs2f5cXFxEnd39wnZ2dmBtxvP4XDiJRKJNCIiQjZr1qxxnZ2dd1yzKZXKqJiYmGjn5xMnTngqlcrrbn92dbiNGQAAAAAABu1wvkHcYu7yvJsxhSIvy8/nR9fdrN9ut1NKSkp4enp6s0aj+ZaI6PTp0x4NDQ1usbGxtruRQ0ZGRjsRtRMRFRYW+jIM0x4fH99DRLRly5aGocYdNWoUs3XrVpNarfYbzHgej2d33tSckpISumnTppGvvPLKd3e6bnNzM/ejjz7yTktL67jTua4CO7sAAAAAADCsaTQaAZfLdaxcufKKs23y5MnWmTNndmVlZQVHRETIIiMjpTt37vRzjlcqlVGPPPLIuNDQUFlKSkqo3W4nIiK1Wu0dGhoqi4+Pj1Kr1b7OeHl5ef7z588fW1xcPOLQoUO+L730UrBEIpFWVFTw5s6dG/Lee+/5ERHt379fEB0dLY2MjJSmpqaGWK1WFhGRSCSSv/DCC2OkUml0ZGSk9OzZs/z/tDNJSUkWNzc3x50+95QpU7qqq6t5RESvvPJKYEREhCwiIkK2YcOGUUREHR0d7KlTp4ZHRUVJIyIiZM7nA5s9SQAAIABJREFUJyJasmTJdzk5OWOG8HW7DOzsAgAAAADAoN1qB/aHotPpPBQKhWVge35+vm9ZWZmHwWCoaGxs5CqVyuiZM2d2EREZDAaPc+fOfRsSEtIXHx8vKS4u9kpMTOxesmRJSHFxcaVMJrOpVKpxA2POmDGje/r06W0qlap9wYIFrdf2WSwWVlZWVuiXX35ZGRsba5szZ05Ibm7uyOzs7MtERAEBAYxerzfk5OSMzMnJCSwoKLg41Gfu6+ujgwcPes+cObPj5MmTnnv27PEvLS01OBwOio+Pj/75z3/eef78eV5QUFDfsWPHqomImpubOc75iYmJXX//+999//73vwt8fHz6h5rHTxl2dgEAAAAA4Cfp5MmTgrS0tBYul0tisZhJSEjoOnXqlCcRkVwu7w4LC+vjcDgkk8ksNTU17ufOneMHBwfb5HK5jc1mU0ZGRvOdrKfVavnBwcE259HpzMzM5lOnTl19lzc9Pb2ViEipVFrq6uqG9D6xzWZjSyQSqVwulwYHB/f+4Q9/aDp27JjXo48+2ubt7W338fGxP/bYY61Hjx4VTJgwwXry5EnvRYsWiQ4cOODl7+//X0XtmjVrGl977bXRQ8nDFaDYBQAAAACAYU0ul1u1Wu117wk7HDc/Gczj8a52cjgcYhiGRUTEYrGGnMet1iMi4vP5DiIiLpfrcK53p5zv7BqNRv37779fx+fzHTdbNzY21vbNN9/o5XK5de3ataLly5f/V2GbkpLSabPZ2KdOnRoxlFx+6lDsAgAAAADAsDZ79uzO3t5e1qZNmwKcbcePH/f08/Nj1Gq1kGEYamho4J45c8YrMTGx+2Zx4uLieurr690rKip4RER79+4V3micl5dXf0dHx3W1UlxcXI/ZbHYvLy/nERHl5+f7JyYmdn7/J7y1adOmdX3++ee+nZ2d7I6ODvbnn3/ul5yc3FlbW+smEAjsixcvblm6dOl3586du+5/CKxatapx27ZtQT90jsMR3tkFAAAAAIBhjc1mU1FRUc3ixYvFW7ZsCeLxeI7g4GDbtm3b6rq6ujjR0dEyFovlWL9+ff3YsWMZnU53wzienp6Obdu2XVSpVOFCoZBJSEjoMhgMHgPHZWRktCxatChkx44dgWq1uuba+Tt27KhNTU0N6+/vJ4VCYVm+fPmVgfOvZTKZuBMnTpR2d3dzWCyW46233go0GAzlQqHQPtjnnzJliiU9Pb15woQJ0URETz/99JWHHnrIum/fPu8XX3wxmM1mE5fLdbz55pvXvSM8b9689g0bNjCDXcuVsG63FQ8AAAAAAPc3rVZbq1Aomu51HnD/0Wq1AQqFImQoc3GMGQAAAAAAAFwOjjEDAAAAAAD8iC5dusSZOnVq1MD2Y8eOVQYFBd2XPxP0Q0CxCwAAAAAA8CMKCgrqNxqN+nudh6vDMWYAAAAAAABwOSh2AQAAAAAAwOWg2AUAAAAAAACXg2IXAAAAAAAAXA6KXQAAAAAAGPZMJhNXpVKNE4vFMWFhYbKkpKRwnU7H+z4xNRqNIDk5OZyIaPfu3T5r1qwJIiLatWuXb2lpKd85bunSpWMKCwsFQ1nj7Nmz/Li4OIm7u/uE7OzswNuN53A48RKJRBoRESGbNm1aeFNTE4eIqLKy0p3P50+QSCTSsLAw2Zw5c0JsNhvr2rkLFiwQjxo1Kra/Hxc6E+E2ZgAAAAAAuAMH/7pF3FR30fNuxgwQP2D5xaKldTfrt9vtlJKSEp6ent6s0Wi+JSI6ffq0R0NDg1tsbKztbuSQkZHRTkTtRESFhYW+DMO0x8fH9xARbdmypWGocUeNGsVs3brVpFar/QYznsfj2Z03NT/xxBMhubm5Izdu3HiJiEgsFtuMRqOeYRiaMmVK5Lvvvuu3aNGiFiKi/v5+OnDggO/o0aN7v/jiC4FKpeocas6uAju7AAAAAAAwrGk0GgGXy3WsXLnyirNt8uTJ1pkzZ3ZlZWUFR0REyCIjI6U7d+70c45XKpVRjzzyyLjQ0FBZSkpKqN1uJyIitVrtHRoaKouPj49Sq9W+znh5eXn+8+fPH1tcXDzi0KFDvi+99FKwRCKRVlRU8ObOnRvy3nvv+RER7d+/XxAdHS2NjIyUpqamhlitVhYRkUgkkr/wwgtjpFJpdGRkpPTs2bP8/7QzSUlJFjc3N8edPvekSZO6zWaz+8B2LpdLEyZM6DabzW7XfkeRkZHW3/zmN1f27NkjdLZ3dHSwU1NTQ2JiYqKjo6OlH3zwge/AeK4KO7sAAAAAADBot9qB/aHodDoPhUJhGdien5/vW1ZW5mEwGCoaGxu5SqUyeubMmV1ERAaDwePcuXPfhoSE9MXHx0uKi4u9EhMTu5csWRJSXFxcKZPJbCqVatzAmDNmzOiePn16m0qlal+wYEHrtX0Wi4WVlZUV+uWXX1bGxsba5syZE5KbmzsyOzv7MhFRQEAAo9frDTk5OSNzcnICCwoKLg71mRmGoaNHjwqeffbZpoF9FouFVVpaOiIvL+/q32LPnj3CtLS0lieffLLtT3/6k8hms7F4PJ5jzZo1o5OTkzs+/vjj2qamJs6DDz4YnZKS0uHt7W0fam4/FdjZBQAAAACAn6STJ08K0tLSWrhcLonFYiYhIaHr1KlTnkREcrm8OywsrI/D4ZBMJrPU1NS4nzt3jh8cHGyTy+U2NptNGRkZzXeynlar5QcHB9ucR6czMzObT506dfVd3vT09FYiIqVSaamrqxvS+8Q2m40tkUikfn5+cW1tbdzHH3+8w9lXV1fHk0gkUn9//ziRSNSbkJBgJSLq6elhHT161Cc9Pb1NKBTa4+Liuj/99FNvIqJjx455b968ebREIpFOmTIlymazsaqrq6/bLXZFKHYBAAAAAGBYk8vlVq1We917wg7HzU8G83i8q50cDocYhmEREbFYrJvOuZ1brUdExOfzHUREXC7X4VzvTjnf2a2trS3r7e1l5eTkjHL2Od/ZNRqN5d98843X7t27fYiI9u3b593Z2cmJiYmRiUQieUlJideHH34odOasVqur/zNP39jYWDZhwoSeoeT2U4NiFwAAAAAAhrXZs2d39vb2sjZt2hTgbDt+/Linn58fo1arhQzDUENDA/fMmTNeiYmJ3TeLExcX11NfX+9eUVHBIyLau3ev8EbjvLy8+js6Oq6rleLi4nrMZrN7eXk5j4goPz/fPzEx8Qe5CMrf378/Ly/PtH379sCBty4/8MADfRs2bKjPzc0d7XyOLVu2XDSbzWVms7mstra27OTJk96dnZ3s5OTkjk2bNgU631n+6quvPH6IfIcjFLsAAAAAADCssdlsKioqqjl8+LC3WCyOCQ8Pl61bt25MZmZmi0wms0ZHR8umTp0auX79+vqxY8cyN4vj6enp2LZt20WVShUeHx8fJRaLe280LiMjoyUvLy8oOjpa6iyMnfN37NhRm5qaGhYZGSlls9m0fPnyKzeK4WQymbiBgYGxb7/9duDmzZtHBwYGxra0tAyqDnvooYes0dHR1nfeeee6m5yfeuqpNqvVyv7ss8+8Tpw44ZOamtrm7PP29rY/+OCDXXv37vXJyclpYBiG5fw5o5deekk0mLVdAet2W/EAAAAAAHB/02q1tQqF4rqLkgB+aFqtNkChUIQMZS52dgEAAAAAAMDl4KeHAAAAAAAAfkSXLl3iTJ06NWpg+7FjxyqDgoL670VOrgjFLgAAAAAAwI8oKCio32g06u91Hq4Ox5gBAAAAAADA5aDYBQAAAAAAAJeDYhcAAAAAAABcDopdAAAAAAAAcDkodgEAAAAAYNgzmUxclUo1TiwWx4SFhcmSkpLCdTod7/vE1Gg0guTk5HAiot27d/usWbMmiIho165dvqWlpXznuKVLl44pLCwUDGWNs2fP8uPi4iTu7u4TsrOzA283nsPhxEskEmlERIRs2rRp4U1NTRwiosrKSnc+nz9BIpFIw8LCZHPmzAmx2Wysa+cuWLBAPGrUqNj+flzoTITbmAEAAAAA4A60qKvEfZe6Pe9mTLegERbhLyPrbtZvt9spJSUlPD09vVmj0XxLRHT69GmPhoYGt9jYWNvdyCEjI6OdiNqJiAoLC30ZhmmPj4/vISLasmVLw1Djjho1itm6datJrVb7DWY8j8ezO29qfuKJJ0Jyc3NHbty48RIRkVgsthmNRj3DMDRlypTId99912/RokUtRET9/f104MAB39GjR/d+8cUXApVK1TnUnF0FdnYBAAAAAGBY02g0Ai6X61i5cuUVZ9vkyZOtM2fO7MrKygqOiIiQRUZGSnfu3OnnHK9UKqMeeeSRcaGhobKUlJRQu91ORERqtdo7NDRUFh8fH6VWq32d8fLy8vznz58/tri4eMShQ4d8X3rppWCJRCKtqKjgzZ07N+S9997zIyLav3+/IDo6WhoZGSlNTU0NsVqtLCIikUgkf+GFF8ZIpdLoyMhI6dmzZ/n/aWeSkpIsbm5ujjt97kmTJnWbzWb3ge1cLpcmTJjQbTab3a79jiIjI62/+c1vruzZs0fobF+2bNmY1NTUEKVSGRUcHCz/85//POpO8/ipws4uAAAAAAAM2q12YH8oOp3OQ6FQWAa25+fn+5aVlXkYDIaKxsZGrlKpjJ45c2YXEZHBYPA4d+7ctyEhIX3x8fGS4uJir8TExO4lS5aEFBcXV8pkMptKpRo3MOaMGTO6p0+f3qZSqdoXLFjQem2fxWJhZWVlhX755ZeVsbGxtjlz5oTk5uaOzM7OvkxEFBAQwOj1ekNOTs7InJycwIKCgotDfWaGYejo0aOCZ599tmlgn8ViYZWWlo7Iy8u7+rfYs2ePMC0treXJJ59s+9Of/iSy2WwsHo/nICKqrq7mnz59urKtrY0THR0ds2LFiivOPleGnV0AAAAAAPhJOnnypCAtLa2Fy+WSWCxmEhISuk6dOuVJRCSXy7vDwsL6OBwOyWQyS01Njfu5c+f4wcHBNrlcbmOz2ZSRkdF8J+tptVp+cHCwzXl0OjMzs/nUqVNX3+VNT09vJSJSKpWWurq6Ib1PbLPZ2BKJROrn5xfX1tbGffzxxzucfXV1dTyJRCL19/ePE4lEvQkJCVYiop6eHtbRo0d90tPT24RCoT0uLq77008/9XbOmzlzZpuHh4dj9OjRjFAo7Kuvr78vNj1R7AIAAAAAwLAml8utWq32uveEHY6bb05eu3PJ4XCIYRgWERGLxbrpnNu51XpERHw+30FExOVyHc717pTznd3a2tqy3t5eVk5OztVjx853do1GY/k333zjtXv3bh8ion379nl3dnZyYmJiZCKRSF5SUuL14YcfCq+JecPvwtWh2AUAAAAAgGFt9uzZnb29vaxNmzYFONuOHz/u6efnx6jVaiHDMNTQ0MA9c+aMV2JiYvfN4sTFxfXU19e7V1RU8IiI9u7dK7zROC8vr/6Ojo7raqW4uLges9nsXl5eziMiys/P909MTPxBLoLy9/fvz8vLM23fvj1w4K3LDzzwQN+GDRvqc3NzRzufY8uWLRfNZnOZ2Wwuq62tLTt58qR3Z2fnfV3v3dcPDwAAAAAAwx+bzaaioqKaw4cPe4vF4pjw8HDZunXrxmRmZrbIZDJrdHS0bOrUqZHr16+vHzt2LHOzOJ6eno5t27ZdVKlU4fHx8VFisbj3RuMyMjJa8vLygqKjo6XOwtg5f8eOHbWpqalhkZGRUjabTcuXL79yoxhOJpOJGxgYGPv2228Hbt68eXRgYGBsS0vLoOqwhx56yBodHW195513rrvJ+amnnmqzWq3szz77zOvEiRM+qampbc4+b29v+4MPPti1d+9en8Gs46pYt9uKBwAAAACA+5tWq61VKBTXXZQE8EPTarUBCoUiZChzsbMLAAAAAAAALue+uIULAAAAAABguLh06RJn6tSpUQPbjx07VhkUFNR/L3JyRSh2AQAAAAAAfkRBQUH9RqNRf6/zcHU4xgwAAAAAAAAuB8UuAAAAAAAAuBwUuwAAAAAAAOByUOwCAAAAAACAy0GxCwAAAAAAw57JZOKqVKpxYrE4JiwsTJaUlBSu0+l43yemRqMRJCcnhxMR7d6922fNmjVBRES7du3yLS0t5TvHLV26dExhYaFgKGucPXuWHxcXJ3F3d5+QnZ0deLvxHA4nXiKRSCMiImTTpk0Lb2pq4hARVVZWuvP5/AkSiUQaFhYmmzNnTojNZmM5n4PFYsXv2bPHxxknOTk5XKPRDClnV4HbmAEAAAAAYNAKCwvFly9f9rybMUeNGmV5/PHH627Wb7fbKSUlJTw9Pb1Zo9F8S0R0+vRpj4aGBrfY2Fjb3cghIyOjnYjaiYgKCwt9GYZpj4+P7yEi2rJlS8NQ444aNYrZunWrSa1W+w1mPI/Hsztvan7iiSdCcnNzR27cuPESEZFYLLYZjUY9wzA0ZcqUyHfffddv0aJFLUREgYGBfRs3bhydnp7ePtRcXQ12dgEAAAAAYFjTaDQCLpfrWLly5RVn2+TJk60zZ87sysrKCo6IiJBFRkZKd+7c6eccr1Qqox555JFxoaGhspSUlFC73U5ERGq12js0NFQWHx8fpVarfZ3x8vLy/OfPnz+2uLh4xKFDh3xfeumlYIlEIq2oqODNnTs35L333vMjItq/f78gOjpaGhkZKU1NTQ2xWq0sIiKRSCR/4YUXxkil0ujIyEjp2bNn+f9pZ5KSkixubm6OO33uSZMmdZvNZveB7VwulyZMmNBtNpvdnG3R0dEWgUDQ/+mnn3oPHH/y5EnPiRMnRslksugpU6ZEXLx40W3gGFeEnV0AAAAAABi0W+3A/lB0Op2HQqGwDGzPz8/3LSsr8zAYDBWNjY1cpVIZPXPmzC4iIoPB4HHu3LlvQ0JC+uLj4yXFxcVeiYmJ3UuWLAkpLi6ulMlkNpVKNW5gzBkzZnRPnz69TaVStS9YsKD12j6LxcLKysoK/fLLLytjY2Ntc+bMCcnNzR2ZnZ19mYgoICCA0ev1hpycnJE5OTmBBQUFF4f6zAzD0NGjRwXPPvts08A+i8XCKi0tHZGXl/dff4uXXnqp8eWXXxbNmTOnw9lms9lYv//978d+9tln1WPGjGF27tzpt3z5ctHHH39cO9TcfiqwswsAAAAAAD9JJ0+eFKSlpbVwuVwSi8VMQkJC16lTpzyJiORyeXdYWFgfh8MhmUxmqampcT937hw/ODjYJpfLbWw2mzIyMprvZD2tVssPDg62OY9OZ2ZmNp86derqe7Hp6emtRERKpdJSV1c3pPeJbTYbWyKRSP38/OLa2tq4jz/++NXCta6ujieRSKT+/v5xIpGoNyEhwXrt3EceeaSLiOjAgQNezjadTsc7f/68x7Rp0yIlEok0Nzd3dENDw32xs4tiFwAAAAAAhjW5XG7VarXXvSfscNz8ZDCPx7vayeFwiGEYFhERi8Uach63Wo+IiM/nO4iIuFyuw7nenXK+s1tbW1vW29vLysnJGeXsc76zazQay7/55huv3bt3+wyc/+KLLza++uqro6/JmRUeHm79zzx9VVWV/quvvjo/lNx+alDsAgAAAADAsDZ79uzO3t5e1qZNmwKcbcePH/f08/Nj1Gq1kGEYamho4J45c8YrMTGx+2Zx4uLieurr690rKip4RER79+4V3micl5dXf0dHx3W1UlxcXI/ZbHYvLy/nERHl5+f7JyYmdn7/J7yev79/f15enmn79u2BzluXnR544IG+DRs21Ofm5o4eOO+JJ57oaG9v5xgMBk8iotjY2J6WlhbuoUOHRhD9+1hzSUkJf+A8V4RiFwAAAAAAhjU2m01FRUU1hw8f9haLxTHh4eGydevWjcnMzGyRyWTW6Oho2dSpUyPXr19fP3bsWOZmcTw9PR3btm27qFKpwuPj46PEYnHvjcZlZGS05OXlBUVHR0udhbFz/o4dO2pTU1PDIiMjpWw2m5YvX37lRjGcTCYTNzAwMPbtt98O3Lx58+jAwMDYlpaWQdVhDz30kDU6Otr6zjvvXHeT81NPPdVmtVrZ1x5Zdlq1alXjd99950b0793mvXv31qxevTo4KipKKpPJpMePH79ujiti3W4rHgAAAAAA7m9arbZWoVBcd1ESwA9Nq9UGKBSKkKHMxc4uAAAAAAAAuBz89BAAAAAAAMCP6NKlS5ypU6dGDWw/duxYZVBQUP+9yMkVodgFAAAAAAD4EQUFBfUbjUb9vc7D1eEYMwAAAAAAALgcFLsAAAAAAADgclDsAgAAAAAAgMtBsQsAAAAAAMOeyWTiqlSqcWKxOCYsLEyWlJQUrtPpeLefeXMajUaQnJwcTkS0e/dunzVr1gQREe3atcu3tLSU7xy3dOnSMYWFhYKhrHH27Fl+XFycxN3dfUJ2dnbg7cZzOJx4iUQijYiIkE2bNi28qamJQ0RUWVnpzufzJ0gkEmlYWJhszpw5ITabjeV8DhaLFb9nzx4fZ5zk5ORwjUYzpJxdBYpdAAAAAAAY1ux2O6WkpIQ//PDDnXV1deU1NTUVr7/+urmhocHtbq2RkZHR/tprr10iIiosLPTV6XQezr4tW7Y0PP74451DiTtq1Chm69atpqysrO8GM57H49mNRqP+/PnzFb6+vkxubu5IZ59YLLYZjUZ9ZWVlRWNjo/u7777r5+wLDAzs27hx4+ih5OiqcBszAAAAAAAMmt6wStzdVeV5N2OO8Iq0SKM31t2sX6PRCLhcrmPlypVXnG2TJ0+22u12ysrKCj5y5IgPi8VyrFixonHhwoWtGo1GsGHDhjFCobCvsrLSQy6XWwoLCy+w2WxSq9XeK1asEAuFQkYul1uc8fLy8vxLSkpGPP30082HDh3y/frrrwUbN24cvW/fvprs7OzRKpWqfcGCBa379+8XrF69Wtzf308KhcKSn59/0cPDwyESieRpaWnNBw8e9GEYhlVQUPDt+PHje0QiESMSiZj9+/f73un3MmnSpO5ri24nLpdLEyZM6DabzVeL/ejoaEtfX9//Z+/e45o+7/7xv3OQACUcAghKoigYcyAEygZ7aKmmP3FtF5nawTZSO+y+jsmNm+2t1tPodM7ig3GPwpjsrg97N4qTLrYR04PFYz1VC60JhwBC5QwekHMw8CH5/bE73gwFkTqL+Ho+Hv7BdXhf1yf56+37+lxhffjhh+7Lli3rGjp+pL096H4eN6jsAgAAAADAhGYymVyUSqVleLtWq/UsKSlxMZvNZcePH69KTU0V1tXVTSEiMpvNLjk5OQ3V1dVl9fX1vMLCQjeLxcJKSUkJLCgoqP7yyy8rr1+/fldlOCYmpnfRokUdO3bsaKyoqCiXy+VWR5/FYmElJSXNys/Pr6mqqipnGIaGVl59fHyY8vJy86uvvnojLS3tvkeWR8MwDJ08eZK/dOnSjuF9FouFVVxc/NSSJUv+JandunVry86dO+9Z3X2Ye3tcoLILAAAAAABjNloF9lE7c+YMPz4+/haXyyWRSMRERUX1nD171tXDw8OmUCh6g4KCBoiI5HK5paamxonP5w8KhUKrQqGwEhFpNJq2PXv2+I6+yv8xGo3OQqHQGhoaaiUiSkxMbMvJyZlKRNeJiBISEtqJiCIjIy0FBQVeo4QakdVqZUskEllTU5NTSEiIZenSpXcS2oaGBp5EIpHV1dXxXnjhhfaoqKi+oXOff/75nt/97nf06aefug2P+zD29rhBZRcAAAAAACY0hULRZzQa7zo6bbfbR5zD4/HudHI4HGIYhkVExGKxxr2P0dYjInJ2drYTEXG5XLtjvQfleGe3tra2pL+/n5WWljbV0ed4Z7eioqL0q6++csvLy/MYPn/Tpk0tf/zjH++q7j6MvT1ukOwCAAAAAMCEtmTJku7+/n5WRkaGj6Pt9OnTrl5eXoxOpxMwDEPNzc3cS5cuuUVHR/eOFCcsLOx2Y2OjU1lZGY+I6ODBg4J7jXNzcxvs6uq6K1cKCwu73dTU5FRaWsojItJqtd7R0dHjurjqfry9vQezsrLqc3Jy/By3LjvMnDlzYPv27Y3p6el3JbXLly/v6uzs5JjN5of6XvXjCMkuAAAAAABMaGw2mwoKCmqOHz/uLhKJQoKDg+Vvvvnm9MTExFtyubxPKpXKFy5cKN62bVvjjBkzmJHiuLq62rOzs+vUanVwRETEXJFI1H+vcRqN5lZWVpa/VCqVORJjx/zc3NzauLi4ILFYLGOz2bRu3bob94rhUF9fz/Xz8wv97//+b78///nP0/z8/EJv3bo1pjxs/vz5fVKptG/Pnj13HTt++eWXO/r6+tj3OrL8xhtvtFy7du2h3VT9uGLdrxQPAAAAAABPNqPRWKtUKm9+1/uAJ4/RaPRRKpWB45mLyi4AAAAAAABMOriNGQAAAAAA4BFqbW3lLFy4cO7w9lOnTlX6+/sPfhd7moyQ7AIAAAAAADxC/v7+gxUVFeXf9T4mOxxjBgAAAAAAgEkHyS4AAAAAAABMOkh2AQAAAAAAYNJBsgsAAAAAAACTDpJdAAAAAACY8Orr67lqtXq2SCQKCQoKki9YsCDYZDLxvk1Mg8HAV6lUwUREeXl5Hps3b/YnItq3b59ncXGxs2Pc2rVrp+v1ev541vj666+dw8LCJE5OTk+npqb6fZv9woPBbcwAAAAAADCh2Ww2io2NDU5ISGgzGAzfEBGdP3/epbm5eUpoaKj1Yayh0Wg6iaiTiEiv13syDNMZERFxm4goMzOzebxxp06dyrz99tv1Op3O62HsE8YOyS4AAAAAAIzZWnO9qKL3tuvDjCl5ytmSKZ3RMFK/wWDgc7lc+4YNG2442ubNm9dns9koKSlJeOLECQ8Wi2Vfv359y6pVq9oNBgN/+/bt0wUCwUBlZaWLQqGw6PX6q2w2m3Q6nfv69etFAoGAUSgUFke8rKws76KioqdWrFjRduzYMc8vvviCv2vXrmmHDh2qSU1NnabKOrh5AAAgAElEQVRWqztXrlzZfvjwYf7GjRtFg4ODpFQqLVqtts7FxcUeEBCgiI+Pbzt69KgHwzCs/Pz8b8LDw28HBAQwAQEBzOHDhz3v9zlUVlY6vfDCC3MiIyN7ioqK3Pz8/PqPHj1a7ebmZs/IyPB59913fQcGBliBgYFWnU53lc/n21566aVAPp8/aDQan7px48aUP/zhD40rV65s//bfyuMPx5gBAAAAAGBCM5lMLkql0jK8XavVepaUlLiYzeay48ePV6Wmpgrr6uqmEBGZzWaXnJychurq6rL6+npeYWGhm8ViYaWkpAQWFBRUf/nll5XXr1+fMjxmTExM76JFizp27NjRWFFRUS6Xy+9Uji0WCyspKWlWfn5+TVVVVTnDMJSenu7r6Pfx8WHKy8vNr7766o20tLRxHVmur693/s1vfnO9urq6zMPDY1Cr1XoREWk0mvbS0lJzZWVl+dy5c/uysrJ8HHOuXbs2paioqOLw4cNX3nzzzYDxrDsZobILAAAAAABjNloF9lE7c+YMPz4+/haXyyWRSMRERUX1nD171tXDw8OmUCh6g4KCBoiI5HK5paamxonP5w8KhUKrQqGwEhFpNJq2PXv2+I6+yv8xGo3OQqHQ6jg6nZiY2JaTkzOViK4TESUkJLQTEUVGRloKCgrGdWw5ICDAOm/evD4iovDwcEttbS2PiKi4uNglNTU1oLu7m9Pb28tZsGBBp2NObGxsB4fDoYiIiNttbW13JfBPKlR2AQAAAABgQlMoFH1Go/Guo9N2u33EOTwe704nh8MhhmFYREQsFmvc+xhtPSIiZ2dnOxERl8u1O9Z7UE5OTkP3fSfOr371q1l/+ctf6quqqsrfeOONZqvVeieXc6w7lj0+SZDsAgAAAADAhLZkyZLu/v5+VkZGxp2ju6dPn3b18vJidDqdgGEYam5u5l66dMktOjq6d6Q4YWFhtxsbG53Kysp4REQHDx4U3Gucm5vbYFdX1125UlhY2O2mpian0tJSHhGRVqv1jo6O7v72T3h/FouFPWPGjAGr1coaad/wr3CMGQAAAAAAJjQ2m00FBQU1ycnJoszMTH8ej2cXCoXW7Ozshp6eHo5UKpWzWCz7tm3bGmfMmMGYTKZ7xnF1dbVnZ2fXqdXqYIFAwERFRfWYzWaX4eM0Gs2t1atXB+bm5vrpdLqaofNzc3Nr4+LighwXVK1bt+7G8PlD1dfXc7///e/Lent7OSwWy/63v/3Nz2w2lwoEAtuDfAYbN25sjoyMlAYEBPRLpVJLT08P50HmP4lYKHMDAAAAAMBojEZjrVKpvPld7wOePEaj0UepVAaOZy6OMQMAAAAAAMCkg2PMAAAAAAAAj1Braytn4cKFc4e3nzp1qtLf33/wu9jTZIRkFwAAAAAA4BHy9/cfrKioKP+u9zHZ4RgzAAAAAAAATDpIdgEAAAAAAGDSQbILAAAAAAAAkw6SXQAAAAAAAJh0kOwCAAAAAMCEV19fz1Wr1bNFIlFIUFCQfMGCBcEmk4n3bWIaDAa+SqUKJiLKy8vz2Lx5sz8R0b59+zyLi4udHePWrl07Xa/X88ezxu7duwVisVgmFotl4eHhkgsXLriMNp7D4URIJBKZ419lZaXTeNYd6vXXX5+emprq923jPG5wGzMAAAAAAExoNpuNYmNjgxMSEtoMBsM3RETnz593aW5unhIaGmp9GGtoNJpOIuokItLr9Z4Mw3RGRETcJiLKzMxsHm/c4OBg67lz5yp9fX0H33//ffekpKSZJpOpYqTxPB7P9qhuah4YGKApU6Y8iqW+E0h2AQAAAABgzNbrjKKq1m7XhxlT7M+3pP9E2TBSv8Fg4HO5XPuGDRtuONrmzZvXZ7PZKCkpSXjixAkPFotlX79+fcuqVavaDQYDf/v27dMFAsFAZWWli0KhsOj1+qtsNpt0Op37+vXrRQKBgFEoFBZHvKysLO+ioqKnVqxY0Xbs2DHPL774gr9r165phw4dqklNTZ2mVqs7V65c2X748GH+xo0bRYODg6RUKi1arbbOxcXFHhAQoIiPj287evSoB8MwrPz8/G/Cw8Nvx8TE9DrWUKlUvSkpKQ9cqWUYhv7jP/5DeO7cOX5/fz9r1apV19evX3+TiOh3v/ud34cffijo7+9n/ehHP+r485//3ExE9MYbb/jn5+f7TJ8+vd/b23sgPDzcQkQUGRk5NzIysufixYtuL774Yse2bduuPeh+HhdIdgEAAAAAYEIzmUwuSqXSMrxdq9V6lpSUuJjN5rKWlhZuZGSkdPHixT1ERGaz2eXy5cvfBAYGDkREREgKCwvdoqOje1NSUgILCwsr5XK5Va1Wzx4eMyYmpnfRokUdjuR2aJ/FYmElJSXN+uyzzypDQ0Oty5YtC0xPT/dNTU29TkTk4+PDlJeXm9PS0nzT0tL88vPz64bOz87O9lGpVJ2jPavVamVLJBIZEZFIJLIWFhbWZGZm+nh4eAyWlpaa+/r6WN///vclS5Ys6SovL3eurq52NplMZrvdTosWLQr+5JNP3Nzc3GwffvihoKSkpHxgYIDCwsJkjmSXiKijo4Pz5ZdfVj7Id/A4QrILAAAAAABjNloF9lE7c+YMPz4+/haXyyWRSMRERUX1nD171tXDw8OmUCh6g4KCBoiI5HK5paamxonP5w8KhUKrQqGwEhFpNJq2PXv2+I51PaPR6CwUCq2Oo9OJiYltOTk5U4noOhFRQkJCOxFRZGSkpaCgwGvo3CNHjvD379/vc/78+RGPMBPd+xjzsWPH3CsqKlwdMbu7uznl5eXOn376qfvnn3/uLpPJZEREFouFXVFR4dzd3c1+8cUXO/h8vo2IaPHixR1D4/385z+/NdZnfpwh2QUAAAAAgAlNoVD06fV6r+Htdrt9xDk8Hu9OJ4fDIYZhWERELBZr3PsYbT0iImdnZzsREZfLtTvWIyK6ePGiS3Jy8syPPvroir+//+A41mVlZGTUv/TSS11D2z/55BP3tWvXtjiONDts37596mjP6UiCJzvcxgwAAAAAABPakiVLuvv7+1kZGRk+jrbTp0+7enl5MTqdTsAwDDU3N3MvXbrkFh0d3TtSnLCwsNuNjY1OZWVlPCKigwcPCu41zs3NbbCrq+uuXCksLOx2U1OTU2lpKY+ISKvVekdHR3ePtvcrV644xcXFBe3du/fqeC/TiomJ6dy9e7ev1WplERGZTCZeV1cX+4UXXujat2+fT2dnJ5uI6OrVq1Oampq4zz33XM9HH33k2dPTw2pvb2cXFhZ6jmfdxx0quwAAAAAAMKGx2WwqKCioSU5OFmVmZvrzeDy7UCi0ZmdnN/T09HCkUqmcxWLZt23b1jhjxgzGZDLdM46rq6s9Ozu7Tq1WBwsEAiYqKqrHbDbf9VNAGo3m1urVqwNzc3P9dDpdzdD5ubm5tXFxcUGOC6rWrVt3Y/j8obZu3Tqto6ODu2bNmplE/6z6lpaWmh/k+V977bWbtbW1PIVCIbXb7SyBQDDw8ccf1yxfvryrrKzM+fvf/77kf/dny8vLu/rMM89Yli1bdiskJEQeEBBgjYyM7HmQ9SYL1v1K8QAAAAAA8GQzGo21SqXy5v1HAjxcRqPRR6lUBo5nLo4xAwAAAAAAwKSDY8wAAAAAAACPUGtrK2fhwoVzh7efOnWqcjwXWMG9IdkFAAAAAAB4hPz9/QeH/7wQPHw4xgwAAAAAAACTDpJdAAAAAAAAmHSQ7AIAAAAAAMCkg2QXAAAAAAAAJh0kuwAAAAAAMOHV19dz1Wr1bJFIFBIUFCRfsGBBsMlk4n2bmAaDga9SqYKJiPLy8jw2b97sT0S0b98+z+LiYmfHuLVr107X6/X88ayxe/dugVgslonFYll4eLjkwoULLqONZ7FYEatWrRI6/k5NTfV7/fXXp49n7ScdbmMGAAAAAIAJzWazUWxsbHBCQkKbwWD4hojo/PnzLs3NzVNCQ0OtD2MNjUbTSUSdRER6vd6TYZjOiIiI20REmZmZzeONGxwcbD137lylr6/v4Pvvv++elJQ002QyVYw03snJyf7xxx97tbS0tE6bNo150PUGBgZoypQp493upIJkFwAAAAAAxk7/HyK6Xu76UGNOlVloaU7DSN0Gg4HP5XLtGzZsuOFomzdvXp/NZqOkpCThiRMnPFgsln39+vUtq1atajcYDPzt27dPFwgEA5WVlS4KhcKi1+uvstls0ul07uvXrxcJBAJGoVBYHPGysrK8i4qKnlqxYkXbsWPHPL/44gv+rl27ph06dKgmNTV1mlqt7ly5cmX74cOH+Rs3bhQNDg6SUqm0aLXaOhcXF3tAQIAiPj6+7ejRox4Mw7Dy8/O/CQ8Pvx0TE9PrWEOlUvWmpKQ4jfZRcDgc+yuvvHJj586dftnZ2U1D+6qqqpx+8YtfBLa1tXG9vb0ZrVZbO2fOnP6XXnop0MvLiykpKXENDQ218Pl8W21trdO1a9em1NbWOu/cubPhwoULbidOnHD38/MbOHbsWDWPx7OP78t6fOAYMwAAAAAATGgmk8lFqVRahrdrtVrPkpISF7PZXHb8+PGq1NRUYV1d3RQiIrPZ7JKTk9NQXV1dVl9fzyssLHSzWCyslJSUwIKCguovv/yy8vr163eVQGNiYnoXLVrUsWPHjsaKiopyuVx+p3JssVhYSUlJs/Lz82uqqqrKGYah9PR0X0e/j48PU15ebn711VdvpKWl+Q2PnZ2d7aNSqTrv97zr16+//sEHHwja2to4Q9t//etfz0hISGirqqoq/+lPf9q2evVqkaOvpqbG+dy5c1XvvPNOIxFRXV0d78SJE9U6na7617/+9aznnnuuq6qqqtzZ2dn2/vvve9xvD5MBKrsAAAAAADB2o1RgH7UzZ87w4+Pjb3G5XBKJRExUVFTP2bNnXT08PGwKhaI3KChogIhILpdbampqnPh8/qBQKLQqFAorEZFGo2nbs2eP7+ir/B+j0egsFAqtjqPTiYmJbTk5OVOJ6DoRUUJCQjsRUWRkpKWgoMBr6NwjR47w9+/f73P+/PkRjzA7CAQCW1xcXFtaWtpUFxcXm6P966+/fuqTTz6pISJavXr1rW3btt15t3f58uXtXO7/pXeLFi3q5PF49sjIyL7BwUHWT37yk67//Sz6rl69Omp1ebJAZRcAAAAAACY0hULRZzQa7zo6bbePfBJ36DFdDodDDMOwiIhYLNa49zHaekREzs7OdiIiLpdrd6xHRHTx4kWX5OTkmXq9vtrf339wLGtt2rTp2oEDB3x6e3vHlLO5ubnZhv7teH4Oh0NcLtfOZv8zDJvNpqF7m8yQ7AIAAAAAwIS2ZMmS7v7+flZGRoaPo+306dOuXl5ejE6nEzAMQ83NzdxLly65RUdH944UJyws7HZjY6NTWVkZj4jo4MGDgnuNc3NzG+zq6rorVwoLC7vd1NTkVFpayiMi0mq13tHR0d2j7f3KlStOcXFxQXv37r36IJdp+fn5DS5ZsqT9wIEDd545PDy8d8+ePV5ERH/7298E3/ve93rGGu9JhGQXAAAAAAAmNDabTQUFBTXHjx93F4lEIcHBwfI333xzemJi4i25XN4nlUrlCxcuFG/btq1xxowZI95g7Orqas/Ozq5Tq9XBERERc0UiUf+9xmk0mltZWVn+UqlU5kiMHfNzc3Nr4+LigsRisYzNZtO6detu3CuGw9atW6d1dHRw16xZM1MikchCQkKkY33uLVu2tHZ0dNw5m7x79+76ffv2+YjFYtnf//5377/+9a8T5kj5RMS6XykeAAAAAACebEajsVapVN78rvcBTx6j0eijVCoDxzMXlV0AAAAAAACYdHAbMwAAAAAAwCPU2trKWbhw4dzh7adOnaoc6wVWcH9IdgEAAAAAAB4hf3//wYqKivLveh+THY4xAwAAAAAAwKSDZBcAAAAAAAAmHSS7AAAAAAAAMOkg2QUAAAAAAIBJB8kuAAAAAABMePX19Vy1Wj1bJBKFBAUFyRcsWBBsMpl43yamwWDgq1SqYCKivLw8j82bN/sTEe3bt8+zuLjY2TFu7dq10/V6PX88a+zevVsgFotlYrFYFh4eLrlw4YLLaOM5HE6ERCKRzZkzR/7CCy/M7u7uHlfO1tzczOVyuU+np6f7jGf+ZIBkFwAAAAAAJjSbzUaxsbHBzz77bHdDQ0NpTU1N2VtvvdXU3Nw85WGtodFoOnfu3NlKRKTX6z1NJtOdpDQzM7N56dKl3eOJGxwcbD137lxlVVVV+aZNm5qTkpJmjjaex+PZKioqyq9cuVI2ZcoUe0ZGhu941tVqtV5KpbL3H//4h/d45k8G+OkhAAAAAAAYs9+d+52our3a9WHGDPYKtvxh/h8aRuo3GAx8Lpdr37Bhww1H27x58/psNhslJSUJT5w44cFisezr169vWbVqVbvBYOBv3759ukAgGKisrHRRKBQWvV5/lc1mk06nc1+/fr1IIBAwCoXC4oiXlZXlXVRU9NSKFSvajh075vnFF1/wd+3aNe3QoUM1qamp09RqdefKlSvbDx8+zN+4caNocHCQlEqlRavV1rm4uNgDAgIU8fHxbUePHvVgGIaVn5//TXh4+O2YmJhexxoqlao3JSXFaayfyzPPPNPjSLp///vf++Xl5fkQEa1YseJGamrq9a6uLnZsbOzslpYWJ5vNxtqwYUPzqlWr2omI/vGPfwj+9Kc/NfziF7+YffXq1SmzZs0aeLBv5fGHyi4AAAAAAExoJpPJRalUWoa3a7Vaz5KSEhez2Vx2/PjxqtTUVGFdXd0UIiKz2eySk5PTUF1dXVZfX88rLCx0s1gsrJSUlMCCgoLqL7/8svL69et3VYZjYmJ6Fy1a1LFjx47GioqKcrlcbnX0WSwWVlJS0qz8/PyaqqqqcoZhKD09/U7l1cfHhykvLze/+uqrN9LS0vyGx87OzvZRqVSdY3nmgYEBOnr0qLtCoeg7c+aM64EDB7yLi4vNRUVFZq1W63vu3DmXDz74wN3f33+gsrKy/MqVK2XLly/vIiKqrq6ecvPmzSkqlcoSGxvb/t577wnG9klPLqjsAgAAAADAmI1WgX3Uzpw5w4+Pj7/F5XJJJBIxUVFRPWfPnnX18PCwKRSK3qCgoAEiIrlcbqmpqXHi8/mDQqHQqlAorEREGo2mbc+ePWM+Jmw0Gp2FQqE1NDTUSkSUmJjYlpOTM5WIrhMRJSQktBMRRUZGWgoKCryGzj1y5Ah///79PufPn68YbQ2r1cqWSCQyIqKoqKju3/72tzfT09N9X3zxxQ53d3cbEdGPfvSj9pMnT/JjY2M7t2zZIlq9enXAj3/8487nn3++h4jovffeE8TGxrYTEa1YseLWL3/5y8Df//7318b6nJMFkl0AAAAAAJjQFApFn16v9xrebrfbR5zD4/HudHI4HGIYhkVExGKxxr2P0dYjInJ2drYTEXG5XLtjPSKiixcvuiQnJ8/86KOPrvj7+w+OFsPxzu5Y1g0NDbV+9dVX5YcOHfLYsmVLwLFjx7r+9Kc/tRw6dEhw8+bNKR988IGAiOj69etTSkpKeI4k/0mBY8wAAAAAADChLVmypLu/v5+VkZFx52bh06dPu3p5eTE6nU7AMAw1NzdzL1265BYdHd07UpywsLDbjY2NTmVlZTwiooMHD97zeK+bm9tgV1fXXblSWFjY7aamJqfS0lIeEZFWq/WOjo4e9eKqK1euOMXFxQXt3bv3qqMi/KCee+65no8//tizu7ub3dXVxf7444+9VCpVd21t7RQ+n29LTk6+tXbt2muXL192NRqNPIvFwrl+/bqpqamppKmpqSQlJaVVq9U+cUeZUdkFAAAAAIAJjc1mU0FBQU1ycrIoMzPTn8fj2YVCoTU7O7uhp6eHI5VK5SwWy75t27bGGTNmMCaT6Z5xXF1d7dnZ2XVqtTpYIBAwUVFRPWaz+a6fAtJoNLdWr14dmJub66fT6WqGzs/Nza2Ni4sLclxQtW7duhvD5w+1devWaR0dHdw1a9bMJPpn1be0tNT8IM//zDPPWBISEtqefvppKdE/L6iaP39+36FDh9w3bdokZLPZxOVy7X/961/r3nvvPe8XX3yxfej8n/3sZ+0JCQmz09PTWx5k3ccd636leAAAAAAAeLIZjcZapVJ587veBzx5jEajj1KpDBzPXBxjBgAAAAAAgEkHx5gBAAAAAAAeodbWVs7ChQvnDm8/depU5f0usIKxQ7ILAAAAAADwCPn7+w8Ov3EZHj4cYwYAAAAAAIBJB8kuAAAAAAAATDpIdgEAAAAAAGDSQbILAAAAAAAAkw6SXQAAAAAAmPDq6+u5arV6tkgkCgkKCpIvWLAg2GQy8b5NTIPBwFepVMFERHl5eR6bN2/2JyLat2+fZ3FxsbNj3Nq1a6fr9Xr+eNbYvXu3QCwWy8RisSw8PFxy4cIFl9HGczicCIlEIpszZ478hRdemN3d3f3AOVtkZOTckJAQqePvzz//3DUyMvKu258nOyS7AAAAAAAwodlsNoqNjQ1+9tlnuxsaGkpramrK3nrrrabm5uYpD2sNjUbTuXPnzlYiIr1e72kyme4kpZmZmc1Lly7tHk/c4OBg67lz5yqrqqrKN23a1JyUlDRztPE8Hs9WUVFRfuXKlbIpU6bYMzIyfMezbltbG/f99993H8/cyQI/PQQAAAAAAGPWvHmLyHrliuvDjMmbM8cyfecfG0bqNxgMfC6Xa9+wYcMNR9u8efP6bDYbJSUlCU+cOOHBYrHs69evb1m1alW7wWDgb9++fbpAIBiorKx0USgUFr1ef5XNZpNOp3Nfv369SCAQMAqFwuKIl5WV5V1UVPTUihUr2o4dO+b5xRdf8Hft2jXt0KFDNampqdPUanXnypUr2w8fPszfuHGjaHBwkJRKpUWr1da5uLjYAwICFPHx8W1Hjx71YBiGlZ+f/014ePjtmJiYXscaKpWqNyUlxWmsn8szzzzT40i6f//73/vl5eX5EBGtWLHiRmpq6vWuri52bGzs7JaWFiebzcbasGFD86pVq9qJiFJSUq6lpaVNj4+P73qwb2PyQGUXAAAAAAAmNJPJ5KJUKi3D27VarWdJSYmL2WwuO378eFVqaqqwrq5uChGR2Wx2ycnJaaiuri6rr6/nFRYWulksFlZKSkpgQUFB9Zdffll5/fr1uyrDMTExvYsWLerYsWNHY0VFRblcLrc6+iwWCyspKWlWfn5+TVVVVTnDMJSenn6n8urj48OUl5ebX3311RtpaWl+w2NnZ2f7qFSqzrE888DAAB09etRdoVD0nTlzxvXAgQPexcXF5qKiIrNWq/U9d+6cywcffODu7+8/UFlZWX7lypWy5cuX30lso6Oje5ycnGxHjhwZ1/HryQCVXQAAAAAAGLPRKrCP2pkzZ/jx8fG3uFwuiUQiJioqqufs2bOuHh4eNoVC0RsUFDRARCSXyy01NTVOfD5/UCgUWhUKhZWISKPRtO3Zs2fMx4SNRqOzUCi0hoaGWomIEhMT23JycqYS0XUiooSEhHYiosjISEtBQYHX0LlHjhzh79+/3+f8+fMVo61htVrZEolERkQUFRXV/dvf/vZmenq674svvtjh7u5uIyL60Y9+1H7y5El+bGxs55YtW0SrV68O+PGPf9z5/PPP9wyNtXnz5padO3dO27VrV+NYn3EyQWUXAAAAAAAmNIVC0Wc0Gu86Om2320ecw+Px7nRyOBxiGIZFRMRisca9j9HWIyJydna2ExFxuVy7Yz0ioosXL7okJyfP1Ov11f7+/oOjxXC8s1tRUVH+3nvvNTg7O9tHWjc0NNT61VdflSsUir4tW7YErFu3btrQ/tjY2G6r1co+e/bsU2N9xskEyS4AAAAAAExoS5Ys6e7v72dlZGT4ONpOnz7t6uXlxeh0OgHDMNTc3My9dOmSW3R0dO9IccLCwm43NjY6lZWV8YiIDh48KLjXODc3t8Gurq67cqWwsLDbTU1NTqWlpTwiIq1W6x0dHT3qxVVXrlxxiouLC9q7d+9VR0X4QT333HM9H3/8sWd3dze7q6uL/fHHH3upVKru2traKXw+35acnHxr7dq11y5fvnzXfwi88cYbLdnZ2f7jWfdxh2PMAAAAAAAwobHZbCooKKhJTk4WZWZm+vN4PLtQKLRmZ2c39PT0cKRSqZzFYtm3bdvWOGPGDMZkMt0zjqurqz07O7tOrVYHCwQCJioqqsdsNt/1U0AajebW6tWrA3Nzc/10Ol3N0Pm5ubm1cXFxQY4LqtatW3dj+Pyhtm7dOq2jo4O7Zs2amUT/rPqWlpaaH+T5n3nmGUtCQkLb008/LSX65wVV8+fP7zt06JD7pk2bhGw2m7hcrv2vf/1r3fC5P/3pTzu3b9/OPMh6kwXrfqV4AAAAAAB4shmNxlqlUnnzu94HPHmMRqOPUqkMHM9cHGMGAAAAAACASQfHmAEAAAAAAB6h1tZWzsKFC+cObz916lTl/S6wgrFDsgsAAAAAAPAI+fv7D1ZUVJR/1/uY7HCMGQAAAAAAACYdJLsAAAAAAAAw6SDZBQAAAAAAgEkHyS4AAAAAAABMOkh2AQAAAABgwquvr+eq1erZIpEoJCgoSL5gwYJgk8nE+zYxDQYDX6VSBRMR5eXleWzevNmfiGjfvn2excXFzo5xa9euna7X6/njWWP37t0CsVgsE4vFsvDwcMmFCxdcRhvP4XAiJBKJbM6cOfLnnnsu+ObNmxwiosrKSidnZ+enJRKJLCgoSL5s2bJAq9XKGjp35cqVoqlTp4YODuJCZyIkuwAAAAAAMMHZbDaKjY0NfvbZZ7sbGhpKa2pqyt56662m5ubmKQ9rDY1G07lz585WIiK9Xu9pMpnuJKWZmZnNS5cu7R5P3ODgYOu5c+cqq6qqyjdt2tSclJQ0c7TxPB7PVlFRUX7lypUyT09PJj093ab48qoAACAASURBVNfRJxKJrBUVFeWVlZVlLS0tTnv37vVy9A0ODtKnn37qOW3atP5PPvlkXIn5ZIOfHgIAAAAAgDE7rjWLbjX1uD7MmIIAN8v/94q0YaR+g8HA53K59g0bNtxwtM2bN6/PZrNRUlKS8MSJEx4sFsu+fv36llWrVrUbDAb+9u3bpwsEgoHKykoXhUJh0ev1V9lsNul0Ovf169eLBAIBo1AoLI54WVlZ3kVFRU+tWLGi7dixY55ffPEFf9euXdMOHTpUk5qaOk2tVneuXLmy/fDhw/yNGzeKBgcHSalUWrRabZ2Li4s9ICBAER8f33b06FEPhmFY+fn534SHh9+OiYnpdayhUql6U1JSnMb6ufzgBz/oHZp0O3C5XHr66ad7m5qa7iT7BoOBLxaL+37yk5+0HzhwQKBWq7uJiLq6uti//OUvZ5jNZpfBwUHWli1bml9++eWOse7hcYbKLgAAAAAATGgmk8lFqVRahrdrtVrPkpISF7PZXHb8+PGq1NRUYV1d3RQiIrPZ7JKTk9NQXV1dVl9fzyssLHSzWCyslJSUwIKCguovv/yy8vr163dVhmNiYnoXLVrUsWPHjsaKiopyuVxudfRZLBZWUlLSrPz8/JqqqqpyhmFoaOXVx8eHKS8vN7/66qs30tLS/IbHzs7O9lGpVJ1jeWaGYejkyZP8pUuX3pWYWiwWVnFx8VNLlizpcrQdOHBAEB8ff0uj0bQfO3bMw3HEefPmzdNUKlVXaWmp+cyZM5Vbt24VdnV1PRF5ICq7AAAAAAAwZqNVYB+1M2fO8OPj429xuVwSiURMVFRUz9mzZ109PDxsCoWiNygoaICISC6XW2pqapz4fP6gUCi0KhQKKxGRRqNp27Nnj+/oq/wfo9HoLBQKraGhoVYiosTExLacnJypRHSdiCghIaGdiCgyMtJSUFDgNXTukSNH+Pv37/c5f/58xWhrWK1WtkQikTU1NTmFhIRYli5deiehbWho4EkkElldXR3vhRdeaI+KiuojIrp9+zbr5MmTHrm5uQ1eXl62sLCw3g8//ND9Zz/7WeepU6fcjx496pmVleX/v/FZ1dXVTk8//fTtsT734+qJyOgBAAAAAODxpVAo+oxG411Hp+12+4hzeDzenU4Oh0MMw7CIiFgs1ohz7me09YiInJ2d7UREXC7X7liPiOjixYsuycnJM/V6fbW/v/+ot0c53tmtra0t6e/vZ6WlpU119Dne2a2oqCj96quv3PLy8jyIiA4dOuTe3d3NCQkJkQcEBCiKiorc/v73vwsce9bpdNX/O6+8paWl5ElIdImQ7AIAAAAAwAS3ZMmS7v7+flZGRoaPo+306dOuXl5ejE6nEzAMQ83NzdxLly65RUdH944UJyws7HZjY6NTWVkZj4jo4MGDgnuNc3NzG7zXUd+wsLDbTU1NTqWlpTwiIq1W6x0dHT3qxVVXrlxxiouLC9q7d+9VR0V4LLy9vQezsrLqc3Jy/Ibfujxz5syB7du3N6anp09zPEdmZmZdU1NTSVNTU0ltbW3JmTNn3Lu7u9kqlaorIyPDz2azERHRuXPnRr0NejJBsgsAAAAAABMam82mgoKCmuPHj7uLRKKQ4OBg+Ztvvjk9MTHxllwu75NKpfKFCxeKt23b1jhjxgxmpDiurq727OzsOrVaHRwRETFXJBL132ucRqO5lZWV5S+VSmWOxNgxPzc3tzYuLi5ILBbL2Gw2rVu37sa9Yjhs3bp1WkdHB3fNmjUzJRKJLCQkRDrW554/f36fVCrt27Nnj9fwvpdffrmjr6+P/dFHH7l9/vnnHnFxcXfe7XV3d7d973vf6zl48KBHWlpaM8MwLMfPGW3dujVgrOs/7lj3K8UDAAAAAMCTzWg01iqVypvf9T7gyWM0Gn2USmXgeOaisgsAAAAAAACTDm5jBgAAAAAAeIRaW1s5CxcunDu8/dSpU5X3u8AKxg7JLgAAAAAAwCPk7+8/WFFRUf5d72OywzFmAAAAAAAAmHSQ7AIAAAAAAMCkg2QXAAAAAAAAJh0kuwAAAAAAADDpINkFAAAAAIAJr76+nqtWq2eLRKKQoKAg+YIFC4JNJhPv28Q0GAx8lUoVTESUl5fnsXnzZn8ion379nkWFxc7O8atXbt2ul6v549njd27dwvEYrFMLBbLwsPDJRcuXHAZbTyHw4mQSCSyOXPmyJ977rngmzdvcoiIKisrnZydnZ+WSCSyoKAg+bJlywKtVitr6NyVK1eKpk6dGjo4iAudiXAbMwAAAAAAPICjuzNFNxvqXB9mTB/RTMsPV69tGKnfZrNRbGxscEJCQpvBYPiGiOj8+fMuzc3NU0JDQ60PYw8ajaaTiDqJiPR6vSfDMJ0RERG3iYgyMzObxxs3ODjYeu7cuUpfX9/B999/3z0pKWmmyWSqGGk8j8ezOW5qXr58eWB6errvrl27WomIRCKRtaKiopxhGHrmmWfEe/fu9Vq9evUtIqLBwUH69NNPPadNm9b/ySef8NVqdfd49zxZoLILAAAAAAATmsFg4HO5XPuGDRtuONrmzZvXt3jx4p6kpCThnDlz5GKxWPbOO+94OcZHRkbOff7552fPmjVLHhsbO8tmsxERkU6nc581a5Y8IiJirk6n83TEy8rK8n7llVdmFBYWPnXs2DHPrVu3CiUSiaysrIz30ksvBb777rteRESHDx/mS6VSmVgslsXFxQX29fWxiIgCAgIUr7322nSZTCYVi8Wyr7/+2pmIKCYmptfX13eQiEilUvW2trY6jfW5f/CDH/Q2NTXdNZ7L5dLTTz/d29TUNGXoZyQWi/v+3//7fzcOHDggcLS//vrr0+Pi4gIjIyPnCoVCxY4dO6aO+YN/zKGyCwAAAAAAYzZaBfbfxWQyuSiVSsvwdq1W61lSUuJiNpvLWlpauJGRkdLFixf3EBGZzWaXy5cvfxMYGDgQEREhKSwsdIuOju5NSUkJLCwsrJTL5Va1Wj17eMyYmJjeRYsWdajV6s6VK1e2D+2zWCyspKSkWZ999lllaGioddmyZYHp6em+qamp14mIfHx8mPLycnNaWppvWlqaX35+ft3Q+dnZ2T4qlapzLM/MMAydPHmS/8tf/vLm8D6LxcIqLi5+Kisr6853ceDAAUF8fPytn//85x1/+MMfAqxWK4vH49mJiKqrq53Pnz9f2dHRwZFKpSHr16+/4eibzFDZBQAAAACAx9KZM2f48fHxt7hcLolEIiYqKqrn7NmzrkRECoWiNygoaIDD4ZBcLrfU1NQ4Xb582VkoFFoVCoWVzWaTRqNpe5D1jEajs1AotDqOTicmJradPXv2zru8CQkJ7UREkZGRloaGhn95n/jIkSP8/fv3+7z99tuNo61htVrZEolE5uXlFdbR0cFdunRpl6OvoaGBJ5FIZN7e3mEBAQH9UVFRfUREt2/fZp08edIjISGhQyAQ2MLCwno//PBDd8e8xYsXd7i4uNinTZvGCASCgcbGxiei6IlkFwAAAAAAJjSFQtFnNBrvek/Ybh+5ODm0csnhcIhhGBYREYvFGnHO/Yy2HhGRs7OznYiIy+XaHesREV28eNElOTl5pl6vr/b39x/19ijHO7u1tbUl/f39rLS0tDvHjh3v7FZUVJR+9dVXbnl5eR5ERIcOHXLv7u7mhISEyAMCAhRFRUVuf//73wVDYt7zs5jskOwCAAAAAMCEtmTJku7+/n5WRkaGj6Pt9OnTrl5eXoxOpxMwDEPNzc3cS5cuuUVHR/eOFCcsLOx2Y2OjU1lZGY+I6ODBg4J7jXNzcxvs6uq6K1cKCwu73dTU5FRaWsojItJqtd7R0dGjXgR15coVp7i4uKC9e/defZDLtLy9vQezsrLqc3Jy/Ibfujxz5syB7du3N6anp09zPEdmZmZdU1NTSVNTU0ltbW3JmTNn3Lu7u5/ofO+JfngAAAAAAJj42Gw2FRQU1Bw/ftxdJBKFBAcHy998883piYmJt+RyeZ9UKpUvXLhQvG3btsYZM2YwI8VxdXW1Z2dn16nV6uCIiIi5IpGo/17jNBrNraysLH+pVCpzJMaO+bm5ubVxcXFBYrFYxmazad26dTfuFcNh69at0zo6Orhr1qyZKZFIZCEhIdKxPvf8+fP7pFJp3549e7yG97388ssdfX197I8++sjt888/94iLi+tw9Lm7u9u+973v9Rw8eNBjrGtNRqz7leIBAAAAAODJZjQaa5VK5V0XJQH8uxmNRh+lUhk4nrmo7AIAAAAAAMCk80TcwgUAAAAAADBRtLa2chYuXDh3ePupU6cq73eBFYwdkl0AAAAAAIBHyN/ff7CioqL8u97HZIdjzAAAAAAAADDpINkFAAAAAACASQfJLgAAAAAAAEw6SHYBAAAAAABg0kGyCwAAAAAAE159fT1XrVbPFolEIUFBQfIFCxYEm0wm3reJaTAY+CqVKpiIKC8vz2Pz5s3+RET79u3zLC4udnaMW7t27XS9Xs8fzxq7d+8WiMVimVgsloWHh0suXLjgMtp4DocTIZFIZHPmzJE/99xzwTdv3uQQEVVWVjo5Ozs/LZFIZEFBQfJly5YFWq1WluM5WCxWxIEDBzwccVQqVbDBYBjXnicL3MYMAAAAAABjdktXJRpo7XV9mDGn+D9lEfxE3DBSv81mo9jY2OCEhIQ2g8HwDRHR+fPnXZqbm6eEhoZaH8YeNBpNJxF1EhHp9XpPhmE6IyIibhMRZWZmNo83bnBwsPXcuXOVvr6+g++//757UlLSTJPJVDHSeB6PZ3Pc1Lx8+fLA9PR03127drUSEYlEImtFRUU5wzD0zDPPiPfu3eu1evXqW0REfn5+A7t27ZqWkJDQOd69Tjao7AIAAAAAwIRmMBj4XC7XvmHDhhuOtnnz5vUtXry4JykpSThnzhy5WCyWvfPOO16O8ZGRkXOff/752bNmzZLHxsbOstlsRESk0+ncZ82aJY+IiJir0+k8HfGysrK8X3nllRmFhYVPHTt2zHPr1q1CiUQiKysr47300kuB7777rhcR0eHDh/lSqVQmFotlcXFxgX19fSwiooCAAMVrr702XSaTScVisezrr792JiKKiYnp9fX1HSQiUqlUva2trU5jfe4f/OAHvU1NTXeN53K59PTTT/c2NTVNcbRJpVILn88f/PDDD92Hjx9pb5MdKrsAAAAAADBmo1Vg/11MJpOLUqm0DG/XarWeJSUlLmazuaylpYUbGRkpXbx4cQ8Rkdlsdrl8+fI3gYGBAxEREZLCwkK36Ojo3pSUlMDCwsJKuVxuVavVs4fHjImJ6V20aFGHWq3uXLlyZfvQPovFwkpKSpr12WefVYaGhlqXLVsWmJ6e7puamnqdiMjHx4cpLy83p6Wl+aalpfnl5+fXDZ2fnZ3to1KpxlR5ZRiGTp48yf/lL395c3ifxWJhFRcXP5WVlfUv38XWrVtbfve73wUsW7asa/ic++1tMkJlFwAAAAAAHktnzpzhx8fH3+JyuSQSiZioqKies2fPuhIRKRSK3qCgoAEOh0NyudxSU1PjdPnyZWehUGhVKBRWNptNGo2m7UHWMxqNzkKh0Oo4Op2YmNh29uzZO+/FJiQktBMRRUZGWhoaGv7lfeIjR47w9+/f7/P22283jraG1WplSyQSmZeXV1hHRwd36dKldxLXhoYGnkQikXl7e4cFBAT0R0VF9Q2d+/zzz/cQEX366aduw+OOtrfJCskuAAAAAABMaAqFos9oNN71nrDdbh9xDo/Hu9PJ4XCIYRgWERGLxRr3PkZbj4jI2dnZTkTE5XLtjvWIiC5evOiSnJw8U6/XV/v7+w+OFsPxzm5tbW1Jf38/Ky0tbaqjz/HObkVFRelXX33llpeX5zF8/qZNm1r++Mc/Thvr3iYzJLsAAAAAADChLVmypLu/v5+VkZHh42g7ffq0q5eXF6PT6QQMw1BzczP30qVLbtHR0b0jxQkLC7vd2NjoVFZWxiMiOnjwoOBe49zc3Aa7urruypXCwsJuNzU1OZWWlvKIiLRarXd0dHT3aHu/cuWKU1xcXNDevXuvPshlWt7e3oNZWVn1OTk5fo5blx1mzpw5sH379sb09PS7ktrly5d3dXZ2csxm80O9ROxxhGQXAAAAAAAmNDabTQUFBTXHjx93F4lEIcHBwfI333xzemJi4i25XN4nlUrlCxcuFG/btq1xxowZzEhxXF1d7dnZ2XVqtTo4IiJirkgk6r/XOI1GcysrK8tfKpXKHImxY35ubm5tXFxckFgslrHZbFq3bt2Ne8Vw2Lp167SOjg7umjVrZkokEllISIh0rM89f/78PqlU2rdnzx6v4X0vv/xyR19fH/teR5bfeOONlmvXrk0Z3v6kYd2vFA8AAAAAAE82o9FYq1Qq77ooCeDfzWg0+iiVysDxzEVlFwAAAAAAACYd/PQQAAAAAADAI9Ta2spZuHDh3OHtp06dqrzfBVYwdkh2AQAAAAAAHiF/f//BioqK8u96H5MdjjEDAAAAAADApINkFwAAAAAAACYdJLsAAAAAAAAw6SDZBQAAAAAAgEkHyS4AAAAAAEx49fX1XLVaPVskEoUEBQXJFyxYEGwymXjfJqbBYOCrVKpgIqK8vDyPzZs3+xMR7du3z7O4uNjZMW7t2rXT9Xo9fzxr7N69WyAWi2VisVgWHh4uuXDhgsu32TOMHW5jBgAAAACAMdPr9aLr16+7PsyYU6dOtSxdurRhpH6bzUaxsbHBCQkJbQaD4RsiovPnz7s0NzdPCQ0NtT6MPWg0mk4i6iQi0uv1ngzDdEZERNwmIsrMzGweb9zg4GDruXPnKn19fQfff/9996SkpJkmk6niYewZRofKLgAAAAAATGgGg4HP5XLtGzZsuOFomzdvXt/ixYt7kpKShHPmzJGLxWLZO++84+UYHxkZOff555+fPWvWLHlsbOwsm81GREQ6nc591qxZ8oiIiLk6nc7TES8rK8v7lVdemVFYWPjUsWPHPLdu3SqUSCSysrIy3ksvvRT47rvvehERHT58mC+VSmVisVgWFxcX2NfXxyIiCggIULz22mvTZTKZVCwWy77++mtnIqKYmJheX1/fQSIilUrV29ra6jTSc1ZWVjrNnj1b/rOf/WxmcHCwfP78+XN6enpYREQZGRk+ISEh0rlz58p++MMfBnV3d7OJiF566aXAxMREUXh4uEQoFCoc+wRUdgEAAAAA4AGMVoH9dzGZTC5KpdIyvF2r1XqWlJS4mM3mspaWFm5kZKR08eLFPUREZrPZ5fLly98EBgYORERESAoLC92io6N7U1JSAgsLCyvlcrlVrVbPHh4zJiamd9GiRR1qtbpz5cqV7UP7LBYLKykpadZnn31WGRoaal22bFlgenq6b2pq6nUiIh8fH6a8vNyclpbmm5aW5pefn183dH52draPSqXqHO1Z6+vrnffv3//NvHnz6l588cXZWq3WKzk5+ZZGo2n/z//8z5tERL/5zW+mZ2Vl+WzZsuU6EdG1a9emFBUVVVy+fNl52bJlwcP3/aRCZRcAAAAAAB5LZ86c4cfHx9/icrkkEomYqKionrNnz7oSESkUit6goKABDodDcrncUlNT43T58mVnoVBoVSgUVjabTRqNpu1B1jMajc5CodDqODqdmJjYdvbs2Tvv8iYkJLQTEUVGRloaGhr+5X3iI0eO8Pfv3+/z9ttvN462RkBAgHXevHl9RETh4eGW2tpaHhFRcXGxS0RExFyxWCw7dOiQd1lZ2Z13imNjYzs4HA5FRETcbmtrm/IgzzSZIdkFAAAAAIAJTaFQ9BmNxrveE7bb7SPO4fF4dzo5HA4xDMMiImKxWOPex2jrERE5OzvbiYi4XK7dsR4R0cWLF12Sk5Nn6vX6an9//8HRYjg5OQ3d9504v/rVr2b95S9/qa+qqip/4403mq1W651czrHuWPb4JEGyCwAAAAAAE9qSJUu6+/v7WRkZGT6OttOnT7t6eXkxOp1OwDAMNTc3cy9duuQWHR3dO1KcsLCw242NjU5lZWU8IqKDBw8K7jXOzc1tsKur665cKSws7HZTU5NTaWkpj4hIq9V6R0dHd4+29ytXrjjFxcUF7d279+q3uUzLYrGwZ8yYMWC1Wlkj7Rv+Fd7ZBQAAAACACY3NZlNBQUFNcnKyKDMz05/H49mFQqE1Ozu7oaenhyOVSuUsFsu+bdu2xhkzZjAmk+mecVxdXe3Z2dl1arU6WCAQMFFRUT1ms/munwLSaDS3Vq9eHZibm+un0+lqhs7Pzc2tjYuLCxocHCSlUmlZt27djeHzh9q6deu0jo4O7po1a2YS/bPqW1paan7Qz2Djxo3NkZGR0oCAgH6pVGrp6enhPGiMJw0LZW4AAAAAABiN0WisVSqVN7/rfcCTx2g0+iiVysDxzMUxZgAAAAAAAJh0cIwZAAAAAADgEWptbeUsXLhw7vD2U6dOVd7vAisYOyS7AAAAAAAAj5C/v/9gRUVF+Xe9j8kOx5gBAAAAAABg0kGyCwAAAAAAAJMOkl0AAAAAAACYdJDsAgAAAAAAwKSDZBcAAAAAACY8DocTIZFIZI5/mzdv9iciioyMnPv555+7Pmi88+fPu+Tn53uM1P/555+7JiYmisaz1/vtKSAgQCEWi+88S2Fh4VPjWedesrKyvL28vJSO2MuWLQt8WLEfN7iNGQAAAAAAxqzc/Iaot6fqgZPL0TzlJrbIpLsaRhvD4/FsD/MG46KiIteioqKnfvrTn3YO7xsYGKBnn33W8uyzz1oe1nrDnT59umratGnMvyP2kiVL2rVabf2/I/bjBJVdAAAAAACYFD744AP3sLAwiUwmk77wwguzOzs72UREp0+fdg0PD5fMnTtXplAopG1tbZy33npr+pEjR7wkEonsnXfe8Xr99den//znP585f/78OcuXL59lMBj4KpUqmIios7OT/ZOf/CRQLBbLxGKx7H/+5388iYg0Gs2MkJAQaXBwsPy1116b/m32PnQ9IqJXXnllRlZWljcRUXJyckBQUJBcLBbLfvWrXwmJiJqbm7k//OEPg0JCQqQhISHSzz777KFVhycLVHYBAAAAAGDM7leB/XexWq1siUQic/z9n//5ny2rVq1qd/zd0tLC3blz57TPP/+8yt3d3bZlyxb/P/zhD347duxo1Wg0QXl5eTULFiyw3Lp1i83n822bNm1qLioqespRAX399dddTCaT68WLFyvc3NzsBoOB74i9cePGae7u7oNVVVXlREQ3btzgEBH913/9V5Ofn98gwzA0b968uRcvXnSJiorqG8vzLFiwQMxms8nJyclmMpkqRhp37do1zscff+z1zTfflLLZbLp58yaHiCgpKUn0+uuvX/vhD3/4/7N391FR12n/wK95YJ54iuFJwBkgnGFmGJlydlG6S0i3wlN4KjPZ8IEW0GRbM2mNyJ/rYpu2dyCh4QN77ltw7Ri57aac0mwLWOsOA7eZEMZRFJ2FgQAHBIYZ5un3Rzu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mpiaP/Pz8kK+//rrR39/f0dPTwzQajd/XZa+99tq/nn32WZPZbGZIpVJlZmZml0wmG7w2ht1uH9NYdrud2OzpW/JN35kDAAAAAMD4/e3XIvq24eZ2FIMUZnr8bcONhnn//fd98vLyQgcHBxnh4eHWQ4cONfv6+jqrqqoE69evF5vNZiaHw3FVVlbqt23bFmqxWJgymcwrOzvb2NjYyDcajR6XL1/mCIVC+5o1azrz8/ODP/vss/M9PT3M9PR0sVarFRAR5ebmtqalpXWnpqaKNRqNp8ViYSYnJ5t27NjRer0cjUajh6enp9PX19dBROTr6+v09fUdHHqd2WxmEhF5e3s7iYjCwsJm//KXv+z87LPPfNasWfOt+zqHw0FLly6NmDlz5mBRUVGrQCC4d/Xq1e2ffvqpz3//93//65FHHum70ed6p8IyZgAAAAAAmPSsVivz2mXMJSUlfteeNxqN7Ndffz2kurpa39DQ0Dhnzhzz1q1bgy0WCyM1NTWqsLDw8tmzZxuqqqrO+vj4OF555ZXW5ORkk06na8jMzDQREWm1WsHx48fPHz169OK1sXNyckJ8fHwcer2+Qa/XNzz66KO9REQFBQUt9fX1jTqd7sznn3/uXVNTw7/ePObNm2cOCAiwiUSi2U899VTEO++884Ol1Js2bZopk8kUYrE49vHHH78SFhb2fRuXx+M56+rqzq5evdpERGSz2RiPP/54pEQisRQVFbUSEQ0MDDCVSuWAVqvVTedClwidXQAAAAAAGI+b0IGdiOstY66srPRsamrixcXFyYi+KwTVanWfVqvlBQUF2RISEsxEREKh0DlSjKSkpG4vLy/X0OPV1dU+hw4duuD+HRgY6CAiKi0tFe7fvz/AbrczOjo6PDQaDW/u3LkDo82DzWZTdXX1uaqqKsHHH3/sk5OTI6qtrfUsKChoJfrPMuaenh7mAw88ID1x4oTnQw891E9EtHLlStO1sbKyssIff/zxK2+88Uab+xiLxaK0tDQTATq7AAAAAABw53O5XHT//fdf1el0DTqdrqGpqelMeXn5JZfLRQwG40cF7HA8PT2HLYT/HeMHx3Q6HWfXrl3BVVVVer1e37BgwYIei8UypvqKyWTSgw8+aN62bVvbn//85wsVFRV3Db3G19fX+V//9V+9VVVVXu5j7iXNbj/72c/6/vGPf/iYzebvk+NwOM7p/J7utVDsAgAAAADAHS8xMbG/trbWq76+nktE1Nvby9RqtVyVSmVpb2/nVFVVCYiITCYT02azkY+Pj6Ovr29M9VBiYuLVgoKCIPfvjo4OlslkYvH5fKdQKHQYDAZ2ZWXliDs7X6u5udnj5MmT37/zXFtbKwgLC/vRO7s2m43q6uq8Zs2aZR0p1po1azoffvjhnsceeyzKZrONZfhpBcUuAAAAAABMekPf2c3Kygq79nxoaKh97969zSkpKXdLpVKFWq2WffPNNzwej+c6ePBg07p168TR0dGKxMREqdlsZi5atKhXr9fzh3v/d6ht27YZu7u7WRKJJCY6Olrx4YcfesfHxw8olUqzRCKJWbFiRYRarR7T+7GDg4OMl156aWZkZGSMTCZTHD582G/Xrl3fLw13v7Mrk8li5HK5eeXKld2jxduyZUu7SqUyP/nkk5EOh2MsKUwbDJdrTB19AAAAAACYpjQaTbNKpeq83XnA9KPRaAJUKlXERO5FZxcAAAAAAACmHLy5DAAAAAAAcAvExsbKBgcHf9BgLCsruxgXFzfqjs1wc6DYBQAAAAAAuAW0Wq3uducwnWEZMwAAAAAAAEw5KHYBAAAAAABgykGxCwAAAAAAAFMOil0AAAAAAACYclDsAgAAAADApMdisdQymUzh/uTm5s4gIoqLi4uurq4WjDfeF198wX/33Xd9RzpfXV0tSEtLE00k14nmBDcXdmMGAAAAAIBJj8vlOnU6XcPNildbWyuora31XLZsWc/QczabjebPn2+eP3+++WaNBz89FLsAAAAAADBm/+/z/yc6bzp/U7uWs/xmmbf+11bDjcZ5//33ffLy8kIHBwcZ4eHh1kOHDjX7+vo6q6qqBOvXrxebzWYmh8NxVVZW6rdt2xZqsViYMpnMKzs729jY2Mg3Go0ely9f5giFQvuaNWs68/Pzgz/77LPzPT09zPT0dLFWqxUQEeXm5rampaV1p6amijUajafFYmEmJyebduzY0TqWPAUCwb3p6enffvzxx748Hs9ZUVFxXiQS2d955x3f7du3h9hsNqafn5/93XffvSASiewbNmwINRgMnEuXLnFbW1s5zz33XPumTZu+vdHnNdVhGTMAAAAAAEx6VquVee0y5pKSEr9rzxuNRvbrr78eUl1drW9oaGicM2eOeevWrcEWi4WRmpoaVVhYePns2bMNVVVVZ318fByvvPJKa3Jyskmn0zVkZmaaiIi0Wq3g+PHj548ePXrx2tg5OTkhPj4+Dr1e36DX6xseffTRXiKigoKClvr6+kadTnfm888/966pqeGPZS4DAwPM+Pj4vrNnzzbEx8f37dy5M5CI6KGHHur7+uuvdY2NjQ1PPfXUlby8vBnue86fP8+rqqrSf/XVV41vvvlmqNVqZdzoM53q0NkFAAAAAIAxuxkd2Im43jLmyspKz6amJl5cXJyMiMhmszHUanWfVqvlBQUF2RISEsxEREKh0DlSjKSkpG4vLy/X0OPV1dU+hw4duuD+HRgY6CAiKi0tFe7fvz/AbrczOjo6PDQaDW/u3LkD15uLh4eHKyUlpYeISK1W93/yySc+REQXL17kPP744zM7Ojo8BgcHmSKRyOq+5+GHH+7m8/kuPp9vFwqFtn/961/sqKgo2/XGms7Q2QUAAAAAgDuey+Wi+++//6pOp2vQ6XQNTU1NZ8rLyy+5XC5iMBg/KmCH4+npOWwh/O8YPzim0+k4u3btCq6qqtLr9fqGBQsW9FgsljHVV2w228VkMt3fyW63M4iInn/+eXFWVta3er2+YdeuXZesVuv38bhc7vdzYLFY398DI0OxCwAAAAAAd7zExMT+2tpar/r6ei4RUW9vL1Or1XJVKpWlvb2dU1VVJSAiMplMTJvNRj4+Po6+vr4x1UOJiYlXCwoKgty/Ozo6WCaTicXn851CodBhMBjYlZWVI+7sPFa9vb0ssVhsIyLav3+//43Gm+5Q7AIAAAAAwKQ39J3drKyssGvPh4aG2vfu3duckpJyt1QqVajVatk333zD4/F4roMHDzatW7dOHB0drUhMTJSazWbmokWLevV6PX+493+H2rZtm7G7u5slkUhioqOjFR9++KF3fHz8gFKpNEskkpgVK1ZEqNXqvhud46uvvtr6y1/+MkqtVkf7+/vbbzTedMdwucbU0QcAAAAAgGlKo9E0q1SqztudB0w/Go0mQKVSRUzkXnR2AQAAAAAAYMrBbswAAAAAAAC3QGxsrGxwcPAHDcaysrKLcXFx192xGW4cil0AAAAAAIBbQKvV6m53DtMZljEDAAAAAADAlINiFwAAAAAAAKYcFLsAAAAAAAAw5aDYBQAAAAAAgCkHxS4AAAAAAEx6LBZLLZPJFO5Pbm7ujNGuz8nJGfX8SKxWKyMrKyssPDxcKZFIYmbPni0vLy/3mVjWPyQQCO4d6dzZs2c5PB5vzrVztFgsjJsx7nSF3ZgBAAAAAGDS43K5Tp1O1zDW64uKikK2b9/eNp4x7HY7vfjii6FtbW0eOp3uDJ/PdxkMBvbx48e9x5/x+IlEIut45gijQ7ELAAAAAABj1pr7qsh67pzgZsbkSiTm0Nf/YBjvfV1dXSy1Wi3/4IMPzqlUKmtycnJkYmJib1NTE9dqtTJlMplCKpUOHDly5GJxcbFw9+7dwTabjTFnzpz+srKyS2w2mwQCwb2rV69u//TTT33eeOONf73zzjuBFy5c0PL5fBcRkUgksmdkZJiIiPbu3SvMz8+f4XK5GL/4xS+6d+/e3UL0Xcc2PT39248//tiXx+M5KyoqzotEIrtOp+OkpKTcbbfbGQsXLuyZyLP57LPPBBs2bBBbLBYmj8dz7t+//6JKpbJOJNZ0g2XMAAAAAAAw6bmLV/enpKTEz9/f37Fjx47Lq1atity3b59fd3c3Ozs7u7O4uLjF3Qk+cuTIxdOnT/MOHz4srK2t1el0ugYmk+nas2ePPxHRwMAAU6lUDmi1Wp1QKHSEhIQMCoVC59Dxm5ubPbZs2RJWWVmpb2hoOPPPf/7T88CBA3e5Y8THx/edPXu2IT4+vm/nzp2BRERZWVnijIyMjvr6+sYZM2bYrjdHg8HAdc9vxYoVYiIilUplOXXqlK6xsbHhd7/7XcvGjRtn3twnO3WhswsAAAAAAGM2kQ7szTDSMuYnnnjianl5ud/GjRvD6+rqzgx377Fjx7zr6+sFKpVKTkRksViYQUFBdiIiFotFaWlppuuNf/LkSc958+b1hoaG2omIli1bdqWqqsprxYoV3R4eHq6UlJQeIiK1Wt3/ySef+BARnT592uujjz5qIiJas2ZN19atW0ctVIdbxnzlyhXWsmXLIpubm3kMBsNls9nwHu8YodgFAAAAAIA7lsPhIL1ez+Nyuc7Ozk52VFTUjzqoLpeLsXTp0q633367Zeg5DofjZLO/K4sUCoXVaDRyTCYT08/Pzzkkxog5sNlsF5PJdH8nu93+fUHKZDJHvnEMXn755bCEhITeEydONJ09e5azYMGC6BuJN51gGTMAAAAAANyx8vLygqVSqaW0tPRCenp6hNVqZRB9V4C6vyclJV2tqKjwa2lpYRMRtbe3s/R6PWdoLG9vb2dKSkpnZmam2L0T8qVLlzyKi4uF8+fP76+pqfE2Go1su91O7733njAxMbFvtNzmzJnTV1JSIiQiKikp8Z/I/K5evcqaOXPmIBHR3r17AyYSY7pCsQsAAAAAAJPe0Hd2s7KywrRaLffAgQMBxcXFhqSkpL558+b15uTkhBARpaamdsjlcsXixYsj1Wq1ZdOmTS0LFy6USqVSxYIFC6QGg8FjuHEKCwtbAgIC7FKpNEYikcQkJydHBQcH28PDw22bN29uSUhIkMrl8pjY2Fjz8uXLu0fLubi4+PK+ffuClEqlvKenhzWReb/88sttW7ZsmTlnzhyZw+GYSIhpizFaOx4AAAAAAECj0TSrVKrO250HTD8ajSZApVJFTORedHYBAAAAAABgysEGVQAAAAAAAD+RU6dO8VeuXBl57TEOh+PUarW625XTVIViFwAAAAAA4CcSFxc3MNxfKMHNh2XMAAAAAAAAMOWg2AUAAAAAAIApB8UuAAAAAAAATDkodgEAAAAAAGDKQbELAAAAAACTHovFUstkMoX7k5ubO2O063NyckY9PxKr1crIysoKCw8PV0okkpjZs2fLy8vLfSaW9Q8JBIJ7Rzv/zTffcB988MFZIpFIGRMTI587d670o48+8hru2rCwsNlGoxEbDo8CDwcAAAAAACY9LpfrHM8uxkVFRSHbt29vG88YdrudXnzxxdC2tjYPnU53hs/nuwwGA/v48ePe4894fMxmMyM5OVnyhz/8wZCamtpDRPTVV1/x/u///s9z0aJFfbd6/KkIxS4AAAAAAIzZ38saRVda+gQ3M6YwzMu8cKXcMN77urq6WGq1Wv7BBx+cU6lU1uTk5MjExMTepqYmrtVqZcpkMoVUKh04cuTIxeLiYuHu3buDbTYbY86cOf1lZWWX2Gw2CQSCe1evXt3+6aef+rzxxhv/eueddwIvXLig5fP5LiIikUhkz8jIMBER7d27V5ifnz/D5XIxfvGLX3Tv3r27hei7jm16evq3H3/8sS+Px3NWVFScF4lEdp1Ox0lJSbnbbrczFi5c2DPaXPbu3es/Z86cPnehS0T085//3PLzn//cQkTU1tbGWrJkyd1XrlzxuPfee/tdLtd4H9e0g2XMAAAAAAAw6bmLV/enpKTEz9/f37Fjx47Lq1atity3b59fd3c3Ozs7u7O4uLjF3Qk+cuTIxdOnT/MOHz4srK2t1el0ugYmk+nas2ePPxHRwMAAU6lUDmi1Wp1QKHSEhIQMCoVC59Dxm5ubPbZs2RJWWVmpb2hoOPPPf/7T88CBA3e5Y8THx/edPXu2IT4+vm/nzp2BRERZWVnijIyMjvr6+sYZM2bYRpvfmTNnePfee695pPM5OTmh8fHxfY2NjQ2LFy/uNhqNnBt7olMfOrsAAAAAADBmE+nA3gwjLWN+4oknrpaXl/tt3LgxvK6u7sxw9x47dsy7vr5eoFKp5EREFouFGRQUZCciYrFYlJaWZrre+CdPnvScN29eb2hoqJ2IaNmyZVeqqqq8VqxY0e3h4eFKSUnpISJSq9X9n3zyiQ8R0enTp70++uijJiKiNWvWdG3dunXmWOf70EMPRTU3N/MiIyMtH3/8cdOXX37p/f77758nIkpJSelZs2aNY6yxpisUuwAAAAAAcMdyOByk1+t5XC7X2dnZyY6KivpRB9XlcjGWLl3a9fbbb7cMPcfhcJxs9ndlkUKhsBqNRo7JZGL6+fk5h8QYMQc2m+1iMpnu72S32xnuc0wmc0zrjWNiYiz/+Mc/vt+M6sSJE03V1dWCl156SXRNrLGEgn/D0wIAAAAAgDtWXl5esFQqtZSWll5IT0+PsFqtDKLvClD396SkpKsVFRV+LS0tbCKi9vZ2ll6v/9EyYG9vb2dKSkpnZmam2GKxMIiILl265FFcXCycP39+f01NjbfRaGTb7XZ67733hImJiaNuHDVnzpy+kpISIRFRSUmJ/2jXZmZmdtXW1nodPHjQ132sv7//+3pt3rx5vf/zP//jT0RUXl7uc/XqVdZYn9F0hWIXAAAAAAAmvaHv7GZlZYVptVrugQMHAoqLiw1JSUl98+bN683JyQkhIkpNTe2Qy+WKxYsXR6rVasumTZtaFi5cKJVKpYoFCxZIDQaDx3DjFBYWtgQEBNilUmmMRCKJSU5OjgoODraHh4fbNm/e3JKQkCCVy+UxsbGx5uXLl3ePlnNxcfHlffv2BSmVSnlPT8+oxamXl5frgw8+OL9v377AmTNnzr7nnntkr732Wkhubm4rEdH27dtbP//8cy+FQiE/fvy4b0hIyOBEn+V0wcAuXgAAAAAAMBqNRtOsUqk6b3ceMP1oNJoAlUoVMZF70dkFAAAAAACAKQcbVAEAAAAAAPxETp06xV+5cmXktcc4HI5Tq9XqbldOUxWKXQAAAAAAgJ9IXFzcwHB/oQQ3H5YxAwAAAAAAwJSDYhcAAAAAAACmHBS7AAAAAAAAMOWg2AUAAAAAAIApB8UuAAAAAABMeiwWSy2TyRTuT25u7ozRrs/JyRn1/EisVisjKysrLDw8XCmRSGJmz54tLy8v95nBsdJ1AAAgAElEQVRY1j8kEAjuHenc2bNnOTweb45MJlNERUXFPPPMM2KHw3Ezhp22sBszAAAAAABMelwu1zmeXYyLiopCtm/f3jaeMex2O7344ouhbW1tHjqd7gyfz3cZDAb28ePHvcef8fiJRCKrTqdrsNlsFB8fH/3nP//5rlWrVnX/FGNPRSh2AQAAAABgzI7vLhR1Gi4JbmbMAFG4+ZG16w3jva+rq4ulVqvlH3zwwTmVSmVNTk6OTExM7G1qauJarVamTCZTSKXSgSNHjlwsLi4W7t69O9hmszHmzJnTX1ZWdonNZpNAILh39erV7Z9++qnPG2+88a933nkn8MKFC1o+n+8iIhKJRPaMjAwTEdHevXuF+fn5M1wuF+MXv/hF9+7du1uIvuvYpqenf/vxxx/78ng8Z0VFxXmRSGTX6XSclJSUu+12O2PhwoU9Y52Xh4cHxcXF9Z07d47rdDpp7dq1Mz/99FNfBoPh+u1vf2vMzMw0jfdZTUdYxgwAAAAAAJOeu3h1f0pKSvz8/f0dO3bsuLxq1arIffv2+XV3d7Ozs7M7i4uLW9yd4CNHjlw8ffo07/Dhw8La2lqdTqdrYDKZrj179vgTEQ0MDDCVSuWAVqvVCYVCR0hIyKBQKHQOHb+5udljy5YtYZWVlfqGhoYz//znPz0PHDhwlztGfHx839mzZxvi4+P7du7cGUhElJWVJc7IyOior69vnDFjhm2sc+3t7WVWV1f7xMbGDpSVld31zTff8BsbG8/8/e9/12/evHnmpUuXPG7Wc53K0NkFAAAAAIAxm0gH9mYYaRnzE088cbW8vNxv48aN4XV1dWeGu/fYsWPe9fX1ApVKJScislgszKCgIDsREYvForS0tOt2Sk+ePOk5b9683tDQUDsR0bJly65UVVV5rVixotvDw8OVkpLSQ0SkVqv7P/nkEx8iotOnT3t99NFHTUREa9as6dq6devM0cYwGAxcmUymYDAYtGjRou6nn376anp6uujpp5++wmazSSQS2efOndt38uRJQXh4+Jg7xdMVil0AAAAAALhjORwO0uv1PC6X6+zs7GRHRUX9qIPqcrkYS5cu7Xr77bdbhp7jcDhONvu7skihUFiNRiPHZDIx/fz8nENijJgDm812MZlM93ey2+0M9zkmkznyjUO439kd67gwOixjBgAAAACAO1ZeXl6wVCq1lJaWXkhPT4+wWq0Mou8KUPf3pKSkqxUVFX4tLS1sIqL29naWXq/nDI3l7e3tTElJ6czMzBRbLBYGEdGlS5c8iouLhfPnz++vqanxNhqNbLvdTu+9954wMTGxb7Tc5syZ01dSUiIkIiopKfGfyPwSEhJ6Dx8+LLTb7dTa2so+deqU1wMPPNA/kVjTDYpdAAAAAACY9Ia+s5uVlRWm1Wq5Bw4cCCguLjYkJSX1zZs3rzcnJyeEiCg1NbVDLpcrFi9eHKlWqy2bNm1qWbhwoVQqlSoWLFggNRgMw773WlhY2BIQEGCXSqUxEokkJjk5OSo4ONgeHh5u27x5c0tCQoJULpfHxMbGmpcvXz7qTsnFxcWX9+3bF6RUKuU9PT2sicx7xYoV3TExMQNyuTwmMTFR+vvf//5fYrHYPpFY0w0DbXEAAAAAABiNRqNpVqlUnbc7D5h+NBpNgEqlipjIvejsAgAAAAAAwJSDDaoAAAAAAAB+IqdOneKvXLky8tpjHA7HqdVqdbcrp6kKxS4AAAAAAMBPJC4ubmC4v1CCmw/LmAEAAAAAAGDKQbELAAAAAAAAUw6KXQAAAAAAAJhyUOwCAAAAAADAlINiFwAAAAAAJj0Wi6WWyWQK9yc3N3fGaNfn5OSMen4kVquVkZWVFRYeHq6USCQxs2fPlpeXl/tMLOsfEggE9w53/NSpU3z3vHx9fe8JCwubLZPJFPfdd5/Ufc3vf//7IC6XO6erq4t1M3KZDrAbMwAAAAAAjNmVw3qRra1fcDNjeszwNAufkhpGu4bL5TrHs4txUVFRyPbt29vGk4fdbqcXX3wxtK2tzUOn053h8/kug8HAPn78uPd44ozXtTs0L1myJOKxxx7refbZZ03XXnP48GF/pVLZf/DgwbvWrVvXdSvzmSrQ2QUAAAAAgDtSV1cXKyIiQqnRaLhERMnJyZH5+fkBWVlZYVarlSmTyRSLFy+OJCIqLi4Wzp49Wy6TyRTPPPNMuN1uJ6Lvuq3r168PjY2NlZ04ccLrnXfeCfzTn/50mc/nu4iIRCKRPSMjw0REtHfvXqFUKlVIJJKYtWvXhrnzEAgE9/7mN78Ji46OVqhUKpnBYGATEel0Os4999wjUyqV8hdeeCF0ovM8c+YM12w2M/Py8lrKy8uFE35g0ww6uwAAAAAAMGbX68DeKu7i1f07OzvbmJmZadqxY8flVatWRWZlZbV3d3ezs7OzO4mI9u/fH+Tulp4+fZp3+PBhYW1trY7L5bqWL18u3rNnj//zzz/fNTAwwFQqlQOFhYWtNTU1/JCQkEGhUOgcOn5zc7PHli1bwurq6hoDAwPtDzzwgPTAgQN3rVixontgYIAZHx/ft3Pnzpbnnntu5s6dOwP/+Mc/GrOyssQZGRkdzz//fNe2bdsCJzr30tJS4ZNPPnklKSmpb/Xq1byWlhZ2WFiYfaLxpgt0dgEAAAAAYNJzL2N2fzIzM01ERE888cRVuVw+sHHjxvD9+/c3D3fvsWPHvOvr6wUqlUouk8kUJ0+e9Llw4QKXiIjFYlFaWpppuPuudfLkSc958+b1hoaG2j08PGjZsmVXqqqqvIiIPDw8XCkpKT1ERGq1uv/SpUscIqLTp097ZWZmXiEiWrNmzYSXHv/1r38Vrly58gqLxaJFixaZysrK/CYaazpBZxcAAAAAAO5YDoeD9Ho9j8vlOjs7O9lRUVG2ode4XC7G0qVLu95+++2Woec4HI6Tzf6uLFIoFFaj0cgxmUxMPz8/55AYI+bAZrNdTCbT/Z3sdjvDfY7JZI584xjU1NTwL126xE1KSpISEdlsNoZIJLK+8sorHTcSdzpAZxcAAAAAAO5YeXl5wVKp1FJaWnohPT09wmq1Moi+K0Dd35OSkq5WVFT4tbS0sImI2tvbWXq9njM0lre3tzMlJaUzMzNTbLFYGEREly5d8iguLhbOnz+/v6amxttoNLLtdju99957wsTExL7RcpszZ05fSUmJkIiopKTEfyLzKysrE2ZnZ7e2tLR809LS8s23336rbWtr4wyXP/wQil0AAAAAAJj03O/suj9ZWVlhWq2We+DAgYDi4mJDUlJS37x583pzcnJCiIhSU1M75HK5YvHixZFqtdqyadOmloULF0qlUqliwYIFUoPB4DHcOIWFhS0BAQF2qVQaI5FIYpKTk6OCg4Pt4eHhts2bN7ckJCRI5XJ5TGxsrHn58uXdo+VcXFx8ed++fUFKpVLe09Mzob8M+tvf/iZ8+umnfzDOokWLTKWlpdio6joYo7XjAQAAAAAANBpNs0ql6rzdecD0o9FoAlQqVcRE7kVnFwAAAAAAAKYcbFAFAAAAAADwEzl16hR/5cqVkdce43A4Tq1Wq7tdOU1VKHYBAAAAAAB+InFxcQPu//+FWwvLmAEAAAAAAGDKQbELAAAAAAAAUw6KXQAAAAAAAJhyUOwCAAAAAADAlINiFwAAAAAAJj0Wi6WWyWQK9yc3N3fGaNfn5OSMen4kVquVkZWVFRYeHq6USCQxs2fPlpeXl/tMLOsfEggE996MODA22I0ZAAAAAADG7G9/+5vo22+/FdzMmEFBQebHH3/cMNo1XC7XOZ5djIuKikK2b9/eNp487HY7vfjii6FtbW0eOp3uDJ/PdxkMBvbx48e9xxMHJgd0dgEAAAAA4I7U1dXFioiIUGo0Gi4RUXJycmR+fn5AVlZWmNVqZcpkMsXixYsjiYiKi4uFs2fPlstkMsUzzzwTbrfbiei7buv69etDY2NjZSdOnPB65513Av/0pz9d5vP5LiIikUhkz8jIMBER7d27VyiVShUSiSRm7dq1Ye48BALBvb/5zW/CoqOjFSqVSmYwGNhERDqdjnPPPffIlEql/IUXXggdbS4VFRXecXFx0UlJSXdHRkbGLF68ONLpdBIR0UsvvRSiVCrlEokk5pe//GW4+3hcXFz02rVrw2bPni2PiIhQHjt2zOsmP+I7Gjq7AAAAAAAwZtfrwN4q7uLV/Ts7O9uYmZlp2rFjx+VVq1ZFZmVltXd3d7Ozs7M7iYj2798f5O4Enz59mnf48GFhbW2tjsvlupYvXy7es2eP//PPP981MDDAVCqVA4WFha01NTX8kJCQQaFQ6Bw6fnNzs8eWLVvC6urqGgMDA+0PPPCA9MCBA3etWLGie2BggBkfH9+3c+fOlueee27mzp07A//4xz8as7KyxBkZGR3PP/9817Zt2wKvN8fGxkb+119/fSEiIsKmVqtlJ06c8HrkkUf6fvvb33775ptvGomIHn/88chDhw75PvPMMz1ERHa7nfHNN980vvvuu755eXmhSUlJ+pv1zO90KHYBAAAAAGDSG2kZ8xNPPHG1vLzcb+PGjeF1dXVnhrv32LFj3vX19QKVSiUnIrJYLMygoCA7ERGLxaK0tDTT9cY/efKk57x583pDQ0PtRETLli27UlVV5bVixYpuDw8PV0pKSg8RkVqt7v/kk098iIhOnz7t9dFHHzUREa1Zs6Zr69atM0cbY/bs2f1RUVE2IqKYmBhzU1MTh4joo48+8i4oKJhhsViY3d3dbIVCMUBEPURES5cuNRER3Xffff2//e1vOdebx3SCYhcAAAAAAO5YDoeD9Ho9j8vlOjs7O9nuYvFaLpeLsXTp0q633367Zeg5DofjZLO/K4sUCoXVaDRyTCYT08/Pzzkkxog5sNlsF5PJdH8nu93OcJ9jMpkj3zgEl8v9/loWi0V2u51hNpsZ2dnZ4TU1NQ2zZs2ybdiwIdRisXz/OiqPx3O5x3U4HIzh4k5XeGcXAAAAAADuWHl5ecFSqdRSWlp6IT09PcJqtTKIvitA3d+TkpKuVlRU+LW0tLCJiNrb21l6vf5HXVBvb29nSkpKZ2ZmpthisTCIiC5duuRRXFwsnD9/fn9NTY230Whk2+12eu+994SJiYl9o+U2Z86cvpKSEiERUUlJif9E5mc2m5lERDNmzLD39PQwjx496jeRONMRil0AAAAAAJj03O/suj9ZWVlhWq2We+DAgYDi4mJDUlJS37x583pzcnJCiIhSU1M75HK5YvHixZFqtdqyadOmloULF0qlUqliwYIFUoPB4DHcOIWFhS0BAQF2qVQaI5FIYpKTk6OCg4Pt4eHhts2bN7ckJCRI5XJ5TGxsrHn58uXdo+VcXFx8ed++fUFKpVLe09PDmsi8AwICHKmpqR0KhSJm0aJFs1QqVf9E4kxHjNHa8QAAAAAAABqNplmlUnXe7jxg+tFoNAEqlSpiIveiswsAAAAAAABTDjaoAgAAAAAA+ImcOnWKv3Llyshrj3E4HKdWq9XdrpymKhS7AAAAAAAAP5G4uLiB4f5CCW4+LGMGAAAAAACAKQfFLgAAAAAAAEw5KHYBAAAAAABgykGxCwAAAAAAAFMOil0AAAAAAJj0WCyWWiaTKdyf3NzcGaNdn5OTM+r5kVgsFsavfvUrkUgkUorFYuWDDz4469y5c5yJZU20YcOG0M2bNwePdH7JkiURYWFhs93zeu2114ImOhb8EHZjBgAAAACAMWtofFnU36cX3MyYnl5Ss0L+hmG0a7hcrnM8uxgXFRWFbN++vW08edjtdlq3bl1YX18f8+LFi/VsNpveeust/8WLF8+qr69vYLFY4wk3Zq+99tq/nn32WdMtCT6NobMLAAAAAAB3pK6uLlZERIRSo9FwiYiSk5Mj8/PzA7KyssKsVitTJpMpFi9eHElEVFxcLJw9e7ZcJpMpnnnmmXC73U5ERAKB4N7169eHxsbGyk6cOOFVXl4esGfPHgOb/V1f8IUXXugSCASODz74wOfs2bMciUQS4x5/8+bNwRs2bAglIsrPzw9QKpXy6OhoxSOPPBLV29t7Q7VWamqqWKlUymfNmhXz4osvht5IrOkKnV0AAAAAABiz63VgbxV38er+nZ2dbczMzDTt2LHj8qpVqyKzsrLau7u72dnZ2Z1ERPv37w9yd4JPnz7NO3z4sLC2tlbH5XJdy5cvF+/Zs8f/+eef7xoYGGAqlcqBwsLC1pqaGn5ISMigUCh0Xjv2PffcY66vr+fFxMRYRsovNTXV5B573bp1oUVFRQGvvvrqt2OZ26ZNm2a+8cYbIUREZWVlF+Pi4gYKCgpagoODHXa7ne67777ompoa/ty5cwfG/+SmLxS7AAAAAAAw6Y20jPmJJ564Wl5e7rdx48bwurq6M8Pde+zYMe/6+nqBSqWSExFZLBZmUFCQnYiIxWJRWlqaiYjI6XQSg8FwDb3f5frRoR+pq6vjb968Oay3t5fV39/PSkhI6Bnr3IZbxlxaWircv39/gN1uZ3R0dHhoNBoeit3xQbELAAAAAAB3LIfDQXq9nsflcp2dnZ3sqKgo29BrXC4XY+nSpV1vv/12y9BzHA7H6V6yHBMTY21tbeWaTCamn5/f991drVYrWLZsmYnNZruczv80fS0Wy/dLlVevXh15+PDh8/Hx8QNFRUX+VVVV3hOdk06n4+zatSu4rq6uMTAw0LFkyZKIa8eCscEDAwAAAACAO1ZeXl6wVCq1lJaWXkhPT4+wWq0MIiI2m+1yf09KSrpaUVHh19LSwiYiam9vZ+n1+h/tsOzj4+N86qmnOteuXStyv9O7a9cufy6X63zooYf6Zs6cab9y5Qq7ra2NNTAwwDh+/Liv+16z2cwUi8U2q9XKOHTokPBG5mQymVh8Pt8pFAodBoOBXVlZ6Xv9u2AodHYBAAAAAGDSG/rO7oIFC3qee+65zgMHDgTU1dU1+vn5OQ8fPtybk5MTsmPHjtbU1NQOuVyuUCqV5iNHjlzctGlTy8KFC6VOp5M8PDxcRUVFl6VS6eDQcXbu3Nmydu3amXfffbfSYrEwhUKhvba2tpHJZBKXy3VlZ2cb4+Li5DNnzrTOmjXr+3d4c3JyWuPi4uRhYWGDcrnc3NfXN+Gtm+Pj4weUSqVZIpHEiMViq1qt7ptorOmMMZb15wAAAAAAMH1pNJpmlUrVebvz+KldvnyZ/fDDD0szMjK+femll6bd/CcDjUYToFKpIiZyLzq7AAAAAAAAwxCLxfbx/LcvTC4odgEAAAAAAG6xFStWiL/66iuva4+tXbu2/YUXXui6XTlNdSh2AQAAAAAAbrEDBw5cvt05TDfYjRkAAAAAAACmHBS7AAAAAAAAMOWg2AUAAAAAAIApB8UuAAAAAAAATDkodgEAAAAAYNJjsVhqmUymcH9yc3NnjHZ9Tk7OqOdHYrFYGL/61a9EIpFIKRaLlQ8++OCsc+fOcSaWNdGGDRtCN2/eHDzaNVu2bAmOjIyMkUqliujoaEVGRsZMq9XKGHpdUVGR/8qVK8UTzWW6wW7MAAAAAAAwZusbL4t0/RbBzYwp8+SZC+Viw2jXcLlc53j+87aoqChk+/btbePJw26307p168L6+vqYFy9erGez2fTWW2/5L168eFZ9fX0Di8UaT7gx+eMf/xj497//3eerr77SBQQEOCwWCyMvLy+4v7+fweVyXTd9wGkEnV0AAAAAALgjdXV1sSIiIpQajYZLRJScnByZn58fkJWVFWa1WpkymUyxePHiSCKi4uJi4ezZs+UymUzxzDPPhNvtdiIiEggE965fvz40NjZWduLECa/y8vKAPXv2GNjs7/qCL7zwQpdAIHB88MEHPmfPnuVIJJIY9/ibN28O3rBhQygRUX5+foBSqZRHR0crHnnkkaje3t4x1VoFBQUhJSUllwICAhxERDwez/X666+3CYVCJxHRW2+95R8REaH8+c9/Hv3FF194jR4NroXOLgAAAAAAjNn1OrC3irt4df/Ozs42ZmZmmnbs2HF51apVkVlZWe3d3d3s7OzsTiKi/fv3B7k7wadPn+YdPnxYWFtbq+Nyua7ly5eL9+zZ4//88893DQwMMJVK5UBhYWFrTU0NPyQkZNBdaLrdc8895vr6el5MTIxlpPxSU1NN7rHXrVsXWlRUFPDqq69+O9qcTCYT02w2M2Uy2eBw5y9duuSxffv20Lq6ukahUOi47777opVKpXnsT216Q7ELAAAAAACT3kjLmJ944omr5eXlfhs3bgyvq6s7M9y9x44d866vrxeoVCo5EZHFYmEGBQXZiYhYLBalpaWZiIicTicxGIwfLR12ua6/mriuro6/efPmsN7eXlZ/fz8rISGh53r3uFwuYjD+82ruX/7yF59XX311Zm9vL2v//v0X2traPObNm9cbGhpqJyJ68sknr+j1et51kwEiwjJmAAAAAAC4gzkcDtLr9Twul+vs7OwctpnncrkYS5cu7dLpdA06na6hubm5vqCgoJWIiMPhON1LlmNiYqytra1ck8n0gzpJq9UK5s6da2az2S6n8z9NX4vF8v11q1evjty1a9dlvV7f8PLLL7dardbr1lpCodDJ5/OdOp2OQ0S0ZMmSqzqdrkEqlQ6477+2GIbxQbELAAAAAAB3rLy8vGCpVGopLS29kJ6eHuHexZjNZrvc35OSkq5WVFT4tbS0sImI2tvbWXq9/kc7LPv4+DifeuqpzrVr14rc7/Tu2rXLn8vlOh966KG+mTNn2q9cucJua2tjDQwMMI4fP+7rvtdsNjPFYrHNarUyDh06JBxr/uvXrzdmZmaGd3Z2soi+6y67C9358+f3f/nll95tbW0sq9XK+Otf/+p3A49q2sEyZgAAAAAAmPSGvrO7YMGCnueee67zwIEDAXV1dY1+fn7Ow4cP9+bk5ITs2LGjNTU1tUMulyuUSqX5yJEjFzdt2tSycOFCqdPpJA8PD1dRUdFlqVT6o3dld+7c2bJ27dqZd999t9JisTCFQqG9tra2kclkEpfLdWVnZxvj4uLkM2fOtM6aNev7d3hzcnJa4+Li5GFhYYNyudzc19c3pq2bN27c2GE2m5k/+9nP5BwOx+np6emMi4vri4+PN/v7+ztefvnl1nnz5skDAwNtsbGxZofDgVbvGDHGsv4cAAAAAACmL41G06xSqTpvdx4/tcuXL7MffvhhaUZGxrcvvfTStJv/ZKDRaAJUKlXERO5FZxcAAAAAAGAYYrHYPp7/9oXJBcUuAAAAAADALbZixQrxV1999YP/yV27dm37Cy+80HW7cprqUOwCAAAAAADcYgcOHLh8u3OYbrAbMwAAAAAAAEw5KHYBAAAAAABgykGxCwAAAAAAAFMOil0AAAAAAJj0WCyWWiaTKdyf3NzcGaNdn5OTM+r5kVgsFsavfvUrkUgkUorFYuWDDz4469y5c5yJZU20YcOG0M2bNwcPd27FihVimUymiIqKiuHxeHPcc/vf//1fPyIim81Gfn5+ql//+tdhEx1/OsMGVQAAAAAAMOlxuVzneP4GqKioKGT79u1t4xnDbrfTunXrwvr6+pgXL16sZ7PZ9NZbb/kvXrx4Vn19fQOLxRp/4qNwb1p19uxZzmOPPSYZOr/333/fNzIy0nrkyBG/nTt3tjCZ6FWOB4pdAAAAAAAYs98e1oj0bb2CmxlTOsPb/N9PqQzjva+rq4ulVqvlH3zwwTmVSmVNTk6OTExM7G1qauJarVamTCb7/+zde1TTV7o//ueThCSERCSCyB3KLQmk0YKxVMShIx09RVcto7aiVMdR6/Smoq21/TqtXVN1KtXiWHtZpx6pnGmVdpRJbS3+VLDYqliacEsQKkXloiiQQELI5fP7oxMPRVSwWBHfr7WyFtnZ+9n7s/3rcT/ZUURFRVny8/PPvvvuu9IdO3b42mw25oEHHujMycn5icfjkUgkGrdkyZLmw4cPj9i0adP5PXv2eP/44486Hu/nVOmFF164nJOT471///4RMTExXampqZFnzpypICJat26db0dHB/ftt99uyMrK8t65c6ePzWZjQkNDrXl5eWclEonz1+zLP//5T+lf/vKX5g8//NDn8OHDHlOmTOn8NfHuNfivAQAAAAAAGPJcyavr9eGHH3qNGjXKsWXLlvqnnnoq7IMPPvBqa2vjZWZmtrz77rsXXCfB+fn5Z7///nthXl6etKSkRK/X6ys5HA773nvvjSIislgsnNjYWItOp9NLpVKHn59ft1Qq/UWSOnbsWHN5ebnwRutLT09vLS8vrzIYDJXR0dGW7Oxs71/zvB0dHczx48clc+bMaZ81a9aV3bt3S39NvHsRTnYBAAAAAKDfbuUEdjBcr4x55syZxj179ni9+OKLIadPn67oa+xXX30lKS8vF6lUKjkRUVdXF2f06NF2IiIul0sLFixoJSJyOp3EMAzbezzLXtN0jdOnT7uvW7cuwGQycTs7O7mTJ09uH+Aj/sKePXtGPvjggyaJROKcN29e69ixY/3tdvs514kz3Bx2CgAAAAAA7loOh4Oqq6uFAoHA2dLSwgsPD7f17sOyLDNr1qzL27dvv9D7Mz6f73QlkDExMdaGhgZBa2srx8vL6+rprk6nE82ZM6eVx+OxTuf/Hfp2dXVdrZRdsmRJWF5eXk1CQoIlOzt7VGFhoeTXPNcnn3wiPX36tDggIEBJRNTe3s7VaDSSxx57zPRr4t5LUMYMAAAAAAB3rfXr1/tGRUV17dq168dFixaFWq1WhoiIx+Oxrr+nTp1q1Gg0XhcuXOARETU3N3Orq6uvuWF5xIgRzj/+8Y8ty5YtC7Lb7URE9I9//GOUQCBwpqSkdAQGBtqvXLnCa2pq4losFubgwYOerrFms5kTHBxss1qtzCeffPKrSo6vXLnCKSkpEZ8/f1534cKFsgsXLpRt3Lix/n//939RyjwAOK2U1YwAACAASURBVNkFAAAAAIAhz/WdXdf7hx9+uP3pp59u+fjjj71Pnz5d5eXl5czLyzOtWbPGb8uWLQ3p6emX5HK5IjY21pyfn3/21VdfvfD73/8+yul0kpubG5udnV0fFRXV3Xuebdu2XVi2bFngfffdF9vV1cWRSqX2kpKSKg6HQwKBgM3MzGxUq9XywMBAa0RERJdr3Jo1axrUarU8ICCgWy6Xmzs6Om756ubdu3d7PfTQQyZ3d/er9dNPPPFE22uvvRZosVjqe7bD9TH9qT8HAAAAAIB7l1arrVOpVC13eh2/tfr6et4jjzwS9ec///niqlWr7rnnHwq0Wq23SqUKvZWxONkFAAAAAADoQ3BwsH0gv+0LQwuSXQAAAAAAgNts/vz5wadOnRL3bFu2bFnzCy+8cPlOrWm4Q7ILAAAAAABwm3388cf1d3oN9xrcxgwAAAAAAADDDpJdAAAAAAAAGHaQ7AIAAAAAAMCwg2QXAAAAAAAAhh0kuwAAAAAAMORxudw4mUymcL3Wrl075kb916xZc8PPr6erq4v505/+FBQUFBQbHBwcm5ycHHHmzBn+ra2aaOXKlf7r1q3zvdXxcOtwGzMAAAAAAAx5AoHAOZDfvM3OzvbbuHFj00DmsNvt9Pzzzwd0dHRwzp49W87j8eidd94ZNWPGjIjy8vJKLpc78IXDHYNkFwAAAAAA+m/fM0F0sVI0qDFHK8z02PZzAx12+fJlblxcnHz//v1nVCqVdfr06WG/+93vTLW1tQKr1cqRyWSKqKgoS35+/tl3331XumPHDl+bzcY88MADnTk5OT/xeDwSiUTjlixZ0nz48OERmzZtOr9nzx7vH3/8Ucfj/ZwqvfDCC5dzcnK89+/fPyImJqYrNTU18syZMxVEROvWrfPt6Ojgvv322w1ZWVneO3fu9LHZbExoaKg1Ly/vrEQicd7sGdRqdXRcXFzHN998M8JkMnHfe++9uqlTp3YYDAb+3LlzwywWC4eI6J133qlPSUnp1Gg0kvXr1/tLpVKbwWBwVyqV5n379p3lcFC02xt2BAAAAAAAhjxX8up6ffjhh16jRo1ybNmypf6pp54K++CDD7za2tp4mZmZLe++++4F10lwfn7+2e+//16Yl5cnLSkp0ev1+koOh8O+9957o4iILBYLJzY21qLT6fRSqdTh5+fXLZVKf5Gkjh071lxeXi680frS09Nby8vLqwwGQ2V0dLQlOzvbu7/PZrfbmbKysqpNmzadW79+vT8Rkb+/v/3YsWPVlZWVVZ9++umPK1asCHb1r6qqct++ffu5mpqaivr6ekFBQYF4YLt5b8DJLgAAAAAA9N8tnMAOhuuVMc+cOdO4Z88erxdffDHk9OnTFX2N/eqrryTl5eUilUolJyLq6urijB492k5ExOVyacGCBa1ERE6nkxiGYXuPZ9lrmq5x+vRp93Xr1gWYTCZuZ2cnd/Lkye39fbZZs2a1EhE99NBDnatXr+YTEXV3dzOLFi0KqaysdOdwOPTTTz8JXP2VSmVneHi4jYgoJibGXFtbe8vfKR7OkOwCAAAAAMBdy+FwUHV1tVAgEDhbWlp4riSwJ5ZlmVmzZl3evn37hd6f8fl8p6tkOSYmxtrQ0CBobW3leHl5XT3d1el0ojlz5rTyeDzW6fy/Q9+urq6rlbJ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xsd35+fm2O3fuVMrlcnOBQKDjcDhG0/XWrl3rIZPJrDQaDWvp0qVde/fubd2+fbtjW1ubWVRUlK+dnZ2utLS0drQ5R0dHqzZv3uxORHTp0iWen5+f+vr162bt7e1sPp9vuHLlikVERES/wWCgZ5991uPcuXMCd3d3rdF4axrd3d0snU7HODk56YiIeDyeUSqVak35i4uL+VlZWU7t7e1mf/rTn5rXr1/f1dPTw3r00Ud9enp62DqdjnnjjTdan3766e7b5yWXy81Xrlzp87e//a0hMjKy/4UXXnA7d+6cYGBggElMTGzbtGlTx1j/LlMNil0AAAAAAJg2dDodFRYWCjZs2NBBRJSTk2NbUVHBq66urlIqlZzQ0FD/JUuWqAoLC62GixcUFNRbWloGm76Fm5qayrO2tta7uLgMlJWVWeTl5dmuWrWqKzc318F0zT179rQ4OTnpdTodRURE+JWWlvK2bNnSduDAAafi4uLa2bNn6+42by8vr0EOh2Osq6szLy4utpo/f/7NlpYWs4KCAr6dnZ3Oz89PbWFhYTxy5IhtfX09t6ampqq5udksKChI8uyzz95wcnLSP/LII90eHh6/ioyM7I2Nje1JSkrqZLNv7ea+fv262cWLFxWXL1+2WL58uc/69eu7LC0tDV9++WW9UCg0KJVKTlhYmHjNmjVDxa5MJuOuXr3a+x//+Me/IyIi1O+8846DjY2NvrKyslqtVjO//vWvxUuXLu0Vi8UDk/6H/A9AsQsAAAAAAGM2ng7sZNJqtSyxWBzQ0tJiHhgY2B8XF9dLRFRSUiKIj4/v5HA45O7urgsLC1OdPXvWcqS4p6dnz3D54+PjO3Nzc4UFBQU2Z86cqbm92D1y5Ijw8OHDDjqdjmlvbzeTyWQWYWFh6vGuISQkRFVYWGj1r3/9i79p06brjY2N5ufOnbOysbHRh4aGqoiIiouLh+bt5eU1GB4ePrRV+fjx41cvXLjQ9tVXXwmysrKcv/32W+tPP/20gYho2bJl3Ww2m0JCQjQ3btwwIyIyGAzMxo0b3b7//ns+i8WitrY28+bmZg4RUWdnJycuLs7nk08+ufLggw9qiIi+/fZba4VCYfn555/bERH19fWx5XK5xXQtdvHMLgAAAAAATHmmZ3YbGhoqBgYGmB07djgSEZm2+d5ppPhIVq9e3Z2Xl2fv6uo6IBQKDaa4QqEw37dvn1NxcXFtbW2tPDo6ukej0UyojgoPD1edP3+er1AoeL/+9a/VixYtUpWVlfG///57/oIFC4ae12UYZsQcoaGh6m3btrUVFBTUfv3110MvpbKwsBhasGnt2dnZwhs3bnAqKiqqFQqF3N7eflCtVrOIiAQCgX727NkDRUVF/NvOY3bv3t2oUCjkCoVC3tLSUrFixYreiax1KkCxCwAAAAAA04a9vb0+Kyurcf/+/U5arZaJiorqy8vLE+p0OmptbeVcuHCBv3DhwpsjxUfKy7jla5UAACAASURBVOfzjenp6c1bt25V3h7v6upi83g8g1Ao1Dc1NXGKiopsTGNWVlb6np6eMddUUVFRqm+//dbW1tZWz+FwyMnJSd/b28v+4Ycf+IsXL775f8f0ffLJJ0KdTkdXr141+/777wVERD09PaxTp04JTLlKS0t5Li4uo3Zce3p62A4ODoNcLtf4xRdfCFpbW81NY2ZmZsavv/76yrFjx+z/9re/CYmIHnnkkZ4DBw7M0mq1DBFReXk5t7e3d9rWjNjGDAAAAAAA00pkZKTa399ffejQIbuUlJTO8+fP8/39/SUMwxgzMjKaPTw8dAkJCd3DxUfLm5SU1HVnLDw8XB0YGNgvEokkHh4e2pCQkKEO7Lp16zpiYmJEjo6Og3d7QRXRra5sd3c3Z8WKFTdMMbFYrL558ybb9NxvQkJC93fffWft5+cnmTNnjiY0NLSP6NaLuHbt2uX04osvelpYWBgsLS0N//jHP/492vV+97vfdcbExPgEBgb6SySS/jlz5vzoDdPW1taG06dP1y9atMiXz+cbXnnllY6GhgZuUFCQv9FoZIRC4WB+fv6Vu61rqmLG294HAAAAAID7i0wma5BKpdP2rbwwfclkMgepVOo1kXOnbUsaAAAAAAAAYCTYxgwAAAAAADBJrl27xl60aJHfnfGioqIaZ2dn/b2Y0/0KxS4AAAAAAMAkcXZ21pu+4Qv3FrYxAwAAAAAAwIyDYhcAAAAAAABmHBS7AAAAAAAAMOOg2AUAAAAAAIAZB8UuAAAAAABMeWw2O0QsFgeIRCJJdHS0T0dHB3uiuZKTk918fHwkycnJbqmpqS4Mw4RUVlZyTeMZGRmODMOEnDlzxnK0PJmZmY59fX1DNZWrq2uQUqkc9iXAGzZscM/MzHQ0/V6wYIHoySef9DT9TkxMdEtPT3e687yVK1d6vf/++3ZERMeOHbPx9/cP8PPzC/D29pbs2rXL4c5jfq6srCz7Z555xmMyct1rKHYBAAAAAGDK43K5BoVCIa+rq6uytbXV7dq1a9ZEcx09enRWRUWFPDs7u5mISCQSqXNycoSm8ZMnTwq9vb01d8uTnZ3tpFKpxlRTRUZGqr7//ns+EZFer6euri5OTU0NzzReVlbG/81vfqMa6XytVsu8/PLLnqdOnaqrqamRV1ZWypcsWdI3lmvfr/DpIQAAAAAAGLPvcqrdO1tUo3Y8x0voyu9/6Bn/prEeP3/+/Jvl5eU8IiKDwUApKSluBQUFNgzDGDdt2qRMTEzsGikeHR3to1arWcHBwf5paWlKIqLY2Nju/Px82507dyrlcrm5QCDQcTgco+l6a9eu9ZDJZFYajYa1dOnSrr1797Zu377dsa2tzSwqKsrXzs5OV1paWjvanKOjo1WbN292JyK6dOkSz8/PT339+nWz9vZ2Np/PN1y5csUiIiKi32Aw0LPPPutx7tw5gbu7u9ZovDWN7u5ulk6nY5ycnHRERDwezyiVSrWm/MXFxfysrCyn9vZ2sz/96U/N69ev7zp16pQgMzPTRSgUDtbU1PCCgoL6T5w48W8Wi0XHjx+3ef31192EQqEuKCio/+rVq9zCwsL6cfzZpjwUuwAAAAAAMG3odDoqLCwUbNiwoYOIKCcnx7aiooJXXV1dpVQqOaGhof5LlixRFRYWWg0XLygoqLe0tAw2fQs3NTWVZ21trXdxcRkoKyuzyMvLs121alVXbm6ug+mae/bsaXFyctLrdDqKiIjwKy0t5W3ZsqXtwIEDTsXFxbWzZ8/W3W3eXl5egxwOx1hXV2deXFxsNX/+/JstLS1mBQUFfDs7O52fn5/awsLCeOTIEdv6+npuTU1NVXNzs1lQUJDk2WefveHk5KR/5JFHuj08PH4VGRnZGxsb25OUlNTJZt/azX39+nWzixcvKi5fvmyxfPlyn/Xr13cREVVXV/MuX778v15eXoMhISHib775hr9w4cKbL7/8smdRUZFCLBYPLF26dM4v8se6x1DsAgAAAADAmI2nAzuZtFotSywWB7S0tJgHBgb2x8XF9RIRlZSUCOLj4zs5HA65u7vrwsLCVGfPnrUcKe7p6dkzXP74+PjO3NxcYUFBgc2ZM2dqbi92jxw5Ijx8+LCDTqdj2tvbzWQymUVYWJh6vGsICQlRFRYWWv3rX//ib9q06XpjY6P5uXPnrGxsbPShoaEqIqLi4uKheXt5eQ2Gh4cPbVU+fvz41QsXLrR99dVXgqysLOdvv/3W+tNPP20gIlq2bFk3m82mkJAQzY0bN8xM5wQFBd309vYeJCKSSCT9V65cMRcIBHp3d3etWCweICJavXp156FDhya8LXyqwjO7AAAAAAAw5Zme2W1oaKgYGBhgduzY4UhEZNrme6eR4iNZvXp1d15enr2rq+uAUCg0mOIKhcJ83759TsXFxbW1tbXy6OjoHo1GM6E6Kjw8XHX+/Hm+QqHg/frXv1YvWrRIVVZWxv/+++/5CxYsGHpel2GYEXOEhoaqt23b1lZQUFD79ddfD72UysLCYmjBt6+dy+UO/WCz2aTT6Zjx3pvpCsUuAAAAAABMG/b29vqsrKzG/fv3O2m1WiYqKqovLy9PqNPpqLW1lXPhwgX+woULb44UHykvn883pqenN2/dulV5e7yrq4vN4/EMQqFQ39TUxCkqKrIxjVlZWel7enrGXFNFRUWpvv32W1tbW1s9h8MhJycnfW9vL/uHH37gL168+Ob/HdP3ySefCHU6HV29etXs+++/FxAR9fT0sE6dOiUw5SotLeW5uLgMjOfemUilUk1TUxO3pqbGnIjo+PHjwrudMx1hGzMAAAAAAEwrkZGRan9/f/WhQ4fsUlJSOs+fP8/39/eXMAxjzMjIaPbw8NAlJCR0DxcfLW9SUlLXnbHw8HB1YGBgv0gkknh4eGhDQkKGOrDr1q3riImJETk6Og7e7QVVRLe6st3d3ZwVK1bcMMXEYrH65s2bbNNzvwkJCd3fffedtZ+fn2TOnDma0NDQPqJbL+LatWuX04svvuhpYWFhsLS0NPzjH//493jumwmfzzfu2bPn6qOPPioSCoW64ODgEf8TYDq7b1rYAAAAAAAwMTKZrEEqlXbc63nA5Onp6WHZ2NgYDAYDPfPMMx4ikUizbdu2tns9rzvJZDIHqVTqNZFzsY0ZAAAAAADgPvPuu+86iMXiAJFIJOnt7WWnpqbOuP/MQGcXAAAAAABGhc7u2F27do29aNEivzvjRUVFNc7Ozvp7Mafp7Od0dvHMLgAAAAAAwCRxdnbWm77hC/cWtjEDAAAAAADAjINiFwAAAAAAAGYcFLsAAAAAAAAw46DYBQAAAAAAgBkHxS4AAAAAAEx5bDY7xPSpnOjoaJ+Ojg72RHMlJye7+fj4SJKTk91SU1NdGIYJqays5JrGMzIyHBmGCTlz5ozlaHkyMzMd+/r6hmoqV1fXIKVSiZcATxH4QwAAAAAAwJidPvCue0fT1VGLwPFycPfs/23KxqbRjuFyuQbTW45XrFjhtWvXrllvv/32tYlc7+jRo7Pa29sv83g8Y2pqqotIJFLn5OQId+7cqSQiOnnypNDb21tztzzZ2dlOiYmJnQKBwDCRecAvC51dAAAAAACYVubPn3+zpaXFnIjIYDBQcnKym0gkkvj6+gYcPHjQbrR4dHS0j1qtZgUHB/ubYrGxsd35+fm2RERyudxcIBDohEKhznS9tWvXegQGBvr7+PhIXnnlFRciou3btzu2tbWZRUVF+YaFhfnebc41NTXmc+fOlaxevdrTx8dHEhkZKVKpVAwR0e7dux0CAwP9/fz8An772996m7rFK1eu9Hr22Wfdg4ODxW5ubkHvv/++3eTeyZkNnV0AAAAAABizu3Vgf2k6nY4KCwsFGzZs6CAiysnJsa2oqOBVV1dXKZVKTmhoqP+SJUtUhYWFVsPFCwoK6i0tLYNNXeLU1FSetbW13sXFZaCsrMwiLy/PdtWqVV25ubkOpmvu2bOnxcnJSa/T6SgiIsKvtLSUt2XLlrYDBw44FRcX186ePVs30nxv19jYaPHBBx/8b0RExNXY2Ni5OTk5ds8//3zn2rVru9LS0jqIiF566SWXrKwshz/+8Y9tRETXr183u3jxouLy5csWy5cv91m/fn3X5N/VmQmdXQAAAAAAmPK0Wi1LLBYH2NnZPdDd3c2Ji4vrJSIqKSkRxMfHd3I4HHJ3d9eFhYWpzp49azlSfKT88fHxnbm5ucIvv/zSbu3atT8qKI8cOSIMCAjwDwgICKirq7OQyWQWE1mDq6urNiIiQk1EFBwc3N/Q0MAlIrp06RIvJCTEz9fXN+DTTz+1r6qqGsq/bNmybjabTSEhIZobN26YTeS69ysUuwAAAAAAMOWZntltaGioGBgYYHbs2OFIRGQ0Goc9fqT4SFavXt2dl5dn7+rqOiAUCoeewVUoFOb79u1zKi4urq2trZVHR0f3aDSaCdVR5ubmQ5Nis9lGnU7HEBElJSXN2bdvX2Ntba38tddea9VqtUP5LSwshs4Z75rudyh2AQAAAABg2rC3t9dnZWU17t+/30mr1TJRUVF9eXl5Qp1OR62trZwLFy7wFy5ceHOk+Eh5+Xy+MT09vXnr1q3K2+NdXV1sHo9nEAqF+qamJk5RUZGNaczKykrf09Pzs2uq/v5+loeHx6BWq2U++ugj4c/NB7fgmV0AAAAAAJhWIiMj1f7+/upDhw7ZpaSkdJ4/f57v7+8vYRjGmJGR0ezh4aFLSEjoHi4+Wt6kpKSfPA8bHh6uDgwM7BeJRBIPDw9tSEiIyjS2bt26jpiYGJGjo+NgaWlp7UTX8/rrr7eGhob6u7q6Dvj7+/erVKoJf1YJ/j8GrXAAAAAAABiNTCZrkEqlHfd6HnD/kclkDlKp1Gsi52IbMwAAAAAAAMw42MYMAAAAAAAwSa5du8ZetGiR353xoqKiGmdnZ/29mNP9CsUuAAD8P/buParJK98f/ycXgQRBCEG5BzSBkIBpioOXeitddQbHotVBC2ql3pDaoiJeaufQYzsdsVr9jkdbKyygOLTQwbaonWpHoUUHkcZDIxLCRQdQLiIgAcwFEvL7oyf+lIJ4oRXx/VqLtSb72c9nf/bT+efj3s9+AAAAYJC4uLiYLN/whccL25gBAAAAAABg2EGxCwAAAAAAAMMOil0AAAAAAAAYdlDsAgAAAAAAwLCDYhcAAAAAAIY8FosVJBaLJSKRSBoSEiJsbm5mPWys6OhoD6FQKI2OjvZQKpXWwcHBfmKxWDJ27FhpRESEgIiooKCAk5WVNWqgWHFxcW4JCQljHiaPjz76iOfr6ysRCoVSPz8/yaJFiwQDzSs4ONgvPz+f27t93759Tq+++qrXw+QxXOE0ZgAAAAAAuG+t2RWe3Y23flFsPYoRLrZa3p98r96rj7W1dY/llOP58+d779q1y3nnzp2NDzNeRkaG840bN37icDjmqVOnimJjY68vWbKkjYioqKiIQ0SkUCi4CoXCdtGiRZqHGWMg2dnZ9gcOHBhz8uTJSh8fn26j0Uj79+93qqurY/P5fHyiaBBgZRcAAAAAAJ4okyZNulVXV2dFRNTT00PR0dEeIpFI6uvrK0lKSnK8V3tISIhQp9Mx5XK5f1JSkmNTU9MIgUDQZYkdHBys0+v1jB07drgdO3bMUSwWS5KSkhwFAkFAfX09m4jIZDKRl5dXQENDw12Lh6WlpdbTpk0TSaVS/6CgIL/i4mKb/uawY8cO18TExGs+Pj7dRERsNpvWr1/fIpPJDERE8fHxrgEBAf4ikUgaEREh6OnpuX1vWlqak1wuF4tEImleXt4v/uGhvr6e/fvf/35cQECAf0BAgP93331n+wiP+4mFlV0AAAAAALhvA63A/tqMRiPl5eXZrVixopmIKD093aGkpIRTVlZW2tDQwA4ODvafNWtWZ15enm1f7bm5uVVcLlduWSXWarXM2bNn+8rl8lsvvPCCZu3atS18Pt/01ltv1SsUCtv09PRaIiK1Wm2TnJzMS0hIaMrJybH39/fXubq6Gu/MbeXKlYJDhw7VBAYGGnJzc21jYmK8CgsLK/qaR1VVFWfKlCna/ua5adOmpt27dzcQEc2bN88nMzNzVGRkpMaSc3Fxsfrbb78duXr1ap/KysrSO++Njo72jIuLu/773/++s7Ky0ur3v/+96MqVK6V9jTOcYWUXAAAAAACGPIPBwBSLxRJHR8dn2tra2PPmzWsnIjpz5ozdwoULW9lsNnl6ehonTpzYefbsWW5/7b3jrlu3rqWkpKR0/vz5rfn5+Xa/+93vxDqdjtG7X0xMTHNmZqYTEVFKSgo/Kiqq+c7rGo2GWVxcPDI8PHycWCyWvP7664KmpqYR9zO3oqIijlgslnh6egZYVqC//fZbu/Hjx4t9fX0lBQUFdpcuXeJY+kdGRrYSEYWGhnZ2dnYye7/n++9//9t+3bp1XmKxWPLSSy8JOzs7WTdv3nzqar+nbsIAAAAAAPDksbyzW11dXdLV1cVITEwcTURkNpv77N9fe1+8vb27169f33L69OnLbDabFAoFp3cfoVDYzefzjUePHrUrLi62DQ8Pv+tdXpPJRHZ2dka1Wq2y/N1rNVUoFOoKCgq4RD9vnVar1arnn3++XafTMbVaLWPjxo2CL7/88nJFRYVqyZIlzXq9/nbtxmDcXYv3/m02m0mhUJRZ8mhqarro6OjYQ08ZFLsAAAAAAPDEcHJyMu3bt6/2wIEDYwwGA2PGjBkd2dnZPKPRSPX19eyioqKR06ZNu9Vfe+942dnZ9gaDgUFEVFtby25ra2MJBIIue3t7U2dn51310vLly2+sXLnSJywsrJXNvvuNUB6P1+Ph4dGVkpJy+53hc+fO/aJotti8eXPj1q1bPS5fvnx79Vev1zOIft6mTETk4uJi1Gg0zGPHjjneee/nn3/uSER08uTJkXZ2diYnJ6e7DrSaOnVq+86dO0dbfhcUFPSbx3CGd3YBAAAAAOCJ8txzz+n8/f11ycnJjjExMa0FBQUj/f39pQwGw7x9+/ZrXl5exqVLl7b11d471okTJ+zj4+O9rK2te4iILP1CQ0M7du/e7SoWiyUbN25sWLVq1c2IiAjNG2+8wVq9enVLX3l9/vnnV1atWiXYuXOnq9FoZLz88sutkydP1vXVd9GiRZqmpiZ2aGioyGQyMezt7U1isVg3d+7cdj6fb1q8ePENiUQi9fDw6JLJZHcV6Y6Ojia5XC7u7OxkHTp06D+9Yx86dOjqypUrvXx9fSUmk4kxceLEjilTptQ+3NN+cjEeZHkfAAAAAACePkqlslomkzUP3HN4y8/P527YsMHzwoUL5Y87l6eFUqnky2Qy74e5Fyu7AAAAAAAAA9i2bZtLWlqac2pq6i9WUmFowsouAAAAAADcE1Z2H96WLVtccnJyeHe2zZ07t3Xnzp2NjyunJ8mjrOyi2AUAAAAAgHtCsQuPy6MUuziNGQAAAAAAAIYdFLsAAAAAAAAw7KDYBQAAAAAAgGEHxS4AAAAAAAAMOyh2AQAAAABgyGOxWEFisVgiEomkISEhwubmZtbDxoqOjvYQCoXS6OhoD6VSaR0cHOwnFoslY8eOlUZERAiIiAoKCjhZWVmjBooVFxfnlpCQMOZh8vjoo494vr6+EqFQKPXz85MsWrRI8CjzsigvL7cSiUTSR4nxKPMaKvCdXQAAAAAAuG9ff/21Z1NTE3cwY44ePVo7b968q/fqY21t3aNWq1VERPPnz/fetWuX88N+vicjI8P5xo0bP3E4HPPUqVNFsbGx15csWdJGRFRUVMQhIlIoFFyFQmG7aNEizcOMMZDs7Gz7AwcOjDl58mSlj49Pt9FopP379zvV1dWx+Xy+6dcYszej0Uhs9vAtCbGyCwAAAAAAT5RJkybdqqursyIi6unpoejoaA+RSCT19fWVJCUlOd6rPSQkRKjT6Zhyudw/KSnJsampaYRAIOiyxA4ODtbp9XrGjh073I4dO+YoFoslSUlJjgKBIKC+vp5NRGQymcjLyyugoaHhrkqxtLTUetq0aSKpVOofFBTkV1xcbNPfHHbs2OGamJh4zcfHp5uIiM1m0/r161tkMpmBiCg+Pt41ICDAXyQSSSMiIgQ9PT2W/PxWrFjhOWHCBL+xY8dKf/jhB+6sWbPGCQSCgNjYWDdLfKPRSPPnz/f29fWV/OEPfxjb0dHBJCJyd3cPjI+Pdw0KCvJLSUlxfJCcnzTDt4wHAAAAAIBBN9AK7K/NaDRSXl6e3YoVK5qJiNLT0x1KSko4ZWVlpQ0NDezg4GD/WbNmdebl5dn21Z6bm1vF5XLlllVirVbLnD17tq9cLr/1wgsvaNauXdvC5/NNb731Vr1CobBNT0+vJSJSq9U2ycnJvISEhKacnBx7f39/naurq/HO3FauXCk4dOhQTWBgoCE3N9c2JibGq7CwsKKveVRVVXGmTJmi7W+emzZtatq9e3cDEdG8efN8MjMzR0VGRmqIiKysrHoUCkX5e++9Nzo8PFz4448/lo0ePdro7e0duG3btutERNXV1TaffPJJ9axZs26Fh4d779q1y/ndd9+9TkRkY2PTc+HChXIiosmTJ/veb85PGqzsAgAAAADAkGcwGJhisVji6Oj4TFtbG3vevHntRERnzpyxW7hwYSubzSZPT0/jxIkTO8+ePcvtr7133HXr1rWUlJSUzp8/vzU/P9/ud7/7nVin0zF694uJiWnOzMx0IiJKSUnhR0VFNd95XaPRMIuLi0eGh4ePE4vFktdff13Q1NQ04n7mVlRUxBGLxRJPT88Aywr0t99+azd+/Hixr6+vpKCgwO7SpUscS/+XX365jYhIJpPphEKhTiAQdHM4HLOnp6fhypUrVkRELi4uXbNmzbpFRLR06dKWgoKCkZb7X3311ZuPmvOTAMUuAAAAAAAMeZZ3dqurq0u6uroYiYmJo4mIzGZzn/37a++Lt7d39/r161tOnz59mc1mk0Kh4PTuIxQKu/l8vvHo0aN2xcXFtuHh4Xe9y2symcjOzs6oVqtVlr8rV66U9jemUCjUFRQUcIl+3jqtVqtVzz//fLtOp2NqtVrGxo0bBV9++eXliooK1ZIlS5r1ev3t2s3GxsZMRMRkMsna2vr2RJlMJhmNRgYREYNxd71+5287O7ueh8n5SYNiFwAAAAAAnhhOTk6mffv21R44cGCMwWBgzJgxoyM7O5tnNBqpvr6eXVRUNHLatGm3+mvvHS87O9veYDAwiIhqa2vZbW1tLIFA0GVvb2/q7Oy8q15avnz5jZUrV/qEhYW19j7Yicfj9Xh4eHSlpKTcfmf43LlzvyiaLTZv3ty4detWj8uXL99eSdXr9Qyin7dWExG5uLgYNRoN89ixY44P+pwaGhqsTp06ZUtE9Nlnn/GmTJnS2bvPg+b8pEGxCwAAAAAAT5TnnntO5+/vr0tOTnZcunRpm1Qq1fn7+0tnzpzpu3379mteXl7G/tp7xzpx4oS9n5+f1M/PT/Liiy/e7hcaGtpRUVHBsRxQRUQUERGh0Wq1rNWrV7f0ldfnn39+JTU1le/n5ycRiUTSI0eOOPQ3h0WLFmnWrFnTFBoaKho3bpxULpeLWSwWzZ07t53P55sWL158QyKRSENDQ4UymewXRfpAxo4dq09JSXHy9fWV3Lx5kx0fH3/jUXN+0jAeZHkfAAAAAACePkqlslomkzUP3HN4y8/P527YsMHTcrgT/PqUSiVfJpN5P8y9OI0ZAAAAAABgANu2bXNJS0tzTk1N/c/jzgXuD1Z2AQAAAADgnrCy+/C2bNnikpOTw7uzbe7cua07d+5sfFw5PUkeZWUXxS4AAAAAANwTil14XB6l2MUBVQAAAAAAADDsoNgFAAAAAACAYQfFLgAAAAAAAAw7KHYBAAAAAABg2EGxCwAAAAAAQx6LxQoSi8USkUgkDQkJETY3N7MeNlZ0dLSHUCiURkdHeyiVSuvg4GA/sVgsGTt2rDQiIkJARFRQUMDJysoaNVCsuLg4t4SEhDEPmkNf97m7uwc2NDSwiYjkcrn4QWPC3fCdXQAAAAAAuG+qsi2etzoruIMZ03akr1biv/PqvfpYW1v3qNVqFRHR/PnzvXft2uX8sJ/vycjIcL5x48ZPHA7HPHXqVFFsbOz1JUuWtBERFRUVcYiIFAoFV6FQ2C5atEjzMGM8quLiYvXjGHc4wcouAAAAAAA8USZNmnSrrq7Oioiop6eHoqOjPUQikdTX11eSlJTkeK/2kJAQoU6nY8rlcv+kpCTHpqamEQKBoMsSOzg4WKfX6xk7duxwO3bsmKNYLJYkJSU5CgSCgPr6ejYRkclkIi8vrwDLKqxFaWmp9bRp00RSqdQ/KCjIr7i42OZh58jlcuVERMePH7ebMGGC34svvjhu3Lhx0sjISC+TyfSwYZ8qWNkFAAAAAID7NtAK7K/NaDRSXl6e3YoVK5qJiNLT0x1KSko4ZWVlpQ0NDezg4GD/WbNmdebl5dn21Z6bm1vF5XLlllVirVbLnD17tq9cLr/1wgsvaNauXdvC5/NNb731Vr1CobBNT0+vJSJSq9U2ycnJvISEhKacnBx7f39/naurq/HO3FauXCk4dOhQTWBgoCE3N9c2JibGq7CwsKK/uRw8eHDMF1984WT53dTUNKKvfiUlJbbFxcWXfH19u6ZPny5KT093fO21124OxvMczrCyCwAAAAAAQ57BYGCKxWKJo6PjM21tbex58+a1ExGdOXPGbuHCha1sNps8PT2NEydO7Dx79iy3v/becdetW9dSUlJSOn/+/Nb8/Hy73/3ud2KdTsfo3S8mJqY5MzPTiYgoJSWFHxUV1XzndY1GwywuLh4ZHh4+TiwWS15//XVBf8WrxZo1a66r1WqV5W/06NHdffULDAy8JZFIuthsNi1cuLD1zJkzIx/k2T2tUOwCAAAAeGLmrwAAIABJREFUAMCQZ3lnt7q6uqSrq4uRmJg4mojIbDb32b+/9r54e3t3r1+/vuX06dOX2Ww2KRQKTu8+QqGwm8/nG48ePWpXXFxsGx4efte7vCaTiezs7Ix3Fq9XrlwpfbBZ9o3BYNzzN/QNxS4AAAAAADwxnJycTPv27as9cODAGIPBwJgxY0ZHdnY2z2g0Un19PbuoqGjktGnTbvXX3jtedna2vcFgYBAR1dbWstva2lgCgaDL3t7e1NnZeVe9tHz58hsrV670CQsLa2Wz734jlMfj9Xh4eHSlpKTcfmf43LlzvyiaH0ZJSYmtWq22MplMlJ2dzZs2bVrHYMQd7lDsAgAAAADAE+W5557T+fv765KTkx2XLl3aJpVKdf7+/tKZM2f6bt++/ZqXl5exv/besU6cOGHv5+cn9fPzk7z44ou3+4WGhnZUVFRwLAdUERFFRERotFota/Xq1S195fX5559fSU1N5fv5+UlEIpH0yJEjDoMx32eeeaZz48aNHr6+vlIvLy/D0qVL2wYj7nDHeJDlfQAAAAAAePoolcpqmUzWPHDP4S0/P5+7YcMGzwsXLpT/VmMeP37c7sMPPxyTl5dX9VuNOZQolUq+TCbzfph7cRozAAAAAADAALZt2+aSlpbmnJqa+p/HnQvcH6zsAgAAAADAPWFl9+Ft2bLFJScnh3dn29y5c1t37tzZ+LhyepI8ysouil0AAAAAALgnFLvwuDxKsYsDqgAAAAAAAGDYQbELAAAAAAAAww6KXQAAAAAAABh2UOwCAAAAAMCQx2KxgsRisUQkEklDQkKEzc3NrIeNFR0d7SEUCqXR0dEeSqXSOjg42E8sFkvGjh0rjYiIEBARFRQUcLKyskYNFCsuLs4tISFhzIPmEBcX58ZgMIIuXbpkbWnbvn37aAaDEZSfn8990HiDbd++fU6vvvqq1+PO41Gg2AUAAAAAgCHP2tq6R61WqyorK0sdHByMu3btcn7YWBkZGc4lJSWqTz755NratWu9YmNjr6vVatWVK1dKN2zY0EREpFAouN98882Axe6jEIlEuvT09NsnNefk5PDGjRun/zXH7EtPTw+ZTKbfethfHb6zCwAAAAAA9219Wa2n+pZ+UFcexbY22v/n73X1fvtPmjTp1sWLFzlEPxdqMTExHrm5uaMYDIZ506ZNDatWrbrZX3tISIhQp9Mx5XK5/8aNGxuamppGCASCLkvs4OBgnV6vZ+zYscNNr9czxWLxyI0bNzb85S9/cT937pzazc3NaDKZyMfHJ+D8+fPqO/MqLS21XrNmjVdrayvbxsamJzk5uUYul/dbvM6ePbvtn//8p8MHH3zQoFKprOzs7IxsNvv253IWL17spVQqbfV6PfOll166uXfv3noiInd398CFCxe2nDx5cpTRaGRkZWVdkcvl+m+++Wbkxo0bvYiIGAwGFRQUqJlMJv3hD38QajQaltFoZCQkJNQvWbKkrby83Co0NFQ0ZcqUjgsXLozMycmp+vbbb+327t3r6uzs3D1u3Di9lZXVE/3pHhS7AAAAAADwxDAajZSXl2e3YsWKZiKi9PR0h5KSEk5ZWVlpQ0MDOzg42H/WrFmdeXl5tn215+bmVnG5XLlarVYREWm1Wubs2bN95XL5rRdeeEGzdu3aFj6fb3rrrbfqFQqFbXp6ei0RkVqttklOTuYlJCQ05eTk2Pv7++tcXV2Nd+a2cuVKwaFDh2oCAwMNubm5tjExMV6FhYUV/c3F3t7e5Obm1vXjjz/aZGdnO/zpT3+6efjwYb7l+p49e+rGjBljMhqNNGXKFL/z589zJk6cqCMi4vP5RpVKVZaYmOicmJg4Jisrq+bDDz902bdvX82sWbNuaTQaJpfL7SEi+uabb6p4PF5PQ0MDe+LEieLIyMg2IqLq6mqbpKSk6r///e+1NTU1IxITE90uXLhQxuPxTFOmTPELCAjQDvZ/v98Sil0AAAAAALhvD7ICO5gMBgNTLBZL6urqrAICArTz5s1rJyI6c+aM3cKFC1vZbDZ5enoaJ06c2Hn27Fluf+0CgUBzZ9x169a1zJ07t/3rr7+2P3bsmENaWpqzSqVS9R4/JiamOSwsTJiQkNCUkpLCj4qKuuu7wxqNhllcXDwyPDx8nKWtq6uLMdC8Fi5c2Hr48GFebm7uqPz8/PI7i91PP/2Ul5aWxjcajYwbN26MUCqVNpZiNzIy8iYRUXBwsPbo0aOORESTJk3qjI+P91y4cGFrRETEzXHjxvUYDAbG+vXrPQoLC0cymUxqamqyunbtGpuIyNXVteuFF164RUSUn59vO2nSpA43NzcjEdH8+fNbKyoqbO73v89QhHd2AQAAAABgyLO8s1tdXV3S1dXFSExMHE1EZDb3vdO2v/a+eHt7d69fv77l9OnTl9lsNikUCk7vPkKhsJvP5xuPHj1qV1xcbBseHn5X0WwymcjOzs6oVqtVlr8rV66UDjT2K6+80padne3k7u7exePxeiztarXaav/+/WN++OGHioqKClVISIhGr9ffrt9sbGzMRERsNttsNBoZRER//etfG5OTk2t0Oh1zypQp/sXFxTaffPIJr6WlhV1SUlKmVqtVTk5O3TqdjklEZFn5tWAwBqzNnygodgEAAAAA4Inh5ORk2rdvX+2BAwfGGAwGxowZMzqys7N5RqOR6uvr2UVFRSOnTZt2q7/23vGys7PtDQYDg4iotraW3dbWxhIIBF329vamzs7Ou+ql5cuX31i5cqVPWFhYK5t99yZZHo/X4+Hh0ZWSkuJI9PO7xOfOnftF0dzbyJEjzf/93/997b/+678a7my/efMmi8Ph9PB4PNPVq1fZ33///YCHZZWWlloHBwfr3n///cbAwMBbly5dstFoNCw+n99tbW1tPnbsmF19fb1VX/dOnz79VmFhoV1jYyPLYDAwvvrqK8eBxhvqsI0ZAAAAAACeKM8995zO399fl5yc7BgTE9NaUFAw0t/fX8pgMMzbt2+/5uXlZVy6dGlbX+29Y504ccI+Pj7ey9rauoeIyNIvNDS0Y/fu3a5isViycePGhlWrVt2MiIjQvPHGG6zVq1e39JXX559/fmXVqlWCnTt3uhqNRsbLL7/cOnnyZN1A81m9evXN3m2TJ0/WBQQEaEUikdTLy8sQFBTUOVCcDz74YHRBQYE9k8k0+/r66v70pz9p2traWKGhocKAgAB/qVSq9fHx6fPALIFA0L1ly5b6SZMm+Ts7O3ePHz9eazKZnuilXsaDLO8DAAAAAMDTR6lUVstksuaBew5v+fn53A0bNnheuHCh/HHn8rRQKpV8mUzm/TD3YmUXAAAAAABgANu2bXNJS0tzTk1N/c/jzgXuD1Z2AQAAAADgnrCy+/C2bNnikpOTw7uzbe7cua07d+5sfFw5PUkeZWUXxS4AAAAAANwTil14XB6l2MVpzAAAAAAAADDsoNgFAAAAAACAYQfFLgAAAAAAAAw7KHYBAAAAAABg2EGxCwAAAAAAQx6LxQoSi8USkUgkDQkJETY3N7MeNlZ0dLSHUCiURkdHeyiVSuvg4GA/sVgsGTt2rDQiIkJARFRQUMDJysoaNVCsuLg4t4SEhDEPmkN/4+7bt8/p1Vdf9XrwWf3s+PHjds8//7zwYe8fTvCdXQAAAAAAGPKsra171Gq1ioho/vz53rt27XJ+2M/3ZGRkON+4ceMnDodjnjp1qig2Nvb6kiVL2oiIioqKOERECoWCq1AobBctWqQZvFn8/9auXevV17gweFDsAgAAAADAfduUrfSsaOzgDmZMXxc77a4/ya7eb/9JkybdunjxIoeIqKenh2JiYjxyc3NHMRgM86ZNmxpWrVp1s7/2kJAQoU6nY8rlcv+NGzc2NDU1jRAIBF2W2MHBwTq9Xs/YsWOHm16vZ4rF4pEbN25s+Mtf/uJ+7tw5tZubm9FkMpGPj0/A+fPn1XfmVVpaar1mzRqv1tZWto2NTU9ycnKNXC7X9zWHvsa1/O/GxsYR06ZNE9XW1lqHhoa2HTx48BoR0Zdffmn/7rvvunV1dTEEAoEhMzOzetSoUT3Z2dn2mzZt8uTxeMbAwEDt/T/54Q3FLgAAAAAAPDGMRiPl5eXZrVixopmIKD093aGkpIRTVlZW2tDQwA4ODvafNWtWZ15enm1f7bm5uVVcLlduWSXWarXM2bNn+8rl8lsvvPCCZu3atS18Pt/01ltv1SsUCtv09PRaIiK1Wm2TnJzMS0hIaMrJybH39/fXubq6Gu/MbeXKlYJDhw7VBAYGGnJzc21jYmK8CgsLK/qax9q1a6/3NS4RkUql4iqVShWHw+kRCoUB8fHx121tbc1//etfXfPz8yvs7e173n77bZf33ntvzLvvvtv4xhtveP/rX/8ql0qlhjlz5oz9df8LPDlQ7AIAAAAAwH17kBXYwWQwGJhisVhSV1dnFRAQoJ03b147EdGZM2fsFi5c2Mpms8nT09M4ceLEzrNnz3L7axcIBHdtS163bl3L3Llz27/++mv7Y8eOOaSlpTmrVCpV7/FjYmKaw8LChAkJCU0pKSn8qKio5juvazQaZnFx8cjw8PBxlrauri5Gf/O517hTp05td3JyMhERCYVC/eXLl61bW1tZly9ftgkODhYTEXV3dzOCgoI6f/rpJxsPDw9DYGCggYho8eLFLcnJyc4P/6SHDxxQBQAAAAAAQ57lnd3q6uqSrq4uRmJi4mgiIrPZ3Gf//tr74u3t3b1+/fqW06dPX2az2aRQKH7x/qxQKOzm8/nGo0eP2hUXF9uGh4ffVTSbTCays7MzqtVqleXvypUrpQ8zrpWV1e3kWSyWubu7m2E2m2nq1KntltiXL18u/eKLL2qIiBiMfmvqpxqKXQAAAAAAeGI4OTmZ9u3bV3vgwIExBoOBMWPGjI7s7Gye0Wik+vp6dlFR0chp06bd6q+9d7zs7Gx7g8HAICKqra1lt7W1sQQCQZe9vb2ps7Pzrnpp+fLlN1auXOkTFhbWymbfvUmWx+P1eHh4dKWkpDgS/fwu8blz5/o9dKq/cfvrP3PmzFsKhWLkpUuXrImIOjo6mBcvXrR+5pln9NeuXbMqLS21JiLKzMzk3ffDHOZQ7AIAAAAAwBPlueee0/n7++uSk5Mdly5d2iaVSnX+/v7SmTNn+m7fvv2al5eXsb/23rFOnDhh7+fnJ/Xz85O8+OKLt/uFhoZ2VFRUcMRisSQpKcmRiCgiIkKj1WpZq1evbukrr88///xKamoq38/PTyISiaRHjhxx6G8O/Y3bX383NzfjJ598Uv3KK6+M9fX1lQQFBYlLSkpsuFyu+X/+539q5syZIwwKCvLz9PTst2B+2jAeZHkfAAAAAACePkqlslomkzUP3HN4y8/P527YsMHzwoUL5Y87l6eFUqnky2Qy74e5FwdUAQAAAAAADGDbtm0uaWlpzqmpqf953LnA/cHKLgAAAAAA3BNWdh/eli1bXHJycu56j3bu3LmtO3fubHxcOT1JHmVlF8UuAAAAAADcE4pdeFwepdjFAVUAAAAAAAAw7KDYBQAAAAAAgGEHxS4AAAAAAAAMOyh2AQAAAAAAYNhBsQsAAAAAAEMei8UKEovFEpFIJA0JCRE2NzezHjZWdHS0h1AolEZHR3solUrr4OBgP7FYLBk7dqw0IiJCQERUUFDAycrKGjVQrLi4OLeEhIQxD5sL/HrwnV0AAAAAABjyrK2te9RqtYqIaP78+d67du1yftjP92RkZDjfuHHjJw6HY546daooNjb2+pIlS9qIiIqKijhERAqFgqtQKGwXLVqkGbxZwG8JxS4AAAAAANy/r9d6UpOKO6gxR0u0NO/A1fvtPmnSpFsXL17kEBH19PRQTEyMR25u7igGg2HetGlTw6pVq2721x4SEiLU6XRMuVzuv3HjxoampqYRAoGgyxI7ODhYp9frGTt27HDT6/VMsVg8cuPGjQ1/+ctf3M+dO6d2c3Mzmkwm8vHxCTh//rz6zrxKS0ut16xZ49Xa2sq2sbHpSU5OrpHL5fq+5rBgwQJvOzs7k1KptL1x48aI995779prr712U6PRMP/whz8INRoNy2g0MhISEuqXLFnSVl5ebhUaGioKDg7uVCgUI8eMGdN18uTJqpEjR+Jbsv1AsQsAAAAAAE8Mo9FIeXl5ditWrGgmIkpPT3coKSnhlJWVlTY0NLCDg4P9Z82a1ZmXl2fbV3tubm4Vl8uVW1aJtVotc/bs2b5yufzWCy+8oFm7dm0Ln883vfXWW/UKhcI2PT29lohIrVbbJCcn8xISEppycnLs/f39da6ursY7c1u5cqXg0KFDNYGBgYbc3FzbmJgYr8LCwor+5nL9+vURCoVC/dNPP9m8/PLLwtdee+0ml8vt+eabb6p4PF5PQ0MDe+LEieLIyMg2IqLa2lqbv//971emTJlSM3v27LHp6emOr7/+euuv97SfbCh2AQAAAADg/j3ACuxgMhgMTLFYLKmrq7MKCAjQzps3r52I6MyZM3YLFy5sZbPZ5OnpaZw4cWLn2bNnuf21CwSCu7Ylr1u3rmXu3LntX3/9tf2xY8cc0tLSnFUqlar3+DExMc1hYWHChISEppSUFH5UVFTzndc1Gg2zuLh4ZHh4+DhLW1dXF+NecwoLC2tjsVgUFBSkb2lpGUFE1NPTw1i/fr1HYWHhSCaTSU1NTVbXrl1jExG5u7sbpkyZoiMiksvl2urqauuHfZ5PAxxQBQAAAAAAQ57lnd3q6uqSrq4uRmJi4mgiIrO57128/bX3xdvbu3v9+vUtp0+fvsxms0mhUHB69xEKhd18Pt949OhRu+LiYtvw8PC7imaTyUR2dnZGtVqtsvxduXKl9F7j2tjY3E7Sku8nn3zCa2lpYZeUlJSp1WqVk5NTt06nYxIRWVlZ3e7PYrHMRqPxnsX00w7FLgAAAAAAPDGcnJxM+/btqz1w4MAYg8HAmDFjRkd2djbPaDRSfX09u6ioaOS0adNu9dfeO152dra9wWBgEBHV1tay29raWAKBoMve3t7U2dl5V720fPnyGytXrvQJCwtrZbPv3iTL4/F6PDw8ulJSUhyJfn6X+Ny5c78omgei0WhYfD6/29ra2nzs2DG7+vp6qweNAT9DsQsAAAAAAE+U5557Tufv769LTk52XLp0aZtUKtX5+/tLZ86c6bt9+/ZrXl5exv7ae8c6ceKEvZ+fn9TPz0/y4osv3u4XGhraUVFRwRGLxZKkpCRHIqKIiAiNVqtlrV69uqWvvD7//PMrqampfD8/P4lIJJIeOXLE4UHntnLlylalUmkbEBDg//e//53n4+PT5wFXMDDGgyzvAwAAAADA00epVFbLZLLmgXsOb/n5+dwNGzZ4Xrhwofxx5/K0UCqVfJlM5v0w9+KAKgAAAAAAgAFs27bNJS0tzTk1NfU/jzsXuD9Y2QUAAAAAgHvCyu7D27Jli0tOTg7vzra5c+e27ty5s/Fx5fQkeZSVXRS7AAAAAABwTyh24XF5lGIXB1QBAAAAAADAsINiFwAAAAAAAIYdFLsAAAAAAAAw7KDYBQAAAAAAgGEHxS4AAAAAAAx5W7ZscREKhVJfX1+JWCyW5Obm2vbXd8GCBd6pqamOA8VMSEgY4+PjIxWJRFI/Pz/J/v37nQYjV3d398CGhgY2EZFcLhcTEZWXl1sdPHjw9qnM+fn53KioKM/BGO9O6enpDgwGI6i4uNimvz53Pp9FixYJLly4YNM778Gyb98+p1dffdVrMGPeL3xnFwAAAAAAhrRTp07Znjx50qGkpETF4XDMDQ0NbIPBwHiUmB988IFzbm6u/YULF8p4PF5PS0sL67PPPnMYrJwtiouL1URElZWV1llZWbw1a9a0EhFNnz5dO336dO1gj5eZmcl79tlnOw8fPsyTy+X1A/XPysqqGewchgoUuwAAAAAAcN/+69//5Vl1s4o7mDGFjkLte8+9d7W/63V1dSN4PJ6Rw+GYiYhcXV2NRETx8fGuJ06ccDAYDMwJEyZ0ZmRk1DCZd29ePXPmDDcuLs5Tq9UyHR0djRkZGdUCgaB77969LqdOnarg8Xg9REROTk6mN998s4WIKCcnx27r1q2eJpOJZDKZNj09vYbD4Zjd3d0DFy5c2HLy5MlRRqORkZWVdUUul+sbGxtZCxYsGNva2jpCLpffuvPzrlwuV67Vaovffvtt9ytXrtiIxWJJREREc1BQkO7DDz8ck5eXV3X9+nXW4sWLvWtra605HE7PoUOHaiZOnKiLi4tzu3r1qlVNTY11fX291Zo1a67/+c9/burvOWk0GqZCoRh56tSp8rlz5wr37NlTT0TU09NDUVFRXv/+97/tPD09DXfmFxwc7Ld79+6r/RXeeXl53Li4OC+9Xs+0sbHpSUtL+49MJjPs27fP6fjx4w46nY5ZW1trHRoa2nbw4MFrRER/+9vfnPbu3evq7OzcPW7cOL2VldVj+d4ttjEDAAAAAMCQNm/evPb6+norb2/vgCVLlnh98803I4mINm3a1HTp0qWyysrKUp1Ox8zMzBx1530Gg4ERGxvrlZOTc7m0tLRs2bJlzfHx8e43b95k3rp1iyWVSg29x9JqtYzo6GifrKysyxUVFSqj0Ui7du1ytlzn8/lGlUpVtnz58huJiYljiIi2bt3qNnny5M6ysjJVWFhYW0NDg1XvuO+//37dhAkTOtVqteqdd965q2DdvHmzm0wm01ZUVKjee++9umXLlvlYrlVVVdn88MMPFT/++GPZ7t273e61op2RkeEwc+ZMzfjx4w0ODg6ms2fPcomIDh8+7FBVVWVdXl5empaWVvO///u/I+/32ctkMn1RUZG6rKxM9c4779Rt3rzZw3JNpVJxv/766ytlZWWlR48edayqqhpRU1MzIjEx0a2goEB95syZioqKCs79jjXYsLILAAAAAAD37V4rsL+WUaNG9Vy6dEl14sQJu9OnT9stW7ZsXEJCwjV7e3vTnj17XPR6PbOtrY0tkUh0RKSx3Hfx4kXryspKTkhIiC/Rzyuczs7O3WazmRiMvmtGpVJp4+HhYRg/fryBiCgqKqrlwIEDo4moiYgoMjLyJhFRcHCw9ujRo45ERIWFhXZffvllFRHRK6+8oomOjjY9yPyKiorsjhw5UkVEFBYW1rF69Wp2S0sLi4ho1qxZbRwOx8zhcIw8Hq/72rVr7HHjxnX3FeeLL77grVu3romIaMGCBa2HDx/mTZ06VfvDDz/YLVy4sJXNZpO3t3f35MmTO+43t9bWVtaiRYt8qqurbRgMhrm7u/v2g5s6dWq7k5OTiYhIKBTqL1++bN3U1MSeNGlSh5ubm5GIaP78+a0VFRX9vj/8a0KxCwAAAAAAQx6bzaY5c+Z0zJkzp2P8+PG6pKQkfnl5Off8+fMqoVDYHRcX56bX6+/auWo2mxlCoVD3008/qXvH43A4PSqVykoikXT1uueeedjY2Jj/Lx+z0Wi8Xfj13j79IPoak8FgmImIrK2tb19ksVh055h3amxsZBUWFtpXVFRw3njjDTKZTAwGg2H++OOPr/1fvIfKbcuWLe4zZszo+Ne//nW5vLzcKiQkxM9y7c7tySwW63Yh/LBjDTZsYwYAAAAAgCFNqVRal5SUWFt+FxcXc4RCoYGIyMXFxajRaJjHjh37xenL48eP17e2trJPnTplS/TztmaFQmFDRLR+/fqGNWvWCFpbW5lERK2trczdu3fzn3nmGX1dXZ3VpUuXrImI0tPTnaZNm3bPldBJkyZ1pKSkOBERffHFF/bt7e2s3n1GjRpl6uzs/EW75f7U1FQnIqLjx4/bOTo6Gi3vEt+vw4cPO86fP7+lvr6+pK6urqSxsfGih4dH13fffTdyxowZHf/4xz94RqORampqRhQWFtrdb9z29naWh4dHFxHRJ598wh+o//Tp028VFhbaNTY2sgwGA+Orr74a8FTsXwtWdgEAAAAAYEhrb29nxcbGerW3t7NYLJbZ29vb8Omnn9Y4ODgYJRKJ1MPDo0smk93qfZ+NjY05MzPzcmxsrFdHRwfLZDIxYmJirk+YMEG/efPmG52dncxnn31WMmLECDObzTa/+eabjVwu13zw4MHq8PDwcZYDquLj42/cK7/ExMT6BQsWjJVIJP6TJ0/udHV17erdJzg4WMdms81+fn6SyMjI5qCgIJ3l2s6dO+sjIyO9fX19JRwOpyctLe0/D/qM/vGPfzht3ry54c62uXPn3jx8+DDv8OHDtadPn7b38/OT+vj46IODg+8q3u9ciZXJZBLL75deeql1y5YtjStXrvTZt2+fy7Rp09oHykMgEHRv2bKlftKkSf7Ozs7d48eP15pMpsey1MsYaJkeAAAAAACebkqlslomkzU/7jxg8Pn6+kqOHj1aJRaLf1GgDwVKpZIvk8m8H+ZebGMGAAAAAAB4Ck2ZMkXk5+enG6qF7qPCNmYAAAAAAIAnRGNjI2vmzJl+vdu///77chcXlwc6BbqgoKBy8DIbelDsAgAAAAAAPCFcXFxMarVa9bjzeBJgGzMAAAAAAAAMOyh2AQAAAAAAYNhBsQsAAAAAAADDDopdAAAAAAAAGHZQ7AIAAAAAwJC3ZcsWF6FQKPX19ZWIxWJJbm6ubX99FyxY4J2amuo4UMyEhIQxPj4+UpFIJPXz85Ps37/faTBydXd3D2xoaGATEcnlcjERUXl5udXBgwd5lj75+fncqKgoz8EY707p6ekODAYjqLi42MbSVl5ebiUSiaRERMePH7d7/vnnhYM97lCEYhcAAAAAAIa0U6dO2Z48edKhpKREVVFRocrLy6sYO3bsI30b9oMPPnDOzc21v3DhQlllZWVpQUFBudlsHqyUbysuLlYTEVVWVlpnZWXdLnanT5+uTUtLuzrY42VmZvKeffbZzsOHD/MG7j284dNDAAAAAABw3+q3ve1pqKzkDmZkJuDJAAAgAElEQVRMa5FI6/bX9/st/Orq6kbweDwjh8MxExG5uroaiYji4+NdT5w44WAwGJgTJkzozMjIqGEy717PO3PmDDcuLs5Tq9UyHR0djRkZGdUCgaB77969LqdOnarg8Xg9REROTk6mN998s4WIKCcnx27r1q2eJpOJZDKZNj09vYbD4Zjd3d0DFy5c2HLy5MlRRqORkZWVdUUul+sbGxtZCxYsGNva2jpCLpffurNo5nK5cq1WW/z222+7X7lyxUYsFksiIiKag4KCdB9++OGYvLy8quvXr7MWL17sXVtba83hcHoOHTpUM3HiRF1cXJzb1atXrWpqaqzr6+ut1qxZc/3Pf/5zU3/PSaPRMBUKxchTp06Vz507V7hnz576ez33/sbVaDTMFStWeF28eJFLRLRt27b6qKiotsWLF3splUpbvV7PfOmll27u3bv3nvEfN6zsAgAAAADAkDZv3rz2+vp6K29v74AlS5Z4ffPNNyOJiDZt2tR06dKlssrKylKdTsfMzMwcded9BoOBERsb65WTk3O5tLS0bNmyZc3x8fHuN2/eZN66dYsllUoNvcfSarWM6Ohon6ysrMsVFRUqo9FIu3btcrZc5/P5RpVKVbZ8+fIbiYmJY4iItm7d6jZ58uTOsrIyVVhYWFtDQ4NV77jvv/9+3YQJEzrVarXqnXfeuatg3bx5s5tMJtNWVFSo3nvvvbply5b5WK5VVVXZ/PDDDxU//vhj2e7du90MBgOjv+eUkZHhMHPmTM348eMNDg4OprNnz97zHyX6G3fr1q2u9vb2poqKClVFRYXqj3/8YwcR0Z49e+ouXbpUplarS//973/bnT9/nnOv+I8bVnYBAAAAAOC+3WsF9tcyatSonkuXLqlOnDhhd/r0abtly5aNS0hIuGZvb2/as2ePi16vZ7a1tbElEomOiDSW+y5evGhdWVnJCQkJ8SUi6unpIWdn526z2UwMRt81o1KptPHw8DCMHz/eQEQUFRXVcuDAgdFE1EREFBkZeZOIKDg4WHv06FFHIqLCwkK7L7/8soqI6JVXXtFER0ebHmR+RUVFdkeOHKkiIgoLC+tYvXo1u6WlhUVENGvWrDYOh2PmcDhGHo/Xfe3aNfa4ceO6+4rzxRdf8NatW9dERLRgwYLWw4cP86ZOnap90HHz8/PtMzMzr1j6OTs7m4iIPv30U15aWhrfaDQybty4MUKpVNpMnDhR9yBz/S2h2AUAAAAAgCGPzWbTnDlzOubMmdMxfvx4XVJSEr+8vJx7/vx5lVAo7I6Li3PT6/V37Vw1m80MoVCo++mnn9S943E4nB6VSmUlkUi6et1zzzxsbGzM/5eP2Wg03q6Ye2+ffhB9jclgMMxERNbW1rcvslgsunPMOzU2NrIKCwvtKyoqOG+88QaZTCYGg8Ewf/zxx9cedNy+/jFArVZb7d+/f8yFCxfKnJ2dTQsWLPDu/byHmiGdHAAAAAAAgFKptC4pKbG2/C4uLuYIhUIDEZGLi4tRo9Ewjx079ovTl8ePH69vbW1lnzp1ypbo523NCoXChoho/fr1DWvWrBG0trYyiYhaW1uZu3fv5j/zzDP6uro6q0uXLlkTEaWnpztNmzat4175TZo0qSMlJcWJiOiLL76wb29vZ/XuM2rUKFNnZ+cv2i33p6amOhH9fFqyo6Oj0fIu8f06fPiw4/z581vq6+tL6urqShobGy96eHh0fffddyPvlXdf486cObN9z549oy39bty4wbp58yaLw+H08Hg809WrV9nff//9qP7iDhVY2QUAAAAAgCGtvb2dFRsb69Xe3s5isVhmb29vw6efflrj4OBglEgkUg8Pjy6ZTHar9302NjbmzMzMy7GxsV4dHR0sk8nEiImJuT5hwgT95s2bb3R2djKfffZZyYgRI8xsNtv85ptvNnK5XPPBgwerw8PDx1kOqIqPj79xr/wSExPrFyxYMFYikfhPnjy509XV9RcnRQcHB+vYbLbZz89PEhkZ2RwUFHR7++/OnTvrIyMjvX19fSUcDqcnLS3tPw/6jP7xj384bd68ueHOtrlz5948fPgwLyEhobGve/obd8eOHQ2vvfaal0gkkjKZTPO2bdvqly1b1hYQEKAViURSLy8vQ1BQUOeD5vhbY/wax2sDAAAAAMDwoVQqq2UyWfPjzgOePkqlki+Tybwf5l5sYwYAAAAAAIBhB9uYAQAAAAAAnhCNjY2smTNn+vVu//7778tdXFwe6BTo4Q7FLgAAAAAAwBPCxcXFpFarVY87jycBtjEDAAAAAADAsINiFwAAAAAAAIYdFLsAAAAAAAAw7KDYBQAAAAAAgGEHxS4AAAAAAAx5W7ZscREKhVJfX1+JWCyW5Obm2vbXd8GCBd6pqamOA8VMSEgY4+PjIxWJRFI/Pz/J/v37nQYjV3d398CGhgY2EZFcLhcTEZWXl1sdPHiQZ+mTn5/PjYqK8hyM8SxYLFaQWCyWWP7Ky8utHjXmBx984Gx5Lvf7XIcKnMYMAAAAAABD2qlTp2xPnjzpUFJSouJwOOaGhga2wWBgPErMDz74wDk3N9f+woULZTwer6elpYX12WefOQxWzhbFxcVqIqLKykrrrKws3po1a1qJiKZPn66dPn26djDHsra27hnsk5o3b958YzDj/ZZQ7AIAAAAAwH07nV7m2VrXyR3MmDz3kdoXXvW/2t/1urq6ETwez8jhcMxERK6urkYiovj4eNcTJ044GAwG5oQJEzozMjJqmMy7N6+eOXOGGxcX56nVapmOjo7GjIyMaoFA0L13716XU6dOVfB4vB4iIicnJ9Obb77ZQkSUk5Njt3XrVk+TyUQymUybnp5ew+FwzO7u7oELFy5sOXny5Cij0cjIysq6IpfL9Y2NjawFCxaMbW1tHSGXy2+Zzebb43O5XLlWqy1+++233a9cuWIjFoslERERzUFBQboPP/xwTF5eXtX169dZixcv9q6trbXmcDg9hw4dqpk4caIuLi7O7erVq1Y1NTXW9fX1VmvWrLn+5z//uelBnm15eblVZGSkj06nYxIR/e1vf6t98cUXbx0/ftxu+/btbs7Ozt0qlYo7e/bsm4GBgbqPPvpojMFgYHz11VeXpVKpIS4uzm3kyJGmd99997olZk5Ojt3+/ftH/+tf/7pMRPTVV1/Zf/zxx87ffffd5QfJ7deGbcwAAAAAADCkzZs3r72+vt7K29s7YMmSJV7ffPPNSCKiTZs2NV26dKmssrKyVKfTMTMzM0fdeZ/BYGDExsZ65eTkXC4tLS1btmxZc3x8vPvNmzeZt27dYkmlUkPvsbRaLSM6OtonKyvrckVFhcpoNNKuXbucLdf5fL5RpVKVLV++/EZiYuIYIqKtW7e6TZ48ubOsrEwVFhbW1tDQ8Ivtw++//37dhAkTOtVqteqdd965q2DdvHmzm0wm01ZUVKjee++9umXLlvlYrlVVVdn88MMPFT/++GPZ7t273e61om0wGJiWLcwvvvjiOCIiNzc345kzZypUKlVZVlbWlQ0bNnhZ+qvVas7HH398taysrDQ7O9upoqLCpqSkpGzp0qXNH3744ej+xnnppZc6qqqqbOrr69lERCkpKU5RUVHN/fV/XLCyCwAAAAAA9+1eK7C/llGjRvVcunRJdeLECbvTp0/bLVu2bFxCQsI1e3t70549e1z0ej2zra2NLZFIdESksdx38eJF68rKSk5ISIgvEVFPTw85Ozt3m81mYjD6rhmVSqWNh4eHYfz48QYioqioqJYDBw6MJqImIqLIyMibRETBwcHao0ePOhIRFRYW2n355ZdVRESvvPKKJjo62vQg8ysqKrI7cuRIFRFRWFhYx+rVq9ktLS0sIqJZs2a1cTgcM4fDMfJ4vO5r166xx40b191XnL62MXd1dTFWrFghUKlUHCaTSTU1NdaWa4GBgbcEAkE3EZGXl5chNDRUQ0Qkk8l0P/zwg11/+TKZTFq4cGFLUlISb+3atS3/+7//O/LLL7/8z4PM+beAYhcAAAAAAIY8NptNc+bM6ZgzZ07H+PHjdUlJSfzy8nLu+fPnVUKhsDsuLs5Nr9fftXPVbDYzhEKh7qefflL3jsfhcHpUKpWVRCLp6nXPPfOwsbEx/18+ZqPReLti7r19+kH0NSaDwTATEVlbW9++yGKx6M4x78f7778/ZvTo0d1Hjhz5T09PD3E4nCDLtTtjM5nM23NjMplkMpnuOU5MTEzLH//4R6GNjY35pZdeujlixIgHSes3gW3MAAAAAAAwpCmVSuuSkpLbK5LFxcUcoVBoICJycXExajQa5rFjx35xSvD48eP1ra2t7FOnTtkS/bytWaFQ2BARrV+/vmHNmjWC1tZWJhFRa2src/fu3fxnnnlGX1dXZ3Xp0iVrIqL09HSnadOmddwrv0mTJnWkpKQ4ERF98cUX9u3t7azefUaNGmXq7Oz8Rbvl/tTUVCciouPHj9s5OjoaLe8SPyqNRsNydXXtZrFY9NFHHzmZTA+06Nwvb2/v7jFjxnR/+OGHrqtWrRpyW5iJsLILAAAAAABDXHt7Oys2Ntarvb2dxWKxzN7e3oZPP/20xsHBwSiRSKQeHh5dMpnsVu/7bGxszJmZmZdjY2O9Ojo6WCaTiRETE3N9woQJ+s2bN9/o7OxkPvvss5IRI0aY2Wy2+c0332zkcrnmgwcPVoeHh4+zHFAVHx9/zxOJExMT6xcsWDBWIpH4T548udPV1bWrd5/g4GAdm802+/n5SSIjI5uDgoJ0lms7d+6sj4yM9Pb19ZVwOJyetLS0QdsSvH79+qYFCxaM+/rrrx2nTp3aweFwBqWIJiJ65ZVXWg4cOMAOCgrSD1bMwcQYaJkeAAAAAACebkqlslomkw3J1Tt4fF599VUvuVyu3bBhw6/2/w2lUsmXyWTeD3MvVnYBAAAAAADggUilUn8Oh9PzySef/OYHlt0vFLsAAAAAAABPiMbGRtbMmTP9erd///335S4uLoPzQu59KC0tLfutxnpYKHYBAAAAAACeEC4uLqbenxeCvuE0ZgAAAAAAABh2UOwCAAAAAADAsINiFwAAAAAAAIYdFLsAAAAAAAAw7KDYBQAAAACAIW/Lli0uQqFQ6uvrKxGLxZLc3Fzb/vouWLDAOzU11XGgmAkJCWN8fHykIpFI6ufnJ9m/f7/TYOTq7u4e2NDQwCYiksvlYiKi8vJyq4MHD/IsffLz87lRUVGegzGeBYPBCFq1apWH5XdCQsKYuLg4t8EcYyD3++x/CziNGQAAAAAAhrRTp07Znjx50qGkpETF4XDMDQ0NbIPBwHiUmB988IFzbm6u/YULF8p4PF5PS0sL67PPPnMYrJwtiouL1URElZWV1llZWbw1a9a0EhFNnz5dO336dO1gjmVlZWX+5z//6djQ0NDo6upqfND7u7u7acSIEYOZ0mOFYhcAAAAAAO7byY//n2fz1RruYMbkewq0v49Zf7W/63V1dSN4PJ6Rw+GYiYgshVx8fLzriRMnHAwGA3PChAmdGRkZNUzm3ZtXz5w5w42Li/PUarVMR0dHY0ZGRrVAIOjeu3evy6lTpyp4PF4PEZGTk5PpzTffbCEiysnJsdu6daunyWQimUymTU9Pr+Fw/j/27jWsyTPbG/jKAZJwJgYTEDGUGEICCQoigj3RXm1nyqZO2XZXBg2l11RodzMdBsTdvq+tTj2NQJ3TrgU7nrY27diZsdQ9OINQy3iqthATDhHpKAqIAkISSSI5vB/a+FIFxENbCv/ftzzPfa/75uHTutZ67ofnnjFjRtwzzzzTe+DAgUCHw8F4//33v5wzZ47t4sWLrMzMzPv6+vq85syZc9Xtdl9f38fHZ87g4GD9a6+9NuPLL7/kymQy+ZIlS3oSEhKspaWlwtra2jPd3d2sn/70p+L29nYOj8dzlZeXn5s/f761oKAg7Pz5897nzp3jdHZ2eufl5XX/n//zfy6N9pxYLJZ72bJll9etWyf83e9+1zH83unTp73VarW4t7eXPW3aNMfOnTvPzp49+1pmZqY4ODjYodfrfZRK5aC/v7/r7Nmz3t3d3V5nz57lrlu37vzRo0f9ampqAoRC4VB1dfUZDofjHs+z/75NrN0AAAAAAADcYNGiRabOzk5vsVgcm52dHbF//34/IqKioqJLBoOhubW1tdFqtTK1Wm3g8Hl2u52h0Wgi9u3b19bY2NisVqt7CgsLZ1y5coV59epVlkKhsN+41uDgIGP58uWR77//ftvp06ebHA4Hbdq0KcRzXyAQOJqamppzc3Mvb9iwQUhEtHLlyrAFCxZYmpubmzIyMvq7urq8b4y7du3ajsTEREtLS0vT66+//o2EdcWKFWEqlWrw9OnTTb/61a861Gp1pOfemTNnuIcOHTp94sSJ5pKSkrBbVbSLioou/fnPf+b39vayhl/Py8uLyMrK6j19+nTTf/zHf/Tm5+dfb6Fua2vjHj58+HRFRcUFIqJz585xampqzuzdu/dMXl5eZFpamun06dNNXC7X9cEHHwSO59lPBKjsAgAAAADAuI1Vgf22BAYGugwGQ1NVVZX/wYMH/dVqddSqVasuBAQEOMvKykQ2m43Z39/PlsvlViIa8Mw7deoUp7W1lZeWliYlInK5XBQSEjLkdruJwRg5Z9TpdNzw8HC7Uqm0ExHl5OT0/uEPf5hORJeIiLKysq4QESUlJQ1+9NFHwUREx44d8//zn/98hojo2WefHVi+fLnzdv6+zz77zP/DDz88Q0SUkZFhfuGFF9ieZPWxxx7r5/F4bh6P5+Dz+UMXLlxgR0VFDY0Wi8/nuxYvXty7YcOG6Twez+W5Xl9f7/u3v/2tjYgoPz+/b/Xq1dff7X366aevsNn/PzV89NFHBzgcjjspKcnqdDoZ//7v/24iIlIoFNZ//etf3kREf/vb3/zHevYTAZJdAAAAAACY8NhsNqWnp5vT09PNSqXSWlFRITAajT7Hjx9vkkgkQwUFBWE2m+0bnatut5shkUisDQ0NLTfG4/F4rqamJm+5XH7thjlj7oPL5bq/3o/b4XBcz5jvpoV3pDUZDIabiIjD4Vy/yWKxaPiao/mv//qv7rlz58qfffbZnvGs7+fn5xr+27Mmi8UiNpvt9vxtTCaTHA4HY3BwkPHLX/5y1ljPfiKYcBsCAAAAAAAYTqfTcfR6Pcfzu76+nieRSOxERCKRyDEwMMCsrKy86QRgpVJp6+vrY1dXV/sSfdXWfPLkSS4R0SuvvNKVl5c3q6+vj0lE1NfXxywpKRHEx8fbOjo6vA0GA4eIaOfOndPuv/9+81j7S05ONv/xj3+cRkT0wQcfBJhMJtaNYwIDA50Wi+Wm657527Ztm0ZE9PHHH/sHBwc7PO8S3wmhUOj8t3/7tyt79uwReK7NmTPn6tatW4OJiN555x1+YmKi5U7jDw4OMonGfvYTASq7AAAAAAAwoZlMJpZGo4kwmUwsFovlFovF9h07dpwLCgpyyOVyRXh4+DWVSnX1xnlcLtet1WrbNBpNhNlsZjmdTkZ+fn53YmKibcWKFZctFgtz7ty5ci8vLzebzXa//PLLF318fNxbtmw5u3jx4ijPAVWFhYWXx9rfhg0bOjMzM++Ty+UxCxYssISGhl67cUxSUpKVzWa7o6Oj5VlZWT0JCQlWz72NGzd2ZmVliaVSqZzH47m2b9/+r7t9Zq+99trFHTt2XH/X+O23325Xq9Xi3/zmNyLPAVV3GlsgEDh/+tOfXh7r2U8EjFuV6QEAAAAAYGrT6XRnVSrVuFpiAe4lnU4nUKlU4juZizZmAAAAAAAAmHTQxgwAAAAAAPADcfHiRdZDDz0UfeP1Tz75xCgSiW7rFOjJDskuAAAAAADAD4RIJHK2tLQ0fd/7+CFAGzMAAAAAAABMOkh2AQAAAAAAYNJBsgsAAAAAAACTDpJdAAAAAAAAmHSQ7AIAAAAAwIRXXFwskkgkCqlUKpfJZPKamhrf0cZmZmaKt23bFnyrmKtWrRJGRkYqZs+erYiOjpb//ve/n3Yv9jpjxoy4rq4uNhHRnDlzZERERqPRe8uWLXzPmE8//dQnJydn5r1Yz4PFYiXIZDL57NmzFT/60Y/uM5vNt5XvrVmzZvrwOQ8++KCkp6eHNdac4X/rRDMhNwUAAAAAABNT397TM4cuXvW5lzG9RL6D/H+Xnh/tfnV1te+BAweC9Hp9E4/Hc3d1dbHtdjvjbtb89a9/HVJTUxPw+eefN/P5fFdvby9rz549QXcTcyT19fUtREStra2c999/n5+Xl9dHRPTAAw8MPvDAA4P3ci0Oh+PynNSckZERWVpaGvLGG290j2euw+Ggd955R/izn/2sz9/f30VEdOjQoTP3cn/fNVR2AQAAAABgQuvo6PDi8/kOHo/nJiIKDQ11iMXiocLCwtDY2NiY2bNnK5YsWTLL5XLdNLeurs5n3rx50QqFImbhwoWzz50750VE9NZbb4neeeeddj6f7yIimjZtmvPll1/uJSLat2+ff0xMjFwqlcoXL14stlqtDKKvqpi/+MUvwuRyeYxUKpXX19dzib769m1qaursmJgYeVZW1iy32319fR8fnzlERK+99tqMkydP+slkMvnq1aunf/zxx/4PP/ywhIiou7ub9eijj0ZJpVK5SqWSHT9+nEdEVFBQELZ48WJxUlJSdHh4eNybb745fbzPbOHChZYzZ85wiIgeffTRKIVCESORSBQlJSWC4Xt75ZVXwpRKpWzlypWhly5d8nrwwQel8+fPl3r+Xk/VdrQYExkquwAAAAAAMG5jVWC/LYsWLTKtX78+TCwWxy5cuNC0ZMmSvieffNJSVFR0qaSkpOvrMZFarTYwKytrwDPPbrczNBpNxP79+8+EhYU5KioqggsLC2eUl5e3X716laVQKOw3rjU4OMhYvnx55N///nejUqm0/+QnPxFv2rQpZNWqVZeIiAQCgaOpqal5w4YNIRs2bBC+//7751auXBm2YMECS0lJSZdWqw187733bkoG165d21FaWiqsra09Q0T08ccf+3vurVixIkylUg1WV1e3ffTRR/5qtTrSU6E9c+YM98iRI8b+/n5WTExMbFFR0WUOh+O+Mf5wQ0NDdODAgYDHHnvMRES0e/fus0Kh0GmxWBhz5syRZ2dnXxGJRE6r1cqMjY21bt68uZOI6L333hMcOnTodGhoqOPGmKPFGN9/8PuBZBcAAAAAACa0wMBAl8FgaKqqqvI/ePCgv1qtjlq1atWFgIAAZ1lZmchmszH7+/vZcrncSkTXk91Tp05xWltbeWlpaVIiIpfLRSEhIUNut5sYjJG7oHU6HTc8PNyuVCrtREQ5OTm9f/jDH6YT0SUioqysrCtERElJSYMfffRRMBHRsWPH/P/85z+fISJ69tlnB5YvX35bSeBnn33m/+GHH54hIsrIyDC/8MIL7N7eXhYR0WOPPdbP4/HcPB7Pwefzhy5cuMCOiooaGimO3W5nymQyORHR/PnzzT//+c97iIg2btwo3L9/fxAR0cWLF70aGxu5IpHoKovFopycnCvj2eNoMW7n7/yuIdkFAAAAAIAJj81mU3p6ujk9Pd2sVCqtFRUVAqPR6HP8+PEmiUQyVFBQEGaz2b7xmqbb7WZIJBJrQ0NDy43xeDyeq6mpyVsul1+7Yc6Y++Byue6v9+N2OBzXM2Ym887fEB1pTQaD4SYiGl7FZbFYNHzNGw1/Z9fj448/9j906JD/yZMnW/z9/V1JSUnRVquVSUTk7e3tYrNvnRKOFWMim/AbBAAAAACAqU2n03H0ej3H87u+vp4nkUjsREQikcgxMDDArKysvOn0ZaVSaevr62NXV1f7En3V1nzy5EkuEdErr7zSlZeXN6uvr49JRNTX18csKSkRxMfH2zo6OrwNBgOHiGjnzp3T7r//fvNY+0tOTjb/8Y9/nEZE9MEHHwSYTKabTjAODAx0WiyWEU82Tk5ONm/btm0a0VeJZXBwsMPzLvHd6u/vZwUGBjr9/f1d9fX1XJ1ON+op1r6+vs6BgYGbcsTbiTGRoLILAAAAAAATmslkYmk0mgiTycRisVhusVhs37Fjx7mgoCCHXC5XhIeHX1OpVDe11HK5XLdWq23TaDQRZrOZ5XQ6Gfn5+d2JiYm2FStWXLZYLMy5c+fKvby83Gw22/3yyy9f9PHxcW/ZsuXs4sWLo5xOJ6lUqsHCwsLLY+1vw4YNnZmZmffJ5fKYBQsWWEJDQ6/dOCYpKcnKZrPd0dHR8qysrJ6EhASr597GjRs7s7KyxFKpVM7j8Vzbt2//1715ckSZmZkD5eXlIVKpVB4VFWUb6Tl5qNXqnh/96Eezp0+fPnT8+PHTdxJjImHcqkwPAAAAAABTm06nO6tSqXq+733A1KPT6QQqlUp8J3PRxgwAAAAAAACTDtqYAQAAAAAAfiAuXrzIeuihh6JvvP7JJ58YJ/qngL5rSHYBAAAAAAB+IEQikfPGE5dhZGhjBgAAAAAAgEkHyS4AAAAAAABMOkh2AQAAAAAAYNJBsgsAAAAAAACTDpJdAAAAAACY8IqLi0USiUQhlUrlMplMXlNT4zva2MzMTPG2bduCbxVz1apVwsjISMXs2bMV0dHR8t///vfT7sVeZ8yYEdfV1cUmIpozZ46MiMhoNHpv2bKF7xnz6aef+uTk5My8F+vByHAaMwAAAAAAjNtf//rXmZcuXfK5lzGnT58+uGjRovOj3a+urvY9cOBAkF6vb+LxeO6uri623W5n3M2av/71r0NqamoCPv/882Y+n+/q7e1l7dmzJ+huYo6kvr6+hYiotbWV8/777/Pz8vL6iIgeeOCBwQceeGDwXq8H/x8quwAAAAAAMKF1dHR48fl8B4/HcxMRhYaGOsRi8VBhYWFobGxszOzZsxVLliyZ5XK5bppbV1fnM2/evGiFQhGzcOHC2efOnfMiImJHS6QAACAASURBVHrrrbdE77zzTjufz3cREU2bNs358ssv9xIR7du3zz8mJkYulUrlixcvFlutVgbRVxXbX/ziF2FyuTxGKpXK6+vruURfffs2NTV1dkxMjDwrK2uW2+2+vr6Pj88cIqLXXnttxsmTJ/1kMpl89erV0z/++GP/hx9+WEJE1N3dzXr00UejpFKpXKVSyY4fP84jIiooKAhbvHixOCkpKTo8PDzuzTffnD7aMzIajd733Xef4tlnn50lkUgUqampsy0WC4OIqLS0VBAbGxsTHR0tf/zxx6PMZjOT6KsKeE5Ozsw5c+bIwsPD48ZTDf8hQWUXAAAAAADGbawK7Le4pmn9+vVhYrE4duHChaYlS5b0Pfnkk5aioqJLJSUlXV+PidRqtYFZWVkDnnl2u52h0Wgi9u/ffyYsLMxRUVERXFhYOKO8vLz96tWrLIVCYb9xrcHBQcby5csj//73vxuVSqX9Jz/5iXjTpk0hq1atukREJBAIHE1NTc0bNmwI2bBhg/D9998/t3LlyrAFCxZYSkpKurRabeB7770nuDHu2rVrO0pLS4W1tbVniIg+/vhjf8+9FStWhKlUqsHq6uq2jz76yF+tVkd6vqV75swZ7pEjR4z9/f2smJiY2KKiosscDsd9Y3wiovb2du7//M//fJmSknLuxz/+8X07d+4MfvHFF/t++tOfXvnlL3/ZQ0Sk0WjCfvvb3wpee+21S0RE3d3dXidPnmxpaGjg/uQnP5E899xzV+7mfzWRoLILAAAAAAATWmBgoMtgMDT9/ve/PxcSEuJQq9VRv/3tb6f97W9/81cqlTKpVCo/cuSIv8Fg4A2fd+rUKU5raysvLS1NKpPJ5Js2bQrt7Oz0crvdxGCM3AWt0+m44eHhdqVSaSciysnJ6f3nP/95PTHNysq6QkSUlJQ0eP78eQ4R0bFjx/xzc3N7iYieffbZgYCAAOft/H2fffaZ//PPP99LRJSRkWHu7+9n9/b2soiIHnvssX4ej+cODQ118Pn8oQsXLoxasJwxY4Y9JSXFSkQ0Z86cwbNnz3KIiD7//HNeQkJCtFQqlX/44YfTGhsbuZ45GRkZ/SwWixISEmy9vb1et7PviQ6VXQAAAAAAmPDYbDalp6eb09PTzUql0lpRUSEwGo0+x48fb5JIJEMFBQVhNpvtG8U8t9vNkEgk1oaGhpYb4/F4PFdTU5O3XC6/dsOcMffB5XLdX+/H7XA4rmfMTOad1xFHWpPBYLiJiIZXcVksFg1f80be3t7Dx7qtViuTiOiFF16I3Lt375kFCxZYf/vb3047dOjQ9eTd8/eMto8fMlR2AQAAAABgQtPpdBy9Xs/x/K6vr+dJJBI7EZFIJHIMDAwwKysrb3rfVKlU2vr6+tjV1dW+RF+1NZ88eZJLRPTKK6905eXlzerr62MSEfX19TFLSkoE8fHxto6ODm+DwcAhItq5c+e0+++/3zzW/pKTk81//OMfpxERffDBBwEmk4l145jAwECnxWK56bpn/rZt26YRfdXeHBwc7PC8S3wvDA4OMiMiIobsdjtDq9Xybz1jckBlFwAAAAAAJjSTycTSaDQRJpOJxWKx3GKx2L5jx45zQUFBDrlcrggPD7+mUqmu3jiPy+W6tVptm0ajiTCbzSyn08nIz8/vTkxMtK1YseKyxWJhzp07V+7l5eVms9nul19++aKPj497y5YtZxcvXhzldDpJpVINFhYWXh5rfxs2bOjMzMy8Ty6XxyxYsMASGhp67cYxSUlJVjab7Y6OjpZnZWX1JCQkWD33Nm7c2JmVlSWWSqVyHo/n2r59+7/uzZP7ysqVKzuTkpJiZsyYcS0mJmZwtKR7smFMtlI1AAAAAADcWzqd7qxKper5vvcBU49OpxOoVCrxncxFGzMAAAAAAABMOmhjBgAAAAAA+IG4ePEi66GHHoq+8fonn3xiFIlEt3UK9GSHZBcAAAAAAOAHQiQSOT3f4IWxoY0ZAAAAAAAAJh0kuwAAAAAAADDpINkFAAAAAACASQfJLgAAAAAAAEw6SHYBAAAAAGDCKy4uFkkkEoVUKpXLZDJ5TU2N72hjMzMzxdu2bQse7f7SpUsjZDKZPCoqSsHlcufKZDK5TCaTjzXnXtFqtYEKhSImKipKERkZqcjPz5/R1dXF5vP5Ks+YqqoqPwaDkdDe3s4mIuru7mYFBwerXC4XPfXUU5EzZsyIi46OlovF4tinn35afPbsWa9ve98/RDiNGQAAAAAAxq2puXjmVctpn3sZ09dPOiiP2Xh+tPvV1dW+Bw4cCNLr9U08Hs/d1dXFttvtjDtdb9euXe1EREaj0Ts9PX32d3W68dGjR3nFxcUzKysrW5VKpX1oaIjKyspCQkNDHQEBAc5Tp05xlEqlva6uzi8mJmawtrbWT61W99fW1vrNmTPnKpP5Va1yw4YN55cuXdrvdDpp9erVwocfflja0tLSxOFw3N/F3/FDgcouAAAAAABMaB0dHV58Pt/B4/HcREShoaEOsVg8VFhYGBobGxsze/ZsxZIlS2a5XK6b5tbV1fnMmzcvWqFQxCxcuHD2uXPnRq2C6nQ6TlxcXIzn9xdffMH1/BYKhcoXX3xxRlxcXIxSqZQ1NTV5ExGdP3+e/dhjj0XFxsbGxMXFxRw8eHDUivO6detERUVFXUql0k5E5OXlRcXFxZeJiObNm2f55JNP/IiIjh075vvSSy91Hz582I+I6PDhw37z58+33BiPxWLRmjVruoOCgpx/+ctfAsb1MKcQVHYBAAAAAGDcxqrAflsWLVpkWr9+fZhYLI5duHChacmSJX1PPvmkpaio6FJJSUnX12MitVptYFZW1oBnnt1uZ2g0moj9+/efCQsLc1RUVAQXFhbO+NOf/nR2pHVUKpWdw+G4Tpw4wZ03b56tvLxckJ2d3eO5Hxwc7NTr9c2bN2+eptFoZlZXV7fl5eVFFBcXX3zkkUeueirFra2tjSPFNxqNvNdff71rpHsLFiywHD161E+j0fR2dnZyli1b1p+WlhZCRPTZZ5/5vvHGG52jPZ+4uLjB5uZmLhENjDZmKkKyCwAAAAAAE1pgYKDLYDA0VVVV+R88eNBfrVZHrVq16kJAQICzrKxMZLPZmP39/Wy5XG6lYQnfqVOnOK2trby0tDQpEZHL5aKQkJChsdZSq9U95eXlgvj4+AuVlZXBOp3ueotzTk5OHxHR8uXL+9asWRNORHT48OGAtrY2rmfMwMAAy2KxMPz8/G6rpfihhx6yvP3220K9Xs8Ri8U2f39/19DQEMNkMjGbm5t97r///qujzXW70b08EiS7AAAAAAAw4bHZbEpPTzenp6eblUqltaKiQmA0Gn2OHz/eJJFIhgoKCsJsNts3XtN0u90MiURibWhoaBnvOjk5OVfi4uJC9+zZY5k7d65FIBA4PfcYDMZNWaXb7aaGhoZmLpd7y4wzOjraeuzYMd/ExETbjffi4+NtPT097L/+9a+B8+fPv0pEFBsbO/i73/1OIBaLbWMlz42NjT7p6emo6t4A7+wCAAAAAMCEptPpOHq9nuP5XV9fz5NIJHYiIpFI5BgYGGBWVlbedJKyUqm09fX1saurq32JvmprPnnyJPfGccP5+/u7UlNTTUVFRRG5ubm9w+/t3LmTT0RUUVHBT0hIsBARpaammjZu3BjiGXPkyBHeaLFXrlx5saSkJNRgMHCIiBwOB73xxhtCIiImk0nx8fFXy8vLpy9cuNBC9FVr85YtW6bPmzdvxKquy+Wi1atXT79y5Qpr0aJFprH+rqkIlV0AAAAAAJjQTCYTS6PRRJhMJhaLxXKLxWL7jh07zgUFBTnkcrkiPDz8mkqluikh5HK5bq1W26bRaCLMZjPL6XQy8vPzu0eqrA63bNmyvtra2sCMjIxvJJCDg4PMuLi4GAaD4dZqtV8SEW3durU9Nzc3QiqVCpxOJyMlJcWckpLSPlLc1NRU69q1a88/88wz99lsNiaDwaAnnnii33M/OTnZcvjw4YDU1NRBIqIHH3zw6n/+539yUlNTv3E41cqVK2e++eabYXa7nTl37lxLbW3taZzEfDMG+rsBAAAAAGAsOp3urEql6rn1yMnh1VdfFdntdkZpaen1w6SEQqGysbGxcXhbM3z7dDqdQKVSie9kLiq7AAAAAAAAX0tLS5N0dnZ6Hzp0yPh97wXuDpJdAAAAAACAr9XU1JwZ6Xp3d/ep8cYoKysTlJeXTx9+LTk52bx9+/bv/LNNUxnamAEAAAAAYExTrY0ZJo67aWPGacwAAAAAAAAw6SDZBQAAAAAAgEkHyS4AAAAAAABMOkh2AQAAAAAAYNJBsgsAAAAAABNecXGxSCKRKKRSqVwmk8lramp8RxubmZkp3rZtW/Bo95cuXRohk8nkUVFRCi6XO1cmk8llMpl8rDn3ilarDVQoFDFRUVGKyMhIRX5+/oxve82pCp8eAgAAAACAcXuluX1my1Wbz72MKfPlDm6OiRj1szzV1dW+Bw4cCNLr9U08Hs/d1dXFttvtjDtdb9euXe1EREaj0Ts9PX12S0tL053Guh1Hjx7lFRcXz6ysrGxVKpX2oaEhKisrC/ku1p6KUNkFAAAAAIAJraOjw4vP5zt4PJ6biCg0NNQhFouHCgsLQ2NjY2Nmz56tWLJkySyXy3XT3Lq6Op958+ZFKxSKmIULF84+d+6c12jr6HQ6TlxcXIzn9xdffMH1/BYKhcoXX3xxRlxcXIxSqZQ1NTV5ExGdP3+e/dhjj0XFxsbGxMXFxRw8eHDUivO6detERUVFXUql0k5E5OXlRcXFxZeJiFpaWrznz58vlUql8pSUlNltbW1eRERPPfVU5HPPPTdzzpw5svDw8LidO3cG3dFDnIJQ2QUAAAAAgHEbqwL7bVm0aJFp/fr1YWKxOHbhwoWmJUuW9D355JOWoqKiSyUlJV1fj4nUarWBWVlZA555drudodFoIvbv338mLCzMUVFREVxYWDjjT3/609mR1lGpVHYOh+M6ceIEd968ebby8nJBdnb29e8LBwcHO/V6ffPmzZunaTSamdXV1W15eXkRxcXFFx955JGrnkpxa2tr40jxjUYj7/XXX+8a6d4LL7wwKycnpyc/P7+vpKRE8NJLL82sqqr6koiop6eH/fnnn7ecOHGC9+yzz963bNmy/rt4nFMGkl0AAAAAAJjQAgMDXQaDoamqqsr/4MGD/mq1OmrVqlUXAgICnGVlZSKbzcbs7+9ny+VyKxFdT3ZPnTrFaW1t5aWlpUmJiFwuF4WEhAyNtZZare4pLy8XxMfHX6isrAzW6XTXW5xzcnL6iIiWL1/et2bNmnAiosOHDwe0tbVxPWMGBgZYFouF4efn576dv1Gn0/nW1NS0EhG9+OKLvevXr7/+Lm9GRkY/k8mk+fPnWy9duuR9O3GnMiS7AAAAAAAw4bHZbEpPTzenp6eblUqltaKiQmA0Gn2OHz/eJJFIhgoKCsJsNts3XtN0u90MiURibWhoaBnvOjk5OVfi4uJC9+zZY5k7d65FIBA4PfcYDMZNCazb7aaGhoZmLpd7y+Q2OjraeuzYMd/ExETbePdDRDQ8ttt9Wzn0lIZ3dgEAAAAAYELT6XQcvV7P8fyur6/nSSQSOxGRSCRyDAwMMCsrK286SVmpVNr6+vrY1dXVvkRftTWfPHmSe+O44fz9/V2pqammoqKiiNzc3N7h93bu3MknIqqoqOAnJCRYiIhSU1NNGzduvH7I1JEjR3ijxV65cuXFkpKSUIPBwCEicjgc9MYbbwiJiOLj4y3vvvsun4hoy5Yt05KSksy3ei4wNlR2AQAAAABgQjOZTCyNRhNhMplYLBbLLRaL7Tt27DgXFBTkkMvlivDw8GsqlerqjfO4XK5bq9W2aTSaCLPZzHI6nYz8/PzuW1VWly1b1ldbWxuYkZFhGn59cHCQGRcXF8NgMNxarfZLIqKtW7e25+bmRkilUoHT6WSkpKSYU1JS2keKm5qaal27du35Z5555j6bzcZkMBj0xBNP9BMRvf322+3PPfecuLS0VCQQCIZ27dp19o4fGBAREQNlcAAAAAAAGItOpzurUql6bj1ycnj11VdFdrudUVpaev0wKaFQqGxsbGwc3tYM3z6dTidQqVTiO5mLyi4AAAAAAMDX0tLSJJ2dnd6HDh0yft97gbuDZBcAAAAAAOBrNTU1Z0a63t3dfWq8McrKygTl5eXTh19LTk42b9++/Tv/bNNUhjZmAAAAAAAY01RrY4aJ427amHEaMwAAAAAAAEw6SHYBAAAAAABg0kGyCwAAAAAAAJMOkl0AAAAAAJjwiouLRRKJRCGVSuUymUxeU1PjO9rYzMxM8bZt24JHu7906dIImUwmj4qKUnC53LkymUwuk8nkY825V7RabaBCoYiJiopSREZGKvLz82fcSRyDwcCRyWTyG68/9dRTkTNmzIiLjo6Wi8Xi2Kefflp89uxZr7vf+Q8PTmMGAAAAAIAJrbq62vfAgQNBer2+icfjubu6uth2u51xp/F27drVTkRkNBq909PTZ7e0tDTdu92O7ujRo7zi4uKZlZWVrUql0j40NERlZWUh93qdDRs2nF+6dGm/0+mk1atXCx9++GFpS0tLE4fDmVKnEyPZBQAAAACAcSvaq5t5+qLZ517GlIr8Bzf9u2rUz/J0dHR48fl8B4/HcxMRhYaGOoiICgsLQ6uqqoLsdjszMTHRsnv37nNM5jebV+vq6nwKCgpmDg4OMoODgx27d+8+O2vWrKGR1tHpdJzs7Oz79Hp9MxHRF198wVWr1ZF6vb5ZKBQqMzMze+vq6gIYDIZbq9V+KZfLr50/f579/PPPz+rs7PRmMBi0efPm9kceeeTqSPHXrVsnKioq6lIqlXYiIi8vLyouLr5MRNTS0uKtVqvFV65cYQsEgqFdu3adjYqKGnrqqaci+Xy+o6Ghwffy5cte69atO79s2bL+8TxXFotFa9as6a6srAz+y1/+EvDss88OjGfeZIE2ZgAAAAAAmNAWLVpk6uzs9BaLxbHZ2dkR+/fv9yMiKioqumQwGJpbW1sbrVYrU6vVBg6fZ7fbGRqNJmLfvn1tjY2NzWq1uqewsHDUtmGVSmXncDiuEydOcImIysvLBdnZ2dc/uRQcHOzU6/XNubm5lzUazUwiory8vIji4uKLBoOhee/evW15eXni0eIbjUZecnLyiInwCy+8MCsnJ6fn9OnTTU8//fSVl156aabnXk9PD/vzzz9v+fDDD8+8/vrrt932HBcXN9jc3My93Xk/dKjsAgAAAADAuI1Vgf22BAYGugwGQ1NVVZX/wYMH/dVqddSqVasuBAQEOMvKykQ2m43Z39/PlsvlViK6Xr08deoUp7W1lZeWliYlInK5XBQSEjJiVddDrVb3lJeXC+Lj4y9UVlYG63S66y3OOTk5fUREy5cv71uzZk04EdHhw4cD2trarieSAwMDLIvFwvDz87utlmGdTudbU1PTSkT04osv9q5fv/56UpuRkdHPZDJp/vz51kuXLnnfTlwiIrd7SnUvX4dkFwAAAAAAJjw2m03p6enm9PR0s1KptFZUVAiMRqPP8ePHmyQSyVBBQUGYzWb7Rueq2+1mSCQSa0NDQ8t418nJybkSFxcXumfPHsvcuXMtAoHA6bnHYDBuyhrdbjc1NDQ0c7ncW2aU0dHR1mPHjvkmJibaxrsfIqLhse8kcW1sbPRJT0+fUi3MRGhjBgAAAACACU6n03H0ej3H87u+vp4nkUjsREQikcgxMDDArKysvOkkZaVSaevr62NXV1f7En3V1nzy5Mkx23n9/f1dqamppqKioojc3Nze4fd27tzJJyKqqKjgJyQkWIiIUlNTTRs3brx+yNSRI0d4o8VeuXLlxZKSklCDwcAhInI4HPTGG28IiYji4+Mt7777Lp+IaMuWLdOSkpLMt3out+JyuWj16tXTr1y5wlq0aJHpbuP90KCyCwAAAAAAE5rJZGJpNJoIk8nEYrFYbrFYbN+xY8e5oKAgh1wuV4SHh19TqVQ3vQvL5XLdWq22TaPRRJjNZpbT6WTk5+d336qyumzZsr7a2trAjIyMbySIg4ODzLi4uBjPAVVERFu3bm3Pzc2NkEqlAqfTyUhJSTGnpKS0jxQ3NTXVunbt2vPPPPPMfTabjclgMOiJJ57oJyJ6++2325977jlxaWmpyHNA1a2eS1tbG1coFCo9vzdt2tRORLRy5cqZb775ZpjdbmfOnTvXUltbe3qqncRMRMSYqv3bAAAAAAAwPjqd7qxKpeq59cjJ4dVXXxXZ7XZGaWlpl+eaUChUNjY2Ng5va4Zvn06nE6hUKvGdzEVlFwAAAAAA4GtpaWmSzs5O70OHDhm/773A3UGyCwAAAAAA8LWampozI13v7u4+Nd4YZWVlgvLy8unDryUnJ5u3b9/+nZ9kPZWhjRkAAAAAAMY01dqYYeK4mzZmnMYMAAAAAAAAkw6SXQAAAAAAAJh0kOwCAAAAAADApINkFwAAAAAAACYdJLsAAAAAADDhFRcXiyQSiUIqlcplMpm8pqbGd7SxmZmZ4m3btgWPdn/p0qURMplMHhUVpeByuXNlMplcJpPJx5pzr2i12kCFQhETFRWliIyMVOTn58+4kzgGg4Ejk8nkN15/6qmnInft2hV09zv94cOnhwAAAAAAYEKrrq72PXDgQJBer2/i8Xjurq4utt1uZ9xpvF27drUTERmNRu/09PTZLS0tTfdut6M7evQor7i4eGZlZWWrUqm0Dw0NUVlZWch3sfZUhGQXAAAAAADG768vzaRLTT73NOZ0+SAt+sOo36Dt6Ojw4vP5Dh6P5yYiCg0NdRARFRYWhlZVVQXZ7XZmYmKiZffu3eeYzG82r9bV1fkUFBTMHBwcZAYHBzt27959dtasWUMjraPT6TjZ2dn36fX6ZiKiL774gqtWqyP1en2zUChUZmZm9tbV1QUwGAy3Vqv9Ui6XXzt//jz7+eefn9XZ2enNYDBo8+bN7Y888sjVkeKvW7dOVFRU1KVUKu1ERF5eXlRcXHyZiKilpcVbrVaLr1y5whYIBEO7du06GxUVNfTUU09F8vl8R0NDg+/ly5e91q1bd37ZsmX9d/CUpxy0MQMAAAAAwIS2aNEiU2dnp7dYLI7Nzs6O2L9/vx8RUVFR0SWDwdDc2traaLVamVqtNnD4PLvdztBoNBH79u1ra2xsbFar1T2FhYWjtg2rVCo7h8NxnThxgktEVF5eLsjOzr7+feHg4GCnXq9vzs3NvazRaGYSEeXl5UUUFxdfNBgMzXv37m3Ly8sTjxbfaDTykpOTR0yEX3jhhVk5OTk9p0+fbnr66aevvPTSSzM993p6etiff/55y4cffnjm9ddfv6O256kIlV0AAAAAABi/MSqw35bAwECXwWBoqqqq8j948KC/Wq2OWrVq1YWAgABnWVmZyGazMfv7+9lyudxKRAOeeadOneK0trby0tLSpERELpeLQkJCRqzqeqjV6p7y8nJBfHz8hcrKymCdTne9xTknJ6ePiGj58uV9a9asCSciOnz4cEBbWxvXM2ZgYIBlsVgYfn5+7tv5G3U6nW9NTU0rEdGLL77Yu379+utJbUZGRj+TyaT58+dbL1265H07cacyJLsAAAAAADDhsdlsSk9PN6enp5uVSqW1oqJCYDQafY4fP94kkUiGCgoKwmw22zc6V91uN0MikVgbGhpaxrtOTk7Olbi4uNA9e/ZY5s6daxEIBE7PPQaDcVMC63a7qaGhoZnL5d4yuY2OjrYeO3bMNzEx0Tbe/RARDY/tdt9WDj2loY0ZAAAAAAAmNJ1Ox9Hr9RzP7/r6ep5EIrETEYlEIsfAwACzsrLyppOUlUqlra+vj11dXe1L9FVb88mTJ7k3jhvO39/flZqaaioqKorIzc3tHX5v586dfCKiiooKfkJCgoWIKDU11bRx48brh0wdOXKEN1rslStXXiwpKQk1GAwcIiKHw0FvvPGGkIgoPj7e8u677/KJiLZs2TItKSnJfKvnAmNDZRcAAAAAACY0k8nE0mg0ESaTicVisdxisdi+Y8eOc0FBQQ65XK4IDw+/plKpbnoXlsvlurVabZtGo4kwm80sp9PJyM/P775VZXXZsmV9tbW1gRkZGabh1wcHB5lxcXExngOqiIi2bt3anpubGyGVSgVOp5ORkpJiTklJaR8pbmpqqnXt2rXnn3nmmftsNhuTwWDQE0880U9E9Pbbb7c/99xz4tLSUpHngKpbPZe2tjauUChUen5v2rRpxHWnKgbK4AAAAAAAMBadTndWpVL13Hrk5PDqq6+K7HY7o7S0tMtzTSgUKhsbGxuHtzXDt0+n0wlUKpX4TuaisgsAAAAAAPC1tLQ0SWdnp/ehQ4eM3/de4O4g2QUAAAAAAPhaTU3NmZGud3d3nxpvjLKyMkF5efn04deSk5PN27dv/85Psp7K0MYMAAAAAABjmmptzDBx3E0bM05jBgAAAAAAgEkHyS4AAAAAAABMOkh2AQAAAAAAYNJBsgsAAAAAAACTDpJdAAAAAACY8IqLi0USiUQhlUrlMplMXlNT4zva2MzMTPG2bduCR7u/dOnSCJlMJo+KilJwudy5MplMLpPJ5GPNuVe0Wm2gQqGIiYqKUkRGRiry8/Nn3Ekcg8HAkclk8nu9v8kEnx4CAAAAAIAJrbq62vfAgQNBer2+icfjubu6uth2u51xp/F27drVTkRkNBq909PTZ7e0tDTdu92O7ujRo7zi4uKZlZWVrUql0j40NERlZWUh38XaUxGSXQAAAAAAGLf/e/j/zjxz5YzPvYwpCZYM/ir1V6N+g7ajo8OLz+c7eDyem4goNDTUQURUWFgYWlVVFWS325mJiYmW3bt3n2Myv9m8WldX51NQUDBzcHCQGRwc7Ni9e/fZWbNmDY20jk6n42RnPkf7awAAIABJREFUZ9+n1+ubiYi++OILrlqtjtTr9c1CoVCZmZnZW1dXF8BgMNxarfZLuVx+7fz58+znn39+VmdnpzeDwaDNmze3P/LII1dHir9u3TpRUVFRl1KptBMReXl5UXFx8WUiopaWFm+1Wi2+cuUKWyAQDO3atetsVFTU0FNPPRXJ5/MdDQ0NvpcvX/Zat27d+WXLlvWPFP/s2bNeGRkZUadOnWqpq6vzeeCBB2L+9a9/nRKLxUMzZsyIa21tNfj4+EyZb8+ijRkAAAAAACa0RYsWmTo7O73FYnFsdnZ2xP79+/2IiIqKii4ZDIbm1tbWRqvVytRqtYHD59ntdoZGo4nYt29fW2NjY7Nare4pLCwctW1YpVLZORyO68SJE1wiovLyckF2dvb17wsHBwc79Xp9c25u7mWNRjOTiCgvLy+iuLj4osFgaN67d29bXl6eeLT4RqORl5ycPGIi/MILL8zKycnpOX36dNPTTz995aWXXprpudfT08P+/PPPWz788MMzr7/++qj7F4vFQ2azmWUymZi1tbV+CoVi8B//+IdfY2MjRyQSXZtKiS4RKrsAAAAAAHAbxqrAflsCAwNdBoOhqaqqyv/gwYP+arU6atWqVRcCAgKcZWVlIpvNxuzv72fL5XIrEQ145p06dYrT2trKS0tLkxIRuVwuCgkJGbGq66FWq3vKy8sF8fHxFyorK4N1Ot31FuecnJw+IqLly5f3rVmzJpyI6PDhwwFtbW1cz5iBgQGWxWJh+Pn53VZiqdPpfGtqalqJiF588cXe9evXX09qMzIy+plMJs2fP9966dIl77HiJCQkXD148KDf4cOH/VesWNH1j3/8I8BqtTIXLFhguZ39TAZIdgEAAAAAYMJjs9mUnp5uTk9PNyuVSmtFRYXAaDT6HD9+vEkikQwVFBSE2Wy2b3Suut1uhkQisTY0NLSMd52cnJwrcXFxoXv27LHMnTvXIhAInJ57DAbjpgTW7XZTQ0NDM5fLvWVyGx0dbT127JhvYmKibbz7ISIaHtvtHnuZhQsXmj/55BO/ixcvei1ZsqT/rbfeEl27do2xePHiK7ez5mSANmYAAAAAAJjQdDodR6/Xczy/6+vreRKJxE5EJBKJHAMDA8zKysqbTlJWKpW2vr4+dnV1tS/RV23NJ0+e5N44bjh/f39XamqqqaioKCI3N7d3+L2dO3fyiYgqKir4CQkJFiKi1NRU08aNG68fMnXkyBHeaLFXrlx5saSkJNRgMHCIiBwOB73xxhtCIqL4+HjLu+++yyci2rJly7SkpCTzrZ7LSB599FHLBx98ME0ikdi8vLzI19fX+emnnwakpaWhsgsAAAAAADCRmEwmlkajiTCZTCwWi+UWi8X2HTt2nAsKCnLI5XJFeHj4NZVKddO7sFwu163Vats0Gk2E2WxmOZ1ORn5+fvetKqvLli3rq62tDczIyDANvz44OMiMi4uL8RxQRUS0devW9tzc3AipVCpwOp2MlJQUc0pKSvtIcVNTU61r1649/8wzz9xns9mYDAaDnnjiiX4iorfffrv9ueeeE5eWloo8B1Td6rm0tbVxhUKh0vN706ZN7cuWLet3Op2M+++/30xElJycbOnr62Pz+XzXreJNNoxblcEBAAAAAGBq0+l0Z1UqVc+tR04Or776qshutzNKS0u7PNeEQqGysbGxcXhbM3z7dDqdQKVSie9kLiq7AAAAAAAAX0tLS5N0dnZ6Hzp0yPh97wXuDpJdAAAAAACAr9XU1JwZ6Xp3d/ep8cYoKysTlJeXTx9+LTk52bx9+/bv/CTrqQxtzAAAAAAAMKap1sYME8fdtDHjNGYAAAAAAACYdJDsAgAAAAAAwKSDZBcAAAAAAAAmHSS7AAAAAAAAMOkg2QUAAAAAgAmvuLhYJJFIFFKpVC6TyeQ1NTW+o43NzMwUb9u2LXi0+0uXLo2QyWTyqKgoBZfLnSuTyeQymUw+1px7oaysTMBkMhNOnjzJ9VyLjIxUtLW1eX2b605V+PQQAAAAAABMaNXV1b4HDhwI0uv1TTwez93V1cW22+2MO423a9eudiIio9HonZ6ePrulpaXp3u12bEKh8NqaNWtCP/roo399V2tOVUh2AQAAAABg3DpffW2mvbXV517G5MyePRi2bu2o36Dt6Ojw4vP5Dh6P5yYiCg0NdRARFRYWhlZVVQXZ7XZmYmKiZffu3eeYzG82r9bV1fkUFBTMHBwcZAYHBzt27959dtasWUMjraPT6TjZ2dn36fX6ZiKiL774gqtWqyP1en2zUChUZmZm9tbV1QUwGAy3Vqv9Ui6XXzt//jz7+eefn9XZ2enNYDBo8+bN7Y888sjV0f6Wxx9/vP/TTz8NMBgMnNjYWPvwex988EHAunXrwq5du8aIjIy0v/fee2ePHz/Oe+utt4T/+7//++X27duD8vPzI/v7+xvsdjsjLi5Ofu7cOcO4H/QUgzZmAAAAAACY0BYtWmTq7Oz0FovFsdnZ2RH79+/3IyIqKiq6ZDAYmltbWxutVitTq9UGDp9nt9sZGo0mYt++fW2NjY3NarW6p7CwcMZo66hUKjuHw3GdOHGCS0RUXl4uyM7Ovv594eDgYKder2/Ozc29rNFoZhIR5eXlRRQXF180GAzNe/fubcvLyxOP9bcwmUzSaDQX16xZIxp+vaOjg71p06bQurq6001NTc2xsbGD69atm/7AAw8M6vV6HyKiTz/91D8qKsp2+PBhn9raWt85c+aMmlQDKrsAAAAAAHAbxqrAflsCAwNdBoOhqaqqyv/gwYP+arU6atWqVRcCAgKcZWVlIpvNxuzv72fL5XIrEQ145p06dYrT2trKS0tLkxIRuVwuCgkJGbGq66FWq3vKy8sF8fHxFyorK4N1Ot31FuecnJw+IqLly5f3rVmzJpyI6PDhwwFtbW3X38EdGBhgWSwWhp+fn3u0NfLz8/veeuut0NbWVm/PtZqaGr8zZ85w582bJyMiGhoaYiQlJVk4HI47LCzsml6v5+h0Op+XXnqpu7a21u/q1aushQsXmm/7YU4hSHYBAAAAAGDCY7PZlJ6ebk5PTzcrlUprRUWFwGg0+hw/frxJIpEMFRQUhNlstm90rrrdboZEIrE2NDS0jHednJycK3FxcaF79uyxzJ071yIQCJyeewwG46YE1u12U0NDQzOXyx01ub0Rh8Nx5+fnd//qV7+6Xt11u9304IMPmv7617/e9C7vggULLH/5y18CuVyu68knnzTl5OSIbTYb8w9/+EP7eNecitDGDAAAAAAAE5pOp+Po9XqO53d9fT1PIpHYiYhEIpFjYGCAWVlZedNJykql0tbX18eurq72JfqqrXn4Scgj8ff3d6WmppqKiooicnNze4ff27lzJ5+IqKKigp+QkGAhIkpNTTVt3LgxxDPmyJEjvPH8TT//+c97amtrAwYGBthERA8//LDl+PHjfk1NTd5ERCaTien5mx966CHz22+/LZw/f/7ViIgIx+XLl73OnTvHmTNnjm08a01VqOwCAAAAAMCEZjKZWBqNJsJkMrFYLJZbLBbbd+zYcS4oKMghl8sV4eHh11Qq1U3vr3K5XLdWq23TaDQRZrOZ5XQ6Gfn5+d2JiYljJonLli3rq62tDczIyDANvz44OMiMi4uL8RxQRUS0devW9tzc3AipVCpwOp2MlJQUc0pKyi0rrjwez/38889fXr16dTgR0cyZMx3//d//fe6ZZ56JGhoaYhARrV69uiMuLs6elpZ29fLly14PPfSQmYhIJpNZBwYGWON/glMTw+0ed7UdAAAAAACmIJ1Od1alUvXceuTk8Oqrr4rsdjujtLS0y3NNKBQqGxsbG4e3NcO3T6fTCVQqlfhO5qKyCwAAAAAA8LW0tDRJZ2en96FDh4zf917g7iDZBQAAAAAA+FpNTc2Zka53d3efGm+MsrIyQXl5+fTh15KTk83bt2//zk+ynsrQxgwAAAAAAGOaam3MMHHcTRszTmMGAAAAAACASQfJLgAAAAAAAEw6SHYBAAAAAABg0kGyCwAAAAAAAJMOkl0AAAAAAJjwiouLRRKJRCGVSuUymUxeU1PjO9rYzMxM8bZt24JHu7906dIImUwmj4qKUnC53LkymUwuk8nkY825V3bs2BEklUrl9913n0Iqlcr37NkT6Lm3efPmae3t7de/mCMUCpU9PT2sb3tPkxU+PQQAAAAAABNadXW174EDB4L0en0Tj8dzd3V1se12O+NO4+3ataudiMhoNHqnp6fPbmlpabp3ux3dP//5T59Vq1aF/+Mf/zgtlUqvNTY2ch5//HGpVCptTUxMtO3atUuQlJQ0GBER4fgu9jPZIdkFAAAAAIBxO7izeWZfh8XnXsbkz/AbfGRZzKjfoO3o6PDi8/kOHo/nJiIKDQ11EBEVFhaGVlVVBdntdmZiYqJl9+7d55jMbzav1tXV+RQUFMwcHBxkBgcHO3bv3n121qxZQyOto9PpONnZ2ffp9fpmIqIvvviCq1arI/V6fbNQKFRmZmb21tXVBTAYDLdWq/1SLpdfO3/+PPv555+f1dnZ6c1gMGjz5s3tjzzyyNWR4m/cuFFYVFTUJZVKrxERKRQK+89//vOuDRs2iB5//PGB5uZmn6ysrCgul+tqaGhoJiJat26d8G9/+1uQ0+mkvXv3timVSvsdPOIpCW3MAAAAAAAwoS1atMjU2dnpLRaLY7OzsyP279/vR0RUVFR0yWAwNLe2tjZarVamVqsNHD7PbrczNBpNxL59+9oaGxub1Wp1T2Fh4YzR1lGpVHYOh+M6ceIEl4iovLxckJ2dff37wsHBwU69Xt+cm5t7WaPRzCQiysvLiyguLr5oMBia9+7d25aXlyceLb7RaOQlJyd/IxFOTk4eNBqNvJ/97GdXYmJiBvfs2dPW0tLSxOVy3UREQqFwqLm5uWnZsmU9GzZsEN7B45uyUNkFAAAAAIBxG6sC+20JDAx0GQyGpqqqKv+DBw/6q9XqqFWrVl0ICAhwlpWViWw2G7O/v58tl8utRDTgmXfq1ClOa2srLy0tTUpE5HK5KCQkZMSqrodare4pLy8XxMfHX6isrAzW6XTXW5xzcnL6iIiWL1/et2bNmnAiosOHDwe0tbVxPWMGBgZYFouF4efn5x4pPoPxze5rt9tNDAZjxLFERFlZWVeIiJKSkq4eOHAgcLRxcDMkuwAAAAAAMOGx2WxKT083p6enm5VKpbWiokJgNBp9jh8/3iSRSIYKCgrCbDbbNzpX3W43QyKRWBsaGlrGu05OTs6VuLi40D179ljmzp1rEQgETs+9kZJSt9tNDQ0NzZ5K7FikUqnt6NGjvgkJCTbPtc8++8xHKpXaRpvjad1msVjkdDrv+D3lqQhtzAAAAAAAMKHpdDqOXq/neH7X19fzJBKJnYhIJBI5BgYGmJWVlTedpKxUKm19fX3s6upqX6Kv2ppPnjzJvXHccP7+/q7U1FRTUVFRRG5ubu/wezt37uQTEVVUVPATEhIsRESpqammjRs3hnjGHDlyhDda7BUrVlwsKSkJbW1t9SYiampq8v7Nb34jKi4uvkhE5Ovr6zKZTDh9+R5BZRcAAAAAACY0k8nE0mg0ESaTicVisdxisdi+Y8eOc0FBQQ65XK4IDw+/plKpbjoUisvlurVabZtGo4kwm80sp9PJyM/P705MTBy1kkpEtGzZsr7a2trAjIwM0/Drg4ODzLi4uBjPAVVERFu3bm3Pzc2NkEqlAqfTyUhJSTGnpKS0jxT3gQceGFy1alXHj3/849kOh4O8vLzc69atuzBv3jzb1+v25OXliYcfUAV3juF237LaDgAAAAAAU5hOpzurUql6bj1ycnj11VdFdrudUVpa2uW5JhQKlY2NjY3D25rh26fT6QQqlUp8J3NR2QUAAAAAAPhaWlqapLOz0/vQoUPG73svcHeQ7AIAAAAAAHytpqbmzEjXu7u7T403RllZmaC8vHz68GvJycnm7du3f+cnWf8/9u48rOkr+x/4yQIkIWGXXUDJ8klCiMjihhtWq1adjlZbV9C2KrZFa/Vrq9Zp7djqr9JxmI5a7SpWsepMbbXVAVGk2jqiENYA0uIKWASSsAWy/P7oxIdaQUSsFN+v5/F5zM29557Px7+O534+eZThGDMAAAAAAHToUTvGDD3H/RxjxtuYAQAAAAAAoNdBsQsAAAAAAAC9DopdAAAAAAAA6HVQ7AIAAAAAAECvg2IXAAAAAAB6vFWrVnmLxWKlVCpVMAyjSE9Pd2xv7rRp04I++eQT1/a+nzt3bgDDMIrg4GAlj8cbyDCMgmEYRUdr7pfZbCZnZ+cBNTU1bCKisrIyOxaLFX78+HFHIiKLxUIuLi4DqqurOQkJCb6enp6hDMMoAgMDQx5//PHg7Oxs3oPKrbfCTw8BAAAAAECPlpaW5njs2DGXvLy8Qj6fb62oqOAajUZWV+MlJydfJiIqLi62nzRpkkSr1RZ2X7Z3xuFwSKVSNZw8eVI4depUfXp6ulAulzdmZmYKx4wZ03DhwgWep6dni4eHh5mI6MUXX6xct27dDSKiDz74wG3cuHHSvLy8Am9vb/ODzrW3QLELAAAAAACddmzblr7VVy4JujOmR9/Axsfjl7X7G7TXrl2zc3NzM/H5fCsRkY+Pj4mIaMWKFT5Hjx51MRqN7IiIiPrPP//8Epv968OrmZmZguXLl/dtbGxku7q6mj7//PPywMDA1jvto9FoHObMmdM/Ly+viIjowoULvNjY2H55eXlFXl5eodOmTbuZmZnpxGKxrCkpKT8qFIqWK1eucJ999tnA69ev27NYLNqyZcvlMWPGNNwp/uDBg+u/++474dSpU/VnzpwRvvDCC1Vff/21CxFRRkaGMCIi4o7rFi1aVHP48GHnTz75xO211177uRO3FAjHmAEAAAAAoId78skn9devX7cPCgoKmTNnTsCRI0eEREQrV668kZ+fX1RaWlrQ1NTETklJcW67zmg0shISEgIOHTpUVlBQUBQbG1u9YsUKv/b2UavVRgcHB8u5c+d4REQ7duzwmDNnzq3fF3Z1dTXn5eUVLViw4OeEhIS+RESLFy8OWLVqVWV+fn7RgQMHyhYvXhzUXvzo6Oj6s2fPOhIR5eTkOMbGxtZevXrVgYjo+++/Fw4dOrS+vbVhYWGNWq0WR5nvATq7AAAAAADQaR11YB8UZ2dnS35+fuHRo0dFx48fF8XGxgavW7fuqpOTk/m9997zbm5uZtfV1XEVCkUTEels63Jzcx1KS0v5MTExUqJfnovt06fPHbu6NrGxsdU7duzwGDBgwNWvv/7aVaPR3DriHBcXV0P0S6d1/fr1/kREp0+fdiorK7tVhOp0Ok59fT1LKBRab489evTohtjYWEedTscmIhIKhVZfX9+W4uJi+6ysLOEbb7xR0V5eVutvwsFdoNgFAAAAAIAej8vl0qRJkwyTJk0yhIaGNu3cudOjuLhYcPbs2UKxWNy6fPly3+bm5l+dXLVarSyxWNyUk5Oj7ew+cXFxtSqVymfPnj31AwcOrLc9Q0tExGKxflNxWq1WysnJKeLxeHetRp2dnS2+vr4t77//vodarW4gIoqKiqo/cOCAS319PSckJMTY3tqcnBzBsGHD2u38wm/hGDMAAAAAAPRoGo3GIS8vz8H2OTs7my8Wi41ERN7e3iadTsf++uuvf/Mm5dDQ0OaamhpuWlqaI9Evx5qzsrI6PAosEoksw4YN069cuTJgwYIFN9t+t2vXLjciop07d7qFh4fXExENGzZMv2nTpj62OWfOnOF3FD8yMrJ++/btnkOGDGkgIoqOjm7Yvn27Z1hYWLuF7Icffuj6ww8/iGydZegcdHYBAAAAAKBH0+v1nISEhAC9Xs/hcDjWoKAg42effXbJxcXFpFAolP7+/i22TmlbPB7PmpKSUpaQkBBgMBg4ZrOZFR8fXxUREdHc0X7z5s2rOXHihPOUKVP0bccbGxvZKpVKbntBFRHRhx9+eHnBggUBUqnUw2w2s4YOHWoYOnTo5fZiDxs2rD45ObnPqFGj6ol+KXYrKyvt4+LifvXiqffff997z549Hk1NTWyZTNb0n//8pwRvYr43LJz9BgAAAACAjmg0mnK1Wl1995m9w+rVq72NRiMrMTHx1jO0Xl5eoQUFBQVtjzXDg6fRaDzUanVQV9aiswsAAAAAAPA/MTEx4uvXr9tnZGQUP+xc4P6g2AUAAAAAAPif9PT0i3car6qqyu1sjPfee89jx44dnm3HBg8ebPj0009/9zdZP8pwjBkAAAAAADr0qB1jhp7jfo4x423MAAAAAAAA0Oug2AUAAAAAAIBeB8UuAAAAAAAA9DoodgEAAAAAAKDXQbELAAAAAAA93qpVq7zFYrFSKpUqGIZRpKenO7Y3d9q0aUGffPKJa3vfz507N4BhGEVwcLCSx+MNZBhGwTCMoqM18MeDnx4CAAAAAIBOqzlQ0re1skHQnTHtvB0b3Z6StvuzPGlpaY7Hjh1zycvLK+Tz+daKigqu0WhkdXW/5OTky0RExcXF9pMmTZJotdrCrsaCngudXQAAAAAA6NGuXbtm5+bmZuLz+VYiIh8fH1NQUFDrihUrfEJCQuQSiUQ5c+bMQIvF8pu1mZmZgsjISJlSqZRHR0dLLl26ZNfePhqNxkGlUsltny9cuMCzffby8gpdsmSJn0qlkoeGhjKFhYX2RERXrlzhjhs3LjgkJESuUqnkx48fb7fjnJCQ4DtjxozAyMhImb+/v+qdd97pY/suJiZGrFQq5WKxWPnee+95EBG1traSSCQasGTJEj+ZTKYYMGAAc+3aNTQsOwk3CgAAAAAAOq2jDuyD8uSTT+rfeecd36CgoJDo6Gj9zJkza5544on6lStX3ti8eXPF/+b0S0lJcZ41a5bOts5oNLISEhICjhw5ctHX19e0c+dO1xUrVvjt37+//E77qNVqo4ODg+XcuXO8yMjI5h07dnjMmTPn1u8Lu7q6mvPy8oq2bNninpCQ0DctLa1s8eLFAatWraocM2ZMg61TXFpaWtDetZSVlfFOnz5dcvPmTY5SqQxZuXLlz1wul/bu3fuTl5eX2WAwsAcMGCCfO3durYuLi7m+vp4zatQow9atW68999xz/v/85z893n777cpuvL29FopdAAAAAADo0ZydnS35+fmFR48eFR0/flwUGxsbvG7duqtOTk7m9957z7u5uZldV1fHVSgUTUR0q9jNzc11KC0t5cfExEiJiCwWC/Xp06e1o71iY2Ord+zY4TFgwICrX3/9tatGo7l1xDkuLq6GiGjRokU169ev9yciOn36tFNZWRnPNken03Hq6+tZQqHQeqf448eP1/F4PKufn5/J2dnZdP36dW5AQIDp7bff9jp69KgLEVFVVZV9UVGRw5AhQxp5PJ5lxowZeiKi8PDwxszMTGGXb+QjBsUuAAAAAAD0eFwulyZNmmSYNGmSITQ0tGnnzp0excXFgrNnzxaKxeLW5cuX+zY3N//qMU2r1coSi8VNOTk52s7uExcXV6tSqXz27NlTP3DgwHoPDw+z7TsWi/WbAtZqtVJOTk4Rj8e7Y3F7OwcHh1tnrdlstrW1tZX15Zdfis6cOSM6f/58kVAotIaHh8uamprY/7vuW3E5HI7VbDZ3+VnlRw2e2QUAAAAAgB5No9E45OXlOdg+Z2dn88VisZGIyNvb26TT6dhff/31b96kHBoa2lxTU8NNS0tzJPrlWHNWVhbv9nltiUQiy7Bhw/QrV64MWLBgwc223+3atcuNiGjnzp1u4eHh9UREw4YN02/atOnWs7dnzpzh3+v11dXVcVxcXExCodCalZXFy8vLa/e5X+g8dHYBAAAAAKBH0+v1nISEhAC9Xs/hcDjWoKAg42effXbJxcXFpFAolP7+/i1qtbrh9nU8Hs+akpJSlpCQEGAwGDhms5kVHx9fFRER0dzRfvPmzas5ceKE85QpU/RtxxsbG9kqlUrOYrGsKSkpPxIRffjhh5cXLFgQIJVKPcxmM2vo0KGGoUOHXr6X65sxY4buww8/7COTyRRisbg5NDT0N9cC945ltXaq2w4AAAAAAI8ojUZTrlarq+8+s3dYvXq1t9FoZCUmJlbYxry8vEILCgoK2h5rhgdPo9F4qNXqoK6sRWcXAAAAAADgf2JiYsTXr1+3z8jIKH7YucD9QbELAAAAAADwP+np6RfvNF5VVZXb2Rjvvfeex44dOzzbjg0ePNjw6aef/u4/2/QowzFmAAAAAADo0KN2jBl6jvs5xoy3MQMAAAAAAECvg2IXAAAAAAAAeh0UuwAAAAAAANDroNgFAAAAAACAXgfFLgAAAAAA9HirVq3yFovFSqlUqmAYRpGenu64fv16T4PB0G01TXJyssv58+d5Hc2ZNm1akJ+fn4phGIVCoZCnpaU5dtf+0L3w00MAAAAAANBpX375Zd8bN24IujOmp6dn45NPPtnuz/KkpaU5Hjt2zCUvL6+Qz+dbKyoquEajkTV37tz+zz//fI1IJLLcvsZkMhGXe2/lzpdffuliMpl04eHhzR3N++tf/3p1/vz5tf/617+clixZElhSUlJ4TxvB7wKdXQAAAAAA6NGuXbtm5+bmZuLz+VYiIh8fH9Pu3btdb9y4YTdy5EjpoEGDpEREAoEgbNmyZb6hoaHM8ePHhZmZmYLIyEiZUqmUR0dHSy5dumRHRFRQUOAwfPhwiVKplIeHh8uys7N5qampjmlpaS5r1671ZxhGUVBQ4HC3vMaPH2+4cuWKAxFRYmKiR0hIiFwmkykef/zxYIPBwL558ybHz89PZTabiYjIYDCwvb29Q41GI+tOOTywG/iIQmcXAAAAAAA6raMO7APcU//OO+/4BgUFhURHR+tnzpxZs3bt2hvbtm3zysjIKPHx8TERETU1NbFDQkKatmyJI417AAAgAElEQVTZct1oNLIGDx4sO3LkyEVfX1/Tzp07XVesWOG3f//+8ueeey5wx44dl1QqlTE9Pd0xPj4+4Icffih57LHH6iZNmqSbP39+bWfySklJcZFIJE1ERLNnz6595ZVXqomIEhISfJOSkjzWrFlzg2GYxm+++UY0efJkQ0pKivPIkSN1Dg4O1vZyeHB38dGDYhcAAAAAAHo0Z2dnS35+fuHRo0dFx48fF8XGxgavW7fu6u3zOBwOxcXF1RIR5ebmOpSWlvJjYmKkREQWi4X69OnTqtPp2NnZ2cLp06cH29a1tLSw7iWftWvX+m/atMnHzc2t9aOPPionIjp//jx/3bp1fgaDgdPQ0MAZOXKkjoho+vTptXv37nWdPHmy4YsvvnBbsmTJz92RA9wdil0AAAAAAOjxuFwuTZo0yTBp0iRDaGhoU3Jysvvtc+zt7S2253StVitLLBY35eTkaNvOqampYYtEIpNWq+3yc7a2Z3bbji1cuLDfgQMHLg4ZMqQpKSnJPSMjQ0RENHPmzLr169f7VVVVcfLz8wWTJ0/W6/X6+84B7g7P7AIAAAAAQI+m0Wgc8vLybj1Dm52dzff3929xdHQ063S6O9Y0oaGhzTU1NVzb25KNRiMrKyuL5+bmZvH392/5+OOPXYl+6fh+//33fCIioVBo1uv1XaqRGhsb2QEBAa1Go5GVkpLiZht3dna2qNXqhkWLFgWMGTNGx+VyqaMcoPug2AUAAAAAgB5Nr9dz5s2b1y84OFgplUoVWq2Wv2nTpuuxsbHVEyZMkNheUNUWj8ezpqSklL366qv+MplMoVQqFRkZGUIior179/74ySefeMhkMoVEIlEePHjQhYho9uzZNUlJSd5yubxTL6hq69VXX70eFRUlHz58uFQikfzqbc4zZsyoPXTokNvMmTNrbGPt5QDdh2W1Wh92DgAAAAAA0INpNJpytVpd/bDzgEePRqPxUKvVQV1Zi84uAAAAAAAA9Dp4QRUAAAAAAMBt5s6dG3Du3Dlh27H4+PiqpUuX3nxYOcG9QbELAAAAAABwm+Tk5MsPOwe4PzjGDAAAAAAAAL0Oil0AAAAAAADodVDsAgAAAAAAQK+DYhcAAAAAAAB6HRS7AAAAAADQ461atcpbLBYrpVKpgmEYRXp6uuP69es9DQZDt9U0ycnJLufPn+d1Vzx4uPA2ZgAAAAAA6LTColV9G+pLBN0Z01EobVTIN11p7/u0tDTHY8eOueTl5RXy+XxrRUUF12g0subOndv/+eefrxGJRJbb15hMJuJy763c+fLLL11MJpMuPDy8uQuXAT0MOrsAAAAAANCjXbt2zc7Nzc3E5/OtREQ+Pj6m3bt3u964ccNu5MiR0kGDBkmJiAQCQdiyZct8Q0NDmePHjwszMzMFkZGRMqVSKY+OjpZcunTJjoiooKDAYfjw4RKlUikPDw+XZWdn81JTUx3T0tJc1q5d688wjKKgoMDhTrlERUXJ4uPj/VQqlTwoKCjk6NGjQiKi4uJi+/DwcJlCoZArFAp5amqqIxHR4cOHRVFRUbLx48f379evn3LKlCn9LJbf1ObwAKCzCwAAAAAAndZRB/ZBefLJJ/XvvPOOb1BQUEh0dLR+5syZNWvXrr2xbds2r4yMjBIfHx8TEVFTUxM7JCSkacuWLdeNRiNr8ODBsiNHjlz09fU17dy503XFihV++/fvL3/uuecCd+zYcUmlUhnT09Md4+PjA3744YeSxx57rG7SpEm6+fPn13aUj8lkYuXl5RXt27fPef369b7jx48v8fX1NWVmZpYIBAJrXl6ew8yZM/vn5+cXEREVFRXxc3JyfgwKCmoNDw9nUlNThY8//nj973HvHmUodgEAAAAAoEdzdna25OfnFx49elR0/PhxUWxsbPC6deuu3j6Pw+FQXFxcLRFRbm6uQ2lpKT8mJkZKRGSxWKhPnz6tOp2OnZ2dLZw+fXqwbV1LSwvrXvKZPn16LRHR0KFDG1auXGlvi/Hss88GFhYW8tlsNl26dOlWZ1ilUjUEBwe3EhEplcrGsrIy+67cB7g3KHYBAAAAAKDH43K5NGnSJMOkSZMMoaGhTcnJye63z7G3t7fYntO1Wq0ssVjclJOTo207p6amhi0SiUxarbawq7nweDyrLSez2cwiItqwYYOXp6dn68GDB3+yWCzE5/PDbfMdHBystr9zOBwymUz3VFxD1+CZXQAAAAAA6NE0Go1DXl7erU5pdnY239/fv8XR0dGs0+nuWNOEhoY219TUcNPS0hyJiIxGIysrK4vn5uZm8ff3b/n4449diX7p+H7//fd8IiKhUGjW6/VdqpF0Oh3Hx8enlcPh0NatW93NZnNXwkA3QrELAAAAAAA9ml6v58ybN69fcHCwUiqVKrRaLX/Tpk3XY2NjqydMmCCxvaCqLR6PZ01JSSl79dVX/WUymUKpVCoyMjKERER79+798ZNPPvGQyWQKiUSiPHjwoAsR0ezZs2uSkpK85XJ5uy+oas+yZctu7N27112tVjMlJSU8Pp+Pt1A9ZCyr1Xr3WQAAAAAA8MjSaDTlarW6+mHnAY8ejUbjoVarg7qyFp1dAAAAAAAA6HXwgioAAAAAAIDbzJ07N+DcuXPCtmPx8fFVS5cuvfmwcoJ7g2IXAAAAAADgNsnJyZcfdg5wf3CMGQAAAAAAAHodFLsAAAAAAADQ66DYBQAAAAAAgF4HxS4AAAAAAAD0Oih2AQAAAACgxysuLraXSCTKtmPLly/3XbdunVdSUpJ7eXm5nW386aefDjx//jyPiMjPz09VUVHBJSIKCwtjbLG2b9/uZpt/6tQpQVxcXN/uypXFYoU///zz/rbP69at81q+fLlvR2s0Go1DVFSUjGEYRf/+/ZUzZ84M7Gj+2LFjg5OTk11sn4OCgkL+7//+z8f2+fHHHw/+7LPPXO68+tGAtzEDAAAAAECnLSu63Ffb0CzozpiMI69xizzgSlfX796922PAgAFNQUFBrURE+/btu3SnednZ2VoiotLSUod9+/a5LV68uIaIaMSIEY0jRoxo7Or+t7O3t7d+8803rhUVFZU+Pj6mzqx54YUXAhISEqrmzJlTR0T03//+l9/R/MGDB9efPn1aOHfu3LrKykqOo6Oj+b///a+j7fvs7GzHnTt33vE+PCrQ2QUAAAAAgD+0/Px8wbx58/ozDKOor69nRUVFyU6dOvWbglwgEIQREa1Zs8YvKytLyDCM4s033/Q8fPiwaPTo0WIiIr1ez54+fXpQSEiIXC6XK3bv3u1CRJSVlcVTqVRyhmEUUqlUkZeX59BePhwOxzpv3ryf3377ba/bvyspKbEfMmSIVCqVKoYMGSItLS21JyK6ceOGXWBgYIttXlRUVBMRkclkokWLFvmHhITIpVKp4t133/UgIhoxYkT9uXPnHImI0tPThePGjdPdvHnTzmKxkFartXdwcLAEBASYiouL7cPDw2UKhUKuUCjkqampjrfn1FuhswsAAAAAAJ12Px3YByUkJKRx8+bNVzrbnd2wYcO1xMRErxMnTlwkIjp8+LDI9t3q1at9Ro8erd+/f395dXU1JyIiQj5lyhT9P/7xjz5Lliypio+Pr2lubmaZTB03bFeuXHlDpVIp33jjjcq244sXLw6YNWvWzZdeeunmli1b3OPj4/umpaWVvfDCC1UTJ06UhoWFNYwZM0b3wgsv3PTw8DBv2bLFw9nZ2Zyfn1/U1NTEioyMZCZPnqyPjo5uLCkp4Tc3N7NOnz4tHD16tOGnn35yyM7O5v33v/8VRERE1BMR+fr6mjIzM0sEAoE1Ly/PYeb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L5s2bL99vrYiIiLZJkybV+/v7y1988UVvpVKpeZjrA91jdddqBwAAAAAAMFGpVFcUCkXd464DBp7/x96dxzV1Zv8DP9kIhM0EBBGBoBBCIKYYxbqgIlaldd+oKGpr62i1at3KWLUWx2VcqqV1q522grZFqFux4q/ubXVsBSasEaGAGDZZQwhgtt8fnfilFhSVjoif9+vF62Xufc55nnvz1/E890ahUDjKZDLh48SiswsAAAAAAACdDopdAAAAAAAA6HRQ7AIAAAAAAECng2IXAAAAAAAAOh0UuwAAAAAAANDpoNgFAAAAAACATgfFLgAAAAAAdHh5eXmckJCQXh4eHv5ubm7+r732mltjYyPjr5yTx+MFEBHduHHDwtvb2898/MyZMzZSqdTX09PTTygU+m/evLlre8wD7QvFLgAAAAAAdGhGo5EmTJjgNW7cuJrCwsKM/Pz8jPr6euaSJUtcnySvTqd75Jhbt26x58yZ47l3797C/Pz8zKtXryoPHTrkGBMT0+VJ1gLtD8UuAAAAAAB0aN99950tl8s1LlmypJKIiM1m0759+4ri4uIc/f39fa9fv25pHhsYGOjz448/8tRqNXPq1KlCf39/X19fX8mhQ4e6EBFFR0c7hIaG9hw+fLhXUFCQqLa2ljlgwACRRCLxFYlE98a1ZseOHU5hYWGVgwcP1hIRubi46Ddt2nR7586d3YiIJk+eLPziiy/45vHmru2jzgNPjv20FwAAAAAAAM+Q4wvdqDyL1645nSRamrC7qLXT6enpVjKZTNv8mEAgMLq4uNwdNWpUzeHDhwV9+/YtLiws5JSXl3OCgoK0ixYtcg0ODlbHx8cXVFRUsPr27es7btw4NRFRSkqKTVpaWqazs7NBp9PRqVOncgUCgbGkpITdv39/cXh4eA2T2XJfMDs722rWrFmVzY8NHjxYm5uba9liwH/xeDzjo8wDTw53FgAAAAAAOjSTyUQMBsPU0vHhw4fXnTx5kk9EFBMTwx87dmw1EdHFixftdu7c6SIWiyWDBw/2aWpqYuTm5loQEQUFBamdnZ0NRERGo5GxdOnSHiKRSBIcHCwqLy+3uH37dqtNwdbW8jCPOg88OdxcAAAAAABouwd0YP8qUqm04cSJE/zmx6qqqpilpaUWQ4YM0Xbp0kV/7do1q6NHjwr2799fSPR7UZqQkJArk8mamsf99NNP1jwez2j+vH//fkFlZSU7PT09m8vlmlxdXaUNDQ2tNgV9fX0bfv31V+sZM2bUmo/9/PPPPKlUqiUiYrPZJoPBQES/P2us0+kYjzMPPDncXAAAAAAA6NDGjRtX19jYyPzkk08ciIj0ej299dZbblOnTq2wtbU1TpkypWrTpk3d6urqWIGBgQ1ERMHBweodO3Y4G42/17U///yzVUu5a2trWY6Ojjoul2v67rvvbIuLiy0etJbly5ffiYuLc7hy5YoVEVFpaSlr3bp1ru+9914xEZGHh8fd5ORkHhHR4cOHu+j1esbjzANPDsUuAAAAAAB0aEwmk44fP5579OhRvoeHh7+np6c/l8s1RkdHq4iIZs6cWX3q1CnB+PHjq8wxW7ZsKdbr9QyxWCzx9vb2W7NmTYtvbn7jjTeqFAqFtb+/v++hQ4cEnp6ejQ9ai4eHh+7zzz/P/9vf/iYUCoX+7u7usgULFpS/8sorGiKit99++86VK1dspVKp77///W9rKysr4+PMA0+OYTI98nZzAAAAAAB4jigUigKZTFbxtNfREW3evLnrF1980fXnn3++0bVrV8PTXk9no1AoHGUymfBxYtHZBQAAAAAAeEx///vf7+Tk5GSh0O14UOwCAAAAAABAp4NiFwAAAAAAADodFLsAAAAAAADQ6aDYBQAAAAAAgE4HxS4AAAAAAAB0Oih2AQAAAACgw2OxWHLzb+aGhob2rKure6xahsfjBTT//MEHHzhxudw+lZWVrPZZKXQUKHYBAAAAAKDD43K5RqVSmXXz5s1MDodj2rFjR9f2yJuQkODg7+9ff/jw4S4tndfpdO0xDTwFKHYBAAAAAOCZMnjwYE1ubi6XiGj9+vXO3t7eft7e3n5RUVFO5jGtHW8uMzOTq9VqmVFRUaojR44IzMejo6MdQkNDew4fPtwrKChIRES0du1aZ39/f1+RSCR55513upvHjhgxopefn5+vl5eX3/bt2x3/uquGR8V+2gsAAAAAAIBnx9qf17rlVufy2jOnF99Lu2HQhqK2jNXpdHTmzBm7kSNHqn/88UfeV1995ZCcnJxtMplILpf7hoSE1BmNRkZLxwcNGtTQPNfBgwcFkyZNqho9erRm3rx5liqViu3q6qonIkpJSbFJS0vLdHZ2Nhw9etQuNzfXMi0tLdtkMtGIESO8Tp8+bRMaGqo5fPhwgbOzs0Gj0TACAgIkM2fOrO7WrZuhPe8PPB50dgEAAAAAoMNrampiisViiVQqlfTo0ePukiVLKi5evGjz8ssv19jZ2Rnt7e2Nr7zySvWFCxdsWzt+f85jx44JZs2aVcVisSg0NLQ6JiaGbz4XFBSkdnZ2NhARJSUl2V2+fNlOIpFI/Pz8JHl5eZZKpdKSiOif//yns4+Pj0Qul/uWlpZyMjMzLf93dwUeBJ1dAAAAAABos7Z2YNub+Znd5sdMJlOLY1s73ty1a9esCgsLuaNHjxYREel0Ooabm1vT3//+9ztERDwez9g839KlS0tWrlxZ0TxHYmKi7aVLl2yvX7+utLW1NQYGBvo0NDSgodhB4IsAAAAAAIBn0vDhwzXff/99l7q6OqZarWZ+//33/ODg4LrWjjePjYmJESxfvrxYpVKlq1Sq9PLy8rTS0lKLnJwci/vnCQ0NVcfGxjrW1tYyiYjy8/M5KpWKXVNTw7K3tzfY2toaU1NTLRUKhfX/6trh4dDZBQAAAACAZ9LgwYO14eHhlX369PElIoqIiLhjfi63teNmx48fFyQmJt5sfiw0NLT64MGDAmdn5z+8gnnSpEnqzMxMy379+omJfu/6Hj58OH/y5Mm1n376aVeRSCTp1atXo0wmq/8rrxceDaMtLX4AAAAAAHh+KRSKAplMVvHwkQDtS6FQOMpkMuHjxGIbMwAAAAAAAHQ6KHYBAAAAAACg00GxCwAAAAAAAJ0Oil0AAAAAAADodFDsAgAAAAAAQKeDYhcAAAAAAAA6HRS7AAAAAADQ4b377rvdvLy8/EQikUQsFkvOnz9v/b9ew7Jly7o7OTn1FovFEm9vb7/Dhw/bt0deHo8X0B554I/YT3sBAAAAAAAAD3L27FnrM2fOdElPT8+ysrIylZSUsJuamhgPi9PpdMThcNp1LfPnzy+LiooqS0lJsQwJCfF59dVXFSwW66Fxf8Va4MHQ2QUAAAAAgA5NpVJxBAKB3srKykRE5OLiohcKhbpLly7xAgICxD4+PhKpVOpbXV3NjI6OdggNDe05fPhwr6CgIBER0dq1a539/f19RSKR5J133uluzrtnzx6BVCr1FYvFkvDwcA+9Xk9Ev3da3377bVcfHx+JTCYTFxUV/alJ2KdPn0YWi0WlpaXsnJwciwEDBohEIpFkwIABops3b1oQEU2ePFn4xhtv9Ojfv7/orbfe6lFbW8ucMmWKUCQSSUQikeTLL7/sYs73sPng0eEmAgAAAABAmxWvfs+t6eZNXnvm5Hp7a7tv2ljU2vkJEyaoN2/e3F0oFPoPHjxYPX369KqQkJD6GTNm9Dp8+HDe0KFDtVVVVUwbGxsjEVFKSopNWlpaprOzs+Ho0aN2ubm5lmlpadkmk4lGjBjhdfr0aRtnZ2d9QkKC4Pr160oul2uaOXOm+759+xwWLVpU2dDQwBwwYIDm448/Vs2fP7/Hxx9/3HXr1q0lzdd0/vx5ayaTaXJxcdGPGDHCKzw8vPLtt9+u3LVrl8OCBQvczp49m0dElJeXZ/nzzz/nsNlsWrBggaudnZ0hJycni4jozp07LCKitswHjw7FLgAAAAAAdGj29vbGjIyMrKSkJNtz587Zzp49u9c777xT4uTkpBs6dKiWiEggEBjN44OCgtTOzs4GIqKkpCS7y5cv20kkEgkRkVarZSqVSsvU1FRGRkYGTyaT+RIRNTY2Mp2cnPRERBwOx/Tqq6/WEhHJ5fL6s2fP2plz79u3z/nIkSMO1tbWhpiYmN+YTCalpqZanz59Oo+IaMGCBVUffPBBD/P4SZMmVbPZv5ddly9ftvvmm29+M5/r2rWr4WHzweNDsQsAAAAAAG32oA7sX4nNZtOYMWPqxowZU9e7d++Gffv2dWUwGKaWxvJ4vHuFr8lkoqVLl5asXLmyovmYjRs3Ok2dOrVy9+7dqhbmMjGZzHvz6vX6e88Hm5/Zbeu6zd1m81oYjD8/avyg+eDx4ZldAAAAAADo0BQKBTc9PZ1r/pyammrl7e3dWFZWZnHp0iUeEVF1dTVTp9P9KTY0NFQdGxvrWFtbyyQiys/P56hUKvbo0aPViYmJfJVKxSYiKisrY+Xk5Fg8zvoCAgLqP/vsMz4R0f79+wV9+/bVtDRu2LBh6g8//NDJ/Nm8jRn+GujsAgAAAABAh6ZWq1mLFy92V6vVLBaLZRIKhU0HDx4szMnJqVi8eLF7Y2Mj09LS0nj58uWc+2MnTZqkzszMtOzXr5+Y6Peu7+HDh/PlcnnjmjVrVCEhISKj0UgcDscUHR19SyQS3X3U9e3du/fW7NmzhR999FE3BwcHfUxMTEFL4zZv3lzy2muvuXt7e/sxmUzT6tWri2fPnl3zyDcE2oRhMrXY+QcAAAAAACAiIoVCUSCTySoePhKgfSkUCkeZTCZ8nFhsYwYAAAAAAIBOB8UuAAAAAAAAdDoodgEAAAAAAKDTQbELAAAAAAAAnQ6KXQAAAAAAAOh0UOwCAAAAAABAp4NiFwAAAAAAOrTS0lKWWCyWiLwjbRwAACAASURBVMViiaOjo8zJyam3+XNjYyPj/vFlZWWsrVu3djV/zsjI4FpaWvYRi8WSnj17+k2ePFmo0+nabX3Dhg3zksvlPs2PjR8/3jM2NrbLo+SJi4uz9/f39/X09PQTi8WSsWPHeubl5XEeFqfT6cjW1vaFR113Z4diFwAAAAAAOrRu3boZlEplllKpzJo1a9ad+fPnl5k/W1pamu4ff+fOHfbnn3/etfkxoVDYqFQqs27cuJF569Yt7pdffslvj7WVlpaybty4YVVZWcm5efOmxePmuXr1qtW7777rdvjw4d/y8/Mzs7KysqZNm1adl5f3p5ztWah3Zih2AQAAAADgmbVmzRpnb29vP29vb7+NGzc6ERGtWLHCtaCgwFIsFkveeust1+bjORwOBQQE1KtUKgsiog8//NBx5MiRvYKDg71cXV2l//znP7uuXbvW2dfXVxIQECCuqKhgERF98MEHTr169fLz8fGRjB8/3tOcLzY2lj9q1Kia8ePHV8XExPyhgE5KSrKTy+U+QqHQ/8iRI3ZERP7+/r4KhYJrHiOXy32uXr1qtWnTpm4rV64slslkTURETCaTIiIiakaOHFlvHvf222+79u3b12fz5s1OmZmZ3N69e4v9/f19ly9f3v2vubvPNvbTXgAAAAAAADw7zsVku1WpNLz2zClwtdGGzPItetS4Cxcu8OLj4x1SUlKy9Xo9yeVy3xEjRtRt375dNWXKFEulUplF9Ps2ZnOMRqNhpKamWs+bN6/QfCwnJ8cqLS0tq7a2lunr6yuNiooqys7Ozpo9e7bbp59+Kli9evWdTz75pFtRUVG6paWlyVwAExHFx8c7bNq06TafzzdERER4btiwocx8rri42OKXX365kZGRwR01apTP2LFj0ydOnFh16NAhgUwmK8nLy+NUV1ezBwwY0HDjxg2r999/v+RB16tWq5nXr1+/QUQ0dOhQr7feeqt8/vz5VRs2bHB61Hv3PEBnFwAAAAAAnkkXL160HTt2bLWtra2Rz+cbQ0NDay5cuGDT0lhzp7dr164veHp6Nvbt27fRfG7QoEFqOzs7o5ubm57H4xmmTp1aQ0QklUobCgoKuERE3t7ejZMmTfLcu3evwMLCwkRElJ+fzykuLrYYPnx4vVwubzQYDIzU1FRLc97JkydXs1gskslkTS4uLnczMjK4ERER1SdOnOATEcXExAjGjx9fff9aVSoVWywWSzw8PPyjoqLuFbIzZsyoMv87NTXV5o033qgiIpo3b17lk97LzgidXQAAAAAAaLPH6cD+VUymPz2u2yrzM7sFBQWcIUOG+MTFxdmHhYXVEhFxudx7iRgMBllZWZmIft9KrNfrGUREly9fzvn+++9tjx071mXbtm0uOTk5mQcPHhTU1tay3NzcpEREdXV1rNjYWEFAQEDxf3P9YYEMBoNEItFda2trY3JysuXRo0cFX375ZT4RkY+PT8O1a9d4ffv2bXR1ddUrlcqs1atXd9NoNPe6yDY2NsbmuRiMP72bC5pBZxcAAAAAAJ5JwcHBdadOneJrNBpGbW0tMykpqcvw4cM19vb2hvr6+hZrHaFQqFu/fr1q69at3do6j16vp99++81i3LhxdXv37r1dXV3NrqurYyYkJAgSExNzVCpVukqlSv/555+zjx49KjDHffvttwKj0UhpaWnckpISC39//yYiokmTJlVt2LDB5e7duwy5XN5IRBQZGVm6devW7s2f59Vqta3Way+88ILmX//6F5+I6LPPPnNo67U8T1DsAgAAAADAMyk4OFg7efLkyoCAAEnfvn19X3/99TuBgYENbm5u+t69e2tFItGfXlBFRDRnzpzq2tpa9tmzZ63bMo9Op2O8+uqrPUUikUQqlUoWLVpUWlxczKmoqOAEBQVpzeOkUmmThYWF8ccff+QREfXs2bOxX79+PuPGjfOOjo4uML85OiIiovq7774TTJgw4d625EGDBjVs3ry5KDw8vKdQKPTv06ePOC8vjztr1qyqP6+IaPfu3UWffPKJs1Qq9dVoNKjrWsB4lNY/AAAAAAA8fxQKRYFMJqt42uuA549CoXCUyWTCx4nF/wAAAAAAAABAp4NiFwAAAAAAADodFLsAAAAAAADQ6aDYBQAAAAAAgE4HxS4AAAAAAAB0Oih2AQAAAAAAoNNBsQsAAAAAAB1aaWkpSywWS8RiscTR0VHm5OTU2/y5sbGRcf/4srIy1tatW7s+LK9OpyNbW9sXiIgyMjK4lpaWfcRiscTHx0fSp08fcXp6OvdJ137y5Enbc+fO3fs935SUFMt+/fr5iMViSc+ePf1mzJjhTkR0/PhxW1tb2xfM1zV48GDvJ537ecd+2gsAAAAAAAB4kG7duhmUSmUWEdGyZcu629jYGKKiospaG3/nzh32559/3nXVqlV3HmUeoVDYaJ5n8+bNXTds2NDtyJEjhU+y9rNnz9o6OjrqQ0JC6omIFi5c6L58+fLSV199tdZoNNL169etzGP79+9fd/bs2bwnmQ/+Dzq7AAAAAADwzFqzZo2zt7e3n7e3t9/GjRudiIhWrFjhWlBQYCkWiyVvvfWWa1VVFfPFF18USSQSX5FIJPn666/tH5ZXrVazunTpYiAi+uWXX6z8/f19xWKxRCQSSbKysiwyMjK43t7eflOnThV6eXn5TZw4Ufjtt9/aBQQEiIVCof/ly5d5mZmZ3K+++qrrJ5980k0sFkt++OEH6/Lyco6Hh8ddIiImk0mBgYENf+0den6hswsAAAAAAG12Zu8ut4qiQl575nR089COWrC06FHjLly4wIuPj3dISUnJ1uv1JJfLfUeMGFG3fft21ZQpUyzNXdqmpibG6dOnc/l8vlGlUrEHDhwonj59eu39+cwFskajYd29e5dx9erVbCKijz76qOuSJUtK33zzzeqGhgaGyWSi3377zSI/P5/7zTff5L3wwguNfn5+kvj4eFNqaqryyy+/7LJp06ZuSUlJv4WHh99xdHTUr1u3rpyIaOHChWUjR4706dOnjyYkJES9cOHCSgcHBwMR0bVr12zFYrGEiGjSpElVmzZtKn2S+/q8Q2cXAAAAAACeSRcvXrQdO3Zsta2trZHP5xtDQ0NrLly4YHP/OJPJRG+//XYPkUgkCQkJEZWWllqUlJT8qfFn3sZ8+/bt9A8++OD2G2+84UFENHDgQM22bdtc1qxZ45yXl2fB4/FMRETu7u5Ncrm8kcVikbe3d0NISIiaiKhPnz4Nt2/fbvF532XLllWkpaVlTpw4sfrixYt2gYGBYvNzx/37969TKpVZSqUyC4Xuk0NnFwAAAAAA2uxxOrB/FZPJ1KZxe/bscVCr1azMzMwsDodDzs7OvbVa7Z9ebNXcq6++WrNy5UoPIqKFCxdWDR06tP7YsWP2o0aNEn322Wf5bm5uOgsLi3sLYDKZZGlpaTL/W6/Xt5rf09NTt3Tp0sqlS5dWenp6+qWmplq26ULgkaCzCwAAAAAAz6Tg4OC6U6dO8TUaDaO2tpaZlJTUZfjw4Rp7e3tDfX39vVqntraW1bVrVz2Hw6Fjx47ZlZeXcx6W+4cffrBxc3NrIiLKysqy8Pf3b1q7dm15SEhIbWpqqtXD4s1sbW2NdXV1LPPnhIQEO51OR0REBQUFHLVazfbw8NA90oVDm6CzCwAAAAAAz6Tg4GDt5MmTKwMCAiRERK+//vod8wufevfurRWJRJIRI0bUvvfee2WhoaFe/v7+vlKpVOvh4dHUUj7zM7smk4ksLCxM+/btKyAi+vLLLx2OHj0qYLPZJmdn57sffvihqrS0tE211JQpU2rCwsJ6JiYm8qOjowtPnTplv2LFCncul2tkMBi0adOmou7du+vb6ZZAM4y2tv4BAAAAAOD5pFAoCmQyWcXTXgc8fxQKhaNMJhM+Tiy2MQMAAAAAAECng2IXAAAAAAAAOh0UuwAAAAAAANDpoNgFAAAAAACATgfFLgAAAAAAAHQ6KHYBAAAAAACg00GxCwAAAAAAHdrcuXPdoqKinMyfBw8e7B0WFuZh/vzmm2/2WL9+vfOTzDF58mThF198wSciCgwM9BEKhf4ikUji6enpN2vWLPeKigrW4+RdtmxZ93Xr1v1pbefOnbPu3bu3WCwWS3r27Om3bNmy7kRE0dHRDnw+XyYWiyVisVgyceJE4ZNc1/OsTT+EDAAAAAAA8LQMGjRIk5CQwCeicoPBQNXV1WyNRnOv+Pz1119tpk+fXtSec8bExPw2ZMgQbWNjI+Ptt992DQ0N9fr1119vtFf+uXPnen799dd5AwYMaNDr9aRQKCzN58aOHVsdExNzq73mel6hswsAAAAAAB3a8OHDNcnJyTZERMnJyVY+Pj4N1tbWhjt37rAaGhoYeXl5lgMGDND+7W9/6+Ht7e0nEokkBw4c4BMRGY1Gau34rFmz3Hv16uU3bNgwr4qKihYbgZaWlqa9e/feLi4utrh69aoVEdGePXsEUqnUVywWS8LDwz30ej0RESUkJNhJJBJfHx8fyYABA0T359qxY4fjkCFDvDUaDaOqqort7u6uIyJis9kkl8sb/5Kb9xxDZxcAAAAAANqsKiHHTVdaz2vPnJxu1lrBFFGrnVmhUKhjs9mmmzdvWly6dMn6xRdfrFepVJzz58/b8Pl8vY+PT0NcXJx9enq6VXZ2dmZJSQk7MDDQd+TIkZoLFy5Yt3T84sWL1rm5udwbN25k3r59myOVSv3mzJlT2dL8bDabfH19tRkZGZZcLteUkJAguH79upLL5Zpmzpzpvm/fPodJkybVLlq0SHjx4kWlWCy+W1ZW9odtz5s2bep69uxZ+zNnzuRaWVmZ5s2bV+br6+vfv3//upEjR9YuXLiwksfjmYiIvvvuO75YLLYhIlqwYEHZkiVLWlwXPBiKXQAAAAAA6PDkcrnmwoUL1levXrVZuXJl2a1btyx+/vlna3t7e0NgYKDmxx9/tJ02bVoVm80mNzc3ff/+/TU//fQTr7Xjly5dundcKBTqBgwYUPeg+U0mExERJSUl2WZkZPBkMpkvEVFjYyPTyclJf/HiRevAwMA6sVh8l4jI2dnZYI6Ni4tzcHFxuXvmzJk8LpdrIiLavn17yWuvvVaVmJhod+TIEYf4+HiHX3755QYRtjG3FxS7AAAAAADQZg/qwP6VBgwYoLly5YqNUqm06tevX0PPnj3v7tq1y9nGxsbw2muvVZw9e9aupThzkdoSBoPRprn1ej3duHGD17t37+KzZ8/aTp06tXL37t2q5mMOHz5s31o+Hx+fhqysLF5+fj7HXAwTEfn5+TX5+fndWbZs2R0HB4cXSktLH+slWNAyPLMLAAAAAAAd3tChQzVnz57t0qVLFwObzSZnZ2eDWq1mpaam2gQHB9cPHTq0LiEhQaDX66m4uJj9yy+/2AQFBT3weHx8vECv11NhYSHn3//+t21L8zY1NTEWLVrUw8XF5W7//v0bRo8erU5MTOSrVCo2EVFZWRkrJyfHIjg4uP7atWu2SqXSwnzcnOOFF17Q7t69u3DcuHFeBQUFHCKib775xt5oNBIRUXp6uiWLxTI5OjoaWlgCPCZ0dgEAAAAAoMMLDAxsqKmpYU+aNOne86tisbihvr6e5eLioo+IiKi5cuWKja+vrx+DwTB98MEHt93d3R94/Ny5c3Y+Pj5+np6ejYGBgX/Yxjxr1qyeFhYWxrt37zKDgoLUp0+fziUiksvljWvWrFGFhISIjEYjcTgcU3R09K2QkJD66OjogokTJ3oZjUZycHDQXbly5aY536hRozSbN2++HRoa6n3+/PmcQ4cOOURGRrpZWloa2Wy26bPPPstns1GetSfGg9r6AAAAAAAACoWiQCaTVTztdcDzR6FQOMpkMuHjxGIbMwAAAAAAAHQ6KHYBAAAAAACg00GxCwAAAAAAAJ0Oil0AAAAAAADodFDsAgAAAAAAQKeDYhcAAAAAAAA6HRS7AAAAAADQoRmNRpLL5T5HjhyxMx/77LPP+EFBQd5Pmnv8+PGerq6uUrFYLPH09PRbtWqVy8NiYmJiuqxdu9aZiGjx4sXdo6KinIiIdu3a5XDr1i38WG4HgS8CAAAAAAA6NCaTSfv27SsMCwvrNWbMmCy9Xs/YsGGD6/fff3/zSfLqdDoiItqyZUtRREREjUajYYhEIv958+ZVeHl56VqLmzVrVk1Lx2NjYx0DAwO17u7u+idZF7QPdHYBAAAAAKDD69evX+PIkSNr165d223VqlXdp02bVunn59f08ccfO0ilUl+xWCyZOXOmu8FgICKi6dOne/j7+/t6eXn5rVix4l631tnZuffKlStd+vTpI46NjeU3n6O+vp7JYDDIxsbGaB5bUVHBIiI6d+6c9cCBA0VERB9++KHj66+/7tY89sCBA/zs7GxeeHh4L7FYLGlsbGT8xbcEHgKdXQAAAAAAaLPjx4+7lZeX89ozp5OTk3bChAlFDxu3devW4t69e0ssLCyMCoUi+9dff7U8ceJEl5SUlGwOh0PTp0/3OHDggGD+/PlVu3btuu3s7GzQ6XT04osv+iQnJ1fL5fJGIiJra2tjSkqKkojoxIkTXSIjI902btzYvbCwkPu3v/2trFu3boZHvYY333yzet++fU4ff/zxrYEDBzY8+l2A9oZiFwAAAAAAngl2dnbGCRMmVNnY2BisrKxMp0+ftktLS7OWSqUSIqLGxkZmjx497hIRff7554LY2FhHvV7PuHPnDictLc3KXOzOnj27qnle8zbm6upqZlBQkM+FCxdqgoODtf/7K4T2hGIXAAAAAADarC0d2L8Sk8kkJvP3pzFNJhNNnz694qOPPipuPiY9PZ27f/9+5+vXr2c7Ojoaxo8f79nQ0HBvW7Gtra2xpdx8Pt84YMCAukuXLtkGBwdr2Wy2ybwtuqGhAY+APmPwhQEAAAAAwDMpNDS07sSJE4KSkhI2EVFpaSnr5s2bFjU1NSxra2sDn883FBYWci5fvmz3sFxERE1NTYyUlBRrLy+vJiIiV1fXu1euXLEmIoqPj+/ysHhra2ujWq1mPck1QftBsQsAAAAAAM+kwMDAhsjIyOLg4GCRSCSShISEiIqLi9mDBg3Sent7N4pEIr85c+Z4yOVyzYPyREZGuonFYomvr69EJpNpw8PDa4iI1q1bV7x06VJ3uVzuY2FhYXrYembNmlUxf/58IV5Q1TEwTKaHfmcAAAAAAPAcUygUBTKZrOJprwOePwqFwlEmkwkfJxadXQAAAAAAAOh0UOwCAAAAAABAp4NiFwAAAAAAADodFLsAAAAAAADQ6aDYBQAAAAAAgE4HxS4AAAAAAAB0Oih2AQAAAACgQzMajSSXy32OHDliZz722Wef8YOCgryfNPf48eM9XV1dpWKxWOLj4yP57rvvbJ8056NYvHhx96ioKCfz58bGRoa9vf0LS5Ys6d5azPHjx21HjBjRq6Vzzs7OvSsqKlh/xVqfNSh2AQAAAACgQ2MymbRv377CyMhIN61Wy1Cr1cwNGza47tu379aT5NXpdEREtGXLliKlUpm1ZcuW20uWLHFvl0U/pm+//dbOy8ur4cSJE4KnuY7OAMUuAAAAAAB0eP369WscOXJk7dq1a7utWrWq+7Rp0yr9/PyaPv74YwepVOorFoslM2fOdDcYDERENH36dA9/f39fLy8vvxUrVriY8zg7O/deuXKlS58+fcSxsbH85nMMHz5cU15ebmH+fOnSJV6/fv18/Pz8fIcMGeJdVFTEJiKSy+U+b7zxRg+5XO7Tq1cvv8uXL/NeeumlXh4eHv7Lli2715Fds2aNs7e3t5+3t7ffxo0b73VvV6xY4SIUCv0HDhzonZeXZ9l8Dd98841g8eLFZQ4ODrpLly7xmh23FwqF/nK53OfYsWNdzMeLi4vZAwcO9JZIJL4zZsxwN5lM7XK/OwP2014AAAAAAAA8O7Ky33Wr1+TwHj6y7axtRFqJ7z+LHjZu69atxb1795ZYWFgYFQpF9q+//mp54sSJLikpKdkcDoemT5/uceDAAcH8+fOrdu3addvZ2dmg0+noxRdf9ElOTq6Wy+WNRETW1tbGlJQUJRHRiRMn7hWOR48etX/ppZeqiYgaGhoYS5cudf/+++9zXVxc9Hv37hWsWrXK9euvvy4kIrKysjIlJyffeP/9952nTp3qdf369SwHBweDUCiUrl69uiw9PZ0bHx/vkJKSkq3X60kul/uOGDGiTqvVMr777jt+RkZGZlNTE7N3796S/v37a4iI1Go189q1a7ZxcXEFJSUlnNjYWMHQoUO1dXV1zCVLlnicP3/+hq+vb1NoaOi9LcyrVq3qPmTIkLotW7aUHjp0qMtXX33VtT2/m2cZil0AAAAAAHgm2NnZGSdMmFBlY2NjsLKyMp0+fdouLS3NWiqVSoiIGhsbmT169LhLRPT5558LYmNjHfV6PePOnTuctLQ0K3OxO3v27KrmeSMjI90iIyPdampq2D/++GM2EVFqaqplbm6uZXBwsIjo9+eGu3XrpjPHTJw4sYaISCaTNfj4+DS4ubnpiYhcXV3v5ufncy5evGg7duzYaltbWyMRUWhoaM2FCxdstFotc+zYsdU2NjYmGxsbw0svvVRjzvn11193GTx4sJrH45lmzZpV3bdvX1+DwXA7NTXV0tPTs9HPz6+JiCg8PLwyNjbWgYjo2rVrtu+///5NIqKZM2fWzJ8/3/jX3P1nD4pdAAAAAABos7Z0YP9KTCaTmMzfn8Y0mUw0ffr0io8++qi4+Zj09HTu/v37na9fv57t6OhoGD9+vGdDQwPDfN5cgJpt2bKlaPr06TVRUVHOc+bMEaalpSlNJhOJRKKG5OTkGy2tw9LS0vjf9ZgsLCzu5WMymSadTsd40HZiBoPR4vG4uDiBQqGwdnV1lRIRVVVVcU6fPm1rZ2dnaC3mv/mwd7kFeGYXAAAAAACeSaGhoXUnTpwQlJSUsImISktLWTdv3rSoqalhWVtbG/h8vqGwsJBz+fJlu4flYrPZtH79+rLGxkbmiRMnbPv06dNYVlZmceHCBR7R729Jvn79uuXD8pgFBwfXnTp1iq/RaBi1tbXMpKSkLsOHD9cEBwfXJSYm8rVaLaOqqop59uzZLkREd+7cYSkUCuvi4uI0lUqVrlKp0jdu3Hjrq6++EgQEBDTm5+dbKpVKC6PRSN988829l1f179+/7vPPP3cgIvrqq6/s6+vrUeP9Fzq7AAAAAADwTAoMDGyIjIwsDg4OFhmNRuJwOKY9e/YUBgUFab29vRtFIpGfu7t7k1wu17QlH5PJpJUrV5Zs27at2/jx429+8803eUuWLHHTaDQsg8HAWLRoUWnfvn0b25IrODhYO3ny5MqAgAAJEdHrr79+JzAwsIGI6JVXXqmWSCR+PXr0aOrfv38dEVFsbCx/8ODBai6Xe69LO3369JqNGze6WlhY3Nq1a1dhaGiot0Ag0AcGBmpu3rxpSfT7c8xTpkzpKZFI+IMGDapzcnLStbSe59ED2+sAAAAAAAAKhaJAJpNVPO11wPNHoVA4ymQy4ePEosUNAAAAAAAAnQ6KXQAAAAAAAOh0UOwCAAAAAABAp4NiFwAAAAAAADodFLsAAAAAAADQ6aDYBQAAAAAAgE4HxS4AAAAAAHRoRqOR5HK5z5EjR+zMxz777DN+UFCQ9/1jd+3a5SASiSQikUji7e3td+jQoS4Pyj158mThF198wb//eGJiom1wcLBX+1wBPA3sp70AAAAAAACAB2EymbRv377CsLCwXmPGjMnS6/WMDRs2uH7//fc3zWOMRiPl5eVZ7Nixw+U///lPtoODg6G2tpZZUlKCmuc5hS8eAAAAAAA6vH79+jWOHDmydu3atd3q6+tZ06ZNq2Sz2aaePXv6DRw4sC45Odlm+/btt6ytrY329vYGIiJ7e3ujvb39XSKiK1euWC1YsMCjoaGB6eHh0fTVV18VdO3a1dB8joSEBLuVK1e6CQQCvVQq1T6N64T2g2IXAAAAAADabGn2LTdlfSOvPXOKrS21u3zdix42buvWrcW9e/eWWFhYGBUKRfatW7c4BQUFlgcOHCg4dOjQLb1eT5s3b9a5ublJBw0aVDdp0qTq8PDwWiKiOXPmeO7cufPWK6+8olm6dGn3d999t/vnn39+b06tVstYtGiR8Icffrjh5+fXNGbMmJ7teY3wv4dndgEAAAAA4JlgZ2dnnDBhQtW0adMqraysTERELi4ud0NCQuqJiNhsNl2+fPnmV199left7d0YGRnptmzZsu6VlZWsuro61iuvvKIhInrzzTcr//3vf9s0z/2f//zHskePHk1SqbSJyWTSjBkzKv/3VwjtCZ1dAAAAAABos7Z0YP9KTCaTmMz/69nxeDzj/eeDg4O1wcHB2tDQUPUbb7whfO+998rakpvBYLTzauFpQmcXAAAAAAA6hYKCAs5PP/10b4v19evXea6urncdHBwMdnZ2hqSkJBsion/9618OAwYM0DSPfeGFFxpv375tkZmZySUi+uabbwT/29VDe0NnFwAAAAAAOoW7d+8yVqxY0aOsrIzD5XJNAoFAd+DAgVtERF988UX+ggULPBYvXsx0d3dv+vrrrwuax/J4PNPHH39cOGbMGC+BQKDv37+/Jjs72+ppXAe0D4bJZHraawAAAAAAgA5MoVAUyGSyiqe9Dnj+KBQKR5lMJnycWGxjBgAAAAAAgE4HxS4AAAAAAAB0Oih2AQAAAAAAoNNBsQsAAAAAAACdDopdAAAAAAAA6HRQ7AIAAAAAAECng2IXAAAAAAAAOh0UuwAAAAAA0OGxWCy5WCyWmP9Wr17d7UHjIyMjH3i+NWFhYR7JycmWj7fKPyouLmaz2ew+27Ztc3zY2MDAQJ/Lly/z7j9eVFTEDg4O9vLx8ZH06tXLb+jQoV5ERImJibbBwcFe7bHOzor93SwZIAAAIABJREFUtBcAAAAAAADwMFwu16hUKrPaOj46Otply5YtpY8yh16vp7i4uMJHjWGzWy6rYmJi+DKZrD4+Pt5h5cqVFY+S1+zdd991HT58uHrt2rXlRETXrl2zepw8zyMUuwAAAAAA0GYrExRuOaV1f+pAPglRN1vttimyokeNq6ysZMnlct8TJ07clMlkTWPHjvUcNmxYXV5eHrepqYkpFoslIpGo4eTJk/l79uwR7N2711mn0zH69OlTHxMTU8hms4nH4wXMmzev7Pz583bbtm27vXbtWtft27cXDRkyRLt//37Bjh07uplMJsaIESNq9u7dqyKiP8WMGjVK09L64uPjBdu3by+aPXt2z/z8fI6np6dOr9dTWFiYMC0tzZrBYJhmzJhR8f7775cTEX355ZcOS5YscddoNKxPP/00Pzg4WFtaWsoZOXJkrTln//79G8z/rq+vZ40ePbrnjRs3rKRSqfb48eP5TCaTXF1dpdOmTas8c+aMvV6vZ8TFxf0WEBDQWFxczJ4yZYpnTU0N+4UXXtBevHjRLjk5OdvFxUX/6N9ax4dtzAAAAAAA0OGZi1fz34EDB/gODg6GnTt33po9e7bnp59+yq+pqWEvX768Ys+ePSpzJ/jkyZP5KSkplgkJCYLr168rlUplFpPJNO3bt8+BiKihoYHp7+/fkJaWpmxetBYUFHDWr1/vevHixZysrKzM1NRU69jY2C4PimkuNzeXU1FRwQkODtaOGzeu+uDBgwIioqtXr/JKSko4N2/ezMzJyclauHBhpTlGq9UyU1NTldHR0YXz5s3zJCJauHBh+dtvvy3s37+/6N133+1WUFDAMY/Pzs622r17d1Fubm7mrVu3uD/88ION+Zyjo6M+Kysr+/XXX7+zZcsWZyKiyMjI7kOHDq3LysrKnjRpUnVJSYlF+35LHQs6uwAAAAAA0GaP04FtD61tY544caL6yJEj/FWrVnkkJydnthSblJRkm5GRwZPJZL5ERI2NjUwnJyc9ERGLxaI5c+ZU3x/z008/Wb/44ot13bt31xMRhYWFVV26dMkmIiKiprWY5g4ePCgYN25cNRFRRERE1dy5c4Xr168vE4vFTUVFRdzZs2e7jR07tnbixIlqc0x4eHgVEVFoaKhGo9EwKyoqWJMnT1YPHjw4/dixY/ZJSUn2crlckp6enklEJJVK63v16qUjIvLz89Pm5eVZNMtVTUQUGBioPXnyJJ+I6JdffrE5fvx4LhHRlClT1HZ2doYHXcOzDsUuAAAAAAA8swwGA+Xk5FhyuVxjRUUF21z8NWcymRhTp06t3L17t+r+cxYWFsaWnrk1mUytztlaTHPffvutoKKignP06FEBEVF5eTknPT2dK5VKmzIyMrKOHTtmt2fPHqe4uDhBfHx8ARERg8H4Qw7zZ2dnZ8P8+fOr5s+fXxUcHOz1//7f/7NxdHQ0cLnce4tksVik1+vvJbC0tDQREbHZbJP5+IOuqTPCNmYAAAAAAHhmRUVFOYtEosaDBw/+NnfuXGFTUxOD6Pciz/zv0aNHqxMTE/kqlYpNRFRWVsbKycl54BbeIUOG1F+7ds22pKSErdfrKT4+XjBs2LAWtyzfT6FQcLVaLau8vDxNpVKlq1Sq9EWLFpXGxMQISkpK2AaDgebMmVPzj3/8Q5Wenn7v+eevv/6aT0R05swZG1tbW4ODg4Ph5MmTtnV1dUwiourqamZhYSHX09Pz7uPcq8DAQE1sbKyAiOjo0aN2arWa9Th5nhXo7AIAAAAAQIdnfmbX/Hn48OG18+fPr4iNjXVMTk7O5vP5xoSEhLrIyEiXnTt3Fs+YMeOOr6+vxN/fX3vy5Mn8NWvWqEJCQkRGo5E4HI4pOjr6lkgkarVo9PDw0K1bt041dOhQkclkYoSEhNTOnDmzpi1rPXjwoMPLL7/8h23Or776anV4eHjPSZMm1cydO1doNBoZRERRUVG3zWP4fL4hICBAbH5BFRHRr7/+ynvnnXfcWSyWyWQyMSIiIiqGDh2qTUxMtH3Ue7hly5biKVOm9JRIJPwBAwZounbtquvSpUun3crMeN5a2QAAAAAA8GgUCkWBTCZ7rJ/OgY6joaGBwWazTRwOh86ePWu9aNEij0f5OaenQaFQOMpkMuHjxKKzCwAAAAAA8BzIzc21mDZtWi9zd3v//v0FT3tNfyUUuwAAAAAAAI/ppZde6lVUVMRtfmzjxo23J0+erG4t5mmRSqVN2dnZHbqT255Q7AIAAAAAADymH374Ie9prwFahrcxAwAAAAAAQKeDYhcAAAAAAAA6HRS7AAAAAAAA0Omg2AUAAAAAAIBOB8UuAAAAAAB0eCwWSy4WiyXmv9WrV3d70PjIyMgHnm9NWFiYR3JysuXjrfKPiouL2Ww2u8+2bdscHzfHsmXLuq9bt865pXPvvvtuNy8vLz+RSCQRi8WS8+fPWxMRubq6SktKSp77lxE/9zcAAAAAAAA6Pi6Xa1QqlW3+2Zzo6GiXLVu2lD7KHHq9nuLi4gofNYbNbrmsiomJ4ctksvr4+HiHlStXVjxK3oc5e/as9ZkzZ7qkp6dnWVlZmUpKSthNTU2M9pzjWYdiFwAAAAAA2u74Qjcqz+K1a04niZYm7C561LDKykqWXC73PXHixE2ZTNY0duxYz2HDhtXl5eVxm5qamGKxWCISiRpOnjyZv2fPHsHevXuddTodo0+fPvUxMTGFbDabeDxewLx588rOnz9vt23btttr16513b59e9GQIUO0+/fvF+zYsaObyWRijBgxombv3r0qIvpTzKhRozQtrS8+Pl6wffv2otmzZ/fMz8/neHp66vR6PYWFhQnT0tKsGQyGacaMGRXvv/9+eWBgoI+/v782NTXVWqPRsD799NP84OBgLRFRdna2VWBgoE9xcbHF/Pnzy9asWVOuUqk4AoFAb2VlZSIicnFx0Tefe+vWrU5nzpyx1+v1jLi4uN8CAgIaly1b1r2oqMiisLCQ2zzXo39hzwZsYwYAAAAAgA7PXLya/w4cOMB3cHAw7Ny589bs2bM9P/30U35NTQ17+fLlFXv27FGZO8EnT57MT0lJsUxISBBcv35dqVQqs5hMpmnfvn0OREQNDQ1Mf3//hrS0NGXzorWgoICzfv1614sXL+ZkZWVlpqamWsfGxnZ5UExzubm5nIqKCk5wcLB23Lhx1QcPHhQQEV29epVXUlLCuXnzZmZOTk7WwoULK80xWq2WmZqaqoyOji6cN2+eZ7NclpcuXcr59ddfs7dv3969qamJMWHCBHVxcbGFUCj0nzlzpvupU6dsms/v6Oioz8rKyn799dfvbNmyxflBudrrO+po0NkFAAAAAIC2e4wObHtobRvzxIkT1UeOHOGvWrXKIzk5ObOl2KSkJNuMjAyeTCbzJSJqbGxkOjk56YmIWCwWzZkzp/r+mJ9++sn6xRdfrOvevbueiCgsLKzq0qVLNhERETWtxTR38OBBwbhx46qJiCIiIqrmzp0rXL9+fZlYLG4qKirizp49223s2LG1EydOVJtjwsPDq4iIQkNDNRqNhllRUcEiIho5cmSNlZWVycrKSi8QCHS3b99m9+rVS5eRkZGVlJRke+7cOdvZs2f3Wrdu3e3FixdX/jdXNRFRYGCg9uTJk3zzHK3levDdfzah2AUAAAAAgGeWwWCgnJwcSy6Xa6yoqGixcDOZTIypU6dW7t69W3X/OQsLC2NLz9yaTKZW52wtprlvv/1WUFFRwTl69KiAiKi8vJyTnp7OlUqlTRkZGVnHjh2z27Nnj1NcXJwgPj6+gIiIwfhjk9X8mcvl3lsMi8UivV7PICJis9k0ZsyYujFjxtT17t27ITY21sFc7FpaWpr+O8ZkHv+gXJ0RtjEDAAAAAMAzKyoqylkkEjUePHjwt7lz5wrN23LZbLbJ/O/Ro0erExMT+SqVik1EVFZWxsrJybF4UN4hQ4bUX7t2zbakpISt1+spPj5eMGzYsBa3LN9PoVBwtVotq7y8PE2lUqWrVKr0RYsWlcbExAhKSkrYBoOB5syZU/OPf/xDlZ6efu/556+//ppPRHTmzBkbW1tbg4ODg+FBc6Snp3PNn1NTU6169Ohxty3re16gswsAAAAAAB2e+Zld8+fhw4fXzp8/vyI2NtYxOTk5m8/nGxMSEuoiIyNddu7cWTxjxow7vr6+En9/f+3Jkyfz16xZowoJCREZjUbicDim6OjoWyKRqNXi0MPDQ7du3TrV0KFDRSaTiRESElI7c+bMmras9eDBgw4vv/zyH7Y5v/rqq9Xh4eE9J02aVDN37lyh0WhkEBFFRUXdNo/h8/mGgIAAsfkFVQ+aQ61WsxYvXuyuVqtZLBbLJBQKmw4ePPhIb5Lu7BgPas8DAAAAAAAoFIoCmUzWrj+dA38UGBjoY34L9NNeS0eiUCgcZTKZ8HFisY0ZAAAAAAAAOh1sYwYAAAAAAHhML730Uq+ioiJu82MbN268PXnyZHVrMS355ZdfbrTvygDFLgAAAAAAwGP64Ycf8p72GqBl2MYMAAAAAAAAnQ6KXQAAAAAAAOh0UOwCAAAAAABAp4NiFwAAAAAAADodFLsAAAAAANDhsVgsuVgslpj/Vq9e3e1B4yMjIx94vjVhYWEeycnJlo+3yv8TGBjoIxQK/cVisaRnz55+27dvd2xtrKurq7SkpORPLw++desWe8yYMT3d3Nz8e/Xq5Td06FCvtLQ0bks54M/wNmYAAAAAAOjwuFyuUalUZrV1fHR0tMuWLVtKH2UOvV5PcXFxhY8aw2a3XFbFxMT8NmTIEG1ZWRnL29tbumjRokpLS0vT/fEtMRqNNG7cOK/w8PDKxMTE34iIrly5YlVcXMzp3bt306Os8XmFYhcAAAAAANps7c9r3XKrc3ntmdOL76XdMGhD0aPGVVZWsuRyue+JEyduymSyprFjx3oOGzasLi8vj9vU1MQUi8USkUjUcPLkyfw9e/YI9u7d66zT6Rh9+vSpj4mJKWSz2cTj8QLmzZtXdv78ebtt27bdXrt2rev27duLhgwZot2/f79gx44d3UwmE2PEiBE1e/fuVRHRn2JGjRqledA61Wo1y8rKyshms00txZvHaTQaRmhoqNeECROqvb29m9hstmnVqlV3zOcHDhzYQPR7IbxgwYIe58+ft2cwGKaVK1eWvPnmm9WJiYm2H3zwQfeuXbvqsrKyeC+//HK1VCpt2LNnj3NTUxPj2LFjeX5+fk2TJ08WWlpaGnNzcy1VKhV3//79+V9++aVjcnKydUBAQP23335b8KjfRUeEbcwAAAAAANDhmYtX89+BAwf4Dg4Ohp07d96aPXu256effsqvqalhL1++vGLPnj0qcyf45MmT+SkpKZYJCQmC69evK5VKZRaTyTTt27fPgYiooaGB6e/v35CWlqZsXrQWFBRw1q9f73rx4sWcrKyszNTUVOvY2NguD4q536xZs3qKRCKJVCr1X7FiRbG5A9xSvFqtZo4cOdI7LCysavny5RVpaWlWMplM21LemJiYLunp6VbZ2dmZ586dy1m3bl2PwsJCDhGRUqm02rt3b1F2dnZmQkKCQ05OjmV6enp2RERExY4dO5zMOWpra9lXr17N2bJlS1FYWJj3ypUry27evJmpVCqtrly5YtUOX9lTh84uAAAAAAC02eN0YNtDa9uYJ06cqD5y5Ah/1apVHsnJyZktxSYlJdlmZGTwZDKZLxFRY2Mj08nJSU9ExGKxaM6cOdX3x/z000/WL774Yl337t31RERhYWFVly5dsomIiKhpLeZ+5m3MxcXF7AEDBojHjx+vFolEd1uKHzdunNfSpUtLFyxYUPWwvD/++KPttGnTqthsNrm5uen79++v+emnn3j29vZGqVRa7+HhoSMicnd3bwoNDa0lIpLJZA2XLl2yNed45ZVXaphMJvXp00fr4OCgCwwMbCAiEolEDXl5eVxzF/lZhs4uAAAAAAA8swwGA+Xk5FhyuVxjRUVFi808k8nEmDp1aqVSqcxSKpVZBQUFGR9++GExEZGFhYWxpWduTSbTn46ZtRbTmu7du+v9/f21ly9ftm4tvl+/fpqkpCR7o9FIRERSqbRBoVC0uF38QWvjcrn3TjKZTDI/I8xkMslgMDDM58zHWSwWWVhY/CFGr9czqBNAsQsAAAAAAM+sqKgoZ5FI1Hjw4MHf5s6dK2xqamIQEbHZbJP536NHj1YnJibyVSoVm4iorKyMlZOTY/GgvEOGDKm/du2abUlJCVuv11N8fLxg2LBhD3w2tzV1dXXMzMxMno+PT6svltq2bVuxQCDQR0REuBMRjR07tu7u3buMHTt23HuL86VLl3inTp2yGTp0aF1CQoJAr9dTcXEx+5dffrEJCgqqf5y1dWb/n517D2vyPv8HfucACVFQwklABIo8JAFMlZW6tgJyqNiJq3WWTkDcrBb5OqdjtVx+0To6e9GfcmHZ5NDWroq2A+lBSjc3WifqWukMbVAwpkVBiIgGCccQc3h+f7i0lBIEar8ivl/Xlesyz/O5P5/7IX/d3p/Pg2IXAAAAAAAmvKFndtPT073r6uoEJSUlrgUFBS3x8fG98+fP78nMzPQkIkpKSroulUplS5cu9Q8LCxvIysrSxMTEMAzDyKKjo5mWlha7kdbz9fU1bt++XRMZGclIpdLgOXPm9CcnJ+vGkvOqVasekEgkMrlcLn3mmWe0CxYsGPYMrtW+fftaDAYDNy0tbSaXy6WKiorGTz75xMnHxydk9uzZwS+++KLXrFmzjCkpKbrg4GC9VCoNjoqKYv7whz+0zpo1a/jXOt/HOCO1wAEAAAAAAJRKZZNcLtfe7Tzg/qNUKl3lcrnfeGLR2QUAAAAAAIBJB29jBgAAAAAAGKe4uLiAlpYWweBrO3fubF2+fHn33coJbkGxCwAAAAAAME5VVVWNdzsHGB62MQMAAAAAAMCkg2IXAAAAAAAAJh0UuwAAAAAAADDpoNgFAAAAAACASQfFLgAAAAAATHg8Hi9MIpHIrJ+tW7fOGGl8ZmbmiPdtSUxM9FUoFMLxZfmt8PDwID8/vxCJRCJ74IEHgnfv3u1qa6y3t3doW1vb914ePPSZL1y4YP9D87qf4G3MAAAAAAAw4QkEAotKpWoY7fj8/HzPnJycq2NZw2QyUWlpafNYY/j84cuqAwcOXIyIiOhvb2/nBQYGhm7YsKFDKBSyQ+NtGeszw3eh2AUAAAAAgFG7svV/fQxffSW6k3MKAgP7vV7e2TLWuI6ODl5YWJj0yJEjX8nlckNCQoJ/VFRUT2Njo8BgMHAlEomMYRh9RUXFpYKCAnFhYaGH0WjkzJs3r+/AgQPNfD6fRCLR3HXr1rUfO3bMadeuXa3btm3z3r17d0tERER/cXGxODc3dwbLspzY2FhdYWGhhoi+F7No0aLekfLs7u7mOTg4WPh8PjtcvHVcb28vZ/HixbOffPLJzoyMDO1wc124cMF+5cqV/nq9nktE9Oqrr16Oi4vrIyLKysryKCsrc+FwOBQTE9NVUFCgqa+vF6Slpc26ceMGXygUWt54443muXPnDoz1b30vQrELAAAAAAATnrV4tX7PyMhoW7t2bWdeXt7l1NRU//T09HadTse3FolvvfWWu7UrWltbKywvLxefOXNGJRAI2OTk5FlFRUUuGzZs6NDr9dyQkBD9nj17rhARbdu2jYiImpqa7Hbs2OGtUCjOu7m5mRYsWMCUlJRMT0lJ0Q2NsWXVqlUP2NvbWy5fvix86aWXLls7wMPFd3d3c5cvX/7AypUrOzZs2NAx9Jl9fHwMVVVVjV5eXqaTJ0+qRSIRe/bsWcEvf/nLB86dO3e+rKzM6aOPPnJWKBQqR0dHS3t7O4+I6Nlnn/V97bXXmkNDQw3Hjh2bsn79+lmnT59W37lfZuJCsQsAAAAAAKM2ng7snWBrS++yZcu6y8rKnLds2eKrUCjqh4s9evSo47lz50RyuVxKRDQwMMB1d3c3ERHxeDxavXp159CYU6dOTZk/f36Pl5eXiYgoMTHxRnV19dSUlBSdrZihrNuYr1y5wv/pT38q+fnPf97NMMzN4eKXLl06e9OmTVfXr19/Y6RnvnnzJmfNmjW+DQ0NDlwul5qbmwVERFVVVU7JyclaR0dHCxGRh4eHuauri/vFF19MXbFiRcDg+NvlPVmg2AUAAAAAgHuW2WwmtVotFAgEFq1Wyw8ICDAOHcOyLGfFihUde/fu1Qy9Z29vbxnuzC3Lst+7drsYW7y8vEwhISH9J06cmMIwzM3h4h966KHeo0ePTnvuueducLm23yO8c+dOD3d3d+O77757yWKxkIODQ5g1Xw7nu3Ws2WwmR0dH0/167hdvYwYAAAAAgHtWdna2B8MwA/v377+4Zs0aP4PBwCEi4vP5rPXf8fHx3ZWVlc4ajYZPRNTe3s5Tq9Ujvtk4IiKir6amxrGtrY1vMpno8OHD4qioqBHP5trS09PDra+vFwUFBRlsjdm1a9cVsVhsSklJmTXSXF1dXTxPT08jj8ejgoICF7PZTES3nrGkpMS1p6eHS3TrGcVisWXmzJk333zzTWciIovFQp999pnDeJ7hXoRiFwAAAAAAJjzr+VXrJz093buurk5QUlLiWlBQ0BIfH987f/78nszMTE8ioqSkpOtSqVS2dOlS/7CwsIGsrCxNTEwMwzCMLDo6mmlpabEbaT1fX1/j9u3bNZGRkYxUKg2eM2dOf3Jysm4sOa9ateoBiUQik8vl0meeeUa7YMGC/pHG79u3r8VgMHDT0tJm2hqzadOma++8846LXC6XqNVqoYODg4WI6Be/+EX34sWLdQ8++KBUIpHIXnrppRlERO+8887Fv/zlL65BQUGywMDA4HfffXf6WJ7hXsYZqT0PAAAAAACgVCqb5HL5sG8HBvgxKZVKV7lc7jeeWHR2AQAAAAAAYNLBC6oAAAAAAADGKS4uLqClpUUw+NrOnTtbly9f3n23coJbUOwCAAAAAACMU1VVVePdzgGGh23MAAAAAAAAMOmg2AUAAAAAAIBJB8UuAAAAAAAATDoodgEAAAAAAGDSQbELAAAAAAATHo/HC5NIJDLrZ+vWrTNGGp+ZmTnifVsSExN9FQqFcHxZfstgMHDS09O9fX19QwIDA4NDQ0OlZWVlTkRE3t7eoQzDyCQSiYxhGNnBgwenW+NEItHc8awXHh4edOLECdEPzXsywduYAQAAAABgwhMIBBaVStUw2vH5+fmeOTk5V8eyhslkotLS0uaxxvD53y+rNm/e7HX16lU7lUpV7+DgwLa0tPD/8Y9/OFrvV1dXqz09PU1KpVKwePFiJjk5WTeWdeH2UOwCAAAAAMCofXLgvM8NTe8d7SCKvaf2x6yStow1rqOjgxcWFiY9cuTIV3K53JCQkOAfFRXV09jYKDAYDNz/dk71FRUVlwoKCsSFhYUeRqORM2/evL4DBw408/l8EolEc9etW9d+7Ngxp127drVu27bNe/fu3S0RERH9xcXF4tzc3Bksy3JiY2N1hYWFGiL6XsyiRYt6B+fV09PDffvtt90uXrxY5+DgwBIR+fj4mJ599tnOoc+g0+l4Tk5O5qHXLRYLrV+/fuaxY8emcTgc9vnnn29bu3ZtJxFRVlaWR1lZmQuHw6GYmJiugoICjTXObDbTihUr/GbOnHkzPz//ylj/ppMJil0AAAAAAJjwrMWr9XtGRkbb2rVrO/Py8i6npqb6p6ent+t0On5GRoaWiOitt95yt3aCa2trheXl5eIzZ86oBAIBm5ycPKuoqMhlw4YNHXq9nhsSEqLfs2fPFSKibdu2ERFRU1OT3Y4dO7wVCsV5Nzc304IFC5iSkpLpKSkpuqExQzU0NAg8PT1visVii63niYyMZFiW5bS2ttq/+eabF4feP3DgwPSzZ886nD9/vr6trY0fHh4uffzxx3tramocPvroI2eFQqFydHS0tLe386wxRqOR8+STT/rLZDL9K6+8Mqau9mSEYhcAAAAAAEZtPB3YO8HWNuZly5Z1l5WVOW/ZssVXoVDUDxd79OhRx3PnzonkcrmUiGhgYIDr7u5uIiLi8Xi0evXq73VcT506NWX+/Pk9Xl5eJiKixMTEG9XV1VNTUlJ0tmLGwrqNub6+XvD4448zTzzxRP20adO+KY5Pnjzp+PTTT9/g8/nk4+Njevjhh3tPnTolOn78uGNycrLW0dHRQkTk4eHxTVc4PT3d98knn7yBQvcWFLsAAAAAAHDPMpvNpFarhQKBwKLVavkBAQHGoWNYluWsWLGiY+/evZqh9+zt7S3DnbllWdbmmrZirGQymaGtrc2+s7OT6+zsbLO7S0QUHBxscHFxMdbW1goXLlzYf7v1WZYlDocz7L2f/OQnvSdPnnTq7+9vF4lEth/gPoG3MQMAAAAAwD0rOzvbg2GYgf37919cs2aNn8Fg4BAR8fl81vrv+Pj47srKSmeNRsMnImpvb+ep1Wr7keaNiIjoq6mpcWxra+ObTCY6fPiwOCoqqnekGCtHR0fLM888o127du2sgYEBDhFRc3OzXUFBgXjoWI1Gw29tbRXMnj375uDrkZGRPeXl5WKTyURXrlzhf/7551MXLFjQFx8f311SUuLa09PDtT6LNea5557TPv74411LliwJMBq/V/Pfd9DZBQAAAACACW/omd3o6OiutLQ0bUlJiatCoTjv7OxsKS8v78nMzPTMy8u7kpSUdF0qlcpCQkL6KyoqLmVlZWliYmIYi8VCdnZ2bH5+/mWGYW7aWs/X19e4fft2jfVsbUxMTNdY3pi8Z88ezaZNm7wZhgkWCASsg4OD+cUXX/zmjG9kZCTD5XLJZDJxtm/f3urj42MaHJ+SkqL79NNPp0ql0mAOh8P+4Q9/aJ01a5Zp1qxZ3bW1taIHH3xQamdnx8bGxnb9+c9//qZjvWPHjvbNmze5qYAuAAAgAElEQVTznnrqKf8PPvjgEo/Ho/sVZ6T2PAAAAAAAgFKpbJLL5dq7nQfcf5RKpatcLvcbTyy2MQMAAAAAAMCkg23MAAAAAAAA4xQXFxfQ0tIiGHxt586drcuXL+++WznBLSh2AQAAAAAAxqmqqqrxbucAw8M2ZgAAAAAAAJh0UOwCAAAAAADApINiFwAAAAAAACYdFLsAAAAAAAAw6aDYBQAAAACACY/H44VJJBKZ9bN169YZI43PzMwc8b4tiYmJvgqFQji+LL9lMBg46enp3r6+viGBgYHBoaGh0rKyMqcfOu9IsrOz3Xt6er6p8by9vUMZhpFJJBIZwzCygwcPTh8u7ne/+53X9u3bPX7M3O4GvI0ZAAAAAAAmPIFAYFGpVA2jHZ+fn++Zk5NzdSxrmEwmKi0tbR5rDJ///bJq8+bNXlevXrVTqVT1Dg4ObEtLC/8f//iH41jmHqvi4mKPtWvX3nB0dLRYr1VXV6s9PT1NSqVSsHjxYiY5OVn3Y+YwkaDYBQAAAACAUftH4R4fbUuz6E7O6erj279o/aaWscZ1dHTwwsLCpEeOHPlKLpcbEhIS/KOionoaGxsFBoOB+9+Opr6iouJSQUGBuLCw0MNoNHLmzZvXd+DAgWY+n08ikWjuunXr2o8dO+a0a9eu1m3btnnv3r27JSIior+4uFicm5s7g2VZTmxsrK6wsFBDRN+LWbRoUe/gvHp6erhvv/2228WLF+scHBxYIiIfHx/Ts88+20lE9N577zllZ2d73bx5k+Pr62v461//2jRt2jSLt7d36LJly26cOnXK0WQycYqKipozMzO9m5ubBb/5zW/at2zZcr2ystIxOzvbSywWGy9cuOAQGhra/8EHH1x6+eWX3a9du2YXGRnJODs7m2pqatSDc9LpdDwnJyez9fsLL7wwo7S01NXLy+umi4uLce7cuf3j+e0mMmxjBgAAAACACc9avFo/r7/+urOLi4s5Ly/vcmpqqv9rr73mrNPp+BkZGdqCggKNtRNcUVFxqba2VlheXi4+c+aMSqVSNXC5XLaoqMiFiEiv13NDQkL0dXV1qsFFa1NTk92OHTu8jx8/rm5oaKj/4osvppSUlEwfKcaqoaFB4OnpeVMsFluG3mtra+O//PLLnidOnFA3NDScnzdvXv9LL730zRZiHx+fm19++aXq4Ycf7v31r3/t9+GHHzbW1NSocnJyvKxjzp8/77B3796Wr7/+uv7y5cuCqqqqqVlZWdfc3d2N1dXV6sGFbmRkJBMYGBgcHx8f9OKLL2qIiE6ePCl6//33xWfPnm2orKz8WqlUTrlTv9NEgs4uAAAAAACM2ng6sHeCrW3My5Yt6y4rK3PesmWLr0KhqB8u9ujRo47nzp0TyeVyKRHRwMAA193d3URExOPxaPXq1Z1DY06dOjVl/vz5PV5eXiYiosTExBvV1dVTU1JSdLZiRuP48eNTGhsbheHh4RIiIqPRyAkLC/umYH766ad1REShoaH9fX19XGdnZ4uzs7NFIBBYtFot77/3+gICAoxERMHBwf2NjY32ttazbmOur68XPP7448wTTzxR/69//WvqE088obNud3788ccn5dZmFLsAAAAAAHDPMpvNpFarhf8tBvnWInAwlmU5K1as6Ni7d69m6D17e3vLcGduWZa1uaatGCuZTGZoa2uz7+zs5Do7O3+nu8uyLD322GPdH3744aXhYoVCIUtExOVyyd7e/pskuFwuGY1GDhGRQCD45jqPxyOTycSxmcx/BQcHG1xcXIy1tbVCIiIO57Yh9zxsYwYAAAAAgHtWdna2B8MwA/v377+4Zs0aP4PBwCEi4vP5rPXf8fHx3ZWVlc4ajYZPRNTe3s5Tq9U2u6FERBEREX01NTWObW1tfJPJRIcPHxZHRUV9b8vycBwdHS3PPPOMdu3atbMGBgY4RETNzc12BQUF4qioqL4zZ85MPXfunIDo1vneuro6wQ/5G1hNmTLF3NXVNWyNp9Fo+K2trYLZs2ffjI6O7v3oo4+m9/b2cjo7O7lVVVXDvqX5XofOLgAAAAAATHjWM7vW79HR0V1paWnakpISV4VCcd7Z2dlSXl7ek5mZ6ZmXl3clKSnpulQqlYWEhPRXVFRcysrK0sTExDAWi4Xs7OzY/Pz8ywzD3LS1nq+vr3H79u2ayMhIhmVZTkxMTNdY3mS8Z88ezaZNm7wZhgkWCASsg4OD+cUXX7zi5eVlKi4ubnrmmWceuHnzJoeI6MUXX9TMmTPH8MP+QkSpqanaxYsXB7q7uxut53YjIyMZLpdLJpOJs3379lYfHx+Tj4+PadmyZTdCQkKCvb29DeHh4aMq4u81nJHa8wAAAAAAAEqlskkul2vvdh5w/1Eqla5yudxvPLHYxgwAAAAAAACTDrYxAwAAAAAAjFNcXFxAS0vLd87c7ty5s3X58uXddysnuAXFLgAAAAAAwDhVVVU13u0cYHjYxgwAAAAAAACTDopdAAAAAAAAmHRQ7AIAAAAAAMCkg2IXAAAAAAAAJh0UuwAAAAAAMOHxeLwwiUQis362bt06Y6TxmZmZI963JTEx0VehUAjHl+W3wsPDg06cOCGyfr9w4YJ9YGBg8O3Gent7hzIMI5NIJDKGYWQHDx6c/kNzGU5lZaXjwoULZ/8Yc08UeBszAAAAAACM2o1ytY/xap/o9iNHz27GlH7xL5iWkcYIBAKLSqVqGO2c+fn5njk5OVfHkofJZKLS0tLmscbw+Xe2rKqurlZ7enqalEqlYPHixUxycrLuh875Y+Q50aGzCwAAAAAA96SOjg6en59fiFKpFBARJSQk+Ofm5rqmp6d7GwwGrkQikS1dutSfiKigoEAcGhoqlUgkspUrV/qaTCYiIhKJRHM3bdrkNWfOHMknn3wydXCXtbi4WMwwjCwwMDB4/fr13tZ1h8aMNe/e3l7OkiVLHmAYRvazn/3sgYGBAc5w43Q6Hc/Jycls/b5jxw6PwMDA4MDAwODs7Gx36/XY2NiA4OBg6ezZs4N3797taivP8vJyJ39//+CwsLCg8vLyH6VjPJHcX6U9AAAAAAD8ILfrwP5YrMWr9XtGRkbb2rVrO/Py8i6npqb6p6ent+t0On5GRoaWiOitt95yt3aCa2trheXl5eIzZ86oBAIBm5ycPKuoqMhlw4YNHXq9nhsSEqLfs2fPFSKibdu2ERFRU1OT3Y4dO7wVCsV5Nzc304IFC5iSkpLpKSkpuqExtqxateoBoVBoISIyGo0cLvdWr3H37t3uDg4OFrVa3VBTU+Pw6KOPygbHRUZGMizLclpbW+3ffPPNi0REJ0+eFL399tsuCoXiPMuyFBYWJo2Jiel59NFH9YcOHWry8PAw9/b2cubOnStLTk7unDFjhnlwnv39/ZwHHnggtKqq6kJwcLBhyZIlD9yp32aiQrELAAAAAAATnq1tzMuWLesuKytz3rJli69CoagfLvbo0aOO586dE8nlcikR0cDAANfd3d1ERMTj8Wj16tWdQ2NOnTo1Zf78+T1eXl4mIqLExMQb1dXVU1NSUnS2YoY6cODAxYiIiH6iW2d2lyxZEvjfuadu3LjxGhHRww8/rGcYpn9wnHUbc319veDxxx9nnnjiifrjx49PfeKJJ3ROTk4WIqKf/exnnf/6178cH330Uf0rr7zi8dFHH00nIrp69apdfX29cMaMGX2D8/zyyy+FM2fONISGhhqIiJKSkjreeOMNt9s9w70MxS4AAAAAANyzzGYzqdVqoUAgsGi1Wn5AQIBx6BiWZTkrVqzo2Lt3r2boPXt7e8twZ1lZlrW5pq2YseBwht25/B3BwcEGFxcXY21trdBWPpWVlY7V1dWOZ86cUTk6OlrCw8OD9Ho9d7g8R7PmZIIzuwAAAAAAcM/Kzs72YBhmYP/+/RfXrFnjZzAYOEREfD6ftf47Pj6+u7Ky0lmj0fCJiNrb23lqtdp+pHkjIiL6ampqHNva2vgmk4kOHz4sjoqK6r0TOT/22GO9Bw8eFBMR/ec//xGq1ephX/il0Wj4ra2tgtmzZ9+Mjo7u/dvf/ja9p6eH293dzf3b3/7mvHDhwh6dTsebNm2a2dHR0fLFF18IlUrllOHmevDBBwdaW1vt6+vrBUREf/3rX8V34lkmMnR2AQAAAABgwht6Zjc6OrorLS1NW1JS4qpQKM47OztbysvLezIzMz3z8vKuJCUlXZdKpbKQkJD+ioqKS1lZWZqYmBjGYrGQnZ0dm5+ff5lhmJu21vP19TVu375dYz0/GxMT03Un3opMRPT73//+2jPPPOPPMIwsODi4PzQ0tG/w/cjISIbL5ZLJZOJs37691cfHx+Tj42NauXJlx7x586RERCkpKdcfffRR/bx58wZee+01N4ZhZAEBAQNyubxvuDVFIhH7pz/9qXnJkiWzxWKx6eGHH+49f/68w514nomKM1J7HgAAAAAAQKlUNsnlcu3dzgPuP0ql0lUul/uNJxbbmAEAAAAAAGDSwTZmAAAAAACAcYqLiwtoaWkRDL62c+fO1uXLl3ffrZzgFhS7AAAAAAAA41RVVdV4t3OA4WEbMwAAAAAAAEw6KHYBAAAAAABg0kGxCwAAAAAAAJMOil0AAAAAAACYdFDsAgAAAADAhMfj8cIkEonM+tm6deuMkcZnZmaOeN+WxMREX4VCIRxflt8KDw8POnHihGi043t6erhLly71ZxhGFhgYGBwWFhbU1dXF1Wq1vJycHLcfms/9CG9jBgAAAACAUfvggw98rl27NuoibjTc3d37n3zyyZaRxggEAotKpWoY7Zz5+fmeOTk5V8eSh8lkotLS0uaxxvD5P7ysevnll93d3d2NFRUVl4iIlEqlwN7enr169Sp/37597pmZmdd/8CL3GXR2AQAAAADgntTR0cHz8/MLUSqVAiKihIQE/9zcXNf09HRvg8HAlUgksqVLl/oTERUUFIhDQ0OlEolEtnLlSl+TyURERCKRaO6mTZu85syZI/nkk0+mDu7IFhcXi62d1vXr13tb1x0ac7s8RSLR3PXr13sHBwdLH3nkEeZf//qXKDw8PGjmzJmhhw4dmkZE1NbWZuft7W20xsjlcoODgwObkZExs6WlRSCRSGTPPffczMrKSseFCxfOto5btWrVrPz8fBciourqatHcuXMlQUFBstDQUGlnZyfXZDLRunXrZjIMI2MYRrZz5073O/LHvwegswsAAAAAAKN2uw7sj8VavFq/Z2RktK1du7YzLy/vcmpqqn96enq7TqfjZ2RkaImI3nrrLXdrJ7i2tlZYXl4uPnPmjEogELDJycmzioqKXDZs2NCh1+u5ISEh+j179lwhItq2bRsRETU1Ndnt2LHDW6FQnHdzczMtWLCAKSkpmZ6SkqIbGnM7er2eu3Dhwp7CwkJNXFxcQFZWlvfJkyfVtbW1wl/96lf+SUlJXevWrdMuWbKEOXLkiHNERET32rVrO0JDQw25ubmtS5YscbA+S2VlpeNwawwMDHCSkpICDh061BgZGdl/48YN7tSpUy25ubluzc3Ngvr6+gY7Oztqb2/n/aAf4h6CYhcAAAAAACY8W9uYly1b1l1WVua8ZcsWX4VCUT9c7NGjRx3PnTsnksvlUiKigYEBrru7u4mIiMfj0erVqzuHxpw6dWrK/Pnze7y8vExERImJiTeqq6unpqSk6GzF2GJnZ8f+4he/6CYiCg4O1gsEAotAIGDDw8P1Go3GnojokUce0V+6dOnsBx984FRVVeX0yCOPSKurq1VTpkyxjGaNuro6obu7uzEyMrKfiEgsFluIiI4dO+aUlpZ23c7OjoiIPDw8zKPN+16HYhcAAAAAAO5ZZrOZ1Gq1UCAQWLRaLT8gIMA4dAzLspwVK1Z07N27VzP0nr29vWW4M7csy9pc01aMLXw+n+Vyb50g5XK5JBAIWKJbhbbZbOZYx02bNs2SmpqqS01N1a1atYqOHDkybeXKld8pqu3s7FiL5dv612AwcKz5cjic7yVt6/r9AGd2AQAAAADgnpWdne3BMMzA/v37L65Zs8bPWvzx+XzW+u/4+PjuyspKZ41Gwyciam9v56nVavuR5o2IiOirqalxbGtr45tMJjp8+LA4Kiqq98d6jn/+859Trl+/ziO6tSVZrVYL/fz8bk6bNs3c19f3Td0WEBBg+Prrrx30ej2no6ODd+rUKSciIrlcPtDe3m5fXV0tIiLq7OzkGo1Gio2N7S4qKnIzGm/9HwC2MQMAAAAAAEwgQ8/sRkdHd6WlpWlLSkpcFQrFeWdnZ0t5eXlPZmamZ15e3pWkpKTrUqlUFhIS0l9RUXEpKytLExMTw1gsFrKzs2Pz8/MvMwxz09Z6vr6+xu3bt2siIyMZlmU5MTExXcnJybof6/nUarVww4YNvkREFouFExsb25WamtrJ5XIpLCysNzAwMDg6OrqruLi4NSEhoVMqlQb7+/sPBAcH9xMRCYVC9tChQ40bN26cNTAwwBUKhZYTJ06oN2/efF2tVgskEkkwn89nU1NTr2/duvW+eLMzZ6T2PAAAAAAAgFKpbJLL5dq7nQfcf5RKpatcLvcbTyy2MQMAAAAAAMCkg23MAAAAAAAA4xQXFxfQ0tIiGHxt586drcuXL+++WznBLSh2AQAAAAAAxqmqqqrxbucAw8M2ZgAAAAAAAJh0UOwCAAAAAADApINiFwAAAAAAACYdFLsAAAAAAAAw6aDYBQAAAACACY/H44VJJBKZ9bN169YZI43PzMwc8b4tiYmJvgqFQji+LL8VHh4e5OnpGWqxWL65FhsbGyASieaOFKfVank5OTluo1nDOpfZbKbVq1f7BAYGBjMMIwsJCZGqVCr7oePz8/NdVq1aNWuMj3LPwtuYAQAAAABg1BrOv+DT16sW3ck5p0xl+mXSV1pGGiMQCCwqlaphtHPm5+d75uTkXB1LHiaTiUpLS5vHGsPnD19WOTo6mquqqqYuWrSoV6vV8q5du2Z3u/k6Ojp4+/btc8/MzLw+2hzeeOMN8dWrV+1UKlU9j8ejxsZGOycnJ8vtIyc3dHYBAAAAAOCe1NHRwfPz8wtRKpUCIqKEhAT/3Nxc1/T0dG+DwcCVSCSypUuX+hMRFRQUiENDQ6USiUS2cuVKX5PJRES3uqObNm3ymjNnjuSTTz6ZGh4eHnTixAkREVFxcbGYYRhZYGBg8Pr1672t6w6NsZXfU089dePQoUNiIqKDBw9OT0hI0A2+v23bNo+QkBApwzCyzZs3exERZWRkzGxpaRFIJBLZc889N7Orq4v705/+lJHJZFKGYWQHDx6cPnSdtrY2Ow8PDyOPxyMiooCAAKObm5uZiOjVV1918fPzC3nooYeCPv30U5u5Tkbo7AIAAAAAwKjdrgP7Y7EWr9bvGRkZbWvXru3My8u7nJqa6p+ent6u0+n4GRkZWiKit956y93aCa6trRWWl5eLz5w5oxIIBGxycvKsoqIilw0bNnTo9XpuSEiIfs+ePVeIiLZt20ZERE1NTXY7duzwVigU593c3EwLFixgSkpKpqekpOiGxtjy+OOP96SlpfmaTCY6fPiw+M0332zOy8vzJCJ67733nL7++mthXV3deZZlKTY2dvbf//73qbm5ua1LlixxsOZuNBrpo48++losFlva2tr4Dz/8sGTlypU6LvfbvmVKSsqNiIgIiUQicVywYEH36tWrOx599FF9c3OzXU5OjpdCoTgvFovNjzzySFBISEj/nf1lJi4UuwAAAAAAMOHZ2sa8bNmy7rKyMuctW7b4KhSK+uFijx496nju3DmRXC6XEhENDAxw3d3dTUREPB6PVq9e3Tk05tSpU1Pmz5/f4+XlZSIiSkxMvFFdXT01JSVFZytmKD6fz4aHh/e+8cYb4oGBAW5QUNDNQTk5nThxwkkmk8mIiPr7+7kqlUr4wAMP3Bw8h8Vi4WzatGnm6dOnp3K5XLp27Zp9a2srf9asWSbrmICAAOPXX3997sMPP3T85JNPnJ544omgAwcONHZ3d/MGP8NTTz11Q61W/+DzyPcKFLsAAAAAAHDPMpvNpFarhQKBwKLVavkBAQHGoWNYluWsWLGiY+/evZqh9+zt7S3DnbllWdbmmrZihpOUlHTjl7/85eznn3/+O11glmVp06ZNbc8//7x28PULFy5858VSxcXF4o6ODv7Zs2fPCwQC1tvbO1Sv13/vOKqDgwP79NNPdz/99NPdHh4exvfee296bGxsD4fDGVWekxHO7AIAAAAAwD0rOzvbg2GYgf37919cs2aNn8Fg4BDd6qpa/x0fH99dWVnprNFo+ERE7e3tPLVa/b23FQ8WERHRV1NT49jW1sa3bkOOiorqHWt+ixYt6t24cWPbr3/96xuDry9evLi7pKTEtauri0tEdOnSJTuNRsOfNm2aua+v75s6rauri+fq6moUCATshx9+6HjlypXv5X3q1ClRU1OTHdGt4v/s2bMOvr6+NyMiIvpOnz7tePXqVZ7BYOC8//77zmPN/16Gzi4AAAAAAEx4Q8/sRkdHd6WlpWlLSkpcFQrFeWdnZ0t5eXlPZmamZ15e3pWkpKTrUqlUFhIS0l9RUXEpKytLExMTw1gsFrKzs2Pz8/MvMwxz09Z6vr6+xu3bt2siIyMZlmU5MTExXcnJyTpb423hcrmUnZ3dPvT6U0891V1fXy986KGHJEREIpHIcujQoUvBwcGGsLCw3sDAwODo6OiuHTt2XF28ePHskJAQaXBwcL+/v//A0LmuXr3Kf+6553xv3rzJJSJ68MEH+zIzM6+JRCL2hRdeuDJ//nypm5ubcc6cOf1ms/m+afVyRmrPAwAAAAAAKJXKJrlcrr39SIA7S6lUusrlcr/xxGIbMwAAAAAAAEw62MYMAAAAAAAwTnFxcQEtLS2Cwdd27tzZunz58u67lRPcgmIXAAAAAABgnKqqqhrvdg4wPGxjBgAAAAAAgEkHxS4AAAAAAABMOih2AQAAAAAAYNJBsQsAAAAAAACTDopdAAAAAACY8Hg8XphEIpFZP1u3bp0x0vjMzMwR79uSmJjoq1AohOPL8lsDAwOcX//61z4+Pj4hvr6+ITExMQGNjY12RERarZaXk5PjZh1bWVnpuHDhwtk/dE34LryNGQAAAAAARm3T+cs+qr4B0Z2cUzJF2L9HOqtlpDECgcCiUqkaRjtnfn6+Z05OztWx5GEymai0tLR5rDF8/vfLqo0bN3r39vZyL126dI7P59Orr77q8uSTT85WKpXnOzo6ePv27XPPzMy8Ppa1bDEajWRnZ3cnpppU0NkFAAAAAIB7UkdHB8/Pzy9EqVQKiIgSEhL8c3NzXdPT070NBgNXIpHIli5d6k9EVFBQIA4NDZVKJBLZypUrfU0mExERiUSiuZs2bfKaM2eO5JNPPpkaHh4edOLECRERUXFxsZhhGFlgYGDw+vXrva3rDo0ZmldPTw+3rKzMtaioqMVaCP/2t7/tsLe3t3z44YeOGRkZM1taWgQSiUT23HPPzSQi6uvr48XHxz/g7+8fvHTpUn+LxUJERCdPnhQ99NBDQcHBwdLHHnsssLm52Y6IKDw8PGjDhg3eDz30UNAf//hHjx/xz3zPQmcXAAAAAABG7XYd2B+LtXi1fs/IyGhbu3ZtZ15e3uXU1FT/9PT0dp1Ox8/IyNASEb311lvu1k5wbW2tsLy8XHzmzBmVQCBgk5OTZxUVFbls2LChQ6/Xc0NCQvR79uy5QkS0bds2IiJqamqy27Fjh7dCoTjv5uZmWrBgAVNSUjI9JSVFNzRmqIaGBoGnp+dNsVhsGXz9wQcf7D979qxDbm5u65IlSxys+VVWVjqeP3/e4csvv7zo5+dnDAsLk1RVVU2Niorq27hx46yPPvroay8vL9Prr7/u/Pvf/9778OHDTUREOp2O95///OfCnf9rTw4odgEAAAAAYMKztY152bJl3WVlZc5btmzxVSgU9cPFHj161PHcuXMiuVwuJSIaGBjguru7m4iIeDwerV69unNozKlTp6bMnz+/x8vLy0RElJiYeKO6unpqSkqKzlaMlcViIQ6Hww69zrIscTicYWNCQ0P7AgICjEREwcHB/Y2NjfZisdj01VdfOURHRzPWed3c3IzWmF/+8pc3bOUAKHYBAAAAAOAeZjabSa1WCwUCgUWr1fKtBeNgLMtyVqxY0bF3717N0Hv29vaW4c7csuz3atXbxlgFBwcbrly5Iujs7OQ6Ozt/092tq6sT/fznP9cNFyMQCL5ZkMfjkclk4rAsy5k9e7b+yy+/VA0X4+joaBnuOtyCM7sAAAAAAHDPys7O9mAYZmD//v0X16xZ42cwGDhERHw+n7X+Oz4+vruystJZo9HwiYja29t5arXafqR5IyIi+mpqahzb2tr4JpOJDh8+LI6KiuodTU5OTk6WX/ziF9r169f7WM8G//nPf3YZGBjgJiQk9EybNs3c19d321pszpw5Azdu3OB//PHHU4iIDAYD58yZMz/4TdH3C3R2AQAAAABgwht6Zjc6OrorLS1NW1JS4qpQKM47OztbysvLezIzMz3z8vKuJCUlXZdKpbKQkJD+ioqKS1lZWZqYmBjGYrGQnZ0dm5+ff5lhmJu21vP19TVu375dExkZybAsy4mJielKTk4etis7nD/96U+atLS0mf7+/iFcLpcCAgIGPvjgg6+5XC7NmDHDHBYW1hsYGBgcHR3dlZCQ0DXcHEKhkP3rX//auHHjxlk9PT08s9nMWb9+fftPfvKTgbH99e5PnJHa8wAAAAAAAEqlskkul2vvdh5w/1Eqla5yudxvPLHYxgwAAAAAAACTDrYxAwAAAAAAjFNcXFxAS0uLYPC1nTt3ti5fvrz7buUEt6DYBQAAAAAAGKeqqqrGu50DDA/bmAEAAAAAAGDSQbELAAAAAAAAkw6KXQAAAAAAAJh0UOwCAAAAAORt390AACAASURBVMCEx+PxwiQSicz62bp164yRxmdmZo5435bExERfhUIhHF+W3woPDw/y8/MLCQoKks2bN0+iVCoF1usnTpwQ/dD5xys/P99l1apVs+7W+v+X8IIqAAAAAACY8AQCgUWlUjWMdnx+fr5nTk7O1bGsYTKZqLS0tHmsMXz+8GXVgQMHLkZERPTv3r3bdfPmzT7Hjh37eixzww+DYhcAAAAAAEbt+XKlj/pqzx3tTDIzHPt3/ULeMta4jo4OXlhYmPTIkSNfyeVyQ0JCgn9UVFRPY2OjwGAwcCUSiYxhGH1FRcWlgoICcWFhoYfRaOTMmzev78CBA818Pp9EItHcdevWtR87dsxp165drdu2bfPevXt3S0RERH9xcbE4Nzd3BsuynNjYWF1hYaGGiL4Xs2jRot6R8oyJiektLCz0GHwtLy/P9dy5cw779u1rISLKzc11PX/+vHDGjBlGoVDIZmVlXVuzZo1PfX29w+nTp9VHjhxxfPPNN12PHDlyyVZetq6/+uqrLnl5eZ5ubm7GgICAAXt7e3asf+t7EbYxAwAAAADAhGctXq2f119/3dnFxcWcl5d3OTU11f+1115z1ul0/IyMDG1BQYHG2gmuqKi4VFtbKywvLxefOXNGpVKpGrhcLltUVORCRKTX67khISH6uro61eCitampyW7Hjh3ex48fVzc0NNR/8cUXU0pKSqaPFGPLe++9N00ikegHX1uzZs2Nf/7zn9MMBgOHiOjgwYOu69at61i4cGHvv//976lERF9++aWor6+PZzAYOCdOnJj62GOP9djKy9b15uZmu5ycHK9PP/1UdfLkSbVarXa4k7/LRIbOLgAAAAAAjNp4OrB3gq1tzMuWLesuKytz3rJli69CoagfLvbo0aOO586dE8nlcikR0cDAANfd3d1ERMTj8Wj16tWdQ2NOnTo1Zf78+T1eXl4mIqLExMQb1dXVU1NSUnS2YoZatWrVA0Kh0DJz5kxDUVHR5cH3nJycLI8++mhPaWnptNDQ0AGj0cgJDw/XGwwGTmpq6pTOzk6uQCBg58yZ03vy5EnRZ5995vinP/3psq28OBwODXediL5z/amnnrqhVqt/8JnkewGKXQAAAAAAuGeZzWZSq9VCgUBg0Wq1/ICAAOPQMSzLclasWNGxd+9ezdB79vb2luHO3LKs7Z2+tmKGsp7ZtXV/3bp12p07d85gGGYgOTlZS0QkEAjYmTNnGvbu3esaHh7eK5fL9R9//LFjc3OzYO7cuQMNDQ3DFqoj5cvhcG6b62SEbcwAAAAAAHDPys7O9mAYZmD//v0X16xZ42fdFszn81nrv+Pj47srKyudNRoNn4iovb2dp1ar7UeaNyIioq+mpsaxra2NbzKZ6PDhw+KoqKjbblkei+jo6L62tjb7999/32XNmjU3rNcfeeSR3r1793pERUX1xMbG9uzfv99NJpP1c7lcm3mNdP306dOOV69e5RkMBs7777/vfCefYSJDZxcAAAAAACY865ld6/fo6OiutLQ0bUlJiatCoTjv7OxsKS8v78nMzPTMy8u7kpSUdF0qlcpCQkL6KyoqLmVlZWliYmIYi8VCdnZ2bH5+/mWGYW7aWs/X19e4fft2TWRkJMOyLCcmJqYrOTlZd6ef68knn+ysq6sTubm5ma3XIiMje/Lz82dER0f3OTk5WQQCAfvoo4/23i4vW9dfeOGFK/Pnz5e6ubkZ58yZ0282m++LVi9npHY3AAAAAACAUqlsksvl2rudx2S0cOHC2Zs2bWr/+c9/3nO3c5mIlEqlq1wu9xtPLLYxAwAAAAAA/B/TarU8Pz+/EKFQaEGh++PANmYAAAAAAIBxiouLC2hpaREMvrZz587W5cuXd48U5+rqam5qajr342Z3f0OxCwAAAAAAME5VVVWNdzsHGB62MQMAAAAAAMCkg2IXAAAAAAAAJh0UuwAAAAAAADDpoNgFAAAAAACASQfFLgAAAAAATHg8Hi9MIpHIrJ+tW7fOGGl8ZmbmiPdtSUxM9FUoFMLxZfmtd955Z5pUKpUFBQXJAgICgnft2uVKRFRSUjL9TswPt4e3MQMAAAAAwIQnEAgsKpWqYbTj8/PzPXNycq6OZQ2TyUSlpaXNY43h879bVhkMBs5vf/tb388+++x8QECAUa/Xc9RqtT0R0QcffDDdZDJ1hYWFDYxlHRg7FLsAAAAAADB6H/yPD11rEN3ROd1l/fTk3paxhnV0dPDCwsKkR44c+UoulxsSEhL8o6KiehobGwUGg4ErkUhkDMPoKyoqLhUUFIgLCws9jEYjZ968eX0HDhxo5vP5JBKJ5q5bt6792LFjTrt27Wrdtm2b9+7du1siIiL6i4uLxbm5uTNYluXExsbqCgsLNUT0vZhFixb1Ds5Lp9NxTSYTx8PDw0RE5ODgwMrlckNVVdWUjz/+ePrp06cdX3nlFc933323sauri7t+/XpfvV7P9fX1Nbz99ttNbm5u5vDw8KCwsLDeU6dOOfX09PCKioqa4uPje00mE/3P//zPzH//+9+ON2/e5Kxdu/ba888/r70zP8Tkgm3MAAAAAAAw4VmLV+vn9ddfd3ZxcTHn5eVdTk1N9X/ttdecdTodPyMjQ1tQUKCxdoIrKiou1dbWCsvLy8VnzpxRqVSqBi6XyxYVFbkQEen1em5ISIi+rq5ONbhobWpqstuxY4f38ePH1Q0NDfVffPHFlJKSkukjxVh5eHiY4+LidLNmzZqTkJDgX1hYKDabzRQXF9cXGxur++Mf/9iqUqkagoODDatXr/Z/+eWXW9VqdUNwcLD+hRde8LLOYzKZOGfPnj3/yiuvtGRnZ3sREe3Zs8d12rRp5nPnzp1XKpXn9+/f76ZSqex//F/g3oPOLgAAAAAAjN44OrB3gq1tzMuWLesuKytz3rJli69CoagfLvbo0aOO586dE8nlcikR0cDAANfd3d1ERMTj8Wj16tWdQ2NOnTo1Zf78+T1eXl4mIqLExMQb1dXVU1NSUnS2YgYrLS1t/vzzz6/9/e9/d8zPz5/x8ccfO7377rtNg8d0dHTwenp6eD/72c96iYjWrl3bsWLFiges91esWNFJRPTII4/0Pf/88/ZERB9//LGTSqUSVVRUOBMR9fT08BoaGoQSieTmSPncj1DsAgAAAADAPctsNpNarRYKBAKLVqvlBwQEGIeOYVmWs2LFio69e/dqht6zt7e3DD1z+98Ym2vaihkqPDxcHx4erl+3bt2N2bNnhxJR022DBhEKhSwREZ/PJ7PZzPlvXpzc3NzLy5cv7x7LXPcjbGMGAAAAAIB7VnZ2tgfDMAP79++/uGbNGj+DwcAhIuLz+az13/Hx8d2VlZXOGo2GT0TU3t7Os74wypaIiIi+mpoax7a2Nr7JZKLDhw+Lo6KivrdleThdXV3cyspKR+v3mpoaBy8vr5tERFOnTjV3d3dziYhcXFzMTk5O5qNHj04lItq3b5/LT3/60xHXiIuL6yosLHSzPltdXZ3AOh98Fzq7AAAAAAAw4VnP7Fq/R0dHd6WlpWlLSkpcFQrFeWdnZ0t5eXlPZmamZ15e3pWkpKTrUqlUFhIS0l9RUXEpKytLExMTw1gsFrKzs2Pz8/MvMwxjc+uvr6+vcfv27ZrIyEiGZVlOTExMV3Jysm40uVosFtq1a5fHhg0bfIVCoUUkEln27dt3iYgoKSnpxvr16/2Kioo8ysvLG//yl79cWr9+ve/GjRu5s2bNMrzzzjtNI829efNmbVNTkyA0NFTKsixHLBYb//a3vzWO7q94f+GM1J4HAAAAAABQKpVNcrkcb/yF/3NKpdJVLpf7jScW7W4AAAAAAACYdLCNGQAAAAAAYJzi4uICWlpaBIOv7dy5sxUvkLr7UOwCAAAAAACMU1VVFc7LTlDYxgwAAAAAAACTDopdAAAAAAAAmHRQ7AIAAAAAAMCkg2IXAAAAAAAAJh0UuwAAAAAAMOHxeLwwiUQis362bt06Y6TxmZmZI963JTEx0VehUAjHl+W33nnnnWlSqVQWFBQkCwgICN61a5crEVFJScn0OzE/3B7exgwAAAAAABOeQCCwqFSqhtGOz8/P98zJybk6ljVMJhOVlpY2jzWGz/9uWWUwGDi//e1vfT/77LPzAQEBRr1ez1Gr1fZERB988MF0k8nUFRYWNjCWdWDsUOwCAAAAAMCobfv3Np+vO78W3ck5ZzvP7n/p0ZdaxhrX0dHBCwsLkx45cuQruVxuSEhI8I+KiuppbGwUGAwGrkQikTEMo6+oqLhUUFAgLiws9DAajZx58+b1HThwoJnP55NIJJq7bt269mPHjjnt2rWrddu2bd67d+9uiYiI6C8uLhbn5ubOYFmWExsbqyssLNQQ0fdiFi1a1Ds4L51OxzWZTBwPDw8TEZGDgwMrl8sNVVVVUz7++OPpp0+fdnzllVc833333cZf/epXftb12tra+D/5yU+kGo3mrMlkovT09JnHjx93IiJKTU3V/u///u+16upq0aZNm2b19/dz7e3t2RMnTlwQCATsqlWrfOvq6kQ8Ho/+3//7fy0JCQk9d+K3uZdhGzMAAAAAAEx41uLV+nn99dedXVxczHl5eZdTU1P9X3vtNWedTsfPyMjQFhQUaKyd4IqKiku1tbXC8vJy8ZkzZ1QqlaqBy+WyRUVFLkREer2eGxISoq+rq1MNLlqbmprsduzY4X38+HF1Q0ND/RdffDGlpKRk+kgxVh4eHua4uDjdrFmz5iQkJPgXFhaKzWYzxcXF9cXGxur++Mc/tqpUqobg4GCDrefNzc11a25uFtTX1zeo1eqGZ599tmNgYICTlJQUsGfPnssXLlxoqK6uvjB16lTLK6+84k5EpFarG95+++2L69at8+vv7+fc+V/h3oLOLgAAAAAAjNp4OrB3gq1tzMuWLesuKytz3rJli69CoagfLvbo0aOO586dE8nlcikR0cDAANfd3d1ERMTj8Wj16tWdQ2NOnTo1Zf78+T1eXl4mIqLExMQb1dXVU1NSUnS2YgYrLS1t/vzzz6/9/e9/d8zPz5/x8ccfO7377rtNo33eY8eOOaWlpV23s7MjolsF9Oeff+7g7u5ujIyM7CciEovFFiKiTz/9dOpvfvOba0REc+fOHfDy8rp59uxZ4cMPP6wf7XqTEYpdAAAAAAC4Z5nNZlKr1UKBQGDRarX8gIAA49AxLMtyVqxY0bF3717N0Hv29vaWoWdu/xtjc01bMUOFh4frw8PD9evWrbsxe/bsUCJqGjqGz+ezZrOZiIgGd2NZliUOh/OdJIa7drtc72fYxgwAAAAAAPes7OxsD4ZhBvbv339xzZo1fgaDgUN0q4i0/js+Pr67srLSWaPR8ImI2tvbedYXRtkSERHRV1NT49jW1sY3mUx0+PBhcVRU1Pe2LA+nq6uLW1lZ6Wj9XlNT4+Dl5XWTiGjq1Knm7u7ub+owHx8fw+effz6FiOjQoUPO1uuxsbHdRUVFbkbjrdq9vb2dJ5fLB9rb2+2rq6tFRESdnZ1co9FIjz32WO/BgwfFRER1dXWCtrY2+zlz5tz3L8BCsQsAAAAAABPe0DO76enp3nV1dYKSkhLXgoKClvj4+N758+f3ZGZmehIRJSUlXZdKpbKlS5f6h4WFDWRlZWliYmIYhmFk0dHRTEtLi91I6/n6+hq3b9+uiYyMZKRSafCcOXP6k5OTdaPJ1WKx0K5duzz8/PxCJBKJLDs723vfvn2X/pvXjfz8/BlSqVRWX18vyMzMbN+3b5/b3LlzJVqt9pt28ebNm6/PnDnzpkQiCQ4KCpLt27dPLBQK2UOHDjVu3LhxVlBQkCwqKorp7+/nbtmy5ZrZbOYwDCNLTEwMKC4ubnJwcLjv270ctLwBAAAAAGAkSqWySS6Xa+92HnD/USqVrnK53G88sejsAgAAAAAAwKSDF1QBAAAAAACMU1xcXEBLS4tg8LWdO3e2Ll++vPtu5QS3oNgFAAAAAAAYp6qqqsa7nQMMD9uYAQAAAAAAYNJBsQsAAAAAAACTDopdAAAAAAAAmHRQ7AIAAAAAAMCkg2IXAAAAAAAmPB6PFyaRSGTWz9atW2eMND4zM3PE+7YkJib6KhQK4fiy/Ja3t3doW1vbNy8ErqysdFy4cOHskWIuXLhgHxgYGPxD14Zb8DZmAAAAAACY8AQCgUWlUjWMdnx+fr5nTk7O1bGsYTKZqLS0tHmsMXw+yqqJCL8KAAAAAACM2pWt/+tj+Oor0Z2cUxAY2O/18s6WscZ1dHTwwsLCpEeOHPlKLpcbEhIS/KOionoaGxsFBoOBK5FIZAzD6CsqKi4VFBSICwsLPYxGI2fevHl9Bw4caObz+SQSieauW7eu/dixY067du1q3bZtm/fu3btbIiIi+ouLi8W5ubkzWJblxMbG6goLCzVE9L2YRYsW9Y4l79/97ndeLS0t9s3NzYIrV67Yp6WltWdlZV0bPKahocF++fLls4uKipqUSqVDZWXldL1ez718+bJg8eLFuqKiolYiouFyfOONN5xPnz495Y033mh96aWX3IuLiz1aW1vP1tfXC1atWuWnUCgueHt7hz799NMd//jHP6aZTCZOaWnpxblz5w6M9TeYyLCNGQAAAAAAJjxr8Wr9vP76684uLi7mvLy8y6mpqf6vvfaas06n42dkZGgLCgo01k5wRUXFpdraWmF5ebn4zJkzKpVK1cDlctmioiIXIiK9Xs8NCQnR19XVqQYXrU1NTXY7duzwPn78uLqhoaH+iy++mFJSUjJ9pJix+Prrr4XV1dXq//znP+d3797tZTAYONZ7SqVSsHz58tn79u27FBkZ2U//n717j4uqzv8H/p6ZMwwMAziAogxXETgM6KToSKQiN5NtbU0w27yBaWrt5jdpvbBmpbnKbmWSiuauJWQtaauiGYSZgrJZA0YJDiMmlwBRkMsMDOPcfn+0049MkJuJ+Ho+HjwezOFzm+NfL9+f8zlEVFJSIjx8+PAPFy9eLM7MzBSXlZXxO1vjtGnT1F999ZUdEdHZs2dFQ4YMMVy5coV/8uRJUUhIyM/rdXZ2NpSUlFxctGjR9S1btrj07l9m4EJlFwAAAAAAuq03Fdj+0Nk25ieeeKLl448/Fq9atcqzoKCg+HZ9s7Ky7C5cuCCUyWQBRETt7e3cYcOGGYiIeDwexcfHN97a58yZM7YhISFqV1dXAxHRnDlzbpw+fVo0f/78ps763AmH83OepWnTpjXZ2NiYbWxsDI6Ojvoff/yRISK6ceMGM3PmzFEHDhy4PH78+J8rrZMmTWpxcnIyEhGNGjWq/fLly4Lr168zna2xra2N29jYyK2pqbGaPXt2w+eff2535swZ0axZs5osYz799NONRERyubwtMzNT3NPvM9ChsgsAAAAAAPcto9FIKpXKWiAQmOrr629bzDObzZzZs2c3KJXKEqVSWVJeXn7hrbfeqiEisrKyMt3umVuz2dzpnJ316UgsFhvq6+t5ls8NDQ08R0dHg+WzQCD4eQIej0cGg4FDRGRnZ2ccMWLEzVOnTolumbNje7Ner+d0tcbg4ODWHTt2OPv4+LSHh4dr8vLyRAUFBaKoqKifK7vW1tZmIiKGYcyW+QcThF0AAAAAALhvbdiwwcXPz6993759PzzzzDNelu3ADMOYLb9Pnz695dixY+Lq6mqGiKiuro6nUqmsuhp3ypQprefOnbOrra1lDAYDHThwwHHq1Knd3rIcGhqq/te//uVE9NMhVvv373eaOnWq+k79+Hy+OSsr6/JHH33ktGvXLsfernHy5MnqHTt2uEyePFkTGhralp+fb2dlZWWyVIcfBNjGDAAAAAAAA57lmV3L54iIiOZly5bVp6enOxcUFFwUi8WmgwcPqtesWTNi69atNXPnzr0eEBAgDQoKasvMzLyybt266sjISD+TyUR8Pt+ckpJS6efnd7Oz+Tw9PfXr16+vDgsL8zObzZzIyMjmefPmNXXW/labN2+ujY+P9/D395eazWaKiIhoWb58eUN3+trb25uys7PLpk6d6icSiUy9WWNkZKRmxYoVVlFRUWqGYWjEiBE3fX19B9UBVHfSZekbAAAAAACgqKioXCaT1d/rdcCDp6ioyFkmk3n1pi+2MQMAAAAAAMCgg23MAAAAAAAAvRQdHe1TVVUl6Hht06ZNP8bGxrbcqzXBTxB2AQAAAAAAeiknJ+fyvV4D3B62MQMAAAAAAMCgg7ALAAAAAAAAgw7CLgAAAAAAAAw6CLsAAAAAAAAw6CDsAgAAAADAgMfj8YJZlpVafpKSkoZ31X7NmjVd/r0zc+bM8SwoKLDu3Sp/snLlStfnn39e0vFafn6+zciRIwOJiMLCwkbV19fzejO2XC73z83NFRIRvf32205+fn5SPz8/qa+vb+AHH3ww5Nb2paWlVr6+voG9met+h9OYAQAAAABgwBMIBCalUlnS3fYpKSkjtmzZcrUncxgMBsrIyKjoaR+G+WWsWrhwYcNjjz3mt2PHjmrLtQ8++MAxNjb2BhHR6dOny3oyx+1cvnyZ/+abb4749ttvLzo5ORmbm5u5tbW1yHcd4GYAAAAAAEC3fZF20f1GtUbYn2M6SkRtkQsCqnrar6GhgRccHBxw5MiRSzKZTDdjxgzvqVOnqi9fvizQ6XRclmWlfn5+2szMzCs7d+50TE1NddHr9Zxx48a1pqWlVTAMQ0KhcOyzzz5bd/LkSft//OMfP7788suSN954o2rKlCltu3fvdnzzzTeHm81mTlRUVFNqamo1Ef2qz6OPPqrpuC6ZTKazt7c3nDx50jYiIqKViCgzM9Pxs88+UxERSSSS0QqF4mJLSws3JibGVy6XaxQKhcjFxeVmdnZ2WUVFhdXs2bNHlpSUXCQi+v777wVPPfXUyOLi4ouWOWpra/m2trYmBwcHIxGRg4ODycHB4SYRUV5ennDx4sVeNjY2pokTJ/5ibQ8SbGMGAAAAAIABzxJeLT979uwROzk5Gbdu3Vq5cOFC73fffVfc1NTEJCYm1u/cubPaUgnOzMy8UlhYaH3w4EFHhUKhVCqVJVwu17xr1y4nIiKtVssNCgrSfvfdd8qOobW8vJz/6quvSk6dOqUqKSkpPn/+vG16evqQrvp0FBsbe2P//v2ORERffPGF7ZAhQwyjR4/W3dqusrLS+oUXXrhWVlZW7ODgYExLSxMHBgbq7OzsjPn5+TZERLt373Z++umnGzr2CwkJaXN2dta7u7uPjouL8/rwww8dLH975plnvN56663Kb7/9Vtkf9/5+hcouAAAAAAB0W28qsP2hs23MTzzxRMvHH38sXrVqlWdBQUHx7fpmZWXZXbhwQSiTyQKIiNrb27nDhg0zEBHxeDyKj49vvLXPmTNnbENCQtSurq4GIqI5c+bcOH36tGj+/PlNnfXpaOHChTcmTZoUYDQaq/bv3+8YFxd343btJBKJLjQ0VEtENHbs2Lby8nIBEVF8fHz9nj17nOVyedWRI0fE33zzzcWO/RiGodzc3EunT58Wfv755/Zr1qxxVygUtn/961/r1Go177HHHtMQES1atKjh5MmTDr+eefBD2AUAAAAAgPuW0WgklUplLRAITPX19YyPj4/+1jZms5kze/bsho7P0FpYWVmZbn3m9n99Op2zsz4djRo1Si+RSHTHjx+3O378uPjs2bMXb9fOysrq54l4PJ5Zq9VyiYgWLlzYmJyc7Prvf/9bPXr06Lbhw4cbb+3L5XIpPDy8LTw8vC0mJqZl8eLFXklJSXUcDqfLtT0osI0ZAAAAAADuWxs2bHDx8/Nr37dv3w/PPPOMl06n4xARMQxjtvw+ffr0lmPHjomrq6sZIqK6ujqeSqWy6mrcKVOmtJ47d86utraWMRgMdODAAcepU6f26PnX2bNn3/jLX/7i7uHhobtdCO+KUCg0h4WFNa9cudIjPj6+/ta/l5eX88+cOfPzs9MKhUIokUhuOjs7G0UikTE7O1tERPT+++879mTewQSVXQAAAAAAGPAsz+xaPkdERDQvW7asPj093bmgoOCiWCw2HTx4UL1mzZoRW7durZk7d+71gIAAaVBQUFtmZuaVdevWVUdGRvqZTCbi8/nmlJSUSj8/v5udzefp6alfv359dVhYmJ/ZbOZERkY2z5s3r6kna16wYEHjunXr3P/2t7/1auv3ggULbnz22WfiWbNmtdz6t5s3b3Jeeuklt7q6Or5AIDA7Ojrq9+zZU0lE9K9//avcckBVRETEr/o+KDhdlecBAAAAAACKiorKZTLZr6qLcHetX7/epbm5mbdt27aae72We6WoqMhZJpN59aYvKrsAAAAAAAADTHR0tE9FRYXg9OnTqnu9lvsVwi4AAAAAAEAvRUdH+1RVVQk6Xtu0adOPsbGxfdo+nJOTc7lvKwOEXQAAAAAAgF5CKB24cBozAAAAAAAADDoIuwAAAAAAADDoIOwCAAAAAADAoIOwCwAAAAAAAIMOwi4AAAAAAAx4PB4vmGVZqeUnKSlpeFft16xZ0+XfOzNnzhzPgoIC696t8icffPDBkKioKB/L57Vr1w738PAIsnz+8MMPHSIiIkbd2i8lJcVpwYIFHrder6qqYsLDw0f5+/tLfXx8AsPCwn7Vl4goNjbW67333hP3Ze2DCU5jBgAAAACAAU8gEJiUSmVJd9unpKSM2LJly9WezGEwGCgjI6Oip30Y5pexKiIiQrNixQpPy+dz586JRCKRsbq6mpFIJIazZ8+KHn74YU1351i9erUkIiKi5eWXX772v/FserLGBxXCLgAAAAAAdFt26tvu9VUVwv4c09nds+3R5f9X1dN+DQ0NvODg4IAjR45ckslkuhkzZnhPnTpVffnyZYFOp+OyLCv18/PTZmZmXtm5c6djamqqi16v54wbN641LS2tgmEYEgqFY5999tm6kydP2v/jH//4N+JQYgAAIABJREFU8eWXX5a88cYbVVOmTGnbvXu345tvvjncbDZzoqKimlJTU6uJ6Fd9Hn300V8EV1dXV4OdnZ3xwoULgqCgIF1dXR1/xowZjSdPnhTNnz+/6euvvxZt3Lixmoho27ZtTlu3bh0xdOhQvY+PT7uVlZX51u959epV/rRp05otnydOnKglIjKZTBQfH+9x9uxZO3d3d53Z/P+7SiSS0U8++WRDdna2g8Fg4GRkZPwwduzY9pqaGiYuLs67qamJeeihh9pOnTplX1BQcHHEiBGGnt7/gQ7bmAEAAAAAYMCzhFfLz549e8ROTk7GrVu3Vi5cuND73XffFTc1NTGJiYn1O3furLZUgjMzM68UFhZaHzx40FGhUCiVSmUJl8s179q1y4mISKvVcoOCgrTfffedsmNoLS8v57/66quSU6dOqUpKSorPnz9vm56ePqSrPh0FBwdrTp06JSoqKhJ4e3vrQkNDW8+ePSvS6/VUWlpqM2XKlNaKigr+li1bXPPz85V5eXkqlUp124rt888/f+3Pf/6z18SJE/1Wr149vLy8nE9ElJ6ePqSsrExQWlpa/P7771cUFhaKOvZzdnY2lJSUXFy0aNH1LVu2uBARrVmzxjUsLExdUlJycdasWY21tbVW/fMvNPCgsgsAAAAAAN3Wmwpsf+hsG/MTTzzR8vHHH4tXrVrlWVBQUHy7vllZWXYXLlwQymSyACKi9vZ27rBhwwxERDwej+Lj4xtv7XPmzBnbkJAQtaurq4GIaM6cOTdOnz4tmj9/flNnfToKDQ3V5Ofn2xqNRpo4caJmypQpra+//rprfn6+0Nvbu10oFJpzc3N/McesWbNuqFSqXz0vHBsb2zJp0qTvDx065JCVleUQHBws/f7774tPnz5t9+STT95gGIa8vLz0Dz/8sLpjv6effrqRiEgul7dlZmaKiYi+/vpr0eHDh8uIiOLi4lrs7e2NXX2P+xnCLgAAAAAA3LeMRiOpVCprgUBgqq+vZ3x8fPS3tjGbzZzZs2c37Nixo/rWv1lZWZlufeb2f306nbOzPh2FhYVpdu/ePcxkMnGWLl16XSwWm3Q6HefEiRN2crn852owh8O5wzf8iYuLi3HZsmU3li1bdiM8PHzU559/LrpTf2trazMREcMwZoPBwLnT9xpssI0ZAAAAAADuWxs2bHDx8/Nr37dv3w/PPPOMl06n4xD9FPAsv0+fPr3l2LFj4urqaoaIqK6ujqdSqbrcvjtlypTWc+fO2dXW1jIGg4EOHDjgOHXq1G4fKjVu3Lj269ev88+dOycKDQ3VEhEFBQVp33///aGPPPKIxjLHV199ZXf16lWeTqfjHDp06LYnKWdmZtqp1WouEVFjYyO3oqJC4O3tfTMsLEx94MABR4PBQBUVFfyvvvrK7k7rksvlmvT0dEciov/85z/2LS0tvO5+p/sNKrsAAAAAADDgWZ7ZtXyOiIhoXrZsWX16erpzQUHBRbFYbDp48KB6zZo1I7Zu3Vozd+7c6wEBAdKgoKC2zMzMK+vWrauOjIz0M5lMxOfzzSkpKZV+fn43O5vP09NTv379+uqwsDA/s9nMiYyMbJ43b15Td9fL5XJJJpO1qtVqnkAgMBMRhYSEaD766CPn8PDwVsscq1evrgkJCQkYOnSofsyYMW1Go/FXpdpvvvlG+OKLL3rweDyz2WzmzJ8/vz4sLKxt8uTJbV988YW9v79/oLe3d7tcLlff2vdWW7ZsqYmLixsplUrFDz/8sGbo0KH6IUOGDMqtzJwHqYwNAAAAAAA9V1RUVC6Tyerv9Tqg77RaLYdhGDOfz6cTJ07Y/ulPf/LsySudfmtFRUXOMpnMqzd9UdkFAAAAAAB4QJSVlVk9+eSTPpYK9+7du8vv9ZruFoRdAAAAAACAXoqOjvapqqoSdLy2adOmH2NjY1vu1Zq6Mnr0aN3FixcHbCW3PyHsAgAAAAAA9FJOTs7le70GuD2cxgwAAAAAAACDDsIuAAAAAAAADDoIuwAAAAAAADDoIOwCAAAAAADAoIOwCwAAAAAAAx6PxwtmWVZq+UlKShreVfs1a9Z0+ffOzJkzx7OgoMC6d6v8yQcffDAkKirKx/J57dq1wz08PIIsnz/88EOHiIiIUbf2S0lJcVqwYIEHEVFRUZFALpf7sywrHTlyZOAf//hHz1vb3CosLGxUfX09ry9rH0xwGjMAAAAAAAx4AoHApFQqu/3KnJSUlBFbtmy52pM5DAYDZWRkVPS0D8P8MlZFRERoVqxY4Wn5fO7cOZFIJDJWV1czEonEcPbsWdHDDz+s6Wrc559/3uOFF16omzdvXhMR0ddff21zp7WcPn26rCdrH+wQdgEAAAAAoNtuHFS566+2CvtzTP5w2zbHOL+qnvZraGjgBQcHBxw5cuSSTCbTzZgxw3vq1Knqy5cvC3Q6HZdlWamfn582MzPzys6dOx1TU1Nd9Ho9Z9y4ca1paWkVDMOQUCgc++yzz9adPHnS/h//+MePL7/8suSNN96omjJlStvu3bsd33zzzeFms5kTFRXVlJqaWk1Ev+rz6KOP/iK4urq6Guzs7IwXLlwQBAUF6erq6vgzZsxoPHnypGj+/PlNX3/9tWjjxo3VRETbtm1z2rp164ihQ4fqfXx82q2srMxERNeuXeN7enretIwpl8u1lt+vXr3Knzx5sm9lZaUgJiamadeuXT8SEUkkktEKheJiS0sLNyYmxlcul2sUCoXIxcXlZnZ2dplIJDKfPn1auGTJEi+hUGiaOHGi5uTJkw6XLl0q7t2/3MCGbcwAAAAAADDgWcKr5WfPnj1iJycn49atWysXLlzo/e6774qbmpqYxMTE+p07d1ZbKsGZmZlXCgsLrQ8ePOioUCiUSqWyhMvlmnft2uVERKTVarlBQUHa7777TtkxtJaXl/NfffVVyalTp1QlJSXF58+ft01PTx/SVZ+OgoODNadOnRIVFRUJvL29daGhoa1nz54V6fV6Ki0ttZkyZUprRUUFf8uWLa75+fnKvLw8lUql+rl6+/zzz9f97ne/85syZYrva6+9Nqzj9uSSkhLh4cOHf7h48WJxZmamuKysjH/r/JWVldYvvPDCtbKysmIHBwdjWlqamIho8eLF3jt27Kj49ttvlTwez9x//0IDDyq7AAAAAADQbb2pwPaHzrYxP/HEEy0ff/yxeNWqVZ4FBQW3rVBmZWXZXbhwQSiTyQKIiNrb27nDhg0zEBHxeDyKj49vvLXPmTNnbENCQtSurq4GIqI5c+bcOH36tGj+/PlNnfXpKDQ0VJOfn29rNBpp4sSJmilTprS+/vrrrvn5+UJvb+92oVBozs3N/cUcs2bNuqFSqayJiFasWNHwhz/8oeXw4cP2R48eHfL+++8PLSkpKSEimjRpUouTk5ORiGjUqFHtly9fFowaNUrfcX6JRKILDQ3VEhGNHTu2rby8XFBfX89rbW3lRkdHtxIRLVy48EZOTs6Qru/8/QuVXQAAAAAAuG8ZjUZSqVTWAoHAVF9ff9tintls5syePbtBqVSWKJXKkvLy8gtvvfVWDRGRlZWV6dZnbv/Xp9M5O+vTUVhYmEahUIj++9//iiZNmqQRi8UmnU7HOXHihJ1cLv+5GszhcDodw8vLS/9///d/DV988cVlhmFIoVDY/G/+nxfH4/HMer3+V4Pc2sZgMHC6+k6DEcIuAAAAAADctzZs2ODi5+fXvm/fvh+eeeYZL51OxyEiYhjGbPl9+vTpLceOHRNXV1czRER1dXU8lUpl1dW4U6ZMaT137pxdbW0tYzAY6MCBA45Tp07t8lCpjsaNG9d+/fp1/rlz50SWCmtQUJD2/fffH/rII49oLHN89dVXdlevXuXpdDrOoUOHxJb+Bw8etLesv7KykmlqauJ1fIa3N4YOHWq0tbU1ffHFF7ZEROnp6Y59GW+gwzZmAAAAAAAY8CzP7Fo+R0RENC9btqw+PT3duaCg4KJYLDYdPHhQvWbNmhFbt26tmTt37vWAgABpUFBQW2Zm5pV169ZVR0ZG+plMJuLz+eaUlJRKPz+/TsOjp6enfv369dVhYWF+ZrOZExkZ2Ww5Gbk7uFwuyWSyVrVazRMIBGYiopCQEM1HH33kHB4e3mqZY/Xq1TUhISEBQ4cO1Y8ZM6bNaDRyiIiysrLsX3rpJQ+BQGAiInrttdd+9PDwMPT2/lns3r27fNmyZZ5CodD0yCOPqO3s7Ix9HXOgeuBK2QAAAAAA0DNFRUXlMpms/l6vA/quubmZ6+DgYCIiSkpKGl5bW8t/77337slz2N1RVFTkLJPJvHrTF5VdAAAAAACAB8THH3/s8Oabb44wGo0ciUSi+/DDD8vv9ZruFoRdAAAAAACAXoqOjvapqqoSdLy2adOmH2NjY1vu1Zq6smTJksYlS5Z0eZL0YIGwCwAAAAAA0Es5OTmX7/Ua4PZwGjMAAAAAAAAMOgi7AAAAAAAAMOgg7AIAAAAAAMCgg7ALAAAAAAAD2tWrV3ksy0pZlpU6OzvLhg0bNsbyub29nXNr+7q6Ot7f//73oXcaV6/Xk52d3UNERBcuXBBYW1uPs4zLsqzUYDDQW2+95bxo0SL32/WvqKjgh4WFjfL395f6+PgERkREjOpqLPht4YAqAAAAAAAY0IYPH25UKpUlREQrV650FYlExg0bNtR11v769evM3r17h65atep6T+bx8vJqt8xzJ3q9nv7yl7+4Tp8+vXnt2rXXiYjOnTtn05ux4O5A2AUAAAAAgG47fPiw+7Vr14T9OeawYcPaZs6cWdWbvuvWrXPJyMhwJiKKj4+//te//vXaSy+9JCkvL7dmWVYaERHR/Prrr9f+7ne/G9XS0sIzGAyc1157rfqPf/xjc0/n+sMf/uA9bNgw/XfffSccO3ZsW11dHd/d3V1v+fvEiRO1vfkOcHcg7AIAAAAAwH3pyy+/FB44cMCpsLDwosFgoODg4ICoqCj1G2+8UR0XF2dtqazqdDrOZ599ViYWi03V1dVMaGgoe7uwawnIREQhISHq999//1cB/MqVK4L8/HwVj8ejjIwMh8WLF3tv3769berUqS3Lly9v8PT01Hd3LLi7EHYBAAAAAKDbeluBvRtOnTplN2PGjEY7OzsTEVFMTEzTl19+Kfr973/f0rGd2WymP//5z25ff/21iMvl0tWrV61qa2sZZ2fnXzxI252tx7GxsY08Ho+IiObMmdMcFhb2/aFDhxyysrIcgoODpRcuXLjQ3bHg7sIBVQAAAAAAcF8ym83dardz506nlpYWXnFxcYlSqSwZMmSIoa2t7VcHW3WHSCQydfw8fPhw4/Lly28cOXLkSkBAQNuJEyfsejMu9D+EXQAAAAAAuC+Fh4erP/30U7FGo+E0Nzdzs7KyhkRERGgcHByMra2tP2ed5uZm3tChQw18Pp8OHTpkf+3aNX5/zH/kyBE7jUbDISK6ceMGt6qqSuDt7a3rj7Gh77CNGQAAAAAA7kvh4eFtsbGxDWPHjpUSES1atOi6XC7XEhGNGTOmzc/PTxoVFdX817/+tS4mJmZUUFBQwOjRo9s8PT37JZCeO3fO9sUXX/RgGMZsNps5ixYtuvbII49oL1y4IOiP8aFvON0t/QMAAAAAwIOpqKioXCaT1d/rdcCDp6ioyFkmk3n1pi+2MQMAAAAAAMCgg7ALAAAAAAAAgw7CLgAAAAAAAAw6CLsAAAAAAAAw6CDsAgAAAAAAwKCDsAsAAAAAAACDDsIuAAAAAAAADDoIuwAAAAAAMODxeLxglmWl/v7+UqlUGpCTk2Pb1zHz8/NtMjIyHLrTNjIy0uehhx5iO15buXKl6/r1611ubXv58mV+ZGSkj6enZ5Cbm9voBQsWeGi1Wk5f1ws9w9zrBQAAAAAAwP2j5OJq91aNStifY9qK/NqkAclVXbURCAQmpVJZQkT0ySef2CclJblFR0eX9mVehUIhVCgUtnPmzGnuql19fT2vuLjYVigUGpVKpRXLsjc7a2symWjmzJmjFi9efG3FihWXDQYDPf30057PPfec23vvvdfld4T+hcouAAAAAADcV5qbm3kODg4GIqKKigr++PHj/VmWlfr6+gZmZWWJiIiEQuHY5cuXSwIDAwNCQ0P9vvzyS6FcLvd3c3MbvX//fof29nbO5s2bXY8ePSpmWVa6Z88ecWfzpaeni6OiopqeeOKJG/v27XPsam1Hjx61EwgEphUrVjQQETEMQ7t27ar65JNPnJqbm5G/fkOo7AIAAAAAQLfdqQJ7t+h0Oi7LslKdTsepr6/nHz9+XEVEtHfvXsfIyMjm5OT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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "alphas_elastic = np.logspace(-10, 6, 100)\n", - "coef_elastic = []\n", - "for i in alphas_elastic:\n", - " elastic = linear_model.ElasticNet(l1_ratio =0.6)\n", - " elastic.set_params(alpha = i)\n", - " elastic.fit(x_onehot, y_log)\n", - " coef_elastic.append(elastic.coef_)\n", - "\n", - "df_coef = pd.DataFrame(coef_elastic, index=alphas_elastic, columns=x_onehot.columns)\n", - "title = 'ElasticNet coefficients as a function of the regularization'\n", - "df_coef.plot(logx=True, title=title)\n", - "plt.xlabel('alpha')\n", - "plt.ylabel('coefficients')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### test prediction" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "best_elas_test_y = para_search_elas.best_estimator_.predict(test_onehot)\n", - "best_elas_test_y = np.expm1(best_elas_test_y)\n", - "sub = pd.DataFrame()\n", - "sub['SalePrice'] = best_elas_test_y\n", - "sub = sub.set_index(np.arange(1461, 2920))\n", - "sub.to_csv('(8) elas_log(y)_onehot_submission.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Kelly/Kaggle_house_price/Regularization Model (one hot yr_month sold).ipynb b/Kelly/Kaggle_house_price/Regularization Model (one hot yr_month sold).ipynb deleted file mode 100644 index 260abfd..0000000 --- a/Kelly/Kaggle_house_price/Regularization Model (one hot yr_month sold).ipynb +++ /dev/null @@ -1,5210 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Multiple linear regression " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "## import data\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "train = pd.read_csv(\"train.csv\").set_index(\"Id\")\n", - "test = pd.read_csv(\"test.csv\").set_index(\"Id\")\n", - "combine = pd.concat([train, test])\n", - "\n", - "# process data before model fitting\n", - "from preprocess3_yrmonthsold import impute\n", - "onehot, encodedDic = impute(combine, True) # process data and onehot encode\n", - "\n", - "# seperate onehot data into train and test\n", - "train_onehot = onehot.iloc[0:1460,]\n", - "test_onehot = onehot.iloc[1460:2919,].drop('SalePrice', axis = 1) # salesprice col were all NA \n", - "\n", - "# split train data frame into x var and y var for model testing\n", - "x_onehot = train_onehot.drop('SalePrice', axis=1)\n", - "y_log = np.log(train_onehot.SalePrice) # convert y to normal distribution for regression models" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### original y vs log(y) distirbution" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([ 5., 11., 13., 61., 58., 126., 165., 180., 122., 130., 121.,\n", - " 78., 61., 64., 49., 36., 36., 25., 13., 25., 16., 11.,\n", - " 4., 11., 9., 5., 4., 4., 4., 2., 1., 1., 1.,\n", - " 0., 1., 0., 2., 0., 1., 0., 2., 0., 0., 0.,\n", - " 0., 0., 0., 0., 0., 2.]),\n", - " array([ 34900., 49302., 63704., 78106., 92508., 106910., 121312.,\n", - " 135714., 150116., 164518., 178920., 193322., 207724., 222126.,\n", - " 236528., 250930., 265332., 279734., 294136., 308538., 322940.,\n", - " 337342., 351744., 366146., 380548., 394950., 409352., 423754.,\n", - " 438156., 452558., 466960., 481362., 495764., 510166., 524568.,\n", - " 538970., 553372., 567774., 582176., 596578., 610980., 625382.,\n", - " 639784., 654186., 668588., 682990., 697392., 711794., 726196.,\n", - " 740598., 755000.]),\n", - " )" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from matplotlib import pyplot as plt\n", - "plt.hist(train_onehot.SalePrice, bins=50) # original y" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([ 2., 2., 1., 0., 0., 0., 2., 3., 4., 3., 5.,\n", - " 1., 5., 21., 22., 23., 18., 29., 58., 56., 65., 100.,\n", - " 122., 93., 90., 82., 108., 91., 64., 55., 58., 51., 46.,\n", - " 42., 23., 29., 22., 13., 13., 13., 7., 5., 4., 1.,\n", - " 2., 2., 2., 0., 0., 2.]),\n", - " array([10.46024211, 10.52172673, 10.58321134, 10.64469596, 10.70618058,\n", - " 10.7676652 , 10.82914982, 10.89063444, 10.95211906, 11.01360367,\n", - " 11.07508829, 11.13657291, 11.19805753, 11.25954215, 11.32102677,\n", - " 11.38251138, 11.443996 , 11.50548062, 11.56696524, 11.62844986,\n", - " 11.68993448, 11.75141909, 11.81290371, 11.87438833, 11.93587295,\n", - " 11.99735757, 12.05884219, 12.12032681, 12.18181142, 12.24329604,\n", - " 12.30478066, 12.36626528, 12.4277499 , 12.48923452, 12.55071913,\n", - " 12.61220375, 12.67368837, 12.73517299, 12.79665761, 12.85814223,\n", - " 12.91962684, 12.98111146, 13.04259608, 13.1040807 , 13.16556532,\n", - " 13.22704994, 13.28853455, 13.35001917, 13.41150379, 13.47298841,\n", - " 13.53447303]),\n", - " )" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist(y_log, bins=50) # log(y)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Ridge regularization" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find best lambda" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'alpha': 0.49770235643321137}\n", - "Lowest RMSE found: 0.14614522543308797\n" - ] - } - ], - "source": [ - "from sklearn.model_selection import GridSearchCV # search for the best lambda\n", - "from sklearn import linear_model\n", - "\n", - "ridge = linear_model.Ridge(normalize=True) # create a ridge regression instance\n", - "\n", - "# find the best alpha (lambda) for ridge\n", - "grid_param = [{'alpha': np.logspace(-4, 2, 100)}]\n", - "para_search_ridge = GridSearchCV(estimator=ridge, param_grid=grid_param, scoring='neg_mean_squared_error', cv=5, return_train_score=True)\n", - "para_search_ridge.fit(x_onehot, y_log)\n", - "\n", - "print(para_search_ridge.best_params_)\n", - "print(\"Lowest RMSE found: \", np.sqrt(np.abs(para_search_ridge.best_score_)))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### RMSE for all train data using best ridge" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.1209304793183424\n" - ] - } - ], - "source": [ - "# fit best ridge equation to all train data \n", - "best_ridge_y = para_search_ridge.best_estimator_.predict(x_onehot)\n", - "print(\"RMSE: \", np.sqrt(np.mean((y_log-best_ridge_y)**2)))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### RMSE across different grid search lambda" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", - " DeprecationWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", - " DeprecationWarning)\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "error = pd.DataFrame(para_search_ridge.grid_scores_)['mean_validation_score']\n", - "error = np.sqrt(np.abs(error))\n", - "alpha = pd.DataFrame(para_search_ridge.grid_scores_)['parameters'].apply(lambda x: x.get('alpha'))\n", - "plt.scatter(alpha, error)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Variable importance for best lambda" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "alpha_100 = np.logspace(-4, 2, 100)\n", - "coef = []\n", - "for i in alpha_100:\n", - " ridge.set_params(alpha = i)\n", - " ridge.fit(x_onehot, y_log)\n", - " coef.append(ridge.coef_)\n", - "\n", - "df_coef = pd.DataFrame(coef, index=alpha_100, columns=x_onehot.columns)\n", - "title = 'Ridge coefficients as a function of the regularization'\n", - "axes = df_coef.plot(logx=True, title=title)\n", - "axes.set_xlabel('alpha')\n", - "axes.set_ylabel('coefficients')\n", - "plt.rcParams['figure.figsize'] = 16, 4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### test prediction" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "best_ridge_test_y = para_search_ridge.best_estimator_.predict(test_onehot)\n", - "best_ridge_test_y = np.expm1(best_ridge_test_y)\n", - "best_ridge_test_y\n", - "sub = pd.DataFrame()\n", - "sub['SalePrice'] = best_ridge_test_y\n", - "sub = sub.set_index(np.arange(1461, 2920))\n", - "sub.to_csv('(11) ridge_log(y)_onehot_submission.csv')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Lasso regularization" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find best lambda" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'alpha': 0.0001232846739442066}\n", - "Lowest RMSE found: 0.14563024917025766\n" - ] - } - ], - "source": [ - "lasso = linear_model.Lasso(normalize=True) # create a ridge regression instance\n", - "\n", - "# find the best alpha (lambda) for lasso \n", - "grid_param = [{'alpha': np.logspace(-4.5, 2, 100)}]\n", - "para_search_lasso = GridSearchCV(estimator=lasso, param_grid=grid_param, scoring='neg_mean_squared_error', cv=5, return_train_score=True)\n", - "para_search_lasso.fit(x_onehot, y_log)\n", - "\n", - "print(para_search_lasso.best_params_)\n", - "print(\"Lowest RMSE found: \", np.sqrt(np.abs(para_search_lasso.best_score_)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### RMSE for all train data using best lasso" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.12104384332174772\n" - ] - } - ], - "source": [ - "# fit best lasso equation to all train data \n", - "best_lasso_y = para_search_lasso.best_estimator_.predict(x_onehot)\n", - "print(\"RMSE: \", np.sqrt(np.mean((y_log-best_lasso_y)**2)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### RMSE across different grid search lambda" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", - " DeprecationWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", - " DeprecationWarning)\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "error = pd.DataFrame(para_search_lasso.grid_scores_)['mean_validation_score']\n", - "error = np.sqrt(np.abs(error))\n", - "alpha = pd.DataFrame(para_search_lasso.grid_scores_)['parameters'].apply(lambda x: x.get('alpha'))\n", - "plt.scatter(alpha, error)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Variable importance for best lambda" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "alpha_100 = np.logspace(-4.5, 2, 100)\n", - "coef = []\n", - "for i in alpha_100:\n", - " lasso.set_params(alpha = i)\n", - " lasso.fit(x_onehot, y_log)\n", - " coef.append(lasso.coef_)\n", - "\n", - "df_coef = pd.DataFrame(coef, index=alpha_100, columns=x_onehot.columns)\n", - "title = 'Lasso coefficients as a function of the regularization'\n", - "axes = df_coef.plot(logx=True, title=title)\n", - "axes.set_xlabel('alpha')\n", - "axes.set_ylabel('coefficients')\n", - "plt.rcParams['figure.figsize'] = 16, 4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### test prediction" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "best_lasso_test_y = para_search_lasso.best_estimator_.predict(test_onehot)\n", - "best_lasso_test_y = np.expm1(best_lasso_test_y)\n", - "sub = pd.DataFrame()\n", - "sub['SalePrice'] = best_lasso_test_y\n", - "sub = sub.set_index(np.arange(1461, 2920))\n", - "sub.to_csv('(12) lasso_log(y)_onehot_submission.csv')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Elastic net regularization" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'alpha': 0.000200923300256505, 'l1_ratio': 0.6000000000000001}\n", - "Lowest RMSE found: 0.14299733584858237\n" - ] - } - ], - "source": [ - "elasticnet = linear_model.ElasticNet(normalize=True) # create a ridge regression instance\n", - "\n", - "# find the best alpha (lambda) for lasso \n", - "grid_param = [{'alpha': np.logspace(-10, 6, 100), 'l1_ratio': np.arange(0, 1.1, 0.1)}]\n", - "para_search_elas = GridSearchCV(estimator=elasticnet, param_grid=grid_param, scoring='neg_mean_squared_error', cv=5, return_train_score=True)\n", - "para_search_elas.fit(x_onehot, y_log)\n", - "\n", - "print(para_search_elas.best_params_)\n", - "print(\"Lowest RMSE found: \", np.sqrt(np.abs(para_search_elas.best_score_)))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### RMSE for all train data using best lasso" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 8.395111090316252e-16\n" - ] - } - ], - "source": [ - "# fit best elastic net equation to all train data \n", - "best_elas_y = para_search_elas.best_estimator_.predict(x_onehot)\n", - "print(\"RMSE: \", np.sqrt(np.mean(y_log-best_elas_y)**2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### RMSE across different grid search lambda" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", - " DeprecationWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", - " DeprecationWarning)\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "error = pd.DataFrame(para_search_elas.grid_scores_)['mean_validation_score']\n", - "error = np.sqrt(np.abs(error))\n", - "alpha = pd.DataFrame(para_search_elas.grid_scores_)['parameters'].apply(lambda x: x.get('alpha'))\n", - "plt.scatter(alpha, error)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "data": { - "image/png": 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gEMQOZX5GRsZEsVgcmZGRMXGga402eBozAAAAAACMaE6nk1JSUsSpqamthYWFt4mIdDod97PPPhtQsWu324nNHt7SJyUlpV2pVOp7Xvvggw8a+puXlpbWQUQdw5rMEHz88ceBzc3N3/P5fNeTzuXngmIXAAAAAAAGrOHtd0TW6mrBcMbkSSSmkH/fZehtvLCw0JfD4biys7Ob3dekUmn3O++801RVVcVNTU2dbDabmUREBw8e1CclJXUVFRX57ty5c3xQUJBNo9EIamtrK1544YXwxsZGrtVqZWZmZt7funVrCxHRgQMHxh48eDA4KCjINmXKFAuXy3UplUp9Q0MDe82aNZPu3r3LJSLav3+/fv78+V295bl06dKw5OTkjjVr1rRPmDBh2muvvdZ64cIFP7vdzjhz5syt2NhYS15enrC0tNRbqVTqP/roI//3338/hMlkunx9fR2lpaVVRET37t3jzJo1S6LX63kLFy58cPTo0SH9DvDSpUvDfH19HSqVyru5uZmzc+fO+jVr1rTPnTtXbDabmbGxsVO3bNnS+M///M/tQ4k/0qHYBQAAAACAEa28vJwfHR1tetxYSEiI/erVqzqBQOAqLy/nrVy5csrNmzcriYjUarX3d999VyGTybqJiD7++OO6cePGOYxGIyM2Nla+atWqdovFwty7d+/4srIyzZgxY5wzZsyQRkZGmomIMjIyRFlZWfdffPFFY3V1NffFF1+U3Lp1q4KIqLCw0F8mk/kQEf3mN7+5v2nTptYf5zZ27Fi7RqOpzMnJCczJyRl35syZOz3Hc3Jyxl+8eFE3efJkW0tLC8t9XaPRCFQqlYbP5zvFYnHU1q1b74vFYttQPrv79+9zSktLtd9//73X4sWLxWvWrGn/61//WiMQCGK1Wq1mKDFHCxS7AAAAAAAwYH11YH8pq1evDv3mm298OByOq7i4WLdu3bpJGo2Gz2Qy6c6dOzz3fdHR0V3uQpeIKDc3d9wXX3wxhuh/uqcVFRVeDQ0NnPj4+M5x48Y5iIgWL17crtPpvIiIrl279kx1dTXfPd9oNLLa29uZRI8/xvxjqamp7UREcXFxpnPnzvn/ePxXv/qVMS0tLWzp0qXtaWlpj7qriYmJD4VCoYOISCwWW2pra3mDKXYZDMajo8mvvPLKAxaLRQqFwtLa2soZaAxPgGIXAAAAAABGtGnTppn//Oc/PyoWf//73+sbGxvZv/rVr6bu2rVrXFBQkO2Pf/zjbafTSXw+X+G+TyAQON1/FxUV+RYXF/uWlpZqfX19nXFxcRFms5npcvX+lVWXy0WlpaWVPj4+Q/peq5eXl4uIiM1mu+x2O+PH46dPn9b/9a9/9T537pzfs88+G/n9999XEBFxudxH67FYLJfNZvuHuW48Hs9psVgY7rXa2trYY8eOtf84B/d+niZ4GjMAAAAAAIxoKSkpnVarlZGbmxvovmY0GplERB0dHazx48fbWCwWHT58WOhwOB4b48GDByw/Pz+Hr6+v87vvvvNSqVTeRESzZs3qun79um9zczPLZrNRz6I6MTHxYW5ubpD7fUlJCf9xsYeqoqKCN3fu3K4PPvigwd/f337r1i3uYGPEx8d3Hj16NICIyGg0Mv70pz/5v/DCC53Dmedohc4uAAAAAACMaEwmkwoLC2vffPNNUV5eXnBAQIBdIBA43nvvvfrnnnvOtHTp0vCzZ8/6JyYmdvL5fOfjYixdurTj2LFjgVKpVB4eHm6JiYnpIiKaPHmybfPmzY2//vWvpwYFBdmkUqnZz8/PQUR07Ngxw/r160OlUqnc4XAw4uPjO2fMmNHn0eXB2Lx588S6ujqey+ViJCYmPnzuuefMpaWlg3r415EjRwxr166ddPTo0XEul4tWrFjRunDhQuNw5TiaMZ62VjYAAAAAAAyOSqWqi4mJaXnSefxcOjo6mH5+fk6bzUYvvvii+PXXX29JT09/8KTzAiKVSjU2JiYmbChzcYwZAAAAAACear/73e9CZDKZXCqVRoaGhlpXrVqFQtcD4BgzAAAAAAA81Y4dOzak37H9JUVHR8u6u7t/0KxUKpW34+LizE8qp5EOxS4AAAAAAMAIp1artU86h9EGx5gBAAAAAADA46DYBQAAAAAAAI+DYhcAAAAAAAA8DopdAAAAAAAA8DgodgEAAAAAYMQzGAzslJSUyRMnTpwWGRk59dlnn5UplcoxTyKXvLw8ob+/f4xMJpPLZDL54sWLw4iI3nrrrZCzZ8/69jX3448/9nv77beD+4qdnp4eOswpP5XwNGYAAAAAABjRnE4npaSkiFNTU1sLCwtvExHpdDruZ599NqBi1263E5s9vKVPSkpKu1Kp1Pe89sEHHzT0Ny8tLa2DiDqGNRl4LBS7AAAAAAAwYP9PWSlqu2sUDGfMgAk+pnnpUw29jRcWFvpyOBxXdnZ2s/uaVCrtfuedd5qqqqq4qampk81mM5OI6ODBg/qkpKSuoqIi3507d44PCgqyaTQaQW1tbcULL7wQ3tjYyLVarczMzMz7W7dubSEiOnDgwNiDBw8GBwUF2aZMmWLhcrkupVKpb2hoYK9Zs2bS3bt3uURE+/fv18+fP7+rtzyXLl0alpyc3LFmzZr2CRMmTHvttddaL1y44Ge32xlnzpy5FRsba8nLyxOWlpZ6K5VK/UcffeT//vvvhzCZTJevr6+jtLS0ioj+P3v3HhBVnfcP/DMzzDAzMI6ADgJyl2EYLoMX1Eh/biVO+2yTEuYtUBBXCdtcWdEnand9MnfDTBcrKtvWFrKe2igM3HZLt0WSgkgaYIYBRBGEQQG5zMAwnLn8/tjf8KMWjMxNxPfrn/V8z/l+z2cO+8+7z7lQR0cHd+nSpSEtLS3OP/3pT3tfeeWVcb8DLBQK56ampl79+OOPxXw+31ZcXHze19fX8tZbb4mfffZZL4Zh2G5ubpZ33nnngq+vryUjI8O7tbWVd+nSJef29nZeWlralaeeeurq9/+rTX64jRkAAAAAACa1mpoaQVRU1OBY+7y9vS2lpaUNWq227p133rmwc+fOkVuAq6urXZ577rm2pqYmDRHR8ePHmzUaTd3XX3+tffXVVz07Ojo4zc3N3IMHD3qVl5fXlZaWNjQ2NvId87dt2+abkZFxpba2tu6DDz5oSktLC3DsKyoqcnPcxpyTk+MxVm0zZsywaLXaus2bN3c+++yznt/e/+yzz3p9/PHHDfX19dq//e1v5x3jWq1WWFhYeKGurk7z4Ycfup0/f5473rUxmUzsu+66y1hfX6+96667jC+88MJMIqK4uDjj119/raurq9OuXr362gxa8qwAACAASURBVNNPPz1y6/T58+f5JSUlDV9++WXdwYMHvc1mM2u89W9n6OwCAAAAAMCEXa8D+2NJSkryq6iocOVyufaSkpKG1NRUf61WK2Cz2XTp0iVnx3FRUVEDMpls2LGdnZ3tefLkyelE/+qeajQafnt7O3fRokUGT09PKxFRfHx8T0NDA5+I6OzZs9MaGxsFjvlGo5HT09PDJhr7NuZv27BhQw8R0cKFCwc//PBDt2/vX7BggfGRRx4JSEhI6HnkkUd6HONLlizp9/DwsBIRzZkzZ6ipqcl5zpw5zFjn4HK59nXr1vUREc2fP3/g1KlT04iILl68yFu1atXszs5O7vDwMNvX19fsmLNixYpegUBgFwgEFnd3d+by5ctOwcHBY65/O0NnFwAAAAAAJrXIyEhTdXX1yK3T+fn5Lf/85z8benp6nPbv3+8pkUiYuro6bU1NjZZhmJGMIxQKbY5/FxcXi0pKSkSVlZW6+vp6bVhYmMlkMrHtdvu457Xb7VRZWVmn0+m0Op1Oe/Xq1Wo3NzfbuBO+hc/n24mInJyc7BaL5d+6p2+99VbLM888097a2sqLjo4O7+jo4BAR8Xi8kaI4HI6dYZhxO69OTk52Npvt+Dc5zvPYY4/5paenX21oaNC++OKLl8xm88h1cXZ2Hr0+jVXbVICwCwAAAAAAk5pKpTKYzWZWdnb2TMeY0WhkExH19fVxvLy8GA6HQ7m5uR5Wq3XMNXp7ezlisdgqEolsVVVVfLVa7UJEtHTp0oHy8nJRZ2cnh2EYOnHixEgHdsmSJf3Z2dkSx3ZZWZlgrLVvlEajcb733nsH/vCHP7S7ublZLly4wLtZaxsMBo6fnx9DRPTGG2+MeZv1VIfbmAEAAAAAYFJjs9lUVFTUtH37dt8jR47Mcnd3twiFQuvevXsvL168eDAhISG4sLDQbcmSJQaBQDBm5zUhIaHv6NGjM6VSqTw4OHhIoVAMEBEFBgYyO3fu1MfExIRJJBJGKpWaxGKxlYjo6NGjrVu2bPGTSqVyq9XKWrRokSE2Nva6ty5/Hzt37pzd3NzsbLfbWUuWLOlfvHixqbKy8qa8/OvJJ59sX79+fbCnp+fwggULBlpaWpy/e9bUwrpe2x4AAAAAAECtVjcrFIquW13Hf0pfXx9bLBbbGIYhpVI5Jzk5uWvjxo29t7ouIFKr1TMUCkXAjczFbcwAAAAAAHBHy8zM9JbJZHKpVBru5+dnTkxMRNCdAnAbMwAAAAAA3NGOHj067ndsJ4uoqCjZ8PDwN5qVeXl5FxcuXGi6VTVNdgi7AAAAAAAAk1x1dbXuVtdwu8FtzAAAAAAAADDlIOwCAAAAAADAlIOwCwAAAAAAAFMOwi4AAAAAAABMOQi7AAAAAAAw6bW2tjqpVKrA2bNnR4aHh4dFR0fL8vLypv/YdeTk5HjIZDK5TCaTc7nceVKpVC6TyeTp6ek+P8b5f/3rX3sKBIK5PT09I1nus88+E7733nvTHNsffvih6PTp0y7XW6e2ttZZJpPJv+t8Ez1uMkLYBQAAAACASc1ms5FKpZqzdOlS4+XLl2s0Gk3du+++e6G1tZU3kfkWi+Wm1bJjx45unU6n1el0WolEwpSUlDTodDptbm5u2007yXUUFBS4y+Xywbfffnsk6FdUVAj/+te/ih3bp06dEp09e/a6YfdOgE8PAQAAAADAhP395T/4drVeEt7MNWf4+g8qH/1l63j7i4qKRFwu17579+5Ox5hUKh1+8sknr9bX1/M2bNgQaDKZ2EREOTk5LXFxcQPFxcWiffv2eUkkEkar1Qqbmpo0y5cvD9br9Tyz2cxOS0u7smvXri4iosOHD8/IycmZJZFImKCgoCEej2fPy8traW9vd0pJSfFva2vjEREdOnSoZcWKFQNj1WixWCgoKCjiyy+/rPP09LRaLBYKCAiI/Oqrr7Rbt271c3V1tep0OkF3dzf3wIEDLWvWrOlnGIYeffTR2eXl5SKz2cxKS0u7mpGR0TXedVCr1c5Wq5X1m9/8pv3w4cOe6enp14xGI+u5557zGhoaYpeVlYkSEhKuvfXWWzPZbLb9rbfemvHCCy9cCg0NNaekpPi3trY6s1gsevnlly9JJBKL1WplrVmzxv/cuXOu3t7ew3/729/OC4VCe0lJifDnP/95gFAotC1cuNB4o3/XWw1hFwAAAAAAJrWamhpBVFTU4Fj7vL29LaWlpQ1CodBeU1PjvH79+qDa2to6IqLq6mqXqqoqjUwmGyYiOn78eLOnp6fVaDSy5s6dK09MTOwZGhpiHzx40OvcuXPa6dOn22JjY6Xh4eEmIqJt27b5ZmRkXFEqlcbGxkaeUqkMuXDhgmasOpycnCg+Pv7a66+/7p6VldVZUFAgVigUA56enlYiovb2dl5FRUV9bW2ts1KpDFWpVDU5OTkzJBKJpaamps5kMrHmzZsXplKp+kNCQobHOsef//xnj4ceeujaAw88YEhLSwvo6OjgzJo1y5qZmamvra0V/OlPf2olIjIYDOwZM2ZYfvOb31wlIlIqlcH33Xdff1ZWVifDMGQwGNjt7e3cixcvOh8/fvzCwoULL61YsSL4zTffnL5169ae1NTUwNzc3OYVK1YMpKam+v7Qv9+tgrALAAAAAAATdr0O7I8lKSnJr6KiwpXL5dpLSkoaUlNT/bVarYDNZtOlS5ecHcdFRUUNOIIuEVF2drbnyZMnpxMRdXR0cDUaDb+9vZ27aNEigyOUxsfH9zQ0NPCJiM6ePTutsbFR4JhvNBo5PT09bDc3N9tYdaWnp3clJCQEZ2VldR47dsxjy5YtI13ahISEHg6HQwqFwuzl5TVcW1vrfOrUqWnnz58XvP/+++5ERAaDgaPVap3HC7sffPCB20cffdTI4XBIqVT25ufnu2VmZo7bCXYoLy8XffjhhxeIiLhcLrm7u9va29vJz8/PvHDhQhMR0dy5cweam5ud9Xq909DQENvRwU5JSek+e/as6LvOMRkh7AIAAAAAwKQWGRlpOnHihJtjOz8/v0Wv1zstWLAgbP/+/Z4SiYQpKCi4aLPZSCAQzHccJxQKR0JpcXGxqKSkRFRZWakTiUS2hQsXhppMJrbdbh/3vHa7nSorK+tcXV3HP2iU0NDQYbFYbCkqKhJpNBphfHx8v2Mfi8X6xhosFovsdjvl5ORcWrlypeG71v7ss8+EbW1tzvfdd18oEdHw8DBLq9UKJxJ2iYjYbPa//QYejzcyxuFwyGKxsMaq9XaFF1QBAAAAAMCkplKpDGazmZWdnT3TMWY0GtlERH19fRwvLy+Gw+FQbm6uh9VqHXON3t5ejlgstopEIltVVRVfrVa7EBEtXbp0oLy8XNTZ2clhGIZGh+olS5b0Z2dnSxzbZWVlgrHWHi05ObkrNTU1cNWqVT0cDmdkvKCgwN1ms1F1dbWzXq/nRUREmOPi4vpzc3MlDMMQ0b+eyTUajayx1s3Pz3ffs2dPW1tbW01bW1vNlStXqltbW3kXL17kikQiq+N6EBGJRCKbwWAYOfnixYv7n3vuuZlE/3q2+Nq1a+PmQC8vL4uzs7P91KlTLkREf/7zn92/6zdPVgi7AAAAAAAwqbHZbCoqKmoqLS0V+fj4REZGRoYlJiYG7N279/Ivf/nLq2+//baHQqGQNTQ08AUCwZi3GCckJPRZLBaWVCqVZ2VleSsUigEiosDAQGbnzp36mJiYsLvvvjtUKpWaxGKxlYjo6NGjrefOnXORSqXy4ODg8BdffHHmWGuPlpSU1GswGDhbt279Rsc1KChoKCYmJvTBBx8MOXLkSDOfz7fv2rWrMzg4eEgul4eHhISEb9u2zZ9hmH8LuzabjT788EO3tWvX9o6+JkqlsveNN95w/9nPfmbQarXCsLAw+bFjx9xWr17de+LECbewsDD5J5984nL06NGWU6dOiaVSqTwyMlKuVqv51/sNr7322sX09HT/6Ohomaur65jX83bAul7bHgAAAAAAQK1WNysUigndLns76uvrY4vFYhvDMKRUKuckJyd3bdy4sfe7Z/6706dPu2RlZfmUl5c3OMZWrlwZuHr16p6kpKQbWvNOplarZygUioAbmYtndgEAAAAA4I6WmZnpfebMmWlms5m1bNmy/sTExBsKpXv27JmVn58/My8v78LNrhG+P3R2AQAAAADguqZ6Z3cyKSsrE2zevDlw9JhAILBVVVXpblVNtxI6uwAAAAAAAFNAbGysSafTaW91HVMBXlAFAAAAAAAAUw7CLgAAAAAAAEw5CLsAAAAAAAAw5SDsAgAAAAAAwJSDsAsAAAAAAJNea2urk0qlCpw9e3ZkeHh4WHR0tCwvL2/6j11HTk6Oh0wmk8tkMjmXy50nlUrlMplMnp6e7vOfPG9tba0zn8+fJ5PJ5KGhofJ58+bJampqnP+T5/y+9u7d6zk4OMi61XU4IOwCAAAAAMCkZrPZSKVSzVm6dKnx8uXLNRqNpu7dd9+90NraypvIfIvFctNq2bFjR7dOp9PqdDqtRCJhSkpKGnQ6nTY3N7ftpp1kHAEBAUM6nU5bX1+vffjhh7v37ds36z99zu/j5Zdf9hwcHJw0GROfHgIAAAAAgAm79l6DL9MxILyZa3JnuQy6r5a2jre/qKhIxOVy7bt37+50jEml0uEnn3zyan19PW/Dhg2BJpOJTUSUk5PTEhcXN1BcXCzat2+fl0QiYbRarbCpqUmzfPnyYL1ezzObzey0tLQru3bt6iIiOnz48IycnJxZEomECQoKGuLxePa8vLyW9vZ2p5SUFP+2tjYeEdGhQ4daVqxYMTBWjRaLhYKCgiK+/PLLOk9PT6vFYqGAgIDIr776Srt161Y/V1dXq06nE3R3d3MPHDjQsmbNmn6GYejRRx+dXV5eLjKbzay0tLSrGRkZE/qecX9/P2f69OlWIiKNRuO8cePGgMHBQQ6bzba/8MILLffee+/AxYsXuatXrw4aGBjgWK1WVm5ubvNPfvKTAXd39+gNGzZ0lZaWitzd3S1PP/10+549e2br9Xre888/37J27dq+8WorLCwUZWdne4lEImtDQ4Ng7ty5xg8++KD5f/7nfyTXrl1zio2NDZ0xY4alrKysYeL/D/jPQNgFAAAAAIBJraamRhAVFTU41j5vb29LaWlpg1AotNfU1DivX78+qLa2to6IqLq62qWqqkojk8mGiYiOHz/e7OnpaTUajay5c+fKExMTe4aGhtgHDx70OnfunHb69Om22NhYaXh4uImIaNu2bb4ZGRlXlEqlsbGxkadUKkMuXLigGasOJycnio+Pv/b666+7Z2VldRYUFIgVCsWAp6enlYiovb2dV1FRUV9bW+usVCpDVSpVTU5OzgyJRGKpqampM5lMrHnz5oWpVKr+kJCQ4bHO0dzczJfJZHKj0cgZHh5mff7553VERH5+fozjGlRVVfE3bdoUUF1drfvjH//ocf/99/ft37+/w2Kx0MDAAJuIyGg0cu6///7+V1999fK99947Z+/evd5lZWX1X3zxhfDRRx/1X7t2bd/zzz8/c6zaiIg0Go2wpqZG4+Pjw0RHR4edPn3a5be//e3V3NzcWWVlZfUzZsyw/rC/+M2BsAsAAAAAABN2vQ7sjyUpKcmvoqLClcvl2ktKShpSU1P9tVqtgM1m06VLl0aeY42KihpwBF0iouzsbM+TJ09OJyLq6OjgajQafnt7O3fRokUGRyiNj4/vaWho4BMRnT17dlpjY6PAMd9oNHJ6enrYbm5utrHqSk9P70pISAjOysrqPHbsmMeWLVtGurQJCQk9HA6HFAqF2cvLa7i2ttb51KlT086fPy94//333YmIDAYDR6vVOo8Xdh23MRMRvfzyy+5btmzx//TTT88PDQ2xUlNT/evq6oQcDsfe2trqTES0aNGigV/84hf+Q0NDrNWrV/feddddJoZhiM/n2+Lj4/uJiORyuUksFlu5XC7FxMSYHF3s8WojIoqOjh7w9/dniIgiIiIGm5qaePfdd9+YHe9bCWEXAAAAAAAmtcjISNOJEyfcHNv5+fkter3eacGCBWH79+/3lEgkTEFBwUWbzUYCgWC+4zihUDgSSouLi0UlJSWiyspKnUgksi1cuDDUZDKx7Xb7uOe12+1UWVlZ5+rqOv5Bo4SGhg6LxWJLUVGRSKPRCB2BkoiIxWJ9Yw0Wi0V2u51ycnIurVy50jDBSzFi3bp1vZmZmf5ERPv27fOcPXv2cGFh4cXh4WGWSCSaS0T04IMPGiIjI+sLCgrEGzduDMrIyNBv2bLlmpOT00gtbDbb7uzsbCMi4nA4dovFwnL89rFqKywsFPF4PNvo+Y45k82keXgYAAAAAABgLCqVymA2m1nZ2dkzHWNGo5FNRNTX18fx8vJiOBwO5ebmelitY99B29vbyxGLxVaRSGSrqqriq9VqFyKipUuXDpSXl4s6Ozs5DMPQ6FC9ZMmS/uzsbIlju6ysTDDW2qMlJyd3paamBq5ataqHw+GMjBcUFLjbbDaqrq521uv1vIiICHNcXFx/bm6uhGEYIiJSq9XORqNxQsHxk08+cfX19TWPvgZsNpteeuklD0eAb2ho4Pn6+jK7du3qWr9+fVdVVdWEn7W+kdpcXFysvb29kyZjorMLAAAAAACTGpvNpqKioqbt27f7HjlyZJa7u7tFKBRa9+7de3nx4sWDCQkJwYWFhW5LliwxCASCMW8xTkhI6Dt69OhMqVQqDw4OHlIoFANERIGBgczOnTv1MTExYRKJhJFKpSaxWGwlIjp69Gjrli1b/KRSqdxqtbIWLVpkiI2NbblerUlJSb2PP/54wNatW7/xoqmgoKChmJiY0O7ubu6RI0ea+Xy+fdeuXZ0tLS08uVweTkTk4eHBfPTRR+eJaMxOsuOZXbvdTjwez/7KK680ExFlZGRcffjhh4Pfe+8992XLlvXzeDw7EdFf//pX0UsvvTTLycnJLhQKrW+//fbFiV7z69Q2rk2bNnUuX7481Nvbe3gyvKCKdb22PQAAAAAAgFqtblYoFBN6S/DtqK+vjy0Wi20Mw5BSqZyTnJzctXHjxt4bWev06dMuWVlZPuXl5SNhb+XKlYGrV6/uSUpKuqE172RqtXqGQqEIuJG56OwCAAAAAMAdLTMz0/vMmTPTzGYza9myZf2JiYk3FEr37NkzKz8/f2ZeXt6Fm10jfH/o7AIAAAAAwHVN9c7uZFJWVibYvHlz4OgxgUBgq6qq0t2qmm4ldHYBAAAAAACmgNjYWJPj80Lww0yaN2UBAAAAAAAA3CwIuwAAAAAAADDlIOwCAAAAAADAlIOwCwAAAAAAAFMOwi4AAAAAAEx6ra2tTiqVKnD27NmR4eHhYdHR0bK8vLzpP3YdOTk5HjKZTC6TyeRcLneeVCqVy2QyeXp6us9/8ry1tbXOfD5/nkwmk4eGhsrnzZsnq6mpcf6ueUuWLAnp6elhMwxDIpEomohIq9Xyjh496uY4prCwULR8+fLg/2T9twLCLgAAAAAATGo2m41UKtWcpUuXGi9fvlyj0Wjq3n333Qutra28icy3WCw3rZYdO3Z063Q6rU6n00okEqakpKRBp9Npc3Nz227aScYREBAwpNPptPX19dqHH364e9++fbO+a85nn33W6ObmZhs91tDQ4Pzuu++6/+cqnRzw6SEAAAAAAJiwwsJC36tXrwpv5poSiWRw1apVrePtLyoqEnG5XPvu3bs7HWNSqXT4ySefvFpfX8/bsGFDoMlkYhMR5eTktMTFxQ0UFxeL9u3b5yWRSBitVitsamrSLF++PFiv1/PMZjM7LS3tyq5du7qIiA4fPjwjJydnlkQiYYKCgoZ4PJ49Ly+vpb293SklJcW/ra2NR0R06NChlhUrVgyMVaPFYqGgoKCIL7/8ss7T09NqsVgoICAg8quvvtJu3brVz9XV1arT6QTd3d3cAwcOtKxZs6afYRh69NFHZ5eXl4vMZjMrLS3takZGxoS+Z9zf38+ZPn269f/VNaO2tlbwpz/9qZWIaOnSpSFPPfWUXqlUGj09PaM0Go1GLBZbHXOfeuqp2ZcuXXKWyWTyxMTEroiICJNj3+OPP+7d0dHBvXjxIl+v1/O2b9/e8cQTT3T+ewWTH8IuAAAAAABMajU1NYKoqKjBsfZ5e3tbSktLG4RCob2mpsZ5/fr1QbW1tXVERNXV1S5VVVUamUw2TER0/PjxZk9PT6vRaGTNnTtXnpiY2DM0NMQ+ePCg17lz57TTp0+3xcbGSsPDw01ERNu2bfPNyMi4olQqjY2NjTylUhly4cIFzVh1ODk5UXx8/LXXX3/dPSsrq7OgoECsUCgGPD09rURE7e3tvIqKivra2lpnpVIZqlKpanJycmZIJBJLTU1NnclkYs2bNy9MpVL1h4SEDI91jubmZr5MJpMbjUbO8PAw6/PPP6+7kev5zDPPXH7xxRclp06daiL6123Mo/c3NTXxz54929Dd3c0JDw+PyMzM7HRyuv2i4+1XMQAAAAAA3DLX68D+WJKSkvwqKipcuVyuvaSkpCE1NdVfq9UK2Gw2Xbp0aeQ51qioqAFH0CUiys7O9jx58uR0IqKOjg6uRqPht7e3cxctWmRwhNL4+PiehoYGPhHR2bNnpzU2Ngoc841GI6enp4f97duCHdLT07sSEhKCs7KyOo8dO+axZcuWkS5tQkJCD4fDIYVCYfby8hqura11PnXq1LTz588L3n//fXciIoPBwNFqtc7jhV3HbcxERC+//LL7li1b/D/99NPzP+RajuX+++/v4/P5dh8fH4tYLLa0t7c7+fn53bx7wX8kCLsAAAAAADCpRUZGmk6cODHyQqX8/PwWvV7vtGDBgrD9+/d7SiQSpqCg4KLNZiOBQDDfcZxQKBwJpcXFxaKSkhJRZWWlTiQS2RYuXBhqMpnYdrt93PPa7XaqrKysc3V1Hf+gUUJDQ4fFYrGlqKhIpNFohPHx8f2OfSwW6xtrsFgsstvtlJOTc2nlypWGCV6KEevWrevNzMz0JyJycnKy22z/P3+bzeYf9G4mZ2fnkcXYbLadYRjWD1nvVsELqgAAAAAAYFJTqVQGs9nMys7OnukYMxqNbCKivr4+jpeXF8PhcCg3N9fDarWOuUZvby9HLBZbRSKRraqqiq9Wq12IiJYuXTpQXl4u6uzs5DAMQ6ND9ZIlS/qzs7Mlju2ysjLBWGuPlpyc3JWamhq4atWqHg6HMzJeUFDgbrPZqLq62lmv1/MiIiLMcXFx/bm5uRKGYYiISK1WOxuNxgkFy08++cTV19fXTEQUFBQ0XFNTI7TZbFRfX8/TaDTXfaZ62rRptoGBAc71jpkK0NkFAAAAAIBJjc1mU1FRUdP27dt9jxw5Msvd3d0iFAqte/fuvbx48eLBhISE4MLCQrclS5YYBALBmLcYJyQk9B09enSmVCqVBwcHDykUigEiosDAQGbnzp36mJiYMIlEwkilUpPjZU5Hjx5t3bJli59UKpVbrVbWokWLDLGxsS3XqzUpKan38ccfD9i6des3XjQVFBQ0FBMTE9rd3c09cuRIM5/Pt+/atauzpaWFJ5fLw4mIPDw8mI8++ug8EY3ZSXY8s2u324nH49lfeeWVZiKi//qv/zK88sorTGhoaHhoaKhJJpON+XyzQ2xs7KDVamWFhobKk5KSvvGCqqmEdb22PQAAAAAAgFqtblYoFBN6S/DtqK+vjy0Wi20Mw5BSqZyTnJzctXHjxt4bWev06dMuWVlZPuXl5Q2OsZUrVwauXr26Jykp6YbWvJOp1eoZCoUi4EbmorMLAAAAAAB3tMzMTO8zZ85MM5vNrGXLlvUnJibeUCjds2fPrPz8/Jl5eXkXbnaN8P2hswsAAAAAANc11Tu7k0lZWZlg8+bNgaPHBAKBraqqSnerarqV0NkFAAAAAACYAmJjY02OzwvBD4O3MQMAAAAAAMCUg7ALAAAAAAAAUw7CLgAAAAAAAEw5CLsAAAAAAAAw5SDsAgAAAADApNfa2uqkUqkCZ8+eHRkeHh4WHR0ty8vLm/5j15GTk+Mhk8nkMplMzuVy50mlUrlMJpOnp6f7/KfPXVVVxf8//+f/hPj7+0cEBQWFP/DAA0FtbW0TfukwwzDE4XDmy2QyeUhISPjPfvazIKPRyJrofKvVSllZWbNurPofH8IuAAAAAABMajabjVQq1ZylS5caL1++XKPRaOrefffdC62trbyJzLdYLDetlh07dnTrdDqtTqfTSiQSpqSkpEGn02lzc3PbbtpJxmA0GlkrV66cs3379quXLl2qvXDhgubnP/9555UrVyYUdhmGISIioVBo1el02vr6eg0R0R/+8IeZE5lvs9mIYRjWCy+8cNuEXXx6CAAAAAAAJkxbt8d3wNggvJlrurhKB+Vh2a3j7S8qKhJxuVz77t27Ox1jUql0+Mknn7xaX1/P27BhQ6DJZGITEeXk5LTExcUNFBcXi/bt2+clkUgYrVYrbGpq0ixfvjxYr9fzzGYzOy0t7cquXbu6iIgOHz48IycnZ5ZEImGCgoKGeDyePS8vr6W9vd0pJSXFv62tjUdEdOjQoZYVK1YMjFWjxWKhoKCgiC+//LLO09PTarFYKCAgIPKrr77Sbt261c/V1dWq0+kE3d3d3AMHDrSsWbOmn2EYevTRR2eXl5eLzGYzKy0t7WpGRsaY3zN+5ZVXPBYtWmRcu3Ztn2Ns5cqVBiIijUbjvHHjxoDBwUEOm822v/DCCy333nvvQGFhoejAgQNe7u7uloaGBr5Goxn5pBGbzaa7777b0NDQwCcieuqppzzfeeedGUREycnJnU8++eTV2tpa5/j4+DkxMTGGqqoq1/Dw8MHBwUGOTCaTh4WFDX7wwQfNE/4j3wIIuwAAAAAAMKnV1NQIoqKiBsfa5+3tbSktLW0QCoX2mpoa5/Xr1wfV1tbWERFVV1e7vMT2BAAAIABJREFUVFVVaWQy2TAR0fHjx5s9PT2tRqORNXfuXHliYmLP0NAQ++DBg17nzp3TTp8+3RYbGysNDw83ERFt27bNNyMj44pSqTQ2NjbylEplyIULFzRj1eHk5ETx8fHXXn/9dfesrKzOgoICsUKhGPD09LQSEbW3t/MqKirqa2trnZVKZahKparJycmZIZFILDU1NXUmk4k1b968MJVK1R8SEjL87fVra2sF8+bNGzNo+/n5MY5rUFVVxd+0aVNAdXW1jojo66+/dlGr1ZqQkJBhR3eXiMhsNrM++eQT8QMPPND76aefCv/yl794nDt3rs5isdD8+fPDli9fbnBxcbE1NTXx//jHP15ctmxZC8Mw5O7uPv12+Q4wwi4AAAAAAEzY9TqwP5akpCS/iooKVy6Xay8pKWlITU3112q1AjabTZcuXXJ2HBcVFTXgCLpERNnZ2Z4nT56cTkTU0dHB1Wg0/Pb2du6iRYsMjlAaHx/f4+h2nj17dlpjY6PAMd9oNHJ6enrYbm5utrHqSk9P70pISAjOysrqPHbsmMeWLVtGurQJCQk9HA6HFAqF2cvLa7i2ttb51KlT086fPy94//333YmIDAYDR6vVOo8Vdq9naGiIlZqa6l9XVyfkcDj21tbWkWsQHR1tHL2eozNLRHTXXXcZHnvsse7f/e53EpVK1SMSiWxERD/96U97P/30U9cHHnig39fX17xs2bIx/0PDZIewCwAAAAAAk1pkZKTpxIkTbo7t/Pz8Fr1e77RgwYKw/fv3e0okEqagoOCizWYjgUAw33GcUCgcCaXFxcWikpISUWVlpU4kEtkWLlwYajKZ2Ha7fdzz2u12qqysrHN1dR3/oFFCQ0OHxWKxpaioSKTRaITx8fH9jn0sFusba7BYLLLb7ZSTk3PJcTvy9YSHh5sqKipciajz2/v27dvnOXv27OHCwsKLw8PDLJFINNexb/Q1+H/b1m93Zq93DQQCwZjB/naAF1QBAAAAAMCkplKpDGazmZWdnT3yMiWj0cgmIurr6+N4eXkxHA6HcnNzPaxW65hr9Pb2csRisVUkEtmqqqr4arXahYho6dKlA+Xl5aLOzk4OwzA0OlQvWbKkPzs7W+LYLisrE4y19mjJycldqampgatWrerhcDgj4wUFBe42m42qq6ud9Xo9LyIiwhwXF9efm5srcdxerFarncd7O3JaWtq1L774wvW9996b5hh75513xF999RXfcQ3YbDa99NJLHtcLr2O55557DCdPnnQzGo2svr4+9t/+9rfp9957r/Hbx3G5XCL6/y+7muwQdgEAAAAAYFJjs9lUVFTUVFpaKvLx8YmMjIwMS0xMDNi7d+/lX/7yl1fffvttD4VCIWtoaOCP14lMSEjos1gsLKlUKs/KyvJWKBQDRESBgYHMzp079TExMWF33313qFQqNYnFYisR0dGjR1vPnTvnIpVK5cHBweEvvvjid765OCkpqddgMHC2bt36jRdNBQUFDcXExIQ++OCDIUeOHGnm8/n2Xbt2dQYHBw/J5fLwkJCQ8G3btvkzDDNm2BWJRLbCwsLzf/jDHzz9/f0jgoODw9988013Ly8vS0ZGxtX8/PwZCoVCdunSJR6Px/teafeee+4ZTEhI6J47d658wYIFYZs3b+5cuHChaaxj161b1yWTycLj4+MDvs85bgXW9039AAAAAABwZ1Gr1c0KhWLMtwRPBX19fWyxWGxjGIaUSuWc5OTkro0bN/beyFqnT592ycrK8ikvL29wjK1cuTJw9erVPUlJSTe05p1MrVbPUCgUATcyF8/sAgAAAADAHS0zM9P7zJkz08xmM2vZsmX9iYmJNxRK9+zZMys/P39mXl7ehZtdI3x/6OwCAAAAAMB1TfXO7mRSVlYm2Lx5c+DoMYFAYKuqqtLdqppuJXR2AQAAAAAApoDY2FjT7fId28kOL6gCAAAAAACAKQdhFwAAAAAAAKYchF0AAAAAAACYchB2AQAAAAAAYMpB2AUAAAAAgEmvtbXVSaVSBc6ePTsyPDw8LDo6WpaXlzf9x64jJyfHQyaTyWUymZzL5c6TSqVymUwmT09P9/lPnre2ttaZz+fPCwsLkwcFBYVHRUXJXnrpJffvmvfZZ58J33vvvWnXO4ZhGBKJRNHftdZEj5ss8DZmAAAAAACY1Gw2G6lUqjkbNmzoLioqukhE1NDQwPvLX/4yobBrsVjIyenmRJ8dO3Z079ixo5uIyMfHJ7KkpKTBy8vLclMW/w4BAQFDdXV1WqJ/hd+HHnoomIho+/bt18abU1FRIaytrRWsXr26/8eocTJB2AUAAAAAgAn7ZV2Lr25gSHgz15S58Af/EObXOt7+oqIiEZfLte/evbvTMSaVSoeffPLJq/X19bwNGzYEmkwmNhFRTk5OS1xc3EBxcbFo3759XhKJhNFqtcKmpibN8uXLg/V6Pc9sNrPT0tKu7Nq1q4uI6PDhwzNycnJmSSQSJigoaIjH49nz8vJa2tvbnVJSUvzb2tp4RESHDh1qWbFixcBYNVosFgoKCor48ssv6zw9Pa0Wi4UCAgIiv/rqK+3WrVv9XF1drTqdTtDd3c09cOBAy5o1a/oZhqFHH310dnl5uchsNrPS0tKuZmRkTOh7xhEREeZnn3328m9+8xuf7du3X+vr62Nv3rzZr6GhQWCxWFi//vWv2x588MH+5557zmtoaIhdVlYm2rNnj37VqlV9qampfrW1tUIiol//+tft69at6yUiSk9P9zl9+rRYIBDYTp48ed7Hx8ei0Wic169fH2iz2Vj33ntv30T/ppMBbmMGAAAAAIBJraamRhAVFTU41j5vb29LaWlpg1arrXvnnXcu7Ny508+xr7q62uW5555ra2pq0hARHT9+vFmj0dR9/fXX2ldffdWzo6OD09zczD148KBXeXl5XWlpaUNjYyPfMX/btm2+GRkZV2pra+s++OCDprS0tIDxanRycqL4+Phrr7/+ujsRUUFBgVihUAx4enpaiYja29t5FRUV9SdOnGh8/PHHA0wmE+v555+fKZFILDU1NXVqtbrutddekzQ2NvImel3uuuuuwYsXL/KJiPbs2eOtVCr7ampq6s6cOVOflZXly2azKTMzUx8fH39Np9NpU1JSenbv3u09Y8YMS0NDg1an02nvv/9+AxGR0Wjk/OQnPzHU19drFyxYYHzppZdmEBGlp6f7pqenX62tra2bOXPmj9LBvlnQ2QUAAAAAgAm7Xgf2x5KUlORXUVHhyuVy7SUlJQ2pqan+Wq1WwGaz6dKlS86O46KiogZkMtmwYzs7O9vz5MmT04mIOjo6uBqNht/e3s5dtGiRwRFK4+PjexoaGvhERGfPnp3W2NgocMw3Go2cnp4etpubm22sutLT07sSEhKCs7KyOo8dO+axZcuWkS5tQkJCD4fDIYVCYfby8hqura11PnXq1LTz588L3n//fXciIoPBwNFqtc4hISHDY63/bXa7feTf//znP6f94x//mHbo0CEvIiKz2cw6f/78vwXnM2fOTDtx4sR5IiI2m00zZ860MgxDfD7ftmbNmn4iovnz5w+Wlpa6EhFVVVW5nj59+jwR0datW7sPHDjgPZHaJgOEXQAAAAAAmNQiIyNNJ06ccHNs5+fnt+j1eqcFCxaE7d+/31MikTAFBQUXbTYbCQSC+Y7jhELhSCgtLi4WlZSUiCorK3Uikci2cOHCUJPJxB4dGL/NbrdTZWVlnaur6/gHjRIaGjosFostRUVFIo1GI4yPjx95TpbFYn1jDRaLRXa7nXJyci6tXLnSMMFL8Q1ffPGFMCgoaMhR6wcffNAUHh5uHn3MqVOnRN/+TSwW69/WcnJyGqmPw+HYrVYry1HnWMffDnAbMwAAAAAATGoqlcpgNptZ2dnZMx1jRqORTUTU19fH8fLyYjgcDuXm5npYrdYx1+jt7eWIxWKrSCSyVVVV8dVqtQsR0dKlSwfKy8tFnZ2dHIZhaHSoXrJkSX92drbEsV1WViYYa+3RkpOTu1JTUwNXrVrVw+FwRsYLCgrcbTYbVVdXO+v1el5ERIQ5Li6uPzc3V8IwDBERqdVqZ6PROKFkqdVqeU888cTstLS0K0RE99xzT//BgwdHaj179qyAiEgkElkd14qI6Cc/+Un/888/LyH614u/Ojs7Od9ee7To6Gjj66+/7kZE9Mc//tFjIrVNFgi7AAAAAAAwqbHZbCoqKmoqLS0V+fj4REZGRoYlJiYG7N279/Ivf/nLq2+//baHQqGQNTQ08AUCwZi3GCckJPRZLBaWVCqVZ2VleSsUigEiosDAQGbnzp36mJiYsLvvvjtUKpWaxGKxlYjo6NGjrefOnXORSqXy4ODg8BdffHHmWGuPlpSU1GswGDhbt279xoumgoKChmJiYkIffPDBkCNHjjTz+Xz7rl27OoODg4fkcnl4SEhI+LZt2/wZhhk37DY3N/PDwsLkgYGB4WvXrg3+xS9+ccXxJuYDBw60m0wmtlQqlc+ZMyf8N7/5jTcR0c9+9jODVqsVhoWFyY8dO+aWnZ3dfvXqVW5ISEh4WFiY/OOPP3a93u956aWXWl988UXPyMjIsNGh+XbAul7bHgAAAAAAQK1WNysUigm9Jfh21NfXxxaLxTaGYUipVM5JTk7u2rhxY++NrHX69GmXrKwsn/Ly8gbH2MqVKwNXr17dk5SUdENr3snUavUMhUIRcCNz8cwuAAAAAADc0TIzM73PnDkzzWw2s5YtW9afmJh4Q6F0z549s/Lz82fm5eVduNk1wveHzi4AAAAAAFzXVO/sTiZlZWWCzZs3B44eEwgEtqqqKt2tqulWQmcXAAAAAABgCoiNjTXpdDrtra5jKritHjAGAAAAAAAAmAiEXQAAAAAAAJhyEHYBAAAAAABgykHYBQAAAACASa+1tdVJpVIFzp49OzI8PDwsOjpalpeXN/3HriMnJ8dDJpPJZTKZnMvlzpNKpXKZTCZPT0/3+bFrgevDC6oAAAAAAGBSs9lspFKp5mzYsKG7qKjoIhFRQ0MD7y9/+cuEwq7FYiEnp5sTfXbs2NG9Y8eObiIiHx+fyJKSkgYvLy/LTVkcbip0dgEAAAAAYFIrKioScblc++7duzsdY1KpdPjJJ5+8Wl9fz5s/f36oXC4Pk8vlYZ988okLEVFxcbFo0aJFUpVKFRgaGhpORLR8+fLg8PDwsDlz5oQfPHhwhmOtw4cPzwgICIhYuHBh6Lp16/w3btzoR0TU3t7upFQqgyMiIsIiIiLCPv74Y5fxarRYLOTn5xdx5coVjmN79uzZkVeuXOGsXLky8JFHHvGbP39+aEBAQMS77747jYiIYRjasmXL7MjIyDCpVCo/dOjQjPHWLywsFN11113SFStWBAcEBETEx8cHOPbt3LnTOyIiIiwkJCR8w4YNfjabjYiI5s+fH5qenu4TGRkZFhAQEOG4NncKdHYBAAAAAGDCMt9T+zZ0GIQ3c03pLNHgc6sVrePtr6mpEURFRQ2Otc/b29tSWlraIBQK7TU1Nc7r168Pqq2trSMiqq6udqmqqtLIZLJhIqLjx483e3p6Wo1GI2vu3LnyxMTEnqGhIfbBgwe9zp07p50+fbotNjZWGh4ebiIi2rZtm29GRsYVpVJpbGxs5CmVypALFy5oxqrDycmJ4uPjr73++uvuWVlZnQUFBWKFQjHg6elpJSJqb2/nVVRU1NfW1jorlcpQlUpVk5OTM0MikVhqamrqTCYTa968eWEqlao/JCRkeKxzaDQaYU1NjcbHx4eJjo4OO336tMt999038N///d9XDh8+3G6z2WjlypWB77333rQ1a9b0ExHZ7XaqqampO378uPjpp5/2jouLa/w+f5vbGcIuAAAAAADcVpKSkvwqKipcuVyuvaSkpCE1NdVfq9UK2Gw2Xbp0ydlxXFRU1IAj6BIRZWdne548eXI6EVFHRwdXo9Hw29vbuYsWLTI4Qml8fHxPQ0MDn4jo7Nmz0xobGwWO+UajkdPT08N2c3OzjVVXenp6V0JCQnBWVlbnsWPHPLZs2dLl2JeQkNDD4XBIoVCYvby8hmtra51PnTo17fz584L333/fnYjIYDBwtFqt83hhNzo6esDf358hIoqIiBhsamri3XfffQMnT56cdvjw4Vlms5nV29vrNHfu3EFH2H344Yd7iYhiY2MHn3rqKd6NXvPbEcIuAAAAAABM2PU6sP8pkZGRphMnTrg5tvPz81v0er3TggULwvbv3+8pkUiYgoKCizabjQQCwXzHcUKhcCSUFhcXi0pKSkSVlZU6kUhkW7hwYajJZGLb7fZxz2u326mysrLO1dV1/INGCQ0NHRaLxZaioiKRRqMRxsfH9zv2sVisb6zBYrHIbrdTTk7OpZUrVxomsj6Pxxv5PWw2226xWFgGg4GdmZnpV1lZqQ0MDGQef/xx76GhoZHHVfl8vo2IiMPh2K1WK2si55kq8MwuAAAAAABMaiqVymA2m1nZ2dkzHWNGo5FNRNTX18fx8vJiOBwO5ebmelit1jHX6O3t5YjFYqtIJLJVVVXx1Wq1CxHR0qVLB8rLy0WdnZ0chmFodKhesmRJf3Z2tsSxXVZWJhhr7dGSk5O7UlNTA1etWtXD4XBGxgsKCtxtNhtVV1c76/V6XkREhDkuLq4/NzdXwjAMERGp1Wpno9H4vQLpwMAAi81m22fNmmXp6elhFxcXu333rDsDOrsAAAAAADCpsdlsKioqatq+fbvvkSNHZrm7u1uEQqF17969lxcvXjyYkJAQXFhY6LZkyRKDQCAY8xbjhISEvqNHj86USqXy4ODgIYVCMUBEFBgYyOzcuVMfExMTJpFIGKlUahKLxVYioqNHj7Zu2bLFTyqVyq1WK2vRokWG2NjYluvVmpSU1Pv4448HbN26tWv0eFBQ0FBMTExod3c398iRI818Pt++a9euzpaWFp5cLg8nIvLw8GA++uij80Q0oU4yEdGsWbOsDz/8cLdMJgv38fEZnjt37sBE5051rOu17QEAAAAAANRqdbNCoej67iNvT319fWyxWGxjGIaUSuWc5OTkro0bN/beyFqnT592ycrK8ikvL29wjK1cuTJw9erVPUlJSTe05p1MrVbPUCgUATcyF51dAAAAAAC4o2VmZnqfOXNmmtlsZi1btqw/MTHxhkLpnj17ZuXn58/My8u7cLNrhO8PnV0AAAAAALiuqd7ZnUzKysoEmzdvDhw9JhAIbFVVVbpbVdOthM4uAAAAAADAFBAbG2vS6XTaW13HVIC3MQMAAAAAAMCUg7ALAAAAAAAAUw7CLgAAAAAAAEw5CLsAAAAAAAAw5SDsAgAAAADApCcUCueO3j5y5IjHxo0b/W5krbKyMsE777wjdmwfP35cnJWVNeuH1jgawzD02GOP+fj7+0fIZDK5TCaT79mz54bPwTAMubm5KbZv3+5zM+ucyhB2AQAAAADgjlJZWSk8efLkSNh95JFH+n73u9913Mxz7Nixw0ev13Pr6uo0Op1O+/nnn+sYhrnh/PX++++LAwMDzR9++KGbzWa7maVOWfj0EAAAAAAATFzhdl+6qhXe1DUl8kFa9VLrjU5vb293SklJ8W9ra+MRER06dKhlxYoVA59++qkwIyPDb2hoiM3n821vvPHGxdDQ0OHf//733kNDQ2yZTOb6q1/9Sm8ymdiVlZUueXl5LQkJCQEikciqVqtdOjs7ufv27buckpLSY7VaadOmTX5ffPGFyNfX12yz2Sg5Obk7JSWl59v1GAwG9ltvvTXz4sWL1UKh0E5E5ObmZjt06FC745jly5cH6/V6ntlsZqelpV3ZtWtXl8ViobVr1wZUV1e7sFgs+yOPPNL129/+9ioR0dtvv+2enp5+5bXXXpv5j3/8w2X58uUDN3q97hQIuwAAAAAAMOmZzWa2TCaTO7b7+vo4cXFxfURE27Zt883IyLiiVCqNjY2NPKVSGXLhwgWNQqEYqqio0HG5XCosLBTt3r179t///vemJ554ot0Rbon+dUv06HNduXKFW1lZqfv666/58fHxc1JSUnry8vLcWltbefX19Zq2tjaniIiIiOTk5O6xatVqtc5eXl7Dbm5u47Zgjx8/3uzp6Wk1Go2suXPnyhMTE3saGxud9Xo9t7GxUUNE1NXVxSEiMhqNrLKyMlF+fv6l3t5ezptvvumOsPvdEHYBAAAAAGDifkAH9odwdna26XQ6rWP7yJEjHpWVlS5ERGfPnp3W2NgocOwzGo2cnp4e9rVr1zhr164NbG5u5rNYLDvDMKyJnOvBBx/s5XA4NH/+/KHu7m4uEVFpaanrQw891MPhcMjPz8+yePFiw0Rrz8nJ8Xj55Zc9e3t7nT777LO6OXPmMNnZ2Z4nT56cTkTU0dHB1Wg0/KioqKHW1lbnTZs2+apUqr74+Ph+IqJ33313+uLFiw0ikciWmJjYEx0d7W2xWFqdnBDnrgfP7AIAAAAAwG3NbrdTZWVlnU6n0+p0Ou3Vq1er3dzcbHv27PFZtmyZobGxUVNUVHR+eHh4QvmHz+fbR689+n8nQi6Xm/V6Pa+np4dNRLRjx45unU6nFYlEVqvVyiouLhaVlJSIKisrdfX19dqwsDCTyWRiz5w501pbW6u95557DLm5uZJ169YFEBH97//+r/vZs2en+fj4RM6fP1/e19fHKS4uFn2PS3RHQtgFAAAAAIDb2pIlS/qzs7Mlju2ysjIBEVF/fz9n9uzZw0REr7766gzH/mnTplmNRuP3ykJLly41FhYWulmtVmptbXUqLy8fN2yKRCLbunXrulJTU/0GBwdZREQWi4UcneXe3l6OWCy2ikQiW1VVFV+tVrsQEen1eier1UrJycm9zzzzTFtNTY3w2rVr7MrKStfLly9Xt7W11bS1tdU8++yzLW+99Zb796n/ToSwCwAAAAAAt7WjR4+2njt3zkUqlcqDg4PDX3zxxZlERHv27OnYu3fv7Hnz5smsVuvI8T/96U8NDQ0NAplMJn/ttdfcJnKOTZs29Xh5eQ1LpdLwlJQUf4VCMTB9+nTreMfn5OS0zZo1i5HJZOFhYWHymJgY2dq1a7v8/f2ZhISEPovFwpJKpfKsrCxvhUIxQETU3NzMXbJkSahMJpNv3rw58Omnn7785ptvusXGxhoEAsFIa3ndunW9n3zyyXSTyTSh27LvVKzv044HAAAAAIA7j1qtblYoFF23uo5bra+vjy0Wi20dHR2cmJiYsLNnz+r8/Pwst7quqUytVs9QKBQBNzIXTzQDAAAAAABMQFxcXEh/fz+HYRhWZmamHkF3ckPYBQAAAAAAmICKior6b4/FxcUFt7a2Oo8e279//+WEhIT+H68yGAvCLgAAAAAAwA365JNPmm51DTA2vKAKAAAAAAAAphyEXQAAAAAAAJhyEHYBAAAAAABgykHYBQAAAAAAgCkHYRcAAAAAACY9oVA4d/T2kSNHPDZu3Oh3I2uVlZUJ3nnnHbFj+/jx4+KsrKxZP7TG0RiGoccee8zH398/QiaTyWUymXzPnj3f+xyff/65QCaTyR3br776qrtAIJhrNptZREQVFRUCqVQqH3+FOxfCLgAAAAAA3FEqKyuFJ0+eHAm7jzzySN/vfve7jpt5jh07dvjo9XpuXV2dRqfTaT///HMdwzDfO38tXLjQ1N7ezuvp6WETEZWVlbkEBgYOlZWVCYiISkpKXGJiYow3s/apAp8eAgAAAACACfv12V/7nu85L7yZa85xmzO47+59rTc6v7293SklJcW/ra2NR0R06NChlhUrVgx8+umnwoyMDL+hoSE2n8+3vfHGGxdDQ0OHf//733sPDQ2xZTKZ669+9Su9yWRiV1ZWuuTl5bUkJCQEiEQiq1qtduns7OTu27fvckpKSo/VaqVNmzb5ffHFFyJfX1+zzWaj5OTk7pSUlJ5v12MwGNhvvfXWzIsXL1YLhUI7EZGbm5vt0KFD7Y5jli9fHqzX63lms5mdlpZ2ZdeuXV0Wi4XWrl0bUF1d7cJiseyPPPJI129/+9urkZGRAyUlJS6rVq0yVFdXu/z85z/vLC0tdb3nnnsGP//8c9f77rsP3/QdA8IuAAAAAABMemazmT36dt6+vj5OXFxcHxHRtm3bfDMyMq4olUpjY2MjT6lUhly4cEGjUCiGKioqdFwulwoLC0W7d++e/fe//73piSeeaHeEW6J/3RI9+lxXrlzhVlZW6r7++mt+fHz8nJSUlJ68vDy31tZWXn19vaatrc0pIiIiIjk5uXusWrVarbOXl9ewm5ubbbzfc/z48WZPT0+r0WhkzZ07V56YmNjT2NjorNfruY2NjRoioq6uLg4R0aJFiwY+++wz13vvvXeAzWbbV6xYYcjMzPQhoqtfffWV6zPPPNM+3nnuZAi7AAAAAAAwYT+kA/tDODs723Q6ndaxfeTIEY/KykoXIqKzZ89Oa2xsFDj2GY1GTk9PD/vatWuctWvXBjY3N/NZLJadYRjWRM714IMP9nI4HJo/f/5Qd3c3l4iotLTU9aGHHurhcDjk5+dnWbx4sWGitefk5Hi8/PLLnr29vU6fffZZ3Zw5c5js7GzPkydPTici6ujo4Go0Gn5UVNRQa2ur86ZNm3xVKlVffHx8PxHR0qVLjYcOHfIsKSkxRkdHD4aHh5ubm5ud29vbnQYHB9lyuXx4orXcSfDMLgAAAAAA/F/27j2sySvdG/+dBAgJRI7KSUKQUwhgKmCkFIunWB3FmRotDFoOc1DYM9Zu3NXdzrxtPQ9jx6mpU0s9jIO1yFyDg9XZjMVfq3RLBYOKSAhRFAyIKIiBcAg5/f7wjS9VQFBbhX4/1+UF63nWWvf9PP3r7lrPYlSzWCykUChqVCqVUqVSKW/dunXRxcXFvG7dOp/4+PjOy5cvVx89evRKX1/fsOofe3t7S/+5+/8cDpFIpG/boTaUAAAgAElEQVRubr7/ne3q1avbVCqVksfjmUwmE+PYsWO8U6dO8RQKhaq2tlYZGhra09PTwxw/frzp0qVLypkzZ3Z+/PHHE5KSkgRERDNmzNBVVVU5lJSUOL744os6IiJPT0/DX//6V9fIyMiuYSf2I4NiFwAAAAAARrW4uLiO7OzsCda29fCmjo4O1sSJE/uIiHJyctyt98eNG2fS6XQjqoWmT5+uKywsdDGZTKTRaGzKysp4g/Xl8XjmpKSk1l/+8pf87u5uBhGR0Wgk68ry3bt3WU5OTiYej2c+f/68fWVlpQMRUXNzs43JZKK0tLS7mzZtaqqqquIS3fve19PTs+/QoUNuM2bM0BERTZs2TffJJ59MiImJweFUg0CxCwAAAAAAo9qnn36qOXfunENwcLAoICAgbOfOneOJiNatW3fz/fffnxgZGSk0mUz3+8+fP79TrVZzhEKhaPfu3S7DiZGamtru5eXVFxwcHJaenu4nFou7nJ2dTYP137FjR5Onp6dBKBSGhYaGiqZOnSpMTExs9fPzM8hkMq3RaGQEBweL3nnnHW+xWNxFRFRfX28bFxcXIhQKRb/4xS/8N2zY0Gidb+rUqbq+vj5mYGCggYjopZde6mpsbGRPnz4dxe4gGCNZjgcAAAAAgB+fysrKerFY3Pqs83jWtFot08nJyXzz5k3W1KlTQ0+fPq3i8/nGZ53XWFZZWekuFosFjzMWB1QBAAAAAAAMg1QqDero6GAZDAbGW2+91YxC9/mGYhcAAAAAAGAYysvLax+8JpVKAzQaDbv/tc2bNzfKZDL87dtnDMUuAAAAAADAYyouLq571jnAwHBAFQAAAAAAAIw5KHYBAAAAAABgzEGxCwAAAAAAAGMOil0AAAAAAAAYc1DsAgAAAADAc4/L5U7p35bL5W4pKSn8x5mrtLSUk5+f72RtHzx40Omdd97xfNIc+zMYDPTb3/7Wx8/PL1woFIqEQqFo3bp1TzUGDA2nMQMAAAAAwI+KQqHgKhQKh8TERC0R0bJly7REpH2aMVavXu3T0tJiW1NTU83lci3t7e3MjRs3PlTsms1mslgsxGKxnmZ4IBS7AAAAAAAwAjfe+Z2v/vJl7tOckx0U1O29ZbPmsXO6ccMmPT3dr6mpyY6IaPv27dfnzp3b9fXXX3OzsrL4vb29THt7e/P+/fuvhYSE9G3dutW7t7eXKRQKHdesWdPc09PDVCgUDrm5uddlMpmAx+OZKisrHW7fvm27cePGxvT09HaTyUSpqan8M2fO8Hx9ffVms5nS0tLa0tPT2x/Mp7Ozk/n555+Pv3bt2kUul2shInJxcTFv3779BhFRbW2t3fz584NiY2M7KyoqHI8cOXJl/fr1npWVlQ69vb3MhISE9j//+c83/v73v4/bv3+/+//8z/9cJSI6duwYb/v27R5fffXVlcd9Vz8mKHYBAAAAAOC5p9frmUKhUGRta7VallQq1RIRrVy50jcrK6vllVde0V2+fNnulVdeCbp69Wq1WCzuLS8vV9na2lJhYSFv7dq1E48fP1739ttv37AWt0T3tkT3j9XS0mKrUChUFy5csH/11VcD09PT23Nzc100Go1dbW1tdVNTk014eHh4Wlpa20C5KpVKtpeXV5+Li4t5sOepr6+33717d/1nn312nYho+/btTR4eHiaj0UixsbEhZWVlnFdffbVj9erVfh0dHcxx48aZ8/LyXJYsWXLnabzPHwMUuwAAAAAAMGxPsgL7JNhstlmlUimtbblc7qZQKByIiE6fPj3u8uXLHOs9nU7Ham9vZ965c4eVmJjoX19fb89gMCwGg4ExnFiLFi26y2KxKCoqqretrc2WiOibb75xXLx4cTuLxSI+n2+MiYnpHG7uO3bscNu1a5fH3bt3bf73f/+3hojIy8urb/bs2V3WPn/7299c9+/f7240Ghm3b9+2raystJ82bVrPjBkzOg4dOuSUnp7e/tVXXznt3Lmzcbhxf+xQ7AIAAAAAwKhmsVhIoVDUODo6Wvpf/9WvfsWPj4/vLC4urqutrbWbNWtWyHDms7e3vz+PxWL5zs/hEIlE+ubmZrv29nami4uLefXq1W2rV69uCwoKCjOZTAwiIi6Xe3/VV6VS2e3cudOjoqKiZvz48SaZTCbo7e1lEhElJSXd+ctf/jLB3d3dNHny5O6hVovhu3AaMwAAAAAAjGpxcXEd2dnZE6zt0tJSDhFRR0cHa+LEiX1ERDk5Oe7W++PGjTPpdLoR1ULTp0/XFRYWuphMJtJoNDZlZWW8wfryeDxzUlJS6y9/+Ut+d3c3g4jIaDTSYCvL7e3tLA6HY3Z1dTVpNBqbkydP3j8pesGCBZ3V1dXc3bt3uy9duhRbmEcAxS4AAAAAAIxqn376qebcuXMOwcHBooCAgLCdO3eOJyJat27dzffff39iZGSk0GQy3e8/f/78TrVazREKhaLdu3e7DCdGampqu5eXV19wcHBYenq6n1gs7nJ2djYN1n/Hjh1Nnp6eBqFQGBYaGiqaOnWqMDExsdXPz8/wYN8XX3yxJzw8vDsoKCjs9ddfF0RFRems92xsbGj27NnaU6dOOVlPj4bhYYxkOR4AAAAAAH58Kisr68ViceuzzuNZ02q1TCcnJ/PNmzdZU6dODT19+rSKz+cbn3VeY1llZaW7WCwWPM5YfLMLAAAAAAAwDFKpNKijo4NlMBgYb731VjMK3ecbil0AAAAAAIBhKC8vr33wmlQqDdBoNOz+1zZv3twok8k6frjMYCAodgEAAAAAAB5TcXFx3bPOAQaGA6oAAAAAAABgzEGxCwAAAAAAAGMOil0AAAAAAAAYc1DsAgAAAAAAwJiDYhcAAAAAAJ57XC53Sv+2XC53S0lJ4T/OXKWlpZz8/Hwna/vgwYNO77zzjueT5thfb28v4xe/+IWvr69vuJ+fX/js2bMD6urqbImIWltbWX/4wx/GW/seO3aMN3PmzMAH5/jss8+c58yZE2Btv/322558Pj/c2v7888+dZs2a9dA4uAfFLgAAAAAA/KgoFAruv/71r/vF7rJly7Rbtmy5+TRjvPHGGz46nY557dq1Sw0NDZcWLVp092c/+1mg2WymtrY21t69eyc8ao5Zs2bpzp8/72htl5WVOTo6OpqamppsiIhOnz7t+OKLL+qeZt5jCf70EAAAAAAADNv/l1vje6dJx32ac7r6OHbPTgnVPO74Gzdu2KSnp/s1NTXZERFt3779+ty5c7u+/vprblZWFr+3t5dpb29v3r9//7WQkJC+rVu3evf29jKFQqHjmjVrmnt6epgKhcIhNzf3ukwmE/B4PFNlZaXD7du3bTdu3NiYnp7ebjKZKDU1lX/mzBmer6+v3mw2U1paWlt6enr7g/l0dnYy//73v7tfvXr1oo3NvZJr9erVbbm5ue5Hjx7l7d69e7xGo2ELhUJRfHx8R0JCgrarq4s1b968SbW1tZyIiIjuwsLCa97e3kYej2e6dOkSOzw8XN/S0mKbkJDQ/tVXXzm+/vrrd8vLyx03btzY9NgvfoxDsQsAAAAAAM89vV7PFAqFImtbq9WypFKploho5cqVvllZWS2vvPKK7vLly3avvPJK0NWrV6vFYnFveXm5ytbWlgoLC3lr166dePz48bq33377hrW4Jbq3Jbp/rJaWFluFQqG6cOGC/auvvhqYnp7enpub66LRaOxqa2urm5qabMLDw8PT0tLaBspVqVSyvby8+lxdXc39r7/wwgvdVVVVnD/96U+NCxcu5KhUKiXRvW3MNTU1nAsXLlwVCASGqKgoYXFxseMrr7yii4qK0p08edLRZDKRv7+/PjY2tquoqMgpKSnpbm1tLefll1/uetrveqxAsQsAAAAAAMP2JCuwT4LNZputxSHRvQJVoVA4EBGdPn163OXLlznWezqdjtXe3s68c+cOKzEx0b++vt6ewWBYDAYDYzixFi1adJfFYlFUVFRvW1ubLRHRN99847h48eJ2FotFfD7fGBMT0znYeLPZTAwGw/LgdYvFQgzGwClERER0BQQEGIiIwsLCuuvq6uyIiGJjY3WlpaUOJpOJpk2bpnv55Ze7Nm3a5F1aWsr19/fv5XK5D8WBe1DsAgAAAADAqGaxWEihUNQ4Ojp+p/D71a9+xY+Pj+8sLi6uq62ttZs1a1bIcOazt7e/P4/FYvnOz+EICwvT37hxg93e3s50cXG5v7p78eJF7k9/+tO7A41hs9n3A7BYLDIajQwiovj4eF1OTs4Es9nMWLly5W0XFxezXq9nnDhxgieRSPC97hBwQBUAAAAAAIxqcXFxHdnZ2fcPfCotLeUQEXV0dLAmTpzYR0SUk5Pjbr0/btw4k06nG1EtNH36dF1hYaGLyWQijUZjU1ZWxhus77hx48xLlixpzczM9DUajUREtHPnTrfe3l5mQkJCp5OTk6mrq2tY8SMjI3tv375tW1ZW5hgbG9tDRBQeHt6zf//+8S+99BKK3SGg2AUAAAAAgFHt008/1Zw7d84hODhYFBAQELZz587xRETr1q27+f7770+MjIwUmkym+/3nz5/fqVarOUKhULR7926X4cRITU1t9/Ly6gsODg5LT0/3E4vFXc7OzqbB+n/00UdNbDbb7O/vH+7n5xd++PBhl8LCwitMJpM8PT1NUVFRuqCgoLCVK1dOHCouk8kksVjc5erqarSu/sbExOgaGxvZM2fOxPe6Q2CMZDkeAAAAAAB+fCorK+vFYnHrs87jWdNqtUwnJyfzzZs3WVOnTg09ffq0is/nG591XmNZZWWlu1g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o0aNHSzIyMnyJiPLz84XJycmB/Z27u+rqalcGgxH/6quv+jo/u3btGpvNZo/tbf6f41kNRHgbMwAAAAAADHkvvPBCyPbt22ufeOKJTqvVSiqVyu2XrsHf39/0r3/9y5OIrhIRKZVKr7CwsK5fuo7BAs0uAAAAAAD02LKqhgBNRxevP2OK3d2MeVGBjb1dt23bNo+1a9eOslgsTC8vL2tRUdH3AQEB1oyMDN/GxkbX+vp6ztWrV13T0tKur1q16gYRUWZmpk9RUZG3r6+vWSgUWuLi4oxERHq9nh0YGGghImKz2RQfH/+jJlOr1bqmpKQE63Q6tlAotCqVyrrw8HCzXC4P5nA49urqaq5Op3N59913G+fPn99qtVrppZde8v/mm2/4ZrOZ8eKLL9544403bt7rftzc3BxhYWGdx44d402cONG4a9cuwYwZM/RXr151vV9+jUbjOm/evNFWq5Xx1FNPtfb2OQ5WOMYMAAAAAAADUmJiouHcuXOaqqoq9axZs/TZ2dk+zrGamhq3o0ePak+dOlWVm5vrazKZGMePH+d9+eWXgoqKCnVxcXGNSqVyd85fvHjx9aioqDGJiYmhOTk53kajkdE9X1paWmBSUpJOq9Wq586dq1MoFAHOscbGRs7Jkyer9+3bd3HZsmVBRqORkZeX5+3h4WGrrKysUqlUVZ988skIjUbjer97mjdvnv6zzz4T1NbWurBYLIevr6/lQfmXLl0a+Ic//KG5srKyysfHx3Lv6EMLdnYBAAAAAKDH+rID+3O5dOmS64wZM/ybm5tdzGYzMyAgwOQcmzJlyi0ul+vgcrlWgUBguXz5Mvvw4cPDpk6deovP59udc5zzc3Nzr6WmpuqLi4uH79y5U/iPf/xDePLkyeo78509e9a9pKSklohIoVDoV69e7e8ck8vlehaLRTExMaaAgADTuXPn3MrKyoZrNBre3r17vYiI2tvbWWq12k0sFpvvdU9yubwtOzvbTyQSWeRyub4n+c+cOTPM+fmSJUt0a9as8f9x5KEHO7sAAAAAADAgvfzyy4FLly69odVq1Rs2bKg3mUy3+xsOh+Nw/s1ischqtTKIiBiMH23Y3iaRSEyZmZnNJ06cqNZoNNympiZWT2vpHpfBYJDD4WB88MEHDRqNRq3RaNRXrlypeO6559ruF8fNzc0RGxtrLCgo8Fm4cGFLT/MzmUzHg2cNLWh2AQAAAABgQGpvb2c5v2e7ZcsW4YPmT5482bB//35Pg8HAaGlpYZaWlno6x3bs2OFht9uJiKiiosKNxWI5vL29bXeuj4uL69i8ebMXEVFhYaEgISHB4Bz74osvvGw2G124cIHT2NjIkUqlXYmJia0FBQUjTCYTg4jo/PnznLa2tgf2YJmZmU1vvfXWZR8fnx7lHzt2rGHTpk0CIqJNmzY98DkMFTjGDAAAAAAAj7yuri6mSCSKdV4rFIrrb7755tX58+eHikQic0JCQkdDQwPnfjHGjx9vnDlzpn7MmDESPz8/k0wmu92sfvbZZ8KVK1cGuLm52dlstmPz5s2X2Oz/bJcKCgoaUlJSgtevX+/jfEGUcywsLMwkk8kidTqdS15eXj2Px3O89tprN+vq6jgxMTFRDoeDIRAILF999VXtg+41ISGhKyEh4UcvyLpX/o8++qhh3rx5oz/66CPR7373ux7vBg92DIcDu90AAAAAAHBvKpWqTiqV3vMtwkOdXC4PnjZtWmtqaioazX6mUqm8pVJpcF/W4hgzAAAAAAAADDo4xgwAAAAAAPAT7Nq1q66nc0+ePMlNTk4OufMzV1dX+/nz5zX9XtgQh2YXAAAAAADgFyKTyTo1Go36YdcxFOAYMwAAAAAAAAw6aHYBAAAAAABg0EGzCwAAAAAAAIMOml0AAAAAAAAYdNDsAgAAAADAI4/H48X1d8yMjAzfrKwsERHRwYMH3WNjY8VisTh69OjRkoyMDF8iovz8fGFycnJgf+fuzmaz0aJFiwLCw8MlERER0WPGjInSaDSuP3fewQxvYwYAAAAAgCHvhRdeCNm+fXvtE0880Wm1WkmlUrn9kvk3b94saGpqctFoNBdYLBbV1ta6DB8+3P5L1jDYoNkFAAAAAIAee+NzVYC2qZ3XnzEjfPjGnFnSxt6u27Ztm8fatWtHWSwWppeXl7WoqOj7gIAAa0ZGhm9jY6NrfX095+rVq65paWnXV61adYOIKDMz06eoqMjb19fXLBQKLXFxcUYiIr1ezw4MDLQQEbHZbIqPj+/qnk+r1bqmpKQE63Q6tlAotCqVyrrw8HCzXC4P5nA49urqaq5Op3N59913G+fPn99qtVrppZde8v/mm2/4ZrOZ8eKLL9544403bt7tXq5du+YiEoksLBaLiIhCQ0MtzrEvvvhieHZ2tq/ZbGYEBQWZduzYUXfgwIFhW7Zs8f7qq6++JyIqLi7m/+UvfxEdOnSoprfPcbDCMWYAAAAAABiQEhMTDefOndNUVVWpZ82apc/OzvZxjtXU1LgdPXpUe+rUqarc3Fxfk8nEOH78OO/LL78UVFRUqIuLi2tUKpW7c/7ixYuvR0VFjUlMTAzNycnxNhqNjO750tLSApOSknRarVY9d+5cnUKhCHCONTY2ck6ePFm9b9++i8uWLQsyGo2MvLw8bw8PD1tlZWWVSqWq+uSTT0bc62jy73//e31ZWZmnWCyOfvHFF/2/+eYbLhHRtWvX2H/+859HHTt2TKtWq6vGjh1rXLNmjWjmzJltZ8+edW9ra2MSEW3fvt1r1qxZ+v58vgMddnYBAAAAAKDH+rID+3O5dOmS64wZM/ybm5tdzGYzMyAgwOQcmzJlyi0ul+vgcrlWgUBguXz5Mvvw4cPDpk6deovP59udc5zzc3Nzr6WmpuqLi4uH79y5U/iPf/xDePLkyeo78509e9a9pKSklohIoVDoV69e7e8ck8vlehaLRTExMaaAgADTuXPn3MrKyoZrNBre3r17vYiI2tvbWWq12k0sFpu730toaKilpqamct++ffyDBw8Onzp1aqRSqaw1Go3M2tpaN5lMJiYislgsjPj4eIOLiwtNmjSpbceOHR6pqakthw4d8tiwYcPl/n7GAxmaXQAAAAAAGJBefvnlwFdffbVpwYIFrcXFxfzs7Gxf5xiHw3E4/2axWGS1WhlERAzGjzZsb5NIJCaJRNKckZHRLBQKH2tqamL1tJbucRkMBjkcDsYHH3zQIJfL23oSg8vlOubMmdM2Z86cNpFIZPniiy88f/vb37aNHz++bd++fZe6z583b57+ww8/HOnt7W2LjY01enl54Tu+d8AxZgAAAAAAGJDa29tZzu/ZbtmyRfig+ZMnTzbs37/f02AwMFpaWpilpaWezrEdO3Z42O0/9IoVFRVuLBbL4e3tbbtzfVxcXMfmzZu9iIgKCwsFCQkJBufYF1984WWz2ejChQucxsZGjlQq7UpMTGwtKCgYYTKZGERE58+f5ziPHXf39ddf8+rq6lyIfngzc0VFBTcoKMg8adKkjtOnTw+rrKzk/N89M8+fP88hIvrv//7v9gsXLvA2bdrkPXv2bBxh7gY7uwAAAAAA8Mjr6upiikSiWOe1QqG4/uabb16dP39+qEgkMickJHQ0NDRw7hdj/PjxxpkzZ+rHjBkj8fPzM8lkstvN6meffSZcuXJlgJubm53NZjs2b958ic3+z3apoKCgISUlJXj9+vU+zhdUOcfCwsJMMpksUqfTueTl5dXzeDzHa6+9drOuro4TExMT5XA4GAKBwPLVV1/V3q22pqYm9pIlS4LMZjOTiOixxx7rWLly5Q0ej+coLCysmzdv3miz2cwgInrrrbeuxMbGmthsNj311FOtn3/+uXDnzp11d4s7lDEcDseDZwEAAAAAwJClUqnqpFLpXd8iDERyuTx42rRprampqS0Pu5bBRqVSeUul0uC+rMUxZgAAAAAAABh0cIwZAAAAAADgJ9i1a1ddT+eePHmSm5ycHHLnZ66urvbz589r+r2wIQ7NLgAAAAAAwC9EJpN1ajQa9cOuYyjAMWYAAAAAAAAYdNDsAgAAAAAAwKCDZhcAAAAAAAAGHTS7AAAAAAAAMOig2QUAAAAAgEcej8eL6++YGRkZvllZWSIiooMHD7rHxsaKxWJx9OjRoyUZGRm+RET5+fnC5OTkwP7ODT8/vI0ZAAAAAACGvBdeeCFk+/bttU888USn1WollUrl9rBrgp8GzS4AAAAAAPTc7pcC6Iaa168xR0YbacaHjb1dtm3bNo+1a9eOslgsTC8vL2tRUdH3AQEB1oyMDN/GxkbX+vp6ztWrV13T0tKur1q16gYRUWZmpk9RUZG3r6+vWSgUWuLi4oxERHq9nh0YGGghImKz2RQfH9/VPZ9Wq3VNSUkJ1ul0bKFQaFUqlXXh4eFmuVwezOFw7NXV1VydTufy7rvvNs6fP7/VarXSSy+95P/NN9/wzWYz48UXX7zxxhtv3LzbvRQXF/Ozs7N9BQKBpbq6mhsTE2PcvXv3JSaTScuXLx/1z3/+09NkMjETEhIMW7durWcymSSTySLj4+MNX3/99fD29nbWxo0b65555hlDb5/jYIVjzAAAAAAAMCAlJiYazp07p6mqqlLPmjVLn52d7eMcq6mpcTt69Kj21KlTVbm5ub4mk4lx/Phx3pdffimoqKhQFxcX16hUKnfn/MWLF1+Piooak5iYGJqTk+NtNBoZ3fOlpaUFJiUl6bRarXru3Lk6hUIR4BxrbGzknDx5snrfvn0Xly1bFmQ0Ghl5eXneHh4etsrKyiqVSlX1ySefjNBoNK73up+qqiruhx9+2FhTU3OhoaGBU1paOoyI6I033rhRWVlZdfHixQudnZ3MHTt2eDjXWK1WRkVFRdV7773XmJ2d7dsfz3WwwM4uAAAAAAD0XB92YH8uly5dcp0xY4Z/c3Ozi9lsZgYEBJicY1OmTLnF5XIdXC7XKhAILJcvX2YfPnx42NSpU2/x+Xy7c45zfm5u7rXU1FR9cXHx8J07dwr/8Y9/CE+ePFl9Z76zZ8+6l5SU1BIRKRQK/erVq/2dY3K5XM9isSgmJsYUEBBgOnfunFtZWdlwjUbD27t3rxcRUXt7O0utVruJxWLz3e4nJiamIzQ01EJEJJFIjLW1ta5ERCUlJfy//OUvPl1dXcxbt26xo6OjO4molYho9uzZLURE48aN63jjjTfu2UgPRWh2AQAAAABgQHr55ZcDX3311aYFCxa0Oo8BO8c4HI7D+TeLxSKr1cogImIwfrRhe5tEIjFJJJLmjIyMZqFQ+FhTUxOrp7V0j8tgMMjhcDA++OCDBrlc3taTGHer2Wg0Ml5//fWg7777Th0WFmbJyMjw7erqun1C183NzUH0w9Frm81275sbgnCMGQAAAAAABqT29naW83u2W7ZsET5o/uTJkw379+/3NBgMjJaWFmZpaamnc2zHjh0edrudiIgqKircWCyWw9vb23bn+ri4uI7Nmzd7EREVFhYKEhISbn8/9osvvvCy2Wx04cIFTmNjI0cqlXYlJia2FhQUjDCZTAwiovPnz3Pa2tp61YMZjUYmEZGPj4+1tbWVuW/fPq/erB/KsLMLAAAAAACPvK6uLqZIJIp1XisUiutvvvnm1fnz54eKRCJzQkJCR0NDA+d+McaPH2+cOXOmfsyYMRI/Pz+TTCa73ax+9tlnwpUrVwa4ubnZ2Wy2Y/PmzZfY7P9slwoKChpSUlKC169f7+N8QZVzLCwszCSTySJ1Op1LXl5ePY/Hc7z22ms36+rqODExMVEOh4MhEAgsX331VW1v7tvb29u2YMGC5ujoaIm/v79ZKpV29Gb9UMZwOBwPngUAAAAAAEOWSqWqk0qld32LMBDJ5fLgadOmtaamprY87FoGG5VK5S2VSoP7shbHmAEAAAAAAGDQwTFmAAAAAACAn2DXrl11PZ178uRJbnJycsidn7m6utrPnz+v6ffChjg0uwAAAAAAAL8QmUzWqdFo1A+7jqEAx5gBAAAAAABg0EGzCwAAAAAAAIMOml0AAAAAAAAYdNDsAgAAAADAI4/H48X1dO6nn37qWV5e7nbnZ1lZWaKQkBBJeHi4JDIyMnrDhg3CvtTR2dnJGDduXIRYLI7etGmT19y5c4O65+qpDRs2CMPDwyVhYWGS0NBQSVZWluh+84uLi/m/+c1vwoiI8vPzhV5eXtKoqKjooKCgMePHjw8vLS11d849ePCge2xsrFgsFkePHj1akpGR4duXGgcyvKAKAAAAAAAGld27d3tardbW+Pj4LiKi999/f8ShQ4eGl5eXVwkEArtOp2Nt27bNsy+xT5w4wbNYLAznS6ZefPHFPv227s6dO4d/9NFHI0tLS7XBwcEWo9HIKCgo6FUDPn369BalUtlARLRv3z7+/Pnzw/71r39Vjx07tuuFF14I2b59e+0TTzzRabVaSaVS9akhH8iwswsAAAAAAAOSVqt1feKJJyIiIiKin3jiiYiLFy+6lpaWupeVlXmuWrXKXywWR1+4cIGzbt06n8LCwgaBQGAnIhIKhbb09HQdEdGePXv4UVFR0REREdGzZ88O7uzsZBAR+fn5xbz22mu+0dHRUREREdFnz551u3LlCjs1NTVEo9FwnbFlMlnksWPHeERE69at8w4ODh4jk8ki582bF5ScnBx4r9rff//9UWvXrr0cHBxsISLi8XiO119//SYR0Z0xr127xvbz84t50LOYPn16+8KFC5s//PDDEUREer2eHRgYaCEiYrPZ5Gz8Dx8+zIuLixNHRUVFx8XFiVUqFYeIyGq10uLFi/0jIiKiIyIiot95552Rff1/eVRgZxcAAAAAAHrs/33z/wJqWmp4/RkzzCvMuObXaxp7uy4tLS0wKSlJl56ersvLyxMqFIqAsrKy2qeffvrWtGnTWlNTU1taWlqYHR0dLIlEYuq+3mg0MpYsWRLyr3/9qzo2NtY0c+bM4JycnBFZWVk3iIi8vb2tarW6au3atSPWrl0rKioqqv/oo4/qP/jgA9Hhw4dr7oxVV1fnkpubO+rMmTNqT09P+7hx4yIkEknnvWq/ePEi99e//rWxt/d8P/Hx8cZNmzaNICJavHjx9aioqDGPP/54+5QpU1pfeuklHY/Hc0il0q6TJ09qXFxcaPfu3fwVK1b4HzhwoPaDDz4YUV9fz7lw4YLaxcWFrl+/zurP2h4G7OwCAAAAAMCAdPbsWffFixfriYgUCoW+vLx8WPc5DoeDGAzGXderVCo3f39/U2xsrImIaNGiRbqvv/6a7xxPSkpqISKSyWTGxsZGzv1qOX78uPvjjz/eLhKJbBwOxzFz5sw+HW/+KRwOx+2/c3Nzr/373/+uevrpp9t27twpnDRpUgQRkV6vZ02dOjU0PDxcsmLFigCtVutGRHTo0KHhaWlpzS4uLkREJBKJbL90/f0NO7sAAAAAANBjfdmBfZgEAoGdy+Xa1Wq1a3R0tPnOsTubw7txc3NzEBGx2WyH1Wq9e8fcw1jdhYWFdX7zzTe83/3ud+3dx9hstsNm+6HXNBqN9817pzNnzvAiIiJu7yZLJBKTRCJpzsjIaBYKhY81NTWxMjMz/Z588sn20tLS2urqatfJkydHOutnMBi9u4lHHHZ2AQAAAABgQIqLi+vYvHmzFxFRYWGhICEhwUBENGzYMFtbW9vtXmfZsmXX0tLSgvR6PZOISK/XM3Nzc70fe+yxritXrrhWVlZyiIiUSqVwwoQJP2o+e2LChAkd3333Hb+5uZllsVhoz549Xvebv2LFiqY//vGP/g0NDWyiH97y/Pbbb48kIgoICDCdPHnSnYho69at943jtH///mGfffbZiKVLl94kItqxY4eH3W4nIqKKigo3Fovl8Pb2trW1tbH8/f3NRESFhYXezvVPP/1028aNG0dYLBYiokFxjBk7uwAAAAAA8Mjr6upiikSiWOe1QqG4XlBQ0JCSkhK8fv16H6FQaFUqlXVERAsWLNArFIrgjRs3ij7//PPaFStWNBsMBubYsWOjXVxcHGw225Gent7E4/EcGzdurJs9e3aozWYjqVRqXL58eXNf6gsJCbG89tpr1/7rv/4rauTIkZaIiIhODw+Pex4Fnjt3bmtTUxP7qaeeinQetV6wYMFNIqKVK1denzt37ugdO3YIJ0yY0HavGPv27fMSi8XDurq6mP7+/qZt27bVjB07touI6LPPPhOuXLkywM3Nzc5msx2bN2++xGazKTMzs+kPf/hDSH5+vs+dsV977bVmrVbLEYvFEjab7UhJSWn+4x//2Kdn8ahg9Ha7HQAAAAAAhhaVSlUnlUpvPuw6HnWtra1MDw8Pu8Viod/+9rdhixYtupmcnHzrYdc1kKlUKm+pVBrcl7U4xgwAAAAAANAP3njjDV+xWBwdEREhCQwMNC1cuBCN7kOEY8wAAAAAAAD94H//938vd/8sMzPTZ8+ePYI7P/uf//kf/Xvvvdf0y1U2NOEYMwAAAAAA3BeOMcPDgmPMAAAAAAAAAHdAswsAAAAAAACDDppdAAAAAAAAGHTQ7AIAAAAAAMCgg2YXAAAAAAAeeTweL66ncz/99FPP8vJytzs/y8rKEoWEhEjCw8MlkZGR0Rs2bBD2pY7Ozk7GuHHjIsRicfSmTZu85s6dG9Q9V09kZGT4jhw5MlYsFkeLxeLopUuX+hERyWSyyODg4DGRkZHRY8aMiTpx4gSXiKi9vZ05adKksJCQEElYWJjEOR/uDT89BAAAAAAAg8ru3bs9rVZra3x8fBcR0fvvvz/i0KFDw8vLy6sEAoFdp9Oxtm3b5tmX2CdOnOBZLBaGRqNRExG9+OKLLX2tMy0t7Xp2dvb17p8rlcrvJ06caFy/fr1w+fLl/idOnLhIRPT6669fnz59entXVxfj17/+dcTOnTuHz5kzp62v+Qc7NLsAAAAAANBjV//4ZoDp4kVef8PbqdsAACAASURBVMbkhIcbff/8TmNv12m1WteUlJRgnU7HFgqFVqVSWVdXV+dSVlbm+e233/Lfe++9Ubt27apdt26dT1lZmVYgENiJiIRCoS09PV1HRLRnzx7+ypUrA2w2G0mlUqNSqazncrkOPz+/mDlz5ugOHDjgYbVaGUVFRd+PHDnSmpqaGtLS0sIWi8XRu3btqk1NTQ3Ozc1tnDhxonHdunXe69ev9xk5cqRl9OjRXa6urg6lUtnQ1+cyceLEjvz8fB8iIj6fb58+fXo7EZGbm5sjNjbW2NjY6NrX2EMBjjEDAAAAAMCAlJaWFpiUlKTTarXquXPn6hQKRUBiYmLH008/fevtt9++rNFo1L6+vpaO/8/enUc1dad9AH+ysCQQgQQJsiMCgSgRoahYta1odWbOqMVlKopgtSad1ipi8XSsVsdORem07mIZtdjWUsUSl6NttfOi1apFkWJCiKIgoCwmEpawZHv/6BtfShGR2ir4/ZzDOd77W+5z73+Pz5N7m5pYYrG4teN6vV7PWLhwoX9WVlaJWq1WGo1G2rBhQ3/ruKurq1GpVBbNmzevdt26dUJPT0/jtm3byiIjIxtVKpWy/Z6lpaU2aWlpA86fP190+vRp9dWrVx/Y2rxjxw6htY05Ozu7X8fxw4cP95s0aVJdx/N37txhffvtt86TJk1CVbcLqOwCAAAAAEC39aQC+3vJz893OHbsWAkRkUwm065evdqr4xyLxUIMBqPT9QUFBfZeXl6tYWFhrURECQkJmq1bt7oRUQ0R0axZs+4SEUVFRekPHTrk0lUsp0+fdhg+fHiDUCg0ERFNnTr1rlqt7jLhvV8bc3x8/MDm5mam2WymvLy8ovZjBoOBXnrppYGvvvpqdWhoaFtX+z/tUNkFAAAAAIA+i8/nmzkcjlmpVP6q5ddisXS51t7e3kJExGazLUajsfOMuZt7PYzMzMzrN2/eLJwyZYp2wYIFPu3HZs2a5Tdw4MCWlStX1jyyC/ZRSHYBAAAAAKBXCg8Pb8rIyHAhIkpPT+dHRkY2EhE5Ojqa6uvr7+U6ixcvvi2VSn21Wi2TiEir1TLT0tJchw4d2lJZWWl75coVOyKizMxMwejRoxt6Esvo0aObzp8/z6utrWUZDAaSy+VdVoIfxM7OzvLhhx9WXr582eHSpUv2RESLFi3yqK+vZ/3nP/95YqrrTzIkuwAAAAAA8MRraWlhCoXCMOvfu+++K9y+ffvNvXv3ugYFBYXu27dPsG3btnIiori4OO2mTZvcQ0JCQhUKhd1bb71VO2bMmPphw4aFBgYGikeNGiXicrlmLpdr2bFjR+n06dMDgoKCQplMJiUnJ9f2JD5/f3/DkiVLbj/zzDMho0aNCg4KCmp2cnIy/ZZ7dnR0tMhksup169YJS0pKbDZv3jzg6tWr9mKxOFQkEoX++9//dv0t+/d1jEdZbgcAAAAAgL6noKCgVCKR3HnccTzpdDod08nJyWwwGOjFF18clJCQcCc+Pv5XL5iC7isoKHCVSCR+PVmLyi4AAAAAAMAjsGzZMg+RSBQaFBQk9vHxaZ09ezYS3ccIb2MGAAAAAAB4BHbu3FnR8VxKSoq7XC7ntz83efJkbWpqatUfF9nTCW3MAAAAAADQJbQxw+OCNmYAAAAAAACAdpDsAgAAAAAAQJ+DZBcAAAAAAAD6HCS7AAAAAAAA0Ocg2QUAAAAAgCcel8sN7+7cvXv3Ol+8eNG+/bmVK1cK/f39xYGBgeLg4ODQLVu2CHoSR3NzMyM6OjpIJBKFfvzxxy4zZ8707Xit7khKSvJwc3MLE4lEoSKRKPS1117zJCKKiooK9vPzGxwcHBw6ePDgkLNnz3Ksa0aPHh0YHBwcOmjQIPGsWbN8jEZjT27hqYFPDwEAAAAAQJ+Sk5PjbDQadRERES1EROvXr+//3Xff9bt48WIRn883azQa1ueff+7ck73Pnj3LNRgMDJVKpSQiWrBgwd2eximVSqvXrFlT3fF8Zmbm9TFjxug3btwoSE5O9jp79uxVIiK5XF7C5/PNZrOZJk2aFLBr1y6XV199tcfX7+uQ7AIAAAAAQLedzCzy1lY2ch/lnnxPR/24+JDyh12nVqtt586d66fRaNgCgcCYmZlZWlpaanPixAnnc+fO8VJTUwdkZ2eXfPjhh+4nTpxQ8/l8MxGRQCAwvfHGGxoiIrlczlu+fLm3yWQiiUSiz8zMLONwOBZPT88hM2bM0Hz99ddORqORkZWVdd3Nzc2YmJjof/fuXbZIJArNzs4uSUxM9EtLSysfM2aM/sMPP3TduHGju5ubm2HgwIEttra2lszMzJs9fS5jxoxp2rRpk/u95/R/8RsMBobBYGAwGIyebv1UQBszAAAAAAD0SlKp1GfWrFkatVqtnDlzpkYmk3mPHz++KSYmpm7t2rUVKpVK6eHhYWhqamKJxeLWjuv1ej1j4cKF/llZWSVqtVppNBppw4YN/a3jrq6uRqVSWTRv3rzadevWCT09PY3btm0ri4yMbFSpVMr2e5aWltqkpaUNOH/+fNHp06fVV69efWBr844dO4TWNubs7Ox+HccPHz7cb9KkSXXtzz377LOB/fv3lzg4OJgSExNR1e0CKrsAAAAAANBtPanA/l7y8/Mdjh07VkJEJJPJtKtXr/bqOMdisdD9KqAFBQX2Xl5erWFhYa1ERAkJCZqtW7e6EVENEdGsWbPuEhFFRUXpDx065NJVLKdPn3YYPnx4g1AoNBERTZ069a5are4y4b1fG3N8fPzA5uZmptlspry8vKL2Y99///1VvV7PmDp16sDDhw/3mzp1an1X13iaobILAAAAAAB9Fp/PN3M4HLNSqbTtOGaxWLpca29vbyEiYrPZFqPR2GXP8IP2ehiZmZnXb968WThlyhTtggULfDqOc7lcy1/+8pe6r776qke/O35aINkFAAAAAIBeKTw8vCkjI8OFiCg9PZ0fGRnZSETk6Ohoqq+vv5frLF68+LZUKvXVarVMIiKtVstMS0tzHTp0aEtlZaXtlStX7IiIMjMzBaNHj27oSSyjR49uOn/+PK+2tpZlMBhILpd3WQl+EDs7O8uHH35YefnyZYdLly7Z63Q6ZllZmQ0RkcFgoOPHjzuJRKLm33KNvg5tzAAAAAAA8MRraWlhCoXCMOuxTCar3r59+825c+f6bdy40d36gioiori4OK1MJvPbsWOH8MCBAyVvvfVWbWNjI3PYsGGhNjY2FjabbXnjjTequFyuZceOHaXTp08PsL6gKjk5ubYn8fn7+xuWLFly+5lnnglxc3MzBAUFNTs5OZl+yz07OjpaZDJZ9bp164QffPBB5Z///OdBbW1tDLPZzBg1alT9smXLehTr04LxKMvtAAAAAADQ9xQUFJRKJJI7jzuOJ51Op2M6OTmZDQYDvfjii4MSEhLuxMfH1z14JdxPQUGBq0Qi8evJWrQxAwAAAAAAPALLli3zEIlEoUFBQWIfH5/W2bNnI9F9jNDGDAAAAAAA8Ajs3LmzouO5lJQUd7lczm9/bvLkydrU1NSqPy6ypxPamAEAAAAAoEtoY4bHBW3MAAAAAAAAAO0g2QUAAAAAAIA+B8kuAAAAAAAA9DlIdgEAAAAAAKDPQbILAAAAAABPPC6XG97duXv37nW+ePGifftzK1euFPr7+4sDAwPFwcHBoVu2bBH0JI7m5mZGdHR0kEgkCv34449dZs6c6dvxWt2RlJTksXLlSmFPYggPDxd1dj42NtZv9+7dLj3Zsy/Cp4cAAAAAAKBPycnJcTYajbqIiIgWIqL169f3/+677/pdvHixiM/nmzUaDevzzz937sneZ8+e5RoMBoZKpVISES1YsODuo4y9O/Lz81V/9DV7IyS7AAAAAADQbV9v/8j7TnkZ91Hu6ertq39Rtrj8Ydep1WrbuXPn+mk0GrZAIDBmZmaWlpaW2pw4ccL53LlzvNTU1AHZ2dklH374ofuJEyfUfD7fTEQkEAhMb7zxhoaISC6X85YvX+5tMplIIpHoMzMzyzgcjsXT03PIjBkzNF9//bWT0WhkZGVlXXdzczMmJib63717ly0SiUKzs7NLEhMT/dLS0srHjBmj//DDD103btzo7ubmZhg4cGCLra2tJTMz8+aD7iMqKio4IiKi8fvvv+/X0NDA2rFjR+nEiRMb8/Ly7BMTE/0NBgPDbDZTdnZ2yZAhQ1q5XG64Xq/PN5vNlJCQ4HPmzBmet7d3a/vPyp4+fZqblJTkrdfrmS4uLsbPPvus1NfX1/Cwz7g3QxszAAAAAAD0SlKp1GfWrFkatVqtnDlzpkYmk3mPHz++KSYmpm7t2rUVKpVK6eHhYWhqamKJxeLWjuv1ej1j4cKF/llZWSVqtVppNBppw4YN/a3jrq6uRqVSWTRv3rzadevWCT09PY3btm0ri4yMbFSpVMr2e5aWltqkpaUNOH/+fNHp06fVV69efajWZqPRyCgsLCxKTU0tX7NmjQcR0ebNm/u/9tpr1SqVSvnTTz8V+fv7t7Vfs3fvXudr167ZFRcXK/bs2VN26dIlRyKi1tZWxqJFi3zkcnmJQqEomjt37p3k5GTPh32+vR0quwAAAAAA0G09qcD+XvLz8x2OHTtWQkQkk8m0q1ev9uo4x2KxEIPB6HR9QUGBvZeXV2tYWFgrEVFCQoJm69atbkRUQ0Q0a9asu0REUVFR+kOHDnX5W9jTp087DB8+vEEoFJqIiKZOnXpXrVZ3O+GdPn36XSKi6OjopmXLltkSEY0cObIpLS1tQEVFhe3f/va3u0OGDPlFwp6bm8ubMWOGls1mk5+fn2HkyJENREQ//fST3dWrVzkvvPBCEBGR2Wym/v37P1VVXSJUdgEAAAAAoA/j8/lmDodjViqVth3H2rf9dsbe3t5CRMRmsy1Go7HzjLmbez1Iu2uRyWRiEBFJpVKtXC6/xuFwzJMmTQo6dOgQr+O6zhJ5i8XCGDRoULNKpVKqVCqlWq1Wnjlz5upvCrAXQrILAAAAAAC9Unh4eFNGRoYLEVF6ejo/MjKykYjI0dHRVF9ffy/XWbx48W2pVOqr1WqZRERarZaZlpbmOnTo0JbKykrbK1eu2BERZWZmCkaPHt3Qk1hGjx7ddP78eV5tbS3LYDCQXC7/zW9FViqVtiEhIa0rVqyomTBhQt3ly5c57cfHjh3bsH//fr7RaKSysjKbc+fO8YiIwsLCWrRaLfvEiRMORD+3Nefl5T30G6N7O7QxAwAAAADAE6+lpYUpFArDrMcymax6+/btN+fOneu3ceNGd+sLqoiI4uLitDKZzG/Hjh3CAwcOlLz11lu1jY2NzGHDhoXa2NhY2Gy25Y033qjicrmWHTt2lE6fPj3A+oKq5OTk2p7E5+/vb1iyZMntZ555JsTNzc0QFBTU7OTkZPot97x3717+/v37BWw229K/f3/D+++/f6v9+Jw5c+pOnjzZLzg4WOzv798SFRXVQPRzlfiLL74oWbRokU9DQwPLZDIxZDJZdWRkZMtviae3YfzWcjsAAAAAAPRtBQUFpRKJ5M7jjuNJp9PpmE5OTmaDwUAvvvjioISEhDvx8fF1jzuu3qygoMBVIpH49WQt2pgBAAAAAAAegWXLlnmIRKLQoKAgsY+PT+vs2bOR6D5GaGMGAAAAAAB4BHbu3FnR8VxKSoq7XC7ntz83efJkbWpqatUfF9nTCW3MAAAAAADQJbQxw+OCNmYAAAAAAACAdpDsAgAAAAAAQJ+DZBcAAAAAAAD6HCS7AAAAAAAA0Ocg2QUAAAAAgCcel8sN7+7cvXv3Ol+8eNG+/bmVK1cK/f39xYGBgeLg4ODQLVu2CHoSR3NzMyM6OjpIJBKFfvzxxy4zZ8707XgteDLg00MAAAAAANCn5OTkOBuNRl1EREQLEdH69ev7f/fdd/0uXrxYxOfzzRqNhvX5558792Tvs2fPcg0GA0OlUimJiBYsWHD3UcYOjw4+PQQAAAAAAF1q/+kh7QG1t6Gqifso97dxd9DzpwWVdzWHy+WG6/X6/Pbn1Gq17dy5c/00Gg1bIBAYMzMzS0tLS22mTZsW6OjoaOLxeKbs7OySmJiYoBMnTqjFYnFrx33lcjlv+fLl3iaTiSQSiT4zM7OMw+FYPD09h8yYMUPz9ddfOxmNRkZWVtZ1Nzc348iRI0V3795le3p6tmVnZ5ckJib6paWllY8ZM0b/4Ycfum7cuNHdzc3NMHDgwBZbW1tLZmbmzc7uJzY21o/H45kKCgocamtrbf75z39WJCYm3tXpdMyJEycO0ul0LKPRyFi5cuWt2bNn1xUXF9tOmjQpMCoqqjEvL89RKBS2ff3119ccHR37dEKHTw8BAAAAAMBTRyqV+syaNUujVquVM2fO1MhkMu/x48c3xcTE1K1du7ZCpVIpPTw8DE1NTazOEl29Xs9YuHChf1ZWVolarVYajUbasGFDf+u4q6urUalUFs2bN6923bp1Qk9PT+O2bdvKIiMjG1UqlbL9nqWlpTZpaWkDzp8/X3T69Gn11atXH9jaXF1dbZOXl6eSy+VXV61a5UlExOVyzUePHr2mVCqLcnNz1W+//baX2WwmIqKbN2/aL1q0qObatWsKJycnU2ZmpssjeZB9FNqYAQAAAACg2x5Ugf0j5efnOxw7dqyEiEgmk2lXr17t1XGOxWIhBoPR6fqCggJ7Ly+v1rCwsFYiooSEBM3WrVvdiKiGiGjWrFl3iYiioqL0hw4d6jKxPH36tMPw4cMbhEKhiYho6tSpd9VqdZcJ71//+tc6FotFERERLRqNxoaIyGw2MxYvXux17tw5RyaTSTU1NbYVFRVsIiJPT8/W6OjoZiKi8PBwfWlpqV1X+z/tUNkFAAAAAIA+i8/nmzkcjlmpVNp2HHvQTzrt7e0tRERsNttiNBo7z5i7uVdX+7dfn56eztdoNOzCwsIilUqlFAgEhubmZiYRka2t7b35LBbrgTE97ZDsAgAAAABArxQeHt6UkZHhQvRzkhgZGdlIROTo6Giqr6+/l+ssXrz4tlQq9dVqtUwiIq1Wy0xLS3MdOnRoS2Vlpe2VK1fsiIgyMzMFo0ePbuhJLKNHj246f/48r7a2lmUwGEgul/eoxVin07FcXV0NdnZ2lsOHD/Nu3br1qyQdugdtzAAAAAAA8MRraWlhCoXCMOuxTCar3r59+825c+f6bdy40d36gioiori4OK1MJvPbsWOH8MCBAyVvvfVWbWNjI3PYsGGhNjY2FjabbXnjjTequFyuZceOHaXTp08PsL6gKjk5ubYn8fn7+xuWLFly+5lnnglxc3MzBAUFNTs5OZkedp/58+drJ02aNGjw4MEhYrFY7+/v39KTeABvYwYAAAAAgAdo/zZmuD+dTsd0cnIyGwwGevHFFwclJCTciY+Pr3vccfVmeBszAAAAAADAY7Zs2TIPkUgUGhQUJPbx8WmdPXs2Et3HCG3MAAAAAAAAj8DOnTsrOp5LSUlxl8vl/PbnJk+erE1NTa364yJ7OqGNGQAAAAAAuoQ2Znhc0MYMAAAAAAAA0A6SXQAAAAAAAOhzkOwCAAAAAABAn4NkFwAAAAAAAPocJLsAAAAAAPDE43K54d2du3fvXueLFy/aW49PnjzpEBYWJhKJRKEDBw4UJyUleRARJSUleaxcuVL4e8TblfbXjY2N9fP09BwiEolCg4ODQ+VyOc86769//au/n5/f4MDAQPH06dP9WltbGX90rL0Zkl0AAAAAAOhTcnJynH/66SeO9fiVV17xT09PL1OpVEq1Wq2Ii4vTPs74Olq7dm2FSqVSpqWllS9atMjXej4uLk57/fr1K8XFxYqWlhbGRx995Po44+xt8J1dAAAAAADotpycHO+amhruo9zTzc1NP2XKlPKHXadWq23nzp3rp9Fo2AKBwJiZmVlaWlpqc+LECedz587xUlNTB2RnZ5dotVq2j4+PgYiIzWZTREREi3WPoqIiTlRUVPCtW7dspVJp9YoVK2qIiGJiYgJu375t29raypRKpdXJycl3iH6uMMfFxdWeOXOG5+TkZMrOzr7u4eFhVCgUdlKp1Eer1bLt7e3NGRkZZeHh4S2dR965cePGNdbU1NhYj2fOnKmz/jsyMrKpoqLC9mGf0dMMlV0AAAAAAOiVpFKpz6xZszRqtVo5c+ZMjUwm8x4/fnxTTExMnbVaKhaLW1999dXqkJCQwePHjw/YsGGDq16vv9cOfO3aNfvc3Fz1jz/+WJSWluZhbRX+7LPPShUKRdHly5eV6enpwqqqKhYRUXNzM3PYsGF6pVJZNGrUqIbly5d7EBHNnz/fd9u2bTcVCkXRhg0bKmQymc/D3k92drZTTExMXcfzra2tjKysLMGf//xnXWfroHOo7AIAAAAAQLf1pAL7e8nPz3c4duxYCRGRTCbTrl692quzeWlpabcTExO1R44c6ffll18K9u/fL7hw4UIxEdGECRPqOByOhcPhGPl8vqGiooIdEBBgSE1NFR49etSZiKiqqspGoVDYu7u7NzGZTJo/f76WiGjevHmal156aZBOp2Pm5+c7Tp8+PcB6zba2tm7/vnbFihVe77zzjpdWq2Xn5uYWdRyfO3euz4gRIxonTpzY+HBP6OmGZBcAAAAAAPo8sVjcKhaLa5OSkmoFAsFQa6XWzs7OYp3DYrHIaDQyjhw5wsvNzeXl5eWpeDyeOSoqKri5ubnTrlgGg0Emk4l4PJ5RpVIpexLb2rVrK+Lj4+++9957bgkJCf4KheJewrt06dIBd+7cYX/99dclPdn7aYY2ZgAAAAAA6JXCw8ObMjIyXIiI0tPT+ZGRkY1ERI6Ojqb6+vp7uc4XX3zhZDabiYiosLDQnsViWVxdXU3327euro7l5ORk4vF45vz8fPuCggIH65jZbKbdu3e7EBHt2bNHEBUV1cDn881eXl5tu3btcrHO+eGHHzj3278zLBaLVqxYUWM2mxnZ2dn9iIj+/e9/u3733XdOOTk511ks1sNsB4RkFwAAAAAAeoGWlhamUCgMs/69++67wu3bt9/cu3eva1BQUOi+ffsE27ZtKyf6+S3GmzZtcg8JCQlVKBR2n376qWDgwIGDRSJRaHx8vH9GRsYNNvv+Ta6xsbE6o9HICAoKCn377bc9JBJJk3WMw+GYFQoFRywWh5w6dYr3/vvv3yYi2rdv3/Xdu3e7BgcHhwYGBoqzs7OdH/YemUwmpaSk3EpLS3MnInrrrbd879y5w46MjAwRiUShycnJAx76wT3FGBaL5cGzAAAAAADgqVVQUFAqkUjuPO44ngRcLjdcr9fnP+44nhYFBQWuEonErydrUdkFAAAAAACAPgcvqAIAAAAAAOimh6nqpqSkuMvlcn77c5MnT9ampqZWPfrIoCO0MQMAAAAAQJfQxgyPC9qYAQAAAAAAANpBsgsAAAAAAAB9DpJdAAAAAAAA6HOQ7AIAAAAAAECfg2QXAAAAAACeeFwuN7y7c/fu3et88eJFe+vxyZMnHcLCwkQikSh04MCB4qSkJA8ioqSkJI+VK1cKf494u9L+urGxsX6enp5DRCJRaHBwcKhcLudZ582YMcM3ODg4NCgoKHTixIkDdTod8reHgIcFAAAAAAB9Sk5OjvNPP/3EsR6/8sor/unp6WUqlUqpVqsVcXFx2scZX0dr166tUKlUyrS0tPJFixb5Ws/v2LGjvLi4WKlWq5VeXl5tqampbo8zzt4G39kFAAAAAIBuUxaleDc1qrmPck8HxyB9aEhq+cOuU6vVtnPnzvXTaDRsgUBgzMzMLC0tLbU5ceKE87lz53ipqakDsrOzS7RaLdvHx8dARMRmsykiIqLFukdRUREnKioq+NatW7ZSqbR6xYoVNUREMTExAbdv37ZtbW1lSqXS6uTk5DtEP1eY4+Lias+cOcNzcnIyZWdnX/fw8DAqFAo7qVTqo9Vq2fb29uaMjIyy8PDwls4j79y4ceMaa2pqbKzHfD7fTERkNpupubmZyWAwHvYRPdVQ2QUAAAAAgF5JKpX6zJo1S6NWq5UzZ87UyGQy7/HjxzfFxMTUWaulYrG49dVXX60OCQkZPH78+IANGza46vX6e1njtWvX7HNzc9U//vhjUVpamkdrayuDiOizzz4rVSgURZcvX1amp6cLq6qqWEREzc3NzGHDhumVSmXRqFGjGpYvX+5BRDR//nzfbdu23VQoFEUbNmyokMlkPg97P9nZ2U4xMTF17c9NmzbNr3///pJr167ZL1++vOa3PbGnCyq7AAAAAADQbT2pwP5e8vPzHY4dO1ZCRCSTybSrV6/26mxeWlra7cTERO2RI0f6ffnll4L9+/cLLly4UExENGHChDoOh2PhcDhGPp9vqKioYAcEBBhSU1OFR48edSYiqqqqslEoFPbu7u5NTCaT5s+fryUimjdvnuall14apNPpmPn5+Y7Tp08PsF6zra2t22XYFStWeL3zzjteWq2WnZubW9R+7MCBA6VGo5ESEhJ8du3a5fLmm29qHv5JPZ1Q2QUAAAAAgD5PLBa3pqSk1J49e7ZYpVJxrJVaOzs7i3UOi8Uio9HIOHLkCC83N5eXl5enKi4uVoaEhDQ3Nzd3mjsxGAwymUzE4/GMKpVKaf27fv26oruxrV27tqKsrKxw+fLllQkJCf4dx9lsNr388svanJwcl57c+9MKyS4AAAAAAPRK4eHhTRkZGS5EROnp6fzIyMhGIiJHR0dTfX39vVzniy++cDKbzUREVFhYaM9isSyurq6m++1bV1fHcnJyMvF4PHN+fr59QUGBg3XMbDbT7t27XYiI9uzZI4iKimrg8/lmLy+vtl27drlY5/zwww+c++3fGRaLRStWrKgxm82M7Ozsfmazma5cuWJn3U8ulzsHBgY+1G+An3ZoYwYAAAAAgCdeS0sLUygUhlmPZTJZ9fbt22/OnTvXb+PGje7WZpRyvQAAIABJREFUF1QREcXFxWllMpnfjh07hAcOHCj59NNPBcuXL/e2t7c3s9lsS0ZGxg02+/6pUGxsrG7nzp39g4KCQgMCAlokEkmTdYzD4ZgVCgVHLBa783g808GDB68TEe3bt+/6ggULfFNTUwcYjUbG1KlTtSNHjmx+mHtkMpmUkpJyKy0tzX3KlCn18fHx/o2NjUyLxcIICQnR79mzp+xhn9vTjGGxWB48CwAAAAAAnloFBQWlEonkzuOO40nA5XLD9Xp9/uOO42lRUFDgKpFI/HqyFm3MAAAAAAAA0OegjRkAAAAAAKCbHqaqm5KS4i6Xy/ntz02ePFmbmppa9egjg47QxgwAAAAAAF1CGzM8LmhjBgAAAAAAAGgHyS4AAAAAAAD0OUh2AQAAAAAAoM9BsgsAAAAAAAB9DpJdAAAAAAB44nG53PDuzt27d6/zxYsX7a3HJ0+edAgLCxOJRKLQgQMHipOSkjyIiJKSkjxWrlwp/D3i7UpSUpKHm5tbmEgkCg0ICBCnp6fzH7wKHhY+PQQAAAAAAH1KTk6Os9Fo1EVERLQQEb3yyiv++/btKxk5cmSz0WikgoIC+wft8XuTSqXVa9asqS4sLLQbOXJkaEJCwl07Ozt8KucRQrILAAAAAADdtrjopreqqYX7KPcUOdjrPwrxKX/YdWq12nbu3Ll+Go2GLRAIjJmZmaWlpaU2J06ccD537hwvNTV1QHZ2dolWq2X7+PgYiIjYbDZZk2AioqKiIk5UVFTwrVu3bKVSafWKFStqiIhiYmICbt++bdva2sqUSqXVycnJd4h+rjDHxcXVnjlzhufk5GTKzs6+7uHhYVQoFHZSqdRHq9Wy7e3tzRkZGWXh4eEtnUf+/4YMGdJqb29vvnPnDsvT0/O++ygUCrtZs2b5m0wmRkxMjG7nzp3Ch/nm79MIbcwAAAAAANArSaVSn1mzZmnUarVy5syZGplM5j1+/PimmJiYurVr11aoVCqlWCxuffXVV6tDQkIGjx8/PmDDhg2uer2eYd3j2rVr9rm5ueoff/yxKC0tzaO1tZVBRPTZZ5+VKhSKosuXLyvT09OFVVVVLCKi5uZm5rBhw/RKpbJo1KhRDcuXL/cgIpo/f77vtm3bbioUiqINGzZUyGQyn+7cw/fff8/19fVt8fT0NHa1z+uvv+792muv1Vy5cqXIw8PD8KifZV+Eyi4AAAAAAHRbTyqwv5f8/HyHY8eOlRARyWQy7erVq706m5eWlnY7MTFRe+TIkX5ffvmlYP/+/YILFy4UExFNmDChjsPhWDgcjpHP5xsqKirYAQEBhtTUVOHRo0ediYiqqqpsFAqFvbu7exOTyaT58+driYjmzZuneemllwbpdDpmfn6+4/Tp0wOs12xra2N0FovVjh07hJmZmf0rKipss7OzrxIRdbVPfn6+4zfffHONiGj+/Pmad999t9N7hf+HZBcAAAAAAPo8sVjcKhaLa5OSkmoFAsFQa6W2/e9kWSwWGY1GxpEjR3i5ubm8vLw8FY/HM0dFRQU3Nzd32hXLYDDIZDIRj8czqlQqZXfjsf5m95NPPnFesGCB//jx4wt7sg/cH9qYAQAAAACgVwoPD2/KyMhwISJKT0/nR0ZGNhIROTo6murr6+/lOl988YWT2WwmIqLCwkJ7FotlcXV1Nd1v37q6OpaTk5OJx+OZ8/Pz7QsKChysY2azmXbv3u1CRLRnzx5BVFRUA5/PN3t5ebXt2rXLxTrnhx9+4HTnHubOnVs3ZMiQpq1btwq62mfo0KGNe/bscSEi2rVrF97e3A1IdgEAAAAA4InX0tLCFAqFYda/d999V7h9+/abe/fudQ0KCgrdt2+fYNu2beVERHFxcdpNmza5h4SEhCoUCrtPP/1UMHDgwMEikSg0Pj7ePyMj4wabff8m19jYWJ3RaGQEBQWFvv322x4SiaTJOsbhcMwKhYIjFotDTp06xXv//fdvExHt27fv+u7du12Dg4NDAwMDxdnZ2c7dvbd333339tatW91NJtN999m8eXP55s2bhUOGDAm5ffu2jaOj432TdfgZw2LB260BAAAAAOD+CgoKSiUSyZ3HHceTgMvlhj+OtyA3NDQwHRwczEwmk3bu3OmSlZXFP3nyZMkfHccfraCgwFUikfj1ZC1+swsAAAAAAPCEO3PmDPfNN9/0sVgs1K9fP9OePXtKH3dMTzokuwAAAAAAAN30MFXdlJQUd7lc/ovf106ePFmbmppa9bDXnThxYmNxcTFeXPUQ0MYMAAAAAABdQhszPC6/pY0ZL6gCAAAAAACAPgfJLgAAAAAAAPQ5SHYBAAAAAACgz0GyCwAAAAAAAH0Okl0AAAAAAHjicbnc8O7O3bt3r/PFixftrccnT550CAsLE4lEotCBAweKk5KSPIiIkpKSPFauXCn8PeKFxw+fHgIAAAAAgD4lJyfH2Wg06iIiIlqIiF555RX/ffv2lYwcObLZaDRSQUGB/YP2gN4PyS4AAAAAAHTbsgMF3uqqBu6j3DPInaffME1S/rDr1Gq17dy5c/00Gg1bIBAYMzMzS0tLS21OnDjhfO7cOV5qauqA7OzsEq1Wy/bx8TEQEbHZbLImwURERUVFnKioqOBbt27ZSqXS6hUrVtQQEcXExATcvn3btrW1lSmVSquTk5PvEP1cYY6Li6s9c+YMz8nJyZSdnX3dw8PDqFAo7KRSqY9Wq2Xb29ubMzIyysLDw1s6izs2NtaPx+OZCgoKHGpra23++c9/ViQmJt7V6XTMiRMnDtLpdCyj0chYuXLlrdmzZ9cVFxfbTpo0KTAqKqoxLy/PUSgUtn399dfXHB0d8R3ZLqCNGQAAAAAAeiWpVOoza9YsjVqtVs6cOVMjk8m8x48f3xQTE1O3du3aCpVKpRSLxa2vvvpqdUhIyODx48cHbNiwwVWv1zOse1y7ds0+NzdX/eOPPxalpaV5tLa2MoiIPvvss1KFQlF0+fJlZXp6urCqqopFRNTc3MwcNmyYXqlUFo0aNaph+fLlHkRE8+fP9922bdtNhUJRtGHDhgqZTObTVezV1dU2eXl5KrlcfnXVqlWeRERcLtd89OjRa0qlsig3N1f99ttve5nNZiIiunnzpv2iRYtqrl27pnBycjJlZma6/E6Ptc9AZRcAAAAAALqtJxXY30t+fr7DsWPHSoiIZDKZdvXq1V6dzUtLS7udmJioPXLkSL8vv/xSsH//fsGFCxeKiYgmTJhQx+FwLBwOx8jn8w0VFRXsgIAAQ2pqqvDo0aPORERVVVU2CoXC3t3dvYnJZNL8+fO1RETz5s3TvPTSS4N0Oh0zPz/fcfr06QHWa7a1tTE6i8Xqr3/9ax2LxaKIiIgWjUZjQ0RkNpsZixcv9jp37pwjk8mkmpoa24qKCjYRkaenZ2t0dHQzEVF4eLi+tLTU7rc/wb4NyS4AAAAAAPR5YrG4VSwW1yYlJdUKBIKh1kqtnZ3dvVZgFotFRqORceTIEV5ubi4vLy9PxePxzFFRUcHNzc2ddsUyGAwymUzE4/GMKpVK2d147O3t713XYvn5n+np6XyNRsMuLCwssrOzs3h6eg6xXtfW1rZ9nJb7xQP/Dw8IAAAAAAB6pfDw8KaMjAwXop8TxcjIyEYiIkdHR1N9ff29XOeLL75wsrYDFxYW2rNYLIurq6vpfvvW1dWxnJycTDwez5yfn29fUFDgYB0zm820e/duFyKiPXv2CKKiohr4fL7Zy8urbdeuXS7WOT/88APnYe9Hp9OxXF1dDXZ2dpbDhw/zbt26Zfuwe8D/Q2UXAAAAAACeeC0tLUyhUBhmPZbJZNXbt2+/OXfuXL+NGze6W19QRUQUFxenlclkfjt27BAeOHCg5NNPPxUsX77c297e3sxmsy0ZGRk32Oz7p0KxsbG6nTt39g8KCgoNCAhokUgkTdYxDodjVigUHLFY7M7j8UwHDx68TkS0b9++6wsWLPBNTU0dYDQaGVOnTtWOHDmy+WHucf78+dpJkyYNGjx4cIhYLNb7+/t3+oIr6B6GtWQOAAAAAADQmYKCglKJRHLnccfxJOByueF6vT7/ccfxtCgoKHCVSCR+PVmLNmYAAAAAAADoc9DGDAAAAAAA0E0PU9VNSUlxl8vl/PbnJk+erE1NTa169JFBR2hjBgAAAACALqGNGR4XtDEDAAAAAAAAtINkFwAAAAAAAPocJLsAAAAAAADQ5yDZBQAAAAAAgD4HyS4AAAAAADzxGAxGxJQpU/ytxwaDgVxcXCTPP//8ICKi8vJy9vPPPz8oODg4NCAgQDx27NhBREQmk4kSEhK8AwMDxUFBQaGDBw8OUalUtl1dKzY21m/37t0unY3997//5UZGRgb7+fkN9vf3F8+cOdO3oaGBuWnTJkF8fLzPo7znjkaPHh0YHBwcOmjQIPGsWbN8jEYjERFVV1ezoqOjA319fQdHR0cH1tbWsjpbv3nzZoGvr+9gX1/fwZs3bxb8nrE+CZDsAgAAAADAE4/D4ZiLi4s5jY2NDCKir776qp9QKDRYx1NSUjxfeOGF+uLiYmVJSYli/fr1lUREGRkZ/KqqKhuVSqVQq9VKuVx+TSAQmHoSQ3l5OTsuLi5g3bp1FaWlpVdKSkoUEydOrK+rq/tD8iq5XF5SXFysVKvVCo1GY7Nr1y4XIqJVq1YNeO655xrKysquPPfccw0rV65077i2urqalZqa6nHhwoWivLy8otTUVI/7JcV9Bb6zCwAAAAAA3Zfzd2+qUXIf6Z5uoXqasrX8QdPGjRun279/v3NiYuLdffv28WNjY7Vnz551JCKqqqqymTBhgs46d/jw4c1ERLdv37YRCoUGFuvnvC4gIOBegszlcsOt383dvXu3y5EjR5yys7NLiYi+/fZb3pYtW9w0Go3N+++/X/7yyy/rPvjgA7cZM2ZoYmJimoiImEwmJSYm3u0Y5+eff+60bt26AQaDgeni4mLMysq67u3tbTx69Kjj0qVLfYiIGAwGnT17VlVfX8+KjY0d2NjYyDKZTIzNmzeXTZw4sbGz++fz+WYiIoPBwDAYDAwGg0FERMePH3fOzc0tJiJauHChZuzYscFEVNl+bU5OjtOYMWP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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "alphas_elastic = np.logspace(-10, 6, 100)\n", - "coef_elastic = []\n", - "for i in alphas_elastic:\n", - " elastic = linear_model.ElasticNet(l1_ratio =0.6)\n", - " elastic.set_params(alpha = i)\n", - " elastic.fit(x_onehot, y_log)\n", - " coef_elastic.append(elastic.coef_)\n", - "\n", - "df_coef = pd.DataFrame(coef_elastic, index=alphas_elastic, columns=x_onehot.columns)\n", - "title = 'ElasticNet coefficients as a function of the regularization'\n", - "df_coef.plot(logx=True, title=title)\n", - "plt.xlabel('alpha')\n", - "plt.ylabel('coefficients')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### test prediction" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "best_elas_test_y = para_search_elas.best_estimator_.predict(test_onehot)\n", - "best_elas_test_y = np.expm1(best_elas_test_y)\n", - "sub = pd.DataFrame()\n", - "sub['SalePrice'] = best_elas_test_y\n", - "sub = sub.set_index(np.arange(1461, 2920))\n", - "sub.to_csv('(13) elas_log(y)_onehot_submission.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Kelly/Kaggle_house_price/Regularization Model.ipynb b/Kelly/Kaggle_house_price/Regularization Model.ipynb deleted file mode 100644 index ce0f996..0000000 --- a/Kelly/Kaggle_house_price/Regularization Model.ipynb +++ /dev/null @@ -1,5349 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Multiple linear regression " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "## import data\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "train = pd.read_csv(\"train.csv\").set_index(\"Id\")\n", - "test = pd.read_csv(\"test.csv\").set_index(\"Id\")\n", - "combine = pd.concat([train, test])\n", - "\n", - "# process data before model fitting\n", - "from preprocess1 import impute\n", - "onehot, encodedDic = impute(combine, True) # process data and onehot encode\n", - "\n", - "# seperate onehot data into train and test\n", - "train_onehot = onehot.iloc[0:1460,]\n", - "test_onehot = onehot.iloc[1460:2919,].drop('SalePrice', axis = 1) # salesprice col were all NA \n", - "\n", - "# split train data frame into x var and y var for model testing\n", - "x_onehot = train_onehot.drop('SalePrice', axis=1)\n", - "y_log = np.log(train_onehot.SalePrice) # convert y to normal distribution for regression models" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### original y vs log(y) distirbution" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([ 5., 11., 13., 61., 58., 126., 165., 180., 122., 130., 121.,\n", - " 78., 61., 64., 49., 36., 36., 25., 13., 25., 16., 11.,\n", - " 4., 11., 9., 5., 4., 4., 4., 2., 1., 1., 1.,\n", - " 0., 1., 0., 2., 0., 1., 0., 2., 0., 0., 0.,\n", - " 0., 0., 0., 0., 0., 2.]),\n", - " array([ 34900., 49302., 63704., 78106., 92508., 106910., 121312.,\n", - " 135714., 150116., 164518., 178920., 193322., 207724., 222126.,\n", - " 236528., 250930., 265332., 279734., 294136., 308538., 322940.,\n", - " 337342., 351744., 366146., 380548., 394950., 409352., 423754.,\n", - " 438156., 452558., 466960., 481362., 495764., 510166., 524568.,\n", - " 538970., 553372., 567774., 582176., 596578., 610980., 625382.,\n", - " 639784., 654186., 668588., 682990., 697392., 711794., 726196.,\n", - " 740598., 755000.]),\n", - " )" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from matplotlib import pyplot as plt\n", - "plt.hist(train_onehot.SalePrice, bins=50) # original y" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([ 2., 2., 1., 0., 0., 0., 2., 3., 4., 3., 5.,\n", - " 1., 5., 21., 22., 23., 18., 29., 58., 56., 65., 100.,\n", - " 122., 93., 90., 82., 108., 91., 64., 55., 58., 51., 46.,\n", - " 42., 23., 29., 22., 13., 13., 13., 7., 5., 4., 1.,\n", - " 2., 2., 2., 0., 0., 2.]),\n", - " array([10.46024211, 10.52172673, 10.58321134, 10.64469596, 10.70618058,\n", - " 10.7676652 , 10.82914982, 10.89063444, 10.95211906, 11.01360367,\n", - " 11.07508829, 11.13657291, 11.19805753, 11.25954215, 11.32102677,\n", - " 11.38251138, 11.443996 , 11.50548062, 11.56696524, 11.62844986,\n", - " 11.68993448, 11.75141909, 11.81290371, 11.87438833, 11.93587295,\n", - " 11.99735757, 12.05884219, 12.12032681, 12.18181142, 12.24329604,\n", - " 12.30478066, 12.36626528, 12.4277499 , 12.48923452, 12.55071913,\n", - " 12.61220375, 12.67368837, 12.73517299, 12.79665761, 12.85814223,\n", - " 12.91962684, 12.98111146, 13.04259608, 13.1040807 , 13.16556532,\n", - " 13.22704994, 13.28853455, 13.35001917, 13.41150379, 13.47298841,\n", - " 13.53447303]),\n", - " )" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist(y_log, bins=50) # log(y)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Ridge regularization" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find best lambda" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Id\n", - "1 61\n", - "2 0\n", - "3 42\n", - "4 307\n", - "5 84\n", - "6 350\n", - "7 57\n", - "8 432\n", - "9 205\n", - "10 4\n", - "11 0\n", - "12 21\n", - "13 176\n", - "14 33\n", - "15 389\n", - "16 112\n", - "17 0\n", - "18 0\n", - "19 102\n", - "20 0\n", - "21 154\n", - "22 205\n", - "23 159\n", - "24 110\n", - "25 90\n", - "26 56\n", - "27 32\n", - "28 50\n", - "29 258\n", - "30 87\n", - " ... \n", - "1431 40\n", - "1432 60\n", - "1433 0\n", - "1434 0\n", - "1435 41\n", - "1436 36\n", - "1437 0\n", - "1438 370\n", - 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\n", 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from matplotlib import pyplot as plt\n", - "plt.scatter(train_onehot.SalePrice,train_onehot.TotalProchSF)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'alpha': 0.49770235643321137}\n", - "Lowest RMSE found: 0.14572897725974193\n" - ] - } - ], - "source": [ - "from sklearn.model_selection import GridSearchCV # search for the best lambda\n", - "from sklearn import linear_model\n", - "\n", - "ridge = linear_model.Ridge(normalize=True) # create a ridge regression instance\n", - "\n", - "# find the best alpha (lambda) for ridge\n", - "grid_param = [{'alpha': np.logspace(-4, 2, 100)}]\n", - "para_search_ridge = GridSearchCV(estimator=ridge, param_grid=grid_param, scoring='neg_mean_squared_error', cv=5, return_train_score=True)\n", - "para_search_ridge.fit(x_onehot, y_log)\n", - "\n", - "print(para_search_ridge.best_params_)\n", - "print(\"Lowest RMSE found: \", np.sqrt(np.abs(para_search_ridge.best_score_)))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### RMSE for all train data using best ridge" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.1212332775621472\n" - ] - } - ], - "source": [ - "# fit best ridge equation to all train data \n", - "best_ridge_y = para_search_ridge.best_estimator_.predict(x_onehot)\n", - "print(\"RMSE: \", np.sqrt(np.mean((y_log-best_ridge_y)**2)))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### RMSE across different grid search lambda" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", - " DeprecationWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", - " DeprecationWarning)\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "error = pd.DataFrame(para_search_ridge.grid_scores_)['mean_validation_score']\n", - "error = np.sqrt(np.abs(error))\n", - "alpha = pd.DataFrame(para_search_ridge.grid_scores_)['parameters'].apply(lambda x: x.get('alpha'))\n", - "plt.scatter(alpha, error)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Variable importance for best lambda" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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l57gzdAqzInKK6G93XHsB0B1fKMbQML3Idnh1t83juok5fUMisURoL/b7jk6bLwzBhcBk3J6dsRSwnRWKxwDUEvdeqW6AlflMmfbi7KK1xPxXmnBNnn8z+xdwuaSrgDcrmJ3KjVFKwyDgHDNr4DsoMfaE8AVlW4CUcTWR6KC0B1MSZvYO7kw9Itf8APCz7EBSnxK3PoGbmcBDUqvlwNy/E+Lz+Fwfh1DYXVTLGGBvScvLy23uA4xton0fSd1iJ/TdSp1L+k7OT7IBviC9R+OaD7U+x/3A4XL9A5LWkrR68UWSNsbrMpQzdSUSiQ5Ce9kxgKeT/lnu+FjgUknP4PMcgzta8xwH3CDpROBePJdQNSwn6Ul8YTw4N941kk7CE+MdVu7mUpjZZLnaOYv1v9rMpoA7fsu0j8LTT7yMLxaVOBS4QNJHuNP+EDNbKOlu4FZJewE/r/U5zOwBSZsAE2LdmQd8H6/7nKUXAd9Z/DDGbPL7SCQS9Uub6hhaiqTlcTu4SToIONjM9mrreWVIOg33ByzES2wejfsAhmW+hVYYY2/g+ch9VO6a4cDO+ML5OfBTM5tQ7vrWJOkYEq1Je9MxtGefh1qgY2hPO4bm0Be4JEws71G+ItkSJ5y1ewBbRz2H1YBl8YysJWszSOpS5OSuhr3xnFBN1YU4ycxujSikK4HeNY6TSCQ6Ce3Cx9BczGysmW1pZr3NbICZvdDWc8qxBvBWRC8RSuv9yNVmAJA0T67BeBLYTtLN0faRpLmhfThMrtm4T9LTksZK2ljS9sCewNAIK12/inmNATLtxpGhX5gm14wsL2llSbPlRYKItlciJLfRHEoNkHQMiUR9U9cLQzvnAeB/JD0v6TJJO5vZxbjeYpdczqQVgBlmti3wJLA2sK6ZLY8nzJtoZtfiQsCfm1lfXGx2WaTBGI3vBvpE1FJTfJeCKO12M+tvZlsC/wccYWbv43mjds5df7+ZfVpqDqUGSDqGRKK+qXdTUrvFzOaFuG0nPPPpKEmnlrh0IXBbfN4IT3fxYDh4uwCvRcTQ9sAtOcfvcjVOaaikX+MO6Sz6a3NJZwNfwPUL90f7KDxi6xE8wumyVppDItEi2rNNvyORFobFSPgLHgUelTQd+GGJyxbk/AoCZprZdvkLJK0EvJeJzZrJSWZ2a1HbcGDvyAw7iIJgbjRwjqRVcD/Ow/jOpqVzSCQSdUAyJS0mJG0kaYNcU1YXoVh3kOc5oEc4rgm7/mZmNhd4SdL+0S5JW8Y9lfprihXxHckyuOYB8N0OHl57EXBP5GaqNIdEItGBSAvD4qM7cJ2kWaHF2BQYQqE2wyPFN5jZJ7iD+g/yOhJTKWROPQQ4Itpn4jWnwUuNniRpSpXO5zy/wf0aD+JFefKMwvUMo3Jt5eaQSCQ6EMmUVIS8BsF0/Lt5CTjUzN6rtR8ze1rSOODbuO3+auBW3J5vwD/j0h0lfdvM/hb3TcWryBXzQ+AfxfUazGwcFWpLS9oIzxP1a0ln4fUUjgrT0WZmVrJiTpidVNT2EvDN6Hcg7oBOJJYYScewZEgLQ2PyieOuw2tG/66ZfR0N9Agdw/14FtS7ou8t4po+QD/gby2bdlkuLjNuIpFIlCSZkiozAU+JndnUh4auYLqkA5toH407bJ+MtjWAOVnHZjZd0rLAmcCBoUM4UNI/JfWIPpaS9IKKChmV0xNIujT6WfSH7yYajJvras3o55+Szsv1v7ukCZImS7pFhTxK35TXt3icQgbXRiQdQyJR36QdQxnklcl2Bf4STfviv+63xOsxT5I0BvcBNGo3sz0lzcvtPpYHHpY0Htc4XGtm70k6HS81+rO4bmPcln8hnnp8mpm9pYb5iYYBx5jZPyVti+sJvmZmPy3xHIeVGjdO98HrVnyM13r4M576/Nd4/YkPJZ0CnBALx1V4nekXaOh7aICZDYs50q9fv/rNuZJIdFLSwtCYLHFcL+Bp3DELXm9hZISWviHpMaB/hfYG9RXM7NowJ30Td9oeXSaq5xrgLnxhOJyGqcipVU/QxLgPhaANSbOAdXAfyKbAuOh/WXzntDHwkpn9M66/ATiq3LiJxOKgo9r02xvJlNSYzMewDv5SzH6FN6feQgPM7FUzuyYS/X2Gi9mKr3kFX2C+hld9+3vRJUsReoLc3ybNHLdcXYkHc31vamaZIC79+k8kOgFpYShD/JI+Fhgccf5jcF9Al/ABDMBj/cu1NyDs88vE5y8DqwL/obQO4Wo80d7NxUn1atUTVBi3HE8AO6hQC3t5SRvi4azr5kJiDy7XQSKRqG/SwlCBqJswDU8LcQfwTBw/DJxsZq9XaC9md2BGaADux5XIr+NpJzbNnM9x7WhcB3FtiX6gNj1BuXHLPfObwCBgZOgvngA2NrMFuOno3nA+v1xhzEQiUcfUdT2GtkIl6iyY2ZNlrh2Oq4eL01Fk5y8FdsDNVuvi6uduwOdNmYhaiqTv4FFR3fAfCXeZ2SmtOUaqx5BoTdqDjqFe/BzqxPUYljgqX2ehWWSRRJJ64XUVbgJ+TC5FxeIgzE8XAt8xs+clLY1nc00kEp2cZEqqnUZ1FszsVUmny2sbzJA0TGpc/1JSX0mPhf7gfklrFF9jZuea2TrAm5Im5u7dJDuWNEfSuZImSnpS0nqSToux35PXcvhQ0hUVnuMU4Cwzez7G/czMLo/+15X0iKRnJD0oae1ov0HSRZLGS3pR0j6lOk46hkSivkkLQ+00qrMQ7ZdEbYPNcdPMHvmbwgH8Z2C/qGdwDRUU1Wb2HLBAUhZBdBgNfQ7vmtk2eDW2883sd7i/4ZtRy2EzPJS2HJvj4biluAyvTd0buAXfWWSsjpu+9gbOKTP3VI8hkahjkimpRirUWfhA0snA8sAq+Ev67tytJWstNDHcX4DDQmS2Py5GyxgZ/44Azo3PuwEb5TYrX5TUzczm1/iY21JY2K4Hzsqdu9PcMfWMpLVq7DeRaBH1Yt+vd9LC0AxK1Fk4Gq+h3M/MXpE0BOhadFvJWgtNcAvwK2AcMKEomV+pqAEB20SW1qaYiddamFnDfKCh9qFqDUcikagfkimpRlS6zsJz8fmtUCbvV+LWkrUWKo1lZh/hIbCX0Dh0NQttPRhfOAD+QUGQh6RKRXXOwzOuZnqFLpJOiHNPAAfE5+/jWo1EItFJSDuG2ukO/FnSF3AV8Qt4fP97eLru2cCk4pvM7BNJ+wEXS1oZ/+4vpOlf7CPw1N0PFbUvH85ooyA2+ylweeRHWhrXSDTKnxTzmSJpMHCzpG7Rz11x+mfAXyT9EngD928kEolOQpvrGCQZ7jw9MY4HA93NbEiFe/YENjWzcytcMxAYbGZ7lDg3Gzf7vNXMOQ8B5hXXRmgppfoN/8VyZnZGaCK+jr/EN8df/k+ZWa8y/Y03s+1LtA+ngrYirhlELrlftD2Kf6dPSfqVmf2+qWdKOoZEa9LWOoZ68nG0RMfQHkxJHwP7qii1dCXMbHSlRWFxEvH+S2qsu3HV9Z9zzQvxdN6V7usCUGpRaEV+tRj7TiQSbUh7WBg+w1M0H198QlIPSbeFPmCSpB2ifZCkS+Lz+pKeiPNnSpqX66K7pFvlNQRGFGkLTgodwMScnX0dSQ9F/P5DknpG+3BJ58vLcf4h7t9U0qMRz39sbs4nhJ5ghqTjqmg/TdJzkv6BRy4twsy+G4ns3sk1Xwi8CeSfE0kDQ3twI27SQtI8ST+Sp9t4U9ICSe8DA3P3fTu+n8clXSzpnpL/lRqOdS6RhVbSiBLnk44hkahj2sPCAHApcEjY3vNchFcf6w98D08uV8xFwEVxzatF57YCjsPTSK+Hx99nzA0dwCUU4vQvAa6P+P0RePWzjA3xGgUnxvHGwDeAbYDfhjO5L26P3xb4KnCkpK2aaD8o5rkvnq67Kf4NPA4cWuLcNsBpZrao1KeZXY2nvZiK7zQ2wVNrI6krroP4lpntCBSLDrICQlnRn37R56lEFloza6TQTjqGRKK+aRfOZzObK+l6PJtpPuZ+N/yXeXa8kqTiTKTb4WIrgBuBvN1/opnNAVChxsLjcW5k7t8Lcn1llcn+ikfuZNxSlOn03lA/fyzpv8CXcEHZHWb2YYx5O653UJn2paL9o2hvUMOhAr/HE+0VG1wnmtdlLmYAhZoRr0p6ONo3Bl7M3TOShjUWRpXwMSQSbUY92fjrmXaxMAQXApNpGJa5FLBdsUBLjbNNlKNUvYEMK/OZMu0fVtF3c2o21Oz9N7MXYqE7oOhU8RybGifpEBKJRCPaiymJsKPfDByRa34AD50EysblP4GbmcDNMtVyYO7fCfF5fK6PQyjsLqplDLC3vIbBCsA+wNgm2veR1C12Qt+tYazfAYNrmNdBoVVYA1dsg9dYWE+ewA8K30k1fKqo85BIJDoW7WnHAPAncgsBblq6VF4XYGn8BXdM0T3HATdIOhE3rbxf5VjLSXoSXxwzHcCxwDWSTsIdvDXF75vZ5AgFzZLfXR01HajQPgq3/7+MLxbVjjVT0mRg6youvwOv1TwdeB54LPqYL+knwH2S3qJEgaEKDMPTYkwu5WdIJBL1S5vqGFpDwyBpedwRapIOwl/yF9ABNAwlrhkM/AiP5FoI/MnMrm/huN0j/5OAy/EF5CPgHDMb1ZK+IekYEq1LW+oY6s2/0RIdQ1vvGDINwznVvqjNbDTueM3oC1wSL7b3gMOBtVt9pixZDUOJsY/BxW3bhLN+ZQpO9/x1XYrLgTbBkZJ+iNeU+DfwhpntVMO8ah0vkUi0c9rax9BiDQMwFPgEuBMvnvNCdFG3GgZJl+bDRMPR/DvgJ1HzGTN738yui+tny+tBPA7sL+nI+M6mxXe4fPgXXpTzBUmfSxpgZhfgdacHAV8BeseY60vaVdIUSdMlXSNpuVLjlfhvl3QMiUQd09YLAyQNQyMNg5n9NDQCfcysDxHaamb/KvUFBgvMbEczuwm4PWpDbAn8H3BE/Kp/Pr6PHfFaDDvFy35tM5uIm6nGxpj/AYYDB5rZFvju8sdlxmtA0jEkEvVNW5uSkoahOg2DaDqsNe8P2FzS2biQrTtwf7SPxTUN6+JFdo7EHdGNkv7hO5iXLCq8AdfhCfmyhbTF/odEolbqzc5fr7SHHQP4y+YIGuYAyjQM2S/ntczsgxr67DAahjAffShpvQqX5ec4HPhZ/NI/g0JtiLH4orQN8Dd84RhI6bTaTWkcKmkmEolEHdMuFoakYahKw3AOHrq7EoCklSQdVebaFYHX5DqDfCjpk8D2wOdmtgAPkz2a0mGyzwK9Mh8MnoLjsSbmmEgkOgBtbkrKkTQMlbkcNwtNkvQp8Cn+nZXiN/gi8DKuXVgx5vixpFfwBZUY8+C4pvh5FsjrOtwS0ViTgCuamGMikegAtHk9hpagEhoGM9trCY29EH+hLg28BBxqDUtv1tLXULwYz99wE81vgQ2yCCtJxwPnA/3NrKwoICKehuX8FrOpoNmo9RnkCul7zGxzVah3kSfpGBKtyZLWMdSzT0N1Xo+hJfQFpsau4ifAiU1c35pk2UU3B96hTKW0KjkaD7U9KY6n09A0th8wq4p+jgOWr2Hc1nyGRCLRQajrhcHMxprZlmbW28wG5DQMS5oJwFoAoRMYGpqF6ZIObKJ9NO50fzLavg2sDvwq9ASzcFPQIkGApMtDJzBT0hnRdiywJvBIaC5a/RmqJekYEon6pj35GOoSebW0XYG/RNO+QB9gS2A13CcwBnf6Nmo3sz0lzQvtAJI2wYvwbA+cDuwFzKGhz+M0M3snxn5IUm8zu1jSCcAutab7qOEZqsLMhuHCRfr161e/tspEopOSFobm0y2nj3gaeDDad6RQ++ANSY/h4rVy7eX0Czfh5qRv4C/t/MJwQEQkLQ2sgYvWnlkCz9CcMRKJVqOebf71RF2bktqY+fErfx08z1Bmn2+OnqEUd+Mhov/O0mAASFoXT7e9a6i076WgU6iVWp8hkUh0AtLC0ELM7H081HVw6AbG4CUxu0jqgSuNJ1ZoL9fvfOAUPEdSnpXwyKX3JX0J+Fbu3AdEaOpieoZEItEJWOILgyST9Kfc8WB5yulK9+wp6dQmrhmoMoXs5UnfVmvWhP3+IfKU1yUJXcI03PRzB25ymQZ3nMXTAAAgAElEQVQ8DJxsZq+XageOqdQv8E3g7shnBLAycBcwBZgJXAOMy10/DPiHpIVhIloT14IsE8/RT1I+B1SeO/C8SpWeIZFIdAKWuI5B0gLgNTwm/y1VUYOhyn4HshhqMIS469e0QQ2GEMZ9Da+NcHksbk+ZWa8KffaioDXogvsN/mJmI5qYy2xaUKeiHEnHkGhNlqSOod79GfWmY2hxqm1JT8T5MyXNy3VRt6m2K3AhcLyKakFUE1IazuOJFMJQF+2qJK0q6QF5Wu0ryfkVJP0mvsMHJY3MdjXx3d8n6WlJYyVtXMX8E4lEndFWUUmX4mUhzytqz1JtPx4v6fuBTUpcc5GZjZQXr8mzFbAZnoJ7HJ5qO8t5NNfMtpH0A/xluweFVNvXSTocT7WdZWvNUm0vjF/2G+O1klcEnpN0OdCbQkpt4VqEx/AFt1x7lmp7aWAyHg1UiX/HMxyKO6QzmgwpldQ1voMekv4XT6nRI8xME4HHzexMSd8Bjop7+uH5p0rNcRhwjJn9U9K2wGX4jqYBETF1FEDPnj2beLxEItHeaBPnc0TZZKm28+yGV2Obiodxlku1fUt8vrHo3EQzm2Nmn+M5iHrlzuVTbW+X6yvr4694mGZGyVTbYWpplGrbzOYBWUrtcu07RftH8R1USrWd5/fASTT877UopNTM3sAT3GU1HdaP7/Bt4EUz2ziij/L1FrYBbgAws3uBd3P93mVm8yOb7d3gJUBxbcUt0feVeKhsI1I9hkSivmlLHcOF+K/Ra3NtWartfF0GGlqEKtJhUm03uMHshXgZH1DlGP8ysz6S1gAelbSneUnUauZSrt+lgPcyIV4i0RbUu92/XmizcNWUaruqVNt5fofrF/JjVwwpNbPXgFOBX5aZ+yEAkr4FfDHaHwe+K6lr7BK+E33NBV6StH/cI0lb1jD/RCJRJ7S1juFPuH0841igXziDZ9E4zTZ4orgTJE3ETRm1ptr+BQXH97HAYfIkfIfGuaoxs8l4UZyJeJrrq81sShPtWart22g61XZ+rJn4Diuj2pDSO4HlJe1U1H4GMEDSZGB33JeBmU3CTVzTcBPYUxS+40OAIyRNw8Nll0gm20QisWSpu7TbWgKpttUxUmobcIOZHRrHS+Nhwk9WSpUdu7T1zOz2+K7HAEfFopa/biAp7XYi0W5RC8JV6zFXUl/cQS3gPeDwxTBGlioCSVmt42IFcrUcDfSIIjlDKKTUPjvO15JS+wbgoyrH/RCv/dwtfDZfB/5TxX198Myup+OpNq4rXhQSibZicesYkg/DaWtTUs20Qart1kypDW7a2SvOr4ebab4AjJCn2X5L0keS/qOWp9T+O+EjwCu1ZZFZSFpB0jVyPcgUSXtJWhY4k4K/4bd49tbxcc14SdVoLxKJRB1TdwvDkkSFdNRZRE9eO7AbMDQif0q2m9meFIrhjIo+5gKvSNocf1mPAv4JHBK7lA3NbHmgJ7CzIqU2rs3Yxcx2qeERbgIOCj1Db9zfkXEa8LCZ9cf1GUOBZfBU36Nyc34WGGBmW8W531fxvaV6DIlEHZMWhtJk6ajfBlahRDrqIu1AJU1BKbKU2nvjTuQ8B4RDeAou1tu0uQ9hZs/gWo6DcR9Hnt2BU+M5H8XNRqXUaCvj2oUZwAUxp6bGTTqGRKKOqUcfw5JgfugAVgbuwX0MF9O6KbWH4nmP5mY6DRVSavc3s3fluZKam1I7YzTwR2AgsGrRnL9nZs/lLw5Fc56zgEfMbB95HqZHWzifRKLZJB/AkiHtGCrQEVJq4xlYzzSz6UXt9wM/Dyc+krYqM87KFJzWg5oxfiKRqDPSwtAEzU2p3VSaajO7qTjax8ymUTml9t9rdD4TKUIuKnHqLNyn8EyYic6K9kfwhIFTw2F+HnCOpHFAl1rGTiQS9Und6RiWJJJOA/4XT4HxOXC0mT1Z5trheLrrW8ucvxRPaLcssC6QmXDOLndPayFPkncm0A3/MXAXbsqaZWarxzU74TufNczsdUmrAM8DPfA8UjvgjvNueKTWL83s1abGTjqGRKJt6Gw6hiWCpO3wDKxbhwZhNfyl3izM7KfRby98AVkiOYcibcWFwHfM7PkQuh1pXgvjXUkbmtnzeIK8KfHv7XiCwQkhJAQ43szulLQUcALwsKQtzOzTJfEciQQsXh1D8l8USKak8qwBvGVmHwOY2Vtm9qqk0yP2f4akYZmNPo+kvpIek9ctuD9CWksiaaNI75Edb5IdS5oj6Vx5DYknQ/eApC9Juj20BR9Kej5MP9lf3sl8CnBWvPwxs8/M7PI4Nw5fCIh/Lyg6Hl88XzP73Lyw0Dt4ZFOpZ0rhqolEHZMWhvI8APxPvHQvk7RztF9iZv3NbHPcrNIgJUQ4qf8M7GdmfXFfQVnVdEQFLQhdA3gdh3zG2XfNbBs8zfX50XYxcF5oCzYDPgndQfb3du7+zSlf82E8hYWgJ56/KQuz3Z6GPo5iJuM1Kko9UwpXTSTqmGRKKoOZzZPUF6+hsAswSl53+gNJJwPL4xqHmTQsoLMR/jJ+MDYTXfAcRZX4C57M7xRgf7xITkamVh4BnBufdwM2ym1WvqhC6otaGAccJ2kD4AUz+0jSsvKMsH2ASRXurTVEN5FoMcncs2RIC0MFzAv1PIrXNJiO5z3qjSeve0We+6hYZyBgppltR/XcAvwKf1FPKErYV65mwjZm9kkVfc/E80vNLHHuWbzg0LcppCKfguefer6JhaYPsOQK8CYSiSVGMiWVIWz/G+Sa+lCIJHpLXqtgvxK3PoeXz9wu+llGUkW1cGRMfRgvNXpt0eksx9LBFEw7/8BFd9lcKzmyzwN+rUKd6y6STohxDU+TcSyFhWECnrCvkX8h7pc8I+yqFBThiUSiA5F2DOXpDvxZ0heAz4AX8DrG7+EZUmdTwtRiZp9I2g+4OJTTS+NRQaV+secZgf9yf6iofflwRhu+OIAvCpdLOiz6f4TcQlE0nymSBgM3S+oW/dyVu2Qcnnk101RMANaj8cJwgTypXxau+rUUkZRIdEySjmExIOnL+GLQHy8JOhs4LosMKnPPqcByZnZGru0dYHsze7bG8YcA8yJ6KKvF8DpwlZn9Mnfd1cD5ZtZk2m9Jvwe6mNkpcbwOviBtXalWRdIxJFqTFK5aPS3RMSRTUisT4at3AI+a2fpmtinuP/hShXvuxpXVfy46tQLw5TL31KJC3h03cR2QD681sx+VWhTK9H0WsJekTeL4IuA3zS1glEgk2i9pYWh9dgE+NbMrsgYzm2pmYyWdFBqIZ1SotdAL+Apulhor6QF5Tej9gE+BK0Ob0E3S7NBRPA7sL+nI6G9aaCamybOlHgMcL+nimMLB+Iv838BXs3lJelRSv/g8T9KZ8vKnjRzn4Yg+AbhMXiN6RTMbUeoLSDqGRKK+SQtD61NSNyBpd2ADYBvckd1X0oA4vQFwqZlthvswvhdpMp4i6jTkIoQWmNmOZnYTcHtoKrbEi/JcHYrqK4ALzOzY8CvsimeJHUnBT1HMCsAMM9vWzB4vdYGZ/Q0Xtl0P/KTcF5B0DIlEfZOcz0uO3eNvShx3xxeEfwMvmdnUaH8ar6FQjlG5z5tLOhuvANcdz5hazB542uyPJN0G/EbS8RGKm2chLnBrikuBbsXpuhOJJUFH8wO0V9LC0PrMpHQYq4BzzOzKBo1uSvo417QQj/wpx4e5z8OBvc1smqRBeM2FYg4GdpA0O45Xxc1d/yi6bkGJxaIUn8dfIpHooCRTUuvzMLCcpCOzBkn98cykh4f+AUlrSVq9ib6aqsGwIvBapOE4pPikpJXw6nI9zayXmfXCw1rLmZMSiUQi7Rham8hGug9wYYSgLiDCVXH/wYQIDJoHfB/fIZRjOHCFpPmUcAgDv8EFai/j2oriRWRfvK5zfkdyF3CepOVqe7JEItFZ6HQ6huZoDKrocyAw2Mz2kLQnsKmZnStpbzy1xKy47kxgjJkVm3GqGeMQPFMq+KLy4yjsU+56wzUKJ8bxYKC7mQ2pdeyWkHQMidZkcekYOqLvIukYqqQ5GoNaMbPRZpYlu9sb2DR37vTmLArBS8DOZtYb1xQMa+L6j4F95XUkaiZEcYlEohPSqRYGymgMgMclDZXXWJguL2mJpIER63+rpGcljcgEYpK+GW2P4yYbon2QpEskbQ/sCQwNHcL6koaHPgFJu8rrKUyXdE1m2gmtwhmSJse5jWOe483s3RjmCWDtJp71M3zxOL74hKR1JD0UeoqHJPWM9uGSzpf0JvCqpNclvSNpbnzeV9J5Ma/7wrfRiKRjSCTqm862MJSrTbAvri3YEk9pPVSF4jpb4f6BTfEcQjtI6gpcBXwXT8vdSJ1sZuOB0cBJoUP4V3Yu7h8OHGhmW+C+nh/nbn/LzLYGLgcGl5jvEbhuoSkuBQ6R52zKcwlwfew+RuD1HTI2BL4cJT+vAGbhkUzfAG7AQ1+3AOYDJfffSceQSNQ3yVzg7AiMjHDNNyQ9hvsg5gITzWwOQKiKe+E2/pfM7J/RfgOeYK9aNor7M7/GdXi00IVxfHv8+zS53UiMtQu+MOzY1CBmNlfS9Xj21HwK7e1y/f4Vz8CacUtR2OrfzexTedrxLsB90T6dynqLRKLV6Yi+gPZIZ9sxZLUJiqlUdKZYY5Atpi3x2jdV5CYbMz8eknoDVwN7FVVpq8SF+EKyQoVr8s/yYdG5rLTp57gZLrv2c9IPi0SiQ9LZFoZyGoN3gQPltQp6AAOAiWX6AC9ws66k9eO4nC6gnA7hWaCXokYCcCjwWKWJhx/gduDQWiKozOwd4GZ8ccgYjyftA9c/lEyBkUgkOiedamGIX7v7AF+X9C9JM4EhwI3AM8A0fPE42cxer9DPAtx0dG84n18uc+lNwEnhZF6/6P7DgFvCRPM5bs+vxOm4rf+ycGbXEgP6JyAfnXQsXkr0GXxR+kUNfSUSiQ5Op9MxNAdJ88yse+54EF7e82fN6KsPsGYkpCOve2jF+S4NnInXj85MQ7eY2e9q7GdL4LpIzIekg/H61CuH32ELYEQ4sUuSdAyJ1mRx6Bg6qt8i6Rjqiz54pTagke6htTgbWBPYIl7qOwElQ0ubYDqwjqTMHLY9bgbbKnc8rtSNiUSifknOwxYSPokrgJ7RdJyZjZO0De747YZHBB2Gi9TOBLpJ2hE4J873M7OfSRqOR0L1w0NgTzazWyUthYeY7hx9LAVcg1dQKy4FKuB/gHXCZIWZfYCbzLI53xnXdAUuMrNh8uI8f4mxDbjGzC6QNAnYFk+61xcPgd0e98FsT+NkfEg6iojS6tmzZ/HpRCLRzkkLQ3V0i1DVjFVwjQJ4AZwLzOzxcBDfD2yC/7IeYGafSdoN+L2ZfU/S6eTMUGGWyrMGHoq6cYxxKx5a2gvYAlgd+D/8xf02vgNZREQuXReLQTkON7N35LUaJsnTcfcC1jKzzaOfL8S144HtJU3AfSGP4gvahfjCcEZR35jZMEKZ3a9fv2SrTCTqjLQwVMf8zM4OBR9DHO4GbKpCxcyVwvSyMnCdpA3wX+DVmnLujNDQWZKyVB074j6Cz4HXJT1S7cQlHYY7l1fF60e/AhwrT/QHvnPYAC/9uZ6kPwP3Ag/E+XHAicBYYJKZ/UvSV2Kn1N3MXqx2LolES+mo/oD2RvIxtJylgO1C3dzHzNaKX+tn4SrhzXGFdNcq+8vrJlT0bzW8APTM/AJmdm0sau8DXeQJ/3aLOW+JFw7qGuk2tsR3BD/F9RLg6Tf644vThGibg4e7jq9hXolEok5IC0PLeQBYFJ0UUUfgO4b/xOdBueubqrFQiseB70laKnYRA8tdaGYf4b6CSyL1BuE/WDY3r3ejotvGRA1oebK9pczsNjyd99bR3wfAK/EM2cIwAU8TkhaGRKIDkhaGlnMs0C8S0s0Cjon284BzJI3DU0lkPIKbnqYqkvVVwW34r/QZwJV4DYb3K1x/GvAaMEPSFNwMdB3wKp7SYunQMJyF7wgA1gIeDV/KcOCXuf7GAcuFGQp8YViPtDAkEh2SpGMAJC3EQzMzbop6Co/idRZqCsQv1iqUON8P+IGZHVtDn93NbF4I6nrhDuxGIjx5Cc8PKBQA+kkk9Gsx4VsZiu+ElsWd7ldVuifpGBKtSWvqGDq6v6IlOobkfHYaOJdbgT64c7rRwiBp6Vhoan1b3hORQhsAQysps4FdzOytGvuvllERWrs6MFPSaDN7YzGNlUgk2oBkSqoSSbtLmhB1Em5RoXZzf0njJU2TNDFSXJ+J516aKulASUMkDZP0AHC9vM7DPXF/d0nXRo2DZyR9L9ovj5oGMyWdYWYDY/GaBGT33hFjLPqjhJM7P14cX5KFyUo6V9KsGPuP0dZD0m2SJsXfDsV9mtl/gX8B65QYL9VjSCTqmLRjcIp1CueY2ajsIByzvwZ2M7MPJZ0CnCDpXGAUXldhkqSVgI/wvEZ5rcIQXBy2o5nNj8igjN8A70eNAyR9MdpPC61BF+AhSb3N7Jn8pM1sH4oIU9IjYR772My2LffQklbBc0dtbGaW0y6U02bk710P9zO8UNxv0jEkEvVNWhicpkxJX8UL9YwLvcKyuAN2I+A1M5sEXv8AIKdpyDPazOaXaN+NQqZTclXaDggF8dK46G1TPNFfNVRrSpoLLACulnQvsROhvDYDfCe0Ix5We3Rkb00klggd3S/QXkgLQ3UIeNDMGqTXDpVxtb+Ii+sc5Ptu0IekdfHKbf3N7F15qoxqdRCl+IyGZsOuAKHK3gbYFV+cfgZ8jYI2o8FCFgvFqOYkD0wkEvVD8jFUxxN4Sc+vAEhaXtKGeNqLNeU1HZC0ojyzaS1ahWIdxBeBlfCF5P3QLXyrhfN/Gd8BLBc+kF1jrO54ptS/4bqEbNdUTpuRSCQ6AWlhcLoVOXEbZDs1szdxgdfIiP9/ArfLfwIcCPxZ0jTgQfzXeC1ahbOBL0qaEX3sYmbTcEXyTDxZXosymIb+4GbcFDUi+gZfvO6JZ3oMOD7ay2kzEolEJ6BT6hjK6RYqXP8rM/t9M8a5GjjfzGY1Y5r5fh7F/QzzgeVwx/CwMtfOxh3fbxW1Fz/z3mY2uyXzqoakY0i0Jq2lY+gMvoqkY6idWnULvwJqWhgkdTGzHzXjnoVlTh9iZk9FJNG/JA2PHUuD+yt039pajUQi0UFJpqRA0sqSnpO0URyPlHRkmJUyU9OIOPf90CxMlXRl9kKWNE/SmZKeBLaT9Khc5Yykg0OrMEPSH3LjNriniql2x/0PY2P8hZJel/QhXr8567ebpPuUq29d4pl7SRor12ZMlrR97tzJMd9pmWlN0vrR59Nx38Zl+k06hkSijumsC0OxT+FAM3sfd7gOl3QQ8EUzu8rMTiV+bZvZIZI2wf0KO8Qv8IUUXsgrADPMbFszezwbTNKawB/wiJ8+QH9Je1e6pwQjwhfwHHBWXN8H/294rJmtYGbXx7XdgbuBG3MpK/LPfEe0/Rf4upltHc90ccz3W8DewLaRgfW8uH4Y8HMz64tHTV1WaqJmNszM+plZvx49elR4pEQi0R5JpqQcZvagpP3xKmVblrl3V1ysNinCN7vhL1jwReK2Evf0Bx4NJzax8xgA3FnhnmIyU1IPYLyk+8zs5TL33wWcZ2Yjcm2lnnkZPAtrtsBtGO27AddGplZCaNcdL8xzS07fsFwV804kWo3O4BtoD3TWhaEk8hKam+BO3lXwjKaNLsMrpP2yxLkFZXwEleoplLunJGb2pqTJeLnNl8vcPw74lqQbrXJ0wfHAG/giuBQudsvmW3zfUsB7yU+RSHR8OqspqRzH42UzDwaukZRVXfs09/khYD95EjkkrSKpUb6gIp4Edpa0WvgjDsbDQ2tG0vLAVnieonKcDrxNGVNPjpVx5fbnwKEU0oM/ABweYyFplVB1vxQ7KuSU21UlEok6prMuDI10CyFY+xFwopmNBcbg+ZHAbevPSBoRoae/Bh4Im/+DeChpWczsNby+wSPANGCymd1V45xHyPM5PQ0MN7Onm7j+OKCrpPMqXHMZ8ENJT+BmpA9jvvfh9aafijEHx/WHAEeE3mImsFeNz5BIJOqADqFjyMXoLw28BBxqZu81s6+hwLfxlNkfAr8FNjCzF+L88cD5eLqKsgH6ko4DhmV2+nL6gtz1XwYuxP0RHwOzgePM7PnmPEdTxG7gKqA3bjp6D/gmsBpwj3lJ0haTdAyJ1iTpGKqnJTqGjrJjyKKGNgfewWsWN5ejga3N7KQ4nk4uyR2wH1CNYO04YPlqBpR7c+/AHdTrm9mmuHbiS1XPunZ+AbxhZlvE93YE8OliHC+RSNQJHWVhyDMBL1OZ2cGHhnZguiI9RYX20Xj46JMqpLK4kzCZyFNNvw8sCs5XUd2EaDsWWBNPf/1IFXPeBX8pf0OFugrD8VQb3ygz14GSHpN0s6Tnwxx2iFxfMV3S+nHdcElXhO7geUl7xJhrUKhJjZk9Z2Yfx2EXSVfFMz0gqVv01V+eJmNCNqdSD5N0DIlEfdOhFoZw7O6K28cB9sV1A1viIZhDJa1Rrt3M9qSw+8jqMcwFXpG0Oe40XlSnITgttmu9cQdzbzO7GK+vvIuZ7VLF1DcHnjazfWLsRX+4JqHUMxBtvwC2wJ3HG5rZNsDVwM9z/fcCdga+A1whqSueg+mUeMmfLWmD3PUbAJea2Wa4iel70X4tcIyZbUehdGgjko4hkahvOkq4alZopxfunH0w2ncERkY45xuSHsNt+OXaRzfq2bkJNyd9A194Dsuda0ndhGooN9e5wKRwbCPpX3g0Ebj5K78g3RyRR/+U9CKeAHBq7IB2xxecSZK2w0N1XzKzrHDR00AveRGfFa1QP/pGYA8SiSVIZ/ANtAc6yo4hE2+tgxfRyXwM5fQDlXQFpbgb/0X+76wYDzSom7CrmfUG7qV5dRNm4qK5Wuf6ce7z57njz2m46BdHGBiAmc0zs9vN7CfADbjTvbjfhdFXrd9ZIpGoUzrKwgBApLU4FhgcuoMxeMWxLqEYHgBMrNBert/5wCnA74pOVaqbUEtNhoeB5ZTLaxT2/J1rnWsZ9pe0VPgd1gOek7SDooyopGXxnc7L5TqIynIfSPpqNB1U7tpEIlHftIkpSZLh6ahPjOPBQHczG1Lhnj2BTZtIjz0Q/wU/DX9x3YAnppuGx+n/xMxel+cKytoNONnMXm9i2hsDawOTswYzmyYpq5vwIg3rJgwD/i7ptUp+Bnk96Hl47eULJZ2KK5Bn44tCH9w0lc31LNxU9L9NzDfPc7ig7ku4j2BBLBKXR0RUdwqpNSqJ9Y4ArpIn7HsUd8QnEokORpvoGCQtAF7DtQBvVbMwVNnvQGCwmTWyfTelI2ii36VxUds8M/tjS+ZYou8h5fqVtBK+4PTM6SGOwb+3I6rsfziuS7i1wjUDKfO9FV3X3czmxedTcZ/KiWb2Wbl7ko4h0Zq0ho6hs/gp6lHH8Bn+i/r44hOSeki6TdKk+Nsh2gdJuiQ+ry/piTh/pqR5uS66S7pV0rOSRsQv4oyTIpxzogplOteR9FCEYT4kqWe0D5d0foSbZmmyN5Wn0n5RHpKazfmECCedIRe2NdV+mjzF9z+Ajcp9SeHPGAN8N9d8EDAy+ukrD1l9WtL9WbSScumxcfPWmrlnuljS+HiG/Up8//0lTZG0nqQVJF0T3/MU4LfycNo5eDTUxhQc3olEooPQlj6GS4FD5DWI81yEVyjrj4dJXl3i3ouAi+KaV4vObYWLyzbF7ek75M7NjXDOS3CVMfH5+nAejyBSTwcbArtlJi/8RfgNYBv8JbmMpL54lNK2wFeBIyVtVaZ9oKTn8N3HAmB1PHS2khBuJGHPl6fv3hDXRywD/BnYL9JgX0PBB5JPj70nnkI7Yw080mkPoIFZTl6P4QpgLzN7ETgNeDi+511wPccOMf+FwMFm9rXiCSvpGBKJuqbNwlXNbK6k63Fn8fzcqd3wX+bZ8UqSip2421F42d0I5M0wE81sDoAKIaxZnYORuX8vyPW1b3z+K4XaAwC3FGUuvTdEYB9L+i9us98RuMPMPowxbwd2wqN4itt7A5cDq5jZ6dF+PvBRqe8ouAe4LMxKBwC3mtlCeV2IzYEH47vqArymptNj3xmhq7PkDvOMTfAFZXczyxbb3YE9w9QHHnHVMz4/aGbvlJqwednRYeCmpArPlkgk2iFtrWO4EHfmXptrWwrYLiKBFtHQIlSRUqGWGVbmM2XaP6yi7+aExFb9sjSz+ZLuw53TB1EwvwmYGWKzwqC+gFRKj51/hvwcX8Nf/FtR2IUJ+J6ZPVc0xrY0/m4SicVOZ/EPtDVtGq4avzhvxqNdMh7AK6kBIC8iU8wTFNS4tYRNHpj7d0J8Hp/r4xAKu4tqGQPsLWl5SSvgL/CxTbTvIy+9uSIN/QflGAmcgO9Qnoi254AeclEaYdbarAXpsd/DldG/D2c0wP3AzzM/jaStqugnkUjUOe1Bx/AnPKNnxrFAv3AGzwKOKXHPccAJkibiNvNqwyaXk9dW/gWFX97HAofJU2gfGueqxswm43mNJuJ1F642sylNtI8CpuLhoWOrGOYB3IE8Kiu8Y2af4An9/iBPgz0VNyFBM9Njm9kb+EJ1aewKzsKrvD0jz4t0VjX9JBKJ+qYu027LU0bPNzOT12c+GHem1m3qbUkXAC+b2YVxfD/wipn9KI7/BPzHzM4vum84EY4qT5B3Fr7gL4M76K9UFSGrRX32opVSb6dw1USibVALwlXb2sfQXPritYqzOgKH42kp+gBIug5Pi1GsVK6Wo4EeZvaxXGeQpd4+O87Xknr7Bio7lzPGA/vjIrel8F3USrnz20d/JYkopWHANmY2R9JyuOM9kegwtETHkPwT1dMeTEk1Y2ZjzWxLM+ttZgOyX/I56jH19rdxn8RUfNFZF1hR0hfjJb8JMCXmfYmkWZLuxX82OOoAACAASURBVENewdNvLI2X9MTMPi5yGg9QkX5BUne5dmNyfAeNTE5yPcMUub6hS3xnk8LUd3QVz5VIJOqMet0xlEWF1Nt/iaZ8iu3V8CyiY/Bf4I3azWxPSfNyu48hNEy9vRfuI8hnWD3NzN6JsR9SpN6WdAKeertJtbWZHSZpF1x38C08ImgtPJz2feAZM/tE0r64KG4L3Bk9C7gmxh8NvCzpITzMdWSEpkJBv7AxnkX2VlxLsU+EDq8GPBF9ZN/lRnhm2cMiG+tRwPtm1j8Wq3GSHjCzl4r+GxwFHAXQs2dPEolEfVGXO4YyZKm33wZWoUTq7XCuNkq9XdRejiz19t54tbU8B0iaDEwBNsPFdc1hHL5gbY/veibkjrN01wNy834VT8AHQPgjdsUd3oNx0VvGnWb2uXnN6ky/IDwK6RngH/hClJ3rAdwFfD+Xgnt34AfxPT8JrIrXbmiApXoMiURd05F2DPPNrI9cSX0P7mO4mNZNvT0UeCp+YXsnhdTb/c3s3XD0Nif1NvjLf3t8NzADeAU4Ed+x5F/yZSMGzGw6MF3SX3En/KA4VUq/cAi+APQ1s0/DOZ7N/f0Yfwc8sim77+dmdn8zni2RaDHJT7Bk6Eg7BqCuU2+D7xj2AN6JHcE7wBdwc1KmuxgDHBTzXoMoyBP+goG5vvpQIY12sDLw31gUdqFhZtVP8N3RDyRlmVzvB34c3yuSNpRrNBKJRAeiI+0YFmFmUyKGvzj19qIU22pG6m0zu6lEW4tTb+eYjvs7bixq657zU9wBfC3an8dNYOC/5k+WdCWeYuRDCruFcowA7pb0FK6DeLbo2T6MENgH5am2r8YjnSZHRNibNMzDlEgkOgDtQsegxVyfobXTcMf9Q1jCabhz1wwGfoRnqV0I/MnMrpf0KP68rSIckPQD4GR80RHu5K7peZOOIZFoGzqCjuFjYF9J51T7ojaz0ZSv0bxYkddnaBPk9Ri+jusV5oZPpVV/tcfzfR3XTexuZq9K6oorwxtdW6keQyLRmjRXx5B8E7XRXnwMnak+w/9Jmi/pP/LaBlMlvSbpn2qiPkPwK7wS3Vxwn4qZXVfie9td0gS5RuEWedZVJJ0e39MMScOy7yOe4/eSHsPTgvwS3328GuMsMLOrylxbPHZKu51I1DHtZWGAzlOfoR8eCfReHB8BvIXrKfalQsisPOneimb2r3LXxHWr4TUTdjOzrYGn8CR8AJeYWf9Id9ENd3ZnfMHMdjazP+EpvZ+uMEz+2gakcNVEor5pL6akzlqfYSd8cb4jl0+pknlMVJey+6v4QjguvrdlKUQ17SLpZLw40Cq40/zuODeqir4zark2kWgVkkloydBuFoYg1WeodJEvnh9KWs+8wlo5hBfSObhBo/sJLsOd7q+Eozuvucg/30w8J9XDlCbVY0gkOijtyZSU6jNUV5/hHDwt9krghXnkKSjyPAHskPObLC9pQwqLwFvhc2hU87lonPMkfTn6WC7vR0kkEh2X9rZjAK/P8LPc8bH4i/AZfL5jaFyj4TjgBkknAvdSe32GpfDU3dl410g6CY/TP6zczaUws8ly9XMmlrvazKbAohTZpdqz+gwv03R9hsuB7nhup0+BT/HvLD+HNyUNAkbKcxoB/NrMnpd0Fa6BmA1MqvAcf5ML9v4RDmqjofo6kUh0UNpEx6BW1i0o6jMAO+N1El4xs72KrplNHeoWJH0Vd64vF3+jKn1PS4LYta1pZn9r6tqkY0i0JilctXpUhzqG1tYt9MWjibrjKSQOaJVZBmpD3QJwHXBAKKy70HQ4a6uh8hqFPnh0VZMLQyKRqD/aysfQqroF4O9mtiXum5gOnKv2o1so1X6apOcq6RYkXRpRVOsDI+LzDyI7KpJWkXRnzPsJSb2jfYik6yQ9IGm2pH0lnSevt3CfCnmO+kp6TNLTku6X511qpFGQtH/Mf5qkMZKWBc7E80xNVaFmRX7uSceQSNQxbel87iy6hVLtB8U8y+oWzOynURPiTDwd9kvAsvLIIoAzgCkx718B1+duXx/4Dl474gbgETPbAje3fScWhz/z/+ydd7hU1dWH359gAVFii9FExYoFERGMXVRMYmJvqMSI0aiJJZqosX2KmhgVW+zRBNGoiD3YO4oNUEQQxY69BDuCirC+P9Y63HOHmblzK/fO3e/z8NyZfXY7Y3L22Xut31qwu5ltgNsO8sEB8xqFk4Gfx8K7o3mu6ZPxI61eZjaP22rSMSQSbZv5dkSSdAsV6RYws9MkXYfnQtgHN5L3i3F3izoPS1oqt8jeExFTJwEdgHujfFL8Ht1xAdsD8Tt3AD7IDZt/2D8BDJN0I3BrubkmEs1Ne7QVzA/mt1dS0i1UQCidLwuPov9JWqpE/1m/30a7OZJmWY2HwZzcnCeb2cYlhpx732Z2iKSf4juQCSruLpxIJKqI+apjSLqFunULkn6Vs5Osji9In0c/A6NOP2BaFj+pAl4GlpG0cbRfUNI6JcZf1czGmNnJeOiOFah/nolEItGGmN87Bki6hbp0C/sC50uagRvtB5rZbLmb61XxO80A9qvHnL+TtDtwYRw/dcR3b5OLVB8iaXV8l/EQnr/ibeC4OKr7ezE7QyKRaLu0inwM9UWhWzAzk7QXsHehbiHqzcbP1YW/aR9mZk8W1qvn2LV8+OVCsiHAe7lq+2TeQ22NWATOB9bCdyZfAqeY2WNF6k6lDm1I0jEkmpKkY6gctUEdQ2PZALg4jlg+B35bot7M8OxB0s/xMA9bNnLsYj78I8zssBL15xuqZ66E8Hi6Cw+3PTLKeuD3O8/CkEgkqpNWFSupUsxstJmtZ2Y9zWwLM3utgmaLA58BSFoufPInhI/+5lE+XdJZ4dv/oKQNVaNb2LESH/4MSbtEH4rxXpH0I7ke47+hKXhZ0ilR/xJ5joaZqsnXsL+kRSXdFTqCF7Ix5RqFpeNzH3n2tkzHcIWk+4Fr5Lmhh8g1HxMlHVzmNxoIPJUtCvFbv2Bmw6LvpeT6iOfkKUSLGtiVdAyJRJumTS4M9aBTPMCn4HqI06N8H+C+2E2sh5/3AywKjArf/q+Av+KZzHYBTivjw58tFNm/TmZ2G/AhcChwJX4ck+WU3hB/CPcC9pDUB9cSfIrnfM7yNUwAfgG8HwthD2pcT8uxAbCTme2DG/a/CM1HX1xPsXKJduvgXmKlOAV43MzWx1XoKxarlHQMiUTbpq0eJVVK/ihpY/wNugcePG6oXOh1u5llC8N31Pb5/zanB+hWZpxSR0mHAy8AT5vZ8Fz5A2b2SczrVlyTYBTXPdwLnCPpLOBOM6vLWA0wMufu+zOgZxibAbri3k1v1tWJpNui7itmtiuwBaH5MLO7JH1WwVwSiSajPdoK5gfVvmOYi5k9RbyNhyF1C9xg/B950nuAQp//uXoAGraI/jj6WVZS/rcutPgbJY5lzOwVfAcwCfi7pJPj0vfU/PdbpKBZXn8h4PDY4fQys5XN7P4S850M9M6NvQswCE/oU2ruiUSiymg3C4OkNXGF7yeSVgI+Ns9h/G9yD8MKqMiHXx547yr82OolalJrAmwrj3XUCVdwP0EJ3YOk5YEZZnYtrvDO5joVXzCgRtNRjPuA36smRtIa0X8xrsfzOOyYK+uc+5zXTmwHLFFm3EQi0Uap9qOkTuFrD/7mvF9oAPrhAfVmAdOB35TqoAiPkPPhj7IBkjbL1fkDHtpjtJmNjrrjJGW+do/j4TdWA643s2eguO4hvKnulfQN/rb+jqRueKykf0s6ARhTMMcTJA0LN9Lz8PhPk+TJer4Gpkr6CtjfzF7OGpnZTEnbA+dJugC3c3SkZlF7GfiLpF2BR3E9QyKRqDLapI6hLRO6hz71cW+VNN3MutSj/tQYY1rWNhaTO8OATXgnbWJmJYVxsYAebWbbN3TuSceQaEoaomNor3aJxugY2s1RUrWhXBjy+H5nPMgrJe++203SaEnj498mUedMYPPwtMpCpC8frravSjq7WMeJRKJtU+1HSa2O0AQMq2ez/JHYm2EUbgirRj9LAT8CXonvwj2wesuVz8NxUdtxzLtj6IWHDP8WeFnSRWb2Tn4QeQ7qgwBWXLGoR2sikWjFpIWhbTDX7baRvJ5z3x2A2xh+IY+XdHG45c7G81CU4iEz+yL6eBFYCai1MJjZFXgiJvr06ZPOKhOJNkZaGNoueXdVmNdltS5GUhPu/CjgI1zstwDwTZl25cKaJxLNSnu1F7Q0ycbQdpkK9JK0gKQVcDV1fdgMeD0+dwU+CL3GvrhbL6Tw2olEuyS97bVdnsDVy5NwdXW5UBYZq+ZsCt8BB0b5pcAtkvbA3XEzgdxE4HtJz+N2kaR0TiTaAcldNdGsJHfVRGL+0Bh31bRjaCCSDLjWzPaN7x3xvMljzGx7ScviquoVgAWBqWb2S0mHAr/LddURD163tpm91IB53I3nf/i8cXc0t79+wH+piaU0DQ8m+Pd8KtC43/eAXmb2QWE/iURzUF8dQ7JJNIy0MDScr4EeEUl1Jh6FNZ+s5zQ8WN4/ACT1BDCzS4BLskqSzgAmNGRRiP5+2cD5l2N05qIKEHGefiKpm5lNjeL+wAtpUUgkqo9kfG4c9wDZK8neuP9/xnLAu9kXM5tY2FjSFsCeeAgNJC0i6SpJkyLnwVZRPkjSrcWEZYq8DCFSe0nSlZImR96ETlGnrzwXw1Py3Awv1Ocmwyh9EzU5s8HzZA8vVl8pH0Mi0aZJC0PjuAHYS575rCe1YxZdgscyekTSiREMby6SfoC7i+5nZl9G8aEAZrYuvtBcHX2DC8sGAOvisZlWKDKf1YFLzGwdPM5RFlzvKuCQOAqaXcF9ZWrnCZJOjLLh+GKApIWBXwK3FGuc8jEkEm2bdJTUCMxsYsQg2pvaqT4xs/skrYIn2tkOeE5SDzPLXqEvw20UT+SabQZcFO2nSHqLGrFZncIyXBWdKaSfBbrFArRYLtf19cD2lKfWUVLMZ5ykLpK64/mgnzaz5KWUaFGSzaBlSDuGxjMSD4c9z7GKmX1qZteHgXocngMCSfvhiX9OL2hSNCdDUImwrFidcn3WlxvwXUPJY6REItH2SQtD4xmKp/2clC+UtLWkzvF5MWBV4O3YRfwNGGhm3xf0lc93sAaeOvNlGkG81X8laaMo2qsR3Q0Hfg1sjS+IiUSiCklHSY3EzN4F/lHk0gZ4/KEsdMW/4jjmn3hu6VulWi/zh+NCs8sjZtH3wCAz+7agXkM4ALhS0tfAKOCLhnRiZi9KmgE8m6UgTSQS1Ue7Wxgk/Qi4AOiLH71MBY6MFJoVk8+PkM9bIGlxSceZ2ZmSXsXzJb8Y9U4DbjKzg8t0PajIWMMkPS3pKTyD24lmNiqudYtq04AeuWZn4cl5dscT6mxhZp/Lc0f/NBTQCwHPAAdEbut+uPJ5bgY3SXcC5+TGW6+iHyiRaAaSjqFlaFdHSfJX79uAUWa2qpmtDZwALNtUY5jZSDM7M77uDKydu3aymT3YwK4/BY7A7RmVMDNyPPfAdygvhZtqX+DViLK6LvAT3GU2413gxHl6SyQS7YZ2tTAAWwGzzOzyrCC8eB7P/PtDQzAAfCcgaZSkmyVNkXRdLC5I+kWUPQ7smvUXmoOLI9nNjsCQcPtcVdKweINH0jahVZgkaWi4gGa6hFPlCXMmyXNVY2Yfm9k4YFYD7ns4cFssEr8FZstThj6Lq66HxA7idOB54AtJ2xZ2UmpuReolHUMi0YZpbwtDD/xhWMiuuE5gPVzRO0TScnFtfeBI/M1/FWDT0BZcCewAbI4nvalFuIeOBI6JN/cskinRfhgwIDQLHYHf55pPM7PeuEvr0Q2+Wx+rA7ANBcZiM7sP2Ah4EfhF7CD+Ly7/FTipRJd1zi3pGBKJtk27szGUYDNguJnNBj6S9Ch+5PIlMDYMzMRbdTdgOq4ZeDXKryUyllVI92if2TWuxsVtF8T3W+Pvs+R2I/WkU26+zwIP5K5lUVZXB24uVGWb2WhJSNq8SL9NMbdEokEkm0HL0N52DJNxb6FCGqIfaExY2rrcjLIxG5MIJ8v6thJuZD40dy3L5LYasJGkHYu0/xvFbQ1NMbdEItGKaW8Lw8PAwpLmRjeV1BfPMzBAUgdJy+BCtLFl+pkCrCxp1fi+d4l6pRLdTMFVyavF932BRyu/jcoJtfQRwNGSFiy49gGe1/n4Iu3uB5bAj9cSiUQ7ol0tDObJJ3YBtpX0uqTJwGA8TMRE3PD6MHCsmX1Ypp9v8KOju8L4/FaJqjcAx4SRedWC9vsDN4VmYQ5weYk+AHezlfQu8CfgJEnvSlq8wvt+Lu6tmLjtdqBziWOjv+FeS4lEoh1RNYl6JM3Gs5l1xHMJ7NvQHAWShuBB4u4G/gX8E/gBsDAeR+ggSb2A5c3s7tI9gaTBwHQzq9TNNN/uFGB1M3styo4CzgP6mtl8zX4jaRDQx8wOK1cvJepJNCVJx1A5akSinmraMeT99j+l9pl6fTkY6G1mxwAXAudH32sRQe5wL6bmyIWQZxK13/J3x72IWhQ51fS/lUQiUYZq/T/7U8CPYe5DrZhGoVT5SFwQNibKCvMqTJK0EJ6IZ0BoFAbI8yQsE30sIOk1SUvnJxVahnslPStpdCkdQI7bgZ2i7Sp4KIu5woDQP8yQNFPSh6oJlf12Mb2BpC1zdZ6TtJg8YupDubrZeFl+h0vxfNIrSNpf0ivhtbVpqUknHUMi0bapuoWhiN9+KY1C0XIz25Ga3ccI4HzgYUn3SDpK0g/M7DvgZGBErt61RAC86O95M5tWML0rgMPNbANcA3BpHbfzJfCOpB64gXtEwfWjzawz0AU3aP8mvI3mUFxvcDRwaNTZHJgJfAPsEnW3As6V5gZn6g5cY2brA98Bp+ILwrbkFN2FJB1DItG2qSZ3w1J++6U0CqXKC4VgV0m6D8+rsBNwsKRinjpD8VzJF+Dq4qvyFyV1ATbBDc5Z8cIV3FcW6vrn+IK3f+7anpIOwv87Loc/rDNNQjG9wRPAeZKuA241s3fDU+kMeTa5OfhOKwsR8paZPR2ff4qHEvlf3M8IanJFJBItQnu2GbQk1bRjKOW3X0ozUHHIUjN738yGmtlOeNTTHkXqvIMvMFvjD9F7CqosAHweO4zs31oVDH8H7s76ttVkekPSyvgOYBsz6wncBSySazeP3iBiOB0IdAKejiOmgcAywAbx+32U66cwgmp1eCokEomyVNPCABT123+M4hqFUuW1kMdEWjA+/whYCniP4hqFf+FHSjfGTiQ/ry+BNyXtEX2pxM6j8H5mAn/BXUfzLI4/uL+QtCyeJa4sklY1s0lmdhYeVXVNoCvwcURX3QpfWIsxBugnaan4Pfaoa7xEItE2qbqFAebx27+N4hqFUuWF/Ax4QdIHeIjuWcC9+Pn/2pnxOeqOxM/711AEyytgIHCApGn4+f4DYTjODMLF2mBmN5jZ+IKy54HncDX3UPyYqC6ukGSSpsT49wCHAZtIeibmN6XEHD7ANR9PAQ/iBulEIlGFVI2OoTmRtDGuH+gXiXOWBhYys/cL6vXBjdWvA3ea2c119Nst6s1zNNUcSDoQN5qPNrMsU9wUoH8WD6qpSTqGRFNSHx1De7dHKOkYmp3lcC+fbwHMbJqZvS/pZEnjwuV1DHALBeElJG0g6dFwUb1PNVFb50FSd0ljc9/Xyr7Llc5nShoraUy4ryJpWUm3hnvoWNWk8CzF7UBv1YTjyI+/naSnwnV1hKRFJW0i6ca4vpukryUtGNderejXSyQSbYq0MFTG/bgf/yuSLpW0ZZRfbGZ9443/FeAwM3s8axRn8RcBu4eL6lDmtRUskx0l4e6oa0vKUoXuT23vps/MbENciX1elF0InB1vBnvido5yzAGGMO8C9kM8btI24bo6EfgjMI6awIOb4wK73njI7qcpQtIxJBJtm2pyV202zGy6pA3wB+NWwAhJxwFfSToW6AwsiZ/335Fr2h33YHogXFQ7AB8UdP+/8AYCQNJ+QE9JHXED7/q5usPj73VAliWuP9A95wK7hKROYbQuxX+A4yWtmCvbBHd3fTL6Wgh4PIzSb0taHeiDu+NugYsARxfr3MyuwDUb9OnTJ51VJhJtjLQwVEh4GY0CRskD3x0M9MTjBb0jj220SEEzAZPNbON6DHUTnm70CeCpgnhPxR6yAjYM0V1FxMP+fODYgn7uNbN9izQZDfwKmAE8hD/0O+OG60SixWjvdoOWIh0lVUCc/a+eK+oFvByfp4V4rZhH0cv4UdHG0c+CktYpN5aZzcC9pC6mQCQHZN5Pe1PjhfQgubhQ8uB+lfBv3MV1yfj+JLBlznaxaO6eH8Ojuj4Znls/AlY1s6IeTIlEom2TdgyV0QW4SNIPcIHba3jY7c/xQHdT8bP4WpjZd+GCeqGkrvjvfQF+5FSO6/AAfQ8VlHcOY7RRkwPiUOAySftH/49QQQDB8K66BDg3vn8k6QD8mGyhqHYC8CruorocvkAAvIBHm00kElVI1bmrSppuZl0qrLsz8IqZvZgrOxpXB3+Pq4bPNbNrGjCPhXE18tLA3/H4QuflxyrT9jhgYTM7Nb7/Bn/Dfw1fFIaWC+MtqR8eR2l7eXjsIXggwC7AG8Cp5jmpCS+mf+DhORbG4z8Nru/9liK5qyYS84fGuKu29x3DzsCdRChrSYfgD/ANzezLeMvfuYF9rw8smDMsFwbAK4qkO4AVgK3j+3bAkXhU1Y3xoHfF7ADlGJHlTQh1862StjKzl/B803ua2fPyAITd69l3ItFiJB1Dy9AubAySVpKHlp4Yf1eUtAmwIx5VdYI8w9oJwB+ymERm9oWZXR19bCMPVT1JHu564SifqoIQ1+H6eS3QK+tb0qgQwCHpgHB9HSXpSkkXZ3M1sx0ijtKnUXQ8/va/vJl9bmbfmNmV0U++z6UlTY02vwI2DxfYLDz4hdH/I7jx+KCo+0PCU8rMZmc7GkkbSnoy7vlJSd2jvIOkc+JeJ0o6vAn/UyUSiVZAu1gYcEPuNRFs7jrgwjhKGQkcE2/1HwOLmdnrhY0lLQIMAwaY2br4Tuv3uSq1Qlyb2cf4cdToeMi/nutreeD/cB3Atni8onL0wCOk1oe7srGpCQ9+RO76+Ny45wMvS7pN0sFxr+ChMbaIkNsnA2dE+UHAysD6ud+zFknHkEi0bdrLwrAxntcZ3Id/syJ1ROnood2BN83slfh+Ne7Ln5EPcd2tjrlsCDxqZp+a2SzcPbWlmSt6MLPTcH3C/cA+eBwo8OB6N0l6AV88Mm+q/sDlZvZ9tM92NnNJ+RgSibZNe7UxzLMAhE3ha0mrmNkbBZfrCtE9T4jrMlQc7juYjCuPHy5y7XtqFvdCDUU51gdeyr7EjuYySVcC/5O0FHA68IiZ7SKP6TQqqpdbQBOJZiXZDVqG9rJjeJKa3MkDgSxsRWHo7L8Dl0haHEDS4vJEOFOAbqqJL7Qv8GgD5zIW1wssEerm3eqo/3fgbHnIbyQtLCk7FppKTbiKopFZC4lwHgcBmZ3iV6qRTa+OL26f4zuG96J8UK6L+4FDYu5IWpJEIlFVVOOOobOkfKTQ8/D8DEMlHYN792RZ0G4ArowH7e64jaALME7SLDzE9rlm9k3oBG6KB+I44PKGTM7M3pN0Bp7f4H3cI+qLMvXvludbeDAe4IbHXAI4B7hR0r4U31FkDJC0Ga5WfhPYLTySwBe58yXNwHcgA81stqSzgasl/amg73/hmdsmxm90JW7DSSQSVUKr0zFIOhE/656NB3w72MzGlKg7jMrCWzeJNqFIv1PxkBjTJD1pZpvEscsmZnZ91OmD52I+IteuS8Rf6ojnhRhqZrfVY1zDNRF/zt1fl6bUH1Qwh2FU8NsnHUMiMX+oGh2DPHTE9kDvfN6DRvbZlNqEkpjZJvGxG76wXR/lz+DZ0vIMltQftwvcj4fCrg/fArtK+ruZTavvXCV1zIzHiURbIukYWoZWtTBQJO8BgKSTgR3wXMVP4ruIWlsdefTT8/CjoGnAoMg6dgKwVV6bgHsVIWkb/DgmOx76fSxIU6PODsCCwB5mNiWMssPxHMljyRmSVaO4PhNYKzQEV+NZ1jIV8pL4MdAqeEC635jZREmDJe2Aez8tiB93TQNuMrPCMN3gO58rgKOAEwt+h5VijGWin/3N7O14w/8UNzyPl/QV7na6HH409CfchXY73LawQwTbq/O3TyQS1UVrMz5XkvegE76rmItK5D2QtBhNpE2IslPwUNTr4xqIfNjqjOOo0S+cX3DtVOC58P8/AcgfZ32L55P+cdxj3xKLQsYlwMDYAeWZR7ORu7YGnq3tz/F9VVwMtxMuyHskfouZUQ51/PbFSDqGRKJt06p2DNb0eQ/qq004FA9yB7W1CbvG5y2yz2Z2l6TP6nmLmxFeSGb2sKSlcg/2u2Kn9K2kj4Fl8fhGRYljsWtww3o+98LGufn+Bzg7d+0m8/DhGffErmAS/ptlGoZJ1Ogxtqrjty82t5SPIdEspOOhlqFVLQzQ9HkPmkGb0JgHXbExs/6+zZVVoocAX8TGM2947mL9A3xdcC07spsjaVbuiGgO0DF2VZdS/rdPJBJVRqs6SlLz5D1oSm3CY7gOIgtut0SROoXaiFLt++HHVV/WMWZJQnV8I3BArriUZqMhZItAud8+kUhUGa1tx9AceQ+aUptwKjBc0nh8EXm7SJ2JwPeSnsdtGM/lrg0GrpI0ETc+71fHeJVwLrUzqZXSbNQbM/s81NAlf/tEIlF9tDodQ3MS/v/XWqSvjAXhA2BMeA0ti+c9WAH3DppqZr+UtAC+0GyNH818g4eqfrPMWMMo4ecvaUPcG2rZ6O9x/IG+J35s0ywpM8MYn8/T/BP89zhSHi32GlxJ/QlulJ9apI9f4PkbOgD/MrMzC+vkSTqGRFOS3FUrp2p0DC3A10APSZ3MbCauilQmhQAAIABJREFUb3gvd/004AEz+weApJ5RPgBYHugZ5/E/Yd7z+oqIxecmYC8zeyrUzLtR+vipyTCzr/DjuWwuz1JjZD8A+MzMVpO0F3AWNalEs/odcG+obXHD+DhJI62C5EOJRKLt0KpsDC3EPdS4Yu6N6xIyliPnCWRmE3PlH5jZnCh/18w+A9cvZPUl7R47hYz+kkaH+23m5nkocLWZPRV9mZndbGYf5ScpaZ8wnM+Q9JWkF+S5HXaIvxPkuRIWk7ScpMei7AVJm9f1I4Qt54fU7CB2IvQdwM3ANrFo5dkQeM3M3jCz7/CQIjsV6Tu5qyYSbZj2uDDcAOwVHjc98ZhFGZcA/5b0iKQT5bkTwA282QP5XEnrVzhWN2BLfCG6PMasNL/CPXiYi864kO0+8/wKBwGHxufNcVfVfXLX1wMmVND/3niehuws8cfAOwChiv4C11XkmVsneDfKapHCbicSbZv2dpREKI274Q/Guwuu3SdpFeAXuAL4OUk9zOxdeQazrePfQ5L2MLOH6hjuxthlvCrpDepOypPnJ7iOYzk8LEhmz3gCOE/SdcCtMbdxuMF5QeB2M6tkYdiL2ilCy7nS1qdOItFstHe7QUvRHncM4Krlc6h9jAS4C6iZXR8G6nFEQh4z+9bM7jGzY/BsZlm8pfyDsdDHv/ChadTkV6iLi3DV8bq4lmORmMeZeEDATsDTktY0s8dinu8B/5H0m3IdS1oP6Ghm+Z3Lu7jRPTPKd8VDaFCsTvATPEJsIpGoItrrwjAUOM3MJuULJW0tqXN8XgwPGfG2pN7ZsVJ4KPUE3opmH0laK8p3KRhnD0kLyPNJr4LrLS4G9pP009y4v1bkW8iRz4ewX67uqmY2yczOwoPzrSmPj/SxeS7ofwO967j/QtsK+GKZjbM78HCRmEjjgNUlrSxpIXzXMbKOsRKJRBujXS4MYTz+R5FLGwDPhM7gKdwdcxxupL1DnuZyIq6xyHIQHAfciecs+KCgv3eAD/Fsad8AjwCb4A/UcyS9LOkl3FZQKHQbjGssRuMB9TKODAPz87h94R6gHzBB0nO4h1Oxe0PSdpKewQPm7SvpnNzlfwNLSXotrh8XbZaXdHf8bt/jmon74p5uNLPJxcZKJBJtl3alY2hJwqPnSdwD6fIoWwnY0cwuqqB9h4K4Ro2dTw/gv8CvIlJsR+AgM7u0wvYNCtWddAyJpqRSHUOyRTROx9AudwwtxNbAd9miAGBmb5nZRZK6hRvr+Pi3CXiYjPCIuh5XGyPpdknPSposD+VBlB8QbrCjJF0p6eIoX0bSLZLGxb9No8mxwN/MbErM5ftsUQgX2DHh/vpgaC2QhwO/QtL9wDWS1pE0NryzJqp2+JJEIlEltDuvpBZkHTzAXTE+BraNsByr4+f92cq+IdAjp6r+rZl9KqkTLii7BVgY+D/clvAVfoz1fNT/B3A+HiqjC+5BNQUPuf3fEvN5HNjIzEzSgfgikoXm3gDYzMxmSroI+IeZXRc2hg7FOosF7CCAFVcsFpk8kUi0ZtLC0EJIugQPu/0d0B+4WFIvPJLqGrmqYwtCbRwhKTNqrwCsDvwIeDSC6CHpplwf/YG14/MsPLzF5nhsp9dKTK+UayzAyFCJg9tdTpQrv281s1eLdZbCbicSbZu0MDQfk4ncCwBmdqg8VekzuGDtI1yMtgBumM6YG2pDHoG1P7Cxmc2QNAp3Wy0XMnyBqJ/P0YCkzE32+SJtLsJzSI+MMQcXm4+ZXS9pDC7Yu0/SgWb2cJm5JBJNSrIdtAzJxtB8PAwsIimfFa5z/O1KTYiNfSlxJBP1PotFYU089SZ4WtEtJS0RRuTdcm3uJxdtNXYlAEOAEyStEeULSPpTbpx5XGMLCfHfG2Z2Ie6m2rNU3UQi0XZJC0MzERqAnfEH+JuSxuKxiP6CJ7/ZT9LT+BFQqYB89+IJcyYCpwNPR9/v4SK7McCDwIt4CAvwKK19wjj8InBItJkIHImHDX8JeAGPAQWlXWMLGQC8IM9nvSa1U5MmEokqIbmrtlEkdTFPhdoRuA0Yama3ze95FZLcVROJ+UNj3FWTjaERqOH5HQ4FfpfrqiPuxbS2mb1U4fCDJfXHbQ6L4OK5JiHsDP8F3sBDb9xpZkfHtUE0Y86IRKIcScfQMqSFoXE0KL+DmV2CR3Ilys8AJtRjUSB7UDcjo2Nx64QHE7zNzJ5o5jETiUQrINkYGk9D8jvMRdIWeOa2P8T3RSRdJWlSCM62ivJBkm6VdK+kVyWdnetjqqSlQzj3UgjeJku6Px7sSOobdoenJA2Rh/eok1jwJlAkvHYplPIxJBJtmrQwNJ6G5HcAQJ7b+ipgPzPLYiUdChBRVfcGro6+wbOvDQDWBQZIykc6zVgduMTM1sFzZWceS1cBh5jZxrh2oiIkLRF9PlZpm5SPIZFo26SjpEbSwPwO2Wv0ZbiNIn9EsxmuKyBiGr1FjXjtITP7AiA8jlaiduIcgDdz+RieBbrFArSYmT0Z5dcD21OezcMbqjtwppl9WEf9RKLZSbaDliHtGJqGeud3kLQfnuHt9IIm5cRr3+Y+z6b4wl6sTrk+SzHazHriu5Pf5/QQiUSiykkLQ9NQ3/wOqwB/AwYWiVj6GDAw2qwBrIjncWgw5vmpv5KUCeT2qkfbV4C/4/qLRCLRDkhHSU2Amb1L8RwIG+Axkb7HF+F/mdk4Sf8EFgVulWq9zB+Oi98ulzQJz/swyMy+LajXEA4ArpT0NTCKGkFcJVwOHC1p5fg+SNLOuesbxW+QSCSqgKoWuEmaTYSvDm6I1Jil6p9gZmc0YJx/4bGGXmzANPP9LIgfLe2GHwnNAE4xs3sa02/03cXMpsfn44DlzOyPko4ErjCzGXFtKh6xdTYequMkM5snKqukwcB0Mzun8FqeJHBLNCVJx1A5SeBWmplmVp+z8RPwUBMVI0+oc2AD2hTzDDodd3HtEbuEZYEt69N3GX4l6Xj8v/lbwKAoPxK4Fl+EMrYys2mSuuOxl0qF604kElVIu7MxSOoqT6nZPb4Pl/Q7SWcCneRJaK6La79WTWKaf0rqEOXTJZ0WkUY3lifL6RPX9g4NwguSzsqNW6tNkXl1xtXQh5vZtwBm9pGZ3RjXfxYahPGSbpLUJcqnSjojrj0jz099n6TXJR0SdfoBv8fDbnfE3VinSboUt2G8I+kreQykvH/p4sBnuTmeGL/dg7i3UqnfOOkYEok2TLUvDNmDPvs3INw9DwOGSdoLWMLMrjSz44gdhpkNlLQWrhnYNHYdswmjMG4feMHMfmpmj2eDhU7hLDx7Wy+gb+4svmibHKsBb+f0DHORh+s+CehvZr3x0N1/ylV5J/QJo4FhwO54JNbTcnXWx3cHawOrxH39AXgbWMHMFov7/B/wSAjgHo1xkbQBbrReH9gV6FvqR086hkSibdMuj5LM7AFJe+ACtPVKtN0GNx6PC8NvJzzzGvgicUuRNn2BUZlOIXYeWwC3l2lTCRvhD/QnYi4L4UlzMkbG30lAFzP7CvdC+iY0DOAJgN6NeU3AXWWLLVBQc5S0Kp4BbhSe7Oe2nC1iZIm2iUSzkWwHLUO1LwxFkbQAsBYwE1iSXNiKfDXgajM7vsi1b0rYCMq5DpVqk/EasKKkxeLBXtjvA2a2d4m2mXZhDrV1DHOo+W9ciQaiFmb2uqSPqMkIV72eColEYi7VfpRUiqOAl3C18tDwBgKYlfv8ELC7pB8CSFpS0kp19DsGz7+wdNgj9saPY+ok3sT/DVwoz6eMpOUk/RrPw7CppNWivHNoHJqCr4DFil2Ie18ZN1Y/BuwiqVNoMnZoovETiUQro9oXhkIbw5nxQD0Q+LOZjcYfeCdF/SuAiZKuC9fTk4D7IzTEA9QktimKmX0AHI+HwH4eeAX4raRXgc6S/pE99EtwEn7G/2Kc8d8O/C+OpgbhSXYm4gvFmiX6OAhAHqYjH5tpiTCkT8FtJVvn7vkeSfmw3Y/EcdMjwHFhBB8PjMAD6t2CH0UlEokqpKp1DPMTuTFgDHCZmV0VO4grgE/N7JhG9NuxiFo6f326mXWJheFOM+sh6Ud4OtCdzWx8GLPvA/7a0OQ+2Th11Us6hkRTknQMldMYHUO17xjmJ1vjdoWrAMK+cBS+gxgnaZ2sYri7biBpUUlD4/pzknaK64PCRfUOfAfTRdJD4bo6KatXhkOBYfHWj5lNA44Fjon+h0naPTefTAhX33ESiUQV0C6Nzy3EOnh007mY2ZeS3ga64N4+H+L/DVYDlgZOBB42s9+GN9HY0AyAax96mtmn8kxxu0R/SwNPSxpppbd/6+D5pvM8Q41RuRTf1HMcwHUMxJHWiiuuWMcQiUSitZF2DM2HKO7FI+AQ/EipF3AlcK6Z3Qf8DDguzvdH4Sk7syfrA2b2aa6PM8Le8CCeRGfZBsylknuozzhA0jEkEm2dtGNoPiZTkyQHAEmL4/mfxwGfyFN9DgAOzqoAu5nZywXtfoqnEc0YiCuUNzCzWfL4RotQmslAH2r0DuAajezwPwvyl9lGMgN5fcdJJJqVZDtoGdKOofl4CPdE+g14fCTgXPysfwae+e1YoGsuXPd9wOHxcEbS+iX67gp8HA/rrfCEPeW4BI+I2iv6XQoP+53lgpiKLxQAOwGZy259x0kkElVAWhiaiTiH3wXYI9xVX8HP7E+IKjfjISZuzDU7HX8oTwx31cIkPhnXAX0kPYO/1U+pYy4fAL8GrpD0MvA+cKGZZRqLK3H9xVggvzup1ziJRKI6SO6q7RBJh+J2ji0iiU+zkdxVE4n5Q2PcVdudjSF8+i/A4xp9ix+jHBmZyhraZz/gaDPbXtKOwNpmdmYE0Hsly9Mg6TTgMTN7sEx3pcZYE7gK6A2cWFceBEmG54j4c3w/Go+jNNjMLsGPlxKJNkXSMbQM7eooKc7ub8MD3a1qZmvjRzt1etpUipmNzCUD2pmcS6iZndyQRSH4FDgCzy1dCd8Cu4abab0Jl9hEItEOaVcLA7AVMMvMLs8KzGwC8LikIfIcCpMkDQDfCYT47GZJUyRdlzMM/yLKHsfDUBPlgyRdLGkTYEdgSITjWDUvJJO0TYjYJoWobeEonyrp1JyobM2Y58dmNg6YVeG9fo8rrY8qvCBppRCuTYy/K0b5MEnnRXiMsyQNlnS1pPtjXrtKOjvmda9q4koV9p/yMSQSbZj2tjD0oEB0FuyK509YD+iPP8yzuEjz5DGQtAhusN0BD0f9o8IOzexJ3D30mMjx8Hp2LdoPAwaY2br4kd7vc82nRd6Fy4CjG3y3flw0UFLXgvKLgWvMrCduYL4wd20NPO/Dn+P7qsCvcG+la4FHYs4zo3weko4hkWjbpOMCZzNgeISt+EjSo7gN4kuK5zGYDrxpZq9G+bWE0rdCukf7zK5xNR624oL4fmv8fZbcbqS+hGL5GvwIambu0sa5fv8DnJ27dlNBePB7wl11Ep4D+t4on0QKpJdoYZLtoGVobzuGydT46+cpl0ehVB6DxrhzlRsvP2ZFeRPq4ALgADyDXCny9/J1wbUszegc/Bguq5vP9ZBIJKqI9rYwPAwsLOl3WYGkvnhe4wGSOkhaBs+6NrZMP1OAleUZzsDzLhSjVK6DKUA3RX4FYF8qzNtQXyKMxo344pDxJK6hANcnlMrklkgk2iHtamHIic62lfS6pMnAYOB6YCKeQ+Fh4Fgz+7BMP9/gR0d3hfH5rYIq2bHSDcCp8hSbm+FHSFtG++HAbXFEMwe4nHnZHlgd3M1W0rt4rueTJL0bITYq4Vw8SN+aEfdoHeAySW/hi9IfK+wnzw+AP9dZK5FItDmSwK0ZUE1OhG1wz6Cf5Y3PUWcUrn0oqf6SNAjoY2aHNcGc1sMT7GxrZm9KWhkPjLenmRUzyNfVXzci30O5eknglmhKko6hchojcGtXO4aWRNLmuOfSr7JFIdw/jw6X1T7AdeHK2klSX0lPSnpenmktO4JaPlxDX5V0dq7/n0l6Ktxab5LUJcqLurvi3k1nmNmbAPH3DOKtP9xy+8TnpeUB85DUTdLo6G98uOEmEokqJi0MzcPCwH/xjGnzxBcys5vxyKYDI/T2bDxt5h/NLHOZzbyIeuERWNfF7SArhGjtJGBP/L/hpsCr4TW1PDCjiLvrPPkhqCwnw8f4LqN3zOPCOuonHUMi0cZJC0PzMAs38B5QV8WgO/BBCNgwsy9z6TsfMrMvwi7xIh7hdCP8gX5H1PkcuDsWmfdxvQH4QtAtPhfLyVCXdxR4UL8rwxZyE3UvJEnHkEi0cZK7YfMwB3+bf1DSCWZ2Rh31yyXSKeYuKzxxTylvqGLurllOhom5er0pkpOB2jkXjgI+wsV/C+ARYhOJ+UKyHbQMacfQTETOhe1x5XGxnUPelXUKbkvoCyBpMZWPVfQ0rsBeLep3lrRGHVM6Bzg+jMaZ8fhIYEhcn0qNxmP3XLuu+G5mDu7B1KGOcRKJRBsn7RiakcjP/AvgMUnTCi4PAy6XNBNXIg8ALpLUCbcv9C/T7//CY2l4FmMJtzmUjBBrZhMk/QW4I9p0A7bKZYs7B7hR0r64y27GpcAtkvYAHmFeAVwikagykrtqGSTNxkM/ZOxsZlObqO8fAPuY2aXxfXk8ec7u5VvWe5xRFHGLlXQm8AfgTWAJoBPwXlxusvtM7qqJxPyhMe6qacdQnplh0G0OfoA/mC8FMLP3qX2E06yY2XHAcdC0eolEojlJOoaWIdkY6okirHbu+53yRD1Imi7pb6FFeFrSslG+rKTbovz50AKcCawaOoYhoRd4IeovIumq0CE8J8+3nI19awldw2XhIjpZ0qmNvMeDJQ3Jff+9PNz2atH/f2JuN8bRVyKRqCLSwlCeTvHgniDptgrqLwo8HVqEx4AsJtOFwKNR3hv3EDoOeD1Cch9T0M+hABHeem/ganmobiiia4jyE2Pb2BPP39yzITccXI8n+cl2lPvjNhFwd9VLYm7fAAcXNk46hkSibZMWhvLMjAd3LzPbpYL63wF3xue8hmBrXGyGmc02sy/q6GczPBw2IZB7C8+TAMV1DQB7ShoPPIeL2erUG5TCzL7CF7btJK0DzM7Sk+Lhwp+Oz9fGXAvbJx1DItGGSTaG+pP394faPv/5sNSNCZldrzDgEffoaKCvmX0maVjBvBrCv/CAfVPxXNMZhd4KyXsh0WIk20HLkHYM9Wcq0EvSAnGMs2EFbR4iMrTJQ3svTumQ3OBv6wOj/hrAisDLJeoCLI67kX4Rdo3tKphTWczsCTx72x54uI6MlTO9BX7MlUJ2JxJVRloY6s8TuIvnJNz3f3wFbf4IbBVhJZ4F1jGzT4An5HmmhxTUvxToEPVHAIPM7FtKYGbP40dIk4GhMcem4GbgsYKjr8nA7+ThuxfFo8cmEokqot3oGBShsJu4z8HAdDM7J74fDRyIHzfNBs41s2sa0G8/4LvIG91sFP4mebfVuLe9gIPN7NG4vhowGvgUv785cX1MqTGSjiGRmD8kHUMrQNIhwLbAhpFruSuwcwO764fnlW70wiCpQ0EO50raLAUcDrydLQpBL/zYqpuZfRtRXhdq7BwTiUpJOoaWoV0vDJJ2wENJLAR8gofB/ijellcEVom/F5jZhdHmROA3wDvA/6gJZX0CHmLiS4A4frk62myDHzt1BMYBv48H69SoswMexXQP3AX0EGC2pF8TD2j8iGiZGHN/M3s7jMx3RhjvfIKgfsApwAf4w3xtSWPwcOB5ih4lmtknki7CF6c8c4CHs2MtMysM85FIJKqA9m5jeBzYyMzWx9NwHpu7tibwc9y4fIqkBSVtgB+vrA/sCswNegcsVpilLa4tgmsABoTvf0fCEB1My+dOiFAUlwPnh5vsaOBi4Boz6wlcRwU5EWLeJ5rZ2gBm9tOc622vUHQvnNNpTABOq6PP+4EVJL0i6VJJWxarlHQMiUTbpr0vDD8B7gsj7zG4/3/GXWb2bbwVfwwsC2wO3GZmM2JnMDLqlgub3R33/c8C3F0NbJG7fmv8zeseCtkYF52B6xvm0Q4UYWyWra0MMwsWipPLVTaz6XgE1oPwncuIsEsU1ks6hkSiDdOuj5KAi4DzzGxkHL8Mzl0rlgcBiiwAYVP4WtIqZvZGweW6kuEUy51QF9kc5moqJIna5/3NEgU17BWjgFGxoO5HjSo6kWhWku2gZWjvO4au1EQU3a+C+o8Bu8hzNC+G2wYy/g5cEhoFJC0u6SA810K38OgBz2mQN+gWo1Dj8CR+hAWub8i0A1OpyaGwE26naDYkdZe0eq6oF67KTiQSVUR72jF0lvRu7vt5+A7hJknv4clvVi7XgZmNlzQCmIA/EEfnLl8GdAHGSZqFp/c818y+kbR/jJMZny+vY653ADdL2gk3Ph8BDJV0DGF8jnpXAv+VNBYX0TX1LuEkSUfmvu+E54z4Ab5beQ0/VkokElXEfNcxhJfPPlTgF1/ohVOmzybRExTpdyru5z9N0pNmtok8E9omZnZ91OkD/MbMjmjseAVj74LbI9aK+EnF6gwjfh9J/8KPyV7Mz7sJ5zOICkJ1Jx1DoilJ7qqV0xgdw3w9SpK0MZ7+snd43PTH3UAb02deT9ADN/RWkvS+XpjZJvGxG76wZeXPNPWiEGThJ/aqq2LM48Bc4LtEIpGomPltY1gOd9ec6xdvZu9LOlnSuAgXcUUYVmshaQNJj0p6VtJ9kpaLSycAf8jrCcxsrp5Ant9gkqShirSYkqZKOlXS+Li2ZpQvJen+aPNPcguMpMzH/0xg83D5PEpSP0l3Rp0lJd0uaaI8P0PPKB8c44+S9IaksguJpC7ApsAB5BYGORdLelHSXcAPc9dGSeoTYrXlgUfyrqmS+kt6Mu7tSUndo125nA/7h6vqozGfUvNN7qqJRBtmfi8MpfziLzazvvHG3wnfVcxF0oK4R9HuZrYBLv76m5pQTxBlpwCPh85hJC52K+Q4YHS4fJ5fcO1U4LnYDZ0A5I+z5tFJlPiNwBXU94bL66eSekf5Lrg77Lp47odNChtGTKb3cfFd3jV1LLBF3NvJwBm5ZvPkfIiF91R8QdiWMmG9k7tqItG2ma/GZzObLheNbQ5shfvFHwd8JelYoDOwJB647Y5c0+5AD+CB2Ex0wFW+9dUTHApcEN/zeoJd4/MW2Wczu0vSZ/W8xc2A3aL9w7ED6RrX7oqd0reSMp3EuyX62Ts3zxvi+/iY3/BwIX1f0sP1mFtXPAHQ6vhvll+YHsoC50nKcj4sDYwys/9F+QhqckQkEi1Csh20DPPdK6mIX/zBeBayPmb2jjw8RWFuAQGTzWzjwv7U9HqCxljni42Z9VdKJ1G7Az8K2hroIcnwRdBi4WzM/E4HHjGzXcKAPip3rWINRyKRqD7mt/G5mF98lndgWpyt716k6cvAMmG8Rh6uIlMtN6WeIJ8XYTtgiSJ1Ks2r0A8/rvqyjjEL2R0Ph7GSmXUzsxXwsN+bRf97yXM8LIfvuiolr+EYVEH9MUC/2PVkcZ0SiUQVMr93DF0o7hf/OZ7vYCru918LM/tO0u7AhXE00xE/aplM0+oJTgWGy1NmPooHsytkIvC9pOdxG8ZzuWuDgavkuQtmUJmIrpC9cQN3nltwT6g/4LuJScArzLvQ5d/wJ0qaE59vBM7Gj5L+BNR5BGVmH8Tu7Sn82G48vntJJBJVxnzXMbQUkmbjD9CMG8ys8IGbr3+CmZ1R6nqZdnP1Aw2YZmFfy+CG48PM7J/1bDsJ2BFfjObmjCioU1RDoibUPSQdQ6IpSTqGylHKx1ARM8Mbp1JOoLanTp3Icx8c2IA2pfIl7IErsvcGKl4YJD0ATDKzNzWvp29WJ68hSbkVEonEXOa3u+p8RVJXSS/nfPiHS/qdpDOBTuHvf11c+7WksVH2T0kdony6pNPk+Q42zvQDcW1vuS7iBUln5cat1SbKlirQGUzAj3tOBX4i6cdRr4OkYdHnJElHRfkoSRdIehLXh2ReTOD5GAo1E0U1JLk2h2teXUdF+oukY0gk2jbtaWHoVPDgHRAumYcBwyTtBSxhZlea2XHUhKQeKGkt3K9/09h1zCaMynje4xci30EW3A5JywNn4TaAXkBfSTuXamNmnxToDHYAPjCzB3GbwIBo2wv4sZn1CD3GVbl7XDQU2X/AtR0ZxTQTdeVWKKbrKNVXLZKOIZFo27T7oyQze0DSHsAlwHol2m6DRzEdF0cznfAcDeCLxC1F2vSltt//dbju4PYybfLshS8I4NqFf+OB/94AVpFnWLsLf8BnDI97eiy8sX4Q5fNoJszsXRXRkJjZsGhTTNdRtC9K6y8SiSYl2Q5ahva0MBRF0gLAWsBMXExX7CEn4GozO77ItW9K2AjK6SZKtcmzN7CspGxnsryk1c3sVUnr4W/thwJ7Ar+NOoWeBGU1E3XkViil66hIf5FIJNou7ekoqRRHAS/hD+KhuaORWbnPDwG7S/ohzI2BtFId/Y4BtpS0dNgj9qZu3QTRf3f8WOjHoV3ohusz9goj8QJmdgvwf0DvXNMB0X4z4ItMvVxqDKXcColEogjt6W2vUxh0M+7Fz+EPxCOxfiXpMeAkPEbSFbjv//iwM5wE3B87jFn423rJB2n4/R8PPILvHu42s/9WONe9gdsKym7Bj5RG4tqIbFHP72I+C+Pz4tTsIkpRSkOSSCTaOa1ex1DK175E3WG0w3wNodGYCXyIC+z2M7MZ9Wh/JHBF1kbS3cA+ZvZ5mTZTqUDrkHQMiaYk6RgqpzE6hlZ9lKSUr6FSZgLP4Abr74BDKm0Yx1xH4gELszn+styikEgkqptWvTDQPvI1vC9ppjz435So959K9AJ5zKyfmT2DpxtdLfq/Pe5/sjxe1Ny5qUZHcSI1+Roeyd3v0uX6KEfSMSQSbZvWvjC0h3wN/zSzTrhu4ZtwqX2d+uVryO6hI7AdNaE/fhv33weR0icWAAAgAElEQVQ4Qh6pFWrrKE6jJl9DsSB8pfooSdIxJBJtm1ZtfLaUr6FSvUDesD4a1zyAP8h3ic8rAKsDn1CZjiKjVB+JRIuTbActQ6teGCDlaygxZiHziPfkYb77Axub2QxJo6j5nSrRUdTVRyKRqFJa9VFSCV/7lK+hMroCn8UDfU1gozJ1S82xPn0kEokqobXvGFK+hoZzL3BI9P0yHqW1FFcA90j6oMDOUJ8+EolEldDqdQzNhdpYfoY4xjk6PI8qqd8ZuBI/dhO+mP4CXyT3MbNLGzOfSkk6hkRTknQMldMYHUNr3zE0J20xP0N9+CPwUXhYZWE2ZgFL49FXW2RhSCQSbY9WbWNoadSK8jMUoSNwXYw3W9JHkmbItRob5jQPO0b95ajJ6YyZvRxeTmcCq0Y/Q5TTVcRcLpY0KD73lfSkpOfjXheT54M4J+5joqTDi/yOSceQSLRh2vPC0KrzMxThe2BgjLcAMMjMOgOfAn/F1dy7AKdF/aHAXyQ9JemvOSP+ccDrcS/HlPpxJC0EjAD+aGbr4d5JM3Ebz8rA+qFGv66wbdIxJBJtm3SUVEArzs+Q5zvcMAxuJ/nWzGaFO2+3uI8JklYBfoY/1MeFl9bMCsfojicKGhf9fRnz7g9cbmbfR/mn9Zh3ItEoku2gZWjPC0NR1HrzM+SZZTVeA3MIzYOZzQmPKuL7dFyYd6ukOcAvmXcB+p7aO8dMp1BKDFhOJJhIJKqA9nyUVIpWl5+hIUjaVNIS8XkhYG08THihZuEtPCf0wuHau02UT8GTA/WNPhaLRed+3IW1Y5Qv2Vz3kEgk5g/tecfQmvIzLBpzEX6sdJiZPdnI+9sKuFYezG+BmNtluEG6q6QvgKFmdpSkG3G9xauEziK0IGfhOossrPdreEyot+O3mIW7xF7cyLkmEolWRLvVMbQmJE03sy7x+efACWa2ZR3N6upzEB425LAS368HHjCzq8r0MRiYbmbnxPdhVJDvIk/SMSSakqRjqJzG6BjSUVLrY3HgMwBJy0l6LLymXpC0eZRPl3SWPBz2g4XuqnF0dBowIPO4yg8Qx0CL5sbZQdIYefjwByUtK08wdAhwVPSxeTTfIlxY35CryxOJRJWRFobWQeY6OwW4HlgrjpaewaOZ/gX3kMqOvhbFPZw2wG0GtdxVzew74GRgRLiljoh2A6Lf93DDehaR9nFgowgffgNwrJlNxUOCnB99jI66y+FRYbfHNRHzkHQMiUTbJi0MrYNMI7Em7sL6PbA+bqD+Ghe9rWtmX0X9QnfVR81sVnzuVmacEeGi+6Oom+kYfgLcF+6uxwDrlGgPcLuZzYkQH8sWq5B0DIlE26Y9G59bJWb2lDx72jJm9pikLYBfAf+RNMQ8N3VF7qplxjBJdwCH42/9F+HxnEbKo7wOLtM8Hw68yVOiJhLlSLaDliHtGFoZ8vDWHYBPwgX2YzO7Ek++07seXZUL9w1+HJRlsutKTfiMfITXuvpIJBJVSNoxtA7yrrMC9jOz2fH2fky4hU4HflOPPh8Bjot+/x5lAyRthr8QvAsMivLBeLjx9/DQ2itH+R3AzZJ2wncXiUSiHZDcVRPNSnJXTSTmD41xV63qHYNqci50BN4E9jWzzxvY1xA8pMTduEH4FGB1M3strh8FnAf0LZczQdKRwBVmNiO+T8X1BdMK6i2FK6zBjcWzgczFZ8PwPKpk3gL+hRuwZwMHmNnY3PXFgHPweEpf4DaLS81saCX9JxItSdIxtAzVbmPIvH164FFID21EXwcDvXMRSScBe+Wu7w5UkoznSKBzXZXM7JOYey9qu432qsei0AHoB6xkZmsDP8Wz3uW5CvgIX+R644vf0kX6quqXiEQiUUO1Lwx5ngJ+DP4WLc9F8II8r8CAOspH4tqBMTmx2O3ATnF9Ffxte67TvqTLwpd/sqRTo+wIYHngEUmPNOZmJN0RArfJkg6Mso6SPpeH2R4LbIi7ti4raUEzm2FmH+f66I7rIwab2RwAM/vYzM6O6/1D8HYD8Jykc+X5sbP2f5X0xyJzSzqGRKIN0y4Whnhz3gaP8wOwK54TIcszMETScqXKzWxHanYfmVjsS+AdST1wvcEIanNinO/1xIPn9TSzC4H3ga0Kcis3hP1C4NYX+JMiYB7uYTTezDY0s6eAD4Cl8ICAhe6l6wATskWhBBvhgrd1cfFbXkW9B3BTYYOkY0gk2jbVfjyQeft0A54FHojyzYDhEer6I0mP4g/YUuUj5+nZuQE/Tvo5vvDsn7u2Z7xdd8TVwmvjgeqaiqNUk63tJ8CquDL6O+A2mGtfuBkXzZ0EDAGOlnQFRfI/SDoZXxyXMrMVovgpM3sbwMzGSVpB0rIx5odm9n4T3lMiUZZkO2gZqn3HkCXjWQlYiBobQylhVn0FW3cA+wJvZ4lsACStDBwNbBNZzu6iJs9Bo5Eny9kCD2OxHr7gZP3PzInflgMWDwP5gUB3eVTYXsBjwGSglzxCLGZ2WvxeS9SMxtcFw98C7IbvHG5oqntKJBKth2pfGAAwT9l5BP62vCD+UBwgz1+8DP6QHVumvFS/M/E4Rn8ruLQ4/kD9It6ut8tdawrRWFfgUzObKWkdfFdTjA+BhSVtERnXfocvWGPNbKaZvYwb0U/NFgdJi1B+gcx2SbtSv6xziUSijVDtR0lzMbPnJD2PP9Suxd03n8ezkR1rZh9Kuq1YeR39zvPWbGbPS3oOfyN/A3gid/kK4B5JHzTCznAXcFDczxQ8CVCxuc2RtBvwD0md8cXqD7jwbRczuw0//joHeF3SJ3jehT+XGjjubRngzbwhO5FIVA9J4NZACjQSL+HG4BnNNNYgIpeCPEfC73APqEVjDidFULtibU8DHjOzB5tgHmcAHczsL/F9JVxh3buUPiQJ3BJNSSU6hmSHcBojcGsXR0nNRF4j8R2eu6ClyDQNq+PeUA/HW3wtJHUws5ObYlEITgd2krRWfP8H8H8NFQ0mEonWSVoYmobRwGoAkm7P6QsOirLfSzo7qyxpkKSL4vOvJY2XNFPSNHlehgmS3pb0WnhGbVpq4HCfvR/YJ/qbKulkSY8De0gaJml3SdvJU3hmc+gnj7CKpJ9JeirmcZOkLiXGmgn8CbhU0nbAYmZ2XWG9pGNIJNo2aWFoJHJF8Hb4kQ7Ab0Nf0Ac4Qh7a4mbcWJsxABgRb94DgJ+aWSfgRjysRmas3hhPwLN2HdMYD6yZ+/6NmW1WYP94ANhI0qIFc1gad2XtH8rnZ/CHf1HM7G5cRX4Nbq8oVifpGBKJNky7MT43A/mIqKPxsNjgi8Eu8XkFPNTE0/JUmBsBrwLdcYP0ocAGwLjQnnUCPsZDV4wys/8BSBoBrFFmLoVeRIViO8zse0n3AjtIuhnP8XAssCW+8DwRc1gIV4mX4xKgU3g1JRItRrIftAxpYWg4mUZiLvIw2f2Bjc1shqRR1OgLRgB74l5Et0WyHAFXm9nxBf3sjHtFVcr6+Jt+RqH2IGMEvhh9Cowzs69iDg+Y2d71GG9O/EskElVIOkpqWroCn8WisCYeTiLjVmBnaofPeAjYXdIPASQtGZ4+Y4B+kpYK3cUepQYMd9SfAcMrmN8oPNnP73JzeBrYVFJmI+ksqdzuJJFIVDlpYWha7gU6SpqIe/A8nV0ws8/w6KsrZWGvw8V0VeAtSTPx5DnHmtkHePKcp4AHcRtCxuZ4OIwJkl4Ffg1snR07lULSQsC5uKp5N2CQpBWj3SBgeMz7aWrbK0qxhqSjK6iXSCTaGEnHMJ+RNN3MinoBNUX9aNMBOAtfFA6K7HD742rwDeoIoleqz8HAdDM7p1y9pGNINCVJx1A5ScdQZUjqKulleVhsJA2X9DtJZxJGb0nXxbVfSxobZf+MRQBJ0yWdJmkM7u66P3BUBAjEzK7C04X2l9RN0gu58Y+OBz8x7jhJz0u6JRTUiUSiikkLw/wne9Bn/wZEbKfDgGGS9gKWMLMrzew4aoR1A3PurpuGIXw2MDD6XRR4wcx+CnxOQaC/4BlKu8IOCK+rI4AFcWP4t8ABdd1Q0jEkEm2b5JU0/5nHuwnAzB6QtAfuGrpeibbbUNzdFXyRyILcieJeTuWC5Y0ws8GStgT+CvwA11UULi7zYGZX4DGh6NOnTzqrTCTaGGlhaKXIo52uhQe1WxI3TM9TjSLursE32bER8BqwkqTFzOyrXJ3euPjue2rvHvMhwocBO0fwvEF4qtBEYr6Q7ActQzpKar0chQfn2xvPvrZglM/KfS7l7loLM/sauBo4L2eD+A3wDS60+wj4YbjHLgxsn2u+GPBBjDmQRCJR9aQdw/wnr6AGd3kdiifW2TBEaI/hYStOwY9oJkoaH3aGk4D7Y4cxCxewvVVknOPxDG4vS+qER2f9f/bOPNzKqvrjny/igKJmRpo5oDgiKMiQs2A4lZpmimQmak5p5oRZlqGWWTgmDjnigPMUaTmDOCHILCaWion6c55QwYH1+2Otl/vec88599yByz337s/z8Nxz9rvfvfd7srPPXmt919oqivp8Ic/C+jTwMi7Cy/hdtL+Cp/1oai2JRCLRymk34aqSDLjBzA6M9x3xeshPm9nu8oI6V+FpLJYG5pjZ9+IL9wJgR9xOPx/Yz8xeLjPXKOAeM7u9yLX+eP2D1WK8x3EH735Eau1meuTCeVfEU3eA/yDYGE+7MShMRCOA1+L6SDO7ssgYfXDTUifgn8AvrZ7/gFK4aiKxZGhKuGp7OjF8AvSQ1CmyhO5EzRchwBl4aogLASRtFu2DgTWAzaLwzZqUTjlRlth8bgP2N7OnIh3FPrTAr/DwLSxyckuajD9zxi0VbEqXAofjIrh/ArsC/2rmpSYSJUk6hpahvfkY/oUnjwO33efTSHyLnIPXzGbk2t/IRGBmNjdUzEial/WXp7YelRtvkKTHJL0gKbPZH407i5+KsczMbjezN/OLlLSHpKclTZX0UGwoSNohF9Y6VdKKkr4laXy0PStpu/o+BEkbAN+k5gRRL5Ky+tFPxSnhOjzFRyKRaGO0t43hZmB/eV3jzahdEvNi4CpJYyWdKmmNaL8Vz0g6TdK5knpXOFdXPHPp94HLYs4ewOQK7n0c2NLMeseaT472k4CjI7x1Ozxi6cfA/dG2OTCtyHiFDMFPCHkz0D6SZki6XdJaRe75NrUjo+ZGWx2SjiGRqG7a1cYQp4Cu+BfjPwuu3Q+sB1yB29+nSupiZnPxNNm/xjOKPizpuxVMd6uZLTSz/+B1nyvJP5SxJnC/pJnAMGDTaH8Cjyw6FviamX0JTAIODqVyz4Jw1FLsT+3T0j+Arma2GZ6b6doi9xTTPBT1L6R6DIlEddOefAwZY3Dn7wBg1fwFM3sPuBG4UdI9wPbAHWa2ADdD/UvSm7gJ5WFqfzHmY/+h7pemAbNwQdrf61njRcB5ZjZGnsp7eKzvbEn3At8DJkgaZGbjJW2Pn0yulzTCzK4rNbCkzYGOZrbo5GJm7+a6XIHnVSpkLr5hZawJvF7PcyQSzUryH7QM7erEEFwNnGFmM/ONknbM8gBFBE834H+StsjMShGhtBk14aBvStok2vemNvtK6iCpG34SmQ2MBA6S9J3cvD+RtHrBvStT4xg/KNe3m5nNNLM/4+ksNg7dwltmdgUeVbVFPc9f6FvJ/AcZe+L6iVpExtePJW0ZTvOfUv8Gl0gkqpB2d2II09CFRS71AUZKylTAV5rZJEm7AleE8AtgIv4FD3AKcA/wKvAskM96Oht4FA9LPdLM5gPzI/fROSFKWwiMx2s15BkO3CbpNTwCaN1oP07SQDzdxXP4KWZ/YJikL/CkeD+t5yPYDz9x5DlW0p64Avo9PA03AJKm5VJ2HEVNuOq/SBFJiUSbpN3oGJobNSD9tbwi2wtRfwF5ic8LgWXjX5aXaDgVpLJubvLzRmTVDsCHuF/hBDN7OPqNxmtZf4FvkEeY2Rflxk46hkRzksJVK6cpOob2aEpaEuxF7Sym1+J1EXrhkUq3LpFVlWZYrO044LJc+2jcid4TPzX8bAmsLZFILGbSxtCMSFpH0sMR9vmwpLUlbY3b7UdEyGs3XEPwBoCZfZWdJILuksZJeimij7Kx75Y0WdIsSYfn2udFGO2UmLNLaCCek/SRpE+jzx6NeKSnyIWkmtk/Q3th+IlhzWI3pXDVRKK6SRtD8zISuC7CPkcDfzWzJ/FIqGFRR+FF4Hw8Z9Fdko4IjUPGxsAuQH/g96pJmHeImfXBTTnHSsoiqlYAppjZFrhP4/dRg+ENvDrb8nh67uMb8Ty7AncXNsaaDsTzOtUhhasmEtVNu3M+L2a2An4Yr68H/lKsk5mdEfb6nXGB2hBq0lnfG+GxCyS9hTuv5+KbQRb5tBawAfAu7sC+JdpvAO6U1BnYGndgZ9NmzvNKGCHpL/jJZssi1y8BxptZxcrpRKI5SP6DliFtDIuXkp79ODlcKukK4O3cCWBBrttXQMfQMgzCs6F+KmkcdXUT+Tk7AB8UKwBUIcPwSKljcX9In+yCpN8DXYAjGjl2IpFo5SRTUvPyJB4+Cl674PF4/TG5RHmSvq+an/Ib4BvAB2XGXRl4PzaFjan9K74D8KN4/WPg8Sjh+bK8AhxySlWBK0rkhroQ6CBplxjnZ7iZa0iWOyqRSLQ90sbQeJaXNDf37wT8F/bBkmbgNvhfRt+bca3B1HA+H4j7GKbhJqcDctXWinEffnKYAZyJaxsyPgE2lWdL3ZGajKkHAIdKmo4rrn/Q0AcMJ/MfqMnVdBlu2noqHOmnNXTMRCLR+mnXOoZQHF8A9MNNOHOA48zshSaMOQA4KWo87Al0j1QWhVqGM3A7/UONmGNj4BpC5WxmZf0Hkr7Ci+x0xFXNB5nZp42YtwueBuMYM/tbJfckHUOiOalPx5B8EDUkHUMjCFPOXXixmm5m1h34Df6LuFkwszFmdna8raVlMLPTGrMpBO/hp5NKhXCfRURUD+Bz4MhGzrsvfloZ0sj7E4lEFdBuNwZgIPCFmS0ScJnZNOBxSSPktQ1mShoMfhIIfcHtkp6XNDrzE0jaNdoepyYqCUlDJY0spmWQNErSj6Lfd8PMNFPS1Vn6DUlzJJ0eGoWZcVLAzN4ys0m4AvnUSh9Y0ql4YsDfxDpek/RmPOtx0WcFSfdKmh7tg3NDDAFOBNaUVDTldoyRdAyJRBXTnjeGUrURfohXOtscjwQaoZokc71xNXB3PDHeNqFBuALYA6+RUJgQjxJaBgDi/lHAYDPriZt7jsrd/k5oFC7F6zE0hT8DU/A6zofiJ4/1cGf2YfJaE7sCr5vZ5nHCuC/WuRawuplNxJXag4uMnz1v0jEkElVMClety7bATeEMflPSo7gP4iNgYiThIxzHXfHEdS9H3QUk3YCXv6yUjeL+zK9xLV7p7YJ4nyXYm0zuNNJAOsV6wau2XYVvPneZ2Sex7jvxje0+PMnfn/G61ZlWYX9qUnfcHGOc18j1JBKNIvkQWob2vDHMoibMM0+xgjQZdTQG8bopHvxy8+XnzM/XUD4r1DTkwmVrYWYvSOqDZ2D9k6QHzOwM3Iy0mqQDousakjbINsREItF2aM+mpEeAZSUdljVI6ge8DwyWtFRE4WyP5wUqxfPAuhGGCqUds7W0DAX3d5W0frw/EE9tsbgZD+wlaXlJK+D1JB6T15741MxuwJ3bW0jaCFjBzL5tZl3NrCvwJ2o0G4lEog3RbjeGiNHfG9hJ0ouSZuF1EG4EZgDT8c3jZDP7vzLjzMdNR/eG8/mVEl0LtQz5+w/G01fMxFNcXFZiDMDDbCXNBU4Afhs6ipUqee7cvFNw38ZEvPb1lWY2Fc+cOjFMT6fiOoYheARXnjtI0UmJRJukanQMEVHzY9ykshCvBfB0ib6jcPv47fWMeRKeOvrLGPfccmUxG7DWOUBfM3tH0pNmtrWkrsDWZnZj9OkL/NTMji09UqPm3hv3S2xiZs9HW1f88+iR11k057ylSDqGRHOSdAyV0+Z1DJK2AnYHtojMpYPwqmlNGfNIYCegf0TfbE/99v4GY2Zbx8uu+MaWtT/T3JtCMARPxZHMPIlEolFUxcYAfAsP21wAYGbvmNnrkk6TNCni7S8v5lCV1EfSo/JaBvfnQk9/A/w88gphZh+a2bVxT4N0BZJWlfRA3PM3chuMpHnx8mxgu9APHB+6iHuiz9fl9RZmSJogabNoHx7z16nPUOQ5V5WnzNgL+AZwSsy1apl7Ss3bWdI18YwzJO0T7ZeGPmGWpNPLjJt0DIlEFVMtG8MDwFqSXpB0iaQdon2kmfWLX/yd8FPFIuR1Ay4CfhS1DK4G/ihpRWDFvJ4gd09jdAW/x5PX9cb1CmsXeYZTgMdCx3B+wbXTgalxGvoNkDdnlarPUAszexdP8329mW2C6xUOifZSlJr3d8CHZtYzrj0S7afG0XQzYIdsIymylqRjSCSqmKoIVzWzeRFCuR2uWL5F0inAx5JOBpYHvo6HoP4jd+tGuJDtwThMLIUXsBGlQ0wboyvYPnttZvdKer+Bj7gtsE/c/0j8+l85rpWqz1CMIbl13hzvpzRi3kHkTFFmlj3PfvLqcR3xU1x33FGfSLQIyYfQMlTFxgBeAhMYB4yL6J0j8F+ufc3sVXlB+8IaBQJmmdlWheNJ+kTSemb2UpF7ylFKV9DcWoZsvFLaidoDuMloR6CHJMM3QYuNs6Hz1tk4Ja2Ln5D6mdn74eAvVRMikUhUMVVhSpK0kaQNck29gNnx+h15xbJiYrXZQJdwXiNpaUmbxrU/ARdnYZ6SVopfw43RFYzH01wjaTdglSJ9SukYCu8fgJurPqpnzkJ+hJcVXSe0BmsBL+OngkrWnZ/3AeCYrJOkVYCV8BTfH0paDditgetLJBJVQrWcGDoDF0n6Gh5a+l9cO/ABnk56DjCp8CYz+1yeqO6vYSLpiJtaZuE+gs7AJElf4AnpzjWz+ZIyXUHHGLesrgC31d8kaQq+ifyvSJ8ZwJfy+gijgKm5a8OBa8J5/ClwUD3zFWMI7uDOcwceCfXnEveUmvcP+Kb5LH5KOd3M7pQ0Ff/sXgKeaMQaE4lEFVA1OobFgZpRGyHpYmAbYBlgXWpONH+oT0/RVCR9Hy/Q0wk/Bf7dzH7ViHHWB24vkj7jBvzZPoo5ngJ+bWav1zdm0jEkmpOkY6icpugYquXE0OwUaCMWSPoG/qXeKMzs6Bi3K76BNLbecoOQl+y8APh+5DnqCBxWz22N4Xgzu1tSB1xx/Yiknmb2xWKYK5FILEGqwsewmFgc2og6hH9kYu79Jtl7eSqLsyVNlPS0pPWifTVJd4YWYKKkLXP3rxr6hGnytBXj8A3t3XiOL83s0ui7rqSxoUV4UNKa0X6DpAslPSnXR+xd6YdmZgvN7Bw8ZffOJZ456RgSiSqmPW8MzaqNKDWJmc0G5kvqEU0H42U5M943s/7A36hJY/1X4C9xDNwPuDI33ruhhegVp5JXge+V0CtcgudA2gy4jZpQVoBv4uahvXBHfEOZgmss6pB0DIlEddNuTUmLQRtRjquAgyX9Ci+P2Tt37ab4O5oa5/EgYKPcYWUVSZ3M7LMGPuZ3qNnYrgPOzF27OxIJzlCZamxlaPb0IYlEfSQfQsvQbjcGaH5tRBluw5XFTwBPmdkH+WUU6S88h9PnFYw9C+gTfxtCXh/RmC/5XkB5T2AikahK2q0paTFpI4piZp/iaSVGUtuMBDUlModQEwL6EK62ztZazpH9Fzz19vrRdylJJ8S1CbgpCuAnuG6hScg5HlgVeLCp4yUSidZHez4xLA5tRDlG41XRHi5oXz6c0UZNfYOjgTdCT9ERGCtpEn6SOSZ/s5lNlacPv1XS6vgJ52/yIkNrAJdLOhP4NjAnHNbr4FqLu4uss7u81gO4IG9ovL5I0k24T+MpYMcUkZRItE3atY6hJQn/xbJmdnqubS7Qo8C0lF2bZ2adc++HUmRjKLhnUR9J+wO7mdlBcW0ONTUiNgIeMLN16lnzojXkwnB7lLunkKRjSDQnScdQOU3RMbRbU1JLIukfeFK6i5ppvD0ivHWqpIfkKSry13vhJqbvRVhrp4IhVsJLmGb9747Q21nytCBIOhvoFPePjq5LSboi+j1QZNxEItEGaM+mpBbDzPYo0b5mmds6hdknYy1goaRt8Uior3CfwQzgZODE3LjTJJ1G7oQREU5jQ5exHjW+B/D03O/FF/0kSXeY2SmSjsmEenFi2AAYYmaHSboVz8x6Q+HCY3M5HGDttYtlIE8kEq2ZtDG0Xj7Lq6cLzEQ9gXPxMNtd8GR5lTAwTEndgIcljTOzecCxOZHbWvgGUEwX8bKZZZvVZLwqXR3M7HLgcnBTUoVrSyQSrYS0MVQnFwHnmdkYeVbU4Q252cxelPQm7mheHtdNbGVmn0oaR+l02oUpwJMpKdGiJB9Cy5B8DNXJysBr8brBmVglfRNP9PdKjPV+bAobA1vmun6hEhXjEolE2yVtDNXJcDwt+GPAOw24b2z4LcYCp5jZm8B9QEd56u0zce1DxuW4Mnp03aESiURbJYWrJhYrKVw1kVgyNCVctd35GEIEdgHQD7eZzwGOy9V4bsyYA4CTzGx3SXsC3c3sbEl7AS+Y2XPR7wxgvJk91Ig5DgCyGgvzgKPMbHqZ/l/hQr2OuHP6QDP7IKKL/o0ruJcBngEONbMv4jnGAnua2T9inHuAc8xsXEPXnEg0N0nH0DK0K1NShGreBYwzs25m1h3PYbRa+Tsrx8zGmFmWDG8voHvu2mmN2RSCl4EdIlPqmUTUTxk+iwysPfAU2Ufnrr0YEU89gTWpHbo6Fzi1kWtMJBJtgHa1MeDhnV+Y2aJSnRF++bikEfIaDDMlDQY/CUgaJ+l2Sc9LGh2bC5J2jbbHgR9m40kaKmmkpK2BPYERIRLrJmlUpNNA0ndDoDZT0tWSlo32OZJOl/YR7u0AACAASURBVDQlrm0c63zSzDJR2gT8C71SnsJTYtQikghOLLg2Ha/rvFNh/1JrK9Iv1WNIJKqY9rYx9MDj7wv5IZ5Eb3M8dHOEaorv9AaOw3/5rwdsI2k54ApgDzxt9+qFA5rZk8AYYFj8cn8xuxb3jwIGm1lP3NxzVO72d8xsC7wu9UlF1nso8K9KHljSUsB3Yy2F15bDU3PfV3DpD8BvSwxZ39pSPYZEosppdz6GEmwL3BS/oN+U9Cjug/gImGhmcwEioqcrbuN/2cz+E+03EErfCtko7s/8Gtfipp6skM6d8XcyudNIzDUQ3xi2rWeOTrn1TqZ2JtRucW0DvMbzjPyNZvaYJCRtV2TckmtLJBY3yYfQMrS3E0NWu6CQcvUICkVd2WbalHCu+uofZHPm50PSZng1tx+UqNiWJ1NOr4M7mYv5GNYHtgyHeSF/pLivoejaEolE26G9bQyPAMtKOixrkNQPTyg3WF7LoAuwPW57L8XzwLqRWgJq0mUX8jGeurrY/V0VNRSAA4FHyy1c0tr4r/UDGxJBZWYfAscCJxWK1czsDeAU4NdF7nsAWAU3ryUSiXZEu9oYopTl3sBOkl6UNAsXi92IJ6Objm8eJ5vZ/5UZZz5uOro3nM+vlOh6MzAsnMzdCu4/GPiPpM9wX8UhEUq6DHBWkbFOw4vjXBLO7IrEAfJ026/Gs+1fpMvdeE2IYmajP9IwJ3cikWgDJIHbEkQFNRfq6dvRzL5sxBxziDoMDb23OUgCt0RzknQMldMUgVu7OjFUAxEie0+8Hi7pckkPANeFqWuEpEmSZkg6InfPeEl3SXpO0mWS6vxvqyJ1F6J91whBnS7p4WhbIcJoJ8WJ5wfRvqmkiXFqmaHa5VETiUQbIDkPlyz5mgsvm9neRfr0AbY1s8/iy/xDM+sXuocJkn6Jm5/Ww30XnwMb4xFDtxeMVafuAv7j4ApgezN7WdLXo++pwCNmdoi8/OlESQ8BRwIXmtloScvgtSFqoVSPIZGoatLGsGSpVXOhBGPM7LN4vTOwWSaSwzOjHoFvBmeY2fYAkg7Bw1kLN4ZidRe64Gk6XgYws/dyc+0prycNnop7bVwsd6qkNYE7s5DdPKkeQyJR3aSNofXzSe61gF+Y2f35DvIcR4VfwFakT7G6CypybzbXPmY2u6D935KeBr4P3C/pZ2b2SIOeKJFoJMmH0DIkH0N1cT9wVBZ2KmlDSSvEtf6S1g3fwmDg8YJ7S9VdeArYQdK6MWZmSrof+IW0KAVI7/i7HvCSmf0VV1NvtjgeNJFILDnSxlBdXAk8B0yR9CzwN2pOfU8BZwPP4gn37iq4t2jdBTN7G/cH3ClpOnBL9D8TWBqvx/BsvAffdJ4N38jGwHXN/ZCJRGLJksJVG4FqUlpn3Bxptsfh6bcbFJ8pqRewhpn9s8T1vsBPzezYEtcHxLy7F7lWdk2ZzsHMtsu1TQM6mlmP+uaujxSumkgsGZoSrpp8DI2jEqdxQ+gF9AXqbAyhX3gGr5uwuFhR0lpm9qqkTfIXWmDuRKJiko6hZUimpMWEpJ0lPRX6gNskdY72fpKeDM3AREkrA2fgKTmmSRpcRL+Q1zZ0lnSNPO31DEn7RBGdV+WprmdJOr2By70VNxGBp/e4Kfcc+bn/Kum0eL1LaCfSf0OJRBsj/Z+6cXSKL/Hs3+D8RUnfwNNWD4oU1c8AJ0Tc/y3AL80sS/H9CZ7u4pZIz53Z+PvgyfJ+XDD373AtQ88o2pNFBJ0ax8bNcGdyQ5zCt1OTKXUP4B8l+p2Cb2ADgb8CB5vZwsJOSvUYEomqJpmSGkd9pqQt8foNT0RQzzK4c3gj4A0zmwRgZh8BRJ9C8vqFPIPI5TzKFe/ZL4RlHYFvxfwz6t5elPeA9yXtj5f9/LRYp4hoOgwYDxyfrzFR0C/pGBKJKiZtDIsHAQ+aWa2sq/ErvtIvyk9KtNfRHUSo6UlAPzN7X9IoXKPQEG4BLgaG1tOvJ/AusEYDx08kmkzyIbQMyZS0eJiAV3pbH0DS8pI2xFNWrCFP9Y2kFSV1pHR67mI8AByTvZG0CrASvpF8KGk1YLdGrPku4C+4fqEoktYBTsSr2u0m6TuNmCeRSLRy0sbQOAp9DGfnL4Y2YChwU+gGJgAbm9nnuJP3otAMPIj/sh8LdC/mryjCH4BV5PWppwMDzWw6MBUvRHQ18ERDH8jMPjazP8ca6xBCt6vw0NfX8SpyV8rLgyYSiTZEq9IxSDLgPDM7Md6fBHQ2s+Fl7tkT6G5mZ5fpM4DScf5zaEJaaknDgXlmdk5j7m/MuJIuBrbBfRfrAlnaij/gzufxZvZQhfMMoMRn0xwkHUMisWRoSzqGBcAPJf2p0i9qMxtDkUL3LUGYgVocMzs65u8K3FPgCC9MnLdYkbRU1MpOJBY75XQMyf/QfLQ2U9KXeDTL8YUXJHWRdIe8PsAkSdtE+1BJI+N1N0kT4voZkublhugs6XZJz0saneUACoaFpmBizi+wjqSHQyvwsLy0JpJGSTpP0ljgz3F/d0njJL0kaZFCWNIJYfJ5VtJxFbSfKmm2PL31Ro35AGN9P4rXcySdJddTzIuxP5K0QNKrknrGbSupSC0HldZizJF0mrx63b6NWWcikWi9tLaNATwy5gC58CvPhcD5ZtYP2AfPG1TIhXitgH7A6wXXegPH4WGc6+GmmIyPzKw/MBK4INpGAteFVmA0HrefsSGuUTgx3m8M7AL0B34vaWlJffDynd/Bw1cPk9S7nvb9Y50/BPqV+5AawKtmthVec2EB8G28XOeyZpal9eiPO5V7At3wU1tRLUZu3Plmtq2Z3Vw4YdIxJBLVTWszJWFmH0m6Di9gn4/jH4T/Ms/erySpMJJnK2CveH0jkLfPTzSzubAoF1BXajKQ3pT7e35urEz0dT0esZNxW4H55F4zWwAskPQWsBpeD+EuM/sk5rwT2A4PNy3W3iHaP4325jKPZePMxP01HwMfS5ovL8AD/tm8FPPeFGufT3EtRsYtlCDpGBKJ6qbVbQzBBcAU4JpcWwe8lkAt0ZeKi8OKsSD3+itqP7uVeE2J9kKNQbGxSy2s3IIXx5dotraF1F7nQmo+g2K1HIpqMXKU0lkkEouN5EdoGVqjKSmrInYrHhKZURi/X0x5PAE3M0FOHVwBg3N/s1/FT+bGOIC69Q3qYzywV2gYVgD2Bh6rp31vSZ3iJLRHA+drCsVqOZTSYiQSiTZOaz0xAJxLbiPATUsXhy6gI/5FemTBPccBN0g6EbgX+LDCuZaVVyXrgCeRy+a7WtIw4G3cL1AxZjZFrkCeGE1XmtlUcAdxifZbgGnAK/hm0VJktRx64p/rXWa2UNJQXIuxbPT7LfBCC64rkUgsAVqVjqGpSFoez2Nk8rw/Q8zsB4thHgNuMLMD431H4A3gaTPbXa4+vgqvq7w0MMfMvifpaOCw3FAdgU1xHca/G7GOfwI/NrMPmvZEi8YbAPwdeAnohIfCnhTXhuJ6j/ypbRz11J9IOoZEc5LCVSunLekYmkofYGSEon4AHLKY5vkE6CGpU/g8dgJey10/A7fPXwiLciRhZhfjUVdE+1nAtMZsCjHe9xq5/nI8FptbJ2CqpLvMrMFK6kQiUb20Sh9DYzGzx8xsczPbzMy2N7P/Lsbp/gVkP1Fq1TDAs5vOza2rTpZTSdsD+wE/j/fLqabOwlR5amtCQ/FBTn/wpqSD49ocSd+Q1FXSvyVdIa/H8EB8sWf1H2aEHmGEvExnvcSGNw0Pb20QKVw1kahu2tTG0MLcDOwvzxW0GfB07trFwFWSxoZorVYm0ggTvQY4KEu9DRwNYGY98Y3m2hj7ejwt9lrAyngYabF0FxsAF5vZpvhpKXPCXwMcGVqGihXK8uR8G+A+h4ysmNC0CPktekw1s8vNrK+Z9e3SpUulUyYSiVZCWzMltRhmNkOekmIIBSU5zex+SesBu+KZTqdK6hHJ9QAuxX0UeRPNtsBFcf/zkl7BhXQAD5vZhwCSngPWAV4tWNLLZjYtXk8GusYGtKKZPRntNwL15UTaLhz8GwFnm9n/5a7dUsTHkEi0GMmP0DKkE0PTGIOL6G4qvGBm75nZjeGgngRsDyDpIFxcd2bBLeX0DeU0GOX6VCzyyPFYqL17AkeVCAtOJBJtmLQxNI2rgTNyqSUAkLRjREgRmoRuwP/iFPFH4AAz+7JgrPG4XoLQC6xNTdbURhHV3T6WtGU0VaztMLMXgD8Bv2rKGhKJRPWRTElNIFJsXFjkUhYd9SW++V5pZpMk/Q1YAbizQLH9C+AS4DJJM/FkgkPNbEEDlN2lOBS4QtInwDgq13YAXAacJK8Ql0gk2gltSseQR9JXeH6gjL3MbM5inG+emXUuc/1ruObgkni/BvBXM/tRM65hDl4NbiHwJvBTIMswey6QzTULGGZmTxeO0dwkHUOiOUk6hspJOobifFZQp2BJ8zU8NPUSgKiC1mybQo6BZvZOaCR+g1dzuww/hUzAK8utCGyyGOZOJBJtgHblYyijFVhU0yHe3xMqYOR1DP4oabq81sNq0b5uaAMmSTozd2/n0B5MiXky5fXZQLcI9RwR2oNnK1jXnZLuk/QfSfkMr/UxHlgfT5f9AbC6mX0/IqM2AP4Ya3lN0mehkzgu5i2ni1hf0kPxeUyR1K3I55x0DIlEFdOWN4Z8Xea7oq2UVqAcKwATzGxz/Ms2S2lxIXBp1H7Ih3TOB/aOGgYDgXPljoJTgBfNrJeZDSuYo9y6euGJ7XriOoK1Knz+3XFT2qa4unqRhsHM7o/T1KG4RuIbwBpEbYjoVkoXMTraNwe2xlOB1CLpGBKJ6qa9mZLKaQVK8TlwT7yejKe/AC/0k31ZXk9NNTcBZ8mVzQtx5fBq9czRVA1DnrHhX5mBJ73bvp55i9WGGENxXcSKwLfN7K5Y6/x6niuRaFaSH6FlaMsbQzFKhfhk0UMZ+VPEF1bjoS9XxyHjAKAL0MfMvgiHcH2nkqZqGPIMzNfLljQL2FxSBzNb2IR5O9XTP5FItBHasimpGKW0AnOAXpI6hKmmfwVjPUHteg0ZKwNvxaYwEP+FDx4tVFhxrr51NRkzexH3M5weJi0kbRC+j1K1IUqN9REwV9JeMc6ymV4jkUi0HdrbxnAJsFRoBW4htAL4l/zLuE3+HLx6XH38Ejha0iR8M8gYDWwt6X3gbuAL4DR8Y3hC0rOSRlS4roqRlIWlrqVcojxJ/XFz1i/w0qPv4SnBXzezKcAovDbE0+RqQ5ThQOBYSW8CLwKrN2SdiUSi9dNmdQxLivhV/jTumL5G0lJ4/eP3ijidGzJuxyJq6fz1eWbWWZ6/6R4z6xERVBOB/c3sqVjbPnjaizcbu5aYbzgwz8zOKdcv6RgSzUnSMVROU3QM7e3E0BLsCMw3s2sAIhroeOCQCG3dNOsoaZykPpJWkHR1XJ+ahbhGuOptkv4BPFAmFLYURwPXmtlTsRYzs9vN7E1JX5d0tzwl9wRFzQhJw2Mt4yS9JOnY3HpPlTRb0kN4kr1EItEGSRtD87MpHsWziLDN/w+PbtoPQNK3gDXMbDJwKvBIhL4OBEaEzR9gKzw9945EKCxungK4VTUpsIv9b9mjcC05TgemRsK83wDX5a5tDOyC+1p+L2lpSX1wn0pv4IdAv1IfQNIxJBLVTdoYmh9RPFpJeK6ifeP9fsBt8Xpn4JT4gh+HRzGtHdceNLP3cmOchUcIfYWHw+4aYbmFEUf1sS0eZouZPQKsKinzldxrZgsiuuktPNx2Ozy09dPY6MaUGjjpGBKJ6qa9hau2BLOo0TcAIGklvNDOJODdMNsMBo7IugD7mNnsgvu+g5cRzWhoKOwsPKHf34tcKxZ6mm1opUJkk0MqsURJfoSWIZ0Ymp+HgeUl/RQgnM/nAqPM7FO88tvJwMq5dN33A7/IhZP2rjssUDoUthQjgYNigyHG/omk1akdIjsAeCdXTa4Y44G9JXUKodse9cydSCSqlLQxNDMhhtsb2FfSf4AXcN/Ab6LL7bit/tbcbWcCSwMzItS0sIhPxmigr6Rn8C/15+tZy5sx1znhNP43bhL6CBgeY83A8zgdVM9YU/BQ2mnAHZTROyQSieomhau2AlSTIly46eaYXDnOxo7ZC3du/zPXthdwBrAMrvYebma3N3L8rkRYbLl+KVw1kVgyNCVcNfkYWgeL8jpJ2gWvnLZDE8fsBfQl6lFL2hwX7+1kZi/Li+88JOnliIxKJFo9ScfQMiRTUutjJeB98JBWSeMjJPVZSdtF+zxJf5Y0WZ4Cu39Od7CnpGXwk8HguHcwcBJwlpm9DBB/zwJOjDHHSeobr78Rju0sBfdjoZ2YImnrlv04EolES5M2htZBliL8eeBKanwMPwayFNmb4/Z98FTg48ysD55q4w941te98RrUn+NpOG6JNN+3UERfgedQ6l7P2t7CTxlb4JFUf63vYZKOIZGobpIpqXWQNyVtBVwnqQce3nq1pKWBu3NpsD8H7ovXM4EFEak0E+haYo5i+opKsqUujdev7oX7P+pLU46ZXY6nAaFv377JiZVIVBlpY2hlRE6jbwBdzGy8vK7D94HrJY0ws+uonQp8IaE7MLOFkkr9bzoL9znMyLVtgZ8aoHbq8bw24ni8fvTmcT3VYEgsMZIfoWVIpqRWhqSNgaVwIdw6uG7hCjwj6hYNGKowzfc5wK8jmiiLKjoOyDK9zsHFcFC7FvXKwBtRy+HAWFsikWjDpBND66BTpMMAN+8cZGZfhfBsmKQvgHnATxsw5lhq0mz8ycxukfQr4B+SlsVNTgNzautz8NxLBwKP5Ma5BLhD0r4xZl6JnUgk2iBJx9BOkXQ28B1gl3BWLxaSjiGRWDK0ax2DvObA+cCWeJjn58BfsrrELbiOg/HiPeCRPrNxZ+19ZnbKYpx3fdwBPRs/bczDC/38p55bewN7AgslfWBmX5O0HtDfzG6OsQfhYru9Ftf6E4mGUErHkHwPzUtV+xgit9DdwHgzWy/CN/cH1qzw/mazl5vZNREa2gt4HTfT9Fqcm0KO2THX5sCNQL1zmtkuZvZxQfN61JQrTSQS7ZSq3hjwojifm9llWYOZvWJmF5USZkkaIGmspBvxX9pEwZrJkmZJOjwbS9Khkl4I8dcVkkZGexdJd8gL60yStE2pBUpaStJ/JX099/4leaGcGyRdGut8QdJu0aejpPMkTZQX0vlZAz6TvEDuZ5IuyK3lPknbxuu5kr5WcO/ZwMDQVBybvyDpD5KukvRorP/oMs+cdAyJRBVT7aakTSldnzkTZs2XtAFwEx6uCV6ApkemAgYOMbP3JHUCJkm6A1gW+B0eCfQx7pCdHv0vBM43s8clrY1nR92k2CLCiXwTLlYbiRfAmRTzgafj3gHYAE9RsT5wKB6N1D8cxRMkPWBm/yvxrBuFk3mlWPd3SvSrj1PImY7ClJRnQ+C7wNeAf0u6LCrUFT5z0jEkElVMtW8MtZB0MV6A5nNgEKWFWRNzmwJ4cfu94/Va+Jf06sCjWZEcSbflxhgEdI8vdoCVJK1YxDSTcRVelGckcAiubs64NUJBZ0t6NebeGdhEUmbWWTnaS20Ms3MCuQOAy4DdS/RtCveEo/otSe/htSH+bzHMk0gUJfkSWoZq3xhqFcUxs6NDHPYM5YVZi0IuIyR0ELCVmX0qaRwu8CqnCu4Q/T+rZJFmNkfS+/IaCr2BB/KXC7vH3D83s4crGb+AMcCl8TovWoPyRX0qoVQBn0Qi0Yaodh/DI8Byko7KtS0ffysVZq0MvB+bwsZ4dBPARGAHSauEmjhfle0B4JjsTZxK6uMqvJ7CzbGmjH3lbIifVv6Dm6Z+nqmYJW0UZq5K2BZ4MV7PAXrH+F2pEbCVolAUl0gk2iFV/YvPzExeY+B8SScDb+OngV/hvodKhFn3AUfKC9bMBibE2K9JOgt4Go8yeg74MO45Frg47umIVzc7sp7l3gVcDYwqaP9v3P9N4HAz+1zS3/Caz9PCXPUW8IMyY2c+BuG/6jMH+qPAa7iT/VlqkvCVYiqwlKTp+Eb2XD39E4lEG6TdCdzkZS0vAPrhX6JzgOPM7IUifTub2bz45X4XcHUxfUSYo04ys90l7Ql0N7OzY9N6wcyek7QlXtbzZ2b2UNx3A3C7md1dwbo3Bq7BneGnmtk59fTPiv90BF4GDjSzD+Lk8G98E1wGN7sdamZf5O69EE+LsVbB6abBJIFbojlJOobKURMEbtVuSmoQoXu4C09Z3c3MuuMlN1crccvw+CX+LP7lWu8XuJmNMbOz4+1euJP6VLws5pBsU2gE7+EnlbIbQo7PQtvQI+7Nh5e+GM7qnrjmY7/sgqQOePruV4HtG7nWRCJRxbSrjQEYiGcmzesepgGPSxohL4YzU17YBuAe4AN8Y9gZuCE2FyTtKul5SY8DP8zGkzRU0ki5bmJPPEndvrjm4ghJP4p+38XDbc+UdHWEpSJpjqTT5dqLmXFSAFgDuAIPZT0+tAaVlv98Cvh2YWOEmk4suDYwnvdSYEjuuYbHOrOCQMdSgqRjSCSqm/a2MfSgbrEa8C/2rBjOIGCEpG/Ftd54FtLuuDJ4G0nL4V/SewDb4aGttYiazWOAYfHLPXMIE/ePAgabWU/c3JN3oL8ThXEuxSuvYWbT4lf+ZbiGopeZ1VtNTa7u/m6spfDacrjm4b5c8xBc83EXsLu8FkTGxrgOoz/w+4Jr+We/3Mz6mlnfLl261LfERCLRyqhq53Mzsi1wU/yCflPSo7gP4iNc8zAXIMxKXfF8RC9n+YjCV3B4sYFLsFHcn/k1rsVNPZlK+c74O5ncaaSBdMqtdzLwYO5at7i2Ae7jmAEgLwn6PeB4M/tY0tP4SSkz7N5rZguABZLewk1wcxu5vkSiwSRfQsvQ3k4MsygesllOs1Aqdr8pXvv6KqdlczZFK5BVhVsHdzIX8zGsD2wZDnOAXfHw3Znyms/bkjMnkXQMiUS7oL1tDI8Ay0o6LGuQ1A/PLTRYnseoC+50nVhmnOeBdSV1i/dDSvQrpQt4Huga6S/AdRaPVv4YlWNmH+JO65MKTT9m9gaeBuPX0TQEj5rqamZdgXWBnSUtTyKRaDe0q40hymHuDewk6UVJs4DheEbSGXgupEeAk82sZKoHM5uPm47uDefzKyW63owX2pma20Sy+w8GbpPXaV6I+w5KIml1SXOBE4DfypPgrVThc0+NZyuWOfVuYHlJO+D+g3tz930CPI77UhKJRDuh3ekYWgPyvEx3ApuY2fOhLbjHzHrkNRHNNNdQPDLqNdykdH6UCm0Rko4h0ZwkHUPlJB1D9TEE/yXeUrUPbgmfwgDgLHlxo0QikShK2hhaGEmdgW1wPULZjUHSCqEdmBTmqB9E+2OSeklaNfQM80JTMS3+rVpsPDN7C8+jtI6k/pKejHGflLRRjP20pE1zaxgnqU+ptZRYd9IxJBJVTNoYWp698HKfLwDvSdqiTN9TgUfMrB8uPBshaQU8bfdQM3sXVy0/b2YbZxXkor0O8tKd6+H5mZ4Htjez3sBpwFnR7eYYk9ByrGFmk8uspQ5Jx5BIVDcp3LDlGUKNXuHmeH9xib47A3tKOineL4cn17sN+J2kYXh9h1H1zDlYXrltAXBEFAlaC7hWXsTIgCxi6VZc8/B7fIO4rZ61/LveJ04kmonkS2gZ0sbQgoSJZ0eghyTDU4EbcEmpW4B9zGx2kbEexDOu7kdNZbpS3GJmxxS0nQmMNbO9w/k9DhZllX1X0mbAYOCI+taSSCTaFsmU1LL8CLjOzNYJrcBaeHK+NUv0vx/4RS4/U+/ctSuBvxJlQhuxlpXxSCWAoQXXbgZOBlY2s5kVrCWRSLQh0sZQgKR5i2HM4WGCGYLXSn46nMT/Bj7FM7x+TdLIglvPxE08MyQ9G+8BCLv/R3gq7oaspWucVt4H/iTpCaAz7pDO5r8dd4zfWm4ti+OzSiQSS55kSmpBzGyApNnAfmY2PRLcbRT1GoYCfc1sHDVmnc+oMeXUQtIa+Mb+QLHruTlHUdcH8RLQ28w2jLGOwgVw2T1vUvDfRrG1pI0h0dIU0zEkv0Pzk04MFSBpj/iVP1XSQ5kOQGVSUUs6VdJsSQ/hSfMyvgm8AZ722szqVEmTtI6khyXNiL9rR/soSZfF5vIKcIeZLYxUHiMilHSGpKKbSY7P8JNL5psYTO50UGb+dSU9FfOcWWTcRCLRBkgbQ2U8DmwZoZ2Z/T2jTipqSX1wU0xvPDtqv1z/84HZku6SdIQ89XUhI3FfxGZ4nei/5q51BTbB04AfGPcfCnTDTT2Glzp9TlKpaCfiOfaXtCaeEO/1Cua/ELg0QlZLpgxJOoZEorpJG0NlrAncL89rNAwvsJNxr5ktMLN38NrMq+E1Gu4ys0/N7CNytRDM7Aw8iugB4MfUroWQsRWevwngejzLacatZrYwUn6/hG9MO+O1JsA3hjfxcqX5jKqF3AfshPs9bqlw/m3wWg1Ze1GSjiGRqG6Sj6EyLgLOM7Mx8lxGw3PXGpyWO4r2XCrpCuDtUkrl/C0lXmfvBfzCzO6vZ5z8Gj6XNBk4Ed/oyiXKKzd/ItFiJH9Cy5BODJWRD+08qIL+44G9JXWStCK5L11J389CPvFCOV/h5UPzPElNuowDcFNWxr6SOsizta4HzMZDSY9SpNWWtGEpVXIB5wK/KqKULjX/EwXtiUSiDZJODHVZXp7eOuM8/IRwm6TXgAl4nYKSmNkUSbcA03An8WO5ywfiPoBPgS+BA8zsq5q9AvD6CVeHsvltPEV3xmy8dsNqwJFmNl/SlbjvYUpsOm/jqTfKYmaz8OJFhZSa/5fAjZJ+CdxR3/iJRKI6aVMbg6R5ZtY5934oHgJaqPotiZmVOkX9vUjf4bm5dge+AP4ev9wvNLONJO0FvJBFH5lZqcR5Q6mp7zwHV0gX4wkzwRLuSwAAIABJREFUOz4372jcZ/EFXlzoCDP7ovAmSV8BmVjtpVz7HPwzGgWMUk3a7zrzm9nLuP+BiMB6VdJoM0unh0SiDdGmNoYlRWwElwP9zWyupGXxX/Dgv9zvAeqEpTYTo4GfxOsbgZ8Blxbpl5X6bC5+DuwWm0Ui0SIkHUPL0G58DPVoA36U6zcv/n5L0vhQKD8rabto3zli+adIuk2eRntFfJN9FyCilGZL2hrYE89EOk1SN0lTcnNtEA7gwrUWmwMzG2pmt+f7mtk/LcBPDGvGGD1Vk4Z7GtBJ0tMN/MyK6jQkXYb7N8ZIOr78KIlEotpoaxtDp4IvwzNy18ppA4rxY+D++JW9OTBN0jeA3wKDzGwL4BnghMhVNAZ4RdJNkg6Q1MHMnoz2YZEO+0XgQ0nZL/eDKVAll5qjvgePU8uBRPirmc3MpeHuhUcTLSVpQpi3KqWOTsPMjsR1DwPN7Pwia0k6hkSiimlrpqRa5pLMxxBvt8LFZuAx+H+pZ6xJuAN2aeBuM5smr4vcHXginMXLAE8BmNnPJPUEBuG+gp2om5wOPPndwZJOwBXH/Quub1lqjnq4BBhvZo+VuL62mb0ur8nwiKSZsVEVCz/Nt91rZguABZIyncbcIvfU3Gx2OW5ao2/fvim8NZGoMtraxtAQsi+sL4mTU0T0LANgZuMlbQ98H7he0gg88dyDZjak6ICeiXSmpOvxrKlDi3S7A6918AgwuUioqMrNUQxJvwe6UCKvUqzt9fj7kqRxuCr7Rdz8tQrwTnT9eu41lNZpJBItTvIntAxtzZRUjlKx+XOAPvH6B0TBGknrAG+Z2RXAVcAWeKjqNpLWjz7Lh2agc0TzZPTCw1QBPsZ9EACY2Xxcd3ApxTOjFp2j1ENJ+hlu6hliZgtL9FklHOKZqWobapzh43ATFPKkfj8BxpaaL5FItH3a08ZwLG7CmYF/Ef4y2q8AdpA0EfgO8Em0D8D9ClOBffDw07fxU8BNMc4E3AYv4GR50rxpwOnUnBZuBobJE/B1i7bR+ImlTmbUMnOU4jLcvPNU+FZOA5DUN/QN4LmVnpE0Hf/SPzuXvO9MYP24NhUv+3lDmfkSiUQbRx7M0jZoDh1DE+beHf+S7YCfOi40s78V6hii70l4EZzf5drG4fqBZxo47zHAcXgSvS6Rs6lYv7yO4X9mtme0z8E/o3fi/YBYx+71zHsscBQwpZyOoW/fvvbMMw16pEQi0QxImmxm9VV3LEqyFzcDDdExSLoL/xIvJWBrKE/E+OPq6Zd0DImqJ+kYWoZ2Y0pqLToGYB0z28zM3mmIjkGepntawb9dzGxqKKUXx2fWKB1DCldNJKqbtrYxtFkdg5ntndclxL+Ks6kCy8WX9WLXMaS024lEddPWTEntWcdQHy2mY0gkFhfJbNQytLWNoSG0GR1DJSQdQyKRqJS2ZkoqR5vUMVRC0jEkEomG0J42hjapY5B0rLx+xJrAjEy7kHQMiUSisbQpHUNDKdQ9NNOYw4F5ZnZOvD8JT4X9JW6KORf4JgU6hgrGHQB8Hg7txUZDPhNJo4B7CjO+5kk6hkRzUhiumnwOpUk6hlaKpCNxJ3R/M/tI0sq4b2FpGq5jGADMw01iTV3XUmb2VVPHSSQSbZP2ZEqqCEl7SHo6TD8PSVot2ovG9Me1U8OM9BCwUW643wA/N7OPAMzsQzPrEyGzm8ccM2PczAcwR9LpoWGYKWljSV2BU4CzJH0m6T+SZsX9leoyBkgaK+lGahTQlXweK8eaMgf98pJejWitUvckHUMiUcWkjaEujwNbmllv3D9wcu5anZh+SX1wp3ZvPBy2H4CkFYEVIyS0FpKWw/ULg82sJ35yOyrX5Z3QMFyKp6eYA5wN/MbMOpnZBnh5zgsaoMsg1n2qmXWv6JPANzNgOrBDNO2B6zvqlA/N3ZN0DIlEFZNMSXVZE7hF0rfw0NV8yodiMf3bAXeZ2acAksZEX1FcIwB+qnjZzF6I99cCRwMXxPs74+9karQXhTRUlwEwsZEpLG7BNRdj8U3wkkaMkUg0meRTaBnSiaEuFwEj45f8EcByuWulYvrrbABhPvokBGWFqJ41ZPM0RDdQVpcRfFJ4U4WMAXaT9HU8tPeRRo6TSCSqgLQx1GVl4LV4fVAF/ccDe0vqFOajPXLX/gRcLGklAEkrSToceB7ommkV8PDZR+uZp5YeggbqMpqCmc3D60lfiEchJcd1ItGGae8bw/KS5ub+nQAMB26T9Bi1FcBFMbMpuKllGq5qzpfWvBQ3v0yS9Cz+5f9piNwOjnlmAgvxugrl+Ae+AU2TJ/RrqC6jUop9JsQz/iT+JhKJNswS1zFIMuA8Mzsx3p8EdDaz4WXu2RPobmZnl+kzgBJ1BVRQg6ARax5OTqvQXNQ3bugGdgA+xE1cN5nZ6SX6jqNIfQdJhwDH46anDrgz+u+SzsBrRj9U0H8AFdRnKEXSMSSak6RjqJxq1zEsAH4o6U+VflGb2Rjc7t3iSFrSn9kwM7s9Ipuek3RdoUM5UlvUQdKawKnAFmb2oTxleBcAMzttcS88kUhUB63BlPQlXuSmWF7/LpLukDQp/m0T7UMljYzX3eSppCdJOiOL2w86S7pd0vOSRoczNmOYpInxL8tLVK5mw3mSxgJ/jvu7l9A0nCCv3/CspOMqaC+lgaiPzCn+SYwzR9Jpkh4H9s2N30HStZL+gCuuPwaWkafueBzI6jy8J89Gi6Rd4zN7nFxUlKQV5JqLSXINxQ+KLSzpGBKJ6qY1bAwAFwMHyJXBeS4Ezjezfni+oivr3Ol9Low+rxdc642XveyOF5bZJnftIzPrj9dpyMJEy9Vs2BCvkXBivC+laTgYt+1vCRwmqXc97XU0EPUwIr7U5wI3m9lbuWvzzWxbM7s53neM53jBzH6L6xHexMNgpwK/y2o74CeweXESuQJ3om8HrJ4b/1TgkfisB8ZaVihcYNIxJBLVzZI2iwAe2inpOtyh+lnu0iD8l3n2fqWI/MmzFV4+E+BGIG+fn2hmcwHiy7QrNdE7N+X+ZsVmymkDbiuIximmadgW1zRkv+LvxL9cVaK9A8U1EOXITEmdgYclbZ3Ln1ToGP4bcKuZ/RHAzL6StCu+AX0XOF9SnwJ/zsa4xuI/saYbgMPj2s7AnuEHAj+1rA38u4J1JxJNJvkUWoZWsTEEFwBTqJ2KugOwlZnlNwtqW4TKUq6WgJV4TYn2wuieYmOXWli5BTfK+29m88LBvC01+ZMK1/gkMFDSuREJhXm0wURgoqQH8c97eIVrErCPmc1uzJoTiUR10FpMSUR5zFuBQ3PNDwDHZG9UUxIzzwTczAQ1cf2VMDj3N6uQVkobUCnjgb3k+YRWAPbGw1fLtZfSQJQlnODfwYvtlOIq4J94WGxHSWtI2iJ3PV83IuN5YF3VpAjPFwy6H/hF5quR1LvS9SYSieqhNZ0YwFNSH5N7fywuEJuBr3U8cGTBPccBN0g6EbgXD+WshGUlPY1vjtmX37F4Oc9hwNu4X6BizGxKhJROjKYrzWwqLAo1LdaeaSBeobYGohQjJP0WVzQ/TE36jFJrOi98N9fjifjOkbQGMB9/xiML+s+Xi/DulfQOvjn2iMtn4ie7GbE5zAEaFcaaSCRaL0tcx9BUJC2P13o2SfsDQ8ysaLRMM833FbWzk95sZmerhG6ggvF6AWuY2T9LXO8L/NTMji12vZ6xG7Wm5iTpGBLNSdIxVI6qXMfQVPoAI+MX7AfAIYt5vs8iiqe56AX0xU0+tZDUMb7U0zdrIpFoMVqNj6GxmNljZra5mW1mZtub2X+X9Jok7SzpKXlNhdsigghJ/SQ9KWl66CdWBs4ABoeWYLC87sMsSR8Bb0v6r6QPJR0sry19jbxOwwxJ+8S4l4ZuYJakokroEuucJ+mPsZ4Jqqk90eCaFAXjJh1DIlHFVP3GsAToFF/i2b/B+YuSvgH8Ftc8bIH/2j9B0jJ4OOkvzWxzPBT3E+A04JbQE2ThpvOB1cxsFbws6GNmdg3wO+BDM+sZWossy+mpcWTcDM+TtFmFz7ICMCHWMx44LNobVJOicNCkY0gkqpu2YEpqaeozJW2JC+qeiOCdZfCop42AN8xsEixKy10q9HZMYYhuMIhc5JWZvR8v9wuHcUfgWzH/jAqe5XPgnng9GS9DCg2vSTG3grkSiSaTfAotQ9oYmh8BD5rZkFqN/iu+Uk9/qYyodYr/SFoXOAnoZ2bvR/TTckXuLcYXVhN9kNd5XIQnNhwjT6I3PHdPOW1IIpFoAyRTUvMzAdhGNfmXlpe0Ia4PWEPSotKfoUUorLNQjkJdxyrASvhG8mH4AnZrhmdoaE2KRCLRhkgbQ8Mp9DHUSv1tZm8DQ4GbQn8xAdjYzD7HxXQTJE0HHsR/2a8O7FrMX1GEPwCryBPxTcd1Ft/G8x7Nws1CTfH2dpB0QaxpgqR3qX1C6IzntHpR0nPAOsC6TZgvkUi0Qqpex1BtSJpnZp1z74fitSGOKX1XybEafW+J8c4BVgEOj7xKB+Oivz64CetJ4Fozuyz69wJWNLOSwrykY0g0J0nHUDntXcfQZpDUBa/ktnY0HWdmT0jqjyuOO+FJBg/GHcJn4CeYbfEyop2IjSJ8DR/hGonVgZMj+V4HPIvsDjFGB+BqXEdxMLBulizQzK6RF/YZhKdH/yLbFOL6tMX2YSQSiSVG2hhank7yTK8ZX6em6FCWZvxxeS2I+4FNcP/E9mb2paRBwFlmto+k08idGOIEkedbeJK9acDoSKWxMrBqtL+FZ0a9Glgf+F8WLZXjGTzKaSEeuVQvESF1OMDaa69dT+9EItHaSBtDy1Mr3DUzB8XbUmnGVwaulbQBbtKpox0owd1mthDYTNLHZtYrfAjTzWxGzD82WwrFo6YqTmWbYWaX48WX6Nu3b7JVJhJVRtoYWhel0oxfBIw1s70ldQXGVThe3nGsgr+F/BdYR9KKZvZxrn0L4HZgWeBHFc6bSCwWkk+hZUhRSa2LUmnG8+GjQ3P9GxLqmvE4sI+85OdqwACAKCJ0LXCeoma0pJ/iKuwncJX1spIydXSW4mOHBs6fSCRaOWljaF0cC/SNPEjPUZMS+y/AnyQ9ASyV6z8WNz1VEuqacQeuVH4Wr/D2NDWpyn+NO7dnS3oNOAH4gQV4HYmdIlx1Fi58KyynmkgkqpwUrtoOkdQ5KsCtiteI2MbM/q+gz+rAfcAl4TNoFClcNZFYMrTrcNUwh5yP5yh6H8//8xczu6uF13Ew8Mt42x2YjaeMuM/MTmmB+YcBp+PJ9z6Oti2Ab5rZffF+R+BT4GxJX8PzIJ2Z3xRCsX17OMhL5oQq6JdItAhJx9AyVPXGIA/fuRsXXf042tYB9qzw/qWymP2mEtlPr4lx5wADzeyd5hi7Qobg4aQ/AG6Iti3w6mv3xfsdgXfMbEALriuRSFQZ1e5j2BH4vEB09YqZXSSpq6TH5DURpkjaGkDSAEljJd1IVGKTdLekyfJ6BodnY0k6VNILUX/gCkkjo72LpDskTYp/25RaoKSl5DUVvp57/5Kkr0u6QV5L4bGYZ7fo01HSefKaDTMk/azchyBpI9z3MJwoUyqpE57S+4DwQfwKT+E9LN5vLWl1SX+POaZL+k4M2VHSVfF5/EvScjFmv+j7FHVLrObXk+oxJBJVTFWfGIBNgSklrr0F7BQ1jDcAbqJGL9Af6GFmWTrpQ8zsvfgynSTpDjw883f4r+6P8aic6dG/lBCtDpFa4ibgx7jieBdgUswHsBauQt4AeChMNIcCb5lZf0nL4nmLHjCz/5V41iF43YSxwDWSVjWzdyWdEc95HHjiPvzEcEG8vwPPBDtSntBveeCbeIrwIWY2U9KdwF4x/ig8XcYTks4vsZakY0gkqpxq3xhqIeliXNH7OS4WGxkhn18BG+a6TsxtCgDHSto7Xq+Ff0mvDjxqZu/F2LflxigqRCuI/89zFXAbvjEcAlyZu3ZriNBmS3o15t4Z2ERewxo8XHUDoNTGsD+wm5ktlHQ3rjf4W4m+eQbEvZjZl8BHkr4J/NfMsrrWk4Gu8gJEnczsiWi/HhhYwRyJRLORfAotQ7VvDLOAfbI3ZnZ0fIE9AxwPvAlsjpvM5ufuW1TvQF5vYBAuLPtU0jg862k5xW9RIVopzGyOpPclDQR643qFRZcLu8fcPzezh+sbOxzM6wJjY6NaFq/kVsnGUGx+KF1zIf36TyTaAdXuY3gEWE7SUbm25ePvynjFtIXAgdSO/8+zMvB+bAob49FN4GGcO0haJcws++TuKSVEK8dVwGjg5lhTxr5yNvx/9s47zKrq+t/vR7CgKJagsWPHDgoYe9fYu8BXjSVqNEaiiRij/gxqLInGWGONYgXEQrAXFEFBwUIRYsdu7A0FVFi/P9Y6zJk79965MwwDM3e/zzMP9+yz270mZ5+91/qshe9WXsePpn4b4yJpnTjmKkZv4Ewz62RmnYAVgNUlrUhdAVzh9ZOErSBsH0uUmnwY0qdL2jyKDqngOycSiRZIi94xmJlJ2hf4p6RT8VwE3wF/wm0Pd0s6CH8AlsqK9jBwnDx3wqt4/gTwHUYb4CN8Af0BWEceyXQ34NJo0xYYIel04OCcIXy1OBp6FX+LH4svQv0Lxn8Dz7e8LH5+/4Oka/EIq+NiF/AJ7m00G0kP4El6VsWN8Ei6DQ9fMQQ/IroFNza/BJwH/AcYLGl/4AR8cfuPpL/Hd3w35lsqFtORwA2SvsPzSWxYol4ikWjBJIFbGVQjBDsH3zGcWUofoQK//vx1vPmPARY2s/VzbW6LOkMaOK9l8OQ804GdMqN0Q/uTtDFu+9jLzF6Vr0L7AJ/mbAlZ3bZhh5h9jRuylyw3RhK4JZqSpGOoHM2BwK2lHyXNbfrJQ2T/FvgCfxNH0qnyLGovSzox6l6I7yjqZHXDdzBrAI9H+6PD22c74FpJx0vqK+klSaPk4jMknSxpcriS3pbr78CYyyA8K1yeXVXX/fX5cGklrp+OReE0XOD2KvgOzMyGZItC1DtP0gjgd5LWkPScpLHUzgOdSCRaES36KGluY2anAEjqB0yNo6se+Pl6D/yoaYykp/CH7JoFO4aMS3BPo3xoifVxt9D2uF3hD2bWVR5J9VDcg+lUYNU4XtpaNXkc1sRjFH2HG4QvyvVbzP11EHAwcK6klYBlzGy8pPXxdKHlWMLMtonv9CBwmZndIen3pRoo5WNIJFo0acfQcLYG7jaz78M9dQjuIluMdeJh/jnuAjopd+8JM/vOzD4GpgL3RflEoFN8ngTcJukQ4MVYdPbAbQ7rmFlXoE0YzTPuNLNZsQvI3F/vBA6K+z3juhaSlo3dzuuSTsrdGpj7vDm+yIC7qxbFzK4zs25m1q1jx46lqiUSifmUtGNoOA1JXPNq2BhWwA3Uu5vZg3Ev7xI6K3c9i5r/Lrvib//7AGdK2gB/sC8DTAnDdAfc0Nwv2tRxfzWzdyRNlbRetD8i7k3CBXyTzOwToIuk0/BdTEbeaG9F+k8kmo1kU2ge0o6h4YwA9pPUTlJ7/KE9kjK5EczsQzyk9Z8rHUSeE2ElM3sC6At0xF1xe+MG58w9tUeUZRRzfwV/0/8zbgCfHGV/B87K2x+ocfctxrP4kRQkd9VEotWSFoYGYmZj8PAaY/EH5dVmNjGOhJ6XNLGI8RncjXTpnA6gPtoCd4RL7IvA33CX1p/jAr5sPq8DMyRtGkWZ++t9hPtrlA/Gw3LcmWv7Ep5z4Q5Jr8rzPaxJ7eOjPH2AkyWNofauIpFItCLmO3dVSQZcYmZ/jOtTgPZm1q9Mm72B9cys2AM5q7MdcIqZ7Vnk3ttAt8ZGQ80Zpy9uTPvG9iupP7AzsLqZzVCovs2skzwF6H9xXcJC+GLxW2AbCn6HqPs0sEpefBf2kWPx46bvzeyWGPN+M7tLrhI/xcxK+qMmd9VEYt4wJ+6q86ONYQawv6QLKn1Qm9lQYOjcnVZxwp9/XjITj790dZF7b+Z0FE/gwfC+KKwUITveww3rTwGEQXvx2CGNmVuTTyQaQtIxNA/z41HST7hb58mFN1Qi3LWkI1QTEnsNSc/G/XMkTc110V7SXZJekXR7CLoy+srDXI/JXE0lrSppmDzU9DB5JFUk9ZeHxX4SP+IBD6o3XB5Su09uzn/IaR5OqqD8jDjWeRx3Z62PS/HjnZILVAjTRuHHRPnfs7tcO7E6fjzWK3e7V5QhqV/s3BKJRBUwPy4MAFfheQQ6FJRn4a6740rkG+q09DqXRZ3CfMRdgZPwDGurA/k8Ct+YWQ9cP3BplF0J3GJmG+Fxji7P1V8bNwL/Ma47415EPYC/SFowzv2PBDbDYzAdI6lrPeW9Yp77A93L/UjBu/gx0GGlKkhaFNiRyD8RZVsA1+A5nd/CbQ/75haYnpS2NZRFKR9DItGimS8XBjP7Bo/z06fgVhZKexx+dLSEPMdAns1xQyvAHQX3xpjZ+3GOPo4avQDE23H8mxmIN8/1cSu19QqDrXb2twfMbEYcf30CLBf17w29wlTgHvy4plT51lH+ffwGlR6PnY97LhX+91wjfqtnYn4PRfm6+K5sryychnl6z0nAjvKggD+a2csVjl+LpGNIJFo28/p8vByX4t44N+XKioa7rn0iVJZS4aShtn9+KYt8vrwwKF+xvktNrNyEG+wNYGZvxAJwcMGtN614TuaP8NDiXam9q8qOkz6mZqFMJOYbkk2heZgvdwwA5gly7sSzmWVUEu76WWpCZPcqcr8UPXP/jo7Po3J9HIIf2TSEEfjxzKKSFgP2wzUP5cozjcTiwF4NGOs8oFI7wFe4gvr88NbKuBvYnTk4RkokEi2f+XZhCP4B/Cx33QfoFsbgyRTPO3wS8IfwtV8e+LrCsRaW9Bzwe2oM332AI0NLcFjcqxgzexEPsz0GeA64wcxeypV/B3wGLIXna1gEF6KNwx/SIxsw1iR8h7WQpN3z9yTtFmf+/8WP6NYN3cVewFWKXM9m9hW+sH5stTPclaIzEfI7kUi0HuY7HcOcEobWaRHwrheeu3if+trNCyRNNbP28XlX4HQz23YO+zwC12T8Lq43wPMw7GFmr4Rx+Vgz+9eczX62juJ+M7urVJ2kY0gk5g2tTccwp2yKG6iFH5kcNY/nUylLAF8CSFoe3zksgf83Ot7MRobr7VW4Ef5L4HQ8rMUq+E7pYeAcoJ08odAF+JHReWb2Csx2Xf1XjLMqcCMebuNT4Egzezce+N8A3XCl9akhaBNwBb5LmELD4kYlEnNM0jE0D/P7UVKDMbORZraxmW1kZtuY2Rvzek5laCePaPoK7np7bpT/H/BIGI43Bg4L4/JieC6GNvjx019x5fN+wDkR/uIsYJCZdTGzQcAGwAslxi/njrs87j21J55rghhnHTxz2zHAFnP4/ROJxHxIa9wxtCSm5fI3bA7cEkc/Y4EbJS0IDDGzY6PODDzng8mzys0wsx8l5UN1N4TNcb0EuDvu33P3hoRb72RJy0XZNsCAcNP9UNITxTpVyseQSLRoWt2OoaViZqNxQ3tHMxuBP4Q/AG6V9Kuo9qPVGIVmh+qOB3ipRX4SfrxW0TRyn/PutypRp3gnSceQSLRo0o5hPiFiE7UBPo+z/w/M7PpwZ90E9yaqhMLw3xcB90h62sxek7QAcJKZXUKNO+6tVOaOOwL4jaRb8Eiv21NXRJhIzDWSTaF5SAvDvKWdatJ1CjjczGaGtqCvpB/x7G6/KtVBEZ4ETot+LzCzQRGLaUB4bBmQWfD64EdWfQnjcz1934sbnicCrxEB9xKJROui1bmrJuYvkrtqIjFvmBN31aqwMUgySbfmrttK+lTS/Y3oa3hoDvJlJ0lqCl3AcEndctedJNUbr0i1I8gmEonEHFEtR0nfARtIahdxlnbGDbuNIYsn9EiurBcexK4iJLUpCMCXSCQqIK9jSPaGuUdV7BiCh3CxF3iO5NlB4iT1kDQqchOMUuRAlrS+PD/DuAjDsRaeonNPSQtHnU7ACsDTkraLt/46OR8kvS3pLElPAwc1dPLynBP3SHpY0uuS/l6kzs8kjZa0Rz1z2TG+60RJN0paOH6De+L+PpKmSVpI0iKS3ory4ZL+Fr/Ja5K2LjHXFHY7kWjBVNPCMBDoJWkRYCM8dlHGK8A2ZtYVF4idH+XH4bkduuAq4PfN7HM89tEvo04vXFCWGWvK5XyYbmZbmVljA9R1wQPcbQj0lLRydiO0Bg8AZ5lZ9lpVZy7x/fsDPc1sQ0JZjcdZ6hrttgZexvNBbEbt36pt5K04CfhLsUkmd9VEomVTLUdJmNmEeLvvDTxYcLsDcHPsCAxYMMpHA2dIWgm4x8xej/LsOOk/8W8+7MYYM3sfZudM7kSNG+ig+qZZT9kwM/s6+p4MrAq8F/MdBpxgZnlPoWJz+RaYYmavRZ2bo92lkt6QtC6ebOgSXEvRhtrB/O6Jf1+gcaK6RKLRpOOj5qGadgzgiW8upm6ugXOBJ81sAzzi6CIAZnYHsDcwDXhEUhZJdAie0GYToF1ES80ol/OhMIdDIZ/joS4ylsajr9bX90/4g7qWUbxE/XLxjUYCuwE/Ao/jITG2wvULhX0WfrdEItFKqLaF4UY8ptDEgvIO1Bijj8gK5bmQ3zKzy/FFZSOAyLo2PPpryoQ2w4FDM1sAcDiuS6gPw3ctnSWdVk/dV4BOirzWeDjxbJcxAj8iGm1mnwLL4KG1J1X8DRKJRIunqhaGSOt5WZFbfwcukPQMfnSS0RN4OY5hOlNbfTwAD3BXzl7QDlcKv4UHpXtI0n5l6l+HH/WMlzQeaI/vcOolvJx6AdtL+m2Japvg9hGAiZLewUNrXBNlz+EpSbMdwgRgQs5+kmdl3OieSCRaGUngNpeIt/5RwM1mdk2UrQrsbWZXVNC+SV1aJW2MJ//Z2cymSFoNPy462MxKRV8t118nPBc4M6XRAAAgAElEQVTDBuXqJYFboilJ7qqVkwRu8yc7AD9kiwKAmb1jZleEcG2kpBfjbwuAcDF9UtIdeNgJJA2R9IKkSfKopUT5r8NldLik6yVdGeUdJd0taWz8ZV5RpwDnZ5nZ4t/zgT9Gu9niunB7fTs+F51rIpFovSTj4dxjfdwFtBh/xpPwGLAQ8LikE/DkNz2ADXKpNY8ysy8ktQPGSrobWBj4f/jR0LfAE8D4qH8Z8E8ze1rSKrgQb92YT+Gx1PPAifV8j0/wXcb08NoagLvulkQp7HYi0aJJC0MzIekq3MPnBzwD25W4LuEHADO7SR48b0xBvuU+ObvEysBaeFa1p8zsi+h7MLB21NkJWK/Gfs0SkhbHvZEKzw0rycC2IJ4RrwvuibR2PfUxs+twewndunVLZ5WJRAsjLQxzj0nAAdmFmZ0g6Wf4W/rJwMe48XoBYHqu3WyX1lgodgI2N7PvJQ3HXWnLPdAXiPrT8oWSJuFv+hNyxZvEfMBdXrOjxUVydcrNNZFoVpJdoXlINoa5xxPAIpKOz5UtGv92AD6KBDuHUdsTKk8H4MtYFDoDv4jyMcC2kpaS1JbcAgQ8Cvwuu4g3ffBjpD+H0TgzHp+E52sAeJuahD4HFsyhkrkmEolWQloY5hLh4rkv/gCfImkMrjL+E/Av4HBJz+JHM6WEbw8DbSVNwEV4z0bfH+CG4+dwz6LJwNfRpg/QTR7baTIe1gMzGxdj3yfpNTyfwvFm9mq0uxg4XtIoPJNcRqVzTSQSrYTkrtpCkdTezKbGjuFe4EYzu7cB7S/E4yDtamY/zK15JnfVRGLeMCfuqsnG0AgkzcTdSdvinkSHmdlXjezrImB3PH7TDcC1wJK459FIMzs2joNWMLN8jKd+knbC7QGPAkMk9QOmmlm9ojgzm62QLtYu3FW7mdlnkkaZWXJTTcxzko6heUgLQ+OYFhFXkXQzcAJwXiP7+g3Q0cxmSHoEdzX9T/S9YdTJorvOXhjM7JTCjnKeSE1KWhQSieoi2RjmnNHAiuBqZ0kXSXpZnuugZz3lQ4HFgOeibHng/axjM5soaSHgHDzM9jhJPeX5GDpGHwvIo6Lm7QJIWkOeu+GFEKh1buwXVGSICwHeCEn3Spos6RpJdf43pJSPIZFo0aQdwxwgqQ2wI/DvKNoff7vfGDfgjpU0AtiiWLmZ7S1pam73sSjwRBiAHwVuMrOvJJ2FH+v8Lup1Bg4BLsXdWcfHkU9+etcBx5nZ65I2w43IO1CakyUdmrsuFQepB57f4R3cOL4/nrxoNknHkEi0bNLC0DjaqSa/wQvAY1G+FTAgYhx9LOkpPNlNqfKh+U5D5PYIngRoHzwA38ZFxr8RzwVxKR5V9ab8TUnt8cVocG6xWLie7/TPIjaGYowxsyyj24D4bneVqJtINCnJrtA8pKOkxpHZGFbFQ1qcEOWlDvkrPvw3sw/N7EYz2wcXndUJUmdm7+ELzA64Z9FDBVUWAL4ysy65v3UrnUN9U6znOpFItHDSwjAHRDa1PsApkhbEw1X3lNQmbADb4GK0UuW1kPTL6AdJP8fzIXyAx0NavKD6DcBtwJ2FUVjN7BtgiqSDoi+V2Hk0hh6SVgvbQk9qstMlEolWQloYSiBpZhh7x6tMVFEzewkPYNcL1xNMiOuXgRFm9r+C8ieAU6O8kF3w/A/j8eB3faPek3j8o3GZ4Ro/hmpPwTESsIIkw3Ms/Dr6eg23WTToJwAukvQmfnT2ArAHbmy/ML7flPhuiUSiFZEEbiUIo3D7+LwrcLqZbduA9v0ooimQ1NbMfmqC+XXD7QJbF5T/HdgceNPMjoiyTlSQO6Ggn4HAW8CZZjYrdjp/BVY0sz1z9crmjUgCt0RTknQMlTMnAre0Y6iMJYAvswtJfeW5DiZIOjtXfoakVyU9DqyTKx8u6fwwOv9e0qqShkX7YfLw2JQp7y/panmuhrckXYunAV1ZUv/cOMLjHB0B7CIpHwyvraSbo++7JC0qaTdJd+babyfpPklr4N5HZ0aMJCLV54BcvVp5IxKJROsheSWVJvM8WgTXF+wAIGkXPPR1D/y4ZaikbfAYQr2Arvjv+iLusZSxZLbjkHQfcIuZ3SzpKOByPK7SlSXKAZaKOewN3IrvCibhrq9dIhbSlsAUM3tTHol1d+CeaL8Ontt5FrA98DrwKZ7/eTEz+w63GQzCczeMzxaFDDMbDgyXR30tzBsxG6V8DIlEiybtGEozLbx5OuPuo7fEG/ku8fcS/vDvjC8UWwP3mtn3YfwdWtDfoNznzYE74vOtuMtnuXKA+yIw30TgYzObGA/uSbjbLEBvanJQD4zrjPfM7DfhTXUQMDY+3wnsJY+5tAfuBluL2AmNk/Rhrrgwb8RszOw6M+tmZt06duxYrEoikZiPSTuGCjCz0XJlcUd8l3CBmV2bryPpJMq7bpaLSlqqXb58Rvw7K/c5u24bYrsDgL0lnRHzXEaepKfYGNn1INzd9gt8sfhWHpV1Y0kLmNksMzsPOE+hgK7g+yQSc4VkV2ge0o6hAkJp3Ab4HPcWOipEZEhaUdKyuEvqfpLaxcN4rzJdjsKPncAVzE/XU14JmQJ6ZTPrZGarAndTcxS1iqTN43PvXN/D8YQ9xxC7GjN7A0/g89dYcAh7xdwJxpRIJOYr0o6hNJmNAfyBeHh43zwqaV1gdKiKpwKHmtmLkgYB4/BwESPL9N0HuFFSX/yc/8h6yiuhN3VdR+8Gjo+5/BfPq3Atbl+4GsDMZkq6HzdYH55rezSexOcNSV8A0/B8DolEopWT3FUTc5XkrppIzBuSu2oDkWSSbs1dt5X0abw5I2lvSaeV7qFkv8PDXXVc/B1Yf6ui/ZwkD6iXSCRydDrtgdl/iblHtR4lfQdsIKmdmU0DdsZDTwBgZkOp61VUKYeY2Zy+Ip+Eh7v4vtIGTSWcSyQSiarcMQQP4e6Z4OfzA7Ibko6QdGV8PkieR2G8PIQ28phHF8tzK0yQdGK5gSQdKmlM7CKuzRl0r5bnLZiUCeUk9cFDXj8p6ckom5rr68BM1BbCt0ui3t8kLSbpRrn47iVJ+5SZ0xGS7pHnbHhdrpjO7tWZV5S/LelseYiQiSqR40EpH0Mi0aKp5oVhINArvG02Ap4rUe8sPC/yxri4DFy8tRrQ1cw2Am7P1b89d5S0TBiqewJbhm5gJu5xBHBGnAFuBGwraSMzuxz4ENjezLav4HusDexkZn8EzgCeMLPuuIjtIkmLlWnbJea2IR7kb+VS88q1+czMNsGN13WyyEHSMSQSLZ1qPUrCzCbIYwj1JpcyswjPAP3loSMyFfFOwDXZ0Y2ZfZGrX+soSVJvYFNcoQzQDvgkbh8cKuG2uLp6PTzYXkMYnItVtAuuY8ge2IsAq+AeScUYFhFiCe3CqsB79cwr+w1ewJP0JBLNRtIxNA9VuzAEQ4GLge3wENd1MLPj5BnQ9gDGSeqCu69W6s4l4GYz+3OtQmk1/I27u5l9GcdDixRpT8FYhXXyQjMBB5jZqxXOLS+Um4kL5eqb14x8/QrHSSQSLYhqPkoCz4R2jpmVDAQnaQ0ze87MzgI+A1bGQ1gfF2EkkLR0mTGGAQeGCA5JS0taFQ/M9x3wtaTlgN1ybQrzL3wsaV15DoT9yoz1CHCiYmsiqWuZuqUoN69EIlEFVPUbn5m9D1xWT7WLJK2Fv40PoybXwtrABEk/AtfjAfBmI2kmNZFHF8SDzy0IdAD2NrNnJb2Exzp6Cz+yyrgOeEjSR2FnOA24Hz/meRnPwwAeSfWpXLtz8XSfE2JxeBvYk4axOG78ngb8CLzRwPaJRKKFkwRucwnl8jlUULdRrqbyvMzdzOyzhrYt0d/P8cxy+4aS+2f4LuSvZtaohDxJ4JZoSlI+hspJArcWgjyPQSai6yfpOkmP4pFb20i6SDV5Hn6TazNC0r2SJku6Jo6UCvseIumFcDE9Nlf+y3AvHS9pWJSVcms9AehvZi8CxIJzKtA32vVXTrSn2kH1EolEK6Gqj5LmMvlYS1PMrJhtYFNgKzObFg/zr82su6SFgWdi0QDPfbAeHoPpYdwb6K6Cvo4ysy8ktcM9oO7GF/5b8bhLPwDLxpzaAeea2VGSlgTGyJMLrQ/cXNDv8zF2xSjlY0gkWjRpYZh7TAvdQjmGhvIa3NV0o9wbeQc8z8MPeO6DtwAkDcDzNBQuDH0kZYvPytG2I/ComR2SryjpeeC0Im6tDfG2KomZXYfbSejWrVs6q0wkWhhpYZi3FLqanmhmj+QryLOllcqlkK+zE7C5mX0vz96Whcku9mAu6tYqaRLQjdrhQDbFdw0APxHHj2HcXqjst0skmphkV2geko1h/uER4PjwXELS2jnVcg9Jq4VtoSd18zR0AL6MRaEz8IsoH40rl1eLPjO32lJurVcBR4RWA0nLAOfh3k7gXk6bxud9cG+rRCLRykg7hvmHG/AUnS/GA/tTapLsjAYuxENXjKBu3oWHcV3FBOBV4FkAM/s0zvvviUXlEzxgYFG3VjP7SNKhwHWSOsR8jjCzzCX2euA/ksbgrrspi1si0QpJ7qrzOXFMdIqZNVSP0BRjnwAcB2xjZl82po/krppIzBuSu2ojkfRzSQMlvRmuoA9KWnsO+8y7pM7O6yBpX0nr5eqdI2mnRo7RWdJoSTNyBuRy9WfKg/q9LGmwKsz1YGZXmdmGERpjeBitsz67hS0jkWg2Uj6G5qFqF4Y4QrkXGG5ma5jZesDpwHJNNYaZDTWzC+NyX3Jun2Z2lpk9XkEfw4vsFr7A04BeXOFUpplZFzPbAPdyOq7CdoUsKymFyEgkWjlVuzDgYal/NLNrsgIzGwc8HUKzl+U5B3rC7J3AcEl3SXpF0u054+0vo+xpchFHFXkdJG2Bh+y+KN7c18iLxSTtGEKziSE8WzjKi+Y/MLNPzGwsHrKioYwE1oz+/xDf82VJJ0XZYpIeCEHcy9n3Dy4CzqxvAKV8DIlEi6aaF4YN8NDRheyP5ynYGHcBvUjS8nGvK55dbT1gdWBLeT6H64G9gK2Bnxd2aGajcBfQvvHm/mZ2L9r3B3qa2Ya4Q8Dxueb15j+oFHnQv92AiZI2BY4ENsO9mI4J76RfAh+a2caxw3g418VoYIaksnkiUj6GRKJlU80LQym2AgaY2Uwz+xgPUtc97o0xs/fNbBYwDvfa6Ywrm183t+Tf1sDx1on2r8X1zcA2ufv5/AedGvplgkyF/TzwLvBv/Hvea2bfmdnUGGdrPPDfTpL+JmnrLF9Djr9Swa4hkZgbvH3hHrP/EnOPal4YJlHjk59HZdrUyV8Qn+fEtavcePkx5yT/QWZj6GJmJ5rZD6XGjQVqU3yBuEDSWQX3n8DFc78o0jyRSLQCqnlheAJYWNIxWYGk7sCXeJrLNpI64m/vY8r08wqwmqQ14rp3iXqFORby7TtJWjOuD6N2KO25xQhgX0mLhpBuP2CkpBWA783sNty4vUmRtufhwfUSiUQrpGoFbmZm8thCl4ZL6XRc6HUSnu9gPL4TONXM/qcSie/NbHqIyB6Q9BmuSt6gSNWBwPWS+uB5FPLtjwQGhw1gLHBNkfazkYfHfh5PqjMrDMfrmdk3Dfj+L8qzs2WL3g1m9pKkXXG7yizcuH18kbYPSkpW5USilVL1AjfVTqgDMDDnYlqs/ulmdn4jxrkBuMTMJjdimlkf/YCF82lC5eErBpjZupIeBP7PzL5qRN/DcSHd85KOAk7GF8YFgDPM7D8F9TsB94eBuiRJ4JZoSlI+hsqZE4Fb1e4YclQSBTXP6UCDFgZJbczs6Ea0mVlQPAB4CMjnj+4F3AFgZrs3ZIwS464EnAFsYmZfS2qPR2lNJBJVQjXbGEoiqYOkVyWtE9cDJB0j6ULCw0fS7XHvUEljouxaSW2ifGqom58DNg8NRLe41zt0CS9L+ltu3FptCucV0VC/krRZrvhgYKCkZST9EP1OljRd0uehr3hUUrvQT7yYG28tSYUuu8vi9pCpMeZUM5sS9TcNfcNoPKlPqd8v6RgSiRZMWhhqHvTZX89w0fwd0F9SL2ApM7vezE6jxsPnEEnr4tFOt4xdx0wgy32wGPCymW1mZrOjoYZx92/ADrheorukfcu1KWAAvktA0i+Az8NV9nPgQ1y4tzu+G9zRzDoDX+Fhtt8Evo7jJ3AdQ/+C/scDHwNTJN0kaa/cvZuAPmZWZ9HKk3QMiUTLJh0llThKMrPHJB2Eh6LeuETbHXHXzrFyEXQ7PIIp+CJxd5E23fEwHJ8CxM5jG2BImTZ5BgKjJP0RXyAGlKg3JZTcUFsDcQNwpKQ/4Itaj3wjM5sp6Zcxzx2Bf4YY7p/AkrlIq7fiYrlEotlIdoXmIS0MJZCHqV4XmAYsDbxfrBpwc94YnGN6ERtB1qYUpdrMxszek/Q2sC1wAEWOnIJCzUW7+Hw38BfcXfeF2GkUjmG4t9IYSY/hO4VLaYLsbolEYv4nHSWV5mTgv7gu4UZFAh3gx9znYcCBkpYFT4QjadV6+n0OT57zs7BH9KbhuoUB+Bv8m2ZWbMEqiZlNxxP1XI0/8GshaQVJee1CF+Cd8HT6WtJWUX5IYdtEItE6SAtDXRvDhfLQ20cDfzSzkbgYLAsDcR2e4GYcMBhPb/mOpNeBx4DlCwcIvUBHADP7CPcqehI/z38R2CX6W1TStNxcDizsKxgMrI8fKzWG2/G3/0cl7S9pIn50NACPlXRxGK3fxQV3v492ywFXh/F5WpF+E4lEK6DqdQyNQdLmwCXAdmY2Q9LPgIXM7MMS9fvjPv931dNvJyrQBswp8hwOHfCw44OAnczsHbl6+zFgLzObJI8W+7vMViHpfWCDhugkko4h0ZRkOoZka6ifOdExpB1D41gej3o6A8DMPjOzDyWdJWlsuKFep7BI5wmXz6ckvSDpEdVEbq2DpHXkaTSz63Wza0nvx+5mjKTnJK0e5ctJuifcRceE51K+z3uBXwGXAX2Bc83snfgeb+IeU6fIw213AQbF7mWh6OIkeYjwCZrDpEaJRGL+JC0MjeNRYGVJr0n6l6Rto/xKM+seb/ztgFoJdsI2cQVwoJltCtyIxx2qQzzABwHrxbHOOOBsatsFvjSzHsC1+A4G4HLg7/GmcDDuhTQbM9vPzDYys8/w46hCHcPzwPpmNgiPINsz3HN/iPsfm1nX6PcPJeaedAyJRAsmeSU1AjObGi6cW+O6gUHyeEvfSjoVWBT3ZJoE3Jdrug4eR+mx2Ey0AT4qMcZ+AJIOBzYC/gS8Dhybq5a5qt4OZGE8dgLWyW1WlpLUzsyK2QREXU+jYmV58mHAiyqtzew63BZDt27d0lllItHCSAtDIwm30uHA8DDe/gZ/gHcLl9J+eHjqPAIm1ScQK2AwHobjGWB0wfl+sYeugB65N/xyTAK6Afn4TZsUXBfSFGHAE4lGkWwLzUM6SmoEcfa/Vq6oC/BqfP5MHl+omEfRq0DHMF4jaUFJ65cby8y+xzUHV1LXvTRLu9kbXzgAHicXriKnci7GxcCZklaJuqvjO5N/xP1SocITiUQrJr3xNY72wBWSlgR+At7Aj3i+wt+22+HhLY6Vh7GeBmBmP4QL6uWSOuC//6X4m3s5bsePbYYVlC8axmijJg/ECbhL6ZHR/5th4F4M303cb2anxHyel3QG8KA85PePuIvuy9HXTcANkqZRoJAG/h8eVymRSLQykrtqExJeSKNwNfQ1UbYqsLeZXVFB+2IRVQn7xcJmdnaurF7XUUkbAP8B9jCzV+Lhf6yZ/auh361I3/2pwAU3uasmEvOG5K46/7AD8EO2KACY2TtmdoWkTpJGSnox/rYAkLSdpCcl3UHkhZA0JNxZJ0kaj8dEukLSr8MTajiwJPD3qN9R0t3hKjtW0pYx/KnAeWb2Sszlp2xRkLSqpGHhdjosd5zUX9LlkkZJeisT2cm5Uh659QHSbiExD+h02gO1cjIk5g7pKKlpWR9XMhfjE2DnyNi2Fu5RlK3mPfC3/ylxfZSZfSGpHZ7RbVvckP3/cOPwt7jdITMwXwb808yejgf8I3icpw2Af0g6Go8Wm2cJ4Gwzu1memOdyIIvyujywFdAZGArchaf+XAfYEFdAT8bdbROJRCsjLQxzEUlX4Q/YH3A30ivDGDwTyIvDxuQWBYA+8rSjACsDawE/B54ysy+i78G5PnbC9Q5Z+yUkzTYam9kNFOgZ5GlI74jLW4ndRzDEzGYBkyUtF2Xb4JniZgIfSnqizPc+lnCrXWWVVUpVSyQS8ylpYWhaJuERTwEwsxPk4TKex4PyfYyH8F4AzzGd8V32QdJ2+IN+czP7Po6NFqF8VNYFon4trYKkSXhY8PEVzD1vbMpHZlWJOqU7SjqGxFwiuas2D8nG0LQ8ASwi6fhc2aLxbwfgo3gTPwwXtxWjA65o/l5SZyALaTEGj8q6VBiRD8i1eZTcUVHORfUi4PQsdIWkBeR5GMCN5L3i8yFAqcRAGSOAXpLahJfT9vXUTyQSLZS0Y2hCzMzk2dj+GQroT/HdwJ9w28Pd8uQ/T5LbJRTwMHCcpAm47uHZ6PsDSefjYbs/xM/4v442fYCrok1b/CF+nJlNkHQSMEDSovgb/wO5NjdK6hvzPLKer3cvblyfCLxGw0OFJxKJFkJyV21BSGof4Tja4g/qG83s3nk9r3Ikd9VEYt6Q3FUbgKSfSxoo6c1wvXxQcxglNFxO74/Pe4fuAEn7SlovV+8cSTs1coxD8DzM04BvcM+kIWXqz5RHRX1Z0n0hxiPcZrOcD5Ml3aKaxENZ28skfSDPYpdIzDckd9Xmoar+jx8CtHvxnMtrmNl6eByi5cq3rBwzG2pmWUC7fYH1cvfOMrPHG9n1FGBtM2uH2xfWsPLbvWkRFXUD4AtyYTLwzG9dcNfTlfAorMDslKb7Ae/hnkiJRKLKqKqFATeY/lggQBsHPC3poni7nijPRZDtBIZLukse+vr2WFyQ9MsoexrYP+tP0hEhBNsC2Bu4KN7O1wjxWCYY21Ge12CipBslLRzlb0s6Wy6CmxgGaMxslJl9GcM8iz/QK2U0sGJhYbiejim4tz3wMp76MwuzgaR+Mc/hIXzrU2owpbDbiUSLptoWhg2om38A/MHeBXcl3Ql/mGcJdLoCJ+Fv/qsDW0paBLge2AsPvf3zwg7NbBQuDusbb+5vZveifX8818GGuME478n0mZltgj+cTyky318DD1XyheV5pXeMuRTeWwTYDDd4Z/TGxXf3AnsWHDN1BnbFBXl/KTyCyjCz68ysm5l169ixYyXTTCQS8xHVtjCUYitCvGVmH+MeN93j3hgzez/cTMcBnfAH5BQzez2Oc25r4HjrRPvX4vpmah/b5HMedMo3lLQ9vjD8qZ4x2smT+3yO54Z4LHdvjdy9d81sQvS9EB6sb4iZfYN7QO2Sa/eAmc2IJD+f0IRHcIlEJbx94R5Jy9AMVNvCkAm+CiknHsuLvfI5CObEnavcePkxa+U8kLQRrmDex8w+r6ePaWFHWBVYiOI2hjWBX0jaO8p/iesoJkp6G18we+falfotEolEK6LaFoYngIUlHZMVSOoOfAn0DPFWR/ztfUyJPgBeAVaTtEZc9y5Rr1Q+g1eATpLWjOvDqEcXII+BdA9wWG6nUS9m9jWuWTil8OjHzD4CTgP+HEW9gaPNrJOZdQJWA3YJDUQikagSqmphiGOf/YCdw111EtAPjxk0AQ8d8QRwqpn9r0w/0/FYQA+E8fmdElUHAn3DyLxGQfsjgcHy7G+zgGtK9JFxFrAM8K8wZlcsDjCzl/Dv1qvI7SF4XodtcfvBA7l23+GK6L0qHSuRSLR8qkrgJunneGKc7vixyNvASQ15Ay/S53bAKWa2ZxzJrGdmF4YC+jUzmxz1zgFGNMZdNTQMmU1hKnC8mZWMfyRpJhHCO9jXzN5u6LgFffYDpprZxQ1plwRuiaYk0zAkO0P9zInArWrOiHMahpvNrFeUdcENqI1eGPKY2VBqvH/2Be4n8ieb2Vlz0PUUYFsz+1LSbniAus3K1M/sC3MdSW3N7KfmGCuRSDQP1XSU1Oo0DJKWif5r/RX78mE/uUieyGeCpN/k7vXNleezxJ0h6VVJj+OeVFn5cEnnS3oK+H2RsZKOIZFowVTNjoHKNAw/A8ZKGhH3uuLJdz4EnsE1DM/jGoYd8FzPgwo7NLNRkoaSS30Za0pew7Cjmb0m6RZcw3BpNP/MzDaR9Ftcw3B0QfezNQzhmVRnZ6AIhxGXU8xsv2j3tZl1j4XoGUmP4rke1sK1CQKGStoGD/LXK36DtngQwPzvt6SZbVvk90xhtxOJFk41LQylmK1hAD6Ot+DueDyiMWb2PkA8aDvhZ/xTzOz1KL+NSEpTIcU0DCdQszDkNQz75xvmNAxb1TNGsaOkXYCNsl0L7pa6VpTvArwU5e2jfHHgXjP7PsYuFMjVWRATiblNsi00D9W0MEwCDixS3tI0DLtVoGEoNe6JZvZIrUJpV+ACM7u2oPwkyn/PUmHDE4lEC6eabAxVp2Eo4BHg+EzLIGltSYtF+VGS2kf5ipKWxXM67CepnTxNaHJZTSSqhKrZMUQSnf2AS+VhsacT7qr48cl4/A35VDP7X2b4LdLPdHlO4wfkeZOfxu0XhQwErpcHmzuwoH2mYWgLjKVhGgaAnxrhhnYDfhT2YhjRP8XdWB+VtC4wOvqeChxqZi9KGoSHAXkHGNnA8RKJRAulqnQMTUlOK9AW+C9weHYePxfGOgLoZma/Cz3BMfiDfbGYw5mZXqJI20brJ0r0NxxYHl9YpwJHmdmrpeonHUOiKUk6hsqZEx1DNR0lNTX5fAc/AMc149j/jLHXwo3AT8QxWC0ktZnDHBClOMTMNsYN5xc1cd+JRGIekxaGpmEkHpAOSUMkvSBpUhw5Iel4SX/PKofe4Yr4fKikMaFBuFYeJhtJR0p6LWR5yCsAACAASURBVLyktiwcMNMw4HGOFgJeij7elXRWaCwOyvQTknaTdGeu/XaS7ovPu0gaHfqJwZm9oQJGZN+7YG5Jx5BItGDSwjCHhJ1gN2pCUBxlZpsC3YA+kpYB7qK262lPYFCc7fcEtgz30pnAIfJcEGfjC8LO5LLAZZjZ57Fr6AKcA9wXn2cB081sKzMbmGvyGB5JdbGCOfwMOBPYKXJAPA/8ocKvvxe1Q29kc0v5GBKJFkzVGJ/nAlm+A/Adw7/jc58wcgOsDKxlZs/Ks579Angd1zI8g+sXNsVFdQDt8DwHm+HpRz8FCCNwubzUhS6wxUR3P0l6GNhL0l3AHsCpwLb4wvNMzGEhPONbOW6X555+GzixnrqJRJORbAvNQ1oYGk8dEZk8oN5OwOZm9n0YaheJ24Pw3Mqv4MIxC++gm83szwX97EvDtBJd8Tf9jFIag0H4YvQFMNbMvo05PGZmpdxui3GImSWLciLRSklHSU1LB+DLWBQ6A7/I3bsHD6zXm5o3+mHAgaEbQNLSklbFM6dtF3aEBYGDSg0o6QBcuTyggvkNBzbBvZqyOTyLh/rIbCSLSiq3O0kkEq2ctDA0LQ8DbSVNAM7FH7oARBC8ycCqZjYmyibj5/uPRpvHgOUjgU4//EjncTxOUZ6Tw9D8OnAosEN27FSOCPtxP24TuT/KPgWOAAbEHJ7FU5cmEokqJR0lNRIzy5TCU7PPZjYj7AHdzOx3RdrsWaRsEPH2Lg8DvkKU3yTpcyK/Q65+P3zRKDWvTgXXR2SfJe0DrJSb+5+BX5vZmkB3SXsBx0T48HqRp//sFjmgE4m5TtIxNA9pxzB/0QXYPbsws6H5RaEJGAVsnrveHPgmO8oCtsCN4olEoopJC8NcRFJHSXfLcx2MlbRllPeQNEqek2GUpHUkLYS7nfaMY6KeoXe4Mtr0l3R51H9LNbkdFpD0r9BN3C/pQdVEUK1FHBt9rZo4TSsCd+MLAvHvqOj3LUnfS5om6X8xp10r/N5Jx5BItGDSwjDntFPtJDnn5O5dhquUuwMH4PGKwD2TtjGzrngcpPPN7If4PCj0CcXCWi+Ph9zeE8h2EvvjMZA2xHM3bF6kXZ5RwBaS1sFdZ5+N67bARnjsJvAjokXxOFKvAL8qjMxaiqRjSCRaNsnGMOfUcltVxDWKy52A9UIfALCEPFJpB+BmSWvhbqkLVjjWEDObBUyWtFyUbQUMjvL/SXqynj6ewXcGbXDj9hh8QeoKvGpm06PewXLldlt8QVoPmFDhPBOJuUKyLTQPaWGYuyyAaxqm5QsjHMaTZrafpE64G2kl5PNDqODfShmFi9LaANeHlmERYDvCviBpNTx7XPfIM92fGj1GIpFo5aSjpLnLo8Bs76TwOgLfMXwQn4/I1S+Vw6EcTwMHhK1hOfwBX47JuOfT1tRkbRuHBwEcFddL4CK5r6PP3Ro4p0Qi0YJJC8PcpQ/QTdIESZOpicD6d+ACSc/gb+4ZT+JHT+Mk9axwjLuB94GXgWtxcdzXpSqbx1l/Ds8t/WMUjwZWJxYGMxuPLxqTgBtJnkqJRFVRVfkYJBlwm5kdFtdtgY+A58xsz3g7/jce42hB4G0z213SAnhO5h1wm8B04GAzm1JmrP7A/WZ2V5F7PYCLgeWiv6fxReRgSmgg6vle7c1sagTsG4MH5ftfPW2GAqtH2HAkLY3rKTrhMZAODlFeYbvDcVEewF/N7OZy46R8DImmJOkYKkdzkI+h2mwM3wEbSGoX5/47U3OkA+5R9JiZXQaz8yyDRyJdAdjIzGZJWolG5jyOxWcw0MvMRkesogNo+BFSnvslLYkHwDu3gkVhfzzJTp7TgGFmdqE8w91pwJ8K2i0N/AU3rhvwgqShxRaQRCLRcqnGo6SH8Mii4HGL8jGGlsePZQAwswm58o/C8wczez97GEqa/YCV5z3on+tvJ0kj5XkVMtXzCXjgvNHRl5nZXWb2cX6SkvaS9FxoHR7PvJAkbZtzj30pvJx6A9/gCYNOkbS1pHvzbrSZDkGea+EPwF8Lfpd98MQ7xL/7FvntdsUXzi/i+z8G/LKwUtIxJBItm2pcGAYCvcITZyP8vD3jKuDfkp6UdIakFaL8Tjxc9ThJ/5DUtcKxOuFhrfcArokxNwBeqKDt08AvQuswEA+RDe4tdEK4yG4NTAP+D3gkyjYGxpnZflm+htzfI3gMp38AhWlIl4sYTcS/y1KXFYH3ctfvR1ktko4hkWjZVNtREmY2IVxEewMPFtx7RNLq+FvwbnhWtA3M7P0QhO0Qf8MkHWRmw+oZ7s7YZbwu6S0aFpxuJTyRzvL4EVFmz3gGuETS7cA9MbexwI3ySKxDzGxcsQ7DK2pNMzs5foOGUsw1tnqMVIl5TrItNA/VuGMAGIobf+uEqo5jkjvCQD0W2CbKZ5jZQ2bWFzifmqOW/IOx0Ne/8KFpuKfPphXM8QrgSjPbEPhN1nfETjoaT+rzrKTOZjYi5vkBcKukX5Xoc3NgU3nwu6eBteU5IwA+jkWI+PeTIu3fxw3zGSsBH1bwXRKJRAuiWheGG4FzzKxWWkpJO0haND4vDqwBvCtpk+xYKTyUNgLeiWYfS1o3yvejNgeFvmAN3B30VeBK4HBJm+XGPVTSzwva5rUOh+fqrmFmE83sb3hyns7yHA6fmNn1uFfVJsW+tJldbWYrRATWrYDXzGy7uD00N87hwH+KdPEIsIukpSQtheeBqChMRiKRaDlU5cIQxuPLitzaFHhenpdgNHCDmY3Fz9vvk/QyHhbiJ/wBD+69cz/wBO76mudV4Cnc4H2cmU0PI3Mv4GJJr0r6L24r+KagbT9gsKSRQD6s9UmSXpY0HrcvPISL2sZJegn3cCr23erjQmBneY6HneMaSd0k3QC+m8JtFGPj75woSyQSrYhqszEsJunWAh3DYGoM0LfgxuKV8UVzR9xQ+ygeDjvTMWyIeypNCZ1CHa1C8IyZnVykfCY1x0yitsgNADP7D0Xe2s2sWI7lm6nxKCqLPIrrlfhiMkvSAWZ2N+6++hmwKh44bwngC/MUnkfnuvgw5t8G12EkEs1Gp9MeSHaGZqDaFobWqmNoCGfgx05rx/HX0lH+azwt6ZqSegF/w793fu5tcM+tnXF7w9jQMUxuprknEolmoBqPklqdjkHS8pJGRNnLoWN4roiOYUPgKOCCGHuW1WRfy+sY7gJ2jEUrTw/gDTN7yzxM+MBoV4ukY0gkWjbVuDBUi45hs0IdAzUahHMlvShpsGrCd8/WKJjZT3i8pWUK5pR0DIlEFVB1C0PsAjpRQseAew9dj2sOXpLU0czeB9YB/gzMwnUMO1Yw3J3xVv460BgdwyOSJgJ9gfWjPNMx9AGWjIf4WOBISf2ADc3s2xJ9to1+nzGzTXAD+8VxrxKNQtIxJOYpyb7QPFTdwhBUq47hc1zxfG9cD6bGtXW2RiGM8h2AQo+jpGNIJKqAal0YqlXHYMB91ORs2BHPzwC1dQwHAk9E/TxjgbUkrRbeTb2iXSKRaEVUm1cS4MZjivv6bwpcKeknfNG8wczGSvolcL2khaPeGOrqGN7DcyK0z/WX6RiWI3QMwPTw+rlY0rL40dQI4J6CufTDdQwf4HmZV4vykyRtj7uMTsaN6b2AvpJ+xN1OS+0YwCOm3irpUuBT4Mgo/3eUv4HvFHoBxIJ4g5ntbmY/SfodLmprA9xoZpPKjJVIJFogVZWPIUPSTCC/WxgYRzSl6p9uZuc3YpwbgEvm1J1THrZidWDV7C1e0hBgJzNrX6bdksD/mdm/Khhjqpm1V4W5JxS5ra2e3BEpH0OiKUk6hspRysfQYKaFl06lnI7bFSpGUhszO7r+mnXazCxx+ytgS+DpeOAvX0GXSwK/BepdGHI0mWYjkUi0TKrVxlAHSR0iRMU6cT1A0jGSLgTahavq7XHvUEljouzaEH4haaqkcyQ9B2wuabikbnGvt6SJoTP4W27cWm3KTHEgcbwD7E/B0ZOkvpLGytOInh39vYSnCp0m6RNJPSQNC1fViZLqaBAor9k4Uq7JeApfpEr9lknHkEi0YKp1Ycge9NlfTzP7Gvgd0D9sAEuZ2fVmdhqxwzCzQySti79Vbxm7jpnAIdHvYsDLoSF4Ohsszun/hh/PdAG6S9q3XJsiDAO2iUWoF56GM+t/F2AtXIDWBbeV9AW6ApPNrJ2ZLQu8COwXrqrbA/8oImIrqtmQR1w9G18QdgbWKzXRpGNIJFo26Sgph5k9JukgXOi2cYm2O+IP3rHxTG1HTYjqmcDdRdp0B4ab2acAsfPYBhhSpk0hM3HRW0+gnZm9nXum7xJ/L8V1e3yheLegDwHnS9oGN3qviBvGZ6cCtRK5J/CQHfnvMAhYu4J5JxJNRrIvNA/VujAUJQyv6+Jq4qXJhcfIV8NDWvy5yL3pJWwExYRh9bUpxkBcg9CvSP8XmNm1tQrrJuM5BOgIbGpmP8rzMhRqLzCzGbi300OSPsY1G8NIYrZEoiqo1qOkUpwM/BdXRWcZ0QB+zH0eBhwYrqZIWjp0BOV4DthW0s/iKKg37sbaUEbicY4KhXmPAEfJ8zkjacWY37fUDs7XAdc7/Bgur3XmXUaz8RywnaRl4rc4qBHzTyQSLYBq3TG0k5RPf/kwLno7Gj8e6YF75rwX/v7XAV9JGmFmu0k6E3g0Hpw/4oHx3qE4a+P5HP4MPIm/3T8YYbUBFpB0uZn1qW/S4ap6ca6onaRuZvZo2D5Gx/HSVOBQPE5TB0nT8UXiZDxO1GfAwsAM/Kjollyfy1Jbs7EMcI2ZfSQPuTEazzvxIkXChScSiZZPVeoYypH58xcpHw6cEvkJGtLfEZTw95fUNmIdNXauJecUbqZPAZuY2dexm+hoZlPkEWDvN7O75IH9JgM7FtEqtMF3SGW/dzk326RjSDQlScdQOXOiY0hHSY1A0i6SRqsmQml2hNNd0ihJ48OdtQOe46Fn5v0kqZ+k6yQ9CtwiaTtJ90f79pJuClfSCZIOiPKrw/1zkqSzK5xmdpQ0FcDMphY++IPMxvBdjPW2pLMkPU3uuEge2uNmSX+N60rdbBOJRAsjLQx1qePKmr8p6WfAmbjqeBM8XtEf5LGDBgG/N7ONgZ3wh+1ZwKBwd81cTDcF9jGz/ysY+0VgT9wDaRbQT9KuwBmx8m+E2yo2on7GAx8DU2Kx2avg/kVxnPY+rvz+JHdvupltZWYD47otcDueI/rMKCvpZpt0DIlEy6ZabQzlqE8V/Qvch/+ZOM9fCD93XwcXho0FMLNvAOrKBAAYap5BrpBvgT0iTPdsJB0n6Vj8v9fyMf6EIu1nY2Yz5TGeuuMutv+UtKmZ9YsqfeMoqT1uZ9jCzEbFvUEF3V2LhxA/L1dW0s3WzK7D7TJ069YtnVUmEi2MtGNoOMLTf2YJcNYzs19HeaUPwVIhJur0IWk1PDnPjma2EfAARVxMi2HOGDO7ABfFHVCkzlRgOLBVmfmNArYPe0RGQ9xsE4kmIdkXmoe0MDScZ4EtJa0JIGlRSWsDrwArSOoe5YvL8xoUuoyW41FcfU30sRSwBP6g/lqebW23SjqStIKkfPjtLhTxnIo5bga8Waa7f+NJjQZH/UQi0YpJC0NdCm0MtaKuhvL3CGCApAn4QtHZPAdyT+AKSeOBx/A3+yfxeEW17BWSZsYZ/w3A1pJOA/4KLCWPpzQe2N7MxuOK5v/hLrXPVPg9FsRDe78iF6ldDqwtaQyueM5sDBPwSLOFYb9rYWaX4DaQW8NNt1a+60Qi0XpI7qrziFJusU1VP9q0Ac7D7RLHmtmM2HVsa2Z3NmzGjZtTcldNJOYNyV21laCmj/C6JXAMcGKEucDMPs4WBZWP+HpeuN0+G4sJ8sxto+VRXM9tzt8mkQDXMSTmPmlhmHc0ZYTXfYG34mhoMTyD29F4Dod3Mw+pPKo/4uuz4XY7Al9cwLPeXW1m3ckF3kskEq2LtDDMO7IHffY3CDzCK37mfxX+cC9GPsLrOHwBuCkXBnwNK8hnXYTZEV9DfZ1FfAX4AU9XCvAC0Ck+b0lNnKZbS3WcdAyJRMsmeZjMZ6hpI7y+AawiaXEz+7ZIH6X40WqMTzOp/b+Teo1SSceQSLRs0o5h/qPJIrya2fe4q+nlocxG0vKSDqVxEV+foSaL3CHlKiYSc4OkY2ge0sIw76jjFht6iKOBP5rZSPx8PwtBcR0wQdLtZjY5yh8Nl9nHKJ0D+kzgU2CypJfx5ECfmtlH1ER8HQ+8mIv4WorfAydIGouH8E4kEq2Q5K6amKskd9VEYt6Q3FVbAZL2k2SSOsd1p3jDR7kIrIlENZPcVZuHtDDMP/TGczr3qq9iIpFIzE3SwjAfEBFOtwR+TT0Lg6TFJN0YIrOXJO0T5SMldcnVe0YlwnPLc0LcKGm4pLck9cndGyLpBXnuh2Nz5UVFbyX6T+6qiUQLJi0M8wf7Ag+b2WvAFwXB7wo5A3giRGbb4zGPFsNjLh0BEEbshc2sXGjuzsCueBrTv+Q8no4ys02BbkAfSctEeSnRWx3M7Doz62Zm3Tp27Fj2iycSifmPtDDMH/QGsqQ4A+O6FLsAp4WwbTgeqG8VYDCwZzzgjwL61zPmA2Y2w8w+Az4Bsh1Anwjg9yywMrBWlJcSvSUSzUZyV20eksBtHhNv5DsAG0gyoA0uIvtXqSbAAWb2apG+HgP2AQ7G3/jLMSP3eSbQVtJ2eOa5zc3se3lO6SwHQznRWyKRaEWkHcO850DgFjNb1cw6mdnKwBRgpRL1HwFOlDw1nKSuuXs34OG1x5rZF42YSwfgy1gUOuPZ6hKJRJWRFoZ5T2/g3oKyu4HTS9Q/F8+1MCHcWWdHOTWzF4BvgJsaOZeH8Z3DhOj32Ub2k0gkWjBVIXBrTC6DCvrsB0w1s4vj+hRctfwTftTyDzO7pRH9bgf8kMu/3JC2K+B2h85mNqueuk3+mxQjCdwSTUmn0x5IdoYKSQK3eYyk44CdgR5mtgEepbRckLpybAds0Yg5/AqPf3RGtihkORoSiUSiIVTtwiBpL0nPhRbg8VwymnI+/mfIE+k8DqyT6+504LdZ3gMz+9rMbo42O8YYE6PfhaP8bUlnS3ox7nWW1Ak4Djg54idtLWlVScMkTYh/V4n2/SUdmJvDv8I+8amkJyXdAbxbEI9pnKSryvwmdcaS1CZ+B0laUtIsSdtE/ZGK3NcF/SQdQyLRgqnahQFXGf/CzLriLqKn5u7V8fGXtCkuPusK7I/nM0DS4sDiZvZm4QCSFsHdRnua2Ya4J8/xuSqfmdkmwNXAKWb2NnAN8M/I0TASuBI3Tm+E50y4vILv1gPfOaxYkPOhi5mdUKZdnbEihPdrwHrAVrir6taxwK1kZm8UdpJ0DIlEy6aaF4aVgEckTQT6Auvn7hXz8d8auNfMvo+dwdCoK0rnKFgHmBLCNYCbqUmGA3BP/FtOF7A5cEd8vhV/ONfHGDObUkG9Sscaic97G+CCKO8OjG3EGIlEo0n2heahmheGK4Ar403+N9T460MRH//4XGcBiEXiO0mrFxmjPjtDNk5DdAHZHH4i/vuF6+pCuTrfVdhXpWONxBfGHsCDwJK4LWREE42TSCTmI6p5YegAfBCfD6+g/ghgP0nt4vhor9y9C4CrJC0BIGmJiDP0CtApdw5/GPUnw/kWWDx3PYrayXGejs9v4+k9wUVtCzLnlBrrOdwgPsvMpgPj8MV0ZBOMmUgk5jOqRb26qKR8isxLgH7AYEkf4P76q5XrwMxelDQIfyi+Q+2H4tVAezwH84/Aj7i76nRJR8Y4bfGjl2vqmet9wF3y4HgnAn3wTG598YQ7R0a964H/SBqDZ3Rr6C6h2G9SdCwzmyHpPWp0DSNx/UV9eaUTiUQLpCp0DBkRcuI2MzssrtsCHwHPmdme4Zn0bzxG0ILA22a2uzwP86V46AoD/j97Zx5n93j98feHKLFXhVIiaO1LSkTtCaHaqlLaSFXRogtV+kO1WkJbtbRUbbU0qEaotWitIRJrEkQSa6uhjaq9CIklzu+Pc76Z79zce2cmM2Pmzpz36zWvuff5Pt/nee6Ee77Pc87nnDnAV+ud40u6GLjRzK6qcm0w8Gvcd2H4k/mhRCoLMzukgz5ytXWNw6u9zY6mnczsxXAm/xHfhbyCO8yfqXL/zsAZeOqOC83spHrzpY4hSbqG9ugYesuOoeAtPCdRXzObjWsPnitdPwG4zczOAFBT2urhwMrARmb2gaRVWMBz/DA+VwJ7mdl94R/Yg+bHR53N3mZW+W39LTwdxicl7QWcjH/ueYQu4mz87zYT3yFdH6VGkyTpIfRGH8NNQBHaMAIYU7q2Ev6FB0ApbfVKwPOFcMzMZprZa+AK4qK/pD1jp1AwLGL9n5K0S7QdDFxiZvfFWGZmV5nZC+VF1tFZbFfSJDwsaSlJK0kaH23TJW0TfT9WqWPAk+tVq9f8JTxqCuAqYIcwWmUGA/8ws3+a2bt4mO+XKgdKHUOSNDa90TBcDuwVGoONcMdqwdnAH0Igdow8xQTAn4Evxpfrb9Q8cV09BgDb4Ybo9zHnBnh4akvU0lkcARxsZgPxSKHZwNeAW6JtY9wPgpm9UqljACYDv43P8rPSl/8ngH/Hfe8DrwNFLQYq+wQzo60ZqWNIksam1xmG2AUMwHcLf6u4dguwBu7YXQd4WFI/M5uJaxJ+DHwAjJW0Qyum+7OZfWBmfwf+GWO2llo6i3uA0+SK7GXjS3wSsL88f9OGZvZmnXH3jhDdbeJnn2ivFlpb6YBqTZ8kSRqcXmcYgutx5++Yygtm9qqZXRYO6kmEIC0EbzeZ2ZHAiXjVNWj+xbhY89Hm+9I04FGawkzrUVVnEc7eA4C+wP2S1jGz8bHO54BL5XmTqmJmz8XvN3Ex2+C4NBN3uhdO+WWAytTd8/oEqwD/acVnSZKkgeithmEUcIKZNQu3lLS9pMXj9VLAmni+oU2KY6WIUNoID1kFeEHSutG+e8U8X5G0kKQ18Z3Ik3jaiX0lbV6a9+uSPl5xb1WdhaQ1zWyamZ2MHwutI2k14EUzuwCPqqpaGlRSH0nLx+tFgF2A6XH5+tI8e+LlQysN2yTgU5JWl/QRXPNwPUmS9Ch6W1QS4M5jPOSykk2BsyQVquILzWxShGheECGdABPxL3iAo/GSl//Gv2TLqayfxAVtKwLfCXHYnIj6+bWkFfCjqfE0pccoGEl1ncVhkobiaunHcGf6XsCRoaGYBdTaMSyKH08tgoeb3o4fm4EblEsl/QPfKewF81J5X2hmnzez9yUdghcLWhgYZWaP1pgrSZIGpcfpGFRRZ0DSfnSyNqA01y54gZuFcB3EGWZ2nqTdgKdaCusMjcERVUJJW5p3NB5t9B5utL5tZu/V0WUMALY0s8tqDNlhpI4hSbqG9ugYeutRUocTT+HnA180s43xLKzj4vJueHbSzmI07tjeEPc9HBDthS5jYzNbD9/dgDvfv9aWCcLvkCRJL6BXGQa1srZBoU2oow/YSdJ98loKV0paEheo9cFVw4Wz+klJWwK7AqfGOGtKeqg016ckzRe+WmOOqpjZ30IPYfiOYRVJD+DHQYeVNAzF9vAkPHX2FEmHS1pM0kXyuhAPx1EVkvaLuW8AbpV0qTxVR7HG0ZJ2rbL21DEkSQPTEw1D3wpB1wmla22tbTCfPiCctz8FhkUthcnAD83sVdwR+6ykMZL2lrSQeYnO64EjQ0vwNPC6pIExx/54zYZ51JqjpQ8eu5Z9gJvNbHM8xcZHgddwtfUr0fVoYEKs53RcdEdEQI0ALgnNBXgq7n3NbHvgwlgvkpbBE+s1C/mNcVLHkCQNTE88HpgdX+RAk48h3m6BF9kBrzdwSgtjTcKTyi0CXGdmUyRthx8L3RPasI8AhYr5AEkbAsNwIdqOwH5Vxr0Q1x38EE87Mbji+mdqzdEC5wDjzQv8YGa3yNOB7wx8DtdlbFDlvq3x8FjM7AlJzwJrxbXbwuhhZndJOjuc5l8Grg4dRZIkPYieaBjaQt3aBmY2Xl7G8gt4xM6p+NP3bWY2ouqAHgI7TdKlwAyqG4argeOAO4AHzeyViuuqN0c1JB0H9MM1D+X1vIrrFS6TdCOud6g2Xy0qc0Jdiqfk3gv4ZmvXlyRJ49ATj5Lq0abaBjX0AfcDWylqLEhaXNJakpaUNKQ010CatA7NaixE2OoteLrui6qss+octT6UpAPwUqQjinxO0V5Vl1G5Hjxcdu/otxbQHw+1rcbFwGHxOTJUNUl6IL3NMByKH+FMxc/ifxDtFwDbyWsbbE7TU/IQ3K/wMJ4B9QwzewnfBYyJce7HI4IEHCXpyfBtHE/TbuFyXGfwsFzsBu7jMODWykXWmaMWv8e1EveFb+XYaN8UmBxj3EfoMoCpwPuSHpF0OH4EtbA8/cYVwH5m9s7800Ak+3uc6gYtSZIeQI/TMXR3JM3FC9z0w0Vin7EqdQ+6K7EDmQZsYmavt9Q/dQxJ0jWkjqGxmI37Hl4BNmgwozAML1d6ZmuMQpIkjUkahi7AzHY3s43M7GXwAjiSTpU0KTQW3472IZLGSbpK0hOS/l0KxX1S0luS/ilporwuQ9VxqlFl7NHheEfSsTHGdEnnF+14CO0Y4GvyGhPb1Bg7dQxJ0sCkYfjwKessro22bwGvm9lmwGbAgZKK3Eifxp296+HZTQ/Bw1sXAYaY2Rp4eOzsFsapRnnsNYCtov0sM9vMzDbAldS7lO7pY2aD477jqg2aOoYkaWx6e7hqV9BMZxHsBGykJvX1MsCngHeBiZH0j3BqD8CL6DwfjmTM7I24XmucWrWpq419NzBU0lHA4sByeKrwG+KeItnfg9E/SZIeRhqG7oGA70ehoKZGD38tRwfNxf/NRPUCOVXHqcN8Y4fi+Rw88eC/5cV/FqtyT7GWJEl6GHmU1D24BfhuKKwJXcQSdfo/Aaws3M8sswAAIABJREFUabPov5Q8yV1bx6lGYQReludn2rNe5yRJeh75xNc9uBA/lnkoHL0v0VQhbj7M7F1Jw4EzJfXF/QvD2jpOjbH/J+kCPCT1GTwtSJIkvYhep2Mo6QgKLo9ymbX6/8TMTlyAeS4ETmupBkMrxhkHrATMwYvwfDOyto5jAWo3dBRqZZ2L1DEkSdfQHh1Db9wxVHP+1uMneI3nViNpYTM7oOWe890zt8blvc1ssqSDgFPxNN5JkiSdQvoY8BTSoQtYO96PkXSgpJNoCi8dHde+HrqBKZLOk7RwtM+SdIK8DsIWoREYFNdGyGsdTJd0cmneZve0YqnjgU9WrP1bkk4vvT9Q0mmSjpJ0qKQNJb0o6c1Y8z8kFfqJWuuq1b5/6Bfuoim0tdrfM3UMSdLA9EbD0Kxeg6ThoeI9BLhYXo/5o2Z2gZkdTewwzGxvSeviabK3il3HXCL5HLAEMN3MNjezIjlfUTP5ZGB7PLHeZvJSnzXvqcMXaX4MBp6HadfC4YzXS7gINyLbRLbXJ/H8Rpvh2VGPqbWuOu0r4fmftsLTidesSJc6hiRpbPIoKTCz2yR9BTgbL8pTjR3wxHSTQgzcF3gxrs3F02lXshkwLhLjFfWZtwWuq3NPJaMlzcadwd+vWPdbku4AdpH0OLCImU0LQ7GpPKvqO8BDeF2KbfBkgrXWZTXaqWi/gqaaDUmS9CB6o2GoiqSFgHXxCJ/lcJXxfN2AS8zsx1WuzanhI6hX66DWPZXs3YKT+ULcF/IEkfXUzN6T9Ay+g7gXz6g6FE+9/Ti1v9Trrbd3RSokSS+lNx4l1eJw/AtzBE1V2wDeK70eC+wpr2CGpOXkNRvq8QCe0nv58EeMAO7qyIWb2QPAqngp0jGlS+PxSnLjgQnAd4ApURu61rrqtQ+R9LH4e3ylIz9DkiTdh95oGCp9DCfJi9McAPwfnhJiJZrqMJ8PzJD0nwg9/Slwq7zGwW3RtyZm9jzwY+BOPKPqIOB4SY/gabc7ij8D95jZa6W2CbG++6KOwhLEjiDW9VPgv8DzwENm9peK9T4Sa94/2kfidR1ux4+m9pO0bAd+hiRJugG9TsfQGuR1ka/Ek8wtDEwBdjazp9sxZh/c2FxlZtdJ2hH4nZmt2wFLRl6283QzG1unzy+Al83st/F+V+AoYAUzq3q0JE+1fYiZzSeUkzQTTx3+v1pzpo4hSbqG9ugYeuOOoUXMbDqeNO5HeAbRP5rZ05L2LYWqnhN+CeSpqSdLelRN1dOQNFPSzyTdA+xeMc19wCdKfTeTdJekByXdJGnFaL87wk8nSHpM0iBJ10r6u6SRkpaV9BRejvOMCDH9fmncY+WhuLfhCfXKjABOA15QpNeIe74Q99yNlzot2vtJuk3SQ5LOpb4/IkmSBiUNQ22Ox8/sPwecEruI3YEtI6qpD031o48Oy7wxsKOkcijnW2a2lZldWTH+znhkEpIWBW7Gnd4L46Ggj0r6bPSdbWbb4HWnr8N9BRsCB8X1r+OO4cG4HuJ7kjaSNBgvSToQz3k0uJhcnkNpO+BvuF9iRLQvDpwHfB6PYFq54m9yp5ltEustX5tH6hiSpLFJw1ADM3sLr398adQ/HoaHeE6Wp6jeDo/wARgh6SH83H1dmsf4X1Ex9OmSZuDRQ7+KtnVxQ1NEKL0BTCplSb0+fk8DppnZC2Y2Bw9fXQX/Ar/azN42szdx47E1HmZ6tZnNDq1GkTobXD19W4xzJbBH7IDWA54ys6fDST26dM+2wJ/i7/MX4M0af7vUMSRJA5PhqvX5IH7Aj01GmdnPyh0kfQr4ATA4EtD9ieZpqt+qGPNw/Av6cNznsHmMPTV2BdUoUl1/QPNU2R/QlIa7FrWcSCOAzSOkFWAF/It/Vp176o2XJEkPIXcMred24KuSlgeIsM3+wNL4k/MboQ7+bJ0xAAjtwm+AxSXtADwGfCKOfpD0EUnrt2Ft44HdJfWVp8r+Eh6RNB74sqTFJC1NVGKT9FHcIK1iZgPMbAAuehsRa1lL0upyFd+Iinn2jjG+CCzVhjUmSdIgpGFoJZFa4njg9ghVvRVYET8+egyYDlwA3NPK8Qz4BXBUHFXtCZwWYawP41/crV3bRNxPMAm4HzjXzKZF+7V42OmV+Bc7uN/hNjN7rzTMdbgP5T3ch3ETblz+WepzHDAsjs2GAM+1do1JkjQOvT5cVfOn4d7NzJ7poLGXBb5mZufE+5XxENUOLX6jFlJwx3HRv8tHVeEn6WNmG8iT/X3DzA5tYZ5R+K7jxagH3SIZrpokXUOGq7aPIkle8fNMB469LPC94o2Z/aejjUIbWErSqgDyZIDzMLPJLRmF4GI8mipJkh5MGoYqSNpP0lml9zfK6y8XqbJ/KekRSfeX9AYrhr7gkfjZEjgJWDN0D6dKGiBpevRfTNJF8vTWD0saWpr7Gkk3h1bhlNI6zi3pJY5v48f6M54ZFtxvMC91hqQhcoEcoY0YJU8b/k9J8wyGmY0HXm3jvEmSNBhpGJqnyLi2Ff2XAO43s43xM/sDo/13wF3RvgnwKHA08HTsRI6sGOdgADPbEP+ivkRSEc00EP8S3xAYXjzpA8fE1nAjPJ/RRm34nFcBX47XX6R56Gol6+BO9MHAcWrKFdUqUseQJI1NGobmR0mV6uRqvAvcGK8fxGssg9cvOBc86ih0A/XYGq+NgJk9ATxLU8bTsWb2emgMHgOKRH1fDcfvw8D61KmJUIVXgdfk9SYeB96u0/evZvaOmb2MpxVfsQ3zpI4hSRqc1DFU532aG82yLuE9a/LYz2XB/4b1tAdlrcJcoI+k1fFMqZuZ2WuSLq5YV2u4Aq83sV8L/eabv43zJEnSwOSOoTrPAAMlLRTHOINb6A+ekvu74PWbQzfwJrVj/cuagLXwXEdP1hl/aVws93r4NT7XijVVci1wCnBLSx2TJOm9pGGozj3ADDyM9de4VqElfgAMlTQNP2Ja38xeAe6JxHanVvQ/B1g4+l8B7Bd6BnBRWvmLfwhebvNh3HcxilbqJSo4F9coTMQNWWvOeZYicjKF4/wRYG1Jr0o6fAHWkCRJN6fX6xi6I/pw0n73xSu+bW1m/27lGPPSdkfm1UPMbEq9e1LHkCRdQ+oYehgfUtrvvnjeo7dLfZeN15+RdHu8PkDSb8s3ShqOR05dEWv5SGf8HZIk6RrSMHRfFiTt9yzgKEmPh7J5BWCpirTfp8e1f+MG55W2LszMrsB3McMjmuvddnzOJEm6GWkYuikLmPa7LzAH+FkYjxeBMyqGPjyufRz4vCJxX0eSOoYkaWwyDLF70xlpvwEwszcl3YXrKSbSPES3rWGwlWOfj9fKZtCgQenESpIGI3cMjUOHpf2O+xfBw3ALh/YzwKbxeo9WDFEvFDdJkgYmdwwNgplNi/xIt4fTuUiPPZmmtN//pOUw1tMljQQWxfUMRXW4kcAFkv6L7yBa4iLgQkmz8d1K+hmSpIeQ4aqdRIjQTgc+A7yGp9I4xcxak4+pI9exPi5s29jMZkfbX3HfxeUVfYcAf8E1HAvhPoqvmdmLkvYDBpnZIZJ2w8t/PtbS/BmumiRdQ4ardjMkCS98M97M1jCzTfEIolVaef/CHbUWM3sUuAY4JsbeDVikilEodo8TItJoI7zwz8FVht2NtuVpSpKkgUjD0DlsD7xrZr8vGszsWTM7M1JvT5D0UPxsCfNSX98p6TKicJCk6yQ9GPqEg4qxJH1L0lORGvsCRYpwSf0kXS1pUvxsFbecAHxF0kA8FfjB0X9kaCBuBf5Y/gBh3JbCdzvl9i2BXYFTQ8OwJkmS9CjSx9A5rE/tNBovAjua2ZyIKBoDFNu9wcAGZjYj3n/TzF4NlfIkSVfjvoGf4am93wTuwNNUgIemnm5md4dj+hZgXTN7W9IReH6m08zs76X1bIqrn2fHUdI2EQ77MTyi6SflxZvZvZKuB240s6uqfcAwYgcB9O/fv/5fKkmSbkcahg8BSWfjYaHv4nqEs+LpfS5NqbYBJpaMAsChkgrF8qrAp3D9wV1m9mqMfWVpjGHAev6wD8DSkpYyszfN7AZJ/8NzNJW5vvA9BBPMbJcY+0d40r3vtOXzZrhqkjQ2aRg6h0cphXya2cERZjoZOBx4AdgYP8qbU7pvnuYgnt6HAVvEE/84XF9QL133QtF/do3rZV3EfHNW4Xrg6jrXkyTpgaSPoXO4A1hM0ndLbYvH72WA583sA2AfPEleNZYBXgujsA4e3QQeSrqdpI+Gw7isObgVOKR4E7uS9rA1TTqHMqlhSJIeTBqGTiAK+eyGf4HPkDQRuARPincOsK+k+/EjoFpP7DfjBXqmAj8H7o+xnwNOBB7ARW+PAUW1uEOBQZKmSnqMNh4BBduEU/kR3HD9X5U+lwNHymtVp/M5SXoYqWNoQCQtaWazYsdwLZ4q40PVR7SW1DEkSdeQOoY6SJobT8DFz4BOnm9WC9eXlfS90vuVJVWN7qnDyIgcmo6L0a6rmOObkqbFzmG6pC9F+36SVm7jXEmS9DJ6g/N5dmQT7S4sC3yPiA4ys/8Ae7ZlADM7otY1SavgYrZNzOx1SUvSVKltP9yY/Kfty06SpLfQ43cM1ZC0mKSL4qn6YUlDo32/QiwW72+M6CAkzZL0S0mPSLo/Ul4gaXVJ94Wg7Oele5eUNDZEbNOKp3ZcYLZm7F5ODcHb9Fas6xpJN0v6u6RT6ny8FXDn8CwAM5tlZjMk7YnrJUbH3H0l7RDzTJM0StKiMd8zko4vrX2daF8i+k2K+75UbQHKtNtJ0tD0BsPQt3SMVJzDHwxgZhsCI4BLJLWUanoJ4H4z2xgXih0Y7WcA55rZZsB/S/3nALub2SbAUOA3oSY+Gng60k4cWTFHvXUNBIYDGwLDJa1aY52P4OGwM8LIfDHGvAoPl907dlCGl/ocHvP1AcpRVC/H2s8Fih3KMcAd8VmH4urnJSoXYGbnm9kgMxvUr19rykonSdKd6A2GYXZ8CQ80s0IstjVwKYCZPQE8S3OhWTXeBW6M1w8CA+L1Vrh6mWLMQMCJEVV0O/AJYMUW5qi3rrFm9rqZzcEjkVarNoCZzQV2xo+nnqIpm2olawMzzOypeH8JsG3p+jVVPutOwNHh3xiH6ypS2pwkPYze4GOoRi2RWLlYDTQvWPOeNYVwzaX5365aaNfe+Nn+pmb2nqRnaLkATj3x2jul15XzNyPWORGYKOk2PEX2yDbMVZ6vPJeAPczsyRbuTZKkgekNO4ZqjMe/uJG0Fv7U+yRerGagpIXiqKY1ZS/voan28t6l9mWAF8MoDKXpCb+eOKzWulpNRDltUmoaiO88Kud+Ahgg6ZPxfh/grhaGvwX4fhyJIenTbVlbkiSNQW/dMZwD/F7SNHyXsJ+ZvSPpHjz8cxoevVMrEV6ZHwCXSfoBnj5iEUmPxrUBkobhxuMJADN7RdI94XC+CTi7Fetqy2dbBLg6opPex5/4fxjXLo7xZwNbAPsDV4YeYhLw+/mHa8ahwGhgahiHZ4Bd2rK4JEm6Pylw60AkbQGcBgyJL/TlgY9ESGq9+/qY2fsduI6RwCwz+7WkdYEJwAqRhqOle2uuRdIsM1uyLWtJgVuSdA1KgVu3YSU8mucdADN72cz+I2kzSfdGqOtESUtFCOqVkm7Acxwh6cgIBZ0qL+NJtH897psi6TxFIZ9aIbRlzOxxfOewvKTVIoR2avzuH+NcLOk0SXcCJ0eobRE2O1XSHqW11J0vSZLGJw1Dx3IrsKq8iM45kraT9BHgCuAHEeo6DCiyn24B7Gtm20vaCU+rPRj3C2wqadt44h8ObBVhpnNp8mUsgYe1GrA6MEXShuUFSdocz6j6EnAW8MeozjYa+F2p61rAMDP7P7zew+tmtmH0vaM0X7WQ3WakjiFJGpve6mPoFCJ/0abANnic/xXAL/FsqpOizxsA4Te4rairgIeC7gQ8HO+XxA3FRngxnUlxT1+82A94CO2aZmaShuMFgKbFE/7hkr6OO5yHR58tgC/HvZfitRYKroxQV3DjVTjUMbOiiltlyO6ONf4OWY8hSRqYNAwdTHy5jgPGhRP5YKqHs0LzzKoCfmVm55U7SPo+cImZ/bjK/fVCaE83s1+3tNw6a6m25nrzJUnSQ8ijpA5E0trycp0FA4HHgZUlbRZ9looooEpuAb4pz22EpE9IWgEYC+wZr5G0nKSq4rZWcC/NQ2vvrtGvsq7DRxdwviRJGpB84utYlgTOlLQs7vD9B177+KJo74v7F4ZV3mhmt4Y/4b44MpoFfN3MHpP0U+BWSQsB7+G7kGcrx2gFhwKjJB2J+xz2r9HvF8DZEVI7FzieJiV0kiQ9nAxX7STaEtopaTfgKTN7LN5fDGxHUwGeUWb2uxq3t2VNQ4B3zeze9o7VWjJcNUm6hvaEq+aOoXuwG+7UfazUdmQkvquKpIVLzuLWMgTfiXxohiFJksYjfQwfItV0BJK2BHbFM5VOUZ1SmaFbOEHSA8AWakPabHmBou/g0UpTJG0j6YuSHogxbldTKvF+km6L+8+T9KxcrFdTU5EkSc8hDcOHy3w6gjjWuR7fIQw0s6ejb2EoytqEJYDpZrY5nkL7YlqZNtvMnsFTXpwe80zAnc+fMbNP43Wcj4p7j8PTa2+Clw4thHD1NBXzSB1DkjQ2aRg+XLYALovXl+JptmtRGIqBZjYt2ubi+ZhgwdJmV7IKcEuE1R4JrB/tW+OGAjO7GSh0DDvQpKmYEu/XqBw06zEkSWOTPoaupa2e/zklv8KCpM2u5EzgNDO7PhzTI1sYW9TWVCRJ0kPIHcOHSy0dQb1U3LVYkLTZlfMsAzwXr/cttd8NfBUgUnUUOoaO1FQkSdJNScPQeSwuaWbp54e4jmB/eVW3ffCU3eDHNkeGE7im87lMVHIr0mZPw/MhtZQ2+wZg98L5jO8QrpQ0AXi51O94YCdJDwGfA54H3oxw2kJTMRW4DU8cmCRJDyKPkjoJM6tldLeX13EeD9wQKuirzGw9SbsAV+EGe7Ckj5VTZFTqIsxsLFCrWM6dkj7A6z/vFf2fwnMvlflLlXtfBz5rZu9HfqWhwAhJg8zsEDwHVJIkPZQ0DF3DO8D2kXRvEeBuSbfjiecGm9nMCD0d0I45hprZy5JOBH6C71ZaJMJP+wN/DqX1u3gW1fXr3pgkSY8hj5K6AHNmxdtF4udd3FC/En3eKWorS/qKpOnyOgjjo20/SddIulnS3yWdUmUq8J3JJ+OeEaFrmC7p5KJDpT4CWJam1OBGVJ/Dcz61NF+SJA1O7hi6iHgyfxD/0j7bzB6QdD3wrKSxuBJ6TFRdOxY/2nku8jAVDMSPkt4BnpR0ppn9u2KqXYBpklYGTsbDTV/D/QS7mdl1NOkjjpXXj3gC10dMkrQ0TUaiNfMh6SA8RxT9+/dv3x8qSZIPndwxdBFmNjdEYqvg/oQNzOwAXBswETgCGBXd7wEulnQgUFYajzWz18MR/RhQjhC6M7QGSwO/AjYDxpnZS1G6czRNuodKfUSz+hGlUp/15it/ttQxJEkDkzuGLsbM/idpHLAz/tQ+DX/CvxSYAexnZt+RV2L7Al6lbWDc/k5pqEq9wlAzmxdpJKme7qFSH1FLX1FvviRJegi5Y+gCIhfRsvG6L56G+4kQmRUMJFJrS1rTzB4ws2PxsNJVF2DaB4DtJC0fx1gjqK57eILW1Y9IkqSHkv/Ddw0rAZfEF/RCwJ/xL+krJJ2Hn+m/BewX/U+VFwASLjJ7BDccrcbMnpf0Y+DOGOdvZjZfqKqZvSsvE1q3fkSSJD2XNAztRNLpwLNm9tt4fwvw7/AXIOk3wHNmdlpxj5lNpbr+4PM1pnkDON7MropjpyfwY52PSDoL+KmZ7VIaf0C1QczsMppyNSFpJFCtbsTCNB0pLYMn4RspCUkv0aSW/iZexjRJkh5EHiW1n3uBLQEi7n95msf8b4k7jzuSvSND60a4gagmUmsPlwAHhXN8A3xHU3BFKbnfNzp43iRJugFpGNrPPYRhwA3CdOBNSR8Nkdq6uMP41NAPTIujGuTUaj9L0mOS/gqsUG1iM3sXT5XdX9LGcW/VegmSdpbXV3gkwmGbIelASTfF8dEKeBqMInrqscr+SZL0XPIoqZ2Y2X8kvS+pP24g7gM+gQvFXgem4lqCgcDG+I5iUgjVtqzRvgUeNrohsCIeGjqKKpjZXEmPAOtIepemegnvSToH2FvSTcAFwLZmNkPScuUxJB0C7ATsZmbvxPHYk3FsdTOeUXVOdB8uqUgXfoaZXVS5ptQxJEljk4ahYyh2DVsCp+GGYUvcMNyL1zcYEyGhL0i6C9cV1GrfttT+H0l3tDB/EYparpcA0Bd4EfgMMN7MZgCY2aule/cBZuJG4b24foKk0bix+BoewTQk+l8R+ZJqYmbn4+k9GDRoUBYVT5IGI4+SOobCz7AhfpR0P/7UX/gX6tU3qEWrvlDjqGhD4HGa6iUUPoC1zWwk9bUJ0/GcTKs0m9zsaTM7Fzc2G0v6WGvWkyRJ45OGoWO4Bz8uejXO5F/F8w1tgR8tjcePYBaW1A/fEUxsoX2vaF8Jz246H/IEfL/Co6CmUrtewn24hmH1or00zMPAt4HrI20Gkr5QEsR9Chez/a/9f6YkSRqBPErqGKbhPoLLKtqWjAyn1+JG4hH8yf0oM/tvC+3bxxhPMb8QbbSkd4BFgduBLwGY2WOSinoJCwHvAQeb2f1x7n9NtL8I7FgMZmZ3SzoC+KukHfHjpdMlvQ28j0dBza0vnk6SpKcgszwC7gwkrQicjp/vv4ZnTz3FzK7twjX9BVjBzLb4sOYcNGiQTZ48+cOaLkmSQNKDZjZoQe7No6ROII5hrsMdvmuY2aZ4sZxV6t857/6FW+7V5jUtC2wCLFscKVXpkzvIJEnSMHQS2wPvmtm8Uptm9qyZnSlpgKQJoSl4SFIhjhsi6U5Jl+FHSEi6TtKDkh6NoyCi/VuSnpI0TtIFoX4ucjBdLWlS/GxVWtMeeGnPy2mqO42kiyWdJulO4GRJS0gaFfc/LOlL0a/qupMk6XnkE2LnsD7wUI1rLwI7mtkcef6jMUCx3RsMbFCElQLfNLNXQ3Q2SdLVuF/hZ/jT/5vAHbiPAuAM4PTwGfQHbsEFduAhp8fjpT6vwp3WBWsBw8KPcCJwh5l9M3YZE+XV5eqtuxmpY0iSxiYNw4eApLNxzcK7eEK6s+Sps+fiX8oFE0tGAeBQSbvH61XxCKGPA3cVWgRJV5bGGAasV3ISLy1pKWBxvCDQ3WZmIcjbwMymR78rS2m3dwJ2DWc0wGJ4qc//1Fl3M1LHkCSNTRqGzuFR/OgGADM7WNLywGTgcPypfWP8KG9O6b63ihfyFNzDgC3M7O1QIS9Gfe3DQtF/drlR0v7AR4EZYTSWxo+Tflo5b4y/R1FWtDTGyDrrTpKkB5E+hs7hDmAxSd8ttS0ev5fBK6R9gIeF1nI0LwO8FkZhHTy6CVznsJ08F1MfSgYIuBWYp0pWU0GfEcDOZjYgMq8WzvBq3AJ8v9AxSCqywLZ23UmSNDhpGDoB8xjg3fAv8BmSJuIZS38EnAPsK+l+/DjmrRrD3Az0kTQV+DmupsbMngNOxAvv3I7nUXo97jkUGCRpqqTHgO9IGoAfBd1fWt8M4A15VbhKfg4sAkyVND3e04Z1J0nS4KSOoZ1EqogiW+nH8fP3l+L94MiAWu6/HPDVcsRSjXH7AC+b2bKSPolHKj0JfAS4G1crLwxcC4xaEH2EpD8BV5nZdRXtCwO/A7bDhXezga+Y2bOSZuK6jMIn8W0ze6DWHKljSJKuoT06hvQxtBMze4Wopqamwje/rnPLcsB3gLqGoQpPmtnASIPxDPA07sy+FbhOUh8ze7+NY9bia8DHgI3M7IOIcHqjdH0bM8sUGUnSQ8mjpE5E0lHyWgvTJX0/mk8C1pbXSzhJ0tKS7ghtwFRJu9QbMzKgjgHOwv0J68X7h+vMiaT9Y/xHJJVTZQ+VdK+kf5YioFaiyZ+Amf0rDUGS9B5yx9BJSBoM7I1rExbG9QB3AUcDn4zqaEUivC+Z2Zvy5Hf3ADfWGXcJXED3o2j6DLCemf2rzpyK/luGLqKcRG8FYCs8Q+uf8aOpy4EJERk1FviTmU0p3TNB0lzgbTObT+iWOoYkaWxyx9B5bANcbWZvm9mbeIqMrav0E644noofC60aoa2VrC1pCjABuNbMbov2+8zsXy3MuT1eR+FVmK8ew3XmTMXrSBDjrQ0cE33uDCMx77NFWu+q6mczO9/MBpnZoH79+tX6+yRJ0k3JHUPn0dpUpN/AQ0E3MbP3w7m7WJV+Txa7jAoqNQi11lIryuCdavdHxba/AX+T9DKewXVcjTGSJOlB5I6h8xgP7C6pr6Ql8S/WCXgai6VK/ZYBXgyjsCPx1N7Bc96O13dYDuarxzAfkjaV14FAnqZ7Q+DZdqwrSZIGIncMnYSZTZQ0BpgUTeeaWZEcb7KkacBf8VKgN0iajOdX+nuNIVeV9CJuRDaoM+cDeFW22cC5uADuQDy89DlJbwA3SloTeLvGXF8EjgqRm3DF9jcj6molmnwMN5vZ0a38kyRJ0iCkjqFBkLQtMAv4Yy3DEP1GUgqZrRVCGyk2jjCzyRXtiwIzcA3GzHg/wMyebGU4bjNSx5AkXUN7dAx5lNQgmNl4oOw0RtKhkh6LMNTLQ+X8HeDwCIfdpjVjS5ol6YTYbWyO7yRfiXnfqcyblCRJzyYNQ2NzNPBpM9sI+I6ZPYML506PqKEJ0a8wFFMkfbbKOEsA081s8zBA1wPPShojae/wM9DKsZIkaXDSMDQ2U/H6z1/HazPXojAUA83slirX5wJXF2/M7ABgBzxh3xHAqDaMhaSDwo8y+aWXXqrWJUmSbkwahsbmC8DZeLbUB7XgpTnnlOoxAGBm08zsdGBHmmdwbZHUMSQMNcanAAAgAElEQVRJY5OGoUGJ451VzexO4ChgWWBJ5g+Hbeu4S1aI2QaSoapJ0qvIcNUGIUJfhwDLhwju58A+kpbBQ0pPN7P/SboBuEpeq/n7NQesMxUeqnoeHvL6FrBfB3yEJEkahAxXTTqVDFdNkq4hw1UXEEkm6dLS+z6SXpJUM4ldnbHGVUbpSDpM0jkdsM6L5QV/pkh6QtJxLaxjvv8YJD0jaVopoqhqnqMkSZJebRjwY5INJPWN9zsCzy3gWGOYv1zmXtHeKuQFcmpxZORKGohXUlu9jfcDDC1FFN3b2nUlSdK76O2GAeAmPLoHvDbyvC9ySYOjVsHD8XvtaF9f0sR48p4q6VPAVcAuoRQmxGYrA3dLGhJP8lfFE//oSDdRPMkfK+lu4CutWG+RYO+tevdLWkjSJZJ+UWugcDSPldeCmBZ+ieLaN9RUv+HSaOsn6WpJk+JnqxrjZrhqkjQwaRi89sBekhYDNsJrKRc8AWxrZp8GjsVrLYOri8+IJ/hBwMyo5DYR2Dn67IWnui6cOJ8GDsML66yB10AomGNmW5vZ5XXWeao87fZM4HIze7HO/X2A0cBTZvbTUr87w5gVn3EOsLuZbQIMBX4jZ3085fb2ZrYx8IPofwbu5N4MD2G9sNpCM1w1SRqbXh+VZGZT4+l+BJ5muswywCWxIzBgkWi/DzhG0irANWZWJL4rjpP+Er+/WRpropnNBIgv+AF47WaAK1qx1CPN7Cp51tSxkrYsHQdV3n8e8Gcz+2VF+1Aze7n0XsCJ8jxMH+CZXVfE6zdcVfQt1W8YBqwXmx2ApSUtFbUfkiTpIeSOwbke+DXz+wN+DtwZSeu+SBzjmNllwK54OOctkraP/tcBO0jaBOhrZg+VxirXPZhLc6NcrqlQFzObhddFKBf9qbz/XrxkZ7W6DmX2BvoBm8bu5wX8M9aq37AQsEXJT/GJNApJ0vNIw+CMAk4o0mKXWIYmZ/R+RaOkNYB/mtnvcKOyETT70h5FG5zObSHUzZsDT9fp9gd893NlC2roohbEe5KGAqtF+1jgq5I+FnMW9RtuxetMF2upVjgoSZIGJw0DYGYzzeyMKpdOAX4l6R68hnLBcGB6HAmtA/yxdG0MsDHuu+hICh/DVGAacE29zmZ2Gl7f4dKKJHhlRgOD5LUg9sZ9KpjZo8AvgbskPYLXjAA4NPpPlfQY7mtJkqSHkQK3BUCSAX8ys33ifR/geeABM9uljWONA35VTkgn6TBgLTP7XjvXeTEegruGmb0jryU92cwGtGfctpACtyTpGlLg9uHTSPqHuTR3gidJktQlDcOC0+H6B0lnxxHNJsCZkv4Ruof26B9+i9dQaOZriLDUUyVNDw3D8Givp7nYVNJdkh6UdIuiLnQlqWNIksYmDcOC0+H6BzM7GPdXFH0OwGsst0f/8C88LHafivYv4yrqjfEw1FNLX/TzaS4kLQKcCexpZpviDvbKcFggdQxJ0uj0eh3DgtJA+gdww3Q98NdS29bAmKjD8IKku4DNgDdqzPk/YAPgtthALIz7VZIk6WHkjqF9NIT+wcz+AUwBvlpqVo3uteYU8GhJw7Chme3UmvmTJGks0jC0j4bRP+DHPkeU3o8HhktaWFI/YFv8SKsWTwL9JG0BIGmRSJ2RJEkPIw1DO+go/YOkFYGP4+f9e0q6T9LuVcZdCpgvKZ6kEyQNa2Gtj+KiuNXk6cGvxTURjwB3AEeZ2X/r3P8usCdwcmgbpgCZujtJeiCpY+hiIuLnXuASM/t9tK0G7GpmZ5b69QFWAW6MI6oFmesUYAvgaTPbr8ZaZGYfLMj41UgdQ5J0DaljaGy2B94tjAKAmT1rZmdK2k/SlfJynbfWGkBeyGdPSZ+T9OdS+5C4t/jS3xM/2tqpyKMkaYCkx+UFhR4CVpW0U+xaHor5l4y+x8rTbU+XdH4RxpokSc8iDUPXsz7+hVyLLYB9zWz7On02B04FfgXsFhqJ/fGjqyJyaStghpk9jfszPl+6f23gjxFe+xbwU2BYpOOeDPww+p1lZpvFjqUvUFXlnTqGJGls0jB0M0Lk9oikSdF0WyntdS0eoKnC28V4eOqluADvL9FnBE35my6P9wXPmtn98fozuH7hnvCF7EtTcr2hkh6QNA3f6VR1PqeOIUkam9QxdD2P4kVvADCzg4ucRtHU6pTcwRXAwcCrwCQzezNSZuwB7CrpGDz09GOSlqoyh3BjVDYcxNHTOcAgM/u3pJE0VZNLkqQHkTuGrucOYDFJ3y21Ld6O8cbhKTUOpOkYaRjwiJmtamYDzGw14Gpgtyr3348rnT8JIGlxSWvRZAReDp/Dnu1YY5Ik3Zg0DF1MlP7cDdhO0gxJE4FLgB/VuGVtSTNLP83yJIWS+Ubgc/Eb/Njo2opxrga+VmU9L+EO6jGSpuKGYh0z+x9wAZ7y+zpgUuW9SZL0DDJctY1ImmVmS3bgeLsBJwAfAd4HRprZVQs41gAinFXSENy/MCMuv2xmwyR9B3jbzP5YfRTfJeBGYCP8aOl/eC6nWZLm4sahYDcze6bWWBmumiRdQ3vCVdPH0IVI2hhPqbGjmc2QtDpwu6QZZvZgB0wxobI+RDkstg4/AF4wsw1jnWsD78W12eHkTpKkh5JHSR2ApNUkjY0w0bGS+keqiX/KWVbSB5K2jf4T4gz/COBEM5sBEL9PBP4v+o2TNCheLy/pmXg9IMZ4KH5arUCWNFLSEaXxT5anAn9K0jbRbSVK9SXM7Ekze6faeEmS9DzSMHQMZ+E6gI3wcpm/i7P+p/DQz62BB4FtJC0KrBKJ7daP9jKT4556vIjvMjbBtQq/q9FvG3nthykRjVSNPmY2GE+zfVy0jQJ+FCK3X8izxBb0LY1Z6bcAUseQJI1OHiV1DFvg9Q3A9QOnxOsJeHK61XHx2YHAXTQ5boWn5S7TGjXxIsBZkgbi2U/XqtFvvqOkKhS1ox/E02tjZlPkCf92wiOaJknawswepxVHSWZ2PnA+uI+hFZ8nSZJuRO4YOofiy3ACsA0wGK/ZsCwwBM9sCq5hqHQOFWpjcGd08W9U1gwcDryAJ90bhDuuF5TiiKhZSm8zm2Vm10Td6T/RXCmdJEkPJg1Dx3AvTXWb96apkM4DeAbSD8xsDp6R9Nu4wQB3PP84oomKqKLD8PQWAM8Am8brsm5gGeD5SHa3D80zuLYbSVtJ+mi8/gh+tPVsR86RJEn3JQ1D21m8QkfwQ+BQYP+I+98Hj+ohHLb/xrUA4AZhKSLc08ym4HqFGyQ9hfskvmtmT0b/XwPflXQvsHxpDecA+0q6Hz9Gaqs6uiXWBO6K1BcP4zuYqzt4jiRJuimpY2gn8jKdZ+NP1QvhorIjo35BW8c6CU+I99ny/ZF+4kDgJfy45ydmdn0HrL2qJiPCU8/Dj74WxX0VB9XSRtSbI3UMSdI1tEfHkDuGdhBpp68BrjOzT+FP70vi1dLajJkdbWZDaxiV08Pp+xVglKRW/dvJ6zi0ld8V85nZusCZpWsTSuU96xqFJEkakzQM7WN7YI6ZXQTz0lEcDnxT0vck/UXSzZKelFSEgiLp66EdmCLpvEhyh6RZkn4Z2VXvl1d2a0ZEBr0PLF9NPxHjXCzpNEl34hXXlpR0kaRp0XeP0lqqzbcSMLM0Z2Xp0iRJejBpGNrHfDoEM3sD+Bd+5DMYd0YPBL4iaZCkdXHtwVaxA5gbfQCWAO43s43xyKUDKyeUtDnwAX6sNJ9+otR1Lbymwv8BPwNeN7MNo+8dLcx3OnCHpJskHS5p2dK4LWojUseQJI1N6hjaRzUdQrn9NjN7BUDSNbjQ7X080miSn0TRFxesAbxLU+K7B4EdS2MeLunrwJvAcDMzSbX0EwBXxg4GXItQRE1hZq/Vm8/MLpJ0C7Az8CXg25G+A1qhjUgdQ5I0NmkY2kezWgoAkpYGVsV3ApVfioYbjUvM7MdVxnvPmqIBmukK8DP/X7ewnvJ8lTUWqn1B15zPzP6DK6BHSZoOLFCd6SRJGo88SmofY/Hw1W8AhK/gN3gVtbeBHSUtJ6kvnlr7nrhnT0krxD3LSVqt2uCtoJZ+opJbgUOKN4VGoRaSdpa0SLz+OPAxSrmTkiTp2aRhaAfxtL077j/4O65DmAP8JLrcjR/xTAGuNrPJZvYYXlP51tA93IY7exeEqvqJKvwC+Kik6ZIeAYa2MO5OQNH3Fjz89r8LuMYkSRqM1DF0EpL2w8tgHtJS3wUc34A/mdk+8b4P8DzwQD0fQEQe/QE/7loEeMbMaqa7UKnGQ5Vr44AjzKymUCF1DEnSNbRHx5A+hsblLWADSX3NbDbuOG7Ncc8JuFP8DABJG3XiGpMkaUDyKKmTMLOLO2u3UOIm4AvxegQwprgQvovrQrdwf8kAVGoUpkZ/STo1jpumSRpeOZmkvpIujzGvwCOqkiTpYaRhaGwuB/aStBhehvOB0rXjgYdDt/AToCjleTbwB0l3SjpG0srR/mVcb7ExHt56qqRK38d38bKgG+Hq7k2pQuoYkqSxScPQwMTT/gB8t/C3istb445vzOwO4GOSljGzW4A18JrO6wAPS+oX/ceY2VwzewGvG7FZxZjb4im4i7mn1ljX+WY2yMwG9evXr/0fNEmSD5U0DI3P9XgW1jEV7dUK/hiAmb1qZpeF43oS/oXfmgJB88ZIkqTnkoah8RkFnFAln9F4ItVGZEV92czekLS9pMWjfSk8xfa/ov9wea3qfrixmFhnzA3w46skSXoYGZXU4JjZTOCMKpdGAheFxuFtYN9o3xQvC1pUh7vQzCZJmoyXKH0E3xUcZWb/jXDVgnNLY05hfsORJEkPIHcM3RRJq4aD+HFJj0pqJl6rVkfBzMaVNAzXAD8PR/HHgdGSpuBP/AcAu8Y9v4nfZmZHhl5hY2CrSIVxAzBb0uoRFvsZ/NhpI2Bx2ldWNEmSbkjuGLov7wP/Z2YPxZHPg5JuC+X0gjDUzF4u3lTsBCi198FrPqwMbGRmH8iLEZVzLzUbK0mSnkUahm6KmT2PK5kxszclPQ58QtI5eFjqULzC2rfMbELkY7oIryT3OG3QGIRK+wvAYngq7htpqildHFclSdJLSMPQAMTT/adp0in0MbPBkj4PHIfrDuZpDELM9lDFMHdKmgu8Y2abV5lmC3yH8GrsEO6WtA2e9O9PZvZwa8eSdBBwEED//v0X7EMnSdJlpI+hmyNpSeBq4LAoAgTuPwCvoTAgXrekMRga5TirGQXwNBmvxv0zgbWBH+NFgcZK2qG1Y6WOIUkam9wxdGMi9fXVwGgzu6Z06Z34XVmzoT0ag7IPATN7B0+5cZOkF/C04WPbMX6SJA1C7hi6KfLybn8AHjez01pxS4dpDCRtUqTKkLRQjPXsgo6XJEljkTuG7stWeI2FaRFmCk11HqrRkRqDFYALJC0a7yfi9aWTJOkFpGHoQiR9jKbjmY/jR0NF1rnBZqaK/ssBlxf1DyJkdEC8nk1Uc4uQ05fNbLKkTwIrArdHjWlwkdsw4Pa492K86lzBI8ALwCfwmg3Lm9mcWmOVaksnSdIDSMPQhZjZK3hGUySNBGa1UNd5OeA7wO/bONWTZjaw3FD6YqeivQ9e8e2vZnZ2tJWPpeYbK0mSnkX6GLopko6K2gjTJX0/mk8C1pY0RdJJkpaWdIekh6JGQs3KbS3M9SdJv5F0J3AiNWo2JEnSO8gdQzdE0mDckTwYWBiYKOku4Gjgk8UTe0QtfSkEcCsA9+DitErWLvkpxpvZoVX6rAnsEErnzwOXSXoIP266KAR3rRordQxJ0tikYeiebANcbWZvA0i6Dq+XcGtFPwEnS9oa1xusKml54H8V/Vpz/HNlSen8N0lrAjsDn8NrNqzf2rHM7HzgfPCazy3MmyRJNyOPkronra2N8A1gGWCT+LJ+GU9rsSBU6hheMbPRZvZ1PMpp6wUcN0mSBiMNQ/dkPLB71FheEvgSMAF4E1iq1G8Z4EUze1/SjngUUbuRtEPkXkLS0sDqeM2GJEl6AXmU1EG0IvT03Yr+ywFfNbP5IozMbKKkMXh1NYABZjYtwkVXkzQbeAMXnX0kaik8BPx9AZe/IrBW6f3ngetD3CZgspk9LOkbwIYlH8MLZvbZBZwzSZJuShqGDqK9oadmNrJivFOAUwpNQunS9JLz+WD8GOlbVcZfNsb5R7GuivEvLL19ACgbroG40fprKLA3iPb/ADeY2W51PleSJA1OHiV9CHRi6OnSwGsxx4aSJsV4UyWtIemTMeeoKPbzR0mflXSvpKckDQon8wHAkXHvlpTCVaOAT2XZ0CRJejC5Y+hkOjH0dGlgUaDIcPo94NdmdkWkshCwCp4l9avAE/hx0ztmtqWkPYCjzWxPSRfiSunfxlpOA8ZLugePhLrIzF6PeYaWjpIuN7OTOuQPlSRJtyF3DJ3PvNBTM3sTKEJPKylCT6fiX8ZF6GklT0bK6zWAo2hSQd8L/FTSUcCqZjYn2v9hZo9FKOpjRBoMYBpNKbubEcdM6wFXATsA90kqSnjeGfMPrGUUJB0kabKkyS+99FK1LkmSdGPSMHQ+nRl6ej1ehwEzuxTYHU/JfZukbaPPO6X+H5Tef0CdHaOZPWdmo8zsi/h/J+u28nNkPYYkaXDSMHQ+nRl6ujXwNICkNczsH2Z2BvBX2pZ2u9laJO0cTm8i/fZHccdzkiS9gPQxdDJVQk/PLZy5cdwyDf8iPw24oRWhp4WPQfjT/0HR/jVJI4D38C/xnwLVjqKq8RfgSklfBg7G1c5nSJqDF/85zMxeqpV4L0mSnoXMMmNB0nkMGjTIJk+e3NXLSJJeh6QHzWzQgtybR0ldhKS5ER76SISobtkBYw6MBHgt9dtZ0kRJT8QarpDUP65dLGnPKvesH+G0T0l6WtLxIYBLkqSHkf9jdx2zI7JnY+DHwK86YMyBuGq5JvKyn2cC+5rZOuHoHk2NCKW4py/u6D7JzNYCNsTDb3/QAWtOkqSbkYahe1AWqq0kaXw8yU+XtE20z5J0sqQHJd0uabCkcZL+KWnXCCc9ARge9w6vMdePgBPN7PGiwcyuN7Pxddb3NeAeM7s1+r8NHAIcWa1zhqsmSWOThqHr6Btf4E8AFwI/j/avAbfEk/zGeGZTgCWAcWa2KR5F9AtgRzxE9YTIxXQscEXsRK6oMe/6uHO7LawPPFhuMLOn4zMsW9k5w1WTpLHJqKSuY3ZJ9bwF8Mc45pkEjAol9HVmVhiGd4Gb4/U0XMH8XkQ1DViQBagp8d/iwPl1cjsJj06q1p4kSQ8jdwzdADO7Dw8t7RdHOtsCzwGXRkZTgPesKYRsnlAtFM1tMfCPApvEva+EcTofWLKFe5pFN0haA0+jUVkUKEmSBicNQzdA0jp4HqVXJK2GC90uAP5AfIm3kkrRXDVOAY6RVFYyL97CPaOBrSUNi/X2BX4HHNeGtSVJ0iCkYeg6Ch/DFOAKPEpoLjAEmCLpYWAP4Iw2jHknsF4953OI636AH109EYny1gUuK3U7T9LM+LnPzGYDu+IG5Sk8Xcc9Zja6bR85SZJGIAVuXYikubi/QHhhn0PM7N52jjkQWNnM/taKvn8BVjCzLUptI6lSS0LSKsDZeHK9pYC+wCAzq1scKAVuSdI1pMCtcekSLQNARBNtAiwrafUW+gq4BneGfwrP43QVHrKaJEkPIw1D96GjtQz/kvRMcVwVP2eX5tsDuAG4HNirhbVtD8wxs4sA4sjrcOAbkRiwGaljSJLGJsNVu5a+4WNYDK+atn20F1qGX0pamCbncKFl+JGka2nSMqwHXGJm10s6Fj/iaelpfgRwPPAC/vRfb7dSTcfwhqRngE/SpLUorp2PRzoxaNCgPKtMkgYjDUPX0iVaBkkr4l/od5uZSXpf0gZmNr3WLaSOIUl6DS0eJUlaR9KPJP1O0hnxutVFW5LW8SFrGYbjNRZmxFP/AOofJ1XTMSwNrAg82YZ5kyRpAOoaBkk/ws+gBUzEn2QFjJF0dOcvr/fwIWsZRgA7m9kAMxsAbEp9wzAWWLwwUHG89RvgrAhlTZKkB9HSU+a3gPXN7L1yo7xY/KNAFoJvH4WPAdzg7mtmcyUNAY6U9B4wCy/72VruBI6OcX9VmTNJ0gCgP3B/0WZmMyS9IWnzaPqppMNK11eRtDtwtqSfAf3wnEy/bMO6kiRpEFoyDB8AKwPPVrSvFNeSjqHQMbwGYGaXAJdUdjKzJUuvR9a41h84rpaOwcyekXQM8KKk54BFgMeBrc3sbUmfA35RJW+S0eRneAsYIekiM7ufJEl6FC35GA4Dxkq6SdL58XMzfrSQufjbT5fpGGjKwro+7tSulaa7mo6hP3AlfiSVJEkPo+6OwcxulrQWXpTlE/iT7UxgUsSyJx1HMx0DniZjafzf6LtmNkHSLFx9PCz6/gTPfdQfN+I34zqGvpK2Bu4DtqiY5x6a6k8jqQ8eBvtanbXNp2OQdDjwrKRjzGxWubOkg4ha1P3792/jnyFJkq6mxUiWiHjJ44LOoUt0DJL2w0VwW8e8T+Fit1qkjiFJehGpfO5aiqOkdYCdcR2D8Cf6/SNv0YZm9mb0r9Qx3BWBAQtSk+GK0FB8PO6vWo0tSB1DkvQi0jB0Ez5kHUN5XsN3C9vW6ZY6hiTpRaRh6CZ8yDqGSrYGnq5zPXUMSdKLSMPQtXRJTYZgePSZCnyapprT4DqGoh7DzNhV7A7sKenvwCvAB6ljSJKeSdZjaAOSDPiTme0T7/sAzwMPmNkubRxrHC5Au6XUdhiwlpl9r53r/AxuTBaNnyvMbGQI597tgJoPWwJjgC+b2YP1+mY9hiTpGtpTjyGT6LWNt4ANJPWNI5QdcT/AgjAGT0NxS6ltL+o7gZshaeEaYcOXAF81s0fi2GftaB+CK6nbZRjCsKzWnjGSJOm+5FFS27kJ+EK8HoF/wQMQ9RHulfRw/F472teXNLE4upH0KTzV9S6SFo0+A3CV+d2ShkSdhavk5TdHR7QSUWPhWEl3A1+pscYVgOcl7Y+HmV4m6THgGGBkrGMbSatJGhtrGiupf8xxcSRNvFde62HP0mc88v/ZO+9wuarq/X9eihAMBAREQCCASAshkFBCRxAbUqTGiAQFRVEUf+AXRTGgIooNCEVACEpJpEpRCC2EUEIglY6SKFEEAgKGEiCs3x9rndxz587MrcnN3Ls+zzNPZvbZZ589F519zl7rXa+kyXHOKdUurvRjSJKGJheG9jMGOFTS8sBAYFLp2BPALma2FXAycFq0Hw2cGemhQ4A5ZvYSXpjwk9HnUHzLp9jb2woXrW0GbADsWLrOW2a2k5mNqTHH3+DZQvsA5wHbm9lmwE+BkZEiew8wCviDmQ0ELgfOKo2xJh6U3puoiSVpL2AjXPA4CBgsqUU2k5ldYGZDzGzI6quvXmOKSZIsqeTC0E7MbAauGRgGVNYj6gdcJekR/Md582i/H/h+VKtdr5TJU2wnEf9eWRrrQTObE6mo02iuU2hWGK/KHE/FF6BxuFjulhpdhwJXxPs/4gtBwfVm9p6ZPYanpQLsFa+pwBRgE3yhSJKkB5ELQ8e4AfglzX/IwTN77jKzAcBncUUzZnYFfvf+JnCrpELhfD2wh6StgT5mNqU01vzS+wU0jwe93toEzezvZnYesAewpaRV2/C9ypkI5eur9O/P4oljkJl9xMx+34ZxkyRpIHJh6BgXA6ea2cyK9n40BaNHFI2SNgCeMbOz8EVlIEDUGBof41UuMh1G0meKmAR+R78AeIWWGof7aHpiGQ5MbGXoW4EvKXyeJa0t6YNdNe8kSZYMcmHoALHFU01b8AvgZ5LuxcVqBYcAj4ReYRPgD6VjVwJb4rGLruIw4Mm43h+B4ZG9dCOwfxF8Bo7FS2/MiHPqVsw1s3H41tP9cjvRq2m/mC5JkiWc1DF0E5IW4DWKCi+Gb3SBvmAQsFYtL4boM4JWiuzVOO99+ML3WXzL6Qng62b2z3rnpY4hSbqHzugY8omh++hOL4aOcBr+dPBRM/sIcA3wZ0n5v6Ek6WHk/6mXDJp5MUiaENs9j8SWD5LmSfq5pIcl3R6aiX9Jmh9ag+l4qesvtqEcRjMkHSy3a0XStyQ9E+83lDRR0grAEcBxhaAuvBnm4d4QleOljiFJGphcGLqPok7SE8BFNNUqKrwYBuGxh8LroPBiGIwHkX+Cp7BuC7wWTx5fwXUJgyq9nlthArBzvN8ZL+S3Np6+eg/uufBPM3ut4ryHcJ1FM1LHkCSNTZbE6D7ejB9/JA3FvRgG4F4MF0taFtcSFAtDpRfDfDN7J4LA/TszETP7j6S+klYE1sEDzLvgi8S1pB9DkvQq8olhCaC7vBgquB/fLnoSf0rYGRfA3Qv8DVgvFo4yW+NPDUmS9CByYVgCUPd6MRRMAI6Pf6cCu+NPJa+a2et4Yb5fR1E+YsF6C184kiTpQeRWUhsopZYWjDGz0zswzmw8VXQuTX7P4Fsyh5vZgiiNfYKkd/Dg7herDtZy7P543GGepL/hBfmexNXXY8ysXPBuhKT9Sp+3x58S1gEmxDyexVNSC76Hm/nMjkykF4GhpaeYJEl6CKljaAOS5plZ3y4YZzZNC0OXEgvDTWY2IBaX481sb0nvxwPYh7bmnRDjLGNm79Y4Nh5/qpiDxzvONbML6o2XOoYk6R5Sx9BNRAnsUyRNkTQztoSIQO4l0TZD0gFVzv1OpKM+IjfoQdL7Jd0saXq0HxLtgyXdHamqt0pas9Q+XdL9wDHV5hjbQA8DG0pavjSvqZJ2j3FGSLpK0o144T0kfTf6TZdUfjo6CC/rsQLweNf8JZMkWZLIraS2Ud72AS8kV6SDzjWzrSV9Hb+bPhL4IfCqmW0BIGmV8mCSBuOB3u3wbaRJku7Gy2v/28w+E/36RXbS2cC+ZvZiLBY/Bb4EXAJ808zulnRGxZw/HHMujHoG4SUvnjKzLTPr+7IAACAASURBVGIRGyfpo9F/KDDQzF6W9ClgP2A7M3tD0gdK4y5jZttK+jTwI2roGPDUWdZdd91W/rRJkixp5BND2yhUysWrrBG4Nv59mKa00T2Bc4oOZvbfivF2Aq4zs9ejkN61eBbQTGDPELLtbGav4j/qA4Db4of+B/iPfj9gZTO7O8b8Y8U11sdTTN8GjjWzTYAXin5m9gTwD6BYGG4zs5dL87/EzN6Ivi+Xxq32fZuROoYkaWzyiaHzFOWpy6Wxa+X9UzreAjN7Kp4mPo0X4xsHXAc8amZDmw0grdzKNe6xlj7U9XQH5VLe9eZf7fsmSdKDyCeGRcM4YGGRusqtJDwldD9JK0RweH/gHklrAW+Y2WW438PWeGbR6iGCQ9KykjY3s1eAVyUV5jrD2zCvCUW/2EJaN8avNv8vRSkMKraSkiTp4eTC0DaK8hXFq7VU1Z8Aq0QAeTquCVhIGPKMxq09JwEXmdlUYAvgwdgyOgn4iZm9DRwI/DzGmgbsEEMdAZwTwec3aZ1zgaVDLT0WGGFm8ys7mdkteID5oZjL8W0YO0mSHkKmqy6hlLQTy+DZP4cXe/7tHKdZqq2k4/BKrmtEDGORkumqSdI9ZLpqz6QIeA/AA8hHd9G4w/B6TPtXOygp4wZJ0svJhaExKCqcVtU/1GsvI2lDoC+e2TSs1F5Nx3CCpMmhwzil1Pf60FM8GmmpSZL0MPLucAkn7uA/BdxSR/+wVLX2iFuUGYZbid4DbCzpg2b2Qhwr6xj2wr2it43xbpC0SxT4+1L06QNMlnSNmb1UMefUMSRJA5NPDEsuhajuIeCfeEG9WvqHWu2VHIrXTXov+hxUOlbWMewVr6nAFNyneqM4dmwEwR/AayttRAWpY0iSxiafGJZcFvo1FEiqpUNo1RdB0kD8R/y2GOZ9wDM0CfEqdQw/M7PfVYyxGy5+GxqK6PF4kb4kSXoQ+cTQWFTVP9RpLzMMGGlm/eO1FrC2vMx3JbfiOoa+AJLWlvRBoB/w31gUNsGrsiZJ0sPIJ4YGwsymSBqN6x+gSf9ArfYSh+KxijLXRfvzFdcZJ2lT4P54upgHfAGvqHq0pBm4MO6BLvhaSZIsYeTCsIRSp8z3r4DLzOwwWBicfg6YFCW396Hkw1yMY2brV7nGd2KMkbjJT5m7gaPNbNOK9oWLS2wlzWv7t0qSpBHIraTG43VgQGQFAXwctwEFwMxu6ICJ0JXAIRVth+Lez0mS9DJyYWhM/gp8Jt4XKajAQk3CqHh/UFGWQ9KEaFta0i/V5BXxTTN7EnhF0nalaxwMjIlzzpP0UGgXyk5wSZL0QHJhaEzGAIdKWh4YiNdbqsbJwCfMbEtgn2j7Cl6SeyszGwhcHu1X4k8JSNoeeMnMno5jJ4W0fiCwa2Q41UTSV2IheejFF1/s2DdMkqTbyIWhATGzGbgXwjDgL3W63guMlnQUbtgDnm56fmHfWdIujAEOlPs5H0rpKQQ4WNIUXNewOaUYRo35pY4hSRqYDD43Ljfgpbl3A1at1sHMjo7toc8A0yQNoobXgpk9K/ek3hU4AFdCI2l9vLrqNmb238h+Su1CkvRg8omhcbkYONXMZtbqIGlDM5tkZicDc3Gl8jg85XSZ6FP2WrgS+A3wdzObE20r4QHvVyWtQcuU1yRJehj5xNBASFqYGho/3GfW6bsfcIykD+FPCXcAjwC344rnGZLeAS4ERsVpV8WY3yxdZ7qkqcCjcd69XfmdkiRZ8kg/hgai0luhlb6jgZvM7OpS26dxA6APAR+xKv/xJS1tZgu6aMrpx5Ak3UT6MfRiJK0n6Y5IPb1D0rqSdsCzkM4Ix7kNo/sw/Ingn5TKWUiaLelkSROBgyRtKOmWKK99T5S/QNJnJU2SNFXS7bG1lCRJDyMXhsZnFPCHUurpWWZ2Hx6cPiHMfv4egrg9gJvwWMKwinHeMrOdzGwMcAHwTTMbjAeez40+E4HtzWwrPIvpu4v6yyVJsvjJGEPjMxT4XLz/I/CLGv32Bu6KAnjXAD+UdFxp22gsQBTO2wG4qlTMdbn498PAWElr4tVZZ1W7UPoxJEljk08MPY9aQaNhwJ6RkvownuK6e+l4UXZ7KeCVeNIoXkW9pLOBUWa2BfBVaqStpo4hSRqbXBgan/sIxTIwHN/uAS+KtyKApJVwM591i7LbwDG03E7CzF4DZkk6KM6VpC3jcD+a6jId3vVfJUmSJYFcGBqLFSTNKb2+AxwLHBGlsA8DvhV9xwAnRKrpQcCdZja/NNafgX0kLUdLhgNfDqe2R4F9o30kvsV0D66LSJKkB5LpqskiJdNVk6R7yHTVxUBZXNaGvvtJ2qyi7XhJT5SqnX6xg/NYLlJFp0k6RNJFlddKkiTpDJmVtGjYD08LfQxA0tG4b8K2ZvaapH7RpyNsBSxb8oMe29nJJkmSlMknhk7QDnHZ94GvR2AXM3vVzC6NMfYIwdhMSRcXe/4hOjtF0pQ4toncd/kyYFAxtqTxkobEOV+W9FS0XajwZagx99GSzpJ0n6RnJB0Y7X3juxTX3Tfa+0t6PMZ9VNI4NZkFJUnSg8iFoXO0Ki4DXgBWNLO/V54s91MYDRwSKaDLAF8rdZlrZlsD5wHHm9kLwJHAPYVwrTTWWsAPcUXzx4FN2jD/NfFspb2BwvXtLWD/uO7uwK/UJGjYCDjHzDYHXsGrsLZA6ceQJA1NLgydYyhN9pd/xH9kK6la5jrYGJhlZk/F50uBXUrHr41/H8b9F+qxLXC3mb1sZu/gBfFa43oze8/MHgOK8hYCTossp9uBtUvHZpnZtNbmlDqGJGlscmHoWqr5HLwGvC5pgyr9VaWtTJFeuoDW40GtjVVv/PL5w4HVgcHxxPM8TUK2cv+2zClJkgYkF4bO0aq4LPgZcE4IzZC0UpSNeALoL+kj0e8w4O4OzuVB3HZzFbnXQtVtnjbQD3jBzN6RtDuwXgfHSZKkQck7vrazgqQ5pc+/xsVlF0s6AXgROCKOjQEulHQscCAeI+gLTJZ7ILwD/MrM3pJ0BC4aWwaYDJzfkcmZ2b8knYb7P/8bz4h6tQNDXQ7cKOkhYBq+eCVJ0otIgdtiRtJJwOfxrZj38JpDQ4ELzOyNTo7d18zmSfocbrZzlpld19k5d4YUuCVJ99AZgVs+MSxGJA3FM4C2NrP5klbDq5SOxdNQWywMap9xzkhJewLr405r13fNzJMk6U1kjGHxsiaegjofwMzm4ltNawF3SboLXGUt6VRJk4ChkgZLultunHNrlL1GFYY6wEXA1/GnkU2BqZJ+FZqH8uuk0Dr8XNKDoX3YOcbsLzfnmRKvHaJ9tzjn6lBwX15KY21GpqsmSYNjZvlaTC88zjANeAo3v9k12mcDq5X6GXBwvF8WD3KvHp8PAS6O93cAG8X77fBCeeDaiANbmct4PM4B8Gng9ni/ArB8vN8IeCje74bHLD6M31DcD+zU2ncePHiwJUmy+Cn+v9uRV24lLUbM9/8HAzvj4rGxkk6s0nUBcE283xgYANwWN+hLA8+pvqFOW6mmk1gWGCVpUMzjo6X+D5rZHABJ0+KciSRJ0qPIhWExYx4vGA+MlzST6r4Gb1lTXEHAo2Y2tNwhUl9fsaaaSR2hmk7iOFy7sCX+ZPBWlf6V5yRJ0oPIGMNiRNLGkjYqNQ0C/kFL3UOZJ4HVI3CNpGUlbW71DXXqjdca/YDnzOw9XFexdAfHSZKkQcmFYfHSF7hU0mNRcmIz3PzmAuCvRfC5jJm9jQeofy43zpmGbyFBbUOdhSY98iJ+7eFc4HBJD+DbSK+30j9Jkh5G6hjaiKQFwEx8+2QWcJiZvdLBsc7AA75/wTOJfgesjMcI7jGzr8Qe/1pm9pdWxhoJzDOzX3ZkLoua1DEkSfeQOobFw5vFfr6kS3HP5J92cKyv4llG8yXdCvzGzP4cY28RfQYBQ/DFI0mSZLGRW0kd43686mixt3+G3JltpqRDWmm/AXg/MCna1gQWltows5mS3gecChyiJqe2pyWtHmMsJelvIZBbSKWuQe7hcE4VHcMRWoR+DKljSJLGJp8Y2omkpYE9gN9H0+fwu/stgdXwekgT8DhAi3Yz20fSvNLTxwrAnZLuA8YBl5jZK5JOBoaY2Tei3yZ4TOG3wJ7AdDObW6ExuwA42syelrQdcK6ZfazG99iVJj+GTXAPiatp8mN4LRaeB2IxA9c1DDOzoyT9CS/Ud1nl2GZ2QcyFIUOG5F5lkjQY+cTQdvpE7v5LwAeA26J9J+BKM1tgZs/j1VG3qdPeDDO7BFcpX4WLyB5QuLhVcDFQ+ER/CbikfLBC1zANj1us2cp3WiR+DEmSNDa5MLSdIsawHl7f6Jhor+WD0GZ/BDP7t5ldbGb7Au/igrbKPs8Cz0v6GK5y/mtFl6UIXUPptWkrl04/hiRJWpALQzsxs1fxctvHS1oWmIDHApaOGMAuuDdCrfZmSPpkjIOkDwGrAv+iuhbhInzr5k9WUVivFV1De0g/hiTp5eTC0AHMbCowHTfpuQ6YEZ/vBL5rZv+p017JXsAjoUW4FfeK/g9wF7BZEXyOvjfgWohLqowDtXUN7eFyYIjcj2E46ceQJL2O1DF0IRFU7lv6PIJSALmdYzXTMUjaB/g4MMjMdu6iKS9yUseQJN1DZ3QM+cSw5DIIF8EVbAbsA3yve6aTJElvIYOHi4mIM5wPrBtN3zazeyVti6eg9gHexO1BZ+E6hj6SdsI9o/8D3GhmEyWNBl7DBXAfwreprpa0FDAK2DXGWAp4BV9UylxlZj+VNBu4FPgsXlX1IDN7otqczOzJeALaBy/NvSFwnZl9t8p3/QrwFYB111238nCSJEs4uTB0LUVKa8EH8LgAwJm4wnmipHXxeMKm+B7+Lmb2rtx97TQzO6CKjmFExbWqaRA+h6eQbgF8EHgcOMrMPl9nznPNbGtJXweOB46sNidcswD+JLMVnqH0pKSzI2NqIaljSJLGJheGrmVh2QxoijHExz3xYHJxeCVJK+JZQJfKq64afufeFq6PCqiPSSp0BjvhTwPvAf9RlaJ8VSh7Mnwu3teb0x2RmYWkx/CspWYLQ5IkjU0uDIuPpYChZvZmuVHS2cBdZra/pP64V0NbqKZBaLN2oso4ZV3Cj+vMKbUMSdLDyeDz4mMcsDA7KbKOwO/O/xXvR5T6d8RTYSJwQNRSWgNXUneEWnNKkqQXkAvD4uNYXB8wI7Zgjo72XwA/k3QvzU1xqukYWuMavCDfI3hJjEm4T3N7qTWnJEl6Aalj6ACSTgI+j2+lvAd81cwm1eg7GrjJzK6ucfwcYEe8zMb6uGMbwE9qndPK3PqGt/SquNJ6xxrCusVC6hiSpHvojI4h94fbidxic29g6/BTWA3/Ue8QZnZMjNsfX0A64+EMcJOklWNOP+7ORSFJksYkt5Laz5p4iud8ADOba2b/lnSypMly/4ULVFEPG0DSYEl3y/0SbpVUs/qp3B/6wdLnTYvPkuZIOl3Sg5ImSdog2tcAXsYL8c0jyllIuk4tPRmukPT7mM8zko4pXevGmOOjko6MtmUkvRLXnS7pfkkfrDH39GNIkgYmF4b2Mw5YR9JTks6V+xoAjDKzbcxsAC4M27t8UhTKOxs40MwG42W0azrAmdmTwFuSikqrR9C8RtJ/zWxbPJbw62g7C/hFPD4ejBfdw8z2r6i6Ogh4Bvd0/jiwPXCq3GsC4PCY4zbAdyStEu39gLvNbEvcrOhLNeZ+gZkNMbMhq6++eq2vmCTJEkpuJbWT2L8fDOwM7A6MlXQi8D9J38VVwR/Ai9jdWDp1Y7yc9m3xMLE08Fwrl/s9cISk/wMOwoVlBVfGv5cDp8f7PYGNSw8rq0jqU5kiW+ImM3sbeEHSy3i57f8Ax0VtJoAP4yrnabhOoyj3/XD8DZIk6WHkwtABouT1eGC8pJm4h/NAXKn8rKSRNHkYFAh41MyGtuNSVwHfB+4F7jezV8rTqNJfwLbxY98WWmgSQum8C7C9mb0paSJN3+Xtyv5tvE6SJA1EbiW1k9j736jUNIimTKK5cie1A6uc+iSwegSvkbSspM3rXcvM3sBLdo+iZantIoV1GL5wgDuulWMFHQlk9wNejkVhc6q4ziVJ0rPJO7720xc4OzJ/3gX+hheMewWYCcwGJleeZGZvSzoQOEtSP/xv/1t8y6kel+NVVu+oaF8hgtGGLw7gi8J5ko6I8e+itFC0kZuBr8g9HZ7AtRBJkvQiUsfQDlTht9BK3/2Ap8JPGUnb44X0lovXWDMbGdtO88zslzXGORFYzsxOKbXNAQZUbC0tkaSOIUm6h9QxLJnsB9wEPBafLwUONrPpkf2zcWsDSLoRWAf42CKbZZIkSQUZY+gkktaTdEeUurhD0rqSdsB9C84IzcCGeBns58CD18WTRLCZpPGhJzi21L4gXvfIPQ4KVgZ+KGlKXHP1mMuGkm4JDcI9kjaRdGQVDcNZkkbHv/fFdQ+MMfrGmFMkzZS0b7T3l/S4pAtD3zBOUp8af5PUMSRJI2Nm+WrjC9/yqWy7Ec/7B8/rvz7ej8Y1C0W/k4H/4l7QXwWWj/aRwH349tJqwEvAsnHsA/FvH7z+0arx2YDhpXFHxfs7gI3i/XbAnXW+y2g862kp3Mjnb9G+DLBSvF8Nj6EI93l4F7cWBfgT8IXW/maDBw+2JEkWP8BD1sHfutxK6jxDafIx+CNegK4FZnaqpMuBvfA6S8Noqn56s7mSer6kF4A18GJ4x0raP/qsA2yELxzvAWOj/TLg2siG2gG4qqRjWK6VuVfzdBBwmqRd4jprx3wAZplZYUT0ML5YJEnSw8iFoeupGc03s7/jWUMXAi/KC91BdT3BbrhgbaiZvSFpPC21EeVrLgW8Yu2rtVTN02E4LnQbbGbvyO0/l6/SfwH+JJMkSQ8jYwyd5z7g0Hg/HPdEgAo/BUmfUdOt/Eb4D2u9rKJ+eNmLNyRtgpetKFiKJq3E54GJZvYaMEvSQXE9SdqyA9+nH/BCLAq74w5tSZL0IvKJoX2sEKmiBb/GfRYulnQC8CJe0whgDHBhBJMPBA4DfiPpDXyvfriZLVDLWnsFtwBHS5qBi+MeKB17Hdhc0sO430IhdhuOP5H8ALfjHANMb+d3vBy4UdJDeBmMJ9p5fpIkDU7qGIJKjYLCr9nMvlH7rC679t64neZS+A/6mWb2u0otRJ25jgeON7MlTjCQOoYk6R5Sx9DARNXVC/AaR3MkLUdTULdSC5EkSbLIyRhDG6imVYj20UX+f3yeF/+uKWlCaAYekbRztO8l9zGYIumqyCRaEV+gXwIws/lm9mQ1LYSkKdGnr6SNYiupcq6V1zilio7hpOg7O44XmoVNon3b0DdMjX83jvYRkq4NrcTTkqpmYKWOIUkam1wYmuhT/vEETi0dGwX8wcwG4nvwZ7Uy1ueBWyNDaEtgmtzp7QfAnma2NfAQ8B0zexm4AfiHpCslDZe0lJndF+0nmHso/B14VU2F8Y7AtQgLqXGNBVbhxWBmZR+IudH3POD4aHsC2MXMtsJ1EqeV+g/CYxpbAIdIWqfyy1v6MSRJQ5NbSU28WU71LGIM8bFNWoUSk/GA9LK4VmCa3NBnM+DeCDi/Dze7wcyOlLQFnp56PG6eM6LKuBfh/gzfwX+ct604vn2ta9Th2vj34dJ37AdcKq8ia3jco+AOM3sVQNJjeNbSs61cI0mSBiIXho5RROzfJZ66IhX1fQBmNiEEYp8B/ijpDFz1fJuZDasyHmY2E5gp6Y/ALKovDNcAP8JLcT9sZi9VHFe9a9Sg0CaU/RV+DNxlZvvLvajHV+lfeU6SJD2E3EpqG7W0CrOBwfF+X+LOWtJ6uBbgQtyFbWs83XRHSR+JPitI+qi8NtFupWsNAv4R75tpIczsLeBWfNun0p+BWtfowPftB/wr3o/owPlJkjQwuTC0jWPxLZwZuB7hW9F+IbCr3BdhO1xfAF7qYpqkqcABePrpi/iP7JUxzgPAJvhd/nclPRmxjVNo+jEeA5wQQeANo+1y/IllXOUk61yjvfwC+Jmke3EL0iRJehGpY6iBpAW48U7BfmY2u4vGXhn4vJmdG5/XAs4ys2rOb5XnHg/0M7MftqHveLpZ35A6hiTpHlLHsGhoFozuYlYGvg6cC2Bm/6a6HWgzJF0HbEj6MyRJsgjJraR2EHn8o0qfbyriA5LmSfqppOmSHlBUK5W0hqTron166BNOBzaM1Ngz5F4Hj0T/5SVdErqCqfJ6RUWWlAH/Bu4vawgknRe6gUclLXR6Kx2/roqW4RN15vxZSZPi+reX2kdKuljVvSPK10sdQ5I0MLkw1Kasa7iuDf3fDzxgZlsCE4Cjov0s4O5o3xr3eD4R+HtoCk6oGOcYADPbAi/NfamkorppLQ3BSfHIOBCPeQwsD2hm+1fRMtxaZ84Tge1DxzAG+G5puE2AT+Cpsj+KlNxmpI4hSRqb3EqqTXu3kt7Gy1eAawI+Hu8/BnwR3LkNF6mtUmecnYCzo/8Tkv4BFJlFtTQEB8sd3pYB1sS1DDM6MecPA2MlrYmn4M4qnVPLOyJJkh5CPjG0j4W6haDsj/CONUXyO5PfX7PcKtV9G9bHRXF7hDL7Zmr7NlRSa85n465wWxBuc/Xm0MZrJUnSIOTC0D5mA4MkLRXbOJXK42rcAXwNQNLSklaiQp9QwQRcK0FoENbFy27XYiU8TfbViAV8qg1zao2yjuHwLhgvSZIGIu/22sfduP/BG8CbuB8yoQ6u5Wa2DrCmpC/jd9hfM7P7Jd0bAef/AqsC60eK7CPAOpI+BTwPjDCz+arh22Bm00Mv8SjwDHBvF3zPkbhF6L9wLcT6XTBmkiQNQuoY2oFKPgiSPgF838x2jYXhJjMbUOWc8bRRS6AKn4WeQOoYkqR76IyOIbeSOs5K+N1+MyT1kTRGXqJ7LKUnCUlflvRUpHteWE59rTLOypESukzp86zYjpoo6bfy8tozJQ2JPn3lpcAfjFTTz9YZ/0hJV0u6VV5C+2elYxeU0l9PLrXPiZTVqfH9OlJuI0mSJZzcSmoffeRlK5bHs3+qCc2+BrxhZgMjbXQKLFQ3/xBPWf0fXgivpu2mmb0iL0nxSTxz6PPAn0p2oMuZ2VBJH8Orrg7CS2TfYmYjIvNpkqTb8C2w5SoucR1eEnxrPKj+lKSzQ2x3opm9HIvSXZKuLrnIPW9mW4WG4TvA0ZVzjwyprwCsu+66tb5ikiRLKPnE0D7eDA3AJvgP9h/UcvN/F+AyADObQVPa6La4nuFlM3sHuKoN17uIJg/pI2heOO/KuMadwAflpj97ASfF4nUXvoCta2bbVeoY8ODy7Wb2PzN7E/dgKH7Fh8lNgaYAm+LprwXlMt39q006dQxJ0tjkE0MHiQDyakC1X75qgZt6aai1rnG3pFFy9fM7ZvZEnWtYXGO/MPVpC9XSXzfCiwRuG08tl1E9XTVTVZOkh5JPDB1EboO5NGHJWaKcbjoAVyMDPIirkleJLZoD2nipy/CKqpVltg+Ja+yGb++8jpfkXlimQtJWbf0+JYp02tdC4PaJDoyRJEkDk3d87aOIMYDfnR9e2vMvOA+4RF72ehq+IGBm/5J0GjAJr3f0GJ762hqX47GDsRXtr0m6D9dDFNtNpwC/lTQTX/T/hvtEtIcpMbdH6Lr01yRJGohMVw20CMtsl67R18zmxRPDW8BBZla1DpOiNDfwMn7XfhJRmlvSROAbZjat2rlLEpmumiTdQ2fSVfOJoYlFWWa7YKSkPfE9+/eA6+v0XRm32HwZ+GRbS3MnSZJ0lowx1EF1SmCr/eW31wd2BIqMpLfNzEJ7cIekKXGdYuvndGAFvNzF0SqV5gb2BL5VY17XSroltAm/kPRptSy5fXUrc+1U2e0kSRqbXBiaqFZmu14J7FrUKmV9JnCemW0D/KfU/y1gfzPbGtgd+FWkwHZJaW5gZpWS28WTxyIpu630Y0iShiYXhibeLP1w7h9tOwF/BC+BDZRLYNeispR1/3i/I6E9KMYMBJwWwerbgbXxUtb1qDevO8zsVTN7Cw8ir9eBuX4YuDWC2CcAm5fOudnM5pvZXKAou92M1DEkSWOTC0N9amkPOlp+u1qkfziuhRgcMY7nab1sdrtKc9fpm2W3kyRpQS4M9alVAns27S+/fS9waLwfXmrvB7xgZu9ErKC4w+/K0tztJctuJ0kvJheG+pwLLB1bKmOJEtj4j/wsPL31l0Q9pFb4FnCMpMn4D2/B5cAQSQ/hP/ZPAJjZS8C9kh6RdEYb59VVjMTLbt8DzO3CcZMkaQB6rI5B0km4DmABnhr6VTObVKPvaLxs9tWtjHk8cCS+lbQA+JWZ/aEL5jobGGJmcyXdZ2Y7yEt572BmV0SfIcAXzayhMoFSx5Ak3UPqGCqQNBTYG9g6TG5Ww72LOzPm0bgn8rZm9pqkfsB+nZ9tc8xsh3jbH1/Yroj2h4D8hU2SZJHTU7eS1gTmFtsrZjbXzP4t6WRJk2N75oIqlVGRNFjS3ZIelnsVrBmHvg983cxeizFfNbNL45w9Iud/ZuT5LxftsyWdUtIobBLtq0oaF+f8jlIwWdK8eHs6sHOkzx4naTdJN0WfD0i6Xu6J8IC8vHdNnUFoEiq1DJ+Q9LjcF+LRmE+f6H9U/J2mS7pG0grRPlrSWZLui/GrCu4yXTVJGpueujCMw+0xn5J0rqRdo32UmW0TTmt98KeKhURO/tnAgWY2GLgY+KmkFYEVq1UtDf3AaOCQyOJZhvB4DuaGRuE84Pho+xEwMXQCN9BU7rrMicA9kT77m4pjpwBTzWwgvmCVt7Na6AxqlN1+EtgIOMfMNgdeoamw37Xxd9oSeBz4cmn8NfF02b3xxasFma6aJI1Nj1wYzGweMBg3i3kRGCtpBLB73D3PxE12Nq84dWNgAHCbvFjeD/CcflE91bQ4Z5aZR87ONwAAIABJREFUPRWfL8U9GQqq+ReUPRtupooTXCuUdQx3AqvG1ha0QWdQYlap3lJ5fgMk3RN/p+E0/ztdb2bvhXFPa3qLJEkakB4ZYwAwswXAeGB8/MB9FS+BPcTMnpU0kpZ6AQGPmtnQyvEkvS5pAzN7pso59ajlX9CZqH+1axbjtUdnUNm3sCEdjRcRnB4L6m41zmm3x0SSJEs+PfKJQdLGcsOZgmLrBGCu3O2s2v74k8DqEbxG0rKSirvlnwHnSFopjq0kt7B8Augv6SPR7zDcSrMeZR3Cp4BVqvRpq45hN3y76rVWrtkeVgSei6214a11TpKkZ9FTnxj6AmfLS1e/i/sSfAXfR5+JC9QmV55kZm9HQPWs2JpZBvgt8CgeI+gLTJb0Dl4M71dm9pakI/C8/2Vi3PNbmd8pwJVy+8y7gX9W6TMDeFfSdPwOfmrp2EiaPB/eoOtFaD/EfSP+gf+9ai1QSZL0QHqsjqESSfPMrG8XjzkSmGdmv4zPXaJziKeAt83svq6bbfeQOoYk6R46o2PokVtJ3UGFzmEAHmDu6B78bsAOrXVq47yW7opxkiTpPfTqhUEd8B2QdJKkJyXdjmckFXSJzkGueD4aOC70BjtLWk/u2TAj/l03zh9d1hIUGojQPNwl6QrgsSoahmmSBqWOIUmSavTqhYF2+g5IGowXwtsK+BywDUBX6hzCTvR84DehObgHGAX8IXQLlwNnteG7bQucZGYbV/FjGITHW1LHkCRJC3r7wtBe34GdgevM7I14Mrgh+na1zqGSoURpDFy/sFMbvtuDZjarlT6pY0iSpAW9fWHoiO9AiwUgFonXJW1Q5Rod1TnUo5jDQl8ISaJ5PajX2zBOre84GvhG/F1OofbfJXUMSdID6e0LQ3t9ByYA+0vqE9tHny0d60qdQ6WG4T6aezlMjPezcYU3wL5AC5vNDpI6hiTpxfRUHUM1VpA0p/T51zT5DvwLeABYv94AZjZF0lhgGp7jf0/pcFfqHG4Erpa0L/BN4FjgYkkn4CU+joh+FwJ/lvQgcAdte0poC6ljSJJeTI/WMUhagP+wFYwxs6oB0+j/fTM7rQPXuQj4dey7dxiVfBni8254QHrvOuf0x70kBnTm2ouK1DEkSffQGR1DT39ieDMycNrK94F2LQySljazIztwzoL2nJMkSbK46HUxBkn9QoewcXy+MvL2Twf6RI7/5XHsC5IejLbfFWIxSfMknSppEjA09A5D4tiw0CQ8Iunnpes2O6cD866prSj12SD0EttIGiHp2tA9zJf0fEnDMFvSY+U5SjpY0q/j/bckPRPvN5Q0Md5X9ZeoMo/UMSRJA9PTF4Y+ai7qOsTMXgW+AYyWdCiwipldaGYnEk8YZjZc0qbAIcCO8dSxgKZA7PuBR8LnoAgEI2kt4Od4Se9BwDaS9qt3Tjtpoa0oXXtj4BrgCDMr6kANwvUW/YC38GD5p/H/7rtWzHECno5L/PuSpLXx1NhyLKWav0QzUseQJI1Nr9xKMrPbJB0EnANsWePcPfCMn8meCUofXM8AvkhcU+WcbYDxZvYiQDx57AJcX+ecZlNrpe3mcKWbL6nstbA68GfgADN7tNT/jlgIkfQYsB6warU5mtn1kvpGttU6uG5iF3yRuLY0Zll38blWvk+SJA1IT39iqIqkpYBNgTeBD9TqBlxaUgtvbGYj49hbNWIE9fL6a51T5iWal+D+ADC39LmW7uBV4Flgx4rxqvWvN8f78YynJ/GnhJ3xba97q4zZHt1FkiQNRK9cGIDj8FIPw/A00GJL5p3S+zuAAyV9EBb6LK/XyriTgF0lrRbxiGG0rlkoMx7XORTF774A3NWG894G9gO+KOnznZjjBHx7aAJe5nt3YH7x1JEkSe+gp9/x9ZFbdBbcgvs4H4lXQf2fpAm4heePgAuAGZKmRJzhB8C4eMJ4BzgGz+2vipk9J+l7+I+5gL+Y2Z/bMd8fA+fJPRgU872sLSea2euS9sZtSWvqGVqZ4z34NtIEM1sg6VlcoJckSS+iR+sYOoMWsX+DpO2BM4Hl4jXWzEbKrTSHmNk3uvLa3UXqGJKke0gdQ2NyKXBw+CovTfMS3kmSJN1Gb40xdAh1kX+DpOuADYHLY6vrYXwLp/J69XwYzpdXQH0qtpCQtLSkM+ReCjMkfbXOd9kt5nu1pCckXa5Iv5J0cozxiKQLSu3jJf1cru14StLONcZOHUOSNDC5MLSPLvFvMLP9gVOBtYFZuCagWpC6ng9Df1yL8BngfLnvw5eBV81sm7jWUZLq1X/aCvg2sBmwAU1ZTaPCj2EAnqZbLsmxjJltG+f9qNqgqWNIksYmF4b20VX+DZjZqcAQYBzweTzQXEk9H4Y/hS/C08Az+MK0F56ZNA3PPloVN+OpxYNmNsfM3sMLA/aP9t3jyWgmLtYrf8+2+EckSdLAZIyhfZyNF8u7QV7gbmTpWJv9GwrC8e08SRcCL0patZXrW433xWcB3zSzW1sZp6DFnOPJ41w8AP5sBMyr+TGkjiFJeij5xNA+usy/QdJnir17/K5+AW6vWaaWDwPAQZKWkrQhvg30JHAr8LVCiyHpo5Le3+Zv5xSLwFxJfYGqvs5JkvRc8o6vNovav+Ew4DeS3sCd2IaHdqA8RC0fBvCF4G58y+ro8H24CN/emRKLzou48K3NmNkr8QQzEzcCmlz/jCRJehqpY+hiVMUfodAv4OUtxpnZv6N9oY+DSl4Mku4zsx1irB3M7IroPwT4IrBSXOPqxfW9OkrqGJKke+iMjiG3khYvI4C1ig9mdmQ1cx8z2yHe9scD00X7Q2bWotx2kiRJV5ILw+JlCKFdiLjDQh+HMpLmxdvTgZ2j/3GhPbjJzEYAfw3txOTQVewb526uJg+JGRHLmFbxmiSpv6THJV0o6VFJ4yT1iTGOinGnS7pG0grRPlrSWZLuC71G1fhD6hiSpLHJhWHx8hAeSxhkZm+2of+JwD3R/zcVx04C7gzNwu7AGRFoPho4M8qND4k+gype28UYGwHnmNnmeOD7gGi/NnQMW+LFBr9cuu6aeNrs3vjC1YLUMSRJY5PB566nVtCmq4M5ewH7SCrMcpYH1sVLZ58k6cP4D/zTdcaYZWZFkcGyLmGApJ8AKwN98WyngutD9/CYQvmdJEnPIheGrqfSUwHcV2FWF19HuDHPkxXtj8vtQz+Di/GONLM7a4xRqWPoE+9HA/tFHacRwG41zqnn7ZAkSYOSW0ldjJnNA56TtAe4jwPwSVyD8D9gxXYMV6//rcA3S3WMtop/NwCeMbOzcKX1wA58jRXjOyxLk51pkiS9hFwYFg1fBH4QpSnuBE4JlfNovK7RtCLQ2wozgHcjCHxcxbEfA8vi/hGPxGdwn+pH4tqbAH/owPx/iJfUuI30Y0iSXkeP0zFIMlwb8P/i8/FA35ItZ7Vz9gE2M7OqwdTosxtwvJntXeXYbEKD0ME5jyR8Gjpy/uIetz2kjiFJuofUMTRnPvA5Sau19QQzu6HeorAokZRxniRJlih64sLwLm7RWbn1gqTVIy9/crx2jPYRkkbF+w0lPRDHTy1pCgD6qop/QXBC6AcelPSRGKuen8KvJd0F/DzO30zV/Ry+I/dFeETSt9vQXun/sEIVHcM0Sauqhr9CaBzukTQlXjtEe00Ph4q/c+oYkqSB6al3q+fge++/qGg/E/iNmU2MH+lbgU2r9DnTzK6UdHTFsa3wEtT/Bu7F/QuKwnavmdm2kr4I/BbP8y/8FC6V9CXcT6GoXfRRYM+ojzQSjwfsjgd+n5R0Hh44PgLYDs8AmiTpbnxBr9Ve+D8sA0wBHg5NQwviN32ZmPencX+FPfGy4R+P+ksbAVfimojW/gaA6xjwxZkhQ4b0rL3KJOkF9MiFwcxek/QHvAhdWUi2J35nXnxeSV71tMxQmn68rwDK+/MPmtkcgAju9qfpR/HK0r+FGG0obtAD7qdQXqiuMrMFpc83m9l8YL6kws9hJ9zP4fW45rW4x4NqtC8V7W9E+w20TjV/hWWBUZIG4WmsH23j3yBJkh5Aj1wYgt/id8yXlNqWAoZWqo6r7IbUopbnAtT3SqjW/nobxq41sXoTbu8dejV/heOA54Et8b/ZW63MM0mSHkRPjDEAYGYvA3+ieTmHccA3ig9xR1zJAzSVhji0yvFaHFL69/54X89PoS1MAPaTtEKUu9gfL91dr72q/0M76Qc8Fwrnw4ClOzhOkiQNSE+/2/sVpYUA31o6R9IM/LtPwGsLlfk2cJmk/wfcDLzaxmstF4rjpYBhpevV8lNolfBzGA08GE0XmdlU8AB2jfZa/g/t4VzgGkkHAXfR8ukmSZIeTI/TMXQESQtwY5plgKeAL5jZG5IOBYaZ2b5deK0RuObhGxF0PgpfNN4fc/hBtVLcce6pwAQzu72r5rOoSR1DknQPndEx9PQnhrbyZpG5I2kc8LSkl/CKo19axNf+TSFAk3QIcKekLcysWZ6npKXN7ORFPJckSZKeG2PoBNcCfzazgcDLwFi5X8FXACR9rZwGGxqIs+P9F9TkhfA7SUtH+xGhE7gbT++sipmNxeMgn4/zZks6WdJE3ON5tKQDJX1K0p9Kc9hN0o3xfi9J94f+4CpJfSWdU0XHcESMf0r0nSlpkxhjW7nnwtT4d+PSd71W0i2Snq6SDlzMJ3UMSdLA5MJQQq5C/hS+pQPwJTMbjOfwHytpVeBqmlJQwYPNYyVtGu93jKePBcBwSWsCp+ALwseBzVqZxhRc01DwlpntZGZjSm23AdtH4Lk8h9WAH+D6iK1x/4fvmNkxVTwZimytudH3PKAo4f0EsIuZbQWcDJxWuvaguN4WwCGS1qn8AunHkCSNTW4lOX0iJx88YPv7eH+spP3j/TrARmb2gFydvD3wNK4uvhc4BhgMTI701z64UGw7YHyxNRTB4bIuoJLKVNSxlR3M7F1JtwCflXQ1XmL7u8Cu+MJzb8zhfTRlSNWirGMoFrx+wKUhbjNc11Bwh5m9Gt/lMWA94NlWrpEkSQORC4OzMMZQIC+atyeue3hD0njcDAf8x/pg/M76OjMz+S/xpWb2vYpx9qN92oKt8Dv9gloZQWPxxehlYLKZ/S/mcJuZDatxTjWq6Rh+DNxlZvtL6g+Mr9K/8pwkSXoIuZVUm37Af2NR2ATYvnTsWlwdPYymO/o7gAMlfRDch0HSenj56t3ktYmWBQ6qdUFJB+DObFfW6lNiPLA1ntVUzOEBYEc11WpaQVK9p5Na9AP+Fe9HdOD8JEkamFwYanMLsExsl4zD7/pHS/oLsDrwGLCemT0IECmmPwDGhU7iNmBNM3sOGAk8iusmplRc57gIBj8NfAH4WEVG0nfVZN8JsJSkucBPgJvwmMhNki6KeY0Arow5PEDzeEVb+QXwM0n3kuK2JOl1pI6hDrE1cx++RXR+tA0CVjSzdonHYivqeDNrkdQfqagLWp7V0lNBXuzuJOBDwEeslf+A9cZeHKSOIUm6h87oGPKJoT67A+8UiwKAmU0zs3sknSAvzT1D0imwsFz145IujBTXcfLyFAfimU2Xx9NBnyqpqEfFeNPlpcFXqDGnYXgF2H9S2t6Sl8MeEu/nyUuGT8IL+bVgUaWqJknS+OTCUJ8BeLZOMyTtBWwEbIunbw6WtEsc3gg4x8w2xwVyB5jZ1XhAeXikihZF/MqpqNea2TZmtiXwOM1rPBXX7QPsgW8hXUlT6Y1K3g88YmbbRYnx6yp1DHggvctTVWOeqWNIkgYmM0o6xl7xmhqf++ILwj+BWWZWpL6WS1lXo5yKOkDST4CVY7xbq/TfG88WekPSNcAPJR1XZatoAXBN8cHM9q84XtiRLpJU1fRjSJLGJheG+jwKHFilXcDPzOx3zRo9tbMynbNPnfHLqaijgf3MbLq8ntJuVfoPw7OOZsfnVfHtrsraSW+1Ma6QqapJkrQgt5LqcydeNfWookHSNsBrwJck9Y22tYs01Tr8D3dnq8WKwHOR0jq88qCklXDjnnXNrL+Z9cd1DO3RLLSFTFVNkl5OLgx1iIyf/YGPS/q7pEfx1NMr4nW/pJl4mYx6P/rgTwTnF8HnKsd/iGsebsP3+Sv5HHBnuLwV/BnYR9Jybf9WrZKpqknSy+lx6aqSDLjMzA6Lz8sAzwGTzGxvSWvgJS/WwffPZ5vZpyUthbu+fQzfW38LONjMZtW51mjgpgguVx7bFrcFXSPGm4j7MxxMlN3uoq9cbV4/Bb4IrGJmfUvtI4AzaHoiGGVmF8Wxw3EdBsBPzOzSKuN+AI+L9Adm43+f/9abS6arJkn3kOmqzXkdD+QWd+Ufp+mHEOBUvGzElma2GXBitB8CrAUMNLMt8CeFVzoygVh8rgL+z8w2BjbFBXOtPVV0FTfiGVPVGFsqpFcsCh8AfoTXddoW+JGkVaqceyIegN4IV3qfWKVPkiQNTk9cGAD+iheWA9+DL5eYWBOYU3wwsxml9sLOEjObU9wNS5pX9JeXvR5dGm9PSffIy2rvHW3H4KK4+2MsM7Orzez58iQlfVbSpNAM3B4LCpJ2LaWWTpW0oqQ1JU2Itkck7Vzry5vZA6G4ppyqii+Kh0j6RMUpn8AXy5fjO98GfLLK0PsCxZPEpXhZkCRJehg9dWEYAxwqaXlgIL53X3AO8HtJd0k6SdJa0f4nvFrpNEm/krRVG6/VH69q+hk8hrA8NfQPVZgIbB+agTF4hVRwTcExUdhvZ+BN3KPh1mjbErfvbBUz2794QsB1Ce8CZ0i6uqRDWJvmaadzoq2SNYoFJ/6tGnBPHUOSNDY9cmGIp4D++NPCXyqO3QpsAFyI1xGaKml1M5uDl9D+HvAecIekPdpwuT+Z2Xtm9jTwDO2rTfRh4NYIYJ8AbB7t9wK/lnQssLKZvQtMBo6Ql8jYwsz+147rFNwI9Dc3Ibqdprv/ylLf0L6KsM1PTD+GJGloeuTCENyAB39bVCqNLZMrIkA9Gdgl2ueb2V/N7ARc8VtslZR/JJdvPlqLH1DD9Q+D2zDHs/EA8BbAV4uxzex04EhcA/GApE3MbELM81/AHyV9sQ3jN5+Y2UulrKYLS3OcgwfjCz4M/LvKEM/LjYeIf19o7xySJFny6ckLw8XAqWY2s9wo6WNFHSJJKwIbAv+UtHWxrRQZSgOBf8Rpz0vaNNorVcQHSVpK0ob4k8iTwCjgcEnbla77BUkfqji3rBk4vNR3QzObaWY/x0tpbCIv4f2CmV2IZ1Vt3d4/SPGjHuyDl94AV1nvJWmVCDrvRXXl9Q2leR6Op8smSdLD6LELQwSPz6xyaDDwkLws9f3ARWY2Gd8vv1HSI8AMfC9+VJxzIl6f6E489bXMk8DdeMD7aDN7K4LMhwK/lPSkpMfxWMFrFeeOBK6SdA8wt9T+7QgwT8fjC3/FldDTJE0FDsAL6VVF0i8kzQFWkDQntp/AHekejXGPJQRsZvYyrnieHK9Tow1JFymK8wGn45qOp/Fsr9NrzSFJksalx+kY2krqHRYPqWNIku6hMzqG3lzrZqHeIaqd1tI7nAkgaWC0l/UO70n6MLXtN+tS0jscamb3SxL+NLC49A5JkiQt6LFbSW2k0fUOj0l6M15vyL0hdm+r3kHu2/BTuQfEA6Vxa11vpKSL5d4Pz0TWVLVxM101SRqY3r4wNLre4e/AnmbWB4+RbB2vtuod3g88EB4QE3D/6HrXA0/H/QRNCulyWW4g01WTpNHpzVtJmNkMeWnpqnoHSRvgCuBP4XqHAWY2R+5q9rF43SHpIDO7o5XL/SmeMp6W1BG9w9jIKnofUMQzCr3D5bjRzxxJk4GL4wf7emvyhqjG23hQHXyB+ngr1wO4OVJe50t6AY+NzCFJkh5Db39igN6td3jHmrIPyv4KVa8XpCdDkvRwcmFIvUM1ql4vSZLeQa+/24tSGLX0DqMkvYsvoBeZ2WRJnwQuVJMHwoO01Ds8CzyCW3QWFHqHNQi9A/CWpELv8EG8FMcEmiw3C0bieod/AQ8A60f7tyXtjt+5P4YH0w8FTpD0DjAPL7/dXmpdL0mSXkCv1THUIu7Wfwtsg2+bzAa+bWZPdWLM3YDjQx+xD7CZmZ0uaT/gKTN7LPqdCkwws0qrzrZcYxPgEvwJ4SQz+2VH59uVpI4hSbqH1DF0EaEjuA5PIT002gbhd/kdXhjKmNkNeFwDPDZxE363j5md3ImhX8aFcVkKO0mSTpExhubsjgdkzy8aIqtnoqQzQhcwU9Ih4E8CkdN/taQnJF0eiwuSPhltE3FbTqJ9hKRRknbA6xWdEamvG0oaLenA6LdH6AhmhnZguWifLekUSVPi2CYxzxeitMc7lV8qNAnTSq/HQodwobxExjiFsZGkoyRNDm3DNaU4y2hJZ0m6L849sNYfMXUMSdLY5MLQnFq6gs8BhS5gT/zHvChItxXwbWAzPKi8Y2gULgQ+i9dIqgwmY2b34U8OJ4Rfwt+LY3H+aOCQyAxaBvha6fS5ZrY1cB7u3VAXM9vOmlzbBgGfBtYFzjGzzXGnugOi+7Vmtk1oGx4Hvlwaak1gJ2Bv6tRJSh1DkjQ2uTC0jZ2AK81sQaiS78ZjEAAPhvr5PVxM1h/XKMwys6cjHfSydl5v4zi/2L66lEiVDYrg9MNxvY4wq6RxKI8zQK7QngkMp8kjAlwX8V7ERNbo4HWTJFnCyYWhObV0BdWMbApq5fV3Jqpf73rla3ZGR1Br3qOBb8STyinU1jC0NsckSRqUXBiacyewnKSiNASStgH+i3slLy1pdfzu/cE64zwBrB+aBXBldTX+R/WCeU8A/SV9JD4fhj+lLA5WBJ4L5fTwxXTNJEmWIHJhKBHbPvvjngN/l/QontN/Be7RMB1fPL5rZv+pM85bwFeAmyP4/I8aXcfgmoOppUWkOP8IXEswE9c3nF9jDMDTbOUeDN8BfiD3YVipLd+7gh/iNaNuwxeoJEl6Galj6CCSFgAz8S2YWcBhZvZKB8c6Aw8I/wUv4f0jYCMz+1scPw74NbCNmdUUBUj6NnCBmb0Rn2fjng5za52zqEkdQ5J0D53RMeQTQ8d5M7J8BuAagmM6MdZXga2j9hL4gnNo6fiBhNahFb4NrNCJeSRJkuTC0EXcD6wNLpKroXmo1X4DXv56UtEGXA/sG8c3AF4FFgoCJJ0XOoFHJZ0SbcfiBkJ3Sborui4Vn6dVvFaV1F/S44tCy5A6hiRpbHJh6CSSlgb2oEnNXEvzULXdzPah6eljbIzxGvCspAF44HoszTkpHhEHArtKGmhmZwH/BnY3s92j33vxeVDF66U4vhGLQMuQOoYkaWxyYeg4fSRNA14CPoAHa6G25qGeFqIaY/DtpP3wMh1lDpY0BZiK6ww26+B3SC1DkiQtyIWh47wZKuL1cDObIsZQK7+/vXn/N+Jpqv80s9cWDiKtj6ud9zCzgcDNtPR+aCupZUiSpAW5MHQSM3sVL153fOT+T6C65qFWe61x3wT+D/hpxaGV8MylV+VezJ8qHauli2gvqWVIkl5MVlftAsxsqqTp+NbPZcBQXPNghOZB0nXV2lsZd0yVtumSpuIq7Wdwe8+CC4C/SnquFGfoCIWW4R94hlRXLDZJkjQIqWNoBUkGXBb2nkhaBngOmBT+CmvgTmnrAMsCs83s05KOAY4qDbUMEQ8ws8c7MI+/AJ/vqFaiu0gdQ5J0D53RMeQTQ+u8jgdj+8T2zsdpsr0EOBW4zczOBJA0EMDMzgHOKTpJOg2Y1pFFIcb7dAfnnyRJ0i4yxtA2/gp8Jt4PA64sHVsTmFN8MLMZlSdL2gU4GPh6fF5e0iWhZ5gqt+csvBqulXSLpKcl/aI0xmxJq7WiP9hG0gxJ95c0E6tW0TFMk3RMnWu10EmU5nCKKrwgqnzf1DEkSQOTC0PbGAMcKvdJGIjvvxecA/xe0l2STpK0VvlESSvjlpuHl7KLjgGIrJ9hwKUxNrjW4RBgCzxYvU6V+dTSH1yC+0kPxbOMMLOXqugYBuFPQrWu1UInUbp2q14QqWNIksYmF4Y2EE8B/fEf8b9UHLsVN+i5EPdhmBpZRwXn4TGKcpB4J+CPcf4TeJD3o3HsDjN7NQrpPYanw1bSQn8QC9CKYQAEXvivNWpdq55Ooiu8IJIkWYLJhaHt3AD8kubbSACY2ctmdkUEqCcTpjqSDsd/PH9ccUpH/B1a69MRTUGLcdqgk+gKL4gkSZZgcmFoOxcDp5rZzHKjpI+VagmtCGwI/DNqHP0UGG5m71aMNYHQB0j6KG6z+WRnJmdm/wX+J2n7aDq0Xv861NNJJEnSC8g7vjZiZnOAM6scGgyMkvQuvtBeZGaTJf0OL453rdTsZv6bwLnA+VFy4l1ghJnNr+jXEb4MXCjpdWA8XnyvXbSik0iSpBeQOoYKJH0I+C1ex2g+MBv4dsl/uSNj7gYcH7qHfXAtw+mS9gOeirpDSDoVmGBmt3fgGsOB7+GF8+bhqmqZ2bc6Ou+uIHUMSdI9pI6hi5Dfsl8HXGpmh0bbILxYXIcXhjJmdgNNlVj3A24ivBbM7ORODD0LN/Mpym8fTlNAO0mSpM1kjKE5uwPvmNlCG83I/pmo6l4Ku0kaL+lqSU/o/7N33uFSVdcbfj+wYbAmaixRFAuxIApi71h+iRprwBLFaIwtxhhNTEwUTYy9kKBGRQSxd4kaUVEEC4IKAhqxYkSNvaGICOv3x1qHOXfuzNy5cLncuXe/z3MfZvY5Z+89o8/Zc/Za3/qkG2JxQdLu0fY4XnKbaO8raYCkrYC98PLbEyR1lvsd7B/n7Rwah0mSBklaPNpLagnM7EkzGxSpqJsAX5nZB5J2K6FhGF5BCzFfXgxxbtIxJBI1TFoY6rIhnoZZTDmPBfCb8Il4SudawNahSbga2BPYFvh+cYeRVjoMOCW0Ba9lx+L6wUDv0DosAhyTu7whLcERuCgPMxteQsPwSxYhoVToAAAgAElEQVSQF0OMmXQMiUQNkxaG6qjkpTDWzKaZ2RxgAp6e2gXXGrxiHsS5vpHjrRfXZ9tXQ4gU2KCslkCuoj4Cr8xaieTFkEgkSpIWhrq8gGcZFTMvuoP5ieo3lJ5UUksQCuWBwE9yLm0N9VHcz2CSF0Mi0aZJC0NdHgEWlzS3KqqkzYBPaISXAvASsKakzvH+wDLnlfNPeAlXM68d73+GP6WURdLq+JPEz+Yng4rkxZBItHnSwpAjtn32AXaR9JqkF4B+eHmJibiXwiM04KUQJSaOAu6L4PObZU69GTglgsydi64/HLgttnTmAP8s00fG6cB3gcsjwDyvOaKZF8ND+AKVSCTaGEnH0EKQNBs3xRG+tXN8ru7RvPbZDVjFzO6vcE4/YLqZXZhrmwr0MLMPK1zXBV/YDNg/HzzPk3QMicTCYX50DOmJoeUwI7KGNsaFauc0QZ/dgAXl47A3cI+ZbVJuUUgkErVJWhhaJkvjcQ0krSxpVGwPTZa0bbRPl3SepGclPSypZ2gqXpe0l6TFgL8CR0uaEfqHTMfw3WomoTLeD5J+hKfoHinp0RLXJR1DIlHDpIWh5dAhbtov4ZlFWUXWg4DhoT/YGE+JBa/DNNLMuuNB7L/i7nL74MX+vgH+BPzTzDqYWaeclqGhjKU89fQOsTX1T+CSUt7SSceQSNQ2qSRGy2FG3PyRtCVwnaQN8TLegyJL6O6c9uAb4IF4PQmYaWazIljdqRHjlgsyZe3l9A6JRKKVkp4YWiBm9hTwPWAFMxuFp8e+DQyVdGicNssKmQNzCI1BCO0as+B/BCxX1LYU/nQA1flDJBKJVkRaGFogkfHTHvhI0hrA+2Z2NXANsGkjuiqnk8gzCthL7iWBpH2B581sduNnnkgkWgPp11/LoYOkbMtGuEf0bHnJ7lMkzcLLaR9aroMSPAqcGv2eY2a3FJ9gZhMlDcALBRrwPnDk/HyQRCJR2yQdQwtgIWoY+gIX4NtUSwBXmtkl8zNuMUnHkEgsHJKOofZZmBqGWyLovTVwmqQfNMHYiUSihkkLQ8ujqTQMZ+H1nSZI6i3p8GJfBrwGEwCRwvoqXlobSSuEH8O4+Ns62jtKulbuBTFR0n4UkXQMiURtk2IMLYMsvrAEfmPeKdozDcPZktoDS0Z7pmH4vaS7KGgY1sfd54ZJOh0va3F8bpxr84PGVlKPeL16jD8xDvfHdQqPx7HhwA/xWkqfRfVVJBVnNGFmVwFXgW8lzeN3kkgkFhJpYWgZLCwNA/hTxY64B8QvooAfuCHR+tLc6tpLR+ZSL6BP1mhmnzRyvEQi0cJJW0ktjGbWMIDHGDbAneYukpS5zbUDtsyppVc1sy/wAHl6CkgkWjFpYWhhNLOGYS6xIA0Ffh1NDwJzt6Eiy6lUe72tpEQiUdu0+YVBkkkamnu/iKQPJN07D32NlLRbUduJki5v4NKsTtIE4BZCwwDsAEyQNB7PVtovzllS0hmV5oEHsNfPgs/R3lHSlSp4TfyBuhad5wGHx5bRCUCPCDC/CBwd5/wVWC6C4c8D9WolJRKJ2ibFGOBL3Oe4g5nNwIO4b89jXzfh++/Dc219gFMqXWRm7bPXktpnqmMzG4L7PSNpMHCvmd0uaQngRUnXmVm/ou6WAUYAn5vZZkXHBgJvAOuY2RxJa+EB5Wwe7wDZVtIXQO8Sc50OHFbp8yQSidqmzT8xBP8GfhyvD8Rv8ABEKuiTcpe1JyWtF+0bSBobv8gnSloHuB3YQ9LicU4nYBVcVbxDPFHcLuklSTcoIrvyktiny93eDqhivpkP85eVrpfUTtIQSX+VO8RtDvwpYhGY2etmdp+8vPZLkgbGk8ANknpJekLSK5J6Rn/9JA3KpcaeMA/fdSKRaOGkhcG5GegTv8S74taWGS8B25nZJrh95t+i/Wigf2QT9QCmhRZgLLB7nNMHD+5mwdpNcB+D9YG1cFFZxtdmto2Z3VxhnhfEVtI04GYze7/C9YsANwAvA6/hVp3LAc/GYnZZUd9r4ymqXYEueKrsNsDJwB9z53UBdgN6AmdExlQdko4hkaht0lYSc+sFdcKfFopLSCwDDIknAgOyG+FTuFJ4NeBOM3sl2rPtpHvi35/n+hprZtMA4gbfCXg8jtWrY1SCU2IrqSMwQtJWudIZxddfCdxqZmfHeB8Bh5vZPmX6fsPMJsW5LwAjzMxKpMDeZ2YzgZmS3sdjFNPyHSUdQyJR26QnhgLDgAvJbSMFfwEeNbMNgT2JbRwzuxHYC5gBDJeUidLuBnaWtCnQwcyey/VVqYT1l9VONPb5R+K/6Mtd/ySwYzwFAbwAbCyp3H/z/Nzm5N4Xp8CmMtyJRCsnLQwFBuHOZ5OK2pehEIzumzVG4PZ1M/s7vqh0hTo37UHUX2SaBEmL4PGCSl7L1+BPP7dJWiR8mZ8BzszFNtaR9JMFMcdEIlG7pIUhMLNpZta/xKHzgXMkPYHrCzJ6A5NjS6gLcF3u2E24DWeleMG8kMUYJuKK5zsrnWxmFwPP4eK4dng57e8Dr8YW0dXAO008x0QiUeOkstsl0MIrg90PmG5mF+bapuI1jz6scN1I4GQza3H1rVPZ7URi4aBUdrvJWZhlsBOJRGKhkhaGhlkgZbArjLeP6pbGXgU4MLQG/5F0taQXJD0oqUP+wrxuITevsyU9L2mMpJWifQ1JI0J/MULS6pLax3wlaVlJcyRtF+ePlrR20jEkEm2DtDCUJitR8RKuFv5LtGdlsLvhMYSs2mlWBrs7rhjOymDvgwe0v8E1ELfEk0il1NS7coXruuExgCyIvQ5wWRS9+xTIeyHM1S2Y2Z9y8xoTTz6jgF9E+wDgOjPrGtf8PdTWL+Mai22AZ4Ft5WK91czs1bg26RgSiVZOWhhKk20ldcHFatdFJs84vJZQP2CjqDYK9ctgP2Zms+J1p0aMWy7gk7W/kSu9/WxR31cCkzPdQm5e95Y4f0vgxng9lELa62i8mut2+PbZNsBm+OfOuM/MZkbMI9Mx1J2s2VVm1sPMeqywwgplPlIikWippIWhAZq5DPZHuDo5z1L40wFU1hAU6xaK51VJc5CdMxovv90TT3VdFi/kNyp3btIxJBKtnLQwNICatwz2KGAveXVTJO0LPJ8V1WuAOrqFBs59koLZzsEU1NdPA1sBc8KwZwLwS3zBSCQSbYT0a680mdUmeMrqYWY2W9IOwCmSZgHTgUPLdVCCR4FTo99zSsUZojTHALzonuFbNUdWO4CZXSxpGfxp5uAKp56AO8OdAnwAHB7Xz5T0FjAmzhuNlwkpFv0lEolWzALXMcQN7mIz+228PxnoWKJcdP6avYD1zezcCufsgOfu71Hi2FQayP1vYM79KNITNAUN9StpC7yQ3eLxd0ul72lBI+kS4E0zuzTeDwfeMrMj4/1FwNshpCtJ0jEkmopOp97H1HN/3PCJCaDl6xhmAvtK+l61F5jZsEqLwoKkim2YBckQ4KjIRtoQuHVhTURSe3zLaat43w6PtWyQO20r4Inmn10ikViQNMfC8C1eafM3xQckrSDpDknj4m/raO8bWypI6hw5+OMknSVpeq6LjirhbxCcIvdLGCtp7eirXv5+tA+WdLGkR3EXM3D3s3r5+pJOkmsYJks6sYr20yRNkfQwkHk5HJ7XKqhQBntF4F0AM5ttZi/G+eU8Ie6X1DVej5d0erz+i6QjJQ1VrhZSfEd7hWbhgvhOJ0r6ZRzfQdKjkm7Et4+eIBYGfEGYDHwhablIY/0hML7Ef9eUrppI1DDN9ev4MmCipPOL2vsDl5jZ43GTHk7OUSx3Tn8zu0nS0UXHNsFvWO/gN7GtKQRSPzeznpE5dCmwB4X8/SGSfg78Hdg7zl8X6BWxhH54vv6OeMB4iqQr8EJ5h+MF7AQ8LekxfIEt194n5rkIXrfoWTO7Fri2+EuS9F6MNRJPfx0SQeDME+JbSb1wT4j98GD1trF19i0Ff4dtgOuBV/AF+Z6IPWyFu68dAXxmZpvFDf4JSQ/GtT2BDc3sjZjTt/HfZiu81PiqeLrrZ8DE0GjUIZXdTiRqm2ZZGMzsc0nX4UHPGblDvfBf5tn7pbOMnBxbUrh534iXxs6o5G9wU+7fS3J97Ruvh+IF8jJuK8r+KeU7sA0uQMuc0+7E0ztVpr1dtH8V7cNKfkGBmZ0l6QZgV1xMdyCeLlrOE2I0/p2+AdwH7CJpSaCTmU3BF5nLJK0Yn/uOWFx2BbpK2j/6WQYXz30T3+kbuWllTw1bARfjC8NW+MIwX/WjEonGkOILzUdz7qdfiv9izv9Sbgdsae61PJe6O0IVqZRTb2VeU6a92M+gVN/lJlZpwo36xRzlsa+QdDXwgaTvUvCE2EduKDQyTh+Hu8e9jju0fQ9XNz+b63IonpKaNw0S8Cszy3tTZwH9Ur4OWwEb4VtJbwG/BT7HS4snEolWRrPpGMzsYzyYekSu+UHg+OyNvAJpMWMolH7oU+J4OXrn/n0qXpfL36+WUcDekpaU9B285MXoBtr3kdQhnoT2rNS5pB/n4iTr4AvSp5TxhIhtnLeAn+Lf02jcijOvOxiM24liZi9E23DgGEU5C0nrxrxL8QS+DfdxxD0+xoVvW1L4XhOJRCuiuTNwLiK3EODbIJdJmhhzGYV7Kec5Ebhe0m/x7ZLPqhxrcUlP44vfgbnx6uXvV4uZPSdpMO7rDDDQzMaDB7DLtN+CC8XepGGh2M+ASyR9hccMDo6Yx/n4VtJJwCNF14wGdjazrySNBlbLj2Nm70n6D+4slzEQ33Z7LhaiDyhs1xUzCX8SubGoreO8pgMnEomWTYv3Y4g98xnhP9wHONDMmsx1TNJ0M+uYe98X10AcX/6qJht7D3ybqB0eN+hvZldK2hsvhvdiA9ePpAEfhvj+JgGbmtlnDYzbjyr1G7Glda+55WlZko4h0VQkHUPj0HzoGGpB+dwdGBC/bD+lsE9e08Q2zlVATzObFtlBneLw3njxu4oLQxVj9MLjABfnFoVK4yYSiUTLr5VkZqPNbGMz62pm2+XKPy9wVFn3sH/uvOnxbzm/hl0lPSXpOUm3SboKD8SvANwXGVUHmdkUSVsBexE2nnIdx3O5sdaRlA8uU2aMjmb2sJmtnimXg6XwHwQfgZfBiAymct/BeZKOzb3vF9t6lb63pGNIJGqYFr8wNAOZ90JmjHNW7lg934IG+qrn1yBXfP8J10hsCjwDTDOzjXCl88rAf4BvJLUztxAdBpwSpb9fAz7LBeYPxwPKcykzxkmlJhjB42HAm5JuknSwXNVcjpspBPLBA923VfoSUtntRKK2qYWtpAXNjLiRA4UYQ7ytpHsoxTg8uL0ocLeZTZC0PW5+80QkHC1GZPOY2ZGSNsL1HCfj5j59S/Q7EPeBOAm/SfcsOr5FuTFK0YhxMbPxklaUtAr+hPOJmf03YgyJRLOR4gvNR1oYGkcWqf+WeNqK2MdiAGY2Sm6H+WO8wukFuC3oQ2Z2YIn+MLNJwCRJQ3GhWt8Sp90BnIFnJD1rZh8VHVelMeZj3Izbgf2B7+NPEIlEohWTtpIqU073MBUPigP8hFAiq7RfwxhgaxXqNS0ZuoGOISjL6IantEKRd0OUxRgOXEGJUhrlxij1gRoYtxw349/D/vgikUgkWjFpYajMCfgWzkRcY/DraL8a2F7SWLw+UqYW3gGPK4zHRXn9zewD/Nf4TdHPGLwOk4DfyQvsTQDOpPCr/Wa8COB4SZ2j7Qb8iSWraTSXCmOUotK4AH+SNC37i/5fwBeqt83s3cpfWSKRqHVavI6hpdGUuocIKK9iZvfH+7I+FHIfi2XM7M+NHGMRPKB+AIUF7Dar6w3d2P7+B1xtZn9o6PykY0g0FUnH0DjmR8eQnhgWLt2AH2VvyvlQSLoLd4vrPw9j/BVYBdgoguzbUijCNy/sCkwBfhrxlUQi0cpIC0MTovL+EvX8FCQthv+S7x2psr1V14disKS/S3oST309y8w+lNRO0uWSXpB0r9yTYf8y8xmGZx1tCoyJraOt8q5wku6W9Gz0d1S0tY/xJ0uaJCnvpXEgvkD9F8+GKjVu0jEkEjVMykpqPHk/aIDlcV0AlPeXqOenYGb7yY115m5DxbZUnpXxUt9dYozb8fTZTni10xVxDUS5Kqd/An5gZptU+Dw/N7OPJXUAxkm6I/pfNSt3IWnZ+LcDsDPwS7yQ3oGUSItNfgyJRG2TFobGU0n3UM5fopyfQkPcbWZzgBclrRRt2+AxgjnA/+Suc1Uh6XA8gP5d/MnhLeAESfvEKT/Aq7pOAdaS9A+8cGEW8N4DL//9VSwgf5b0myIfi0RigZDiC81H2kpqWjJ/iW7xt6qZfUHBT2FDvPT2ElX2l/eEUNG/1fAqsHosTpjZtbGofQa0j7TVXjHnjXGbziXM7BN8+2okcBwusAN/Qugld4x7Fl9gdmzEfBKJRA2QFoampZy/REk/BYr0ClXyOLBfxBpWwlNkSxLOcdfgRQiXiDm1JwR5Ma9P4gmgCxEziBIb7czsDuDPwKaSlsafVlY3s05m1glfNKoW1SUSidogLQxNywlAD3nRvRcpeEucD5wj6Qmgfe78R/GtpwmSelMddwDTcDe1K4GnqexRcRrwLjA59BWj8RpN7+C+0ouE9uEvuP4B3L5zZMRSBgN/wGMbj4TdacY9wF7yCq2JRKK1YGZt5g/f3x+ae78IblJzb7xfCS93/Txe8vr+aG+HF9CbjHsbjAPWbGCswcD+ZY71xE2JpuCB6YHAkvjTxIAqPkfH+Pe7wGvA9xvxHfQGJgIvAOfn2hcHbsG3n57GfaNLXb97zPtV4NSGxuvevbslEk3BGr+/d2FPoaYAnrF5vFe2teDzl8CGkjqY+0zvQmGLBzx99CEz6w8gqWu098a1AF3NbI6k1ajvjVwVsf1zG9DHzJ4KLcB+NG5L6d7IFFoM+IuZ/a/Ksb8LXAB0N7MPJA2RtLOZjcAtVz8xs7XlhkjnUbeqarYNdRn+vU3Ds5iGWQOGQolEorZoi1tJ/8aL3IHvj9+UO7YyfsMDwMwm5trfNc8EwsymmQdo53oxxOv95RafGb0kjZb0stw1DXxffoiZZRVWzcxuN7P38pOUtKekp0P78HCWlSSv1rpsnDYTuEPuA/GhpBnx90psT+1W9NnXwp3hMnHBwxT8tH+CbzGBp8XuXELA1hN41cxeN/ebvjmuq0PSMSQStU1bXBhuBvpEMLYrvm2ScRlwjaRHJZ0mLzUNcCuwZ9xsL5JUSReQpxOwPb4Q/TPG3BDP6GmIx4EtzDUINwO/i/aTgeOsoGKegftAXGJmHYCOuI1nNzMbXtTnq0AXSZ3kpS32xlNUweMKbwGY2bd43OK7RdfPPSeYFm11sOTHkEjUNG1uYYingE7408L9RceG47+qr8ZFZeMlrWBm04D18CDsHGCEpJ2rGO5WM5tjZq8Ar1O+sF0pVgOGS5oEnAJsEO1PABdLOgFYNm7i4/Bif/3w0hdflOownnKOwWMJo/Eqsd/G4VJpsMXitGrOSSQWCEnH0Hy0uYUhGAZcSN1tJMAdzszsRjP7GX7D3S7aZ5rZv83sFOBv+K9tqHtjLNYnFN80DQ/6dqdh/oEHojfClcZLxDzOBY4EOuBlLrqY2aiY59u4D8Sh5To1s3+Z2eZmtiUeRH4lDk0jnh7iaWIZ4OOiy+eeE6yGZzclEolWRFtdGAbhtYcm5Rsl7SRpyXi9FNAZ+K+kTbNtJbkNZlcKHgbvSfphtO9DXQ4IvUFn/ElkCm4XepikzXPjHiLp+0XX5rUPh+XO7Wxmk8zsPNzCs4tK+0CURNKK8e9ywLEUxGvDcuPsj6emFi9s44B1JK0pr/XUh0I5kEQi0Upoa1lJgAePKV2ptDsuBssc2gaa2ThJuwNX5/L1x+I3eIBT8RTXt/B01o65/qYAj+FpsEebG+58HVk/F8ZNeg6eunpn0Vz6AbdJehvXF6wZ7SdK2hGYjafU/hu/QZ8iaRYwHa/EWo7+kjaO12eZ2cvx+hr8aeNV/EmhD0AsiAPN7EfmtZ6Ox2tAtQcGmXs1JBKJVkSb8mOQNBvXIQi/sR5vZk/K/YvvtSgaV3TNSOBkMytpKiDpNNzrALywXfYUMsjM/t6kH6DuuL1wsdvrwHdwEdt5Ft4O89jnNGBDM/u0aWaZ/BgSTUfyY2gcmg8/hrb2xDC3AF6kcp6DZw3NM+aGN2dHn9MtV2CvGXjUzPaOsTcF7pJ0qJk91oxzSCQSrYy2GmMAWBr4pLhRUgdJN0dZi1vwIG927IjQJIyUdLXCO6EUkpaV9HoEcrP3b8i9Dh6XdKmkp+R+Bz3inI5yH4SxoV/Ys9oPY2bP4QtUVsL7wxhvQvzNlrSRpF6Rjnu3pBclXVZCr4Ckw2IeE+T+D+0krRUaieXjczwpaacS1yYdQyJRw7S1haFD3OiyMhR/KXHOMcBXZtYVv9F2h7l77X/GC83tQgOpp7Ed8wReQgJca3CrFUpULx6ZQb+mEAA+HXjAzHoCOwEXhfahWp7LzesB4DehZ+iGPy1l21ybAyfiW18/pEikJmlDPJC+VVy7CK7Ufh24CLgc11WMN7NHSnz2pGNIJGqYtrYwzIgbZRf8hn1diV/L2wHXw1zNQ6Z+7gk8Fumss/CyFg0xEDg8Xh8OXJs7dlOM8QiwoqSOuG3mafLidY/iKaqrN+LzVVuSe4yZTY1F6ma8amqeXsBmwDMxl+3xDC3M7J/ACvF5fkci0Uyk+ELz0dZiDHOJOkXfw29y9Q6XaGu0v7GZPSZpQGQRzTKzlyqMYTHG3mb2WmPHCjbBHd3AhWvtYG6No/x/61Jj5xEePP9z8QCxgK2MZyV1ZB5rRiUSiZZLW3timIvcf6A98FHRoVHAwXHOhrhmATxFdXtJy0XcYD+q43rgBuo+LUAUqJOb5bxnZl/iaaAn5OZYbemNzPvhj3hZD3BVcyak24e65b63kLR6LBg/xctv5HkY+GksnEj6rtyqFLwI32C84OCV1c4vkUjUDm3tiSHv1yzgMDObXbSbdAVwrdyjYAK+IGBmb0v6G15b6R1cQ1DJByHjBjx2cEtR++eSnsSrqmbbTWcCl8rLYLTDaxvVK1KXY0e5x8KSwHvAsbmMpCuBeyTtghsI5X0UnsRjBRvgLm11RGpmNknSmcDDIdybBRwttybdGE/znS1pP0k/M7OhVXwPiUSiRmh1OoZIGe2Ye98X6GFmx5e/quq+O5rZ9HhiuAvfbrkrd3wPPKDdDvd17o8vHr8AfpWVp5b0OH5znVDU/0gqaCYqzOt4PJjcGVjBzD6M9pVw4doPYj5TgYvx2MBgM7uxUV/APJB0DImmIukYGsf86Bja7FbSPNIvnjgmA28Ad2cHJC0KXAXsae6fvAketP0L8Cmw/gKc1xN4wPjNovbMX2JjM1sfV2mDP2Ec1JgBsrTbRCLR+mlTC4OkNSSNCI3CiGzfPLQD++fOmx7/rixpVKS4TgbuifTNE/CsnWcl3RYB2aXwrbmPYG7RvYPw+kM7AhdEP52BJbOnBUnrSKpXhlvSrqFzeFXSpzHnTJNwe/5cMxtvZlNLfOR6/hJm9nDMc9vo6zeSlpB0bWgqxkewHEl94/P9C3hQ0lBJc7e2JN0gaa8Sc086hkSihmmNC0OH3A10Av6rOWMAcF1oFG7A7TorcRAwPBaDjYEJEZD9E9DLzDbFC9mdZGYf43v1b0q6SdLBktqZ2ZPRfkqkyr4GfBbBYvD4wuD8oEVjrI0rtG/PNAlmtj/VUc5f4lRgdPR1CW4eRFRyPRAYktNPbInHYnYil34raRlgK4pKl0c/SceQSNQwrXF7YG7ZCyjEGOLtlripPcBQ4PwG+hoHDIptorvNbILcQW194IkIWi8GZG5sR0raCN/WORkXwvUt0e9A3D/hJDw7qWfR8S3KjdEYzGy4pLVwzcb/4f4S9epB4TqGf8Q1L0l6E1g3jj0Ui16WfnuZvPjfvsAd4QeRSCxwUnyh+WiNC0NjyCLv+Zx/4TdizGyUpO1wB7ahki7Ay2g8ZGYHluzQ1cWTJA3F4xB9S5x2B3AG8AjwrJkVp8yq0hiNIW7qNwI3SroXF/CVGq8cxTqFoXg6bx/g5/M7v0Qi0fJojVtJlXiSKCeN39yy/P2pFHL+f4Jn8KDSPgdjgK0lrR3nLClpXXmdox1yY3WjEAz+Ao9BABDlt4cTqbEl5llyjMZ+WJXxlyieD3W1G+viauspZbodjGdAkUpuJxKtk7a2MJyAb+FMBH6G1ykCt/LcXtJYvI5Q9it5BzyuMB4XtPU3sw/wp4Cbop8xeH0iAb+TNCViG2dSeFq4GfdLGB/BZ/AYh+EagzpUGKMkkk6Ql8xeDZgoKau91B0vazER34oaaGbj8DIf30p6XtJv8NpH7eX6iVuAvmY2s/5IYGbv4erqUgtaIpFoBbQ6HcOCQAUfh4ybzS02y53/RzP7WwN9ngwsky87ETf0izO9w3zMt56ewsyulLQ38PL89B9PIJOATc2sQYFf0jEkmoqkY2gc86NjaOsxhmqpE9Cugj/ivtAlkXQXvq2zU66tvZkd2ZhJxTWzi9oyPUVPM5smd53rFIf3xt3m5mlhkJsDDcIXr2pU34lEogZpa1tJTYakZWLbaL14f5OkX0g6l0LK7A1x7BAVvA2uBPaPlNmpks6S9DSwpdznIfNmODB0BZMlnRdtd8l9Ff4n6UvgJbnhUJ5SeoopkrYC9iKnp5DUTdIYuUbiLrkPNDGP82LOL0vaNvp+FN9qOjiu+WWZ7ybpGBKJGiYtDNVRRxshqXf8Yj4eGCz3cF7OzK42s1MplPc+WNIP8ZTUreOpYzYR6MUtOSeb2eZmNreQXegNzsOfKLoBm0na28z2wf+bnWBm3zGzdcxseH6ijdRTXAf8PhapSXimVMYi5r4QJ+bajwA+M7PNcIHfLyStSRFJx6U2z28AACAASURBVJBI1DZpK6k6Sm4lmdlDkg7AhWQbl7l2ZzwIPC40CR2A9+PYbDx1tZjNgJERhCaePLbDS3CUuyY/rwb1FCFQWzZXdG8IdT0m7ox/n6WwFbUr0FUFlfgywDp4Wm4isUBJ8YXmIy0M84G88ugPgRnA8uTKT+RPA4aY2R9KHPu6OEaQu6Yc5a6pQ5V6ikpkWUmzKfx/IrwY4PDSlyQSidZA2kqaP36Dp24eSEEhDTAr93oEsH+ohZH7Ja/RQL9P4+mz35N7JhwIPNbANUT/VekpYivsk1z84GdVjDEcOCb7bKHf+E4180okErVDemKojryPA7if8iDgSDz75wtJo/D6RmfgWUETJT0XcYY/4UXoMm+D46hfCXUuZvaupD/gwV4B95vZPeXOl+9RjcY9qh/H9RQ34g5rk6irp7hN0uUx/iLAQEkzgdcp+ELk+QdejRW8lEcn4DkVCgd2LnFNIpGoYZKOoZUgr4F0G17uuz1uMrR7ZhMai8cP8MVmUzP7LG7uK5hZ2RiBpMHAvWZ2e1H7Drh3xB6V5pV0DImmIukYGkfSMSQws8ny8ti/x7OdrgNmS/oPvhhsiWcYfQFMj2umZ6/l1V7/iT8dvAb83Mw+yY8haXfgUuBD4Llm+FiJRGIhkBaGGiaEcvl00XbAcrgxUA/cj2E94HAzOzbiFe8Bb0gaAdxpZv+Ka6/DA8uPSToL3xI7MTfWEnjpkJ1wy9Fiq9L8vI4CjgJYffXVy52WSCRaKGlhqGFC11CHuKlPN7OZkR77ppmNifNnx6/+zfA02kskdQcuoXLqKnitpjfM7JUY53ri5l9iXlfhcRZ69OiR9ioTiRojZSW1PubEX0adstnmjDWzc/BKs/s1ou90k08sNFJ8oflIC0MbQtIqkjbNNXXDnyiqSV19CVhTheqw8+0VkUgkWiZpK6ltsShwYZTc+Br4ADg6jh0G/DOqp9ZLXTWzryN2cJ+kD/G02FJucIlEosZp9emqklbC99C3wN3XvgHON7O7FsJc+gIXAG9H00QzOzTiAqPM7OEK1+4FrF+u3Hf03cPMjq9yLlPxDCXDv5dDzezNOFZcZnxvM5taTb/FpHTVRGLhMD/pqq16Kyly9+/Gb7prmVl3fF99tSqvb78ApnVLFLHrZmaHApjZ6ZUWhThnWCUPiHlkxyigNxIX52XMyM2x27wuColEU9Lp1PsW9hTaDK16YcBTK78xs39mDWb2ppn9Q1InSaMlPRd/W4ELtyQ9GsrhSdF2t6RnJb0Q2ylE+xFRlnqkpKslDYj2FSTdIWlc/G1daZKSBmeF6SRNlXRmzGmSpC7R3jfX/wHyctzPh+I6YxVJD0h6RdL5jfiengJWbWCOfSXdOY/9JxKJGqK1xxg2oLwQ631gl9g7Xwe4Cc/9B+gJbJhTBP/czD6W1AGvknoHsDjwZ9wH+gvgEeD5OL8/cImZPS5pdbzG0A/jWG9J22TnmVkpi8wPzWxTScfi1VGLDXxOB3Yzs7clLZtr74Yrn2cCUyT9w8zeKvfl5Ngdf7LKyJcAeSOXFltV/0nHkEjUNq19YaiDpMuAbfA4Qy9gQCh+ZwPr5k4dW1Qm4gRJ2c3xB3ip6e8Dj4X/AZJuy/XRC1g/dAQAS0taKl7fUkUcIF/yet8Sx5/AfSBuzZ0LMCJzVpP0IrAGUGlheDRiMO9TYiupxPlV9Z90DIlEbdPaF4YXyOXpm9lxkr4HPINXRn0P91Foh2fpZMzN/Y+aQL2ALc3sK0kjgSWoXBq7XZw/I9+YWygaolTJ67mY2dGSNgd+DEyIxS1/Xdlri9gR/6yDgbOAk6qcV7X9JxJNRtIxNB+tPcbwCLCEpGNybVml0GWAd81sDp63Xy7QvAzwSSwKXfDsJoCxeGns5SQtQl2h2IO4uxswtw5RkyGps5k9bWan43WLfjCvfcXidSJwqKTlm2qOiUSidmnVC4N5Lu7e+A38DUlj8XIPvwcuBw6TNAbfAvqyTDcPAItImgj8BcjKS7wN/A33TngYeBH4LK45Aegh90V+kYJWoKm4IALTk4FRFGIb84SZvYvHWI5risklEonapsXpGFqK7kDS4cCv4+36wBR8++SB8HVGUkczmx5PDHcBg+Z3npLWxrOhpuDbVdOBvlmNopaApJOAy83s64bOTTqGRFOQpaqm7aTqaTU6hpakOzCza7M8fuAdPOe/W7YoBP0ie2cybp95d6m+5oEpMdbGwI3AqQ1d0MychMdZEolEK6RFLQzUgO5AUntJr0pa3sxOBroDiwH9gKGSroh5vizp/+KaRSRdLGlsbC8Vp59WYmn8yQlJnaPv8fH5No/2VSU9LmlC6Bu2ijE/lfSOpK8kfR5zmi7pLUk/qjQ3Sb0kjQjtwhRJ10X7b4AVgdGSSoryJB0l6RlJz3zwwQeN+KiJRKIl0NKySlqi7qAOUbr6JuAgYACwGzAuxgMPBG+Pp7Q+HFtDRwDvm1lPSYsDYyQ9aGb/LfNZ14snkaVj3ptH+7u576ALHi/ZHDgE+JeZnRdPTR3i/GWAA8zsIbmJzyL4ttjGwJXA/bjeoN7c4vpN4/z3o30LM7tE0m+Bbc3s0zLfUUpXTSRqmJa2MNRhYesOzOyLMlO7BvcrGAD8HPdCzrg1Mp2mSHorxt4V+KGkPnHOMtFebmGYkukIJB2MO6vtgS8SAyRtDHxLwW95HHCl3EznbjN7PuIeM8zsoThnEvCZmX0raRLu3UyFuQGMicA0sVB1IoLviURzkmILzUtLWxhalO6gHGY2VdInknbElcAP5g8Xnx5jH2tmI6rpv4hhwBXx+re4oOwQvFJqZtH5SHzuHwM3SDoHd1j7JtfPHAo6hDkU/tuXnJukXiTdQiLRJmlpMYZa0h1cA9wA3BxzyjhAzrr408or+NbUsTEuktaLba5q2Ab3YM4+27uRhnsYsdhJWgP4X2zhDMYXq2qZl7l9ASzVwDmJRKJGaVG/AM3MJO2NW07+DvcL+BLXHTwH3CHpANzcvpLu4Gi57mAKOd2BpEx38A71dQeXxTWL4NqAhrQHdwGD8Btxnlfj+hWBo8zsG0lXAqvjKmXwPfufVOg7izEI/9WeBdAHALdLOhDXTmS/6HcGTpI0C3+KOKSBuedp7NzA4wcPS3rLzHo1YqxEIlEDtDgdw4JETag7kLQFcA6wHQXvgtWBU+OXe7MiqROeMvtXM/tztH0PD1hfWak+U1z7H3whXQxf2I4tehKaJ5KOIdEUJB1D41Fr0TE0A02iO5B0Gr6H/0dy3gV4ls/7TTXZeeB1PEidcQAet6mG1+IzdMUzkfbOH9SC8aZIJBItkBa1lbSgCd1BU/RzNnA21C2MZ2aHRFt74FxgBzyT6DIzuzICxP3w+kbd8ZhBlpnUAVgZX7Bm4ttDX5Xqp8LUZgD/kdTDzJ4BegO3AqvEvAYDn+Npvt8Hfmdmtxd9tm8lPQmsHfM9A3/q6BYpr2+a2eXRXz/gCzO7KN+HUtntRKKmaWtPDAuCDiEsmyAp25Y6Ak8N3QzYDPiFpDXj2CZ40brO+NbN8bgOY1Fg51A798Jv8pX6KcfNQB9Jq+GZRO8UHV8ZD2jvgS86dZB7Pu9MYXusJ3Cama0ffffOnf5TPG23DmZ2lZn1MLMeK6ywQgPTTSQSLY029cSwgCjlXbAr0FXhykZBG/ANrrmYBnW0AZ/h2UbjAMzs8zherp+8ZqOYB/Bif+/h213F3B2xgxfldakyOsd8DLjHzP4dTwxzNSJmNl7SipJWAVbAs7/KaTESiSYjxRaal7QwLBgE/MrMhtdp9BttKW2AqK9/KNtPJSIL6llc87ABsGfRKfnx89qO10oscFA/++t2YH98K+rmaueVSCRqh7SVtGAYDhwjaVEASetK+k6F81/C/Zo3i/OXisypxvaTcRHwezP7aL4+RWluxgsb7o8vEolEopWRnhgWDAPxLaLn5NHpDyjK8skTv/J7A/8IcdkMPM7QqH5y/b1A9dlIjcLMXpDblL6dlctIJBKti1arY5A0Gw+gCt+yOd7MnpzPPrsBq5jZ/fG+L3AB8HbutIPM7MX5GWdhMD86iEokHUOiKUg6hsaTdAylyfQFGwN/wMVo80s34EdFbbdkOob4axGLQlbiopHMjw4ikUi0ElrzwpAn72mwsqRRKngXbBvt0yWdJ/c5eFhST7lvw+uS9pK0GHAW0Duu7V1uMEn7RB+K8V6W9H1JfSXdI+kBucfBGblrTor5TJZ0YrR9R9J9kp6P9t6SNpL0jdzac0L0k2Ux9ZN0lbxs9nVy74gL5B4TEyX9soHvaa4OIt5nOohsjoMl/V3Sk/G97F+qEyU/hkSipmnNMYYOkX65BJ67v1O0HwQMN7OzQ4iWFen7DjDSzH4feoS/ArvgKuAhZjZM0ulAj2xbJbaSekvaJjfulmZ2l6T9cA/l3YEzzOx/IYbrCWyIi9fGSboPz0g6HPdWEPC0pMeAtYB3zOzHMd4yZvaZpMxR7sO4iV+YG787sI2ZzQih2Wdmtpnca+EJuQ9EpXTXTAfxPwo6iFVyxzMdRBe88mu9AHTyY0gkapvWvDDM1RdI2hL/Bb0h7l0wKDJ97jazCXH+N7gGADw2MdPMZqmud0Epbimz//4rvPTGGDO7Kdf+UJYtJOlO/CZrwF1m9mWufduYz4WSzgPuNbPRVXzuYbny4c2pg0gkFhgpttC8tImtJDN7CvgesIKZjcIL372NW3EeGqfNskIkfq53QdwE52UBXTX6WUlS/nsu59dQat4v408Ak4Bz4okF3KQn67PYezmvO8h0EFn8Y00ze5AKmNk3QKaDuKPEKeV0EIlEopXQJhYGuS9De+AjuXfB+2Z2Ne6psGkjuqrKhyACv9fi21b/AU7KHd5F0vKRlro38ARezXRvSUuGTmEf3FN5FeArM7se3y7K5joVXzCgrq9EMS1RB5FIJFo4rXkrKYsxgP+yPczcr3kH4BQVvAsOLddBCR4FTo1+syyn4hjDsbgGYbSZjY5zs1gCwOPAUGBt4MYodpcVuBsb5wyM8hO7ARdImgPMAjIDozOBayT9EfeXKEeL00EkEomWT4vRMUgy4GIz+228PxnoaGb9KlyzF7C+mdUrBpc7ZwfgZDPbo8SxqXgw+cN5nHM/YLqZXdjQuXF+X3LB6/npN76fI/FtpdnARWZ2XYXzR+LfwzPVfu7GjlGKpGNINAVJx9B41Ep0DDOBfeWiqqows2GVFoUFieZNJ9BUYx+NZ0z1NLMN8ZhJk+73VzuGkk9DItHqaEkLw7d4iuNvig9IWkHSHZGPP07S1tHeV9KAeN1Z0pg4fpak6bkuOkq6XdJLkm6IbZWMUySNjb+1o681JI2I3P8RklaP9sGSLpb0KHBeXL++CnqHE3JzrqdLMLPBwOvF7XH+aXJNwsPAeg18V3/EHdY+j34/M7Mh0c/OksbLdQ6DIk01/11uhKefPirXQbwraYakhyTdFE8JDY0xVdLpkh7HRXDF/72SjiGRqGFa0sIAcBlwsKRlitr7A5eEL8F++N55Mf2B/nFOsQdB5oGwPq4N2Dp37HMz64n7KV8abQOA68ysK3AD8Pfc+esCvbItLzyffzdcn3CGpEUldaegS9gC91HYpIH2PjHPfXHvhZLI6xQtZWavlTi2BO5B3dvMNsJjSMfkzzGzSfH97IhvE70HLB/j9mhojBxfm9k2ZlavwmryY0gkapsWFXw2s88lXQecgKtwM3rhv8yz90vHzSvPlhQCqzdSV/RVygPh8Th2U+7fS3J97RuvhwLn5/q6zcxm597fZ2YzgZmS3gdWwrUJpXQJKtPeLtq/ivZhJb8gp1yJbvAnjTcizRVgCC6yu7TM+dvg3gszYtx/VTFGRimNQyKxQEixhealRS0MwaXAc3i6Z0Y7XFGcXyzq2Go2QCkPhAwr85oy7cX+BOX8FUpRacJVZQHE4vmlpLXM7PVG9F/1fBoYI6P4e0gkEq2ElraVhJl9jNfnOSLX/CBugQnMrXJazBgKOf19GjFk79y/T8XrJ3N9HEzh6aJaSuoSGmjfR1KHeBIqNtcp5hzgMklLA0haWl7+4iWgUxYrAX4GPFahn8eBPSUtIakjkP9ZVm6MRCLRymmJTwzgAqt8SucJ+E1qIj7nUcDRRdecCFwv6bfAfbhdZjUsLulpfJE8MDfeIEmn4Ln/hzdm8mb2XCldApTWK0T7LcAE4E18sajEFUBHXB8xC9c4XGRmX0s6HLgtsqbGAf+sMM9xsW31fIz7DIXvreQY1X0DiUSilmkxOob5RW5iP8PMTFIf/Ca/ByU8GeTeA/dGGmZxPyOJfP8y45xGIRNno+gfYJCZ/b3UNU2BpN2BvwGL4nWdfmtmI+NYH+AM3DynV5X9CTgFD0B/g38/SwH7m9lzTTXvpGNINAVJx9B45kfH0FKfGOaF7sCAuOF9Cvwc2DlXSG83fHtk+/kZxMzOBs6OPqdbaZ/kBcH7wI/N7F1JGwP3Aj+IY0cCR1VZZC/jODwzaQKeabUk8ExTLgqJRKI2aTULQ9wUN863FQWn53oyFJ3TAQ90r4/XNeqQO3YE8Hs8vfMVvOJqSdWypGXxoPm6ZvZtvB+Pl754DN+m2Rzfnjk8FMgd8dTY9fEngdPN7F+5Pi+jbmotkvrj2UYd5TWQ/oynvg6Ulwt/FTcTWgzYAM+o6ojXbZoB/MjMPsV1Clua2ZslPssuuDNdezx2c5y5/eg0PFX4J3Fs/1wGVP76o4CjAFZfffVSX1cikWjBtLjgcxPTIURcL+E3tL+UOOcYvFBdV/xJoDuAvIBddtPdBdcrlCVutk/g/gvgN+Jbc6mti5vZlsCvKegwTgceCB3FTsBFoUXI+jzO6rrDdTOza4GfAk+b2SwzOx3/1d/bzE6NSzfAg+lb4EK8T8xsE7xq6iGSlgMWLbMoLAkMAvYLLcSSxE0+eC/6Gkjd4oD57yLpGBKJGqa1LwyZvWcX/IZ9nernuG4HXA9gZhOBidHeE3jMzD42s1nAbVWMN5BCoPpw6qbc3hRjPAKsGE8LuwKnhbbiUbyEdsWf2KFc/itFwrUiHjGzL83sPbxQYPYUknlLVEpr/SHwSk7cdh3+HWXcGf8+S2WfikSiyZh67o9TfKEZaTVbSQ1hZk/J6zCV+glbKgLf6NpDZvaYpAGSdsT9HV6qMEbmw7B3AwrjwoS8NMedwCFW2YUtr62Yk3s/B1jEzD6WNEvS6mb23+JhGphG1lexHiSRSLQSWvsTw1yU82QoOjQK1yogd3jrGu1jge0lLRepn5V8D/Jcj5fRuLaovXeMsQO+HfMl7peQr6+0SYX5L4en4Z5sZmOqnEslzgUuzxTkkpaV9AvgRWAdSWvFeYdQWQuRSCRaGa39F185T4b8OVcA14ZGYgKhMTCztyX9Dfc7eAe/YVajjbgBjx0Ul4z4XNKTeEpott10JnCp3D60HR44/kmZfn8NrAmcKenMaNvZ5t1M5x+4z/Wzkr7BdQrnm9lXEXS/U1459Wng6nkcI5FI1CCtRscwP0iaTUGPAHCzmZ0raTRe7XUCcBeuVbirgb4yDcWVZnZ/tD2OaygmxPsewKFmdkL5nsr2P5LKOoupuNNcFvQ+1syebOw4Zfrui2crvR1NE82sotFR0jEkmoKkY2g8Sccw/8woo0fohD8BGF6W4+5KnUi6Ai/4dxWeMnp/iXMWiZv6grxb7mjzaD5UBbeUS9lNJBKtgzYTY5hHXgMOjqyme4EnJT0n6bbIKkLSZpKelPQ8nuraA/gVbvk5QVJv4GHgWEkP4plRO0i6N67vKOlauX/CRElnx3UfSvpK7pXw4vx8iPx48X5A/PpH0rmSXoyxL4y2kv4XjRgv+TEkEjVMWhicTO+Q/fXOH4xspj/hPgyb4r/2T5K0GB5L+LWZbYw/LXxJxBgiVTaLNXQHfmJmBxWN/WfgMzPbKLQUF8bTy7pmtiQuTntfUleqJzPhqeQHjaTl8UJ+G8TYf41Dlfwveue+p5I1pJKOIZGobdJWklNuKyljC1yd/EQErhfDK7GuB7xrZuPAy1VD2XLgw4rLhge9yFWDNbNMnf3TUBAvAqwc40+sf3lJqt1K+hz4GldN34c/FWVzKud/kbaSEs1Oii00L2lhqA4BD5nZgXUa/Vd8tdH7cv4F9UxxJK0JnAxsZmafyCuyLlHi2mr5lrpPh0sAROmOnsDO+OJ0PK7Anl//i0QiUcOkraTqGANsrYIn9JKS1sX9D1aRtFm0LxWahy/wtNRqKPaaWA6v6/Ql8JmklYD/m8/5v4k/ASwut03dOcbqCCwT2VMnAtlTUzX+F4lEopWSFganOMZwbv6gmX0A9AVuCr3DGKCLmX2DC9f+EcHnh/Bf44/iN+J68YoS/BVYTtLk6GNHM3seL8D3Al636In5+XBm9hZufjQRz7IaH4eWAu6Nz/QYnpoLLrrrEQHpF6nvfZFIJFoxbVrHUEK/sLeZTV2A4003s44Vji8LHGRml8f7VYC/m9n+TTiHn+MLgOE/DE4zs3tiu+peM7u9wrUjqaChKEXSMSSagqRjaDxJxzDvNBR0bm6WBY4FLgcws3eAplwUVgNOAzY1s89iKymlDSUSiTqkraQi5P7Hma5gvLwgHpL6ShqQO+9eed0jJE0P/cHzksZEXABJa0p6KrQAf8ld21HSiNBETJKUlcE4F+gcW1AXSOokaXKZeb0Y5/1X0qeSPpf0pqTzK3y8FfH4x3QAM5teqhifpNNjzpMlXaW6UedD5LqNyRG4LvUdJh1DIlHDtPWFIR9byEpdHAcQXgQHAkOU80gow3eAMaFlGAX8Itr7A1eEHuB/ufO/BvYJTcSOuA+DgFOB10L/cErRGMXz6oin0Z4OfIy7ua2H6wx+QGmeB94D3ohFZs8y5w0ws83MrU874Bapcz+rmW2FP9kMKnVx0jEkErVN2kqqv5W0DV5gDjN7SdKbuPVlJb6hoAF4Fjf2AXdfy6qyDsVNc8BTVP8maTu8FPaqwEoNjFFpXiPM7DOACBavAbxV3EEUENwd2AzPTLpEUncz61d06o6Sfoeb9CyPB8EzT4fMV2KUpKUlLRsmRYnEAiPFFpqXtv7EUIpyyfoltQDBLCtE8Yt9CkpF9w/G9/a7x8L0Hg3rFCqJCPL+CxV9EswZa2bn4NqFOuXE4+nocty2cyO8smp+bqV8JRKJRCsiLQz1yfszrIs7qk0BpgLdJLWLrZqS++tFPEFB1Xxwrn0Z4H0zmxUxjDWivZL+ody8qkbSKpI2zTV1wzUOebJF4MMIThcHvzNfiW3wUh7VlCJPJBI1RJtaGCSZpKFFbR+oUNBuJbwcxD6SvgbG4fqFWbjP8vrAV3gZ7oqF7SL98wHgOEnj8MUg40XgsBjjLuBT/Ia8JzAzArsXFHV5OdBe7t1wC9DXzGbSOBbFi/h9LWkGHuzuF8cWw+s2jcPFdS/g1WTHFfWxalw7ArijkeMnEokaoK3FGL4ENpTUIco9HACckzt+FjDczH4EXvLCzCZKOhBYBVjezOZE2ueXWV2jvDYhdAC3x8Lwvpltmev/3Fh8BgK7hd2o8O2crEbSg0W1iDaMfr/GF6k6mNlgYHDu/R7F5+R4G89MWs3MPowMpj3wooDTcH+FcyWdCixnZr8vun7fOHdVfAvpWUn9c/WdEokmJ9MwQIo1NBdt6okh+DeQ/d91IBFMDVbGb5AAmNnEXPu7ZjYn2qdlN0NJ07PzJe0fC0JGL0mjJb0sKbthHwcMMbOnoi8zs9vN7L38JCXtKenpSE19OJcCu30uk2q8vAzHypJGRdtkSduW+eyKv+/EgrQ07k4H7hw3JF4PAfYucf1ueM2oj+PzPwTsXmasRCJRo7TFheFmoE8EWbvi1pUZlwHXSHpU0mly5TF4OYk948Z7kSp4MxfRCdgeX4j+GWNuiGcuNcTjwBZmtknM+XfRfjJwXASttwVmAAfhTzrdgI2BCbGo5Mt8TAC6AMfgau938K2xa6LflczsXYD4d8USc1qVutlO06KtDknHkEjUNm1uYYingE7408L9RceGA2vhmThdgPGSVjCzabhG4A94eukISTtXMdytZjbHzF4BXo8+q2U1YHjEFE4BNoj2J4CLJZ0ALGtm3+JxgMMl9QM2MrMvzGzz0EPM/cOL/h0DbIJvjU2Mz1QtpTKj6mUlJR1DIlHbtLmFIRgGXEjdbSQAYpvkRjP7GX7D3S7aZ5rZv0N49jcKWy35G2Nxymmp1M4XcNOehvgHLjTbCPglhVLZ5wJH4sKzMZK6mNmomOfbwFBJ5XyYu0Ufr0V67a3AVnHsPUkrA8S/75e4fhoupMtYjcJWVCKxQJh67o/n/iWah7a6MAwCzjKzfAE9JO0kacl4vRTQGfivpE2zbSVJ7fAtqCzN8z1JP4z2fYrGOSDSWzvjTyJTgAF4RtLmuXEPkfT9omuXwW/0AIflzu1sZpPM7Dw8ENxF0hp4oPtqfGtoU0rzNl71NfsZvwvwn3g9LDfOYcA9Ja4fDuwqaTl5efBdoy2RSLQi2lpWEuDBY7xcRTHdgQGSMjHbQDMbF2rhqyUtHueNxW/w4GUs7sX33ifjpSoypuDlrFcCjo7Moq8l9QEulLQivjU1CrizaC79gNskvY2X+V4z2k8M7cNsPO3137hW4hRJs/A6SCWfGMzsHUlnAqPi3DcpZDqdC9wq6Qjgv3jGFpJ6xNyPNLOP5TWfshTWs8zs41JjJRKJ2qXVLQySDLg+toKQG+e8CzxtZh0ju+cafEtkUWCqmf0ofvH/AL9RCw/q3glgZg/gmoTisQbjpao7l5jK5fh2VXbz31vSI7geooeZlcocGhx/mNk9lPjVbma/KnHdEAoZRcgL+h2Kp5x2zLWvgd/wZ+H1lY4xs4/i8B64aA7guuyGHyW2j4zrl8djM4YL/u4uMZdEIlHjtLqFgfpahV0obMmAaxUeMrP+cAJfeAAAIABJREFUMNeeE1zRuwrQNa9VmJcJxOJzG9CnSKtQravb/PIv/InmlaL2C/Gb/hBJO+Eajp/FDf8MoAcFfcKwEvqEU/G6TJnW4VSgWOuQSDQpScfQ/LTWGENb1ipgZmPwX/N1nOnwbKQRcdqjuHYBqtcnVKN1SCQSNU5rXRjahFahUsdmtjlRPTaXrjqWQtG8fYClJH2XKvUJVKd1SDqGRKLGaZULQ1vRKjRinIyTge0ljccXs7fxqrFV6ROqJekYEonaplUuDEFb1SqUxczeMbN94wnltGj7jOr1CdVoHRKJJiXpGJqf1rwwtFWtQlkkfS8+A/iTUebAVq0+oRqtQyKRqHFa7cIQweNyWoVnJE0EniK0Cvh++b/kHssT8S2WYq3CI3jqa55Mq/BvQqsQQeZMqzBF0n/wWMHnRdf2w7UKo4EPc+0nRoD5eTy+8G9gB7wG0ng8TlDqswEg6XxJ04AlJU2L7SeijymSXsa1FWfHd/UxkOkTxpHTJ0gaGFoGcK3DLpJewbO9zi03h0QiUbvIrO0YcDWgcdijAY3DpcBO+FbR18BPzeyNCmMNxjUOt5c41hPf5lop+nscOIGCxuH44muaCnkJ8T/GuO8Ah5iX4O6He1Vn0eI/mtn9Ja7fHV+U2uOLasXFoUePHvbMM8804SdIJBLVIOlZM+vR8Jn1aY06hkq0aY1DLIT9gfWt4MdwPAWznkvM7MIK17fHs7p2weMS40LvUNG0KJGYH5KOoflptVtJFWhNGocvJU2MbafpkmZIeqWCxqGSH0M19AReNbPXzewbPMX2J8UnpXTVRKK2aYsLQ2vSOKyEB6GvBc4xsw54umxJjYOZzaK8HwPA8bHQDIogdDFV6R1SumoiUdu0uYWhLWscJC1KeT+GK/AMrW543OWiUl2UaGs7QarEQiGlqzY/bW5hCNqqxqGsH4OZvWdms2O77Gp826iY5MeQSLQB2urC0FY1DmX9GDLhWrAPXkK8mHHAOpLWlLQYnpI7rMxYiUSiRmlrWUlA8mOgtB/D+ZK6USip/UuAWBAHmtmPzOxbScfj4rf2wCAze6HUWIlEonZpsTqG0BxcbGa/jfcnAx3NrF+Fa/bCUzHL5tZL2gE42cz2KHFsKq4j+LD4WJVz7gdMr5TyuSD6lbQFvtAtHn+3AGfimoR1zOyTeCJ4B9jWzB6P6z7A4x6/yvov1ZeZ9Ss1h2q+r6RjSMwvKV113pgfHUNL3kqaCewr6XvVXmBmwxoSXC0oQiOwsBgCHBWZShviQW/DM662jHO2AsbHv0haD/jQCkY9ZftqhvknEokWREteGL4FrgJ+U3xA0gqS7pA0Lv62jva+kgbE686SxsTxs/J6A6CjpNslvSTphsjpzzhF0tj4Wzv6WkPSiEjlHCFp9WgfLOliSY8C58X160saKen1yBzK5nxS6A0mSzqxivbT5OU0HsYzoiqxIlGqIwLIL0p6Gs8+GiT3YrgCv8nnF4onq+mrgbHrkXQMiURt05IXBnBdwcGSlilq74+rdDfDVcMDS1zbH+gf5xRnzmwCnIjn8a8FbJ079rmZ9cRjCJdG2wDc+awrcAPw99z56wK9si0vfGtmNzyr5wxJi0rqDhwObA5sAfxC0iYNtPeJee4LbFbpSwIuwWsg3SXpl5KWMPdjOAh4MX79Z4HvLKtoKzz1tcG+Ghi7HknHkEjUNi06+Gxmn0u6Dq8jNCN3qBf+yzx7v3RkEeXZkkJK6Y14emrG2AhAE7+mO+GCMiiksN6E3ySzvvaN10OB83N93WZms3Pv7zOzmcBMSe/jgedtgLvM7MsY8068qJ7KtLeL9q+ivWLmj5mdJekGvCrqQbhGYwc8SL6JpO8Ai5rZ9HiSWRtfGOppFSr0VS4Y1TKDVIlWQ4orND8temEILgWew9W9/8/eeYdLVV1v+P0AC4gSTdBooqLEjgqIDbuxJDGxRUWiRo0aTVSiUaOJ+dlbYqKx9y52xRiT2FGQIiggLWLF3jsWbOv3x1qHOXc4M3fu3Eu59+73eXjuzCl77xl9zp691/rWl9EB2DDqHc2i4Y5QVWbmXn9Nw+/BKrymwvHymklFbVcaWLUBN+mBa2bPARdJugx4W9K3zexdSc8Cv8K/Q/AMp5/gW0bTam0LeBcvDZJnUeCDpowzkUjM/8zvW0lZSehbgP1yh+/Di78BEGmW5YymZGO5exO6HJD7Oypej8y1sQel1UWtDAN2lNQlfr3vBAxv5PhOkjrHSuhn1RqXtF0uTrISPiFlD+wR+LZZ9llGAb8DRltBSlqVtoYB22crM0k7A0+WrZYSiUQboDWsGMC3PPKlqAcBF8g9FTrhD62Dyu45DLhe0hHAv4EPa+xroQjcdsC3UbL+rpR0FJ4Cum9TBm9m4+TF9cbEocvNbDzMKs9ddPxmvObRi/hkUY29gLMlfYoH7ffIPbBH4BNBNjGMwxXLRXGZam1NjMD+o5FK/BauwE4kEm2M+VbH0FzkCubPzMwkfQN8DGT+CTc1onX4k5mdVkefl+Pai7rLUEvaAdjXzHaM938E9jOzLEPqZ8ABZrZ92X37UODloAoeEwX9Xk0F/4jmkHQMieaSdAz10RwdQ2tZMdRDpmIWvl+/jpk9W+O9f8LrIdWMpI5m1qRf0HFP+VbMSDxNN2ND4CNJS5rZW1TOJqpEJY+JRCKRKGS+jzHUi5kNN7O1I8X0s/JJQVK30AmsEu9vlHSApDOAzvIS24Pj3J6ha5gg6RK5YQ1yD4STYutpw9Av9ItzAyVNCn3CX3L9NrinYNxvAx9mGgq8rPXtQH9JF+Aq5f1jLC9JekPSIzRMuc1T6DEh53xJUyX9Gw9GZ2OcLulESePiM6wax7tLuj+OXyLpRRUIEJV0DIlEq6bNTgxlZA/67N8AM/sQj1tcLa9dtLiZXWZmx+ATSW8z20PSanggeqPQA3yNB6ABFgEmm9n6WZkJmFVf6C+4FWhvYF1JO1a7p4yR+ESwCvAMHkjvj8cKvgTWBH4c166JF8NbvUJblTwmdsKFc2vilp79y+57x8z64sK4I+PY8cBDcXwIsFxRh0nHkEi0btryVlKez+Kh3gAzu1/SrvjDc+0K9/4Q35YaG8k6nfHAK/gkcXvBPesCD8evf2LlsSlwZ5V78ozAH9Qd8aDxGOA4XPA2zcw+l1dnzfdxMy62K/+M90paEfgRPpmMl9QrxnNjbGW9Jumhsluzon5PUNJwbExUkDWzeyS938jnSCSaTYorzH3ay8RQiLxU9mq4eG4Jclsu+ctwK84/Fpz7vEK6ZjV9QqV78ozEt4w6ApeZ2cdyBfLmNIwv1JQ5ECm/NwA3SLqb8Jho5P5Mj5HXedQsFEkkEq2X9rKVVInDcT+CgXg66gJx/Mvc6weBXeQlspG0hNz/oBqPAZtJ+k7EIwbi5bdrZSrusLYJXvgOPHX1IEr1jR4DNpf07RjrrkUNqYLHBJ7iu7ukjvLKq1vUMK5Hgd2irW2AIvvPRCLRymkvK4bO8tIXGffgZj37A+vFL/JhwJ/xffRL8bz9cRFn+DNwX6wwvgQOpmTUMxtm9nqkmQ7Ff2X/x8z+WetgI8X2MaCbuU8z+JbSr4mJIfo4IY6/jusTOhY0V8lj4nE8BjIJeJraJq4TgRslDYjrX8fTgBOJRBuizeoYakHSDDPr2viVEMHjp/MaBblHxP64EOxr4O9mdm0d41gIF+F9BzgdDyY3WQ+hZvhBSBppZuUB6Ab6hhjn1+aGPRsCFxXFbvIkHUOiuSQdQ30kHcPcYUfcqW0qgKSD8Af4elHsrxulon1NpQ9e5C57yN7c3ME2laJJoYDlgFti5fQFns2USCTaGO09xjAbKvBekNQf2B44M9Jde+IiuN+a2UcAZvahmV0TbfxQ0vjQAFwZv7SL9AH3S5qMb8tsLOkzuadEXg+xn6Sn49hlCr+JGj7Hw5L+ItdfPC1pkzi+hkqajImSVorjM+JvRX0DsBjwEb5Ceo/iYH3SMSQSrZw0MczObN4LZjYSN70/Kn7VvwUsGlVIGxDZQ1cDA8xsTXxV9pvcJXl9wItm1gv3b/ivmXU2s6tzbS0D/B/u1bA17vXQFDqFt8RheOwEPIB9TnyOfsz+cC/UN0SA+zxgFzNbB4/RnFrUadIxJBKtm7SVNDvVvBcysjIbRawCvGBmT8f7a/BgdWb6U6QPqMR6wCORboqkWynQKlQh31ePeD0KOFbS94E7zOyZsnsq6RtWwa0+7w89R0fC6S2RmJOkuMLcJ60YGme2CSC2jz4J4Vg5jeX6F+kDKtFc3cBsfZnZDfi22GfAvZK2LLivaNITMCUU4b3NbE0z26aZ40skEvMhaWKYnUreCx/jxjQZp+OlvxcDkLSYpF8DTwE9VKp1tBdN0zDkGYPrIRaX1ImSv0TdxGT2vJmdi2+PlRfVq6RvmAZ0j2wk5JalazR3PIlEYv6jvW8ldZGU32M/i8reCzcBl0kaBOyCxwi64qUyvsT1DX+PchX7ArfGw3wscHE9gzOzVyWdhovZXsMzomr1lajEAGDPGPMbePXVPEMo0DeY2ReSdgHOjQysTvj22JRmjieRSMxntGkdg9xQ5noz2yved8L3xR8zs59K2h5YvZo3Q4V2H8arlmbWoqfU42Mg6TDgUgtv5wrXdDX3au6EP7SvNLMhTeijC3AZvjIQ7sb2I1wzcXcEv+uiFh1I0jEkmkvSMdRH0jFU5hOgl6TO5v7QWwOvZifN7C58O6Ue9jCz5j7xDgOuBypODMAJkrYCFsYtTf/VxD5+B7wZGVLIK7Z+Wf2WRCLRnmkPMYb/AtnPjIHAjdmJ0AycH693lXsnPBnlMYh99r+F5mCipEOrdaTKvg0XRV7/FEknxrFBeD2koZKGxrEZubZ2kXS1mR2J10n6D55C+hdJJ0h6V9Kn8e95ScdWGNbSNJwMp5lZFpTuGNqIKZLuk9Q5+u4p6R5JT0garpIfwwqSRkkaK+nkKt9D0jEkEq2Y9jAx3IQHUxfGt1Meq3DdccC2ZrY2nrUDXptoBaBPTteQMVglf4dvq7pvw7GxpFsLDyavFcHf14AtzKyWAnYrA1uZ2RHAgsDvzKwLPrl8RSkdtpwrgaPjgX5KJmgLVgIuMLM18C2mLLh9KXBo6BWOBC6M4+fgZTDWxeMThSQdQyLRumnrW0mY2URJPfDVwn+qXDoCN+25hVL+/1bAxWb2VbT1Xu76BltJkgZS2bdht8hY6oT/gl8dmNjEj3Jrrlz3NsD28lpN4NtMy+GVYhtgZhMiE2mb+DxjI7PoM1xvkRUXfALPpuqKi9pujc8BsFD83YjS5HEdbkaUSMxRUlxh7tPmJ4bgLuBvuJ/Bt4suMLOD5OY32wETJPWmupCtnELfBkkr4L+61zWz9+VF6Rau0Ea+r/JrPinr6+dmNq2WgZnZDHyyu0PSN8BPcLOgmbnLvsYnsw7AB1WK47XdbIVEIgG0j60k8O2Uk8xsUqULJPU0s8fM7DjgHWBZPNh7UGQEIWmJKn1U8m1YDH+ofyhpKUqWnDC7NuJNSavJi9TtVKWve4FDFT/pJfWp8rk2krR4vF4QX61UKxn+EfCC3Nkuq52UuduNoKHGI5FItEHaxcRgZq+Y2TmNXHZmBJkn4yKvJ4HLcVObiZKeBH5RpY+puJ/DfZImAvcDS5vZk7jZzhR8gso7sF0K/DcLPgPH4BVcH6J6uYmTgQViXJPjfSV6Ao9ImhTjeJzGrUX3APaLzzwF2CGO/w44WNJYoFsjbSQSiVZKm9YxNEZk8vwC30b5BjjQzAqD08r5ElQ4fwG+B78gHrDOtnnq0jg0BUnb4UK1bCvon2Z2dB3t/AC4rTGPhaaQdAyJ5pJ0DPWRdAx1EAHYnwJ9zWympO/gD/W6MLODo90e+ATSYg/XasQ2zz+A7czs6dj2Sj4JiUSibtrFVlIFlsZLYM8EMLN3zOw1ScdFnv5kSZdm+/h5JK0j6ZHI879XXlOoEEmrSBqTe79a9l7SK5LOCO3DY5E9hKSlJN0RWoAxkjao8jmOxreSVpDblz4OHBhptPdKGirXYNwvr6iKpOslnSNpZGggKsYzJC0jtxnNPrfJy4Ej6YVIAy6/J+kYEolWTHueGO4DlpWb2FwoabM4fr6ZrRulIjrjq4pZqAm+BOCCMuBzSVnpiX2Bq3KXvB+eCZfgtZoAzgX+GsvA3fBYRyV6AU+Y2b25yqe9Y8XyDe7xvBZwKw21DkviW1874gUBK43/NaCbpEWATfCJZxO5WdErZvZ5wT1Jx5BItGLa7VZS1B9aB3/YbQHcLOkY4GNJfwC6AEvgwdd8GYp6fAmuAPaVdDSwK27lmZEpsQcDWc2mrYBVcouVxVUq69EU1qc0sV1LwyD1neYBpomSvtdIO6NwbcMmwGkxvs7A8CaOJ5FoMimuMPdptxMDQAjGHgYejqydA3F1cj8ze1nSCcyuJ8h8CTZsQle34lagI4BRZvZBfhgF1wv3kv6ihran4MK6plY5zWsYGvN9GI4b+HwPnySPwkVvczSonkgk5g3tdisp9v7z5SF6U8okeicUwLsU3NpkX4KonvoQbht6VdnpAfF3IKVU1gdw17dsrNUC2X8F/hwZRVl9p9/HudH4VhTAnngabj0MA/YGngoV+Me4knpkne0lEon5mPa8YugKnCfpW3itoWfx2kgf4F4E03EvhQY0w5dgMK44frDseJcIRhs+OYBPChfJfR06AUPJTRRl4xkvL41xi7wIngH/jNOHAFdI+iPwJiVviWqsroYeFYea2ZDIdsomlhFA9xDDJRKJNka71jE0FdXgP5C7dkfg6RC+ZTqHHXBV9ULAzcD+eNntd8zsb3Nm1BXHdwKe1vo2nqZ7spndWPWmOkg6hkRzSTqG+miOjqHdbiXNBXbEy08g6V/AfsDAyBbqBdwyD8eWcXaMZwfgksi4SiQS7Zw0MTQTSctLejC0Ag9KWk5Sf7x095mhLTgMr2Y6DTzobWZTzez7wOf49s3DoSkYlGv7ztBKTJF0rUplvr+W9Fb8e1BS97i+0EehMczsGdwsKKupVMmPoaek0aHzOEk5/4iy7yTpGBKJVkyaGJrP+cC1Ob+Gc81sJF7R9ajQFDwHnA1MkzRE0oFlwrBVgW2B9YDjc7/cfxVaiX5AX+CH8Qu/A3C4mS2JezIfH9dX8lGoiqS+wDNmlpUJr+bHcE74MbxWqb2kY0gkWjftOfjcUmwI7Byvr8OzhGbDzE6SNBjP5vkFHmjePE7/OxTYMyW9BSwFvAIMyqmSl8WNdd7FhWs3x/Hr8XLa1XwUKnG4pAOAFXEfaBppZ0N8iwzgBryUeSIxR0lxhblPmhhanorR/Fg5XCTpMuBtSZk3RLkvQidJm+NCsg3N7FNJD1Pdx6ExH4Uizjazv0naGbg21Mz1tJNIJNoQaSup+YykoUfBo/G6gdeCpO1U+gm+Ej4B5IVu5XTDy2V8Gnv8+XpJHShpLH4BPNqIj0JVzOwOvNTF3o20M5qSg9vus7eUSCTaAm1qYoig7ITcvx4t3EUXeeG77N9MYBBe7mIisBfuWQDuNX20pJfjl/hewLOSPsC3nPbIWXUWcQ++cpiIl7IYnTv3CbCGpCeArYHFJD0HfAfPLnoG11WMj+/hSUnjIiieVYD9bVl/JwG/l5sEVfJjOAxfWYzDixB+WPM3l0gkWg1tSsfQFJ3B3OhPpRLcvSpd09x+Jd0EvAAca2bfyCu0rmZm/y67blvgT2a2Wb3jktQFmIoHw7fC0293qHZP0jEkmkvSMdRH0jFUQdLCkq6Su7ONl7RFHN9H0vm56+6OfX0kzZB0avzSHi235ETSCpJGRbrmybl7u0ba6LjoJ3tYngH0jF/tZ0rqIXdca2xcd0S66DOSCoPZcW1PvFDen83sGwAze97M/l1w+WLA+034fjpK+lscnyjpULwm0zJ4JtQheHG/5P2QSLQx2lrwubNcNwDwgpntRJSSMLM1Y6/+PkkrN9LOIsBoMzs2HswHAKfg6ZoXmdm1kvIlKj4HdjKzj+SGP6Ml3YVbdfbKArllW1vVxtUbr8A6E09xPc/MXs5uzK1S1gAmVNmS6izpdVyfsAC+lTUBtx1tbBz74k50fczsK0lLmNl7kl4DtsNLgV9rZteWdyrp13h5EZZbbrkKQ0skEvMrbW3F8FnOjyBL89wY39PHzJ4CXgQamxi+wL2XAZ4AesTrjSiVyb4ud72A0yIe8ABehXSpRvqoNq4HzezD8DqYCizfSFuV+MzMljazhaO/r/AJ54IaxrEVcHEUzcPM3svd80/gqqJJIa5NOoZEohXT1lYMRVQqKf0VDSfGfCrol1YKvnxNw++pKCizB9AdWMfMvpQ0ncqppY2NCwrSVytcNwVYW1KHbCupEmY2KlYz5U/qSuMQlVNvRwA/lnRD7ntKJOYIKa4w92lrK4YihuEPbmKLZDm8NMV0oLekDpKWxVXHjTGChqmpGd2At2JS2ILSL/wGKas1jqtmQhfxOHCi5KmwklbKxThmEdtEHXGBXC3juA84SF5VFUlL5O45LtqpSVmdSCRaF+1hYrgQ6Cg34rkZ2CdUxiPwbJ5JuIJ3XA1t/Q44WNJYfDLIGAz0k/Q4/pB9CsDM3gVGyP2jz6xxXE1lf+C7ePxgEnAZpXIVnSPwPSH62LsgHlFpHJcDL+EOb0/ieok8hwELVwuOJxKJ1kmbSledm0g6Fn9Yfo2XqDjQzB6rcO3VeHpooeOZvCT3Rnj56xUorRxOqXRPcwm9wjtAjwiafx94GVdaj44VyDtAT7xe0r54ie5FgIl4euxTjfWT0lUTiXlDc9JV20OMocWRu7f9FOhrZjNj737Betszs4Oj3R74BDLHy1GE5mEsrqi+D5+YxuN1kkbjJcNfM7MPYpfqTDP7R4xzIDBUUq9YFSUSc4ykY5j7tIetpDnB0ri5zkwAM3vHzF6TdFxoHCZLujTb988jaR1Jj8hLWt8raelKncjtR8fE68ck/U/SJ7E99IWkKySNiXMrxnVLhQ7i8Ti3QaX28e20/vG6P14BNv++0LozDH2GkspiJBJtkjQx1Md9wLKSnpZ0oaTN4vj5ZrZuKIo746uKWcjLaZ8H7BIlra8ETq3UiZlNAz6PX+brA/8CjowVxVu4Q9x6wCXAWXHbucBfYwm5Gx4rqMRIShPBusBtlFJz+1PyoC5iHF4ufDaU/BgSiVZN2kqqAzObIWkdYBNgC+BmSccAH0v6A9AFWAJPJ/1X7tZVcPe2+2Mx0RF4vZHursBrMR0N7IrrEDIyTcVgXGUNrj9YJbdYWVxSZzP7rKDt0XjQvGt8rs8kvRRbWv2pMmlRJd3WzC7FPR3o169fCmIlEq2MNDHUSWT3PAw8HBk9BwJrAf3M7GW5p3K5lkHAFDPbsAld3Qr8Cf/1PsrM8hVZix66AtYzsy9q+AwzJL2IB5azCPFo4GdANzN7tsrtfShVkk0k5hgprjD3SVtJdRB7/yvlDvWmlEn0TvwC32X2O5kGdI/gNZIWkLRGtb7M7FPgIdwp7qqy0wPi70BK2z4PEGUuoo/GAtkj8NTTUfF+VNn72ZC0G7FSaqTtRCLRCkkrhvroCpwn6Vu4gvpZvDbQB7guYjowtvwmM/tC0i7AuZK64d//P/Atp2oMBn4CPFh2vEsEpw2fHMAnhYsk7RvtDyU3URQwIs5nE8HjuFvcxWXXHSVpHzxddRKwRcpISiTaJnNFxyDJgLPM7Ih4fyTQ1cxOqHLP9sDqZnZGlWs2x4OxPy04Nx3f1nmnzjGfAMwwsxa1r2ys3dA8bA2smEuFfQafQHoD15jZnZJeAT6L96fEvbcDg8N4B0nn4CuXZRsrmTGnSDqGRGLe0Bp0DDOBnSWdXuuD2szuAu6as8MqJisDMQ/5GvgVcBG+WlgEz2baDw8K34lvA36C+zBnbEisDkLAthMuWtsUj4ckEq2KvIYBUrxhbjG3Ygxf4Vkqh5efkNRd0u2R/z9W0kZxfJZfgqSecl+EsZJOkjQj10RXSbdJekrS4DLtwFGRyz9G0g+ireXl3gkT4+9ycfxqSWdJGgr8Je5fXdLDkp6XNCg35t+HVmGypMNqOH6spGmSHsAzkxrjH8DhMUHtgQvN3qOh7uBAvMppdzkr4NVU34jzWwCT8cnlRJVc7d6Q9J6kFyVNl7SzpL/KfRfuiZTainoLSYMkTY3v76YaPksikWhlzM3g8wXAHrG3nucc3JR+XdxPuCjv/hzgnLjmtbJzffBg6erAiriCN+OjyPM/H3/YEq+vNbO18F/j5+auXxnYKtvywvP0t8UL7B0fweJ18Cye9XHV8AGS+jRyfPcY5864XqAxXsIzfvYqO/4E0EvSgvgEMQoPaK/G7LqDgXg66xDgB8C6oX+4GC/l/QPcsvN6YKiZrYlvTW3XiN7iGNyjYS3goKLBJx1DItG6mWtbJlGP51rcIzmfU78V/ss8e7+YpPKKpBsCO8brG/CidxljzOwVAHmxuB6U0ihvzP09O9fWzvH6OiBfBO7WsiJz/w5180xJb+EeCxsDQ8zsk+jzDlzPoArHO8TxT+N4rdtjp+FbabPW0hFzmAL0xSefv+KTYX984hkZfSyIB6sPN7OPJT0GbJNr679RCXYSrqW4J45Pwr+/anqLicBgSXfiW1qzkXQMiZYibR3NG+b2Xvo/cMVsPu2yA164rYEAS7NXk6hENe8Cq/CaCsc/qaHtav4FlWjyw9HMMre13cpOjcRjBoua2fuSRuM2m30oZRL9CK/+Oim+xy7Ap5QmhqyUxzeS8t4T31D6jJX0FttF/9sD/ydpjczMJ5FItA3mqo4h9slvwYOoGffhDzagYt79aHybCZpWn2dA7m+WjjmShp4KTRVpDQN2lNRF0iJ4gHd4I8d3ktQ5VkI/a0Jfp+KVTfOMwOMLT8b7ifjqYTlKaa8Dgf3NrIeZ9cArtm4jqUvgI5mgAAAgAElEQVSN/RbqLSKgvayZDQX+AHwLT91NJBJtiHmRffN3chMBvrV0gdwWsxP+IC3fuz4MuF7SEfiv3g9r7Guh2EbpQCnPfxBwpaSj8DLS+zZl8GY2LlJKx8Shy81sPMxKNS06fjMwAbfNHN6EvqZIGodvHWWMxLePTo9rvoptrpdjBdAFj4scmGvnE0mPUuOkVEVv8TT+36Ebvqo4u0yJnUgk2gCtwo8hHnafmZlJ2h0YaGY7lF0zw8y65t7vg+sYDmEOI+mnwMn4BLQAHii/RNKOeKG7qY3c/zCux2hSwn+Vfk8ADsAnPoB7zOyYprTdUiQdQ6I5pHTV+lEr0DE0l3WA8yMV9QM8x3++IDJ4LsXrE70iaSFKFUp3BO7Gs4DmZr/gv+ZbVJxXYRwdC1zhEolEK6ZV1Eoys+FmtraZrWVmmzZS3G02GtEu7JK7bkb8XVrSsMj7nyxpkzi+jaRRksZJulVeE2lRfIJ9N8Y608ymSeqPB2jPjHZ6xrZQ1tf1kj6NAHM/PNNn3wp9FFHYbyPfww8ljQ/NwpWSFopjQ3LXbB0ZVZU+L3L9w3GxPbVrQT8pXTWRaMW0iomhRmb5G8fD9qTcuWrahSJ+Adwbef9rAxPkpSn+jOsc+uI1hX4fAfW7gBcl3ShpD0kdzGxkHD/KzHqb2XPAh7ng+kvA0dFH5hX9r6I+igZYqd/cJYfnvo9tJS0MXA0MCM1CJ+A3eIG+1SR1j/v2Ba6q9Hlz7X9uZhub2WwiNzO71Mz6mVm/7t27l59OJBLzOa1lK6kWPrOcJWYWY4i31bQLRYzFA9QLAHea2QS5Gc/qwIhIAV2QyHQys/0lrYlrMo7Eax3tU9Du5bi3wu/xTKn1ys5vUKmPIhrpt8FWkqS1gRfM7Ok4dA1wsJn9Q9J1wJ6SrsK/q1/iKa/VxpIqqybmOCmmMG9oSxNDU8gi7l8Rq6aIXywIYGbDJG2K5+xfJ+lM4H3gfjMbWNAeZjYJ1w1cB7xA8cRwO3A8/iv9iYLqpKrWRzP6zdquxFX4auVzXOT3VXwf1cZSrvlIJBJthLa0lVSNStqF6XhgG7w8RFYnaHngLTO7DHdQ64trKTZSqeZSF0krS+oqr/Ka0RtPSwX4GI8FAGBmnwP34vWLyr0VqNRH0QdqpN8ingJ6ZG3j5TYeiXG9hpca+TO+3dSksSQSibZFe5kYBuFbOBPxB+Lv4vhlwGZyT4P1Kf0K3hyPK4zHhXXnmNnb+K/xG6Od0XgtJQF/kBfJmwCcSOlX+014Ib/xknrGscH4iuW+8kFW6aOIav3ORkxK+wK3ykthfENDz4XBuBZiah1jSSQSbYhWoWNoCSR9jdcCyripEa+HP5nZaXX0cznuPVGYoir3ouhmZv/XSDsPk9M2yH2Y7zazXtWulftQfIyX8OgI/NnM/lnDuM8HxpvZFY1dG9dvTgUvjDxJx5BoDknHUD9qBzqGlqBBcLoG/oQXsqsZeU7//lXODwF6AluW3dPSOoAtzOwdSavgK5OqE4OkJ/DV0hFVrkl6hUSindBetpIKkdQttmJWifc3SjpA0hmU0l8Hx7k95b4OEyRdIqljHJ8h94h4DNhQ7t/QL84NDM3AZEl/MbOdImV2ev6eGsY5BPgP0DP6fzL6mSgvt9G5wq2L4UHzrJ1CvwjczGcRYJykX+euL/9sP5L7XjxKKcuraLxJx5BItGLa04qhc+zFZ5xuZjdLOgS4Wm6DuXgEnJF0SLbCkLQanl66UZSrvhAPYl+LP1Anm9lxcS3xdxnc8Gcd/OF8n6QdzezO8nuqMFhSvursc2bWO9Jde5nZ5pLWwivW5hkaWUUrEtVZ1dAvQsBjkh6Jek6/MrP3JHUGxkq6PTKmZo1TroN4Bl/tPEuVdNVUdjuRaN20p4mhcCvJzO6XtCtuJLR2hXt/iD/gx8aDvzPwVpz7Gk9DLWdd4OEI4hIrj01xD4NK95SzR3mMIY5vSoj0zGxiBIfzZFtJPYEHIwZRyUdiPDBI0k5x77LASriiOj/OVXEdxDNx//XArNVFIjEnSDGFeUN7mhgKkauFV8PNg5YAXim6DLjGzP5YcO7zCnvv1XQDle5pCo3+Ejez5yS9iQvVCscTQeStcE+MT2MSWbjCONOv/0SiHdCuYwzB4cD/8LLcmdoZ4Mvc6weBXSQtCSBpidA6VOMxPBX2OxGPGEjoBlqAYfhWFpJ6AWsVXRTjXQHXN1Tyi+gGvB+Twqq4+rqIp4AVcmm3NYvwEolE66I9rRjKYwz34F7G++MVSj+WNAwXeR2P75FPlDTOzPaQ9Gc8TtAB+BI4mCqCMjN7XdIfgaH4r/X/1JI2WiMX4fWMJuI+D2PKzg+N9NwFgGPM7E3gTRX4RUiaChwUbU3D9QpFn+fzCEz/W9I7uEhwttTZRCLR+mk3Oobmohb0e5AX0lvGzP4T77cHVq+mq6ijjwXxmlA/w8VsU/HaSK9I+hbwCzO7MK7dnAJNgqQdgH3NbMd4/0dgPzPL1NA/Aw4ws+0rjSPpGBLNIekY6qc5Ooa0lTRv6A38JHtjZne15KQQnIaX41jZzFbCg953RLbSt4Df1tDGSBqm024IfJRtqQH9cavRRCLRhkgTQwsgqbuk2yWNjX8bxfH1JI2Ul8QYKWmV+CV/EjAgNAljJL0k6e14/56kO+P65xV+EZI6SLpQ0hRJd0v6j3JeEmXj6YKnph6eBY/N7CpgJp5uegYlTcSZcVtXSbeFTmGwJEVG1Ycq1Vf6Hp6l1D/e98cnj/L+k44hkWjFtKcYQ3Mpj1EsgfshAJyDl7l+VG4CdC+e6fQUsGlUK90KOM3Mfi7pOHLbUPltqYgDLIKnl64afdyGC8p6AGsCS+IB8ysrjPUHwEtm9lHZ8ceBNYBjcB1EptPYHOgT517DVwEb4XGEkUD/CKA/g8cgtpV0Nx70HlveedIxJBKtmzQx1E41v4etgNUzcRuwmKRF8YyfaySthKd6LkBt3Glm3wBTJS0VxzbGS2J/A7whaWiV+0Vxamml4wBjzOwVgJgAe+ATwwh8ZdAR92MYAxyHTyTTojhfIjFHSDGFeUOaGFqGDrgOIK9SRtJ5wFAz2ykEag/X2N7MfDNlf2vhWWB5SYua2ce5431x34XG+vya0v8bI4FD8YnhssjeWhivQJviC4lEGyTFGFqG+4BZ2Ukq2Xd2A16N1/vkrm/g01AjjwI/j1jDUviDuZBQN18DnKVSTadfAl1wk6Cm9D8VWIaSSho8RfYgCuILiUSi9ZMmhpZhENBPXtRuKv7QBE8XPV3SCPwXd8ZQfOtpgqQBNfZxO67KngxcggvoPqxy/R9xR7anJT0D7ArsZM67uGXn5FzwuRDzfObHgHfM7Ms4PAqvw5QmhkSiDdJmdQwtqTuoo++fAifjE+8CuNHPJZJ2BJ6u5NWQu/9hcl4MueNdzWyGpG/je/0bmdkbufOD8bjHl3H+wNzDPN9O3pvipUyHEP0ujZcHATjFzG6TNNLM+pe3UwtJx5BoDknHUD/N0TGkGEMLE2U0LsXV1K9IWggP5ALsiBfCqzoxVOHuEKctCJycnxSCwcCe8foGXNV9UUE71bwp9iifkOqdFBKJROukXW4lSVpe0oOx9fNgpJgi6eq8NkDSjPi7tKRhsfUzWdImcXwbSaMkjZN0q6Su+N59J7w6KWY208ymSeoPbA+cGe30lDQu19dKcsOc8rHO6gN4G9jYzFY3s6vj/JBobwIuahsPbIOvGL7fQt9X9j1sLveBaKB3KLg+6RgSiVZMW54YMqOd7KF5Uu7c+cC1YZozmChhXYVfAPfGr+y1cT/o7+B1lbYys764RuD3ZvYerj14UW78s4ekDmY2Mo4fZWa9zew5XDyW/XLfF7g632mlPvLXhPlP7/w/PMC8F14PqoiF48E9Ora38gzOfW/fLri3D3AYXrF1RVzv0AAzu9TM+plZv+7du1cYQiKRmF9py1tJ1XQHG1JyILsODxJXYyylyqt3mtkESZvhD8cR8aN5QTwoi5ntL2lNXN9wJLA1DbOSMi4H9pUb7wwA1is7v0GlPhrhQmCYmQ2vcH45M3tN0orAQ5ImxUQFBVtJZVTSOyQSLU6KKcwb2vLE0BSyCPxXxCoqtkgWBDCzYZI2BbYDrotMnveB+82ssPy0mU0CJkm6DniB4onhdryS60PAE5EtlEfV+ihC0vFAd+DASteY2Wvx9/kIOPcBnqt0fRmV9A6JRKKN0Ja3kqoxEtg9Xu9B6RfvdNypDWAHQqks9154y9z28wpcKDYa2EhRR0juc7CypK5RYiKjN6Xy3A30A6Eavpcoo10wzsI+Kn0oSfsD2wIDQyFddM3iERDPtqo2ov5geCKRaIO014lhEL6FMxHfi/9dHL8MN9cZg3sjfxLHN8fjCuOBn+Ppp2/jq4Abo53ReG0jAX+QNC22Wk6ktFq4CThKXlQvM7wZjK9Y7isfZJU+KnExsBQwKmIEmQ91P0mXxzWrAY9LehLXU5zRWPpsIpFoX7QZHUMuN1/4FschEfBtTpvlvgn7AGdSUjOD+xrU/WCVdCTQzcz+rzljnV9JOoZEc0g6hvpJOgZnVrBZ0rbA6cBmzWyzNx6w/k/u2M0tJZKTNAToiZfCbm5bnczsq+aPKpFItHfa6lbSYnhwuJoGYYakv0h6QtIDcu+Eh+UeCNtrdt+EiqUrJO0UbSj6e1rSdyXtI+mfku6JraXjc/f8HlgJ/2+wZxxbRNK/JT0ZYx0Qx6dHPCDbFsq8G96Q9I6kj3Db0Y6SzpR7QkyUVDEArSqaBEnHRRuTJV2aO/5wfGdj4jNuUqHtpGNIJFoxbWliyHQLT+FpoCfH8dk0CHF8EeBhM1sHDwqfgqeV7gScZGZf4OWlbw59wM1xXzZRZP86m9kQ4A3cB/oy4PicKnk9PMDdG9g1Huzr4LqF9fGU1AMk9QF+BLxmZmubWS8q6xCmxOe5GA9sL2VmWwL7AR+a2brAutHuClW+s0qahPPNbN0YQ2cgb/nZyczWi/uOp4CkY0gkWjdtdStpQ+BaSb0o0CDE9V9QevBOAmaa2ZeSJlEqYVFEpa2kQ/ECd6PN7Mbc8fuzNFRJd+C+CgYMiSqo2fFNYjx/k/QX4O4qOoQ8d+XKfW8DrKWSersbvip5ocK9lTQJW0j6A16NdQlgCqVy3XfE3yeo/j0lEs0mxRTmDW1pxTALMxsFfAfobmbDgE3xgPF18vLTAF9aKfL+DZGfH2me9UyY34t2lpKU/17Lo/tGBW8FM3saT5edhFdlPS5OzdJXAAuX3fZJ7rWAQ3Mq6BXMbLZspxyzaRLkXgsXAruY2Zr4CmjhgnuShiGRaKO0yYlB0qp4met3VaxBqJWafAskdcJ1CL/ALTfzZSu2lrSEpM54Eb0RwDBgR7kuYRF8+2q4pGWAT83seuBvubFOp6Sv+HmVodwL/CZWR8h1FYvU9ElLZJPAO/LaT4W+0olEou3Sln7x5T2ZBextZl/LxWZHSfoSmAH8slIDBQwFjol2T49jAyRtnLvmt3jpi+FmNjyuHSspy7N7FC+78QPghqzchNzbeUxcc7mZjY9sqjMlfYOXzv5NnD8RuELSn3BvhEpcjm/vjIuA8dv4ZFQzZvaBpMvwVct0CjydE4lE26bN6BjqRQ29CQB2NLPpLdT2b4HdzGzzeL8McK6ZteivcFXwb8idnw68bGab5I5NwAPJvST1A35pZoOq9LEscC3wXXzL7FIzO6exsSUdQ6JeyjUMkGIOTUFJx9AsqnkTNJcuwJrZm6hRNK+2ZhaVtKyZvSxptfyJmFAae3p/BRxhZuMkLQo8Ien+pJpOJNoebTLG0FxCf3B+7v3dsSWV6R9ODa3BaLn/MpKWknsjPBn/+uPiuC6R1nqmpB6SJsf1C0u6StIkeYmMLXJ93yHXPjwj6a+5cVwU+oApkk6s8bOsiXs2LwmMjpXC/cCNuWs2l3R3vD5B0pUqaToGAZjZ62Y2Ll5/jMdSvlehz6RjSCRaMWliaOjbMKSG6xfBU1LXxoPIB8Txc4FH4nhfPMXzGOC5yBA6qqydgwEi82cgcE1kBIFrHgbgq40BsY0DcGwsDdfCazqt1dhgo8rra3i9pxdjdfQ2pfTTIlbFi/GtBxyfBbMzJPXANRCF8Y6kY0gkWjdpK6npW0lf4Pac4Ln8W8frLYnAtpl9jZvwLF6lnY2B8+L6pyS9CGSVUx80sw8BJE0FlgdeBnaT9Gv8v9vSuDBtYo3jfg94X9Lu+K/9T6tc+28zmwnMlPQWXpgv0zt0xcuFH2ZmH9XYdyLRZFI8Yd6RJoZi8roBaJjHn9c/NCeXv1DLEBTpC1bATX/WNbP3I6upXNPQGDcDF1DsDVG1f5jlZ307MNjM7ii6MZFItH7SVlIx04HekjrENk65s1oRDxLppfKaRYtRXQcxDC+VgdxjYTlgWpX2F8PFbB9GXOPHNYypnCG4W929Tb0x0l+vAP5nZmfV0XcikWglpImhmBF4GYlJuNBsXA33/A4vJTEJ32JaI0phjJAXozuz7PoLgY5x/c3APrF9U4iZPQmMx2MXV8YYm4SZfWxmf4k6UE1lI9y7YstcTOYndbSTSCTmc9q9jqFe5qT+oYnj6IFPYqdkng7ySqyvA5dUKxEewfZrzOzOeD8NuM7MTon3zd42SjqGRL0kHUPzaI6OIa0Y6uezXE2i3vNiUsjxPA0roO6KrywaYyTQH0DSt3Fl+Ia58xvGNYlEoh2RJoYWRBX8EFTd+2BdSSND+zBG0qKV2qnCZ8D/YstqAu4ytySe6rqmpKslnRv9PK9S9dURxMQQf+8GustZAZ/83pD0nyw1NjQXmWXoyXKf6fLvIekYEolWTMpKqp98baYXzGwncn4IkhbC4wtZddM+wBq4pmAEsJHcW/pmYICZjY2A9WeV2jGzSuWzwf2kXwP+AVyD12fqZ2aTYg5aGk+RXRW4C7gNj4X0kpsS9QcewX0ZVovxZnGMYcAm8tIaX1HybdgYuL58IGZ2KXAp+FZSI99jIpGYz0gTQ/0U6R8q+SF8QbH3wYfA62Y2FiDTBUhqqq8CuJfDycCb+GRTzp1RUnxqZDVhZjMlTcEFeRvgGUsr4pNEH0rbSMOBQdH/v/GKsV2AHmZWLZMqkaibFE+Yd6SJoWXJ/BAapIPKy2kUaQPE7H4NFduphpl9IekJ4Ah8ZfKzskvy/ec1FCNxv4pFQx8xGjgEnxgujmvG4uU9nsfLaXwHV3w/Uev4EolE6yHFGFqWpvohPAUsI2nduH5RubdDvb4KfweOzhzjamQEcCDwZLyfiK8eliMC2JHe+jKwGzAaX0EcGX8TiUQbo92vGHJpp53wrZK9zOyDOptbCd9/f03S+0B3/IHaLddfb7zExePxK38AcJ7cyOcz3Nvhclwz8JKkd6nRV8HMpgBTJO2Jbyt1kbQJ7tv8UIXbRuLbR19J6mdmj0cZjJfN7BtJ++CrheHAD83sU0nDge+TJoZEok3S7icGGnpFX4MXtzu1sZvMrGvB4QNxO9GZku4FjjKzf0bbWfnt3ngxu6ujnbH4L/QGSHoQ+KeZ/a2RcUwHeuXu+xFwOLCBmb0qqSOwN7mCd/mxm9lbfpsezh3bvKCf/wP+L16/RvWSHolEs0k6hnlH2kpqyCiilHSkbJ4ZKaCT4pd9teN34ZVXH4tjSxOF58CrnEb2z0l4GukESQPkpbW7RxsdJD0bArVZSOopL8P9hKThcuvSShyLm/a8Gv1+bWZXZkFiScdFGuxkSZdmabPBnpHSOlnSbGVAJHWXdHvcP1bSRuXXJBKJ1k9aMQTxy/qHeD0ggJ3xX/dr48HWsZKG4Rk7sx03s+0lzcitProAD0kaCdwHXBW2mcfhaaSHxHWr4jWT/oFvIz1pZu80fF5zKXAWvpJZKvp8DphpZuuXfZQ1qF7C43wzOyn6vg4XxmUluBcxs/6SNsXLbvQqu/cc4Gwze1TScngsZLWya5BXgP01wHLLLVdlKIlEYn4krRhKeoR3gSXwrBvwHP0b4xf3m3iO/7pVjjfAzK7CH5q34l4Io0OTUM6VlHyofwVclT8pL3Pdn9L21qfAK6G2Lp8UGiAXt02Q9Fy2ssHrOT0mr9G0JT6RZNwYYx8GLCbpW2VNbgWcH9/XXXHNbEUCkx9DItG6SSuGiDFI6oYrfw/GTXcq7aHXvLcee/FXAlfKndvKf4ETVptvStoSWJ+ouJqjA/BBEzwjMl3C0DDp6S13o+ssNwK6EF+xvCzpBBqW7i5PnS1/3wHY0Mw+q3EsiUTdpHjCvCOtGIIwxhkEHBlposPwWEDHiAFsCoypcrwBkn6USzf9LvBt4FWKS3FfjiuIbwmTn/y4PgJekLRrtCVJa1f5KKcDf5P0/dyxzvE3mwTeiZVIuf90Fi/ZGFdef1h2/j5c45B9xjnllZ1IJOYhaWLIYWbj8Xz+3XHvgonx/iHgD2b2RpXj5WwDTJb0JL4Xf1RcNxRYPQs+x7V3AV0p20bKsQewX7Q1Bdihymf4D77i+a+kqRHj+Bq4N9JwL8PTc+/EhWt53o/rL8bLcpQzCOgnr980FTio0jgSiUTrpdWV3ZaXczgbT/F8Hy838Vczq8WvuSXHsS/uwQBusTkNfwDfY2bHNLGtfnhQd5Marv0B/mB/Cl8BfIQHlK9r5L6+wJJmdk+VazoB75hZeWyhrusgld1OJOYVakbZ7VYVY4jUyjtxD4FfxLHlge1rvL9j+VZNvURw+apodzqwhZm909R2JB2DO7+VxxaqMc3M+sT9PwCGSKKRyaEvHuOoODEkEvMTSccw72htW0lbAl+YWVbDBzN70czOk9QjcvzHxb/MZ2BzSUMl3UAY60i6MzQBUyK1kji+n6Sn5SWyL4ugbZPy9yP28KykJXLvn5e0hKTrJV0U43xa0o/N7AygJ7CzvOz2RBWUsi7o59jIDroN6AJcEse6ystsj5GXyP6ZXFV9HLBHbGHtIi+/cY1cizFR0o65ts+QlwEfJWnJONYzspnGAifU+N8rkUi0QlrbxFAtR/8tYGsz64sHUc/NnVsPONbMVo/3vzKzdfBSD4MkfVvSMriydwNga7w8dUaWv78u8HM8WFxIrEhuBH4Rh7YFxprZe/F+WWAzvMjdpZHC+mvgLTNbD099PTh0AhUxs1MzkyA8m+lrMzsVnwDuiba2xOsnGS6sGxz33IY/3N82szVxTcYj0XQ34BEzWxsX/P0qjp8HnBPfQVWTBSU/hkSiVdOqtpLKkXQBriv4glKOfW98r3/l3KVjyrwMBknaKV4vi9c4+i7+QHwv2r4118ZWeMA4u38xSYua2ccVhnYFrl84H3+w5ieSW6L89TRJL0ff2wCrSdo9rsnKbL9U2zfRIIV2G+DHsUUFHocommS2IuovmQea3o/YwWdm9t+45gkgi3tsSKli63XAiZUGk/wYEi1B2jaad7S2iWEK/osdADM7WF4+4nG8PtCb+K/fDsDnufs+yV7IS2BvhefjfyqvEbQw1fUJTcrfN7Ppkt6XtAVevvq+/Onyy6Pv35rZg7W0X0Af4H/xWrj/9HP5C+Rq5gaHCsYCPslmZOXBs3Gmh3wi0Q5obVtJDwELS/pN7liX+NsNN735Bq9M2rFCG92A92NSWJVSAbsxwGaSFo9fzj/P3VNP/v4VwGDgphhTxq6hRVgZX608g6ez/jb6RdIqERdoFEkr4lae58Whe/G00ux8n3hZrp+Y9ZliPIs30tVovOw2NC1QnkgkWhmtamKILY8d8Qf4C3JrzGuAo3FF795yo5mVya0SyrgH6CRpIl6aenS0/SpwGl6F9AFgKu6wBvXl7w/BJ6Gry44/i4vk/gX8OrwOLsEniAlyhfRFVF/NrRKB5adwS8+/5zKSTsTLbU+Su7OdEMcfAtaO+3aJ65aK/iZQ2jKqxCDg8PjOiyrLJhKJNkKr0zG0FCrWQ5xrZjfGL/chwJX16iMkbQCcbmZb5I5dD9xmZnfmjrWYHmJ+JOkYEvWS0lWbh9qLjqGlkCrqIa6UdDQec7gvrim6v6oeQtKxeKbR7pWuyWgpPUQikUi0FK1qK6kFqaSH+CG+VfU2nu30RD16COAN3GP59LweAg+Q71WjHqKvpJmxJTQh/n1eSQ8R93SSdFYteghJW0l6UNIdkqZJujZ37kSVPBsujokUSY+GxmFM3NO/QtspXTWRaMW0yxUDtekhPpe0Eq5JyJZj6wG9cqmvvzKz9yJQPFbS7cBCuB6iLx7wfYiSn3JNfgYAZjZO0l+BN83sfEk/AfaO/qCkh1gJeECugN6P0EOEPmK0pPvMrFLaa198++qtuHYDMxuN6xWOjwnhBuBHQJbCqmh/e1wz8aOCsad01USiFdNeJ4YGtGM9xGgzez3GOQHogQfjfyjpKHxL7Tu4niGbGO6Iv0/E9YnEHCHFE+Yd7XViSHoIZ2bu9dd4tlYXfCLqa+4ZfQoNPRtm5q+vsZ9EItGKaK8xhqSHqExn4Bvcs2HRsvEnEol2QLv8xWdmJi8ad7akP+DB5k9wPcQ44Ha5Mc5QqushDgo9xDRyeghJmR7iNWbXQ1wQ93TC9QyNaSKG4C5wV5cdz/QQSxJ6CEmX4OUvJsR21VtU8W4owszelXQNMBl4MT5HIpFoR7R5HUMFvcIc9W+Q1NXMZpTrISTtg6uUX41LJ5rZLyWdBAwzswcK2toAd2U7G1jdzM6ooIfYB7fsPKS8jXlJ0jEk6iXpGJpH0jFUoIpeYU77N5wgaSuK9RA3lz+8zey4Cv3P0kOY2Sjc6S2RSCTmKG09xjBP/BuAvwDP4emqGwKF+f65dq6Wl6lA0vTQEYzDBXLbmtkoSfvk2v8ncIrcM2FYrqllJN0j6ZlIdUVSb5V0ENm/kZJmSDo12hgdKyvk/g2PyUtnPJA7foKkK+OzPi9pEBVIOoZEonXTplcMzJ96hQGSNs6uC+VzOal8QeMAACAASURBVO+YWV9JvwWOBMqFasfhE8arkvL2mr3x7KWZeBrreWY2IY43QJLh6arHxiRyAHAK8CiwQcRh9gf+ABwRt60KbIEX45sm6SIz+7K87aRjSCRaN219YmjAvNYrxOvZtpIKyGsFdi44PwK4WtItuWsBHjSzD2M8U4HlgZcr9PEFcHeun63j9feBmyUtDSwI5L+Hf5vZTGCmpLeApYBXGvksiURdpHjCvKOtTwzzlV4hN1E0RlWtgJkdJGl9YDs8A6l32X0V783xpZUyD/LXngecZWZ3xWc/oWBctbSfSCRaKW09xtCa9Ao1I6mnmT0WQet38FVMS9GNUtbU3i3YbiKRaCW06Ymhlfk3NIUz5cX1JuNahicbu6EJnADcKmk4PukkEol2xnylY4iA6FlmdkS8PxLoamYnVLlneyK/v8o1mwNHmtlPC85Nx/P/m/wQlNQVDw5/ihvd1O3fUND2CcAMM/tbhfNX43GBFc1sZrZFZmY9ctccjmsglspiD3ObpGNI1EvSMTSP5ugY5rcVw0xg53jI1YSZ3VVtUpjDnIivBo7Eg7SF/g1zkK/x4nqVGAiMBXaqck0ikUg0YH6bGL7C0xwPLz8hqbuk21XmZZDP75fUM3Lyx0o6SdKMXBNdJd0m6SlJg9UwEnyU3GNgjLx8NZKWl/sVTIy/y8Xxq+WeB0Pj3ovx7J61gOfy+f2Sfi/3NJgs6bAajh8r9zl4AFilhu/rH7jd5mxBYEk9cQvObwPn5jQML0l6SNK/YnvtkBjP+Pjulsh9l/fI9RvDI76CpF1j3OUainzfSceQSLRi5reJAeACYA9J3cqOZ9qAdfFA7+Wz3enXnBPXvFZ2rg9wGO4/sCKQN8n5yMzWw6uK/iOOnQ9ca2Zr4UXszs1dvzKwVbblhef3b4vrH46XtICkdYB9gfXxgPUBkvo0cnz3GOfOwLrVvqTgJVx3sFfBuYG4NqMX8B6wjZn1xjUQywG/iPGeCnxqZn2AUcAv4/5LgUPNbB18RXRhHM80FGtTQUFuZpeaWT8z69e9e/caPkYikZifmO/SDc3sI7mb2CAgn+5ZTRuQsSEebAY3mMnvz48xs1eggffAo3Huxtzfs3NtZRqC64C/5tq6taxURlF+/8bAEDP7JPq8A49DqMLxDnH80zhea/mL0/BSGeUbsrsDO5nZN9HHrvikCzA0PCA+lvQh8K84PglYK2In/fEgdNbeQvG3koYikWhRUjxh3jHfTQzBP3DFcl4V3FLaAJg9B98qvKbC8fIMpqK2Kw2s2oCbnAlgZs/GRLfbrA6ktXAR3v3x/SwIPE9pYsiP95vc+29i7B2AD2KFUd7fbBoKM3u3qeNOJBLzL/PjVhKhJr4Ft6rMqEUbMJqSnmD3gvOVGJD7Oypej8y1sQel1UWtDAN2lNRF0iJ4AHh4I8d3ktQ5VkI/a0Jfp+LbPRkDgRPMrEf8Wwb4nryAYKOY2UfAC/LS48hZO17PSQ1FIpGYD5hfVwwAfyc3EVCbl8FhwPWSjsC3VmpN0VxI0mP4RDkw19+VcovLt/G4QM2EZ/PVuBAO4HIzGw+zUk2Ljt8MTMB9EIY3oa8p8qJ7fePQ7sCPyy4bEsffrLHZPYCLJP0ZWAC4CddLnCmvLSXgQVpWQ5FIJOYD5isdQ3OR21J+FgXgdgcGmlmTjGrK2pthZl1bboQN9Qlyr4Vz8P37hfA6SidoLnkrSOqAb9ttiW9jfQ7sVlYnqlkkHUOiXpKOoXk0R8cwP68Y6mEdvDCegA+onuM/P3AN/iB+UlJHaktRbUkGAMsAa0WQ+vtUVoAnEol2wnwZY6gXMxtuZmub2VpmtqmZPdvSfagOv4Iq+oQlgddj7F+b2dSC/q6R9LGkz+LvFEn7hp7i4tAYPC3pp3F9R0lnyrUcEyUdWOXjLE2pXhRm9oqZvR/tbCNplNyr4lZJXSX9OLKRsrFtLulf5Y0mHUMi0bppUxPDXCLzK+iD77v/IXeukp6hkj7hbNzXYIikAyUtXNDfEsAhZtYZ+B3wTM7DoQewGZ4hdHHcvx/wYWg51sV1EitU+Cy3AD+TC9/+LqkPgFx5/mdcq9EXr0b7e+B+YIMImoOvOG4ubzTpGBKJ1k1b20qaGzTVr2ATKugTzOwkSYOBbXDB2UBg87L+qukpbolf+89Ieh6fmLbBtQi7xDXd8NTV2eIGZvaKpFXwGMOWwIORidQZFwKOyKW7jjKzryTdg08mt+ET0h/K200kWoIUT5h3pImh6dTjV1Axwm9mz+HZP5cBb0v6diP9V9NcGJ4tdKiZ3dtIO1n/M4H/Av+V9CYuELwPuN/MBhbccjNwMK6mHhtCuUQi0YZIW0lNp6l+BRX1CZK2U0mhtxI+mXxQdn81PcWukjrI6yKtCEzDbUR/I2mB6GPl3NZPAyT1lbRMvO6A13t6EdeDbKRS3agukjJ3uofxtNgDKNhGSiQSrZ+0YqhOF0l568qzKPkVvIo/QCvt3wOz9AyV9Al7AWdL+hQvILiHmX2thmruanqKacAj+JbVQeFffTkeexgXk87blMqElLMkcJmkrNzFGOD8aGcf4MbcuT8DT8f47gb2IRn5JBJtkjalY5gbyD0jrjezveJ9Jzyz6DEz+2lkKV2BK4IXAKab2U8kHYz/ys7oBKyBe0n8r45xvAL8ycyubd4nmtXe5sA/8dIZnYG7zezIOLcPXp5kKzN7MI7thNdK2tXMbqvUbtIxJOqhSMMAKe7QFJqjY0hbSU3nE6CXpM7xfmtKW0sAJ+H782ub2erAMQBmdoGZ9c7+4YXvBtczKQQP4AZBLcnwyLbqA/xUUdo8mERJFQ6+vZVUz4lEGyRNDPXxXzwjB0rlrTOWBmZtP5nZxPKbJW2KF737bbxfWNJVcrvO8ZK2iOP7SLpD7ovwjKR8RtLmwMOSekj6n6TLQuNwXzZpSVo3tAxPSnortBCZL8NjlT5cFCqcAHwvd3g4sF6k4HYFfhDXzEbSMSQSrZs0MdTHTcDuoRtYC/d9zrgAuELSULmwbZn8jZK+hW/L7B3F6sCzfDCzNfGJ5pqcpqE3rhdYExggqaho3UrABWa2Bh68zgoJXoXHHtYGrgSey61a1q/04SQtHm3mjXgMX6VsC+yAr3gKSTqGRKJ1k4LPdWBmEyX1wB/i/yk7d6+kFYEf4YXsxkvqZWbZT+eL8BjFiNxtG+NpsJjZU5JexM2AAB7M/JolTQWWB14uG9ILZpb9en8C6BET0KJmNjKO3wDM5nldxibyIoWrAGeY2Rtl52/Cg+HdgCOAPzXSXiJRFymWMG9JK4b6uQs3Arqx/ISZvWdmN0SAeiywKYCkvfGMoZPLbqnm0VDNR6LaNTUbVeQYHo51a+Iprw1Km5vZGNwR7jtm9nQd7ScSiVZAmhjq50rgJDOblD8oaUt5lVdCt9ATeClWEafiKalflbU1DNcoEHqB5fBU1LqJmkcfyyu4QhP8KeKhfzpwdMHpP5JWColEmyZtJdVJ2ISeU3Aqq/D6FT7xXm5mYyVdAiwC3FGmUzgU91O+WNIkXM+wj5nNVO3udJXYD9cpfIIL02r1pwC4GDiyvM6Smf23uYNKJBLzN21Ox9AMnUGTvQnkhjt3F+XxS1oP32paKtp7FN+f34057LUg6VTgl8DimZ+EpGOAnwDfwSeft4FfmdmLcX5vXMQGcIqZXVPQ7hK42rkHMB3/ft6vNpakY0g0lUoaBkixh6aQdAwNqUtnQENvgjVxy83y8hQ1EZPPrcDRZrYKsBpwD7BoPe3Vwb/wCq+dIjV1Ml7M7yx8UloLuI0oyBcP/OOB9SlVhl28oN1j8GD4Srh72zEF1yQSiVZOW5wYoD6dQTVvghnZ9ZJ2iZVCxlYq80TA00+vMbNR0ZaZ2W1m1sBWU5W9HTbL6Q3GS1pU0tKShmUPekmbVPrwZjbazF4HvorU1F5mtp2Z3YlnHk3AbVC3i9f34pPle/GZ78ezqsrZATcXIv4WltpIOoZEonXTVieGenQGhd4ENdCD2T0ReuFpo41RydvhSODgUEhvAnyGl+W+N46tTQVxWWOYWdbG/XhKam98eyifAvsKDcVtGUvFhEP8XbJCH0nHkEi0Ytpk8LlOnUGhN0FWG6gKRZ4ItVLJ22EEcJbcq+GOGNtYvJjeAsCdOd1Ck5G0J9APn9CgOLW1bQWfEq2GFEeY97TVFQPUoTMws5lm9l8zOwo4jdJWSf4hWe6yVuSJMAXPTmqM8/BqpmsCB2Ztm9kZwP54MbvRklY1s2ExzleB6yT9sob2Z0PSVsCxwPbhxQC+Qsgrqr8PvFZw+5sxiRF/36pnDIlEYv6mLU8MTdUZVPImAH8grhbHdyrrp8gT4Xxgb0mzyk5I2lPSd8vuLfR2kNTTzCaZ2V9wW81VJS0PvGVml+FZVX2b+oXE9tgl+KSQf6jfC2wjafEIOv8/e2cet+lY9vHvz9hGY6lMRcVIlmQZDBpLZGsXIkRvvNFGIpTiLVSWiGTLki1jCfF6KUsyDDPMWMZYspSllELWYcb6e/84jmuea+657+e5n4V5lvP7+Tyfue/zus7zOu9LXed1HsfxO47Ns62Ry2rz/DKRjbVQKAwyBu3CkM7jVjqDWzP1wyRSZ0DYy/8vI3imESGdx2ef/YHLgT8Roa91qpoIfyBrIqSTeXvgKEn3S/oz4St4vqHvQURthwnAU7X2vdLBfCfhX/gDkTRvqqQ7iFxIzX4bAJJ+pkjLvZCkxyQdlIeOBEbkNacqy4zafppQY0/Jv0OyDUmnSapC3g4HNpP0IBHtdXirORQKhYHLoNMxvBlIep1IO11xfpp7Wp3/A9uH9uA6pxFlQ+/twTTr48xPhKJ+FngDuJdwZj+WOZS+aPvEPHcjYF/bXeVR6hFFx1DoLkXH0DcUHcObz4x6LYXOFoWk2ykjJA2zvWt3FgVJw1ocOpTQTCyfmoNLCcW1gMXIdN99QQoIC4XCIKIsDD1E0qJpJlohv58naTdJhwPD01QzLo/tJGlytp1cPdAlTZd0iKI2wlhJ4yuzjaQdFPUZ7pZ0RO269T7TanqH6m8tovzn3rZfB7B9BpFob2PC/LNsnntkDjtC0kWS7pM0LhcQJK0p6XpJt0m6quZ4Hi/pUEnXA99ucm+KjqFQGMCUt732GJ5CsIrDbF8gaQ/gTEnHEuknTgWQtEfqA5D0IUJVvZ7tVyWdSCTMO5vInXS37R/mueS/SwJHEP6QZ4CrJW2ZArXZ+jQiaVXgb+6o9VBxK1FKdH9g5dr8NiIqtn2YiES6CVgvF57jgM/ZflLSdkQSwP/O8RazvSFNsH0KcAqEKanlXS0UCv2SsjC0x4zqQVrH9jWStiVEc6u16LsJ8YCfkg/+4XSEeb4OXNykz1rAeGcNh9x5fJQwCbXqUyGaaxBatQNMzqSA5AI4ikgHsjJwTc57GLM73i/oZA6FQo8pfoS5T1kYekGGr36IiBx6B7VUG/XTiPQY329ybGZl7mnSpxWt+lT8BVha0sK2X6i1r0HkUGpGq3oO99ge26LPi53MoVAoDGCKj6F37A38mVBYV6pkgFdrn68FtpH0LoiEdalJ6IxbgA0lLZ7+iB2IkNgusf0ikcfo6Jov47+AhYhw2xdoL5nf/cBISWNzjPkkfbidORQKhYHNkFkYJL1b0rmSHkpn6iRJjWK1VlTO5OrvcEVBnV2BfWxPIIrtVGmrTyEcw+MyyuhAwk8wjchRtKukJ2vjnp39RgFrZx6i7wPXAXcCt9uuxGTDFCm0W/3OnQkF9UzggdQcbAtslcn8/gPclE7tIwmNRJ0VgZ1svwJsAxyReoqpwLq18/aUdE/NgV0oFAYJQ0LHkFE2EwmTzq+ybWlCAXxcG/2HdWG+6e58duZNqsnQ3bElTa9qNnSnv6TngZG1tBpNKTqGQncpOoa+oegYumZj4JVqUQCw/ajt4ySNUqTNvj3/1oWI1lFkYD2XFLdJujR3G/dI+mo1lqSvKNJuj5d0qqTjs32kpIslTcm/9TqbpKQzJW2Tnx+RdHDO6S5JK2b7zrXxt803/zsl3VAbaklJV0p6UNLPenrTcj6/lDQxd1rV3C4joqNuyWilQqEwiBgqzucPA7e3OPYEsJntmZKWI5LuVavs2kRoZ5X19L9tP60oAjRF0sXAAsD/EM7dFwg7/p15/rHAMbZvlLQUkX/oQ3lsO0nrV+el1qCRp2yvIembRCruXRuOn0Y4vA28PSOK/giMJkJQXwbul3Sc7b/TM5YA1idMTJcBF9neIncac0RqQegYgK8CLLXUUj28bKFQmFsMlYVhNiSdQDzsXgE2JWo0jyYicpavnTrZs5f23LPml3g/sBzwHuD6Wm6hC2tjbAqspI7azYsoEvcBXNCGued3+e9twNZNjp9HJAH8LZGe+z9pClrU9nM5n3uBpZm93kJX1O2Ll2Za8XuVhYS67Fx0DIXCgGaoLAz3EInnALC9u6TFCdHX3sC/CR3CPITTtmJWSGYKwTYFxtp+SdJ4wsnbWWjpPHn+jHpjbaHoisp+X4WQzobtrysyuH6aSLA3uqFfy741ZkiaP53NEGG39YR+9bHannih0FOKH2HuM1R8DH8CFpT0jVrbQvnvonSU9PwSIeRqxqLAM7korAh8JNsnE6Glb1fkDfp8rc/VwKxdQe3B3Sco0nPfkirop5i9pkK7XA/slOMNB75AREMVCoUhypBYGByhV1sSD/CHJU0mYv2/B5xI1E64mTABtRJuXQnMmyGnPwZuzrH/QSStu4Ww798LPJd99gTGSJqWJp2v9/FPOzId03cT4bJ3dtWhCd8Gtk7/xM3AhVkUqFAoDFH6RbiqJBPppvfJ7/sCI2wf1EmfLYCVukh/vREtUkpLeoQIy3yq8Vibcz4ImG77KEkjbE/PHcMlwOm2L+ntuC2On0mU5HyOMGWdZ/vgPDae+L19Eh+qKI+6ru1zezpGCVctFOYOvQlX7S8+hpeJt9bD2n1Q276MiJJ5y9GcqaYPUpTMXJAwH136Jk9hP9sXSVqQcAqf3eAk7ytGAV8EerwwFArdpegY5j79xZT0GhHFsnfjgVZagIZ4/mUl3ZzHD5E0vTZE05TSyX6KdNiTJX0wx1pa0rVp/rk2w0yrmP6jJV1HZD6FiDgaT0QMnW57Rdt7AnunvuBuSXvVfst3WrQfoEjh/UdghW7ct6r+9Bzmr/o9kLSNpCcVKuu7JD0r6aWcR3U/N1SHsvuOjJ46HNgg2/aWNEzSkXmfp0n6WjfmWigUBgj9ZWGAyFC6o6RFG9orLcBahGP3tCZ9jyW0AGsxZxH71YG9gJWImsx1kdnzttcmSnj+ItuOB862vSowDvhl7fzlgU0rkxcR2/9xQu/wI0U+oTWJegjrEA7q3SSt3kX79jnPrYnMql1xZPoEHiOqyT3RVQfgitQd3AV8xvZCwKfouJ/7ElXeRhNlSGcQKbonZHGiY4CvAM/lfV4rf8MyjRdSqcdQKAxo+ospCdvPK3IG7Uk8lCo60wJUjCWcyxBmj7p9vllK6Rvz2Hm1f4+pjVVpBn5DlMisuLAhNcYVmRLiZUlPAO8m9BGXZDI7JP2OeNCqRfs82f5StrdjHqtMSSOAayWta3tiG/2g9f28iUi8N47QRDzWJKx2c2BVpQKaiNRaDpjNjFV0DIXCwKbfLAzJLwiFcl0F3FdaAJgzpt8tPtOivdFk0ypddTM6m3CPHp7p8B5PLEaNC0N9zAVrn5veT+BwSVcQu4ib02fSiIBv2b6qJ/MtFNqh+BHmPv3JlESqh39LmCwq2tEC3EyHfmD7blxyu9q/k/LzxNoYO9Kxu2iXG4AtJS0k6W3AVsCELtq3kjQ839w/2+6F0gm+DvDXJof/LelDipoR9SyyTe+nQhNxl+0jCOHfisyZovsq4BvKlOKSls/fUigUBhH9bccA8HNqDy7CtHSCQj8wL/EgbdQD7AWcI2kf4Ao6dARdsYCihOU8RM2D6nqnS9oPeJLwC7SN7dszpHRyNp1m+w6YFWrarP0CIq31o8Ri0RVHSjoQmJ+o9/C7JufsD1xOpMK4G6gyqLa6n3tJ+hix87mXSMf9BvCaIu32mYQvZxRwezrxn6TDhFcoFAYJ/ULH0FskLUSU37Sk7YEdbH9O0uuEs1XEA2+PbtjiW11rNLCk7d/n952BI4F/1E77YtZhGFAodAsPA3tW6cgz8utW22f2ZMyiYyh0lxKu2jdoEOgYesuaRCI8EbWKq4L1s2o1S/o4cBghDusNo4nsq7+vtbWTEO8tR9K8tl/rZrcngG9LOrmWP6lQKAwh+pWPoafYnmB7Ndur2v6o7b80OW0R4BkASUtIuiHj8++WtEG2T5d0hKLmwh8lra2osfCQpC0kzQ8cQqTMnqpOahFI2irHUF7vAUnvUegv/ldRL+F+ST+q9anrHCbkNaZJek7SDEmPVddU1GtYPD+PSSc0kg6SdIqkq4Gze6A9eJIwT325yW8ardCLTJN0iaS3t/jtJVy1UBjADJYdQyuGZ4jqgkRdgY2z/YvAVbZ/qqiLXCXUexsw3vb3JF0C/ATYjNBAnGX7Mkk/pFbhLE1J9doKEFE/l0j6PLA78AngR7b/ldFUawMrAy8RdR2uIKKIKp2DiNxLOxHai0/Y3i2v16jzaMaawPq2ZyhqIzxney1JCxBlPa/uQil9OPAHSac3tJ9NRCVdL+kQ4EeEf2c2SrhqoTCwGewLQ92UNJZ4g14ZmEI4mOcj6g1MzfNfIZLlQfgmXrb9qqS7CKdrK1qZkr5FOH5vtn1erf0aR+3lSs+wPrEwNNM5XAkcJekI4HJHfemuuKwWjtqW9qCO7SrR4BertlyQFrN9fTadBVzYxlwKhW5R/Ahzn0FhSmoH25OAxYk6xTcAHyUcxr+R9F952qvu8Ma/QeoUMiV3TxbR9+Y4786w0VnTaZweLXQOth8gdgB3AYfljgUijUg15oIN3ep6i0p7MDr/lrF9dRtzP5TIPjtk/jdSKBSCIfN/ekUNhWHAfyQtDTxh+1Tg10RZznZpjO1vdb15CaHeF4E/A9+pHd5M0jsU9Q+2JFTHTXUOkpYEXrJ9DqHorub6CLFgwOw1IBrpkfbA9n1E2Opn8vtzwDOVP4aoXXF9i+6FQmEAM9hNSZWPAeLN+cu2X1ek495P0qvAdOC/Wg3QhOuA/XPcw7Kt0cfwTSL1xATbE/LcypcAIZr7DfBB4NwqTbaa6BwymupISW8ArwJVsaGDgV9L+gHhj2jFafRce/BT4I7a9y8Dv8rw4IfopsajUCgMDPqNjkEDvCZDN/rsTM153ZNxJX2EEJstkH8XdHaf5iZFx1DoLkXH0DdokOgYBnpNhreSs4Av2L4zo6q6k6q7T5E0rCGxYKFQGOD0Jx/DgK7JoNA67Fmbc9PaC8A7gI0a29W9mgzvAh4HsP16pbJW6C4mKuopTJS0Qrb/XtKq+fkOSccrNBL/kvQ3SU9Lur82l3EK3UZTDYSkjSRdJ+lcwine+N+r6BgKhQFMf1oYoNRkaLcmwzHA/QqR2dcUldwA7gM+ant14IdEZBGEY3sDSYsQC/ByGcb7Z0KnsVV+rsJS1yWU3Z3VX1gbOMD2So2Ts32K7TG2x4wcObKLn1IoFPob/cmUVGoytFmTwfYhiroJmxNRTzsAGxEahbMkLUeEwM6XXSYQ9/RhIsngZulAHmX7fmKROUHSu/J3X2z7NUmtNBCv5D19M8qJFoY4xY8w9+lXC0NSajK0ge2/AidJOhV4UtI7gR8D19neSpEQb3yePoXI7/QQcA2h59gNuK025G+INOPb05Frqmn9hXToz1FOtFAoDA76mymp1GRooyaDpE/X/CTLEQvSs8QbfZXldefq/EyG93fgC8R9mkCU8qyrqM8k01vYvifbSv2FQmEI0h93DFBqMnSV9uJLwOWSZub3KUTY6s8IU9J3gD819JkAbGL7JUkTgPcRArrptkfY/rekvwOLS3qAEAOeQ4jcKg3E64SS+x3AOyUdZXvf7tybQqHQ/+k3OobeohY1Geb2vN4sqgd6fh4H3Gb76J6OI+kdwL+BbW1fmvfzYuBq28cockz9L/Bp2/cpwnW/avvEzsYvOoZCd+hMwwDF/9Ad1AsdQ78zJfWCNYGpuav4JrBPF+cPJiYQKuqWYbKdhM+iqO/8IDDV9qUA6QjfA9gvT/su8NNMlYHt17paFAqFwsCkv5qSuk1mHV1tbs+jL5F0ArOH1kKE5J5RO2de4JPAlQ3hsAJukXQ98QIwR3tlxrL9R0lnEWasWdj+a/o9FiPShP+8zXl/FfgqwFJLLdXNX10oFOY2g2ZhGIzY3r2Tw/U8UBOIZIDfoHthsvU8SKJ5ZFTboV+1eZd6DIXCAKYsDAOXWbUmKmqRSo2083C/h0hFXh/vA8BTtp+VdA9hrruzJ5MtFNqh+BD6B4PJx1DofphsnXHA+ulvQJES/JdElTaAI4EfSFo+j8+T0U+FQmGQUXYMg4iehMnW+s5QZKs9TtKJRJGhn9gel8enpdP6vIxYMhEWXCgUBhmDJly10LdI2hI4GviY7Ue7Or8VJVy1UJg79CZctewYGpB0AJF/qBJzfc1200I4+RZ+ue2LWhyvoormB5YBqgymP2nVp6+Q9GngEGA4YTL8X9vfa7d/hq1eKumDkqY28WecA1xUhbcWCn1B0TH0D8rCUEPSWKKU5Rq2X5a0OPFQ7xFVVFHmLbq88eH6ZiFpNSLn1KdtP5Ahrbu9FdcuFAoDn+J8np0liCiclwFsP2X7n5J+qKhJcLekU5pF/0haU9L1km6TdJWkJVpdRNIKkibXvn+o+i7pMUmHK+pD3JKRQUh6t6TfKeocTFZUcWvF94Af234gf8drtk/KcZZR1FKYJukaSe/L9nMkHauo4/CQpK26ffc6fk+px1AoDGDKwjA7VwPvl/SApBMlbZjtx9tey/bKhGlmtjKh0sVYigAAIABJREFUmWTuOGAb22sCpxP1kpuSqa5nZpoJCPFZPZvsM1kj4mTCzg8RIfSztBl+geY1KSpWZvbMqXVOJJzPqwIX0lGDAqIA0HpE+vLDmvRti1KPoVAY2BRTUg3b01M9vAHwMeACSfsDL0j6LrAQkUDuHuD/al1XIB7G1+RmYhhZYa0Tfg3sIul7wLZEkZ6KqkbEOKCqZ70psEJts/J2ScMbU5G3wTp0LGxnE6m6Ky51RCNMk/Tebo5bKPSa4kPoH5SFoYEswjMeGC/pLuBrwKrAGNt/l3QQsGBDNwH32B7bjUtdCPwAuAmYZPvZ+jSanC9g7Uyh3RWVGO2erk5soF5botuK50KhMDgopqQaaftfrtY0mo5IoqckjQC2mbMn9wMj03mNorznhzu7Viap+xNRRvSMhsNVjYgdiIUD4I/ArBQZal6TouJnwIHqqGE9rCZGu5kwRQHsRIjfCoVCYRZlxzA7IwiB12JEbeS/EMngniWK3j9C1D6YDduvKMpf/lJRM3lewnbf1Rv7OOBTwLUN7QulM9p01IjYnajYtkuOfx21haJhPndI2hf4bSqYTaTMhsiY+mtJ3yfSbLdTa2IlSY/Vvn+rjT6FQmGAUgRubaJa/YM2zt0SeMD2vbW2fYFdiQXndSJT6ZLAArYPrp33GLByg2mpPvYChOJ4ccJBvBlwdP1abc7xICKEtQobutL2/pLGE9FZM4nazrvZnpp9rsxj8xIpNXZvqH89B0XgVugORcfQdxSBW/9jS+ByovoZkr5OPMDXtv187irGE3b8jbs59urAfDVNxAW9mOcxto9q0r6j7Vtzd3IkMXeAL+T8BVxEOM3P78X1C4VCP6QsDL1A0tJEaOpIOkqAvg/YAthQ0oFEHeofEKklngew/RwZhSRpE0lHEf8tpgDLprjuEeAsov7zfMRD+Gmi3OZIRcrtC4ADiDrPM4iIqYWJDKgPAi/brpdI7S6T6CjUQzX/nOv8NHeSl3oMhcIApzife8fxwNmpCRgH/NL2ROAyYL98q38CWNj2Xxs7S1oQOBPYzvYqxAP3G7VTnrK9BnASsK/tJwhz1ATbo20fBtwK7Ej4Kt4AliXe8FdsY/57S5qafx9vcvwTwGwpLyRdlb/pBWLXMAdFx1AoDGzKjqF3jAW2zs+/IaKBGmlVAAdC//BwpVAmdgi70yE6+13+e1vtOq1YG7je9tMAki4Elu+iTytT0rhMzz0MWKN+wPbHc0EbR5jBruniGoVC2xQfQv+g7Bj6ljkWgDS/vFiltmigK61ApSt4na4X8b7UHexIJP07Fzih8aDtmcSu6HN9eM1CodBPKAtD75gIbJ+fdwRuzM8vELb+isOAEyQtAiBpkbTD3weMqvQGwJeA63s4l8mEX+PtmTTv8z0cBwDbrwIHAh/JXE4jqvxPOf6ncv6FQmGQURaG9lkoE9xVf98B9iTSWkwjHurfznPPB/aTdIekZQkfwXXAFEl3Ew//l/LNexfgwlRZvwH8qieTs/0P4FDgFkIMdy/wXE9/bI45gwir3Rd4G3BZ/tY7CT9Dj+ZaKBT6N8XH0D4v2X5fk/Zm4aYjiYR6dR3DG3SYmubJP2xfy+x5ksj2UbXPtwIb5ddJwIIZlXQYIcJ7KY+da/uUfKO/hEgK2Bl7S9opP19pe//8PE5SXcfwc0XVticI5/brwIu2X+ti/EKhWxQdQ/+gLAxvDu3oGLbs4did6RgOUtRsXpBYFLoqotNdHcNRtq+TND9wraRP2v5DD39HoVDopxRTUi+QtLSka7O2wbWSlpK0LqFjODLDQJcldAzfrOsYbJ+VY2ySJqe7JJ2eymYkPSLpYEm357EVJb2L0DGMrsaWNF5SpW78M5EB9l9EevDjJB1QC0mt/g5o8ydOImo/Y/sl29fl51eA2wnNRrP7UuoxFAoDmLIw9I65rWP4a22sJYH/AT5CTcdg+6d5bv2vqhXRbR1DXmsxQnjXmOOJvGbRMRQKA5hiSuodQ07HkP6L84hF8KEuxi8UukXxIfQPyo6hbxkKOoZTgAdt/2KOXoVCYVBQFobeMWR0DDnvnwCLAnv1ZuxCodC/KQtD+wxpHYOk9xEJ+1YCbk+/xK69Gb9QKPRPhlQ9BkkGzrH9pfw+L1Gb+Rbbn5H0bqIW8/uJjKaP2P6UpHkIu//GhLloJpGC+uFOrnUmcLntORLNSVobOAp4d453I7HIfIEoIdqjjKiSRmTd6krHcLrtS1qcexnwAdsr5/eDmL0+ww9s/75Jv08AxxL+h9NsH954Tp1Sj6HQHYqOoe9QqcfQNi8CK0sanm/DmxEpqysOAa6xfSyApFWzfTuiqM6qtt/It+cXezKBXHwuBLa3PUmSCLPPwp33bIu2dAyStgamNznUyhld9RtG+Bw2Ax4jdkCXdbdIUKFQ6N8MRVPSH4DqtWMHIsKmYgnigQeA7Wm19sdtv5Htj9l+BqKyW3W+pG1yp1CxqaQJkh6Q9Jls2x04y/akHMu2L7L97/okJX1W0i1pjvpjLihI2rAWYnqHpIUlLSHpBmBTYrHfzfaett1ExzANOBr4SQ/u3drAX2w/lFqG82mSSK/oGAqFgc1QXBjOB7ZPDcGqhE2+4gSiHvJ1+UBdMtt/C3w2H6w/lzRHCosWjAI2JBaiX+U1VybCT7viRuAjtlfPOX832/clSmqOBjYgCvR8Ebgq21YDplaDNOoYCO3B3nSk0aizR4r1Tpf09ibH3wv8vfb9sWybjaJjKBQGNkPNlITtaZJGEbuF3zccuyrDSj8BfBK4Q9LKth+TtALhY9iYSAexbeY56ozf5i7jQUkP0V7xnIr3ARcoMprOD1T+jJuAoyWNA36Xc5sCnC5pPuBSZ43mRiSNBj5oe++8B3VOAn5M+Dx+TDid/7txiCbDDh0nVeFNp/gQ+gdDcccAoUw+itnNSADYftr2uemgngJ8NNtftv0H2/sR0T9VrqP6g3HBxuGafL8HWLONOR4HHJ+K6K9VY6ezd1ci5cXNkla0fUPO8x/AbyT9V4sxxwJrKsqG3ggsL2l8jvtv26/nQnYqYTZq5DHCMV/xPuCfbfyWQqEwgBiqC8PpwCG276o3StpYkUUUSQsTmUT/JmmNyqyUEUqrAo9mt38r6hXMA2zVcJ1tJc2TIasfAO4n0mh8WdI6tevuJOk9DX0XpcMx/uXaucvavsv2EURZzxUVtaefsH0qEVW1Bk2wfZLtJTNz6/rAA7Y3ynGXqJ26FXB3kyGmAMtJWkaRSG97YpEtFAqDiH65MEiypJ/Xvu+b4ZSd9dlC0v6dnQMMk3R5Oo+Pbej/CGGzvzUdtJOIcMwpwLuA/0sNwjTgNeIBD7A/kUn1ISL8tM79hGbhD8DXbc9MJ/P2wFGS7pf057zu8w19DyL0DY8SKuSKvSTdLelOwr/wB0JA96KkGYRTeUSalZD0e0Vuo1mOckkbEQtInZ8pkvVNAz5G+CGQtKSk3wNkmu09gKuI3cMfbN/T7EYXCoWBS7/UMShqATwOrGX7KUn7AiNsH9TLcTciktF9psmxRwgNwVM9GHdeQiU8vbNwz56QC2LLcRUpvbckwl+fzTf57wAnVtlca+dOtz2is/vQ5pyGEU7sfR21IlpSdAyFdulKwwDFB9EdeqNj6Jc7BuKN/BTyrbWOpJGSLpY0Jf/Wy/adJR2fn5eVdHMeP6QeUkq8TV8k6T5J4yTVHar7SZqcfx/MseZIrZ3tZ0o6WtJ1wBHZfyVFGuyHJO1Zm/N38i3/bkl7tdF+QO4m/kgk2uuMA4Bv2H4WIiW27cOrRUGRvnvxJv0WkXSJpHsl/SpNYUjaXNIkRbrvCyWNqI3zQ0k3Eo77MUSyvamShncxx0KhMIDorwsDROjojoqiNnWOJYRYaxHCsNOa9D0WODbPaXSOrk7k+lmJsPuvVzv2vO21CTNRlSRujtTatfOXBza1vU9+XxH4OOG4/ZGk+SStSaS9WIdIib2bpNW7aN8+57k1sFarG5R+kBHNFNgKDcRUQph3XX6u//deG9gHWIXwpWydC8iB+ZvWIHwY36n1mWl7fdvn5LEdMwx2RsO1i46hUBjA9NtwVUels7OJVBH1B8+mxJt59X2RfEDWGUtH1NC5RARSxWTbjwHkw3IUHcnvzqv9e0xtrFaptS+0/Xrt+xW2XwZelvQE4XNYH7jE9ot5zd8RPgW1aJ8n21/K9s6cu7Ol9FbUVDgCWAz4ou2JaSL7WJrk6junyVXabEnn5TxnEgvmTXl/5yd8LRX1anEtsX0KseNjzJgx/c9WWSgUOqXfLgzJL4hKYWfU2uYBxjZ5S213zJdrnxvTWbvFZ1q0N6bFaDZ2q4l1NuG2Hqa5eL4oaRnbD9u+CrhK0uXEQ73T7k2+i0gJskOLPj1KA1IotEPxH/Qf+rMpCUfRmd8CX6k1X01ExgCzRFuN3ExH2untmxxvxXa1f6s35VaptdvlBmBLSQspit9sBUzoon0rScNzJ/TZLsY/DDipFnkk5tRTNGNtRdjpPMTvvZG4b+vV/CsLSWpV7KcxtXihUBgk9PcdA4QCt55tdE+itsE0Yv43AF9v6LMXcI6kfYAraD/99AKSbiEWzOqteU9CVbwfkXl0l+5M3vbtivxJk7PpNNt3wKwMrM3aLyDSWjxKLBadcRJR5/kWSS8TyfFuAu7oot8k4HDCx3ADYb56Q9LOwHnK2tOEz+GBJv3PJNJ8zKDJDq5QKAxc+k24qiIl9tGVI1dthKhK2gJYqTH1s0KkNiOTyB0CfNV2o4CsVyGq2f8g5k6I6plEiu53234h244lFrGR6U+YaHtdReqLdW2f28U1H6Eb90JthryWcNVCYe6gQZJ2+2UiMuawdh9Oti+jufJ2TeD4NKuYKFrTpyi0C3OTvxCZTc9Jc9DHqKUQt71ufhxFJNnrdGEoFOY2RcfQf+hPPoY+0y4Q9QKWzRDTbwPzaIBrFySdkJqBqcAWwOJEplWAjQjz0Wu186sIpMOBDbLv3pKGSTpKqXKW9K3aZb6l0C/cJWnFHOdtimyrUxRpvudIs10oFAYX/WlhgKJdaKldsL17LXX2ZYTZaKYiPfYORGruZuwPTMi+xwBfJVJsrF77fRVPpX7hJDoWnQOAP+V9/RhwZDrLW6KiYygUBjT9amFItW6lXaizKWEamko8FFtpFy7Mz41mk8mZH+kNwqk7qnasrl0YWxurGuM3RIx/RVPtQpq/5tAu2J4OVBqFVu0bZPtLeQ/aTUz3O2JBWYeundQVmwK/yrxHVeRXfTyIehGj8vPmwP5578cTEU9LdXYBl3oMhcKAZm7byZtRtAvtcz5xr87KiKJ2+swmimug+i31eyTg87bvn22QrChXKPQVxX/Qf+hXOwYo2oU2tQsA2P4bYeo5sZPTGvUGVwNfr5znkt7RxWWuInwPyvPbrV5XKBQGKP1xxwBFu9CuWQjbJ3dxyjTgNUWa7jOJAkDLA9MkvUoU5Tm+dXd+TOzipuXi8AjQo6yshUJhYNBvdAy9pUG7sD2wg+3P5bHXgXpRnvMbtQ8NY/3A9qE9mMNphBajx+GxGfWzi+0t8/v3ga/YriKmPgvsZnuLhn47EzqEPRRlSE8mciYtQDifv1o/p8l1f0/kV3q2p3NvRtExFNqlhKv2LYNFx9Bb6tqFZ5m9XvGMjOZplx8Q5TvbRtIw27v2oM/rDc0TyQR0yVjgeUnvsv0EsC4RmtoZvySiuP43r7NKV3Ox/an2Z14oFAYz/c7H0FNsT7C9mu1VbX/U9l86O1/SoqkbWCG/nydpN0mHA8Mz7n9cHtspdQ5TJZ2sKFSDpOmKeg+3AGNTzzAmj+2QeoC7JR1Ru+5sfZr8jieB5yR9UNIJRBTREkTKi6mEX2JijrWLpAckXc/sIbhLEBXWqjHru6UlJV0p6UFJszLFKus2SBol6c+STpV0j6SrlfUWJK2V2odJko5UVLRrdm9LuGqhMIAZNAtDF1QP+upvO9vPEX6MM9P09Hbbp9ren9xh2N5R0ocIx/R6uet4nXBIA7wNuNv2OrZnOagV9aGPADYGRgNrSdqysz4NTCR2Br8kQld3IkJxxxDhsFMUNZoPJhaEzQiNRsUxwJ8k/UEhalusdmx0/p5VgO0kvb/J9ZcDTrD9YWL3VTn1zyBKlI7N+9CUEq5aKAxsBpMpqTOampJsXyNpW0JYt1qLvpsQZqopGZgznNArQDwcL27SZy1gfL79kzuPjwKXdtKnzk3EwjCMiJSaDPyQEMDdb3umpHUarnEB4VTG9hmSrgI+QaTN+Jqk6vddm4siku4Flgb+3nD9h21Pzc+3AaNycVnY9sRsP5fihC70IcV/0H8YKgtDUxQ5hj5EFAJ6BzXzS/00Qifw/SbHZjbxEVR9WtGqT52JwLeIheFU2y9IWpCO1BcVLSMHbP8TOJ2IrrobWDkPdabpoMU5w+n8NxUKhUHEUDEltWJv4M9EmOrpkubL9ldrn68FtpH0Loi4f0lLdzHuLcCGabMfluNf34153UuU5NyAjvTZU4kQ3eqN/RZgI0nvzLluW3WW9Ilq/pLeA7yTWoK9nmD7GeAFSR/Jpu5oRQqFwgBiqOwYhqfjtuJK4m16V2DtfCO/gag98CMiKmiapNvTz3AgcHXuMF4Fdif0Bk2x/XiGmV5HvGn/vooQaocMub0FWNT2q9k8ichzNLF2jYOy/XFCAT0sz90cOFbSzPy+n+1/qX2leCu+Apwq6UUiPUa7WpFCoTCAGDQ6hlY00TBsafuRN/F6022P6OR4VY/5xPy+JPBL29v00fW/DSxje6/8fjKRaXbT/P4tYDnbjfmo2hl7ROZ4QpEFdgHbG3TWp+gYCu1SdAx9S290DEPBlFRFGFV/j8zl+SwGfLP6YvuffbUoJFVEU8VoYNEqxJb2dBCt+HRGdd1NJNL7Y8+nWSgU+itDYWGYA0kLSjojdQZ3SPpYts+q75DfL1dUKqv0Bz+VdKei7sO7s32ZjOufIunHtb4jFLUcqvoGVR2Dw4Fl8wH7gKR7Jc3I73dKuqrFvH7XTH/QhDuA5RV5lxYFXiL8E5XIbV06dBDdqhkBfJBwRP+LiFZqTChY9S86hkJhADMUfAx1/8LDtrcifATYXkVRkOZqtS56X/E24GbbB+SDeTfgJ0QdiJNsny1p99r5M4GtbD8vaXHgZkmXEfURVq7CZxWlNy+3PVqR52ll2x9vMq/RRLjqy8D9ko6z3Rhmiu3X8veuRTzEbwEeBNaV9ARhPvy7Zq8NIUJAdz3xstCqvaoZMS/h07it2Y2yfQqp3h4zZszgtlUWCoOQobAwNNMwrE8kk8P2fZIeJTUAnfAKcHl+vo0QlUEIzCoB2G/oqOwm4FBJHwXeAN5LiNM6o7N5taM/qKh0EMMJ5/SDRJqPJ+mIappVGyLHrGpDqEX7PNn+Ura3WzOiUGiL4j/oPwyFhaEZrcJzXmN289qCtc+vusNT31lNh4odgZHAmrZflfRIw3jdmRe0pz+omAh8La93ArEgrJT/Vv6Ft6pmRKFQGGAMSR8DkbZ7R4A01SwF3E+klB4taR5Fqoi12xjrJmav3VCxKPBELgofI97wYc76CO3Mq7tMJEqHjrT9RC5oTxIq6GrH8JbUjCgUCgOPobpjOBH4laS7iF3CzrZflnQT8DAR3no3YUfvim8D52aYaD3VxTjg/yTdSjh/7wOw/R9JN2Vkzx+IN/qu5tWtH2f7GUlPAvfUmicRZq8785y3rGZEoVAYWAx6HUN3aaJ76Le1G3KczxDFdOYB5gOOtX2yImnfA70dv7cUHUOhHdrRMEDxQ3SH3ugYhuqOoTMGTO0GRdqLUwj19mOSFgBG5eEtCWf5XF0YCoXCwGOo+hi6hfpp7QbCPDQSuCJDVG8B5pe0LrAFcGTOY9mG6y2eznAkDZN0VF5/mkIZXdVemJjaismSFlYL/UeT+1V0DIXCAKbsGOakMa/SYbYvkFTVbjiWrN0AIGmPmiahXrvhVUknEs7ks+mow/DDPJf8t6rdsCbwDKFd2NL2pY19GrE9Jk1SWxDJ/i4H7rH9RoaTXm77ovr1mvBVYBlg9dRAvEPS/MAFwHa2p0hahMhA++287mz6D9sz6wMWHUOhMLApC8OcDKjaDbZ3VZTu3BTYl9BX7NxZnwY2BX5l+7Uc7+kc73HbU7Lt+ZxbK53FtG5cr1CYg+I76F+UhaFN1H9rN1SlO++S9BsiqmrnJqfVNRp1PYWYU5/QrK2ruRYKhUFC8TG0T7+r3aDIx7RRrWk0HenAG/USjxC7GYB60r6rga9LmreaMxFau6SktbJt4TzeVzqLQqHQjykLw5w01oc+PB+CuwL72K7EXgfm+VXthnEZGlrVbpgGXAMs0dnFbD8OVLUb7gRu70btBgHfTcf4VKIG9M557Hxgv3QSLwscBXxD0kRg8doYpwF/y99wJ5ES/BXCV3Jctl1D7DJOBIalzuICUmfR5lwLhcIAYcjpGBQVzX5B2PZfJt6k97L9QC/G3AjY1/ZnJG0BrGT78EYtgaRDgBtsdztddTp7zwDWAA6wfVQX51d6jHkJ89KXbD+rSNr3Z+JNf37gVuAr6SzfiFigtrD9fznO5cBRtsd3d85QdAyF9ig6hr6nNzqGIbVjUHiELyGcvcvaXonQIXSV3K5tbF9WE8RtSeQoqo79sCeLQvI0sCfx5t8OVR2KlbNvPfPrX9PBvgrwPuALtWOPAQf0cI6FQmEQMKQWBuBjRDK8X1UNtqcCN0o6MnUEd0naDmInkPH/F0m6T9K4XFyqusr3SboR2LoaT1nToYWW4ExJ2+R5m6SZ5y5JpyvEaUh6RNLB6qjjsGIOfTJwKlFec+8c8+Nt/u5JRHbX2UjH9uSGY3cCz0narPF8SWtKul7SbYq6EU3NZEXHUCgMbIbawrAyzWsIbE04blcjwjePrD30Vgf2It78PwCsJ2lB4iH9WSIl9XsaB7Q9EbiMqLc82vZfq2PZ/0xCJ7AKYe75Rq37U7bXAE4iQlCxvVW+5f8KOCbHvKqrH5wO7U1yLo3HFiTqLlzZcOgndPhQqnPnI0JVt7G9JlEz+6fNrmn7FNtjbI8ZOXJkV1MsFAr9jBKuGqwPnJdv0P9WFKZZC3gemGz7MYB08I4CphNFfx7M9nMIoVi7rJD9K7/GWYSp5xf5/Xf5723UdiPdZHhtvrcRDuSKZfPYcsBFtmfTIdieIAlJ9XrOKxAL6zW5aRoGPN7DuRUKs1F8B/2LobZjuIeOkM06PamD0BuvfVd6gOqaXdVd6IxKqLc04WRu5mP4IPCRdJg38lNm9zWIUFVXtbNXsb15D+dWKBT6MUNtYfgTsICk3aqGjNV/BthOkTdoJKE8ntxiDIg4/2UyDBRCe9CMVrUX7gNGSfpgfv8SbWoXuktWfdsT2Lemt6iOPU6UGp1DkGf7auDtdKi87wdGShoLYVqS9OE3Y86FQmHuMqQWhixYsxWwmaS/SroHOAg4l0jrcCexeHzX9r86GWcmYTq6Ip3Pj7Y4tVFLUO+/C3BhagLeIHwHLZH0HkmPAd8BDpT0mCKHUTu/+478bds3OXwpsFCD2ajip0TUEqlt2AY4IrUNU4nyoYVCYZAx6HUMtXh+EaaZPWxPzHj+yzOcs7HPeEKX0DQAX9IBwLb5dRU66jecbvuXffoDOq75KTrSe38Q+AeRnuMO27v0Ytx/AR+0Pb2Nc1ckihfVNR+H2b6gVZ+iYyi0Q9Ex9D290TEMBefzrKR4Gd55GLBhbwa0/VMyIkfS9G7Wb+jpNX8P/D6veSOxwE3tvNebwr09/R9boVAYGAwpUxKwCOFPmA1FHePzFfUILiCyolbHviLpgdQznCrp+FaDS1pM0kPqyDu0mKSH03dxo6RfSJqU+oSqNsKI1DdMTpNT27WUJb0z9QwvSPpzfp4p6aA8fqSiPsQnJF0j6RJF+owzGobaJ699Z+X3kLRZfr8zNRULNl6/k3kVHUOhMIAZCgtDlfvoPiIv0I+bnPMN4CXbqxI7gTVhVq2E/wE+QqSzXrFJ31nYfha4CfhENn0R+G0tQ+oCtscSdQ1Oy7YfAlfaXhvYGPh5uw9h2//J3crPCUX0ZoRZa408ZX06ajOvkb9zJWC1amFK/ml7dSJsdq9s+y7w37ZXI3ZYr2T7Spo9l9Q6TeZVdAyFwgBmqJmSxgJnS2r0K3wU+CWA7WmKBHgAawPX2346+19I1B/ojNOIKKDLCQfzl2rHzstr/EnSuySNADYHPilp/zxnQSJraXdyN00A/ovYDV0MbCnpbcBI248qCghNrBzq6TweReRJgtl1Exvn55uIJHrnAhfbfiH1C8WUVOhziu+gfzEUdgyzsD2JyCza7DW2T+oP2L4eWF5R9vJV2/d1cg3nNbas6QOW6kFCv0nErmYDIvPr3UQ22Jtr57TSY9SPzWq3fRCxw1gEuFXSB7o5p0KhMEAZUgtDRtUMA/7TcKheZ2BlYNVsn0zUSnh7+g0+3+alzgHGEdlQ68zKwQT82/aLwFXEDqOa4+rt/p4K2y8Ru4VPEbuACUQqjQmd9esMScvavtP2oYR5qqudUqFQGCQMBVNSvYazgC/bfl2z10A+CTgjTUhTSXGb7X9IOpQopvNP4F7guTauOY7wHTSGcT6vqIewMGFmgqih8IvUM8wD/AX4XPd+IhCLwJq2X5E0gdAf9HhhAL6nSAT4BnFPrgWWJX0MtfN+VU9KWCgUBj6DXsdQRz2oxSBphO3puWO4hNAqXFI7vhENtRhy3N2Ab9VqMfwN+Kntk3sw757WYqjY0vYj3b1uw5gHAdO7unYjRcdQaIeiY+h7io6hDaRZtRjOsr19to0majF0ZtM/SNKmhFP4akIp3BTbl0n6JJEaexqxSNybh/9G7Dx6QlWLYcs2z5/lcH+zkTSv7dfeimsVCoW3hqHkY+hRLQYiemdBImLn27atTmoxEA7cL+ekkPeSAAAgAElEQVT1ZtViIExElUagq1oMf5E0o9ImEAvS94BXe/rjU0txpKQpCr3G12rH9qu1H1xrPyB1D38ksqtW7eMlHarIQvvtJtcqOoZCYQAzZHYMtFeLYXFgiqQb8tjqwIcJ/8JNRC2GW4laDBsTD/s50kFkyo3LiJQbFwFUPg111GLYxPYDks4mon+qlNtP2f6gpG8Ca9jetRq3Eq61Qd2v8rDtrYhdzHO218qF6CZJVxOpt5cjQnMFXCbpo8CLRG6l1Yn/ndzecP8Ws91UQW77FKIWNmPGjBk6tspCYZAwlBaGVgzGWgzNTEmbA6sqK8gBixILwub5d0e2j8j2hYFLMuKJXOjqtMyPVCh0l+I76F8MpYXhHiI7aCODsRZDq+t+q7HqmzJ/VKNTXNJedP47X+zDuRUKhX7EUPIxDLlaDA1cBXxDWZNB0vKpjr4K+O9UYSPpvZLeRWg7tlLkkVqYKGNaKBSGAENmx5BO460IzcD+wEwyXJUwn9xJvCF/1/a/MkS02TgzJVW1GJ4CbiT8F42cD5wqaU9qO5XsX9VimBeYQhu1GAjh2iLAG/k2v5Lt59u/A5xGmMJuzwitJ4kw1qszZcak9INMB3ayfbsioeBUot5EbzQRhUJhADGkdAxvFm+GbqAXc1kOOAb4EPAs4Sv5ke0bmpz7CDCGKOH5qO1fZPtVwN8rx7eknwP/sH10d+dTdAyFrmhXwwDFF9EdeqNjGEqmpDeTGbVcR6Pn4qKwIHAFcIrtZW2vCXwL6CrP0USyGpukeYjorHrZznWJqKxCoTAEKAvDm0Qr3UClj5B0UWohxqVpB0lrSZqoqIEwWdLCnYxT1WKY9UcI9W63PSuCyPbdts+s9bk6NRQn0+EIv4mOMp0fJpLwvaDIEbUAsfu4Q8Ecmo8mv73oGAqFAcyQ8TG8yXRHNwDN9RGTiRDQ7WxPUdRzntFqHNsPE/qLWUg6mtb1pwF+BNxo+xBJnybDbG3/U9JrkpYiFohJwHuBsURuqGmZg+nzNNF82H68fpGiYygUBjZlYegbuqMbeIXm+ojngMdtTwGoHMuSWo3zcFeTknRJnvuA7a2JiKutc/wrJNWr2VW7hnWBo4mFYd2c18Q8p5Xmo1HjUCi0TfEb9D/KwvDm0Uo3sBHN9RGidU2IOcZpwT3Ewx8A21spKrXVE9+1eoOv/AyrEKakvwP7EM7r02tzKRQKg5ziY3jzaKUbaMV9wJKprSD9C/N2c5xzCbPUFrW2hWqf63UnPgm8vXbsJuAzwNO2X8+qdYsR5qRJtf7d0XwUCoUBSNkx9ABJ021XgrBPAQulff5TRO3os4mEd3+jQTfQMM7OxMP11rThb0eU0xxO+Bc2pYX+oNm8bM+Q9ChwVpqJ/k0I7V5OU9TBwHmSbidEdX+rdb+L8Bucm3P7BFFidBHgj5LuB/YjForZNB89uIWFQqEfU3QMPaBaGCRtQjhZN7f914ZzxhN1GloG8efCMMb2Hn00r8WIB/x04FPpoEbSmdQS+rUxzspEivItbP8527YAnm3UQ6iLtNtFx1DoiqJjeHMoOoa5gKQNiCyrn64WBUkHSdo3387HAOMylHR4s1DUHGpJSVdKelDSz2rjby5pkqTbJV2ojpQVVWru2zNktK7Q/jzwf4TqevuGKW8qaYKkByR9Jse6RdIsvYIijHZNIsX3odWiAFFroloU1EXa7UKhMLApC0PPWAD4X0LhfF/jwXwzvxXYMaOVXidCUb9tezXCRDQjTx9N1IJehbDfv1/S4sCBwKa218ixvlO7xHzEf7vFiPDVqZJuIfI2nZd/jTmcRgEbAp8GfqUQw50PfAFA0hLAkrZvI0Jpb+/iHixme0PbP288UHQMhcLApiwMPeNVIornK22evwINoag188u1tp+zPZOo9rY08BGi+ttNGc765WyvOCEXnG2AO/LzFkQhoBszpfdraRKq+K3tNzJd+EPAisBvgW3z+BeACxsnrg4h3QOS9q0dapl22/YptsfYHjNy5Mh27k+hUOhHFOdzz3iDeJD+UdIPbB/axfmtQlGhdejqNbZbZW5tlpp7OyLK6OHwUbMIYU46MI83Xt+2/yHpP5JWzf5VVbd7iPrSd9r+DzA6F4URtf4l7XahTyh+g/5H2TH0kCxg8xlgR0nNdg71tNutQlFbcTMRdlqVAl1I0vJdTGkH4BO2R9keBazJ7H6GbSXNo0gX/gHg/mw/H/gusKjtKhHgz4ADFFlXK+phr4VCYRBTdgy9wPbTGdZ5gyIFd50zCVv+DCLEs1koaqtxn8yIpfMyDQbEm/8Dzc6XNIoILb25NsbDkp6XtE423U+EqL4b+HqargAuAo4Fflzre5ekbwNnp5P8P0Ro649a341CoTBY6NfhqpIOAL5ImEzeAL5m+5YW555JGyGZaRLZFXgtx/156g56O9dHiNDTpyRNtL1uPrDXtV1pA8YA/2V7z95er3bdPk/5LenrpB6ju6GujZRw1UJh7tCbcNV+u2OQNJYw1axh++WM1Jm/l2N+HdgMWNv285IWpYVYrDfYrjKVjiIWtnOz/VYiwqgvaZanqVfY7rRwUKHQlxQdQ/+jP/sYlgCesv0ygO2nMgvoDxUpqO+WdEqqgWdD0pqSrpd0m6SrMhQT4AfAN6sEdRkNdFb22USRjvouSadXJpxWugG1TmGNpOn58XBgg4zq2VuRcvvyPOcdki5VpNK+OR3AlRbi9NQKPKSoANctJI1KzcLt+VfVWtgo78tvM8rocEk7KnQVd6X/YZYeo2HMTRRJ+arvm0n6XXfnVigU+j/9eWG4Gnh/PsBOlLRhth9vey3bKwPDiV3FLBQ5hY4DtslCNacDP01b+cKNCuXssyDhE9jO9irETuobtVOeSj3BSUD1wKxSWK9OZBddqslv2B+YkMV7jmk4djARaroqsWDVzVkrAh8H1gZ+lL+pFcPVUZOhenA/AWyWc94O+GXt/NUIUdoqRL3p5W2vTaTe+FYn1/kT8CFFjiSAXYAzmp1YdAyFwsCm3y4MtqcTkTVfJfIDXZAO2Y8pFLt3ARsze6UxCM3AysA1qQE4EHgfnYeMrkDUUaicu2dRy1IKVG/GtxHmIfL4OTnXK4B6Cut2WB/4Tfb/E/DONG0BXGH7ZdtPEQ/5d3cyTr163FbZNh9Rb/ouQpuwUu38KbYfz53YX4kFGMJPMYoWOJxRvwF2UqTeGAv8ocW5RcdQKAxg+q2PASDz/o8HxudD7mvAqoST9++SDgIWbOgm4B7bYxvHk/SipA/YfqhJn85ophuA1gtNOzS7ZjVeM21Dd9ibSKC3GrH4z6wdq4/9Ru37G21c5wwi5cZM4MLOciQVCu1S/Ab9j367Y5C0gqKwfcVoOmLvn1LkDtpmzp7cD4xM5zWS5lNHPqDDgBMU1dGQtIikrxI6g1FK3QBhYrm+iyl2lsK6oq5l6Kz/RoS56vkurtkuixJK6zeI3zKsLwa1/U+i6tyBhOmtUCgMQvrzjmEEEfe/GBFa+hfCrPQsYfZ4BJjS2CnTV28D/DJNM/MCvyDUvCfluFMkvUqktvi57ZmSdgEuVAjPpgBdReZ0lsK6YhqRmuJO4kF6R+3YQcAZkqYBLxFpL/qKE4GLJW0LXEffqpTHASNt39uHYxYKhX7EW6pjkGTgaNv75Pd9gRG2D+qkzxbASrYP7+ScjYgU159pcuwRUl/QwzkfBEy3fVRX5/bluBltdQCxYBj4B7CH7Xvy+CM0+V3VuMAywHpEiO8ydOy2ftJTTUKOfzzhNP91O+cXHUOhFd0JU60oZqf2GUg6hpeBrSUd1u6D2vZlzKWawuo8bcWbze5Eqc3VbL+kqP18maQP11TLLbG9O8xSRV/eF1oHSbcRu499ejtWoVDov7zVPobXiMI2ezcekDRS0sUKjcIUSetl+875loqkZTPmf4qkQ2p6AYARki6SdJ+kcfnGXbFfxupPVkf+oaUlXZs6gmsVFdiQdKakoyVdBxyR/VdqpiuQ9B2FnuJuSXu10X6ApPsl/ZGIhOqM7xG1nl+S9E4if9HbgQcy2mrJ/N7dcZG0RkZ2Tct7vqikJRWpuysdiCUtmd8fzpDePxPmsOvyXmzVYvwSrlooDGDmhvP5BCLx3KIN7ccCx9heiyg4c1qTvscCx+Y5/2w4tjqwFxGa+QHCjFLxfMbqH0/4G8jPZ6eOYByzx/ovT9RCqN6M59AVKAra7AKsQ6TJ3k3S6l20b5/z3BpYq9UNSuf42yrNhe3/5Bv/wcBF+fmfwDPdGbfGOcA++dvvB/4nHcuLKupJb0AotDdQiN4eq+1S3kXc2y0JZ/4clHDVQmFg85abSjIVxdnAnnQUq4FIKrdS7UV/EXVUOasYS0cKi3OBun1+su3HAPKNehRwYx47r/ZvJTQbSzxIIeLzZ1VPI0IxX699vyLj/l+WVOkK1gcusf1iXvN3xANVLdrnyfaXsr0n5rFmWowNujNu7j4WtF3dm7NIPQUwiTBfbQAcSvw3GQ5MqA1xaWoapkl6bw9+Q6EAFH9Bf2Zuhav+gihy87aGuYytibXea/uFbozZWey/W3ymRXtjFE+rmgnN6EwT0ZanP8NWX5T0gYZDaxDFfHo0btLZ/CYQwr33EnqF1YkFsF7nuX4vutJ/FAqFAchcWRhsP01UD6vXMbga2KP6IqmZs/RmwswEc9Y07oztav9Oys8Ta2PsSMfuol1uALZU1Ep4G7AV8WDtrH0rRf3nhYHPdjH+kUTI7XAASZsSD+lzm8yj7XHT6T9DmT+J2TUbNxBRUPeleO0FYHPiXhUKhSHC3Iy6+Tm1hYAwLZ2Qcf3zEg+przf02Qs4R9I+wBXAc21ea4F0rM5DRy3kPYHTJe1HpNzYpTuTt327IiX15Gw6zfYdMCsFeLP2C4CpwKPMbp5pxnGEc/kuRWrtfwGfs103v1Xz6M64EIvBSbno/IX87bb/kpFY1Q7hJkKz0FfCu0KhMADol/UY1ELvQPgBZti2pO2BHWx/Ls8ZqnqHM4ENiUVyQeA82wd3ce7zhO/gZuD7tv/RxRzGE/et24KEomMoNNIT/UJF8Uu0T290DP01JUald1i8oX1NYGruKr5JLZ7e9mWdLQpvJpq7egeA/TJSaTTwZUnLNJ4gaVjt3NWIsNYq9LRXdS4KhcLgor8uDE31DrYnEJEyDxJvvOcNAr3DfyTNkPQPdaTPvrg7uoQaVULBKiLqEUX9ihuBbRvupTMV+L+AT+b5J6X+4B5JrXYdm0uapKjzcKEiZ1XjOUXHUCgMYPrrwgBDR++wFDCSyAG1C+GQX57u6RKOzBDdx4DzbT9ROzbT9vq2z2/R9/acN8ABufVcFdhQWTyoIndwB+ZvXoPQOnynccCiYygUBjZz2wTSkqJ36JbeYT/bF+Xb+7WS1rVdRRJd0EXf+o7pC4pss/MSFfRWIhIBVnwk227K+z8/HVFehUJbFD9B/6ffLgzJL4g32nqlsErvMFt0juas8NmKQaN3mKOTPT0dxevTEWLaVWbV1YnFZBmiOt1atp9JR3WzWhfX2N6BQqEwaOnPpqSid2hP7zCLdIKvQ1Rm6+pcpR9kCeBKYBFiEXlO0rtJv0MDNwPr1fwvC0lavt35FQqFgUF/3zFA0Tu0o0s4UtKBhGnnWjpKkbY693+AhYgH/cdsvwLcKekOom7FQ4SGofG3PKkor3qepAWy+UDggcZzC4XCwKVf6hh6i6SFaNA7AJ8hCvxUnN+F5uEHtg/twbVPIzQYvS5kI2kk4Tzfw/bJXZw7niZag3z7/zXwfqIW9CO2P6VONB19SdExFBopOoa3ht7oGAbCjqEnrAkcn6GozwL/DWzSzZoEPyASybWNpGG2d+1Bn9dbHN6WeKvfAeh0YeiEQwi/wLF5vVW7OL9QKAxx+rWPoafYnmB7Ndur2v6o7b80O09Rh+B+SSvk9/Mk7SbpcGB4agrG5bGdUt8wVdLJSsGYpOkKrcQtwNjUMYzJYztIuiv1C0fUrjtbn05+yg6EiO99qZuodA5Pp/bhMUl1rcdOkibm9dbOtiWIMNbq3tSjjJpqOlL/cHBqFe6StGK2j5R0TbafLOlRzSlCLDqGQmGAMygXhhYMrz1Yp0razvZzhP/izDQ5vd32qbb3J0xRo23vKOlDhEN6vdx1vE44oiEyxN5te51aKmsURW6OADYmFMlrSdqysz51JL0feI/tyYQDflpe+yvAbbaH234fs0dsvc32uoQq/PRsOwH4taTrFAV9lqyd35mm46nUKpxERCsB/Aj4U7ZfQmgw5qDoGAqFgc1gNSU1Y0YzU5LtayRtSzxAV2vRdxPCPDUlX6qHA5WI7HXg4iZ91gLG234SIHceHwUu7aRPne2JBQHgfMJPcDThGP6ApOMIx/rVtT7n5W+6QdIikhazfZUiffcniEijOyStnOd3pumoHNi30aHjWJ+InsL2lZKe6eI3FApzUPwE/Z+htDA0RdI8wIcIEd07qJld6qcBZ9n+fpNjM1v4CDrTKbTqU2cH4N2Sqp3JkpKWs/2gpNUIhfXuwBcIHwrMqX8wzAr7PRc4V9LlxAL1HzrXdLzcpL3UXygUhgBDyZTUir2JWsY7EGGp82X7q7XP1wLbSHoXgKR3SFq6i3FvIdJKLJ7+iB3oqHvQKenzeFsWKxplexRRRnP7tOnPY/ti4H+I4j0V22X/9YHnbD8naeOM0kKhi1gW+Fs782jCjcRChKTNyZrThUJhcDGUdgzD01xScSVhh98VWNv2C5JuIOLyf0Qk8Zsm6fb0MxwIXJ07jFeJt/VHW13M9uOSvg9cR7xp/972/7Y51x0IG36diwmT0mXAGTkPgPou5hlJEwmxWrWLqCK0XiNeBE6zPSXDVbvLwYSGYTtikft/9s473Irq+t/vB0kURYnGHgtKVEREVOwNu8nX3g0aNdbEEmyJif6M0Rh77x272LuiogioFJVqL6BijMZeseD6/bHWcObOPefccy/13Lvf5+G55+zZe8+eEzN7Zq31Wet9vJhPIpFoRbRKHcO0IC+Kk9c7bG9mE6fT3L8Afmdml8T3xYELzGzn6TF/7jyDqFI/QV574l0z2yDXNhpob2bdy42JPnMCU8zsR0nrAI8BvavVaUg6hkSRpGOYOSQdw/SlrJN6OvELPGLoEgAz+w8wXTeFZjCvpCXN7N2IuqqFpYDb4m3le+DVGbe8RCIxq0g+hhpQrtZDfH8gM8WEJuEUSWPkNSAWifZFJN0d7WPkNZZPA7pEuOyZkjpL+iK+j1GpNsMbkjbOnfsuSY9Iel3SGbl1NFk/oQq3UcoNtQelzLJImkvStaFhGJWtBXfMv4qbxt7C62aU+72SjiGRqGPSxtCYvN6haOcvxzzAsKiKNhg4INovAJ6K9tXwHETHAm+GPuKY6PdOvKFcD9xnZh3w9B3XScqym/bEb+IrA7uFxgGaqJ/QBHdQCkPdBrg/d+wQADNbGd80srX8EfgmalOcgvsvGpF0DIlEfZNMSY1prinpe+CB+Pw8sHl83gT4PUCEpn4uqVoUz/rAhdH/FUlv4wV7AAaGGA9JLwFLA+/SdP2EanyCO6t3x6OyvqlhLRsShYrMbKw8kWEi0SySn2D2J20MtZFF9GTk6xT8YCUPflEL0ByqaQQa6Q1UW/2EpuiPC/v2acZaUrRCItHKSaak2pgI9JTULsw4azbRH1z78EfwRHmS5sNDO4vV5jIGE2k25DUOlqK6c7eW+glNcTdekW5AjWvJt3fHTViJRKKVkTaG2ngamICHsZ6FV5Vrij8DG0sah5uYVjKzj/GymOMlnVnofwkwR/TvD+wTZULLYmZjgKx+wjWUqZ/QFGb2pZmdHvUYalnLpXjivbHAXyjVkkgkEq2IVmdKkmTAjWa2V3xvjwuxhpvZ1qpcn6AdXkp0YtwQJwO7mtmEMBX1KXOufuTMMGZ2B+7UBfcDzI8XzzFgP0lj8NxGn+Scz91j7GQam3Qws35Av9z3rXOfG/UPngbuljS/mXXMrXcp4DrgU+AJScea2UNxeA9gLkmvAodXmHtRYBk8V9ScuIahuKkkElVJOobZn9b4xvA10F1Sh/i+OfBe7nhWn2AVM+uGRwqBR/0sDvSIaJwd8FoOzSY2n9uBv5rZCngupkeobEaa3txPeXPX8cBtZrYqnqQvE9p1i+8r4cn2Lok0HkVOB841s+XwzWW/Mn0SiUSd0xo3BoCHgezRokGMPpXrEywGvG9mP0X7JDP7FFyrkPWXtHO8KWRsJmmIpNckZU/zh+BJ956NuczM7jCzD/KLlLSNpOGhFXg8p4HYKBcyO0rSvJIWkzQ42sZL2oAKmNkwM3ufQqpxPDw1i3TqhFeHA9gOr2j3nZlNAN6gsLFIEh5plb0RXQdsTxmSjiGRqG9a68ZwK55wbi7cQTo8d6xSfYLbgG3iJnq2pFVrPFdnYCN8I7osztkd9ys0xVBg7XiCvxW324NHGx0SYbMb4JlffwcMiLZV8JrQTZHVlOgZ41bGN7JJwEPAYdHvV3j4a8akaMvzS+AzM/uxSh8g6RgSiXqn1fkYYGqMfWf8beGhwrGy9QnMbJI8q+km8W+gpF3MbGATp7st3jJel/QW0LUZS10C6C9pMdwXMSHanwbOkddwuCvWNpJS9td7zKyWjaHIHkA/MztbnuvohoguKheeWgxLraVPItEkyU8w+9Na3xjAs5CeRUMzEuD1Cczs5nBQj8SFW4Qp5eFwDP+bkqkkfwMsagXK1UB4kQqq4AIXAheFT+OgbG4zOw3P+toBGCapq5kNjnW+h9/Qf1/D/EX2I4r/hJlrLmBB/Ol/yVy/JSiZmTI+An4RzvxKfRKJRCugNW8M1wAnmVk+UyqqUJ9A0mqZWSkilHpQSqv9gaQVo32Hwnl2CX1DF7w85qvARcDektbKnXdPSYsWxnai5BjfO9e3i5mNM7PTgeeArvL6Dx+a2ZV4VNVqNJ938Gp0yBPnzQX8D99Ed5c0ZwjnlqMQihqRWU9SSvq3N1BrGvFEIlFHtNqNIZzH55c5tDrwXMTiP0vUJwAWBu6XNB5PK/EjfoMHj1x6AHgCD33N8ypem+Bh4GAzmxxO5t2BsyS9Kull3FfwRWHsicDtkobgT+QZfcPBPAb3LzwM9AZGSxoF7ASUuzYAJJ0RfoS5JU2SdGIcOgo4IOa9BdcnmJm9iL9JvIRHTx0SaTyQ9FDOD/NX4EhJb+A+h6srrSGRSNQvbbYeg0p1F4SnmTjUzJ4J38QD5eoSqOk6B8cBu8TXlSnVdbjGzC6YrhdQOudvcbMXwK/xN5BvgVFmtu80zPtf4Ndm9lWTnauQ6jEkiiQdw8xBqR5Di5iaLE/SlnjpzI2mZUIzOwXPOoqkr2ZgXYf8OR8iHOyShuIbXEsc04lEIgG0YlNSM5kPF2w1QFIHSbdKGiupP+4Mzo7tF9qFQZKuVK5eQ5l5fiHprcxxG98nyHMoDZV0nqRn5fUPekWfjpL6SRoRWoZtysw7vKBT6Im/NWTHn5DnOkLSy5L+Ep/PDJ/HVpIek9eNeFXStYVTHBXnHiPp1zF2c5VqTLygUmrw/LqSjiGRqGPa8saQib9eAa4CTi7Tp2z9gbC5/z9gbVxZXTVE1cw+w0NQt4qm3+FhrlPi+5xmtg6eX+mqaDsBeMTM1sTDZ88u3oTNbK2CTmE0Lk7LGAxsIGkh4Cs8nTbxd0h8Xi2usxuwSrYxBf8JjcV1QN9o+wvwB/M6ExvhaceL15t0DIlEHZNMSUDE9F8fMf15KtUfWBMvwvNJjL+dkqK4ElcBh+NO7H2BvXLHbolzPCFpYUkdgS2A30jKUnbMhWc5fa0Z1zgErwnxKXAnsL2keYCFzOztiEx6xsz+G9cxBhfsZU6Bu+Lv8/jmBL7BXSjpZuBOM/uyGetJJJKfoA5oy28MU4mY/gWBco+35bzz1eoVVDrHU8Dy8jKZP5jZK1XOYXGO7XNvBEuZWXM2BfCoq7XxiKjBwHhcHzEs16dRrYcyx6a2m9mJ+BvGfHh017LNXFMikZjNSRsDIKkrMAfwceFQpfoDI/BSmvOH32CnGk91I3ATULTl7xbn6A18YGZf4zUSDs+tsdYUHVMxs2/wt4Xf4m8BQ/B0G0OqjatGaCzGmNm/8airpt6UEolEndGWTUkdwmEL/nS+t5lNkRq8DFwKXBsmpNGE6MvM3pP0bzwH03/w+P/PazjnTbjvoH+h/QtJz+DZV7MQ038C58lTgLfDfQfbNe8SAd8EVjez70MvsQTTsDEAf5W0LvAT/ps0lTIkkUjUGa1mY8jpEtrjOYf2CqdvWcysXFppzGyipIclvYiHgf4DuBx/W5gTOBB/+h6HO2QfxSuhPVqYp2Os60TgKzM7C3f63mZmRaHbbWb298L4ryU9BWTq6c6SrsJ1FGWvy8zWL9N2TO7zW74kdZY0PrQaj+SO75/7vGju81DCcW5mBxau64dya0kkyjEtGgZI/omZRavZGGjoTL4OT319SgvnOgh30H4naQBeg+DemHvl6HMC0As3Pz0K3FNtQkmXAptRikyqiqStgCOA38Qbyhx4GopFaGGdiOYiaY5c5FQikWgjtFYfw7NESmg5Z8pTTIyTtFsT7fcB8wDDo61Yv2GcpJ/jRW3AK709DbwWYaHIcye9IWnB3Lg/4pvCxZKel9dw6Gpm61cQpB2Hvx28F+OnAP/Fs7GOlvS+pG8kfS7pCoUNTK6rOFdeu+FlSWtIukvS65L+lZu/vaTr5BqNO1TKHzVR0glysdwukrpIeiS/5qZ+/KRjSCTqm9b0xgD4Uy6eKC7L47MjLvxaBY88GilpMLBuuXYz21Y51XLcMJ8IH8CjwLVm9pmkE4BeZnZo9OuKO6rPw98MxpjZRwWfxRV4PqXX5Qn2LqEUBlpkJQq1pQsq5wVy4bI3AFvjldsAvjezDSX9GU90tzrwCfCmpHOjzwrAfmb2tHD8RPwAACAASURBVKRrgD/h2WgBJmdmKUkDm7HmbJ1XxLXSq1evtplzJZGoY1rTxpA5kzvjcfePRfv6wC3xxP1B2O3XqNJ+X35SM7s2zElb4c7fgyStUub81+A34fOAP1CIPAptwrp40rysec5aLizMVzfgzum/m1l/YGO5knluYAE81Xe2MWTXMA540byaG/J6EUvipqh3zezp6HcjHgGVbQz9p3XNiUQ5ko+gPmhNpqTMx7A0XvTmkGivpDmoWYtgZv8xs2vMbDs862qjBHtm9i6+wWyCO4wfLnRph1dA65n7t2KV075IpNaOFNw9Y84OcgX0JcDO5rUcrqRhnYhMf/ATDXUKP1F6GCinncj4uoVrTiQSrYDWtDEAYGaf40+/R8urnQ0GdpPnJVoIVzOPqNLeAHk+oZ/F50XxdNPvAV/iT/B5rsKfvvPpLrJ1fQFMkLRLzKUKbx4Zp+Jpu5fItWW5mrJN4KN4qt+Z5rOUXPENXtltaLFDC9acSCRaAa1uYwiew4vgvIFHD80FjMHrKfwlUkDcjdddGIObXLL2IlsAWW2EAcAx0e9J4Lfh4N0t+t4HdKSxgC2jD7BfzPUioUsIh/Gr4VR+WdKB4U+4AHhY0kvh45iC53Rqj78ljMOjoUYCHSXdir+t3CbpIVyzUImX8WJCY3FT1KXNWXMikWi9tMp6DOE87jij+seYRqGc8gR055rZBrWOifZBRJ0HSQsAbwKLmNn3xfFxrJeZfZRrF/AMcJ2ZXRZtPYF5zWxaxGzTTKrHkMiTdAwzD01DPYbW+sbQCEmd4ql8hfh+i6QDJJ1GKdPqTXFsT3m669GSLo8bMpK+knSSpOHAOvGkn6XJvgW/Of9K0um58zYYU8NSO+I2/qyCWtnx8pTgj0g6AMjyL12WHTez0WY2JMw/5cJye0t6StJt8vThp0nqE9c9Tl6qFHnq70slPSlPHb6RpGvizaZfy/7XSCQSszOtdWPIbvTZv93C93Ao0E/S7sD8ZnalmR1LOK7NrI884+huwHrh8J1C5EvC9Q3jI931VJu8PA33erh2YnlgDUnbVxuTG3scLpQbKulbYCLwXO7Notz4jngE0s3mNaC745FY5ciH624GnClpsTi2Cp7qe2U82+vy5mm+rwIOy80xPx6iekSc91w8nHbleDMpXlPSMSQSdUxr3RiyG332rz+AmT2G2+UvxrOMlmNTPO5/pDz8dVMgyyA6BU9fXWQNYJCZ/c/MfsRzIm3YxBhiTafgPpH1zawDrmxeSdLSVcbfi+sprq80b46pYbnmtaizsFyAkWb2vpl9h5uosrQe4/Cw34z7zW2O4/Akf+PM7Cfc55Dvl11TqseQSNQxrUnH0CSS2gEr4jWRFyCnaM53w231fytzbHKFFBHVQl8rjSmLmf1P0gu4E/ntCuOfxms13Bw37BepHJlUbW3FUNZ8mGu59NvVwl8TiSZJPoL6oLW+MVTiCDwaZw/gmiwMFfgh93kgsLOkhcEVxrmn90oMx9NwLxj+iD3wJ/NmI1dar4o/wVfiBDxH0yXx/QlgzvA3ZPOsIWkjagzLTSQSiYzWujEUfQynyWsf7w8cFZE6g4Hjo/8VwFhJN5nZS9H+aIRyPobnS6pIKIv/hoewjgFeyJLuNYObwnT1PNDPzCr5DDL6AnNJOiPeGnYANpf0pjwz7Il4SvB8WG4+XDeRSCTK0irDVZtCLlQ7D7e1f4c7fPta8yuk5efsjYecbi1pW6CbmZ0WTujXYsNB0knAYDN7vAXn6IprJFYDjjNP5V2tf9lU5JI6429Or+Iq8efwvEk/5Maej5unlgx/QotI4aqJxKxhWsJV25x9OGL+78b9CLtHW0/c6dvijSGPmd1HKV/R9nid55fi2AnTMPUnuKp7+6Y6BtVSkb9pZj3D9PUYsCvuNM98MTsA7+Kmp0HTsOZEAph2DQMkH8XMorWakqpRNuYfDxetFO8/SJ6a+hVJN8XmkqXLeEWeonrHbD5J+0i6SF7pbFs8RHS0pEclfSJpQnx/I/6NC23AnDF+oqR/SnohjnWNdX5oZiOBlhTHmZqKPE84tkcUjm2M14e+FPeXZNc1T6xzpKRRkpIKOpFohbTFjaFSzH+1eP9VcZt+Nzx0dT15IrsrgW2ADYBFixOa2TP4m8MxETa7RfYdWBvPVPrbSITXHvhjbvhHZrYafnM+elouWKVU5PeVOTYXHgH1SK55D+AW/M1q65xj/jjgCTNbA988zpQ0T5k5k44hkahj2uLGUIlq8f4jzGxS2Nqz1N5dgQlm9no4f29s5vlWiPGZ+eo6StoHgLvi7/OU0QrUSJaK/GM8PPex3LEuuWPvmNlYAHkRot8C90QSveF4viji77ExbhCeg2qp4kmTjiGRqG/anI+ByjH/tcb7T6Fy6urm0FTa7+yc+fM1l2/Dj9AJ93Mcgifmg5KPYTFgkKRtwzeyFZ6AcFxYzOYGvgEejDXvZGavtnA9iTZM8g/UD23xjaFszD/wKc2L938FWEaRU4icLb5AufTc2fjOkn4d3/eihdqHpiiTijx/7H3gWDzcFvw69jezzmbWGVgG2CL0FQOAw3I+llVnxHoTicSspc1tDFVi/m+mGfH+ZjYZOBB4MJzPb1foeitwTDhruxTG74tXRxuHq4gvqzAH4GG2kiYBRwLHS5okab4ar3tUXNvuZQ7fA8wdgrgt8beDbNzXeK2GbfCU3z/DNR/j43sikWhltBkdgyQDbjSzveJ7e+B9YHhRe9DMeQfhArhvo+lfZnZHC9bXF7jCzL5p7thmnmd94Bwg21DOMa/RTBnNxSAiHXhLz5d0DInErCHpGGrja6C7pA5m9i2wOV6JDWikPWgufabl5hn0xR3YNW8MktpH0r5a+y+Kvxltb2YvSFoQGCDpPTN7kILmYkasIdE2mR4aBkh+iplFWzMlPQxk/2VlIZlASXsQn3cJPcMYSYOjbQ5JZ4WuYKykwxrNnkOVazpcGqGcL0r6Z7QdDiwOPCnpyWj7KjfXzoraB/L6COdEv9MlLSnpY0nfxL+34py/LLOsQ/B0Gy8AmBf7+QseaVTUXGRmr13iOl6TtEHut7pd0v2UMrLmrz2FqyYSdUxb2xhuBXaP2P0eeChmOU4AtjSzVfCbJbg/YRlgVTPrQaiEg5tUysv0S1Wv6XBcvN71wBPv9TCzC/C8Rhub2cY1XMfywGZmdhSuffizmc2Nby4/xnk/LjNuJRprOJ4DViqjuciS+LWPGg19gX/kxq0D7G1mmxRPksJVE4n6pi2ZkjCzsfI8QXsAD1Xp+jRe0Oc2SnqCzYDLMrOJmX2S69/AlCRpD0o1HQA6AB/G4V0lHYj/9ovhormxzbyU23OpuLcAtpWUieAybcHLZcaJ8iG21RxNlfQUjxV+g0SiIskEVF+0qY0huA84C+gNlDO3YGYHS1oLNzuNludSqnRTLUfZmg6SlsFVzGuY2adhHpqrwhz5cxX7fF04V63aghfxanF5X8rqVPcpVNJTfF2mbyKRaAW0NVMSwDXASWY2rlIHSV3MbHgkvPsIWBK3pR8c0UxIWqDKOSrVdJgPv6F+LmkR4De5MUW9wweSVlQpoV0lmqMtuBjYJzY6wg9xOnBGhTUkEok2SJt7YzCzScD5TXQ7U9Jy+NP4QDz+fzxu2x8r6Qc8T9JFFc7xkqSspkM7POndIWY2TNIo/Mn9LdxklXEF8LCk98PPcCweIfRunLtjhbWejKcQHxubw0Rg6wrrel/SnsCVkuaN6zvPzO6PLrfGscOpXBEukUi0clrdxlBFrzAovi8CXI2/BfwMv5ECXA+sFsItAyYDu5rZhDj+Iy4sOzJ3rn7ARRVCVScAX+DpvH8O7C8v/DMI+MrMDs13NrMLgQtz3+8AGukhzGyfwvdvgYMKv8EpwO+B+c2suKEsCsyDm4bGmNmlMWZvSoWLTg/nc+/ceT6StJqkx3Bfw0RJ85vZp2WuPZFoQApXrS9aoylpql4hvjfQKwAn4Y7TVcysG/5kDh5FtDjQI7Kd7gB81pIFxOZzO/BXM1sBrzP9CDPPTHM/sGaZdS2Hp75Yz8xWwiONMrPYP/Asq2sC/5A0f5l5jwUGmtly+JvUsWX6JBKJOqc1bgxQRa+ARwJNyr5kWUWj/f2sWllkU/0UKmsKgs0kDYk4/8yEcwjufH425jIzuyOytpKbaxtJw+XpMh6PDQVJG+XCX0dJmlfSYpIGR9v4TFNQgU7xG+RLnN4NHABcnF2XmWWRUlsSUUZx7DE8mV6R7fAssMTfsgWDko4hkahvWuvGUE2vcDFwtaQnJR0nafFovw3YJm6iZzfhxM3TGdgI34gui3NWqvlQZCiwtpmtGmv+S7QfjfskeuK1Hr4FfgcMiLZV8PTfZTGzrN+3oUnoaWY74D6S5SU9LWmYpOzm/yvcl5ExiTJFfYBFIulelnxv4QrnTzqGRKKOaXU+BqiuVzCzAZKWxZ+IfwOMktTdzCZJWgHYJP4NlLSLmQ1s4nS3xVvG65Lewus01MoSQH956uuf434JcKf0OZJuAu6KtY0ErpFnR73HvOpcc2kPLIf7DpYAhkjqTvkU4G0jiVZippB8A/VFa31jgJJe4ZbigTCZ3BwO6pFEgRwz+87MHjazY4B/UzKVVNMUFG+ghkcdrV7DGi/Endcr4w7kuWIdpwH748K4YZK6mtngWOd7wA2Sfl/D/EUmAfea2Q/hVH8V3ygm4c74jCVwJXaRD2ITI/5+WKZPIpGoc1rzxlBWryBpE3ltASJkswvwTkTcLB7t7XATVJZKu5qmYBdJ7eS5hZbFb7YXAXuHSC47757yJHZ5OlFyjO+d69vFzMaZ2el4yoquoYP40MyuxKOqVmvBb3IPXpITeQK95fGw2QF4zYX5w+m8RbQVuS+3zr2Be1uwhkQiMZvTajeGcB6X0yusDjwXoaPPAleZ2UjcXn5/hKuOxcNTM51Cpil4Ag99zfMqXmDnYeBgM5scTubdgbMkvSrpZdxX8EVh7Il4PYYhuJAuo284mMfg/oWHcfPP6NBB7EQVLYakM+R1G+aW12w4MQ4NAD6W9BLwJJ4X6eNIbXEy/vY0Et9QP4m5rpKUpe49Da9j8Toe7dWsFOWJRKI+qKt6DBG1cy6wNl5x7XvgDDO7eyavY1/gz/G1G745TAEeMbMZGsIpqSv+G/wa37zGAIfnIoyaGt8eT3MxDvc5vAjsE3qIWsa3w4sY1bQppHoMiemlYYDkq2gOmoZ6DHXzxhCq3nuAwWa2rJmtjj+VL1Hj+Dmm11rM7Nos2odSVtSeM2FT6IC/uVxoZsuZ2Yq4Artszqcy47Nggy9j7SvH9wMqDCmOF76ZJP1CItGKqZuNAY8U+t7Mppa/NLO3zexCSZ1DS/BC/FsXQFLvCEu9GX9CRtI9kp6X10M4MJtL0n6hRRgk6UqVajMsJOlOSSPj33qVFiiv2fBGCMay72/JcyXdKK/FkGkefhN92svrK4yQ13nYv8pvsBe+MT4UY4cDZwO3SHpJ0leSXo7rWyv6bCbXSNwKjMpPZv66OAR/+0DSX8KENV5Rb0LSr+P7ZcALwOXAvBHWe32F3yHpGBKJOqaewlVXwm9M5fgQ2NzMJsvVvbfgWUTBlbzdc6kt/mBmn8TT90hJdwJzAv8Pd+h+ifsSxkT/84FzzWyopKVwO/2K5RZhZlMk3YJrDi7ChWMj43zgkT8b4ZFAj0v6NbAf7lReU9KceBTSo2b2TplTNNBHmFneuT038FP8Bl1xAVp2fG28bOk7ubcGIvR1K+BeSWviNSPWBOYARkh6Cq8o1w3YN7LOtgd2iDeOspiXCr0C3JRUqV8ikZg9qaeNoQGSLgbWx/0MmwEXybOGTsGjbTJG5DYFgMMlZZFFS+I36UWBp3IO19tzc2wGdIsbO8B8kuY1sy8rLO1qPB3GRcAfgKtyxzLNw6uS3o1zbwGsKGn36NMp2sttDNWYE/8NVsF9D11yx54tbDTzSsp0EE8B/YDDgTstak5Lugf/fR8F3gwHfSLRbJJfoP6op43hRTwaBwAzOyRCLp8DjgA+wBXB7fAEeBlT6wZI6o3f6Ncxs2/kxe7norzAK6Nd9K/JOWtmEyV9KmljYFUalr4sp3kQ8KcahHTgv8FaFY4dhauX98STA36VO1asnfBl8YlfuZ2vDKn2QiLRhqgnH8MTwFyS/phrmzv+dqKU52gv3BRSjk7Ap7EpdMVNLAAj8DKb84epZKfcmEeBqZlQ462kKa7GS3/emuVeCnaRszz+tvI6bpr6k0p1HlZQKQFgkRtinVPzGEn6raRuud/AcI1BtRt9OQYDO0jqIKkjnhdpSLGTRQW7vEkqkUi0LupmY4gb3vb4jXGCpBG4Hf2vwCW4oGwYbgKq9IT7CNBermE4GRgWc7+HK52HA4/jFc0+jzGHA73CMfwScHANy70bv1H3K7S/gd+A7wcONLPvcWfu67hGYTxwKRXe5MLMsw1whKTXYz17Av/DTVf7x2+wNKXKazVhZiNw38xI/He5tCgOzHE1Xv+hrPM5kUjUN3WlY2guqlybYbiZbS1pW9wpe5qkjmb2VfS5G7imkj4iTFCL4eIzgH+Z10/Ijq8NnGpecCdruxG4w8zuybX1Ba7I7PrTG0lb4hXawCOP3os1jzWzZqfUCGf1KcCOuLnua+AEMyunkgaSjqGtMz01DJD8Fc1B06BjaO3mgKm1GcJH0KA2g5ndR6n+8YmSNsN9Do/imolq9LEyBXokHQcciGssmqIvcCMe+VMTktpn5pymiBv2gBg3CDi63JqbwanAAvhm+r08X1LF8N1EIlGf1I0paRqoWJtB0j6ZXgE3I2Wq4J5mZqFDOEvSuDAlHVbtRPKymdvhqux9FKI6SZfiWVdPkfTPaDscLwz0pKQno+2r+NszzGWfhF7gE0n/iX6nS5pH0jVyXcUoSds190dRBf1E6B4GSrpLns7j+mifF9gHV1l/D556O/+mlJs76RgSiTqmtb8xgNc5OEHSA3hivGvwvEVFTgC2NLP3JP0i2g4ElgFWNbMfFcK14CZJmSlpUzzX0m54dbQfJF2C6wKuB44LLcMceDrvHmZ2gaQjcdV0Pk8SZjZa0jHA1ma2j7ww0ILAdqGV+DfwhJn9IdY6QtLjZtac6KEDKaOfiGOr4dqFD6N9bTwseIKZfVV+ugbrTzqGRKKOafUbg1WpzVDgaaCfpNuAu6JtM+CyzHST6RyCBqYkSXvgCfpGRuRnB0ppqXeVq6zb476JbniivuZwu5lNic9bANtKOjq+zwUsBbzcjPkq6ScAhlkU5Am9Q2fgtWauN5FIPoE6pdVvDEFWm6E3FfIKhap3LdzsNDrCUkXtBWuEl/P8W4NGaRm8ItsaZvZpPP0XazpMXUbuc7FP/m1AwE5m9mqNa6u03kb6ifCz5COapuD/nbwOLCNpnma+mSQSiTqjLfgYoEJthjzyGgjDzewEPAX2krgTOksDQcGUVGQgsLOkhbO+8hoK8+E39c/l2WF/kxvzJTBv7nu1ug95BgCHKV5NVHsZ0uIcteonCKX39cB5EZ2EpMUl9WnBuROJxGxMm9gYqtRmyHNmOJnH41qDMXg6i3fwmP0xeA6kSud4CTgeeDR0Eo8Bi5nZGDx53Yv4BvV0btgVwMOZ85nqdR/ynIyrm8fGek9u4trKUbN+IsexuL7jZUnjcJNbquKWSLQyZrqOIbQF55jZUfH9aKCjmZ1YZcxUvUGVPr3xcMytyxybCPQqOnmbseYTga/M7KyWjG/pvGF22oiS2O4bM1u3TL+JTMP11bDOzsADZta9uWOTjqHtMr01DJB8Fs1hWnQMs+KN4TtgR3meo5ows/uqbQozEs361A/HZLUfym0KMwJNx9oViUSi/pgVG8OPuAnliOIBVah9kNcbSOoiaVgcPymL/Q86SrpD0iuSbsps8MExEbM/Qp7uGklLR8z+2Pi7VLT3ixj/Jykph7vJazW8JdcgZGs+UqUaBn1raD8u9AGPAyu05AeU9EtJj8o1DJfjjuSN5VqHSXLtw4eS/hf9N5Urr5HXhHhOXo/in7k5J0o6QdJQPKfT6pLGSHoWOCTXb6X4DUfH77YcBZR0DIlEXTOrfAwXA30kdSq0Z7UP1sAT2V3VaKT3OT/6/KdwbFVcTdwNWJaGqtwvzGxNPKfQedF2EXC9mfXAk95dkOu/PLBZZvLCBWpb4vUK/iHpZ5JWB/bFM56uDRwgadUm2nePde4IrFHtRwrOjJvwaEk3Rds/gKFmtioecbUUXsN5RzzFdk+83OiEcBSvTykh3nHxetkDzzvVI3euyWa2vpndClyLi9nWKaznYPz374nXvJhUXLCZXWFmvcys10ILLVTDJSYSidmJWWImMbMv5IrawynlG4IKtQ8Kw9fBk+kB3IyHoWaMMLNJ0CD+fmgcuyX399zcXDvG5xuAM3Jz5XUDAA+a2XfAd5I+BBbBb7h3Z+Gbku7CxXOq0N4u2rOaB/fRNMeUURdvmK3bzB6U9Gm0Pw+sHr/Zd3hho15x7uwtp5qmon+sqxPwCzN7KvfbZNFUzwLHSVoCuMvMXq/hGhJtkOQPqF9mZVTSeXj1snlybVntg8ym/iurXBCnHOXi7zOswmcqtBdj9cvNXSm1dbWU19PL299oHjP7AZiIv608g78lbIwX7XlZJU3FpvGW9CAN9RLZNVfUb5jZzcC2+IY+QNIm0+NiEonE7MMs2xhCRXwbvjlk1FL7YBilegm1JKrL2C3399n4/Exujj6U3i5qZTCwvaS5Jc2Daw+GNNGe1TyYF0+h3RIGx3qR146ev3Ds6Pg7BDf9jI605dU0FVMxs8+iz/rRNFWrIGlZ4C0zuwA3Y/UoM0UikahjZnXEzdnkNgLc3HGxXAfQHr+5Fesf9AVulHQU/sT7ObUxp6Th+Ga4R+5818jzEv0Pf9KuGTN7IUJKR0TTVWY2CqaGmpZr7w+MBt6mTCGcMpwp6fjc9zWBfwK3SHoBL82ZL9s5BDgO9zV8LWlydh4zGyMp01S8RUNNRZF98d/mGyJDa7AbsKekH4D/AifVcA2JRKKOqLt6DPKi999G9tPdgT3MrFnZRSUtipuy1sBNRBOBvmbW4nxAyuko1LDOw/bAayGAQ9JJwGAze7wF5+iDFyYCL935xxDQVeo/BRiHb7ITgL3M7DO5LuFl3EH9c7w86n5hisrGng/sDCxpDavQNYukY2hbzAjtQp7kt6gd1ZmOYVpZHVfrjgX+hNc6rpkIYb0bGGRmXcysG/B33Jk8XSjoLrbHHbzZsRNasikEE4CNwj9wMpHBtArfhq+mO/AJubBT4M2ILFoZWALYNTugUkqOd3FHdyKRaEPU3cZgZkPMbBUz62FmG5rZG82cYmPgBzO7LDfnaGCopDNDdzBO0m7gbwKhX2ikj5C0VbQNpRTdNFV3IWld3FGbhZx2kWskdo5+m4YW4eP4Nyb6fS/pXkkvxFq6xjqfMbMsAmkYfkOvlWeBXxUbI/JqROHYxkCWJiMzu6Ea60AkHUMiUd/U3cYwHeiOh3UW2RHoCayCh82eKa9QBmX0EZLmAq7EHcgbAIsWJzSzZ3AHbaZefjM7FuP7AbuZ2S9xf8m18RT/H+BxM1sNvzkfXZwbd9o/XMsFy5XMm1KqVpc/Nheut3gk15wVNLob2Dq0EOC+iydCQ7Ix/hvlo8qy6046hkSijpnVzufZifWBW+IJ+gNJT+E+iC8or4/4Ci9c83q034gXv6mVFWJ85te4Djf1ZOK7rCbE8+TeRuJcG+Mbw/pUp0Nuvc/jif0yusSx5fBa1GNj7p8DvwWOMLMvw2G/Bb5xTY86EIlWTPIBtA7a4hvDi7ifokg17UElfcS0eO6rnS9/zgZ6jFAqX4VXc/u4iTm+jTeQpXEnczkfw6+BtcNhDrAVXrRnnDw53/qUzElZHYhMZ7KUmaVNIZFoZbTFjeEJPHT1gKxB0hp4nebd5HWeF8KdriMqzAHwCl64pkt836NCv2LNhfz4zoq8TcBeeOhpReS5nO7Co4tqjqAys8/x0Nyjc2ah7Nj7eDrtrMDQHsD+ZtbZzDrjpU23iGiw6VEHIpFIzOa0uY0hhF47AJtLelPSi8CJeHqNsXgdhieAv5jZf6vMMxk3HT0Yzue3K3S9FU/gNyq3iWTj9wVul9c2+Am4rMIcGSfgFeguCSd1zXGgoaMYQ3lR4D3A3JI2wvNBPZgb9zUu/NuG6VMHIpFIzObUnY5hWpDXgrjRzPaK7+3xgjjDQ3+wCHA1Xr3tZ8BEM/tthG+eB2yCm48mA7ua2YQq5+qH1zAo5jlC0pp4jqdFYr6h+BP9rnhdhUOLY6YXkh7BcyS1x4Vvh5jZFHl1uv64P2Iifn2flhm/N16QCOBfZnZdtfMlHUPbYUZrGCD5MJpDW9MxTAtfA91VKmG5OfBe7vhJwGMRDtsNN7GAq30XB3qY2cr4G8dnLVlAbD63A381sxWAFfGIoHLmphnBrma2Ch6dtRCwS7QfCww0s+XwMqXHFgfG5vEPPIopyzI7f7FfIpGob9raxgAe4pk9dmRhmRmLkUsjnUXqRPv7mQLYvFTopwDK1YOQtHO8KWRsJmmIpNckZZXlDgGuM7NnYy4zszvM7IP8IiVtI2l4mKAejw0FSRuplIZ7lLymxHhJX0n6Nv69LumX5S7ezL6Ij+1xh3T2yrgdHhlF/N2exmyJb5yfxPU/hjurG5B0DIlEfdMWN4Zbgd0jfr8HMDx37GLgaklPygvqLB7ttwHbxM347GY4XTvjpTn/D7gszllJR1FkKLC2ec2FW4G/RPvRuPmnJ66feA+vnXCqmXUAOgKrVYtYkjQAr9X8JZCZuhYJR3TmkF64zNBf4WrojEmUF80lHUMiUce0OR2DmY2V5wraA3iocGyAPHvoVnjm0VGSupvZJEkr4D6GTYCBknYxs4FNnO62eMt4XdJbFabRHwAAIABJREFUeLGfWlkC6B8iu5/j6TDAE9+dIy/ac1esbSSe8O5nwD2h5K6ImW0Zm9RNcT2PVeufo1yIbdtxUiWqkuz/rYe2+MYArgA+i4ZmJMDTgZvZzeGgHknkCjKz78zsYTM7Bvg3JVNL/sY4V8PZGt00jco6iiIXAheFT+OgbO7IwbQ/0AEYJqmrmQ2Odb4H3CDp901NHlFR9+EmJHBR32IA8ffDMsMm4Y75jCVoXEUvkUjUOW11Y7gGOMnMxuUbJW0S8frI6yV0Ad6RtFpmVooIpR6UwlM/kLSiSonn8uwiqV2EqS6LZzO9CNhb0lq58+4pz/iapxMlx/jeub5dzGycmZ2OZ0XtKmlp4EMzuxKPqlqt3EVL6pi7+bfHFc6vxOH7cufZG7i3zBQDcE3D/OF03oKGKbkTiUQroE1uDOE8Pr/ModWBryV9iz8xz4fnGFoYuD9i98cCP+I3eHAfxQO49uH9wnyv4qK1h4GDzWxyOJl3B+6VNEHSy7iv4IvC2BNxjcMQ4KNce99wNo/Bq6g9hvtGsnWfjNd7QNJEeRK+0aGV2BG4T56Z9ru4xkw7cRqu7Xgdj9Y6LeboJemq+N0+iXFj8bepk6ItkUi0ItqUjqEWJH1lZh1nVP8YM4c1rCfd4jGSTsOjpg40s+8iemkjM7tNntKil5l9FD6SR81s6ZauO8YNwutO1CROSDqG1s3M0C7kSX6M2kk6hhmMpE6SXo2bK5JukXRA3JQ7xBP5TXFsT0kjou1yeWZTIpz0JHlSunXkqbx7xbE94sl+vKTTc+dtMKbMuuYGDgAOM7PvAMzsAzO7rcxlzIen/SjOIZVJNx7H/hJtY+Ja8+PaSbpO0r+a+XMmEonZnDYXlVQDWUbSjFPNrL+kQ4F+8spm84c9H0mHRugoklbExXDrmdkPki7B6yVfD8wDjDezE6Iv8Xdx4HTcjPUp8Kik7c3snuKYMvwaeCenTZhKbCaLA+/GueYEDiszRz7d+ILASEmDo217YC0z+0Yubstoj0c0jTezU8qc+0Ai0+xSSy1VYemJRGJ2JW0MjckykjbAzB6TtAtuz1+lwthN8Rv8yLgZd6AU3TMFuLPMmDXwanL/A4g3jw3x/EWVxjSJma1VMCV1wcNsrzOzr3JdK6Ub3wivD/FNzJf3JVyOh+I22hSi7xVEdblevXolW2UiUWekjaFGIupoRdzhuwA5hXS+G65q/luZY5Mr+BWqpd+uNCbjDWApSfOa2ZdV+mFmb0r6AC82lM8aW+n8orJG4RlgY0lnR9hroo2SbP6tk+RjqJ0j8II0e1ASkwH8kPs8ENhZ0sLguYUilLQaw4GNJC0Y/og9aCL9dkY8zV8NXCAvsIOkxSTtWewba1qGxllgB1M+3fijwB9y4bt5U9LVuDjw9gh7TSQSrYj0f+rGFH0Mj+C6h/2BNc2rmg3GM4z+AzeZjJX0gpn1kXQ87idoB/yA50aqlJIbM3tf0t+AJ/Gn9IfMrJyGoBLHA/8CXpI0GU8UmPdJPClpCp4t9thiTia8fOc6eEpuo5Ru/BFJPYHnJH2PbwR/z637HEmdcEFdnyyPVCKRqH9m+cYgT4V9jpkdFd+PBjqa2YlVxmwLdAsVcKU+vfGwyq3LHJtI2N6Lx8xsjgpTrpj7/AVe2hMz+yvw19z4/nj66uK8HQvfe+c+3wzcLOnEbN5yY5RL5R1ho4vhuoKf4xvY8Wb2WfRdAr/Zd8PfDB8AbpS0Je7sfkOeAPA93Dz2Ar4B3ivpWNw/8kBkmUXSPpL+F/1vlHS5mf2jwm+VSCTqmFm+MeA3th0lnVruRl0OM7uPMoXtZwazmemkj5k9F2akU3G18kZyz/ddwKVmtl2YqK4ATomUHgOgsSYhNtMh5rUpOuC5ou42s6fjfP3N7FB55tZXJd1hZvmkeok2wszWL2Qkn8bMYXbwMfyI37SOKB6QtJCkOyWNjH/rRfs+ki6Kz10kDYvjJymXBhvoKOkOSa9IuklZjKhzjFxvMEJRXlOewnqgpLHxd6lo7yfpHElP4k/bAN3kWoS3JB2eW/ORoQkYL6lvDe3HyTUSjwMrVPqRJN0NbAucGaauXsDaAGb2PZ59dSlJq+CJ8Sab2bVxfEr8vlN9Bk1hZt/iCupy2VM/xh3fi9UyVyKRqC9mh40BPAS0T9is85wPnGtmawA7AVeVGXs+cH70KSZ0WxXoi5tTlgXWyx37wszWxFNbnBdtFwHXm1kPPE7/glz/5YHNMpMXnil1S0oFa34maXW8XOda+E37AEmrNtG+e6xzRzxMtCxmtgP+lnRMhNM+BwzLHZ+Cm466AitRSO0dWod3cO1Dk8hzIS2HO6eLx5bCk/qNLR6L46keQyJRx8wWG0PctK7Hy1vm2Qy4KJ6Q7wPmkye3y7MOXhENvG5znhGRF+kn/Om3c+7YLbm/map4ndwcN+Ax/hm3F0JHH4yMqx/hWoVFov/dZvZ1aAXuwvMgVWrfINq/id9gWs1jyv0tF2paLQQ1YwN5LqX/4j6GfN3r3eQ1st/CN+OyoaqW6jEkEnXN7GQvPw93gF6ba2sHrBNmjak0tAhV5bvc5yk0vF6r8JkK7V/XMHc1TUAlposALPwIK+MhtR/jb1j54/PhKbPfbGKqzMewPDA0fAxZlFbmY1gHeFDSw4WNI9FGSLb+1s1s8cYAU5W1twH75ZofBQ7NvkT4ZJFhlG6CuzfjlLvl/j4bn5/JzdEHr6LWHAYD20uaW9I8eBruIU207yCpQ7wJbdPM8wEg11GcCrxrXo50IDC3oi5DbBpnA/0yJXNTmNlrMedfyxx7Fn+j+nNL1ptIJGZvZpuNITgbz9eTcTjQK5zBLwEHlxnTFzhS0gjcGfp5jeeaU55P6M+UHN+HA/uGKWUvmnnjM7MXgH64QGw4cJWZjWqivT9u5roT3yyaw02x1vF4XqXtYh2Gbz67yNNovwZMJqdDqJHLgA0lLVPm2On4b1U07SUSiTqn7tNuR5TNt2ZmknYH9jCz7ZoaNx3O2yBttaR9cG3EoZVHVZyrJ7C4mT0U35vUabTgHO2Bk4BdKJnFbq+U72h6kdJuJxKzBk1D2u3ZycfQUlbHHdQCPgP+MIvX0xJ64uGnD8EM02n8C1gUWNnMJseT/lHFTvE7KimZE7NKq1CN5NuYOcxupqRmY2ZDzGwVM+thZhua2Ruzek1V9BdrSnpG0qj4u0KI007CI35GS9otNBT/i++fxOfXQzOxc8zVTtIlkl6U9ICkh7JjZdaTr9swGcDMvszU5ZI6S3pZnib8BWBJSZdGyOmLkv4Z/X4j6bbcvL0l3T/DfshEIjFLaA1vDLOKYk6lBSg95Wf6i6ER8z8AT6nxCrChmf0oaTPg32a2k6QTyJmh5KrjXhEB1A/3H+yGaxTuA+7AdQ+d8UikhfFopGsqrDWr21AtA+sKwL5m9qdYw3Fm9kk4rgdK6oGXEb1c0jxm9nWsqVH6D6V6DIlEXZM2hpbToG5D5mOIr5vhyujscKa/6ARcJ2k5PEz1Z9TGPWHaeUleuhNcG3F7tP9XrsquCUn74o71XwLrRvPbZjYs123XuMG3x5363cxsrKRHgG0k3QH8H664bkCqx9A6SGabtkvaGGYMlfQXFwJPmtkOkjoDg2qcL6+ZUOFvLTSo2xCpMq6VNB7IkgZO1WlEFNLRwBpm9mm8tcwVh/vjGWM/AUY2VQcikUjUH3XvY5hNqaS/6IRnJwXYJ9f/S6C5YZ9DgZ3C17AI0LtSx1zdhoskzRVrmgPPylqO+fCN4vOY+ze5Y4OA1XCfRSMzUiKRqH/SxjBjqKS/OAM4VdLTlJ7UwWsxdMuczzWe4068itx4vNTmcKprOI4D3gfGSxqFayauo3F+KcxsDDAKeBH3WzydOzYFT+H9m/ibSCRaGXWvY5hVFHUMTfTdHnjNzF6K72vjDuo5419/MztRUY/BzM6qcd6OZvaVPA32CGC9lqSoyJ83zEYb4ZuMgCPNbGD0uxr3owgXze1jDetHNyLpGOqL2TFENU/ye9TOtOgY0hvDzGF7PMNrxnXAgeG87o6nAmkJD0Rk1BDg5OmYtyjL4NoXVz9nHJGFBuOZWpst5kskErM/aWOYjqhMPQdJ65KroyCpCx5e+j64aSZ7kwgq1Xm4R9LzoSs4MMb2xkNRHwYOj3M+GOd5SdIXkr6RNE5S1xZc0rPk6jFEBthMBNeBCgkAldJuJxJ1TdoYpi+N6jmY2TPk6iiY2ZvAuXgFtLslHZQ5hINGdR6i/Q9mtjpuyjk8zEfgGocXzGw14ClgQjztvw+sbmZz4/WqL2nB9WwF3JNvkHQtnpK7K3BhuUEp7XYiUd+kcNXpyzq48Aw8++gZ5TqZ2UmSbgK2AH4H7EEpquhBM/sO+E5SVudhEr4Z7BB9lsSL6HwM/EQpOuhG4C5JHXF9wu05LcWczbiOMyWdgb/ZrF1Y+74R0XQhLnC7tsz4RJ2SbPgJSG8MM5qKnn0ze9PMLgU2BVbJvQE0qvMgr8W8Ga6NWAWPGMq/ZRTP2Q74LN5Qsn8rNmPdx+AmquNxf0hx7VPwzWin4rFEIlH/pI1h+lKpnkMDnYKk/1PpUX45fAP4rMq8nYBPzeyb8BXkn+LbAVmOpN8BQ8MXMEHSLnE+yWtB10woqs8H2knaMubIamMLrx3xSnPmTCQS9UEyJbWcuSVNyn0/B9cvXCPpGOB/eJ1ngFuBK8OZvDNe6+FcSd8APwJ9zGyKKlemewQ4WF574VVytZ5xIdpKkp7HQ0wzHUQf4FJJx+OpN27Fa0LXTKQy/xee9uIxPJ3HfHi46hjgj82ZL5FI1AczTMcgyYBzzOyo+H400DHL6FlhTJN1CMKscrSZbV3m2EQ8+dxHLVzziTRDRzC95g3twK7AIlmKCUnn4xvNQtWupxY9RbXfrMb1z4OHpy5rZp/n2u8BbjaziuG2Sccw+zG7axWqkXwgtTO76hi+A3aUtGCTPQMzu296FqdpDvJCNrOSN4gKbJLaARtTSp8xS4lMqo/iegwAJHXCE/kl9XMi0cqYkRvDj3iGzSOKB1S5XsE+ki6Kz10kDYvjJ0nKK2w7SrpD0iuSbsrZ6wGOkTQi/mU28Ub6gmjvJ+kceWbS02N8JR3BkZLGx7++NbQfJ+lVSY/jKa2b4hZKZqDeeBqKH3Pz7RnXNFrS5REZBHCDCnUTov9W8fsMJSKlYk3jJH0u6VtJX0u6II6Nk/SL8CV8rFK96BvkKcJvoWFN7R2AR8rVkFbSMSQSdc2Mdj5fDPSJp8s8Wb2CNfDIlqvKjD0fOD/6FPP5rIqrcrsBywLr5Y59YWZr4pqC86Ktkb4g1395YLPM5EUZHYGk1XF/wVq44/cASas20b57rHNHYI1qP1LwOrCQpPnx8NVbswOSVsQ3jfVCozAF9yEAHBeviz2AjST1kOsirsQdxBvglduIMp6DcBNfhzi+YczzdPyOKwFvxTjiuobhfo7VVYqe2h3fLBqRdAyJRH0zQ80nZvaFpOtxW3k+BXWlegV51qFkurgZyNvnR5jZJAB5SojOlCKAbsn9PTc3VyV9we0RfplRTkewPnB3mFSQdBd+41SF9nbR/k2011qm8y78hrsWcFCufVO8hOnI+M06AB/GsUZ1E+L8E8zs9Tj/jUThnLiWnQDM7AlJv4yNewi+SbwNXAocKOlXwCdZPqS4jp0l3YmXI320xutKzEYkO32iKWaGXf08vFxkXghVqV5BrXM2ivXPfbcKn6nQ/nXhWLm5Ky2s2oJb4tW/Ff+trjOzn3K/h6Ltbw1OXr1uQqXzl1uzAYPxOgtL4ZlYd8AjqIbk+t2CaxsE3GtmPzTr6hKJRF0ww3UMZvYJniRuv1xzpXoFeYZRElDtXuZ4JXbL/X02PlfSF9TKYGB7SXNHhM4O+A2zWvsOkjrEm9A2tZzEzN7Bb8rF9BUD8Sf1hQEkLSBpaSrXTXgFWEaelwncNJW/lj4xT2/gIzP7wszeBRYEljOzt/Df6GgabgxP4rqLQ6hgRkokEvXPzIrEOZuGmTgPBy6Wx+W3x29WBxfG9AVulHQU8CDVaw3kmVPScHzTy26IlfQFNWFmL8TT+IhousrMRsHUUNNy7f2B0bhpZgg1YmaXl2l7Sa5HeFQesfQDcIiZDZPXVngR9ws8Hf0nh3npQUkf4Tf57jHdiXj1trHAN8DeuVMNp1QnYghwKrlNNN5i7gR2wf83SyQSrZDZth6DpLnxusomaXdgDzPbbiaefwowLte0vZlNnE5z/wL4nZldEt8XxxPu7Vx9ZLPPMwjXL5QVEkj6Ax41lqXROM7M7p2ea0g6hplDPWsTmkPyj9SOpkHHMKtj96uxOl6KUni6iD/M5PN/GxFAM4JfAH8iTEZm9h9KaS1mCpKWwM1Wq5nZ5/LEeymEKJFIzL65ksxsSFYUxsw2NLM3ZvWalNNZxPcHwk6PpK8knSJpjFx/sUi0LyJPrz1G0keSXsOT4HULLcHDkjpLGh/955J0begKRknaOHfuuyQ9Iul1efbTbB2XltMyNMHCeA6nrwDM7CszmxDzdYnzPC9piKSukjpJmhimLMKv8q5KacHzv1PSMSQSdcxsuzHMBnSQi8lGS7q7hv7zAMMi++lg4IBovwB4KtoXwTUNqwIvmVkHM/tNYZ5DAMxsZdxHcp1K9Rp64k71lYHdJC0Z7Y20DDWsdwzwAZ5s71pJeQf5FcBhUf/haOCSSIUxBi/7Ce5QH1AuMinpGBKJ+mZ2NiXNapprSvqeUnqI54HN4/MmwO9harrqz0PEVon1iQI4ZvaKpLdxER7AwCxXkaSXgKWBdymvZRhbbbGRtG8rfKPaFE/qtzquF6lUy6E/vjE9iUd5taT4T2IGkGzvielJ2hiax480fMvK10T4wUqe/KK2ojlU00aUq9VQTctQlVjvCGCEpMdwrck5RC2HMkPuA06VtADuA3qilvMkEon6IpmSmsdEoKekdmHGWbOGMQOJ9NSS5pCnrW5Qn6FAXmewPC44e7XK/JW0DFWRtLik1XJNPYG3q9VyCAX0CDxdyQMFxXgikWglpI2heTwNTMDDWM/CVcpN8WdgY0njcBPTSmb2MfC0PPHemYX+lwBzRP/+wD6RoqMsZjYGd2a/CFwTa6yFnwFnyRPtjcZNRH+OY32A/SSNiXnzYcL9gT0plRNNJBKtjNlWxzAzaSOahYnAu2a2Qa5tNNDezLqXG1Ncr6TNgdOAn+M+lWPMrKo5KekYZgxtRbdQJPlSaqe16hhmJq1as5BjXklLmtm78oytTVJY70fANmb2H0ndgQHAr2bQWhOJxCwimZIqMB00C2MkrYs/YXeJsNczZ4VmQZ4iZHFcuzAs3hT+TC7fUaxriKQX4t+6ufbxAGY2KjYKcBPTXJLmpEDSMSQS9U3aGJwZoVlYDb95Hgu8aWY9zeyYwjwzRbNgZmvhNS164w7mnnhq7/tz3T4ENjez1eK8FxTnKbATMKqc/yPpGBKJ+iaZkpxWrVnI8QnwqTz31Mt4Er2Mn+EpSLJCQMuXGU+sZyW84t0WNZ43MZ1JtvbEjCRtDJVpVZqFHP3xynr7FNqPwJXQq+DXPbnsgj3H0t3A783szWaeO5FI1AHJlFSZibQSzUKBu/EKdgMK7Z2A983sJ2AvSum3pxIRVg8CfzOzWsNiE4lEnZE2hsq0Js1Cfo4vzex0M/u+zFr2ljQMNyPlK9tlb0eHAr8G/l/OJ7Nwc9eQSCRmb9qkKUlSZ1y52x3AzDpKOhHPNPoR8GhE3/SRdBVwThTLmSipV/R/xszWBZ7D4/oxsw8knYybWQ7PzmdmvyssITvvZBqbdDCzfkC/WKvF+QfF4fHARDM7sTCmd+76VgAux0Nl5wSGmFnnMueZCHQPh/t1ZtYjxr+KR1MB/BJYVNKOZvYv4F/FeRIzl7aqYYDkW5lZpDeGxuyDh3YCYGb7m9lLxU6xKQB0Bn6Xa38uvylMB74DdpS0YDPGXACcG5FQKxIO7io8gyfOQ9Iv8Q1yHUm98JDWOaJPIpFoA6SNoTG9gJvCTNJB0qC4QTZA0lfx8TRgg+h/hKTekh6IPvNIukbSyNApbBftK0kaEWPGSlquynp+xNNgH1FmDUtLGhhzDJS0VGgWNgTOyMw9uf5zhJZiZIw5KA49TWwM8fcBvGjP88CWwKdm9t9KWocy60o6hkSijkkbQ2OeA/rE0/a3NfQ/FjfV9DSzcwvHjgOeMLM1gI2BMyXNg9e3Pj9CZHsBk5o4x8W4WatTof0i4PowAd2Ep65YC1daLwq8D1yHh7kC7Ad8HutZAzggIp2ex01KP8c3hmdxJ/iK8T3zZdSkdUg6hkSivmmTPgZKztRa21vKFsC2ko6O73PhkUfPAsdF6OddZvZ6tUnM7AtJ1wOHA/nNah1gx/h8Ax5thJldK2kAsBWeAO8geYbULYAekrIUF52A5cxsgqQXcVHe2jHPsvimsColM1LNWofEjCPZ2RMzmra6MXwMFIVnC+BRSNMTATuZWTEE9eUw+fwfMEDS/k0lowPOwyOjrq3SZ+rGFs7za4BrIqVF91jPYWZWDFUFv/lvCMwbGolheBTSqsBl0acmrUMikahv2qQpKeoKvC9pUwB54ZmtgKFU1x2Uo1r/AcBhkpdCk7Rq/F0WeMvMLsCL39SS1uIT4DbcHJTxDF5JDVwPMTTm30pRi1nSonhk0Xuxnj/mji0fpi1wc9FBePlOcDX12vgbzovR1qTWIZFI1D9tcmMIfg8cH87ZJ4B/hpK3H3BZ5nyuYZ6xwI/ypHlFB/HJuPllbDy1nxztuwHj49xdgetrXPPZQD466XBgX0lj8Rt1Vk9hi5h/DL4ZHGNm/wWuAl4CXoj1XE7prfEZ3Hz0LICZ/Yj7FJ6LjQCqax0SiUQroa2akghdwh64iWYN3ObfB+hrZivkuvbOjemc+9wx/v6A10wGQJGBNdgcmGBmB0naHngtxpwam85gM3u8iXV2zH3+AJhbUp/YDMBDS/cK8VvW70jgyNyapoSfoz2eI2ktM8vnScLMPqSQoqOgjRgEdMxpHXrhPo7pQluOzU/UTvKvzBza7BtDmHfuBgaZWRcz6wb8HVhkep3DzO4zs0wotj2e8C47dkJTm0IVJgAbxU36ZDyctRrfRtRUdzwB4MEtPO/CklqShiORSNQRbXZjwMNHfzCzzLGKmY0Ghkas/3h5nYTdwN8EQtNwh7wc5k0538FW0TaUUpTQ1JoOEe+/LR6uOlpSF0n9suggSTtI+kbSt5I+DrPUaEnvSPpnaAbGSeoa63zGzD6N0wwDlmjGdQ/B01og6ci4zvGS+kbbPJIejDWMz64/OBM4vqkTJB1DIlHftOWNoTsev19kR7wWwirAZvjNfLE4tirQl//f3tmGSFVGcfz3p1CEqJAoLC01Jagow5ckTQkNKygT/JCgmUh+qw+BJRh+iN6EIOlDERplSRkuVkIl5BolZGmbsWH4spnkomimFdirePpwntU70+zOHXdn5+7u+cFwZ+597nP/Ozsz597nued//Mx/NDBFXj9hNXAvcDueP1CCmX2BTzIvTWfuZ11J0/4vAePMbAhuUvd6ynE4AxxPeQOv4M6q5SwGPs7zB0u6EDfe+07SeGARXpdhMp7TcAs+CX/YzG5OVxibM11sB/5WKijUGZHHEAR9mwE7x9AFU4F3Uj2Fo5I+w+cgfgd2mFk7nK2XPBIf4/+xIxdB0jpgSQ3Huy7tvy+9XosX8FmVXm9MyxYyVyPpWHfggWFqlWMMyWRAbwNew11g3zOzU6mvjXhg2wy8IGkl7ie1rayvp/Grhidy/4U5iLHjICgOA/mKYTcwvsL6mmokpOfdSYzr6njZY5bUfZBXblsDzE4Orl3RMccwzsweSc6qFY+bAtR43FX2OUkryrZvxRP1Jlc5ZhAEfZSBfMWwFXhW0sNmthpA0kTgJF5Kcy2e9DYNWIrfVlqJPcAoSdemIaJ5nbTrLN9hDzBS0hgza8NvO/2sK+GSrsavJBZkrjRq5XPgDUnP40FiDrBA0pXACTNbJ/eDeqjCvs/gSW8Hqh2kpaXluLwyXaO5DHfOLRqhqzaKqKuImsBHI86LARsYzMwkzQFWSVqGZ/EexOcQLsITvQx4PBnIVQwMZvaXvNTmh5KO40lmN1Zouh5YLelRYG7Z/ouADWkOYCfnMo07YwWetPZymv8+bV4HOjdm9o28AtyOtGqNme2SNAufVzkD/EsqPFS270eScs0qm1khJhkkfV3re9QbhK7aKKKuImoC13Xe+5r1tD1QEBSPIn95Q1d+iqiriJqge7oG8hxDEARBUIEBO5TU35AX2GmusGlGjsnpgUC1JMBGEbpqo4i6iqgJuqErhpKCIAiCEmIoKQiCICghAkMQBEFQQgSGoF8iaaikTyTtT8vywkwdNbNbki/Vbknnay7Y07rGSdqeNLWW+VU1TFdqt1nSr0p1zeuk5S5JeyW1pVvJy7cPlvRu2v6VpJH10lKjrmnJ1+y0zlVJLIKuxyR9r3O14a+p1mcEhqC/sgxoNrOx+KT8/74weE3s25Iv1a3AspTg12hdfwAPmtkNuHfVKkmXFkAXuJHignqJkHQBXuP8btyTbJ6k68uaLQZOmtkY4EVgZb301KjrJzwh9O1666lR1y5gQnJjbiKVAO6KCAxBf2U27jtFWt5f3sDM/jGzDsuRwfTO9yGPrn0d3lupROsxoN6JglV1JT3NeBZ/vZgEtJnZgWTdsj5py5LV2gTMUMr0bKQuMztoZq24+WVvkUfXp5n6K7ncmCMwBP2VK8zsCEBaXl6pkaQR8qJHh4CV6Ye44boy+iYBg4AfumrX27rqyFX4/6KD9rSuYptUafA33Amg0boaQa26crkxRx5D0GeRtIUKNufA8rx9mNkh4KY0hPS+pKZUKa+hulI/w4C3gIWZ8qoN11VnKp35l99Tn6dNT9Oj9PSOAAABW0lEQVSIY+Yhty5J84EJwPRqnUZgCPosZjazs22SjkoaZmZH0g/ssSp9HZa0G7ceb2q0LkkX47U5njSzL7ujpyd19QLtwIjM6+FA+VVcR5v25C92CXCiALoaQS5dkmbiJwDTM8OnnRJDSUF/ZROwMD1fCHxQ3kDScHntbdJdOFOAvQXQNQgvO/ummW2os57cunqJncBYSaPS+/BA0pYlq3UusNXqn6mbR1cjqKpLXoDrVeC+VNu9OmYWj3j0uwc+5twM7E/LoWn9BNxJFuBOoBV30m0FlhRE13zc2fbbzGNco3Wl19uAn4E/8bPVWXXQcg+wD59XWZ7WPZV+2MDrgWwA2nB34NG99Jmqpmtiek9OAb8AuwuiawtwNPNZ2lStz7DECIIgCEqIoaQgCIKghAgMQRAEQQkRGIIgCIISIjAEQRAEJURgCIIgCEqIwBAEQRCUEIEhCIIgKOE/bvjuvi0vIrUAAAAASUVORK5CYII=\n", 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gxo0bbNmyhSNHjiCEIDc3FyEEzz//vMH0UkpWrFhBixYtiuzr1asXa9asYdmyZUbVn4YNG8bcuXMLbJs8eTLdu3dn1apVJCQkEBUVBYC/vz9eXl4cOnSIpUuX8tlnnxm1RaHIj4ooFWVi+fLljBw5krNnz5KQkMD58+dp3LgxFy5cMJi+T58+zJkzh7xXkBw4cEC/Lzo6mpdeeomIiIhim+4lkZSUhL+/P6CNjOdn+PDhvPvuuyQlJRESEmLUFoUiP8pRKsrEkiVLGDhwYIFtgwcPZubMmQbTT548mezsbFq3bk1wcDCTJ//zevjw8HA8PDwYPXp0qWwZP348EyZMoHPnzuTm5hbYN2TIEL7//nsefvhhk2xRKPJTLV8u1q5dO1lThHuPHTtGy5YtrW2GRbh48SJRUVEcP34cGxv1DK8KGKp/QogYKWU7K5lULqjaqKgUfP3113To0IG33npLOUlFpUMN5igqBSNHjmTkyJEFti1cuJAPP/ywwLbOnTvz8ccfV6RpCoVylIrKy+jRo0vdX6lQWBLVxlEoFAojKEepUCgURlCOUqFQKIygHKVCoVAYQTlKhUWoSD1KhaKiUY5SYREqWo9SoahIlKNUlJmK1qOcNm0aY8aMISoqiiZNmvDRRx/p9z300EOEh4cTFBTE559/rt/u5ubGpEmTaNOmDR07dlRCvwqzUPMoqxPrX4fLhy2bp28I3DerxCTW0KM8fvw4v/32GykpKbRo0YLnnnsOe3t7vvzySzw9PcnIyCAiIoLBgwfj5eVFWloaHTt25K233mL8+PHMnz+fN954o0yXRlFzUBGlosxYQ4+yX79+ODo64u3tTd26dfUR4kcffaSPGs+fP098fDwADg4O+n7S8PBwEhISyn7iihqDiiirE0Yiv/LAWnqUjo6O+u+2trbk5OSwdetWNm/ezJ9//omLi4veEQPY29vrX6+al16hMBUVUSrKRGXTo6xTpw4uLi4cP36cXbt2le6kFIpCKEepKBOVSY+yb9++5OTk0Lp1ayZPnkzHjh1LlY9CURilR1nFUXqUCmui9CgVigpE6VEqKjNqMEdRKVB6lIrKjHKUikqL0qNUVBZUG0ehUCiMoBylQqFQGEE5SoVCoTCCcpQKhUJhBOUoFWXG1taW0NBQ2rRpQ1hYGH/88QdQUJOyMFFRUSUuU3zrrbcIDQ0lNDRUn39oaGgBpaDyYMOGDYSFhRESEkJ4eDhbt27V7/v+++9p2bIlPXv2NDk/KSXvvvsuLVq0IDg4mNDQUL799ttysFxRrkgpq90nPDxc1hTi4uKsbYJ0dXXVf9+wYYPs2rWrlFLKM2fOyKCgIIPHdOvWTe7du9fs/MubmJgYefHiRSmllLGxsbJ+/fr6fffee6/cvn27WfnNmTNH9u3bVyYnJ0sppUxMTJRfffWV5Qy2MobqH7BPVgI/YMmPmh5UjXhnzzscv3nconkGegbyWvvXTE6fnJxMnTp1imzPyMhg9OjRxMXF0bJlSzIyMvT7FixYwDvvvIOfnx933303jo6OzJ0712D+t27dIiwsjBMnTmBnZ8etW7do27YtJ0+epFu3brRr147du3eTmprKwoULadeuHampqYwdO5a4uDiys7OZMWMGDzzwgMH8w8LC9N9DQkJITU0lOzubN998k127dhEdHc3AgQNp1qwZ69at4/bt2xw9epTx48eTmprKd999h7OzM+vWraN27drMnDmTP//8E3d3dwBq166tny+6adMmXn31VXJzc+nYsSMff/wxDg4O1K9fn+joaNasWUNubi7Lly+nefPmJv8HCsujmt6KMpORkUFoaCiBgYFER0cXWL+dx7x583BxceHQoUNMmjSJmJgYQFu2mOeENm3apH+VRHHUrl2bzp07s2HDBgC+++47Hn74YWxtbQHIysrizz//5MMPPyQ6OhqAGTNm0LdvX/bs2cOWLVt4+eWX9apCJbFs2TI6dOiAvb09M2bMIDQ0lKVLlzJrlqbSdPToUZYuXcquXbt47bXXqFOnDgcOHCA8PJzFixeTmJhIdnY2jRo1KpJ3eno6Y8aMYcWKFRw+fJj09PQCQsP16tXjwIEDREdH8/777xu1VVG+qIiyGmFO5GdJnJ2d9erkf/75JyNHjuTIkSMF0mzfvp2XXnoJgNatW+tFeffs2UO3bt30akFDhw7lxIkTJZYXHR3NRx99RP/+/Vm4cCHffPONft8jjzwCQI8ePbh69Sqpqals3LiR9evX6x1cngZmSVHa4cOHeeONN9i0aVOxaXr06IGrqyuurq64ubnpo9SQkBBOnDihV0gyxLFjx7j77rtp2rQpoK1MWrBgAWPHjgVg0KBBgCYUsm7duhKvh6L8UY5SYVHuuecerl+/zrVr14rsy9ODzE9JzqQ4unXrxtixY/ntt9+wt7cnMDCw2DKEEEgpWb16td4pGePcuXMMGjSIxYsX07hx42LT5dfEtLGx0f+2sbEhJycHT09P7O3tOXfuHA0bNixwrLHzzstLaWdWDlTTW2FRjh8/Tm5uLl5eXgW2d+3aVT/ae+TIEf37cNq3b8+2bdtITEwkJyeHFStWmFTOY489xqOPPlpkiePSpUsB7c2P9erVw9XVlT59+hQYLc+vgVmYxMRE+vXrx+zZsy0i0/b666/z/PPPk5KSAmh9rPPnz6dVq1bEx8dz+vRpABYvXky3bt3KXJ6ifFARpaLM5PVRghYpffXVV/o+wzyee+45Ro8eTevWrQkNDaV9+/YA+Pv7M3HiRDp06ICfnx+tWrWiVq1aRst89NFHmTFjBsOGDSuw3cPDg06dOpGSksLChQsBmDp1KuPGjSMkJIQ7d+7QrFkz/UvNCvPhhx9y5swZpk6dytSpUwH49ddfizh+U3nxxRdJS0sjPDwcBwcH7O3tGT9+PC4uLixYsIBBgwaRm5tLhw4deOqpp0pVhqICsPawe3l81PSgqkVKSoqUUsrs7GzZv39/uXLlSqPHLFmyRI4aNarAts6dO8sDBw6Ui40Kw6jpQQpFBTFt2jQ2b95MZmYmvXv35qGHHiox/XPPPcfmzZv1I98KRXmjHKXC6syePdus9PPmzTO4fceOHSbnsW7dOiZOnFhgW7NmzVi+fLlZtihqBspRKmok999/P/fff7+1zVBUEdSot0KhUBhBOUqFQqEwgnKUCoVCYQTlKBUKhcIIylEqykx10qPcvHkztWrVom3btjRv3pxu3bqVea11/fr1uXXrloUsVFgDNeqtKDP5RTF++eUXJkyYwLZt28qU56RJk5g0aRIAbm5u+vwrgu7du7N69WoA9u/fz8CBA/n666/VEsMajHKU1YjLM2eSdcyyepSOLQPxLTTfsCSquh5lYcLCwpg0aRJz586lW7duPPbYYwwZMkQ/Kd7NzY3U1FQ2b97MW2+9Ra1atThx4gTdu3dn7ty5RUQ6vvrqKz7++GNu375Np06dmDt3LgkJCfTp04fdu3dTq1YtIiMj+c9//kOPHj1MveyKckY5SkWZyVvrnZmZyaVLl9iyZUuRNPn1KA8dOqQXyM3To9y/fz/u7u706NGDNm3aFFtWfj3K/v37F6tHuWXLFqKjo4mNjdXrUS5atIjExEQ6dOhAr169cHJyMun8wsLCmDNnjtF0u3fvJi4ujgYNGtCrVy/WrFlTYJXRkSNHWLVqFX/88Qd2dnY8/fTTfP/994wYMYKXX36Z559/njZt2tC2bVvlJCsZylFWI8yJ/CxJddSjzI80UQquY8eOBAQEADB8+HB27NhRwFFu3ryZvXv30q5dO0B7wDRo0ACAZ599lh9++IGFCxeWqG6ksA7KUSosSnXQoyzMgQMHaNmyJQB2dnbcuXMHgNzc3AJakYbKzo+UkjFjxvDmm28WKSM1NZVLly6Rm5tLamoqrq6upbJVUT6oUW+FRanqepSFiY2NZebMmbzwwgsABAQE6F9jsWrVKnJzc/Vpd+3axblz58jNzWXZsmV06dKlQF49e/Zk2bJlXL9+HYAbN25w7tw5AF599VVGjRrFlClTeOaZZ0y2T1ExqIhSUWaqkx4lwG+//Ubbtm1JT0+nXr16fPLJJ/oR72eeeYYHH3yQTZs20bt37wIq5506deLll1/m6NGjREVFMWDAgAL5hoSEMHXqVHr27MmdO3ewt7fn008/JT4+noMHDzJ37lxsbW1ZsWIF33zzDY8//rjR66CoIKyt81YeH6VHWbWoDnqUmzZtkg8++KBVyrYmSo9SoagglB6lorIjZCk60ys77dq1kyWt+qhOHDt2TD/QoDAdpUdpGQzVPyFEjJSynZVMKhdURKmokSg9SoU5qFFvhUKhMIJylAqFQmEE5SgVCoXCCMpRKhQKhRGUo1SUmeqiR7lu3Tp9OW5ubrRo0YLQ0NAiq3/MxdfXl9TUVAtZqbAGatRbUWaqix5l/pHwLl26MHfuXP2KI0XNRjnKasTvy05w/bxlIxfvBm5EPmyayg5UPz3KPHr06MGnn35K8+bNadmyJaNHj2b8+PG8+uqrtGnTBm9vb9577z3c3NyIi4ujU6dO+iWUAO+99x6rV6/mzp07rFixgmbNmrFp0yZeeeUVQIvK//jjD5Ol3xQVi2p6K8pM3lrvwMBAoqOjmTx5cpE0+fUoJ02apBeWyNOj3LVrF5s2beL48ZKFh/PrUQLF6lF++OGHREdHA+j1KPfs2cOWLVt4+eWXyczMNOscu3btyu+//861a9dwc3Njx44dAOzYsYPIyEhAU0OfN28ecXFxHDx4sEDXgp+fHwcOHOCJJ57ggw8+AODdd9/lyy+/5ODBg2zbtg0HBwezbFJUHCqirEaYE/lZkuquRwkQGRnJ119/TZ06dRg8eDCrV68mLS2Na9eu0ahRI44dO0anTp3w9fUFoE2bNiQkJOi1JwcNGgRAeHi4Xti4c+fOvPjii4wYMYLBgwfj7u5usj2KikVFlAqLUlF6lCdOnDBbjzI2NpbY2FiznSRo57Vr1y5+//13unbtSnBwMF988QUdO3bUp8mvJGRra1tAqzJvX/7t06ZNY968eSQnJ9OuXTtOnz5tlk2KikM5SoVFqW56lHm4uLhQp04d1q1bR7t27YiMjGT27Nn6ZndpOHXqFG3atGHixImEhIQYjaQV1kM1vRVlprrpURZHZGQkMTExODg4EBkZyYULF8rkKN955x3++OMPbGxsCA0N5d577y11Xopyxto6b+XxUXqUVYvqoEdZU1F6lApFBaH0KBWVHeUoFVZn9uzZZqWfN2+ewe15U3ZMQelRKsxBOUpFjUTpUSrMQY16KxQKhRGUo1QoFAoj1FhHmXz9KstmTCTx8kVrm6JQKCo5NdZR5ubkcO1cAivfnkp6cpK1zVEoFJWYGuso6/j68dCrk0m5cZ01s98i5/Zta5tUZVF6lCVjjh7l8ePHcXFx0dsRGhqqX22ksCLWnshZHh9zJpwf/2O7nP1wP/nLZx+ZfExlojJMOHd1ddV/37Bhg+zatauUUsozZ87IoKAgg8d069ZN7t271+z8KwpLTl6vV6+eflK9MY4dOyar0oIJNeG8htDinkiunjnFnjXLqd8ymFaR3a1tUqn5bdHnXD1rWWGFuo2a0H3U0yanV3qUZdOjVFROamzTOz+dhz2Of2AQm+bP5caF89Y2p8qh9Cgtq0cZFxdXoOm9e/dus2xVWJ4aH1EC2Nja0v9f4/lq/ItsmPc/HpnxX2wKiTpUBcyJ/CyJ0qO0rB5lq1atSuy/VVQ8KqLU4ebpxb2jn+HyyRPE/Lza2uZUWZQepYbSo6xeKEeZjxadutIs4h52LlvMzYsXrG1OlUTpUZqO0qOsOihHmQ8hBD2jn8fOwYEtCz8rVbRTE8nrowwNDWXYsGHF6lGmpqbSunVr3n33XYN6lD179jRLjzIpKalYPcoXX3yR+fPnA5oeZXp6OiEhIQQFBTFt2rRSnWdkZCT+/v4W1aMMDg6mdevW1K1bV69HWbiP8tNPPy11GQoLYc0hd8AJ2AMcBI4C0w2kcQSWAieB3UCAsXzLOr0iZt0aOfvhfjJ+764y5VMRVIbpQWVF6VFWXWrK9CBrR5RZQA8pZRsgFOgrhOhYKM2TQKKUshnwP+Cd8jaqTa89yFomAAAgAElEQVT78fRvwLavvyAnO7u8i6vxTJs2jdDQUIKDg2ncuLFJepSTJ0/mjTfeqCALFTUdq456654+eUsW7HWfwu3dB4Fpuu/LgblCCKE7tlywtbOj+8hoVrw9lQMbfiTigUHlVZQCpUepqPxYfXqQEMIWiAGaAR9LKQtPGvMHzgNIKXOEEEmAF3C9PO0KCA2nUeu27F2znDa97sPBybk8i1NUMEqPUmEO1m56I6XMlVKGAvWB9kKIwouDi84pKRp1IoR4WgixTwixz9DUlNLQaeijZKQkc2DDTxbJT6FQVE2s7ijzkFLeArYCfQvtugA0ABBC2AG1gJsGjv9cStlOStnOx8fHIjb5NQ+kcWg4+35cye2MdIvkqVAoqh5WdZRCCB8hRG3dd2egJ1B4Ddta4And9yHAlvLsnyxMp6GPkpmaoqJKhaIGY+2I8i7gNyHEIWAvsElK+ZMQYoYQYoAuzQLASwhxEvg38HpFGujbrDkBbcI48MtP5OaoEXCFoiZiVUcppTwkpWwrpWwtpQyWUs7QbZ8ipVyr+54ppRwqpWwmpWwvpazwdV7h9z9IWuJN/vrj94ouukpQXfQoFYrisPqod1WgUZswvOo3JObnNbSM7G5wzXJNJr8oxi+//MKECRPYtm1bmfKcNGkSkyZNAsDNzU2fv0JhDZSjNAEhBGH3P8imz+dwIe4wDYJaW9skg9z68RS3L6ZZNE8HP1dqP9DU5PRVXY/yiy++YMOGDaSkpHD69GmGDBnC22+/DcDTTz/N/v37ycjIYNiwYUyZMgWA+vXrEx0dzZo1a8jNzWX58uVmi24oKjfW7qOsMrSMjMLZ3UMN6higuulRHjx4kOXLl3Po0CEWL17MxYvaC+hmzZrFvn37OHjwIJs2bSIuLk5/TL169Thw4ADR0dG8//77Zlw9RVVARZQmYu/gSKtu93Jg/VrSk27hUqu2tU0qgjmRnyWpbnqUPXv21GtDBgYGcu7cOfz8/FiyZAkLFiwgJyeHixcvEhcXR6tWrYCCepPr1q0z8copqgoqojSDkO69uZObS9z2LdY2pdJSHfQoDelKxsfH8+GHH7JlyxYOHTpE3759C0SlhvQmFdUH5SjNwKt+A+5qHsjhLRuVBFsxVFc9yuTkZNzd3fHw8ODSpUv88ssvZuehqLqopreZhPTozcZPP+LiieP4t2hpbXMqBXl9lKBFiMXpUY4ePZrWrVsTGhpqUI/Sz8/PLD3KGTNmFKtHmZKSon+519SpUxk3bhwhISHcuXOHZs2asWbNGrPOMSwsjFatWhEcHEyTJk3o3LmzWccrqjjW1nkrj095vu4zKyNdfjhyiNww78NyK8MclB7lPyg9yopH6VEqDOLg5MzdER2J37NTrdSxEEqPUlHZUU3vUtCiU1fifv+NhIMHaBre3trmVHmUHqWisqMcZSlo1DoUJ1c3/vrzd+UoqyhKj1JhDqrpXQps7exp1r4Tp/btIuf2bWubo1AoyhnlKEtJi06R3M7I4EyselG9QlHdUY6ylDQMao2zRy2lKKRQ1ACUoywlNra2NA3vwJnYGHJr+EqM4mTWykJsbGyBpYCLFi3Cx8enwPuu86+1rmrEx8fTv39/mjZtSnh4ON27d2f79u0G0wYEBHD9erm+IkphBOUoy0CT8AhuZ6Tz9/Gj1jbFquSt9T548CBvv/02EyZMKHOehR0lwLBhw/TLEGNjY/XrrK2NuUsWMzMz6devH08//TSnTp0iJiaGOXPmcPp0hUutKkxEOcoyEBDSFlt7e07F7LG2KZWG/DJrly5domvXrvo5kr//rnVTuLm58dprrxEeHk7Pnj3Zs2cPUVFRNGnShLVr13L79m2mTJnC0qVLCQ0N1S9LNMSqVavo2bMnUkouXbpE8+bNuXz5MosWLeLBBx+kb9++tGjRgunTp+uPef/99wkODiY4OJgPPvgAgLS0NPr160ebNm0IDg7Wl5k/mtu3bx9RUVGANvfz6aefpnfv3owcOZLc3FxeffVVIiIiaN26NZ999lmxNn/77bfcc889DBgwQL8tODiYUaNGAXDjxg169+5N27ZteeaZZ9Ry2UqAmh5UBuydnGgY1JrTMXuIGhltdUHf9evXc/nyZYvm6evry3333VdimrwljJmZmVy6dIktWzTRkO+++44+ffowadIkcnNzSU/XXtCWlpZGVFQU77zzDgMHDuSNN97Qy5Y98cQTDBgwgBkzZrBv3z69LuWiRYtYunRpgbmSf/75JwMHDmTFihV8/PHHbNiwgenTp+Pr6wtoykRHjhzBxcWFiIgI+vXrhxCChQsXsnv3bqSUdOjQgW7dunH69Gn8/Pz4+eefAUhKSjJ6bWJiYtixYwfOzs58/vnn1KpVi71795KVlUXnzp3p3bs3jRs3LnLc0aNHCQsLKzbf6dOn06VLF6ZMmcLPP//M559/btQWRfmiHGUZaRLWnl+/nMfNixfw8m9gbXOsQnEyaxEREYwZM4bs7Gweeugh/XpwBwcH+vbVXrYZEhKCo6Mj9vb2hISEkJCQUGw5w4YNMyjoO2fOHIKDg+nYsaNeZg2gV69eenGOQYMGsWPHDoQQDBw4EFdXV/3233//nb59+/LKK6/w2muv0b9/fyIjI42e94ABA3B21t73vnHjRg4dOqSfsJ6UlER8fLxBR1mYgQMHEh8fT/PmzVm5ciXbt29n5cqVAPTr18+gELKiYlGOsow0CY/g1y/ncTpmj9UdpbHIryLIL7PWtWtXtm/fzs8//8zjjz/Oq6++ysiRI7G3t9dH3zY2NnqJMhsbm1JJlP3999/Y2Nhw5coV7ty5g42N1qNUnOSaIZo3b05MTAzr1q1jwoQJ9O7dmylTpmBnZ8edO3cAioj95jlb0DQT5syZQ58+fYzaGxQUVGDgZtWqVezbt49XXnmlgK2KyoPqoywjHt518WnUmNMH9lrblEpBfpm1s2fPUrduXZ566imefPJJ9u/fb3I+7u7upKSkGE2Xk5PD6NGj+e6772jZsmUBdfFNmzZx8+ZNMjIyWL16NZ07d6Zr166sXr2a9PR00tLSWLVqFZGRkVy8eBEXFxcee+wxXnnlFb2tAQEBejX2kiTg+vTpw7x588jO1tb/nzhxgrQ0w6/lGDFiBDt37mTt2rX6bXndElBQkm79+vUkJiYavQ6K8kVFlBagUeu27F+3luzMTOydnKxtToVTnMza1q1b+e9//4u9vT1ubm58/fXXJufZvXt3Zs2aRWhoqH4UvXAf5SeffMLmzZuJjIwkMjKS0NBQfV8kQJcuXXj88cc5efIkI0aMoF27dgCMGjVKL/MWHR1N27Zt+eWXX3j11VexsbHB3t5ev5586tSpPPnkk8ycOZMOHToUa290dDQJCQmEhYUhpcTHx4fVq1cbTOvs7MxPP/3Ev//9b8aNG0e9evVwd3fXi3xMnTqVRx55hLCwMLp160bDhg1Nvm6K8kFUxxG1du3ayZJehWppEmJjWPH2VAZPmE5AaHiFlQtw7NgxWrZUupiFWbRoUYHBIEX5YKj+CSFipJTtrGRSuaCa3hbAPzAIWzs7zh45aG1TFApFOaCa3hbA3smJu5oHcu6wcpSVhVGjRunnJVqLw4cP8/jjjxfY5ujoyO7du61kkaK0KEdpIRoFh7Jz2WLSk5Nw8TD+KgNF9SckJEQ/bUpRtVFNbwvRMKQNAOePHrayJQqFwtIoR2khfJs2x8HZmXNHVAShUFQ3lKO0EDa2ttRvFcL5o4esbYpCobAwylFaEP8WrUi8dJH0pFvWNkWhUFgQ5SgtiH9gEAB/nzhmZUsqFqVHaR4JCQkIIZg8ebJ+2/Xr17G3t2fs2LFWtExRHMpRWpB6TZpha2/P38er5g1cWpQepfnr05s0acJPP/2k//3DDz8QFBRkSbMUFkQ5SgtiZ2+Pb9O7ufhXzXKU+VF6lMb1KEF7uLRs2ZK8FWRLly7l4Ycf1u8fNWoUL730Ep06daJJkybqNbpWRs2jtDB+LVoR89Nqsm9nYe/gWKFlnzjxJimplm32u7u1pHnzySWmUXqU5ulR5jF8+HC+//57fH19sbW1xc/Pj4sXL+r3X7p0iR07dnD8+HEGDBjAkCFDjNqkKB+Uo7Qw/i1asXfNcq6cjKd+q2Brm1MhKD3K0ulR9u3bl8mTJ1OvXj2GDRtWZP9DDz2EjY0NrVq14sqVK0btUZQfylFaGL8WmkDA33/FVbijNBb5VQRKj9K4HmUeDg4OhIeH895773H06FF+/PHHAvvzrkte/grrofooLYyzmzte9Rvydw3tp1R6lMb1KPPz8ssv88477+gjX0XlREWU5YBf80Did/+BlLJGKFUrPUrz9CjzExQUpEa7qwBKj7IcOLR5A5vmz+XJj76gdj3fci1L6VEaRulRVgxKj1JRauo1aQbA5VMnrGyJQqGwBKrpXQ54N2yErb09V06fJLBTV2ubUyNRepQKS6IcZTlga2ePT6PGKqKs4Sg9yuqDanqXE75N7+bK6VNI3dQShUJRdVGOspyo1+RusjMzuHnxb2ubolAoykiNdZTyjiT1z4vInPKJ+Hyb3g3AldPx5ZK/QqGoOGqso7ydkMStNadIXBFfLqsePP3rY+/oxOVTylEqFFWdGusoHZvUxqNXI9IPXCXl13MWz9/Gxpa6jZtyuQZElEqP0jzy9CjnzJmj3zZ27FgWLVpkPaMUJVJjHSWAe48GuITVJXnzOdIPXbN4/vWaNONawhnu3Mm1eN6VCaVHaf769Lp16/Lhhx9y+/btcrBIYWlqtKMUQlBn0N04NHQncUU8OdczLJq/T6PG5NzO4tblSxbNtzKj9ChN06P08fHh3nvv5auvviqyLzY2lo4dO9K6dWsGDhxIYmKiiVdfUV7U+HmUws4Gz0cCufLRAW4sOU7d59og7Czz/PBppElsXTubgKdffYvkWRKT4y9wJNWyzj7YzZk37y7ZdqVHWTo9ytdff5377ruPMWPGFNg+cuRI5syZQ7du3ZgyZQrTp0/XO3SFdajxjhLAro4TnkObc+PrOJI2naX2fcVXbnPw8m+AsLHh2tkztLini0XyrIwoPcrS6VE2btyY9u3b89133+m3JSUlcevWLbp16wbAE088wdChQ43aoihflKPU4dzKC9cIX1K3X8Al2BuHBu5lztPOwQFPv/pcO3fGAhYax1jkVxEoPUrT9SgBJk6cyJAhQ+jaVS11rczU6D7KwtTq1xgbdwcSV5yw2PxKn0aNuXa2YhxlZUDpUZqnRxkYGEirVq30LxqrVasWderU0ffnfvPNN/roUmE9VESZDxsnO+o81IwbX8eR8vsFPLo3LHOePo0ac3znNjLTUnFydbOAlZUPpUdZej1KgEmTJtG2bVv976+++opnn32W9PR0mjRpwsKFC02+boryQelRGuD613FknUzE95UIbD0cymTLmQP7WDlrGsOmzaJ+S8u/GkLpURpG6VFWDEqPsgZTu19jZK4k6ZeEMuf1z8h3zWl+KxTVDdX0NoCdlzNunf1J3X4Bt3vuwqF+6Qd2XOt44uTuwbVzCZYzUGEUpUepsCTKURaDR48GpMdcJmnjWXzGlL7JLISgbqMAFVHWQJQeZfVBNb2LwcbJDveuDcg6kUjW2eQy5eXdsDHXz59V2pQKRRVFOcoScL3nLmxc7Uguo2iGV/0G5GRlkXzd8uvJFQpF+aMcZQnYONhaJKr09G8AwM2/z1vKNIVCUYEoR2mEvKgyZWvpnZyXzlHeUI5SoaiSKEdpBBsHW1w73EXm8ZulVhdydvfA2aMWNy5UT0ep9CgV1R3lKE3AraMf2AhS/7hY6jy86jeotk1vpUdp/vp0RdVCOUoTsPVwwKW1D2n7LnMno3Q3hZe/5iir40qo/Cg9SuN6lFu3biUqKoohQ4YQGBjIo48+qq8XM2bMICIiguDgYJ5++mn99qioKF577TXat29P8+bN9ddSUTGoeZQm4tbFn/QDV0nbexn3ruar9Hj6NyAzLZX0pFu41q5TDhbC9B+PEnexbFOZCtPKz4OpDwSVmEbpUZqvR3ngwAGOHj2Kn58fnTt3ZufOnXTp0oWxY8cyZcoUAB5//HF++uknHnjgAUCLXPfs2cO6deuYPn06mzdvNmqjwjIoR2kiDv5uODTyIG3PZdwi/YtIeBkjb+T7xoXz5eYorYXSozRfj7J9+/bUr689cENDQ0lISKBLly789ttvvPvuu6Snp3Pz5k2CgoL0jnLQoEEAhIeHl3idFJZHOUozcI2oR+LyeG6fTcYxoJZZx3rV/2eKUMPg1uVhntHIryJQepSm6VHmnTNog2E5OTlkZmby/PPPs2/fPho0aMC0adMKlJl3TF56RcWh+ijNwDnEB+FgS9reK2Yf61bHCwdn52o/RUjpUZqnR5mfPKfo7e1NamqqPjpVWB8VUZqBjaMtzq29yTh0jTsDmmDjaPrlE0Lg6V89R76VHmXZ9CjzqF27Nk899RQhISEEBAQQERFh1vGK8kPpUZpJ1tlkrs07SJ3Bd+Ma4WvWsRs++R8Jhw7w7KemOwxjKD1Kwyg9yopB6VEqDOLQ0B07H2fS9l81+1hP/wakJd4kSzf6q1Aoqgaq6W0mQghc2viQ/Os5cpNvm6WAXsfXD4Bbly9Sr0mz8jJRgdKjVFgW5ShLgXNrH5I3nyPj8DXcOvubfFxt37sASFSOskag9CirD6rpXQrs67pg7+tC+qHrZh2X5yhvXb5UHmYpFIpyQjnKUuIc4sPts8nkJGWZfIy9oxNunl7culz6NeMKhaLiUY6ylDi39gYgoxRRZeIl5SgViqqEcpSlxN7HBfu7XMk4Yp6jrOPrR6KKKBWKKoVylGXAqZUXt88lk5uWbfIxtX39yEhOIivdvFUblZnLly8zfPhwmjZtSqtWrbj//vs5ceKE2fksWrSIixfNf4hMmzaN2bNn63/n5OTg7e1dRO4tOjraZA3LiRMn8tprr+l/nz17liZNmnDr1i2z7VNUfZSjLAPOgZ4gIfNEosnH/DNFqHoM6EgpGThwIFFRUZw6dYq4uDhmzpzJlSvmL/MsyVHm5uaanM/GjRtp0aIFy5YtK7C2+4svvjCoYWko78mTJ7NmzRqOHTsGwL/+9S/efPNNateubbIdiuqDcpRlwN7fDRt3ezKP3TD5mNp3aY4y8dLf5WVWhfLbb79hb2/Ps88+q98WGhpKZGQk//3vf/X6jFOnTgUgISGBli1b8tRTTxEUFETv3r3JyMhg+fLl7Nu3j0cffZTQ0FAyMjIICAhgxowZdOnShR9++IH58+cTERFBmzZtGDx4sF62rTBLlizhX//6Fw0bNmTXrl367VFRUeSt2HJzc2PKlCl06NCBP//8s0gezs7OvP/++zz//POsX7+elJQUHn30UUteOkUVQs2jLAPCRuDUwpOMI9eRuXcQtsafO7Xracsey6Wfcv3rcPmwZfP0DYH7ZhW7+8iRI4SHhxfZvnHjRuLj49mzZw9SSgYMGMD27dtp2LAh8fHxLFmyhPnz5/Pwww+zYsUKHnvsMebOncvs2bP1a7IBnJyc9Ou7b9y4wVNPPQXAG2+8wYIFC3jxxRcLlJuRkcGvv/7KZ599xq1bt1iyZAn33HNPEfvS0tIIDg5mxowZxZ7b/fffz4IFCxg5cmSBNeaKmkeNjij/OHm9zIrjzi09kZm5ZCWYJpj7zxSh6tH0Lo6NGzeyceNG2rZtS1hYGMePHyc+Ph6Axo0b60U0jGkrDhs2TP/9yJEjREZGEhISwrfffsvRo0eLpP/pp5/o3r07Li4uDB48mFWrVhlsWtva2jJ48GCj5/HCCy8QERFBixYtjKZVVF9qbET5x6nrjPhiN89FNeW1voGlzsexWR2wFWQeu4lTU9P6r8pt5LuEyK+8CAoKMigHJqVkwoQJPPPMMwW2JyQkFNFizMgo/qVt+TUfR40axerVq2nTpg2LFi1i69atRdIvWbKEnTt3EhAQAGhR6G+//UbPnj0LpHNycsLW1tbo+dnY2Oj1LRU1lxpbA+5p4sWjHRoyb+spvvj9dKnzsXG0xbFJLTJP3DT5mNp3+XGrmsyl7NGjB1lZWcyfP1+/be/evXh4ePDll1+SmpoKaOK6V6+WLCRiTIMyJSWFu+66i+zsbL799tsi+5OTk9mxYwfnzp0jISGBhIQEPv74Y5YsWVLKs1MoNGpsRCmEYMaDwSSm3+Y/Px/Dt5YT/Vv7lSovp2Z1SFp/htzkLGw9HI2mr+PrR0ZKMlnpaTi6uBpNX5kRQrBq1SrGjRvHrFmzcHJyIiAggA8++IDatWvr+wfd3NxYvHhxiVHcqFGjePbZZ3F2djY4wPLmm2/SoUMHGjVqREhISBGnunLlSnr06FEgYn3wwQcZP348WVmmr6BSKApT4/Uos3JyeXT+bo5eTGbFc51o5edhdnm3/07l6pwD1BnWAte2dY2mP7FrBz/+bxaPv/MRdQOamF1efpQepcKaKD3KGoKjnS2fPBaGh7MdzyzeR2LabbPzsL/LFRsXO7JOmjYZ2cOnHgBJVy+bXZZCoah4aryjBKjr7sSnj4VzOSmT11ceMnskXNgIHJvUIuvkLZOOrVU3z1GaPylbYXkGDhxIaGhogc8vv/xibbMUlYga20dZmLYN6/BK7xa8vf44P8Rc4OF2Dcw63rFZbTKO3CDnRib23s4lpnVyc8fB2Znka+arpCssz6pVq6xtgqKSoyLKfERHNqFDY0+mrz3KuRvmva7BUTc1yJTmtxCCWj71VNNboagiKEeZD1sbwfvDQhFCMGn1YbOa4HbeztjWciDrlIn9lHXrqaa3QlFFUI6yEP61nXm5d3N+j7/OusOmR3xCCByb1CbrTJJp/ZQ+9Ui6dqXMK4MUCkX5oxylAR7v2IggPw9m/HSUlEzTJdQcAjy4k5pN7o1Mo2lr1a1HTlYWGclJZTFVoVBUAMpRGsDO1oa3BoZwNSWLub+dNPk4xwBtDqYp67496mriGEnXqn7zuzrqUSoU+VGOshhCG9RmYFt/Fu1M4FJS8WuR82Pn44JwsuP2WeOOspaPNjG9qvdTVlc9SoUiP2p6UAn8u1dzfjp4iQ82xfPOkNZG0wsbgWMjd7LOGm9Ol8dcynf2vMPxm8ctlh9AoGcgr7V/rdj9xelRAvz3v/9l2bJlZGVlMXDgQKZPn05CQgL33XcfXbp04Y8//sDf3581a9bw888/6/Uo85YwtmzZkjFjxrBx40bGjh1LSkoKn3/+Obdv36ZZs2Z88803uLi4FLEpT49y3rx57Nq1S7+MMioqSi/j5ubmxr///W9++eUX3nvvPbp06VIkn4CAAJ544gl+/PFHsrOz+eGHHwgMDGTPnj2MGzeOjIwMnJ2dWbhwIS1atGDRokWsXbuW9PR0Tp06xcCBA3n33XfL+hcoKgEqoiyB+nVceKxjI36IOc/Jq8WLNeTHIcCDnKsZ3EkvuW/TwdkFJ3cPkqt4RGmKHmVsbCwxMTFs374dgPj4eF544QWOHj1K7dq1WbFiBUOGDKFdu3Z8++23xMbG4uyszUXN06McPnw4gwYNYu/evRw8eJCWLVuyYMGCIuXm6VH279+fRx55pFhBjDw9yt27dxt0knl4e3uzf/9+nnvuOX3zPjAwkO3bt3PgwAFmzJjBxIkT9eljY2NZunQphw8fZunSpZw/f970i6motKiI0ghjezRj2b7zfLA5nrkjwoymd2yk66c8m4xzS68S0+aNfFuKkiK/iia/HiVAamoq8fHxNGzYsEx6lG+88Qa3bt0iNTWVPn36FElfWI/yzTff5H//+18RMQ5T9SgHDRqkt3PlypUAJCUl8cQTTxAfH48Qguzsfx6K9957L7Vq1QKgVatWnD17lgYNzFu8oKh8qIjSCJ6uDjzWsRHrDl8i4brxF4LZ13cHW8FtEwZ0avnUJbmKD+YEBQURExNTZHueHmVsbCyxsbGcPHmSJ598EqCIHmVOTk6x+RfWo5w7dy6HDx9m6tSpZGYWnV2wZMkSNm/eTEBAAOHh4Xo9ysKYqkeZZ2t+OydPnkz37t05cuQIP/74YwE7zDk3RdVBOUoTGNM5ADtbGz43QbfSxsEWBz83skwY0PGoW4/ka1eRd+5YwkyrUBP1KJOSkvD39we0AShF9Uc5ShOo6+HEkPD6LN93gavJxudIOjR0J/vvVGRuyZPJa9X1JTcnh9RE00V/Kxt5epSbNm2iadOmBAUFMW3aNEaMGMGIESO45557CAkJYciQISU6QfhHjzLv5WKFydOj7NWrF4GBRVXpi9OjXLt2rUX1KMePH8+ECRPo3LmzGjGvIdR4PUpTSbieRo/3tvJMN+Ovjkg/cJWbS/+i3rgw7H2LF+Y9vX8vq96ZziNv/he/5qXTlFR6lAprovQoFQUI8HalV6t6LN17nqyckqMI+/puANw+X3IE5e7lDUDKjeuWMVKhUJQLylGawWMdG3Ez7TbrjawBt/NyRjjacvvv1BLTuXv5AJBy/ZrFbFSYj9KjVBhDTQ8yg85NvQnwcuGbXWd5qK1/semEjcDB343bF0qOKB1dXbF3cib5hnKU1kTpUSqMoSJKM7CxETzWsRExZxM5dqnkUW37Bu5kX0pD5hQ/oi2EwN3Lm5TrqumtUFRmlKM0kyHh9XG0s+Hb3WdLTOfg7wa5kuzLJc+99PD2UX2UCkUlRzlKM6nt4kDfYF9+PHipxEEdh/ruACb0U3qTopreCkWlRjnKUjCwrT9JGdls/at4B2dbxxEbFzsTRr59SE+6Rc5t89/+qFAoKgblKEtBl2beeLs5svrA38WmEUJgX9+d7AtGIkpv3cj3zarb/LaUHmV+tm7dSv/+/QFYu3Yts2bNAmD16tUFNCWnTJnC5s2bS1XG8ePHueeee3B0dCygZ1kctra2BUbGS1qjbiqFtTQVlRM16l0K7GxtGNDGjx8vxxcAACAASURBVMW7zpKUnk0tF3uD6Rz83Eg5eQGZcwdhZ/iZpJ9Lef06dXz9ys3m8iJPj/KJJ57g+++/BzQFnStXrtC8eXOLlDFgwAAGDBgAaI6yf//+el3JGTNmlDpfT09PPvroI1avXm1SemdnZ2JjY0tdnjnk5ORgZ6duz8qC+idKycC2/ny58wzrjlzikfYNDaaxv8sF7kiyr6bj4OdmMI1HXkRpgX7KyzNnknXMsnqUji0D8c0nI1aY4vQopZS8+uqrrF+/HiEEb7zxBsOGDWPr1q1MmzYNb29vvUTb4sWLEUKwYcMGxo0bh7e3N2Fh/yg1LVq0iH379jFixAjWrl3Ltm3b+M9//sOKFSt488036d+/P0OGDOHXX3/llVdeIScnh4iICObNm4ejo2OxupJ169albt26/Pzzz6W+Prm5ubz++uts3bqVrKwsXnjhBZ555hnAsB4nwFtvvcXXX39NgwYN8PHx0cvURUVF0alTJ3bu3MmAAQN4+eWXS22XwrIoR1lKgv09aOrjyprYv0twlJpzzL6cVqyjdNNHlFVzQKc4PcqVK1cSGxvLwYMHuX79OhEREXTt2hWAAwcOcPToUfz8/OjcuTM7d+6kXbt2PPXUU2zZsoVmzZoVkFfLo1OnTgwYMEDvGPOTmZnJqFGj+PXXX2nevDkjR45k3rx5jBs3DvhHV/KTTz5h9uzZfPHFF2afa0ZGhl4ernHjxqxatYoFCxZQq1Yt9u7dS1ZWFp07d6Z3797Ex8fr9TillAwYMIDt27fj6urK999/z4EDB8jJySEsLKzA9bt16xbbtm0z2zZF+aIcZSkRQnB/yF18/NtJbqbdxtPVoUgaOy9nsBMlThGyd3DE2d3DIlOESor8KpodO3bwyCOPYGtrS7169ejWrZteVah9+/bUr18fQN/X5+bmRuPGjbn77rsBeOyxx/j8889NLu+vv/6icePG+ub+E088wccff6x3lIZ0Jc3FUNN748aNHDp0iOXLlwOaslB8fHyxepwpKSkMHDhQr8ye16WQh6EHhML6qMGcMtAnyJc7EjYfM6wpKWwF9vVcyb5U8lxKd2+fKjtFqCQ9yuIoTrNRCFFqO4yJuxjSlbQEUkrmzJmj1908c+YMvXv3LlGPs6TzzK+/qag8KEdZBoL8PPCv7czGo8Wv/bb3NcFRevmQXEWb3sXpUdapU4elS5eSm5vLtWvX2L59O+3bty82n8DAQM6cOcOpU6cAitWQLE6zMjAwkISEBE6e1N6a+c0339CtW7eynJpJ9OnTh3nz5ulVzk+cOEFaWhp9+vQxqMfZtWtXVq1aRUZGBikpKfz444/lbqOi7KimdxkQQtA7qB7f7j5HalYObo5FL6f9Xa6kx1whN+U2tu5Fm+egDeicP3qovM0tF/L0KMeNG8esWbNwcnIiICCADz74gNTUVNq0aYMQgnfffRdfX1+OHzc82OTk5MTnn39Ov3798Pb2pkuXLhw5cqRIuuHDh/PUU0/x0Ucf6Zu7eccvXLiQoUOH6gdz8g8wGeLy5cu0a9eO5ORkbGxs+OCDD4iLi8PDw8Pk84+OjiYhIYGwsDCklPj4+LB69Wp69+7NsWPH9C82c3NzY/HixYSFhTFs2DBCQ0Np1KgRkZGRJpelsB5Kj7KM7Dp9g+Gf7+LjEWH0a31Xkf2ZJ29x/YvDeD8ZjNPddQzmsXftCrZ/u5CxC5fi6GJe00vpUSqsidKjVJhERIAnXq4O/FJM89v+Ls3xldT8VrqUCkXlRjW9y4itjaB7YF02xV0h947E1qZgR72tqz02Hg4ljny7eWpva0xNvIl3g0blaq+iZG7cuMG9995bZPuvv/6Kl1fJb9VUVF+Uo7QAXZv7sDzmAocu3KJtw6LNa2MDOm51dI7y5o1ys1FhGl5eXhW2+kZRdVBNbwsQ2cwbIWDbCcMj1/Z3uZJ9Nb3Yl425enoCkFaFXzKmUFRnlKO0AHVcHWhdv3bxjrKuC+RKcm4WfbMgaJPOnVzdSE1UEaVCURlRjtJCdLvbm4Pnb3Ervahcmn1dbRVGzlXDjhLAtY6nanorFJUU5SgtRLcWPtyRsONk0ZFrOx9nALKvpRd7vJunV5V+v7dCUZ1RjtJCtKlfG3cnO7YbaH7bONlh6+FAztUSHGWdqusoa4oepRCigKLP7NmzmTZtWqnKVlQtlKO0EHa2NnRp5s2O+OsG1x3b1XUhuyRH6elJWuJN7twp+Z3hlY08PcqoqChOnTpFXFwcM2fO5MoVw+vfS8OAAQN4/fXXgaKOcsaMGfTs2bNU+ebpUb7yyismpXd0dGTlypVcL+XL4Cy5xlxRsajpQRakQ2NP1h+5zIXEDBp4/j97Zx4eZXU27vudfc1khyQskV0gIaCAICBi3apScQGrlFIVv8+vavlVqbVCpQqtrdYFa6lV1K8tH65FoRVEKe4CguwKRCRAFrJnksxk9vf3x5uZyTATSGKSmZBzXxeXMu+aaJ4855znuY8p4pg204RjRzmyLMeUIlhS0pADAZrq6zEnx+7gORMfv3aYqhOnN6q3l/T+FqbObl3A25t8lBqNhjvuuIMnn3yS5cuXRxw7duwYt956K5WVlWRkZPDSSy8xYMAA5s+fT2pqKrt27WLcuHFYrVaOHj1KWVkZhw8f5oknnmDr1q1s2LCBnJwc1q9fj1YbWwQtiB8io+xEJg5S6iG3fhu9KKPJNCJ7/PjtsffGCZYI9bQFnbb4KN9//30WLVpEWVkZoPgog33V3377LZ9++ikul4sFCxawfv16Pv74Y06ejO50CvooH3vsMXbv3s3gwYNDx4I+yldffZV9+/bh8/lYuXJl6HjQR3nnnXd+p60XfvrTn7J69WrsdnvE53fddRfz5s1j79693HLLLdxzzz2hY4cPH+b999/nj3/8IwBHjhzh3//+N2+//TZz587l4osvZt++fRiNxu8kERZ0HSKj7ESG97GSbNKy7WgNN57fP+JYeOXbiSZZH3WtJaU5UNZW04chHXr+6TK/7uZs9FECJCUlMW/ePFasWIHRaAx9/vnnn4fu+6Mf/Yhf/OIXoWM33ngjarU69Pcrr7wSrVZLXl4efr+fK664AoC8vLxO2YdH0PmIjLITUakkxuemsu1orIxSCZStzVOG2hhretaCTm/0US5cuJBVq1bhcLTebdXyaznVMRl8F5VKhVarDZ2rUqnEPGaCIgJlJzPxnFRO1DRRWhdZM6kya1GZNPhaKREy21JAknrcyndv9FGmpqYye/ZsVq1aFfps8uTJoc3VVq9ezZQpU7rk2YL4IAJlJ3NB8zzl9qORAU+SJDQZra98q9RqzLbkHjdHGfRRvvfeewwePJhRo0axdOlSbr75ZvLz8xkzZgwzZswI+Shbo6WPcsqUKQwcGFsOctNNN/HYY48xduzYUFANXh/0Uebl5aFSqdrko+zXrx9PPPEEy5Yto1+/ftTX17fp67733nsjVr9XrFjBSy+9RH5+Pn//+995+umn23QfQc9A+Cg7GX9ApuDhTVydn8XvrsuPOFb7ZiFNX1WTveSCmNf+44GFmJJsXPfAb9r8POGjFMQT4aMUdAh1aJ4yegityTAScHgJOL0xrxVtjAJBYtJrA2VTo4cNf9lHQ42r0+993sAUvq10RPV9a9KVVVJfdexnWkUbY9yprq6moKAg6k91tfgF1pvpteVBjjoPxQdrWPf0bq67bxzGVvaz6Qhj+ycDsPtEHdOHZ4Y+16QZAPBVN6Hrb426zpySSlNDPT6vF40oOo4LwkcpiEWvzSjT+1m46qf5NNS4+Nef9uBxdV5ZRn7/ZCRJCZQt0aQaQQJfVWyLUFDgK7yUAkFi0WsDJUD20BQuXzCayhONvP/SV8iBzlnYsug1DMu0sut4ZKCUtCrUNv1pAmWw6FwESoEgkei1gVKWZfbs2cOAUSlceP0Qju6pYseGok67/9gByew+URdVCK1JN7Y6R2lq7vF22ms77T0EAsF3p9cGyqKiItauXcvbb79N3sU5DL+gL9vXH6VoX+fshDh2QDL2Ji9HqyK7NzRpBnzVsTPKoAzDUVcX87hAIIgPvTZQnnPOOVx88cXs3buXDRs2cNEPh5GWY+E/f/saZ31scUV7KOivBL1Th9+aNCMBpy9miZApyQaShKOuZ2WUvcVHqVarKSgoYPTo0dx44404na1r805HZWUlWq2W5557rkPXC7qfXhsoAaZNm8bkyZP54osv+HzbZ1x660g8TX62/OPgGXuHz8SQTAsWvSZ6Qae5RMgbY55SpVZjSrL1qKF3b/JRGo1Gdu/ezf79+9HpdPzlL3/p0HNff/11LrjgglbbNAWJR68tDwKl/e7SSy+loaGB//znP2RkZDBp1mA+eb2Qrz8rY+SF2R2+t1olMaa/jS+PRwa9YKD0V7tgQFLUdWZbcoeH3lte/isVx77t0LWtkTlwEBfPv6P1Z/YiH2VLpk6dyt69ewF44oknePHFFwG4/fbbWbhwIQ6Hg9mzZ1NcXIzf72fJkiXMmTMHUPrY//jHP3LzzTdTUlJCTk5Ou58v6F56dUYJSrCcOXMm2dnZ/POf/6TvKB3ZQ5P57M1vaGr4bkPw/H7JHDrZgNsXtpZrUgwgxc4oQVnQcfagoXdv81GCYirfsGEDeXl57Ny5k5deeolt27axdetWnn/+eXbt2sXGjRvJzs5mz5497N+/P6RSO3HiBCdPnmTChAnMnj2bV1999Tu9i6B76NUZZRCtVstNN93Ec889xxtvvsGNN97Cm4/u4rN/fsMlPx7Z4fuOzrbhC8gUljcyOscGtCgRam1Bx5ZMbVlJh553usyvuzkbfZRNTU0UFBQASkZ52223sXLlSmbNmhVSqV133XV8/PHHXHHFFdx3333cf//9XH311UydOhWAV155hdmzZwOK4OO2227j5z//eYfeR9B99PqMMkhSUhKzZs2isrKSL/Z9SsFlAzj4+UlKCzue3Y3KVobWB0ojbdhnKhFy1NV+5znS7qI3+SiDc5S7d+/mmWeeQafTtfrcYcOGsXPnTvLy8njggQd4+OGHAWXY/fLLL5Obm8vMmTPZs2cPhYWFHXofQffRawNlIODj2LG/4veHA9aQIUOYNGkSX3zxBbahHiypej55/ZsOF6IPSDVh0WvYXxKp7tKkGVotOjcnp+D3enE7W5fCJhK90UfZkmnTpvHWW2/hdDpxOBysXbuWqVOnUlpaislkYu7cudx33318+eWXHDp0CIfDQUlJCUVFRRQVFfHAAw+EPJaCxKXXBkq7/Uu+OfIHDnz1/5Dl8BziJZdcQmZmJhvf3cC47/ej8ngD33xZ0aFnqFQSI7OSojPKNCNyU+wSIXOo6Lxn1FL2Vh9lkHHjxjF//nwmTJjAxIkTuf322xk7diz79u1jwoQJFBQUsHz5chYvXsyaNWuYNWtWxPXXX3+9WP3uAfRqH+XxEy9RWLiMfv3mMWzor0NDv+LiYl544QUmTJiAfUcafm+AHy6diFrd/t8rv1l/gFe2n2D/by5HrVLu37S/iup/fE3mXQXo+kXKMY7v38PrjzzI7Id+R/+ReWe8v/BRCuKJ8FH2Agb0/wn9+99KcfHfKClZHfq8X79+jB8/nu3btzPkIgv2yia+/qS0Q88YlW2jyevnaFV4G1l1SrNFqNYddX64O6fnrHwLBGc7vTpQAgwd8gBpaRdzuHAZ9vo9oc8vueQSLBYLewo/p885Sex89xh+f6Dd9w8v6ISHdJpUJVD6a6MXdEw2RdHWk0qEziaEj1IQi14fKCVJxaiRj6PX92H/vrvwepUAZTAYuPjiiykuLiY930djjZtvvmh/t8mQTAs6jSoiUKqMGiSDBl8MabDBbEGl1oiMMk4EfZSn/klLS4v3qwniSK8PlABabTJ5o5/B7ani4KFfhz4fO3YsmZmZ7Dm8jZQsIzvfPd7uFXCtWsWIvtboBZ0UfcyMUlKpMCV3vDtHIBB0PiJQNpOUlM+gc+6houIdysuVljaVSsWll15KbW0t1hGN1JY5OLq3/XahUdlJHCitj6i5U6ca8MUIlKBsXduT+r0FgrMdEShbMGDAApKs+Rw6/BBujxIQhwwZwsCBAzlcvBdLqo49m0+0+74j+iZR5/RS2RBevNGkGPDXumMWLJtFRikQJBQiULZApdIwcuRj+HwOvin8HaDUCU6bNo2GhgasIxyUFtZRXdp4hjtFMrSPBYBD5eFCaU2KHtkbINAYu5bSITJKgSBhEIHyFMzmIQwccDsny9+irk6pxRw0aBA5OTkcq/oKSSNz4KP2lQoN76PUSh46GQ6U6uaV71gLOubkFJz2OgIBf9SxRET4KNvO9OnTOf/8cInhjh07mD59ervvI+heem2g9AVkVh6voClGyU9u7p3o9X05dPg3yLI/lFXa6+3Yhrs4uLWsXZuRpVn0pFt0FJaHM9EzlQjJgQCuGK16iYbwUbafiooKNmzY0KFrBfGh19qDvqh38PCRUnbUO/jrqFzULYQMarWJoUMeYP+Bn1Fa+ho5OT9k2LBhZGZmUuMtQnKN4PD2ckZPa7tHcGimNWLoHS46j51RglJ0HqyrbAt164/gKe3cHnFdtpnkawa3elz4KNvvo1y0aBHLli3jyiuvbPdzBfGh12aUk5It/GZINv+utPPrwpKoRZXMzKuw2cZxtOhP+P1uJEli4sSJVNdWYsrx8vWn7Rx+97VSWN4Qeo5Kp0Zl1uKvie7OCW4y5ugB/d7CR9k+HyUQGu5v2bLlO72HoPvotRklwB39MylxeXmuuJLBJj239ssIHZMkiUGDfs6uXXMpKf0/BvT/CXl5ebz33nv4LeVU7NdRe9JBSl9zm541rI8Vh8dPSV0T/VJMQOslQqYkxV3ZVG+POnY6Tpf5dTfCRxnbRxlk8eLFLFu2jN///vcdehdB99JrM8ogDw3J5tK0JB76ppSd9shha2rKJFJSJlFUtBK/34lOp2Ps2LGU1RwjoHZzaFt01tMaw5pXvg+fsvIdO1A2tzHa2xco44HwUbbPRxlkxowZuFwutm7d2qF3EXQvvT5QqiSJZ84dQJZey4IDRVR5In+IBg36f3i91RQX/x2A8ePHEwgE0PWv4/C28jZ36gwNrXy3WNBJMeCvc0fdQ282o1KrcdYn/tBb+Cjb7qM8lQcffJA//OEPXf6Ogu9Orw+UAMlaDatG51Lt9fGLQycisoRk23mkplzI8RMvEwh4SE1NZciQIdgpob6mibIjbQtmNqOWLJuBwvJTSoT8Mv76yHlKSZIwJtnaPfSOB8JH2XYf5al8//vfJyMjI8ZdBYlGr/ZRnsqzxyt45Egpz547gOv7poY+r67+iN17fsLIc/9AVtb17N+/nzfeeIPUhjHkjT2Xi+eOaNP95724nepGN/++R5mvajpUQ/VLB8j473z0ubaIc//2i7tJysjk2kVLTntP4aMUxBPho+yF/Hf/DMYnmflVYQkn3eGOmdTUqZjNwzh+fBWyLDN8+HD0ej3qPnV8u7uSQBv1a8P7WCisaMTfPNTW2JR5M789euXbmGTrMZZzgeBsRwTKFqgliafPHYAnEODX34R3QpQkiQEDbqPRcYiamk/QarWMHj2aGncJTkcTZUfaNkQenGHB4wtQWqfsl6NObg6UddHb4pqSbDS1cxgo+O4IH6UgFiJQnsIgk567B/ZhXUUdn9SG5xP79rkGnS6DE8UvAzBmzBj8fh9eczXf7qps270zlJXvI5XKgo7KoEHSq/HVxS4R6gmLOWcbwkcpiIUIlDH4n/6ZDDDoeLCwBG/zMFml0pOddSPV1R/hcpXSv39/UlNTkVNq+HZ3ZZu2lx2UodTafVsZLkNSJ+vx22NklLZkPE1N+DzRxwQCQfciAmUMDGoVjwzN4ZDDxf+Whv2T2dmzgQClZW8gSRKjR4+m0VtFfV0DlcfP3JedZtZhNWj4tuX+OTZ9K3OUyhYSzh6w8i0QnO2IQNkKl6UlMSXZwlNF5Tj8isXHaOxPaupUSktfQ5b9nHvuucjIeIw1HGnD8FuSJAZlWDhaFc4oNcl6/DGH3krReU8oERIIznZEoGwFSZL45aAsqrw+XixumVXOwe0uo7r6I/r27UtKSgqk1lHURvP54HRz5NDbpifg8CF7I5VqJptSLiQySoEg/ohAeRrOt5n5XloSzx6vwO5VOnYy0i9Bq02jtOx1JEli5MiRNPorqSqro7GVrR1aMijDTJndhbO5Ayi48u07ZZ7S2Nzv3RNKhHqjj/Kaa66hrtlCX1RUhNFopKCggJEjRzJv3jy83kgh889+9jNycnIIBNq/k6cg/ohAeQZ+eU5f6nx+/lqsDK1VKh19+lxNdfUWfL4GRo4ciSzLeAzVHP+q5oz3C658B7PKcIlQZJAN9XsneEbZW32UqampPPvss6FjgwcPZvfu3ezbt4/i4mJee+210LFAIMDatWvp378/H330UYfeVRBferU9qC2Mtpq4PD2Jl0qq+OmAPpjUKvr2uYbi4v+lsnIT2dnXYbPZ8PhrOH6gmpEXZp/2fqGV7yoHo3Ns4aLzU2opdUYjao2mXXOUGzZsiKkn+y707dv3tN7E3uqjnDRpUshH2RK1Ws2ECRMoKQnX4W7ZsoXRo0czZ84c1qxZEzKaOxwO7r777pAWbunSpfzgBz9o97sIuh6RUbaBO/tnUuP189pJJWNMSirAYOjPyfL1SJLE8OHDcWtqOXGw6oxdOrlpZiQJjgYzyla6cyRJwmhLTniDUG/0Ufr9fjZv3szMmTOjjrlcLrZt2xbhn1yzZg0//OEPmTVrFv/6179Cw/Lly5czY8YMvvjiC7Zs2cKiRYtwODpXvCzoHERG2QYm2syMtZp47kQFP8pOQy1J9OlzNceP/xWPp5qhQ4eyfft2Gn3VlBc1kDXY1uq9DFo12TZjqERI0qpQWbT462IIfJNsNDW0PVAmkjH7bPZRFhUVcd5553HppZeGjh05coSCggIKCwu54YYbyM/PB8Dj8fDOO+/w5JNPYrVamThxIps2beKqq65i06ZNrFu3LhS4XS4Xx48fF737CYjIKNuAJEncOSCTo00eNlYpgatvn2uQZT8VFRvIzc1Fo9HgMSjD7zMxKCN65dsXo5bS1AP6vXujj/LYsWN4PJ6Yc5TffPMNW7duZd26dQBs3LgRu91OXl4eubm5fPLJJyGFnCzLvPnmm6HuHxEkExcRKNvI99Nt9DfoWNVcKmSxDMdsHkp5xTtotVpyc3Pxm+1tWtAZnGHh28rG0A+3OlnfakaZ6Is5vdFHabPZWLFiBY8//njU6nZWVhaPPvoov/udst3xmjVreOGFFygqKqKoqIijR4+yadMmnE4nl19+Oc8880zo/4Ndu3Z1yfsKvjsiULYRjUriR9lpfFbXyBGnskKdkXEZdXVf4PXWMnToUDyyg7KS8jPu0HhOuhmHx09loxIcNTYlUJ6aFRl7QKDsrT7KsWPHMmbMGF555ZWoY9deey1Op5MPP/yQd999l6uuuip0zGw2M2XKFNavX8+SJUvwer3k5+czevRoliw5vVJPEEdkWT7r/px33nlyV1Du8sg5W3bJvykskWVZlu32PfL7mwfJpaX/lKurq+WHHnpI/sP/+5tctL/qtPf5z8FyeeD9/5K/OFoty7Is1394Qj5x/0ey3+mNOG/bW6/Lj8++SvY0NbV6r6+++uo7flUCQceJ9f8fsENOgDjQmX9ERtkOMvVaLk+38crJatyBAFbraHS6TKqqNpOamkpqaipefQ2lh08/rzggVdlc7HiNE2h95Tu4yZiwCAkE8UUEynYyNyuNGq+fjVV2JElFRvolVNd8RCDgZsiQIXj19Zw4dPoFnX4pRiQJjlUHA6UOAH99K905CT78PpsQPkpBLER5UDu5KNVKP4OWNaU1/CAzhfT0SygpXUNt7VZyc3PZvn07ZWWleN1+tHp1zHvoNWqykgzhjNIaDJSnZJTBfu8Er6U8mwj6KAWCloiMsp2oJInr+6TycV0DlR4vKSmTUatNVFa9T25uLgBuTS0nz2A9H5BmCgfKpOah96kZpVUJlK7GMyvcBAJB1yECZQe4NjMZvwzrK+pQq/WkplxIdfWHGI1GMjP74NXbKTlce9p7DEw1h4beklaFyqSJESiVLW6Fak0giC8iUHaAcy1GRpgNrC1XFllSU6fgcpXQ1FTEoEHn4NPVU3z49HNaA9JMVDW6QxYhlVUXFSh1RhMqtZqmBrF3jkAQT0Sg7CDX9Unhi3oHx5vcpKZeCEBNzafk5uYiE6C0tBS/r/W+71gr3zH397YmJXygFJo1oVk72xGBsoP8IFPRoL1dUYfRmIvBkENNzScMHDgQCQmXpoaq4sZWrx+YpgTK0Mp3jIwSaA6UiTtHKQvNGiA0a2c7IlB2kIFGPeclmVhfUYckSaSmTqGm9nP0eq0yT6mzU3609bnFYEZ5oiZcIhRo8CAHIrtzDFZrQmeUrWnWpkyZwqJFixg9ejR5eXm8+uqrgJIpTp8+nRtuuIERI0Zwyy23hDqSNm7cyIgRI5gyZUqEuOLll1/mrrvu4rPPPmPdunUsWrSIgoICjhw5wvz583njjTcA2Lx5M2PHjiUvL49bb70Vt1vJ0HNzc3nooYcYN24ceXl5HDx4EIDMzEzGjx+PVqtt99c9adKkCJVakNNp1u68886I1sylS5dy6623Mn36dAYNGsSKFSva/R6C7kGUB30Hrki3sfzbMkpdHlJTp1Ba+ioNDXsZNPgcKsq3UXqkjvyL+8e8NtmkI8mgCWeUSTqQIdDoVf69GaM1ieriE216n8OHH6Gh8evv/oW1wGo5l2HDWm+ta4tmraqqivHjxzNt2jRA6Wk+cOAA2dnZXHjhhXz66aecf/75LFiwgP/85z8MGTKEOXPmRN0zqFkL+idbEtSsbd68mWHDhjFv3jxWrlwZsgcFNWt/uxoo0gAAIABJREFU/vOfefzxx3nhhRc6/D0JatZuu+22qGNBzdrTTz8d+iyoWfvBD37Ar371K7xebyg4Hzx4kC1bttDQ0MDw4cO58847OxS4BV2LyCi/A1ekK+U771bXk5oyCZCorvmUfv36IUsBThQVn/b6AWkmjoVqKYMlQpHzlEZrUo8sD2pNswaENGsqlSqkLTt48GBIsyZJEnPnzm3X82Jp1loOc1tq1oqKijr0NQU1a2lpadTU1MTUrKWlpTFgwIAozdq1115LUlJSSLMW5KqrrkKv15Oenk5mZmanTlkIOg+RUX4Hhpj0DDLqebfSzk9yBmO1jqa25lOGDv0xAHXOKpz1HkwtMsSWDEw181WZMqxutTvHaqOpoR45EEBSnf732ukyv65i1KhRoaFvS4LD6Vj0dM2a3W7n6quv5tlnn+Wee+4BwnOUZWVlTJ8+nXXr1jFz5swIzRqA0+nEZDKFRBmtfS8EiYXIKL8DkiRxeXoSn9Y1Uu/zk5IyEXv9HsxmPWaTFZ+2nvKi1ucXB6SZKK514g/IoeF2rFpKORDA7XR26dfSUYRmreOaNUHPQQTK78gV6Ta8ssx/qutJto1Hlj3UN+xl4MD+eHX1lH97+gUdr1+mzN6EyqwDKfbQG2iX6bw7EZq1jmvWBD0H6UxDlp7I+eefL+/YsaNbnuWXZfI+3c9FKVZWDLPx0cfnM3jQvZw8OY6NGzcy0noZs++dHPPajw5XMu/F7bx6xwVMHJRG6W+3YRiWQuoNw0LnHN21g38+upQfPvI42cNGRN3j66+/FlZsQdyI9f+fJEk7ZVk+P06v1CWIjPI7opYkZqQm8WFtA2pNMmbzUOrqvgjtCVN2siSq5CdITooRgJK6JuVeSdG1lIZgG2MClwgJBGc7YjGnE5ieauWN8lr2NzaRbDufk+XrGTUqA7VKjYs67FVNJGeaoq7LSW4OlLXNgdKqw18bub93UIwhAmX3UF1dzSWXXBL1+ebNm0lLS4vDGwkSAREoO4FpKUrW92FNAzcmj6ekdA0u1zdkZvSl0lVP5fGGmIHSoFWTbtGFM0qbHs+xyIAYnKN0iUDZLQjNmiAWYujdCWTqtZxrNvBhTQPJyeMBqKv7goG5/fFpG6k43nqQy0k2hgOlVUfA6UNu0SOuMxpRqTUioxQI4ogIlJ3ERalWttsdBLR9MeizqavbQU6/HJBkThRFt7oFyUkxhofeMWopFTFGYrcxCgRnOyJQdhIXpVjxyDLb6hqxJZ9PnX0nWVlZAFRUlrdaEB3MKGVZbtV03hMMQgLB2YwIlJ3ExGQLOkniw9oGbElj8HgqMJs9aNRanL46HDH27QYlULp9AaoaPaiaA2WgMbKIOdENQgLB2Y4IlJ2ESa1igs3Mx7UNJCWNAaChcR8ZaZn4tI1UnoitXMtJURZ5SuqawhllQ3SJUCJnlMJH2bqP8oMPPkCSpIgC86uvvpoPPvigQ+8siA8iUHYik5ItfNXowm8YjiRpqK/fS/+B/fBpHZQfi91Z07JESGXWKt05Dae2MSbu0Fv4KBVO56Ps168fy5cv79A7ChIDESg7kYnJZmRgZ4MXi2UE9fV76Nc/B6QAxUdLY14TLjp3IqkkVGZtjKG3DVdjA3IC2rGFjzKSWD7KMWPGYLPZeO+996LO37lzJxdddBHnnXcel19+OWVlZe1+F0HXI+ooO5FxSWa0ksQ2u4MbksZw8uTbjDy3DwAny2P/ANiMWqx6TWTReYyMMijGMFgsrT5/SWEx+xubOumrURhtMfLI0H6tHhc+ykhi+SgBFi9ezOLFiyPUbF6vl7vvvpu3336bjIwMXn31VR588EFefPHFDr+boGsQGWUnYlKryLca2VbnICkpH7+/EaOxHrVKQ6OnDrfTG/O6nJRwLaXKqsMflVEG2xgTU4wRC+GjzI+4durUqQB8/PHHEe+8f/9+Lr30UgoKCli2bBnFxad3mArig8goO5mJNgvPF1eisyg/KI2N+0hPzaTa1UhNqYOsIclR1+QkGykOZpQWLb6KSAVX2CBUT0pWTqvPPl3m11UIH+XpfZQtefDBB1m+fDkajSb0zqNGjeLzzz/v0PsIug+RUXYyFySb8coyh3x9UKst1NfvJTsnC5+2kaqS2CU+URllgyfiBz8cKBOvREj4KE/vo2zJZZddRm1tLXv27AFg+PDhVFZWhgKl1+vlwIEDXfLOgu+GCJSdzASbGQnYbm/Cah2FvX43/QcqCzolx2KvBOckG2lw+ah3eVFbdOCXkZvCWY+hRUaZaAgfZes+ypbD7CAPPvhgaHit0+l44403uP/++xkzZgwFBQV89tlnbXq+oHsRPsou4OLtB8nUaVlmeY0TJ/7GsKEbeeGFl8g1nc/8X1wddf6/9pZy1//tYuPCqQw46aLmlUP0+fl5aJtFGi5HI8/eehPT593OeVddG3Gt8FEK4onwUQo6zHibmS/rHZjNI5FlD2azMlSsrauOeX6WzQDASbsr1J3TcuVbbzQhSaoeucmYQHA2IBZzuoBxSSb+VlpNpWY4AC73N5gNSTS5GmJuNtbXptRSnrS7UOcq5T+BxhZiDJUKvcVCU2Ps7h5B5yF8lIJYiEDZBYxNMgNw0JtGpkpHY8NXpKf1p7ixlJrSRkxJqRHnZ1r1SBKU2V2oLUrxs7/hlBIhi0U4KbsB4aMUxEIMvbuAISY9FrWK3Y1uzObhNDR+TXa/vvjVTVSciK6F1KpVZFj0nLS7kIwaUEsETu33tlhxOURGKRDEAxEouwC1JDHGamJXvROr5VwaGw+S0z8LJCg+HrtDp6/NwMl6F5IkobZEd+cYLFYxRykQxAkRKLuIsUkmDjQ2oTOPxOutIS1N+VZXlFfEPL9vkoGTdmW/HJVVG9WdIwKlQBA/RKDsIsYmmfDKMiVqpXRCrSlDJampq6+O2UWSZTNQZm+xJURURmnBJRZzBIK4IAJlFzHWqtRAHvIrRdZOxyGSzMm4pQacdk/U+X1tRupdPhxunyLGaDwlUJqtuJ0OAn5/1798OxE+SuGjPNsRgbKLyNJr6aPTsKfRj9EwgIbGr8lIz8SvcVJb7ow+P1hLWe9CZVFUay33AzdYFDFGoi3oCB+lgvBRnt2IQNlFSJLE2CQTuxucWKzn0tDwFVn9+hJQe6gorok6v09SuOhcbdWBDAFHeJ7S2KxXS7Tht/BRRtJeH2Vr7yZILEQdZReSZzHxblU9qvSRNFVuIjtbMQeVnihnHIMjzg1mlGV2F2Mtyr/7Gzyh7SFCGeVpFnR+s/4AX5V2bq3lyOwkHrpmVKvHhY8ykvb4KIN05rsJugaRUXYho61GZKBUPRyQsVgdAFRWVkWd27c5UJbXu1BblQynpek8PPTuGSvfwkd5Zh9lZ76boGsRGWUXMsqitCYWyTkMBtTqciRU2Buih94GrZoUk5Yye/PeOYDf0TJQnnnofbrMr6sQPsqO+yg7890EXYvIKLuQHL2WZI2aQrcZSdLgbDqC2ZCE09OA3xu9/01fm1GZo7REb1vblqF3PBA+yo77KAU9BxEouxBJkhhlMXLA4cZkOgeHo5AUWyp+jZO6yuiV775JesrsLiSDWmljdLQwCJmV/vFEC5TCR9lxH6Wg5yB8lF3MQ4Ul/K20irUpL+B0fEV12X3s3LONm2f+F8POy4o494F/7mPTgZPsXHIppb/dhmFYCqk3DAsd/9Otcxg5dQYzfvJfoc+Ej1IQT4SPshuQJKm/JElbJEn6WpKkA5Ik/SzGOdMlSbJLkrS7+c+v4/GuHWWkxUhTQKZGn0dT03Gy+6eCBKXHo+sMs2wGqh0e3D4/arM2ojwIwGhJ3P29BYKzmXgv5viAe2VZ/lKSJCuwU5Kk92RZ/uqU8z6WZTlaDd4DGG1VFnSOS0PoT4DkVKWfu7w0uuc7uPJdUe/GYIne39tgsSRcwfnZhvBRCmIR10Apy3IZUNb87w2SJH0N5ACnBsoey1CTHq0kcdSfSX9Ar1cs5zV10SvffZPCtZRDzFrc1a6I40KM0fUIH6UgFgmzmCNJUi4wFtgW4/AkSZL2SJK0QZKk7q+B+Q7oVCqGmw0cduuRJDVu91F0GiMNzrqoczOTlDKRigYXKosuRkYpAqVAEA8SIlBKkmQB3gQWyrJ86iTcl8BAWZbHAM8Ab7VyjzskSdohSdKOysrKrn3hdhJc+TYac3E4v8FmTsYjO3CdEggzreGht8qsRfb4kb1hCYYwCAkE8SHugVKSJC1KkFwty/I/Tz0uy3K9LMuNzf/+DqCVJCk9xnl/lWX5fFmWz8/IyOjy924PI8wGKjw+/MZROBzfkJqWrpQIVUSWCKWYtGjVEhUN7vCWEI7IWkqXoxE5EF2DKRAIuo54r3pLwCrga1mWn2jlnL7N5yFJ0gSUd469nWGCMtzc3J6oHU1T0zEy+6Ygq/yUl0S2MkqSRIZFrwy9zTHaGM1WkGXczugaTIFA0HXEO6O8EPgRMKNF+c/3JUn6b0mSgtXCNwD7JUnaA6wAbpJ7WPHnsOZAWSoNRJb9ZGQpGWF5afQUQUaSgcoGN6pgRtkYq40xseYpe4uPUtB7ifeq9yfAaRt8ZVn+E/Cn7nmjriFHr8WsVnHcn85IwNK8z3dVVfTKd6ZVz/FqJ+pgRulorY0xK+raeBD0Uf74xz8Odans3r2b8vLykKDiuzJz5sxQ3/Rbb73F1VdfzciRIwHFR9lRgj7Kt96KOe0tEISId0bZK5AkiWEmA0c9SmYpqSoBiTp7jJVva/PQ23Iag1ACZZS9xUdZVFTEueeey4IFCxg1ahSXXXYZTU3K1h3PP/8848ePZ8yYMVx//fU4m6dG5s+fzz333MPkyZMZNGhQTHmIoGcQ74LzXsMws4EtNfVotam4XMcwaJJxNEVvXdsnyUCt04tXJYFGFdMg1NRa0fmGX8LJfZ374n3z4MpHWz3cm3yUhYWFrFmzhueff57Zs2fz5ptvMnfuXK677joWLFgAKN7JVatWcffddwNQVlbGJ598wsGDB5k5c2bUewt6BiKj7CaGh1a+R+B0HsViSsLtd+LzRO6Bk2lVaimrHB6ljbHF3jnGBMwoW+Ns9FGec845FBQURN1n//79TJ06lby8PFavXs2BAwdC11x77bWoVCpGjhzZqdtjCLoXkVF2E8EFnUpNHlrH26Qkp1BdV0l9tYvULHPovFDReb2LbEtkv3fQIORurZbyNJlfV9GbfJSnvndw6D1//nzeeustxowZw8svvxyxcVjLa3rYGqSgBSKj7CaGmZQfmFLVYNzuk6RnWpBVPipLaiPOCxadl9crtZQth95qjRaNXo/L6ei+Fz8DvdFHeSoNDQ1kZWXh9XpZvXp1tzxT0L2IQNlN9DPoMKlVnJAVJ2NGHyWrKSuOlGMEh96VzbWUUW2MZgvuBBJj9FYfZUseeeQRJk6cyKWXXsqIESPafb0g8RE+ym7k8h2HsEhu/sc+i/79HuH//u8wI7MnM/uOy0Ln+AMyQx98h59ePITbfFoaPysj55HJoWHpy/f+DylZOfzgvgcB4aMUxBfhoxR0OsPNBo64lG+5Wq00F9XVRQ691SqJNIueinq3UkvpCyB7wi2LBosFdwINvQWC3oBYzOlGhpoMvHayFr8uF5f7GDpVBo3O6BKhUC1lbnDvHA8qveK11JstNFRH7+Io6ByEj1IQCxEou5HBzQs6tfoCUpxFmI2DaaxrRA7ISKrwim+fJAPl9eGic7/DiyZNCZQGk5mq40Xd/u69BeGjFMRCDL27kUHNgbJKMwyn8yi2pGR8qiYcdnfEeUpG6Q63MbZY0NFbLLgdYugtEHQnIlB2I7kGPRJwUuqHz1dHWoYeWe2l+mTkSmumVU91oxvZqCT8AWdLg5AyRxkIRBaqCwSCrkMEym7EoFaRY9BSGlDmutLSlWB3siTSIpSRZCAgQy3KIk7AGS6Q1puUNkaPs6k7XlkgECACZbcz2GjghE+Zb0xKVYJdVUWkRShYS1nh8jbv7x1DtZZAtZQCwdmOCJTdzCCTniKXhAzodIo9qLY2skQoozlQVjd6UJkiu3P0puY2xgQKlD3VR7l69Wry8/PJz89n8uTJ7Nmz57Tnq9VqCgoKQn862jPekqVLlwoXZg9ArHp3M4OMeur9AVyaAfgDFUjoqG+ILBHKsDR35zS6GWnWRAy9DebEyih7so/ynHPO4cMPPyQlJYUNGzZwxx13sG1brL3tFIxGY7etiPt8PjQa8eOZKIiMspsJrnxX6/JxNZ3AoDHjdEUGvfTmQFnV6EZl0kYs5uibh96JklH2ZB/l5MmTSUlJAeCCCy6guLi43V+/3+9n0aJFjB8/nvz8fJ577rnQscceeyz0+UMPPRT6fPny5QwfPpzvfe97HDp0KPT59OnT+dWvfsVFF13E008/3e53EXQd4ldWNxOspazQDKHJ9U9Mhgupr6tHluVQm6JRp8asU1PV4EFl1uI9GS4HCg69Y2WUv9/+ew7WHOzU9x2ROoL7J9zf6vGzxUe5atUqrrzyytN+L5qamkKatXPOOYe1a9eyatUqbDYbX3zxBW63mwsvvJDLLruMwsJCCgsL2b59O7IsM3PmTD766CPMZjOvvPIKu3btwufzMW7cuIjvX11dHR9++OFp30PQ/YhA2c300+vQSFAh9cPlKiXJaqGmvgJXoxejVRc6L82ip9rhRmWyRJYHhTLKxK6lbM1HmZSUFPJRAqG5PovFEvJRAsydO5e//vWvbX5eLB/ls88+GwqULX2ULbNVULLiVatW8cknn5z2GbGG3ps2bWLv3r2hrNZut1NYWMimTZvYtGkTY8eOBaCxsZHCwkIaGhqYNWsWJpMJIDSlECTWLwhB/BGBspvRqCRyjXpKA+nIso/UdDha5qO6vJ5+1vAuvOkWnTL0Tk0h4PSFune0egMqtTpmRnm6zK+r6Ok+yr1793L77bezYcOGDrUoyrLMM888w+WXXx7x+bvvvssDDzzAf/3Xf0V8/tRTT5326zSbza0eE8QPMUcZBwYZ9ZzwKT8QyWmKwfxkcWQtZbpFrwy9TVqQQXaFg4neZE6YOcqe7KM8fvw41113HX//+987vPB0+eWXs3LlSrxeJes/fPgwDoeDyy+/nBdffJHGZslySUkJFRUVTJs2jbVr19LU1ERDQwPr16/v0HMF3YvIKOPAIJOeD2tUBJBISlY2oqqqiNyqPM2i58vjtaE2Rr/DqwRNlOG3K0GG3kEf5cKFC3n00UcxGAzk5uby1FNP0djYyJgxY5AkKeSjDC6knEpLH2V6ejpTpkxh//79UefddNNNLFiwgBUrVkRksi19lD6fj/Hjx5/RR/nwww9TXV3N//zP/wCg0Whor57v9ttvp6ioiHHjxiHLMhkZGbz11ltcdtllfP3110yaNAkAi8XCP/7xD8aNG8ecOXMoKChg4MCBTJ06tV3PE8QH4aOMA/9bUsX9h4t5hv9mdNa1vPqKi2F9zuPmO68JnfPEpkP8acs37Jt3AbX/+xUZd45BPzAJgNUP/hyD2cL1v3pY+CgFcUX4KAVdxkCjsmhTpx2J33cSCRX2U8za6VY9ARnsauXvgVOKzhN9MUcgOJsQQ+84MMDQrFvTDqXJtQO9uh9OV+S8W5pZOacmECCZaDFGfWXkFhKCzkH4KAWxEIEyDvQzaJGAatUAmprWYjJcRkP9qUXnStYZCpSOFt05FkvCdOacbQgfpSAWYugdB3QqFdl6LRVk4vVWk2TV45Oa8DSFg2F6sN+7WYzhd0YPvc/G+WWBIBERgTJODDDqKPMrizOpaQFklS/CSxlsY6x2KN05kft7Wwj4ffjckcJfgUDQNYhAGScGGvSU+pThtS1FySTLy8J74SQZNOjUKiob3ahN2oQWYwgEZzsiUMaJgUYdFV4JDzosNsVLWV0R1q1JkkSaRdesWtNEZZSQOGIMgeBsRwTKODHAoGSTVVIWepOy4l1bE+mlTLfolTZGszZq1RsSJ6PsLT5KSZK49957Q39//PHHWbp0aYeeLehZiEAZJwYam0uENMNBrgYZ6hsiaynTgv3epwRKvTloEIp/LWXQRzl9+nSOHDnCV199xW9/+1vKy8s77RkzZ87kl7/8JRAdKB9++GG+973vdei+QR/l3r17WbJkCXfcccdpz9fr9fzzn/+kqqpj2wW37DEX9CxEeVCcCGaUNZpBuN070UiZOBzRXsqDZQ3K0LuFGMPQYuitMieFzj/529/i/rpzNWv6c0fQ91e/avV4az5KWZZZtGgRGzZsQJIkFi9ezJw5c/jggw9YunQp6enpIUXbP/7xDyRJYuPGjSxcuJD09HTGjRsXut/LL7/Mjh07uPnmm1m3bh0ffvghy5Yt48033+SRRx4Jadc2b97MfffdF2phXLlyJXq9ntzcXH784x+zfv16vF4vr7/+OiNGjGDy5MmhZ7TFR6nRaLjjjjt48sknWb58ecSxY8eOceutt1JZWUlGRgYvvfQSAwYMYP78+aSmprJr1y7GjRuH1Wrl6NGjlJWVcfjwYZ544gm2bt3Khg0byMnJYf369Wi12vb+ZxJ0MSKjjBMZOg1GlUSllIPLXYpea6LJE5khpjer1iSjBmQINJcPJZK8ty0+yvfff59FixZRVlYGKD7Kp556iq+++opvv/2WTz/9FJfLxYIFC1i/fj0ff/wxJ0+ejLpn0Ef52GOPsXv3bgYPHhw6FvRRvvrqq+zbtw+fz8fKlStDx4M+yjvvvDPm1gtt8VEC/PSnP2X16tXY7ZFW+rvuuot58+axd+9ebrnlFu65557QscOHD/P+++/zxz/+EYAjR47w73//m7fffpu5c+dy8cUXs2/fPoxGI//+97/P+A6C7kdklHFCkiQGGPVUBtJxu09iMpiodZ06R6nD65dx6JTfZwGnF7VZi96ouAzdTifGFuefLvPrbs5GHyVAUlIS8+bNY8WKFRiN4e/+559/Hrrvj370I37xi1+Ejt14442o1erQ36+88kq0Wi15eXn4/X6uuOIKAPLy8jplHx5B5yMyyjgywKDUUsqyH1sy+CQX7qbwXGSwlrJWUgrLgyVCKrUarcGI2xn/jHLUqFHs3Lkz6vOe5qN8++2329yiuHDhQlatWoXjNHPELb+WUx2TwXdRqVRotdrQuSqVSsxjJigiUMaRgUYdpT49MmBL9oIqQE1FdNF5jdy8v3eEGMOE2+ns1veNRW/0UaampjJ79mxWrVoV+mzy5MmhzdVWr17NlClT2nw/QeIjAmUcGWDQ4QhINGLFalO6bCrKwl7KtGC/tz9WoEwMg1DQR/nee+8xePBgRo0axdKlS7n55pvJz89nzJgxzJgxI+SjbI2WPsopU6YwcODAmOfddNNNPPbYY4wdOzYUVIPXB32UeXl5qFSqdvkoCwoKOP/8tpvB7r333ojV7xUrVvDSSy+Rn5/P3//+d7E52FmG8FHGkQ2VdfxkfxHL5EXkm6fzzgYv08ZdyYyZEwGoqHcx4bebefiqc5nx7xJs3z8H6zRlbm/NkkVodDpG3zBX+CgFcUP4KAVdTk5ziVCteiB6Q7DovC50PMXcfNztA1V41RuUWkq3M/4ZpUDQGxCr3nEkR68EQrvmHGSOgtw/QuCrVauwGjTUODyojKcUnZvM1JaVdPs7n+0IH6UgFiJQxpFUrRqjSqJGlYPb9SkaaQgOxyndOWYdNU6vUnTeMqNMkDnKsw3hoxTEQgy944gkSfQz6KgmQyk615ho8kSuZKeaddQ43KiMmgiDUKKsegsEvQERKONMjl5HlWzD52vAYtLg8UcHSsUgdGq/t+KkPBsX4wSCREMEyjiTY9BS7lc6PGzJPnySG68nnDmmmnXUOj2hfu8gepPSnSMCpUDQ9YhAGWdyDDqqfGq8aLDa/CAFqC4P9xGnmvXUODxIBnXUHCWAHAh0+zsLBL0NESjjTHDlu5p0rEkeACpKWxSdm5V+b6dOjez2IzcXnwcDJQmQUfYWH6VaraagoIDRo0dz44034uzgHHFlZSVarZbnnnuuQ9cLuh8RKONMjkFRalWTjtGsmM5rqqJrKe3N9Qkhg1Bz/3C8h969yUdpNBrZvXs3+/fvR6fT8Ze//KVDz3399de54IILWm3TFCQeojwozvRrLjqvU+ei1TcAVux14RKhtGCgRCYJRYyhtujCQ285PPT++LXDVJ3oXFFGen8LU2e33gfdm3yULZk6dSp79+4F4IknnuDFF18E4Pbbb2fhwoU4HA5mz55NcXExfr+fJUuWMGfOHEDpY//jH//IzTffTElJCTk5OW1+riA+iIwyzmTplT2+a9UDkNSKZq2+RdF5arA7h2aDUDCjNCVGRtnbfJSgmMo3bNhAXl4eO3fu5KWXXmLbtm1s3bqV559/nl27drFx40ays7PZs2cP+/fvD6nUTpw4wcmTJ5kwYQKzZ8/m1VdfbdMzBfFFZJRxRq9SkanTUCP1xeP5AJU8OELfFQyUdUExRnOJUHgxJxwoT5f5dTdno4+yqamJgoICQMkob7vtNlauXMmsWbNCKrXrrruOjz/+mCuuuIL77ruP+++/n6uvvpqpU6cC8MorrzB79mxAEXzcdttt/PznP2/z1ymIDyJQJgA5Bh3VLkXgq1MZaHLFCpR+IOyk1Oj1qNTqiKF3PBg1ahRvvPFG1Oc9zUe5YcOGM7YoBuco2/LcYcOGsXPnTt555x0eeOABLrvsMn7961+zZs0aysvLWb16NQClpaUUFhaGfkEIEhMx9E4AcvQ6KgJWvN4aDDodLl9T6JhJp0avUVHrVX64g0NvSZLQmcxxX/XujT7KlkybNo233noLp9OJw+Fg7dq1TJ06ldLSUkwmE3PnzuW+++7jyy+/5NChQzgcDkpKSigqKqKoqIgHHngg5LEUJC4iUCYAOQYtFX4DMmBNCuANhAOlJElKv7fbBxKniDFMEUPveNADq5epAAAgAElEQVRbfZRBxo0bx/z585kwYQITJ07k9ttvZ+zYsezbt48JEyZQUFDA8uXLWbx4MWvWrGHWrFkR119//fVi9bsHIHyUCcALxZUsLixhpfwTvMVz+epIA7/61YPo9Erp0FUrPqZPkoFlx30Y8zNIuXYIAH//5c8Yee1NnHfBpHi+vqAXI3yUgm4jWx+spUzDbPWABFUnw7WUqWYd1Y7mfu9TunPOxl90AkGiIRZzEoC+zYGyljQGW5QtIarKa8kemAEotZRF1Q5UZnOUk1IEys5F+CgFsRCBMgHICgZKqQ8GkwMwR3Xn1Dq8qDI0+E/ZN0f0encuwkcpiIUYeicAGVotKqBe3R+NTik2r6sNizHSzDoa3T58enWkQcgsMkqBoDsQgTIB0KgkMnVa7Ko+INWADA314RKYVLNSA2jXStGqNVkWwVIg6GJEoEwQ+uq11ErpeDwVqNDhcIZ7tkNtjGqQXb5QSZDeZAGEak0g6GpEoEwQsvRaqgNW3O4KdCp9zO4ce/N/rXC/tyLvDYhAKRB0KSJQJghZei1VASOy7MFklHB5w0XnoTbGU8UY5sSQ9wofZduZPn16RGH7jh07mD59ervvI+heRKBMELL0WhoCGtzosFq9eAOu0LEUk7IqXi+3Jsbwd/PbhhE+yvZTUVHBhg0bOnStID6I8qAEoWUtpcXiJyB58Hi86HRabEblmD1kEGqhWqupCw29t7z8VyqOfdup75U5cBAXz289gAgfZft9lIsWLWLZsmVt1roJ4o/IKBOEYC1lDamYrV6QoPqk4qfUqFUkGTThQHmqkzKOQ2/ho2yfjxJg0qRJ6PV6tmzZ0qbnCeKPyCgThHBGmYrR7AZ0VJ6sJWtAJqAUndc1784YGno3z1EGM8rTZX7djfBRxvZRBlm8eDHLli3j97//fZu/RkH8EBllgpClUwJlnSoHvUFZJKitCZvOk0066txKoJSjMsr4zVGOGjWKnTt3Rn3e03yUb7/9dpt9lLt37+aZZ55Bp9Od0UeZl5fHAw88wMMPPxxxfMaMGbhcLrZu3dqWL08QZ0SgTBDMGjVWtQq7KguNTqmhrG+xd06KSUtdkxdJF962VqVWgyTFVbUmfJRt91GeyoMPPsgf/vCHDj1X0L2IoXcC0VevpdaTiaSyA/1oaAgXnaeYdHxT0YjKaCHgCmeQkiQRiGNGGfRRLly4kEcffRSDwUBubi5PPfUUjY2NjBkzBkmSQj7KgwcPxrxPSx9leno6U6ZMYf/+/VHn3XTTTSxYsIAVK1ZEmNVb+iiDiznt8VECaDQa2qvna+mjBEI+ynfffZdFixahUqnQarUR86VBvv/975ORkdGu5wnig/BRJhCzd39DRcNRfqv+Le9tvITstHNY8LNbAPjN+gO8vqOYzSkZqFMNpM8bCcAXn37C0CGDSe6TFc9XF/RShI9S0O301WupkS24PZVoJB1NTeGC5hRTsxjDoCLQFDYISZIU94JzgeBsRwy9E4gsvY5qvwGf7MagV+Fu0Z2T3Fx03qBTkVIfXoxApRItjJ2I8FEKYiECZQLRV6/Fj4SdJCwmP1V14e6cZJPSxtigkbC1sJyLjLJzET5KQSzE0DuBCJYI1ZKG2eLDJ7tDx4JtjA0qIraDEIFSIOh6RKBMIDL1SoJfRzImkw9Z8uNyKcEypTmjrFeB7PEj+5VFOGXVWwRKgaArEYEygchszijtJDd354Q3GQvOUdqlZoOQK1ykLQcCQt4rEHQhIlAmEBm65oxSSsdgau7Oad47J5hR2uXIfm8k5T+hGH4LBF2HCJQJhF6lIkWjplHVF60+2Mao7J1j0qnRqVXYmwNisI1RUiltf7Icv0DZG32U11xzDXV1yi+xoqIijEYjBQUFjBw5knnz5uH1eiOu/dnPfkZOTo6YJumhiECZYGTqtdhVmag1SvtifZ3SridJEskmLXZfZEYZ7I+O1w9gb/VRpqam8uyzz4aODR48mN27d7Nv3z6Ki4t57bXXQscCgQBr166lf//+fPTRRx16V0F8EeVBCUamTkOVOxkkJZNsuclYikmH3au0K4YDZXjoXbf+CJ5SB52JLttM8jWDWz3eW32UkyZNCvkoW6JWq5kwYQIlJSUR36PRo0czZ84c1qxZEzKaOxwO7r777pAWbunSpfzgBz9o8zsIug+RUSYYfXRaamUrXl8lUkCDwxkOfMkmbVi15grOUTYPveOUUfZGH6Xf72fz5s3MnDkz6pjL5WLbtm0R/sk1a9bwwx/+kFmzZvGvf/0rNCxfvnw5M2bM4IsvvmDLli0sWrQIh6Nzf9EJOgeRUSYYGToNNQEDnkAtWpUG5yltjEcqGgFV1NBbDgROm/l1N2ezj7KoqIjzzjuPSy+9NHTsyJEjFBQUUFhYyA033EB+fj4AHo+Hd955hyeffBKr1crEiRPZtGkTV111FZs2bWLdunWhwO1yuTh+/HhU77Qg/oiMMsHI1GnxyGqaMGEyBHB7wm2MKWYttU0eUEsJM0fZG32Ux44dw+PxxJyj/Oabb9i6dSvr1q0DYOPGjdjtdvLy8sjNzeWTTz4JKeRkWebNN98MOS5FkExcRKBMMPo0m87rSMFk8uHxR7Yx1jm9SAZ1eNU7zkPv3uijtNlsrFixgscffzxqdTsrK4tHH32U3/3ud6Gv44UXXqCoqIiioiKOHj3Kpk2bcDqdXH755TzzzDOhIL9r1642v4OgexGBMsHI1IW7c8wmf1Qboy8g4zKoYw6940HQR/nee+8xePBgRo0axdKlS7n55pvJz89nzJgxzJgxI+SjbI2WPsopU6YwcODAmOfddNNNPPbYY4wdOzYUVIPXB32UeXl5qFSqdvkoCwoKIraRPRNjx45lzJgxvPLKK1HHrr32WpxOJx9++CHvvvsuV111VeiY2WxmypQprF+/niVLluD1esnPz2f06NEsWbKkzc8XdC/CR5lgHHa4mLb9ID+Vn2RUeQ57D+n45S8fwGDQ89qOE/zijb2sze5DP5OejNvz+Prrr0k16DBak0hKFxJYQfcifJSCuBDMKO0kYzR5AKiuiOzOadBK4VVvQKVSic4cgaALEaveCYZNo0avkrAHMtEbywALNZV15AzoEzII2dUSgcYWBiERKDsN4aMUxEIEygRDkiQydBrqPZno9N8CUNfcxhhyUqrCLYygBErRGtc5CB+lIBZi6J2A9NFpqZfSUGmad2O0K/8MZpT1kkygyRdaLRVDb4GgaxGBMgHJ1GmpxQaS0u/d2LwbY5KxWd4LIIPsVtoZJZUqrlIMgeBsRwTKBCRDp6FWNuP31yAF1DidSneOVq3CrFNTf4pqTRl6x2/LWoHgbEfMUSYgfXRa7AEDTn89arQ0ucJtjMkmHQ2ByEApht4CQdciMsoEJLglhB0LBp2Myx1uY0wyarH7og1CckCOm+Vc+CiFj/JsRwTKBKSPLtzGaDT48PhatDEatdQ3q9bC8l618vc4/BAKH6WC8FGe3YihdwKS3lx0Xo+NNJOfmkZP6JjNqOUbuwtQK0XnZmXoDYqAobyiolPfpW/fvqfVjwkfZSTt8VEuXbqU48eP8+2333L8+HEWLlzIPffc0+Z3EHQfIqNMQNK1LbtzvPhlT2jIlmzSUu9udlK2WMyBMxt0ugLho4ykPT5KgIMHD/Luu++yfft2fvOb30QN2QWJgcgoE5D05qF3PTaljVHS4mpyYTKbsBm12F1ekPQEXOHyIIBLL7kEndEYt/duifBRntlHCXDVVVeh1+vR6/VkZmZSXl4e+t4IEgeRUSYgJrUKs1pFPcnoDYo9qKZa6c5JMmpx+wJ4dKoWc5TKf8Z4LBQIH6VCR3yULd8r1rsJEgcRKBOUdK2GRlUGOoOy4l1XpRSfB/f3btSrQmKM4BxlPBZzhI+y4z5KQc9BBMoEJV2noUFKRaNT9lCx1yrBwdbcndOoVUUNveMRKIWPsuM+SkHPQfgoE5Qf7/uWwppv+IPqT7y/eRwTx1zElbMu5pPCKuau2sZzfTMYZzJQNVXL8OHDqTh6BGtaGubk1Hi/uqAXIXyUgriSrtVgx4LcvG1tY73S7x3KKDWn7JsjQcAvipkFgq5ArHonKOk6LXUBA165DimgDm1bGwyUDSoIOJuH3pKktDEKMcZ3RvgoBbEQgTJBSddq8KOiPgAaSUVTk7KoY2tezGmQZAJNYRGGpFKLfu9OQPgoBbEQQ+8EpWV3jkHvC/V7W/UaJAnqkZFbbAchSZIIlAJBFyECZYIS7M6px4bJ4Mfd3O+tUknYjFoakBUnZfNinKJaO/sW5gSCREAEygSlZUZpNHnx+sPb1tqMWuqDCzfNsVGo1gSCrkMEygQlPbQbow2jwRfZ7x0RKMMZZbwWc4RmrXXN2gcffIAkSRF1k1dffTUffPBBh95ZEB9EoExQUrUaJJrnKI0+kGRcLmX4nWTU0tDspAxmlIqTUmjW2kN3adb69evH8uXLO/SOgsRABMoERS1JpGo1NEgp6IzKsLtld47dc0qgjNNOjK1p1qZMmcKiRYsYPXo0eXl5vPrqq4CSYU2fPp0bbriBESNGcMstt4TmWTdu3MiIESOYMmVKhLji5Zdf5q677uKzzz5j3bp1LFq0iIKCAo4cOcL8+fN54403AKWEZ+zYseTl5XHrrbfidivft9zcXB566CHGjRtHXl4eBw8eBBQbUUpKCtAxzVpLlVqQWJq1MWPGYLPZeO+996LOb+3dBImFKA9KYNJ1Gup9Gej0XwNQU2UnK6cPySYtdo8P0EbsxFha9WeKa0uBjsslTsVqOZdhw5a0erwtmrWqqirGjx/PtGnTAEWzduDAAbKzs7nwwgv59NNPOf/881mwYAH/+c9/GDJkCHPmzIm6Z1CzFvRPtiSoWdu8eTPDhg1j3rx5rFy5MmQPCmrW/vznP/P444/zwgsvRFzfEc3abbfdFnUsqFl7+umnIz5fvHgx/5+9846Tqjwb9nXOzGwvbGEXEGSRqMAWlg6LoEbFKKKhKCIJQV80iD1iSyxgJK/xRSOWWHiNGEMUXw0a+IIdC6yFBZaOIkWqFIXtdc7z/XHmnJ3Zxi7szu4c7uv3Q3bOnDnnmVm5537add9///0BxiGL47VNaHsko2zHJPsySneYuTSo4KgpxoiP9FBYXo3yzXxDzX5v2snEd0OaNcDWrOm6bmvLtm7damvWNE3jV7/6VbPuV59mzd8m7q9Z27VrV8BrLc3an//850bvYWnWkpKS+Omnn+rVrCUlJXH66afbmjWLESNGAPD555/XuW5jbRPaB5JRtmOSw9zsIg7dZW5fLPLV9+4QGYZXKUohYDKnc+J0OnZPw+X2BK2N6enpdtfXn1DTrC1btqzJmrWCggIuu+wynn32WdtIbo1RHjhwgPPOO49///vfdcS+f/jDH5gzZw5ud+A/u4baJrQfJKNsxyR73BxTUaCZY5PFRaaay9rGWOiiTkapgryWUjRrjWvW/Bk1ahRHjx497uy60P6QQNmOSQ5zU6LCKfeWgKFTau33tpyUYbqdUepa28h7RbPWsGatvm72H/7wh2ZNGgntA9GstWNe3X+Eu77Zy1Pqer755JekJnXjhtum8OWOH7n6xS95Ji6BtFExZAzMorKsjJ/27yWh82mER0W1ddOFUwTRrAltTkdPTe2ccI9BeYW5jtLuersJGKOEtpH3CoLTkcmcdkzN7pwORIRXU1llBkq7HIRbqztGKaq1k0I0a0J9SKBsxyT6xBhFxNElopricnMBtb+TsiajNGeM22LRuZMQzZpQH9L1bsckelwAFBNLREQ11YYZKCM9Lty6RolGjRRDk663ILQWEijbMfFuFy7MjDIysgqDarxeL5pmqdYMK6FE03VxUgpCKyGBsh2jWfu9iSU8ohLANp3HRXooMhQoFeCklEApCC2PBMp2TqKvbG1YhNnttnbnxEW4KbaCoiGBUhBaEwmU7ZxEj4sSLQGPb7/3sZ/MXSlmRmkFSvMvTdcx2mDW+1TxUQqnLjLr3c5J9LjZq8Xh8piBsvCYL1BGeNhXbWaXSik0zAmdYGeUlo/yN7/5jb1LJT8/n4MHDzZrW2BjXH755fa+6bfffpvLLruMPn36AObumhPF8lEmJCSwbNkybrjhBr766qsWabPgLCSjbOckedwUqug6Yoy4SA9FlpOyDbvep4qPcteuXfTu3Zvrr7+e9PR0Ro0aZY8Xz58/n0GDBtG3b1/Gjx9Paam5J3/q1Knceuut5OTkcMYZZ9QrDxFCA8ko2zlJHjdFKgIvPjFGsbnfOy7S7XNSYq+l/O9DxWwurcBzeFuL3T8jJpI/ntm1wedPJR/ltm3beO2115g/fz5XXXUVb731Fr/61a8YN24c119/PWB6J1966SVuueUWAA4cOMCKFSvYunUrl19+eZ12C6GBZJTtnESPGwOdIlUJSqOkxMxW4iI8VHoVCrCHJVvO13vSONFH2aNHD7Kzs+tcZ+PGjYwYMYLMzEwWLlzIpk2b7Nf88pe/RNd1+vTp06LlMYTgIhllO8dadF6oInBrUFbmC5S+3TkK7K73A6clUFpQQOoZPwta+04lH2Xtdltd76lTp/L222/Tt29fFixYEFA4zP81ThTQnCpIRtnOSbTre8cR7vbWFBiLMI8r/Gp7azrKb11lMDgVfZS1KSoqonPnzlRVVbFw4cITvo7QfpFA2c5JDKvZ7x0RXk1FVU0lRgBDI2AyB4K7jfFU9VH688c//pEhQ4Zw0UUX0atXrxO6htC+ER9lO2dPeSWDvtjM9eqvnL4pgSNHO3LfgzNZs/so4/6ay1tXdKHv2b1xJ0ZQWlhA4eFDdDw9DZcneOUghFMX8VEK7YIk2yAUS0RElS3GiIvwyyj9KjGCGIQEoaWRyZx2TpRLJ0LXKPLGER7xI15VhWEYxEX6jVHW7nqLk/KEER+lUB8SKEOARI/bDJThP4BmijHiIiIAMLR65L2SUZ4w4qMU6kO63iFAksdNsZ5IWLjZ7S4pLiHC4yLMrZu1vY2aWW+QQCkILY0EyhDAVK11wOMLlAVHzeUx8ZGegOVBMkYpCK2DBMoQINHjophYW4xhBcq4CLcpDvKzB0Hwa3sLgtORQBkCJIW5KSQa3WXuyikqsPZ7e8xet2+ReU2g9LZVUwXBkUigDAESPW5KVDjVmIHSFmNEeLBzR0OhaZpZDiLIa2NdLhfZ2dn2H8sded5553Ei61nz8/P5z3/+0+DzeXl53HrrrSfU1uO1KS0tjczMTPu95ObmntB96mPBggV07NjRvvaUKVNa7NpC6yKz3iGAvY1RGaA0Sktq9nvbo5F+M9/BnsyJjIxs0Zni/Px88vLyuPTSS+s8V11dzcCBA094F01TWL58OcnJya1y7YkTJ/LMM8+0yrWF1kMyyhDACpTHlAddaZSV+urmRLjt7NF/LWV7nMx5//33GTZsGP379+fKK6+kuNj0aq5atYqcnBz69u3L4MGDKSgo4MEHH2TRokVkZ2ezaNEiZs2axQ033MCoUaOYMmVKgP28uLiYa6+9lszMTLKysnjrrbcAuPHGGxk4cCDp6ek89NBDJ9V2//sB3HzzzSxYsACAe++9lz59+pCVlcXMmTMBOHz4MOPHj2fQoEEMGjSIlStXntT9hbZHMsoQwDIIFRFHmNugvMLc7x0f6cFQVeZJSjF7ySbW7TyMpmm4w3a2yL37dInjoTHpjZ5TVlZm68cA7rvvvgCf5JEjR3jkkUf48MMPiY6O5s9//jNPPPEE9957LxMnTmTRokUMGjSIwsJCoqKiePjhh8nLy7Mzr1mzZrF69WpWrFhBZGRkgJ3nj3/8I/Hx8WzYsAGAo0ePAjBnzhwSExPxer1ccMEFrF+/nqysrCa95/PPPx+Xy0V4eHijxvOffvqJxYsXs3XrVjRN49ixYwDcdttt3HHHHZxzzjns3r2biy++mC1btgCwaNEiVqxYYZ937bXXNqlNQtsigTIEqNnGGEeUp5qKyhoxhoLAmW8NILhjlMfren/55Zds3ryZ4cOHA1BZWcmwYcP45ptv6Ny5M4MGDQIgLi6uwWtcfvnlREZG1jn+4Ycf2iUoANtY/sYbb/Diiy9SXV3NgQMH2Lx5c5MDZVO73nFxcURERDBt2jRGjx5tZ50ffvhhQF2fwsJC23gkXe/QRAJlCJDgC5TFxJAQ7qWkzG+/t9eMkUopHhqTztED+zG81SR1Pb0NWxyIUoqLLrqojjpt/fr1TXZQRkdHN3jt2tfYuXMnc+fOtXVvU6dOtfV0J4Lb7Q4YzrCu5Xa7+frrr/noo494/fXXeeaZZ/j4448xDIMvvvii3sAuhCYyRhkCdHCbXe8SYggP81Ll9QVK335vL/ip1rR2tzNn6NChrFy50nZFlpaW8u2339KrVy/2799vm8+Lioqorq5u0DlZH6NGjQrI0I4ePUphYSHR0dHEx8dz8OBBli1bdlLt7969O5s3b6aiooKCggI++ugjwBwfLSgo4NJLL+XJJ5+0s+rabZItkaGPBMoQIMKlE6n7G4TMcUnbIIQKmPUOdslaa4zS+nPvvfcGPN+xY0cWLFjApEmTyMrKYujQoWzdupWwsDAWLVrELbfcQt++fbnooosoLy/n/PPPZ/PmzfZkTmPcf//9HD16lIyMDPr27cvy5cvp27cv/fr1Iz09neuuu87u8p8o3bp146qrriIrK4vJkyfTr18/wAzsl112GVlZWZx77rn85S9/AeCpp54iLy+PrKws+vTpw/PPP39S9xfaHvFRhgj9czfys/L3uXLfDjZtT+aBBx5g4/4iDu3ZwYDTf0ZcbDju+HCKjhymtKiQ1B4927rJwimA+CiFdkWC202xFkd4RCXgMwjZlnPNdlJa6yid+AUoCG2FTOaECAkeN4e1GjFGWWkZcVHmLLFXo+5+b2Wgaa62aGpIMGTIELvut8Wrr75KZmZmG7VIaM9IoAwROnhc7CIOt8eccS0sKOZ0n0jWoB55r6Gkv9AIja2PFITayD+lECHR46aYaNsgVHSsGI9LR9cCy0FYS2Xa28y3IIQyEihDhA5uF0UqAs1lCjEKC82/NU2rtTxI5L2C0NJIoAwROnjceHFRRjUAJUVmoNQ1a8G5eZ4VKIO9REgQnIwEyhAhwd7vbYBSlPgMQpqmmesofRmlLuUgBKHFkUAZIiS4ffu9VQQuDdsgpGu+nTmq7bre4qNsGv4+yj59+jB//vwWu7bQusisd4hgZZTFxBDm8lJebgVKzWc5N2e6a5YHBW8dpfgom44lxTh06BDp6elcfvnlpKamtsq9hJZDMsoQoYMtxoglzO21VWuaBl4rKCqFprfPWW/xUQaSkpJCz549+f7770+qbUJwkIwyREj0yygTw6sp8y2W1jWtxnL+7n1ohzaQWF6Gy+0Bt+fkb9wpEy55tNFTxEfZdB+lxY4dO9ixYwc/+9nPmtQmoW2RQBkixLutQBlLWFglBSVmoNQ0MJTyOTEUGhoQ3Lo54qNsuo/SEveGh4fzwgsvkJiY2KQ2CW2LBMoQIUzXiXFpFFfHEBFxiGrD3POt+xaYG4C64E9oEW6Ofb+T8Kgo4ju2j7Ev8VHWIOLe0ETGKEOIDm6XL6OsxquqMAwD35BkwMy3rgW/wFhjiI9SfJShjgTKECLR46FEiyMs3MwmKyoq7GzK3O9tnmfKe4PX9RYfpfgonY74KEOIq/K/48Cx9dx7bDGr8nty6623snvvfvTErpyOTmyHCFwxYfy0fy8oSDyta1s3WXA44qMU2h0JHjclxOLxmBM5JSUldkYZsN9bC77lXBCcjEzmhBDmGGU0Lku1drQEXceuxOi/31tVSaBsDPFRCs1BAmUIkehxU6wiwG0GyqKCYmITo/DiU61Z+7319jWZ0x4RH6XQHKTrHUJ08Lgw0CnXvAAUF5VgrYwxzAWVgG8yR7regtBiSKAMITpYYgzM6FhSXIqmaeg+J6Xd9dZ0lKGkbo4gtBASKEMIS4xRqMyByVKfQcil+7YxtqFBSBCcjATKECLBX4zhMigr8zMIaaqu5Vy634LQIkigDCH8VWse3UuFzyDk0mt1vf0LjAUBp/koR4wYEXAsOzubjIyMk763ELrIrHcIYY1RlhBLvMdLZaW13xu8ioAtjEDA/uTWxGk+yqKiIvbs2UO3bt3qWH9a+95C+0QyyhDCMgiVEE14WDWV1eY6wFAYowwlH+VVV11lb5187bXXmDRpkv2c/71vvfVWHn74YQDee+89Ro4cGbQvJyG4SEYZQnh0jWgdSrwxhIX/RFVRTaD0KsXcb+axbcMOlGFQVVGBe2sYust1UvfsldiLewbf0+g5TvNRTpgwgalTpzJz5kyWLFnCwoULefXVV+uc9+ijjzJo0CBGjBjBrbfeyn/+8x90XXIPJyKBMsSId7so8UYTHnaQaqMK8GWU1nCkApqoLmspnOajTExMJCEhgddff53evXsTFRVV73lRUVHMnz+fkSNH8pe//IWePXs26fpC6CGBMsTo4HFTUhGDJ7wag2qUUj7LueLOs24nrFM0BgaHv99JXHIKUfHxbd3kkPRRTpw4kZtuusku+dAQGzZsICkpif379zfr+kJoIf2EECPe46ZEMy3nYAYKl+6vWvMvMOZtq2YGEIo+yrFjx3L33Xdz8cUXN3jO999/z+OPP87atWtZtmyZbIt0MBIoQ4wObjelWhyeMHN8UimFy98g5JdhBWt5kBN9lLGxsdxzzz2EhYXV+7xSiv/6r/9i7ty5dOnShZdeeolp06adlEldaL+IjzLEuH3Lbj44uIMnSp7hi1UZjB8/ntN/1ovvfywhDRcxyZHoEW4O7txOZGwccckd27rJgoMRH6XQLol3uyhRUbh9BiH/jNJA2VmkGIQEoeWQyZwQI97jopwwvG6z620YCpfv686/bo6m67KFsRHERyk0BwmUIYa16LzUF7EzfvYAACAASURBVByVodD9JnOsIt9aOysw1t6QiRehOUjXO8ToYNf3NlHKCJjMUcrPSRnEAmOC4GQkUIYY8ZZBSLnQUL6uty+j1KjJKKXrLQgthgTKEKODvd/bNAhZC67NRefUjFFquuw7FoQWQgJliOEvxvC4vSjMwOjStbp1cySjFIQWQQJliNHB459RVttjklZGWVMOInhjlE7yUf7tb3+zTUQZGRm88847AEydOpUePXqQnZ1Nr169mD179gnfQwg9ZNY7xPDPKMPCq+zI6NI1DCPQcq4Mo9690C2NU3yUe/fuZc6cOaxZs4b4+HiKi4s5fPiw/fz//M//MGHCBMrLy+nTpw9TpkyhR48eAdfwepu2bdTr9eI6SbOTEDwkowwxwnSdSN3MKMM8XqycUdfqrqM0H7aPme9Q8FEeOnSI2NhYYmJiAIiJiakTCAF7m6Il6khLS+Phhx/mnHPO4f/+7//s8wzD4De/+Q3333+/fb0HH3yQIUOG8MUXX5zIxyi0EZJRhiDxbhel3mjCww8EZJQVzzzB3u3b0MNceL3VeKuqKA+POKmMMrx3Lzr9/veNnuMUH2Xfvn1JTU2lR48eXHDBBYwbN44xY8bYz99111088sgjfPfdd9x6662kpKTYz0VERLBixQoAnn/+eaqrq5k8eTIZGRn84Q9/AKCkpISMjAxb9iuEDhIoQxBLtRYWVo1C2aq1QILnpHSKj9LlcvHuu++yatUqPvroI+644w5Wr17NrFmzgJqud3FxMRdccAG5ubnk5OQABHwxAPz2t7/lqquusoOkdf3x48c32gahfSKBMgTp4HZToMXhCasGalRrxk13cJrS8ZwWQ0VpCcd+OEBS1254wiPatL2h5KPUNI3BgwczePBgLrroIq699lo7UFrExMRw3nnnsWLFCjtQ1m5fTk4Oy5cv58477yQiwvz8IyIiZFwyRJExyhAk3uOilFg8PielYRjomlkOAgBlrqOE4KnWGiNUfJT79+9nzZo19uP8/Hy6d+9e57zq6mq++uqrRo3m//Vf/8Wll17KlVdeSXV1dZPuL7RfJFCGIPFuFyVaDO4Aea/5nLXoPJi1vZ3io6yqqmLmzJn06tXLvve8efPs5++66y6ys7PJysoiMzOTcePGNXq93/3ud/Tv359f//rXsvg/xBEfZQjy4LZ9vLp3Ly+WzyGmw33079+f4irYd6yMn+EiMjUar6riyJ7ddEjtRERMbFs3WXAo4qMU2i3xbhdlhKN8qjWv12vv97bEGFqQa3sLgpORyZwQJN63O6fMpRELGF4D3V0j70UpNFf7WkfZ3hAfpdAcJFCGILYYwzKbG0ZAgTEMU7MGiJOyAcRHKTQH6XqHINY2xiLl6257A+W9Vtfb3O8tgVIQThYJlCFIB5+TsgRzAbZhGLh8Swm9ELjfWwxCgnDSSKAMQeL9nJSaZnavdc2v620ZhKTAmCC0CBIoQ5AOfgYhDYWhVMAYpbXIPJiqNUFwMhIoQxBr1rsUc9ucUkaN5VwjwCBkBKHr7SQfpSDUh8x6hyDhuk64BqUqCk1T9hIg00lJ0CsxOsVHKQgNIYEyRIl1a5RWRptjlL5Aue3dPZT8UEK4rqO5daorK1BK4Qk/fJyrNUxytxhGXHXWSbf3/fff56GHHqKiooKePXvy8ssvExMTw6pVq7jtttsoKSkhPDycDz74gAcffJCysjJWrFjBfffdx5YtW9i/fz+7du0iOTmZG264gblz57J06VKKi4u55ZZbyMvLQ9M0HnroIcaPH8+NN97IqlWrKCsrY8KECY0ayf2JiYnhtttuY+nSpURGRvLOO++QmprKkiVLeOSRR6isrCQpKYmFCxeSmprKrFmz2L17Nzt27GD37t3cfvvtJ5ztCu0X6XqHKPFuFyVEgylaA8CW6FjDkq1sNreovde79v5sfx/lmjVrGDhwIE888QSVlZVMnDiRefPmsW7dOttX+fDDDzNx4kTy8/Ntfdnq1at55513+Oc//xlwbX8f5fr16/n5z38OmD7KvLw81q9fz6effsr69eub9F5KSkoYOnQo69atY+TIkcyfPx+Ac845hy+//JK1a9dy9dVX89hjj9mv2bp1K++99x5ff/01s2fPpqqq6oQ/S6F9IhlliBLv9lBKtC8Wmt3v9NHd8VYZpLndeFKiKDx8iPKSYlLSzmjVtjjFRwkQFhZm29MHDBjABx98AJhlIiZOnMiBAweorKwMMJ+PHj2a8PBwwsPDSUlJ4eDBg3Tt2vW49xJCB8koQ5R4j5tSLRZNM9NH20mpVMBkTntYHmT5KPPz88nPz2fz5s289NJLzarncyI+yo8++oj169czevToJvsoPR6PfT2Xy2Ur0m655RZuvvlmNmzYwAsvvBBwvfDwcPtn/9cIzkECZYgS53ZRRrQtMreclObyIPOYpmsopdp8v3eo+Cgbo6CggNNOOw2AV1555aSvJ4QWEihDlDi3y+x6UyujNB8A/vLe1s0qneKjbIxZs2Zx5ZVXMmLECJKTk0/6ekJoIT7KEOWP2/fz4u79vBNfQqdOZ5KUlMTRcsWhonJ64SKsayylhQUUHj5Ex+5puNyetm6y4EDERym0a+LdLqr85uJMg5DvZ3xjd3r7KQchCKGMzHqHKHG+bYzKkmF4DXTN/HWaqjUVtK53KCI+SqE5SKAMUaxAaYVAw2vg8tRYzlHUqNfEIFQH8VEKzUG63iGKnVH6/msEGIRUra63BEpBOBkkUIYo8XagtCznqq7lvB2VrBWEUEYCZYgS5xcoNepzUqqachDS9RaEk0ICZYjin1FqmsJQtWa9ZTJHEFoMCZQhSqzbV44WDc03JmlllNZkTrDGKGv7KHft2tWq92uMbdu2cdlll9GzZ08GDBjA+eefz2effVbvuWlpaRw5coQ77riDJ5980j5+8cUXM23aNPvxnXfeyRNPPNHqbRfaLxIoQ5QoXccNKHSfk9IIKDCGbw+0pmmtvoXRkmJYf9LS0lr1fg1RXl7O6NGjueGGG9i+fTurV6/m6aefZseOHY2+Licnh9zcXMBcj3rkyBE2bdpkP5+bm9siu3uE0EWWB4UomqYR59Z8XW9zMufTV+az89ttuIEwl47m1qgqL0fXXbjCTmxnTkr3Mzh/6g3Nfp3X6+Xee+/lk08+oaKigptuuonf/va3fPLJJ8yaNYvk5GQ2btzIgAED+Mc//oGmaXXclB999BFRUVH1Xqc+Fi5cyLBhw7j88svtYxkZGWRkZADw448/MmnSJA4fPszgwYPtL5Dhw4dzxx13ALBp0yYyMjI4cOAAR48eJSoqii1bttCvXz+UUtx9990sW7YMTdO4//77bQ2c4GwkUIYwcW6XGSh1hfL69nfXc56idTNKa683QI8ePVi8eDEvvfQS8fHxrFq1ioqKCoYPH86oUaMAWLt2LZs2baJLly4MHz6clStXMnjwYCZOnMiiRYsYNGgQhYWFREZGNngdf82ZxaZNm+jfv3+D7Zw9ezbnnHMODz74IP/v//0/XnzxRQC6dOmC2+1m9+7d5ObmMmzYMPbt28cXX3xBfHw8WVlZhIWF8dZbb5Gfn8+6des4cuQIgwYNYuTIkXTu3LkVPlWhPSGBMoSJc7sxrK43ivOn3sA3PxQR7lV0jQ7D3SGCI3t24/K4SejUpdXaUZ+P8v3332f9+vW8+eabgGnf2bZtG2FhYQwePNj2NVpjmvHx8fW6KRu6Tn2BsjZjx45l27ZtnHXWWfzrX//is88+41//+hdgOiQtdyWYWWVubi65ubn87ne/Y9++feTm5hIfH09OTg4AK1asYNKkSbhcLlJTUzn33HNZtWpVQAYrOBMJlCFMvMeNUprtpDQXndesowSfaq0N1lEqpXj66ae5+OKLA45/8skn9fobG3JTNnSd+khPTw+YuFm8eDF5eXnMnDnTPtaQ/9Iap9ywYQMZGRl069aNxx9/nLi4OK677jq7LcKpiUzmhDBm11tH95P36rpm78wBn7y3DdZRXnzxxTz33HN2WYRvv/2WkpKSBs9vyE3ZnOtcc801rFy5kn//+9/2sdLSUvvnkSNHsnDhQgCWLVvG0aNH7eeGDx/O0qVLSUxMxOVykZiYyLFjx/jiiy8YNmyY/fpFixbh9Xo5fPgwn332GYMHDz6Rj0cIMSSjDGHi3S5UlQZ+GaVL06gE8GWRuqZTbQTfuD1t2jR27dpF//79UUrRsWNH3n777QbP93dTlpWVERkZyYcfftis60RGRrJ06VJ+97vfcfvtt5OamkpsbCz3338/AA899BCTJk2if//+nHvuuZx++un2azMzMzly5AjXXHNNwLHi4mLbPzl27Fi++OIL+vbti6ZpPPbYY3Tq1KklPi6hndOmPkpN07oBfwc6YXYWX1RKzat1jgbMAy4FSoGpSqk1jV33VPBRAsz6bh/Diw4zuEc0JaXRJCcn80NxNaVlVfT0ePCkRFFw6Acqy8vpeHpaWzdXcCCnio+yrTPKauBOpdQaTdNigdWapn2glNrsd84lwJm+P0OA53x/n/JYs96BGaVvwblRYzmXnTmCcHK0aaBUSh0ADvh+LtI0bQtwGuAfKK8A/q7M1PdLTdM6aJrW2ffaUxpbteZfN0fXMGrqi7WbAmMtyYYNG/j1r38dcCw8PFzUaUKr0dYZpY2maWlAP6D2/+2nAXv8Hu/1HTvlA2W87aT07cjxyXsVyp7p9i8w1tSKh+2dzMzMRsvjCkJL0y5mvTVNiwHeAm5XShXWfrqel9QZWNU07QZN0/I0Tcs7fPhwazSz3RFfW95rGDWqNTs4ihhDEE6WNg+UmqZ5MIPkQqXUv+o5ZS/Qze9xV2B/7ZOUUi8qpQYqpQZ27NixdRrbzoj1zyiVwvCqWqo1PzGGqNYE4YRp00Dpm9F+CdiilGpIz/JvYIpmMhQokPFJk5qMUvft9zYnc8xj+JyUIu8VhJOlrccohwO/BjZommYNOv0eOB1AKfU88B/MpUHfYS4PurYN2tkuifMLlDqmk9KjW6o1ZWaUvgxTut6CcOK0aUaplFqhlNKUUllKqWzfn/8opZ73BUmUyU1KqZ5KqUyllPMXSDaR2hmlMgK73spQQel6txcf5a5du9A0jQceeMA+duTIETweDzfffPNxXxsZGUl2djZ9+vRh+vTpGPLlIvho8zFK4cSJdulogIHLz3Ie6KTUg1A3p734KAHOOOMMli5daj/+v//7P9LT05v02p49e5Kfn8/69evZvHlznR1AXq+3RdsqhA5t3fUWTgJd03yBUkfDS9UnByk4doDYSi8VwBGPC6UpqivKOeopQnc1/9cd1iWaDmN6Nvt1beGjBDNo9+7dm7y8PAYOHMiiRYu46qqr2L/fnP+bOnUqcXFx5OXl8cMPP/DYY48xYcKEgGu43W5ycnL47rvv+OSTT5g9ezadO3cmPz+fMWPG0L17d2bMmAHArFmziI2N5c4772z2ZySEDhIoQxzTFqSjWX0D32SOVcY2GCsn24uP0uLqq6/m9ddfp1OnTrhcLrp06WIHSoADBw6wYsUKtm7dyuWXX14nUJaWlvLRRx/x8MMPA/D111+zceNGevTowdq1a7n99tvtQPnGG2/w7rvvtujnKbQ/JFCGOBqaWTdHU2gjk0np3IkN+wpIRicpIRItQufQrh3EJnckOr5Dq7Shvfkof/GLX/DAAw+Qmppar4H8l7/8Jbqu06dPHw4ePGgf3759O9nZ2WiaxhVXXMEll1zCJ598wuDBg+379evXj0OHDrF//34OHz5MQkJCgFxDcCYSKEMcO6PUFFYeqWvmNkYMv5K1QZ6YaAsfpUVYWBgDBgzg8ccfZ9OmTSxZsiTgef/7+0thrDHK2kRHRwc8njBhAm+++SY//PADV199dZPbJYQuMpkT4uho9mQOYFdjNFC+AmO6WWAsyIGyLXyU/tx55538+c9/JikpqQXeTSBW1/7NN9+s020XnIlklCGOFpBRmoHSpWsYhvITY2hB35nTFj5Kf9LT05s8291c0tPTKSoq4rTTTpN6OacIbeqjbC1OFR8lQO76DcSlpdHVe4jSskg6duzIrp/KcVUbnB4VjjshgsPf7yQsMor4lNS2bq7gME4VH6V0vUMcczInMKM0y0Fgu9acqFoThGAiXe8QR9eoR95rloOwetua1jZ1c1oL8VEKwUYCZYhjdQksea8y6sso26YSY2shPkoh2EjXO8TR/LVqgNdnELI0a2B2vQ0HZZSCEGwkUIY4dkZp/e0rB+H1raMEqZsjCCeLBMoQR7f9kxqgUIaBrmkoFIYKLAchCMKJIYEyxLH2shi+X6VhKL9yEOZzumSUgnBSSKAMcWr8k+YSIcOXUYIZNKFmeVBrZZVO8FGOHTs2YCH72WefzSOPPGI/Hj9+PP/6V32VSoRTAQmUIY5/RqlRtxyEUn77vVspUDrBR5mTk0Nubi4AP/74IzExMXzxxRf281988QU5OTkt32AhJJDlQSGOaaIEhc7K3C/48cej6C435VVevkbDFe7C8FZTXVVFWPjn5p7HZtCpUycuueSSZrcr1HyUw4cP5+677wYgNzeXyy67jGXLlqGUsu3nnTp14tJLL+XRRx8lKyuLfv36MXbsWB588EEeeOABunfvzrRp05r9WQntHwmUIY6m+fZ7Kx3NrpNjPqfs/2j249bwUzrBRzlgwAA2btxIZWUlubm5nHvuuezYsYMtW7awdu1ahg8fDsDIkSP5/PPPSUtLw+12s3LlSgBWrFjBr371q1b4dIX2gARKB+DC7HqPOGcIXiOCDsnJfHuwiC7oJKZGU1lRxrGDB0jqejoeP8VYS+EEH2V4eDjp6emsWbOGL7/8krvvvpsdO3aQm5vL2rVr7W73iBEjeOqpp+jRowejR4/mgw8+oLS0lF27dnH22WefzMcotGMkUDoA0z/pKzCmjMDa3gZ+Y5TBm/kORR9lTk4On332GUVFRSQkJDB06FCeeeYZ1q5dy/Tp0wEYNGgQeXl5nHHGGVx00UUcOXKE+fPnM2DAgCa3Twg9ZDLHAbj8VGsKha/wYk1tb7vAWPACZSj6KIcPH84LL7xA3759AcjKyuLLL79k9+7d9oRQWFgY3bp144033mDo0KGMGDGCuXPnMmLEiCbfRwg9JKN0AC5No8rPIBSQUSq/krVB3O8dij7KnJwcduzYwX333QeYRcZSUlLo1q0bul6TU4wYMcKeaBoxYgR79+6VQOlwxEcZ4mzZsoXItDMorSqnc9WPlFVE0KlTJzYfKKKD0uicEIkKUxzZ/T3xKalExsa1dZMFByE+SiFk0Am0nFuLzv3LQUDw6+YIglOQrrcDcGm+Soy6fzkIMAzM5UJW19shBiHxUQrBRgKlA3Bp5oJzy6vmn1EqQ9ljlk5xUoqPUgg20vV2ALpvGbkt761VDkLTNCkHIQgngQRKB+CyMkZrj7evHESAvFcT1ZognCgSKB2AvwQDwPBaXW9q5L26jiEZpSCcEBIoHYDLXjdp6dUMdN2yB5nnaLqzCowJQjCRyRwHUFMOwrScG74JnIACY60o73W5XGRmZtqP33777TZVrQlCSyOB0gG4/OW9+MYoPTWz3oA5udNKgbI+KYYgOAkJlA7AGqP8YfdfqSjeYS4w111UVhus0zT0fS6qKytMt+LBiGZdOzamN2ed9cDxT6xFW/goG7v2ww8/zJIlSygrKyMnJ4cXXngBTdM477zzGDJkCMuXL+fYsWO89NJLsh1RqIOMUToAe50k/iJKav2sBR5vQSwfZXZ2NmPHjgUI8EiuWrWK+fPns3PnTsD0UT755JNs3ryZHTt2sHLlSiorK5k4cSLz5s1j3bp1fPjhh3V8lLWvUx/1XRvg5ptvZtWqVWzcuJGysrIAC3p1dTVff/01Tz75JLNnz26dD0kIaSSjdADWt13S6bcRWWqgaRGExcaz+6dSztBcxJwWS+HhQ5SXFJOSdkaL3789+Sjru/Y555zD8uXLeeyxxygtLeWnn34iPT2dMWPGADBu3DgABgwY0Gb1foT2jQRKB6BpmqlaU2ZhiAAnpbLq5gR3wXlb+Sjru3Z5eTkzZswgLy+Pbt26MWvWLMrLy+u8xjpfEGojXW+HoPu2MWqaMnfm2BM8VnkIc8F5sBadt7WP0h8rKCYnJ1NcXGxnp4LQVCSjdAguNJ9ByNrCaB6v66Q00FyuVm9PW/so/enQoQPXX389mZmZpKWl2d17QWgq4qMMcSwf4LaScgxvMckVxVRUhZHUMZVvfHVzklJjKCsrovDwITp274HLLd+PQssgPkohpDDLQbhsJ6Xmt63Rf/xPxBiC0HwktXAILk2zxyihpixtna63A7Yxio9SCDYSKB2Cbo9RWoHS56YE32RO8OvmtBbioxSCjXS9HYLuq8SIVmM5t/Z7K0O1SclaQXAKEigdgtn11uxAWeOkVLUySgmUgtBcJFA6BGu/twqwnNc3Rhn6XW9BCDYSKB2CVQ7C33LuL+9tzVlvl8tl7/W2tg22JjExMY0+f+zYMf7617/aj/fv38+ECRNatA3FxcX89re/pWfPnqSnpzNy5Eh7Msn6PPr27Uv//v3Jzc0FYNeuXWRkZJzQ/dLS0jhy5EiLtV9oHjKZ4xDqWM6Nmro5SoHeirPe7U2zZgXKGTNmANClS5cW340zbdo0evTowbZt29B1nR07drBlyxYg8PN47733uO+++/j0009b9P5CcJGM0iHUbFk0Cayb459RBqfrXV5ezrXXXktmZib9+vVj+fLlACxYsICbb77ZPu+yyy7jk08+AcxM8Q9/+AN9+/Zl6NChHDx4EICdO3cybNgwBg0axAMP1CjfiouLueCCC+jfvz+ZmZm88847ANx7771s376d7Oxs7rrrroBMrrF2jRs3jl/84heceeaZ3H333Q2+t+3bt/PVV1/xyCOP2F9AZ5xxBqNHj65zbmFhIQkJCU3+fLxeLzNnziQzM5OsrCyefvrpgNeVlZXxi1/8gvnz5zfy6QstjWSUDsEFPLfnEHtKy8BbictVSLXS8BqKSF1D8+hUlZWj/3gQl+enJl83IyaSP57ZtdFzLM0aQI8ePVi8eDHPPvssYK553Lp1K6NGjeLbb79t9DolJSUMHTqUOXPmcPfddzN//nzuv/9+brvtNm688UamTJliXxcgIiKCxYsXExcXx5EjRxg6dCiXX345jz76KBs3brSzOv+hgMbalZ+fz9q1awkPD+fss8/mlltuoVu3bnXauWnTJrKzs3E1sBXU+jzKy8s5cOAAH3/8cZ1zGmrHyy+/zM6dO1m7di1ut5uffqr5XRUXF3P11VczZcoUpkyZ0uhnKbQsklE6BN0271jLgOrJHM1KES2O1dXMz89n8eLFAKxYscJeFN6rVy+6d+9+3EAZFhbGZZddBgQqz1auXMmkSZMAAhaaK6X4/e9/T1ZWFhdeeCH79u2zs9CGaKxdF1xwAfHx8URERNCnTx++//77Zn4SJtbnsXXrVt59912mTJlS5/fRUDs+/PBDpk+fjtu3zTQxMdF+zRVXXMG1114rQbINkIzSIbg0uLFbCsmAXuImzBNFpSeSH4sqOTsiDE9yJId378ITHkGH1E6t3p6GZtfdbndASQp/3ZnH47GHCGorz+pTsC1cuJDDhw+zevVqPB4PaWlpAddrTrugfkVbfaSnp7Nu3TpfEbfGc41hw4Zx5MgRDh8+3KR2NKSbAxg+fDjLli3jmmuuafAcoXWQjNIhWLPeZt0c5bfgvKZuTjArMY4cOZKFCxcCphpt9+7dnH322aSlpZGfn49hGOzZs4evv/76uNcaPnw4r7/+OoB9TTAlvikpKXg8HpYvX25ngLGxsRQVFTWrXc2hZ8+eDBw4kIceesgOeNu2bbPHSP3ZunUrXq+XpKSkJrVj1KhRPP/883aQ9u96P/zwwyQlJdmTVELwkEDpEGpmvU3VmrU8CMAIqMQYnMmcGTNm4PV6yczMZOLEiSxYsIDw8HCGDx9Ojx49yMzMZObMmfTv3/+415o3bx7PPvssgwYNoqCgwD4+efJk8vLyGDhwIAsXLqRXr14AJCUlMXz4cDIyMrjrrrua1K7m8r//+7/88MMP/OxnPyMzM5Prr7+eLl26AIGlMSZOnMgrr7xSZzyzoXZMmzaN008/naysLPr27cs///nPgNc9+eSTlJeXNzrZJLQ8olkLcfw1VxuKSolRx4gurUYRgTs2nn1Hy/iZy01U5xh+OrAPZRgknVZ3gkIQTgTRrAkhhw62GEMpVVPG1vdlqLdibW9BcDIymeMgXJqGUrqv4KIKqJsDoOmabGFsJkOGDKGioiLg2KuvvkpmZmYbtUhoCyRQOgjLIGTXzfEZg7y+WjmaZJTNRhyXAkjX21GYO3EsJ6UK3NaoCHolRkFwChIoHURdea+J/zbGYFZiFASnIIHSQbj8StZCrUBp4KhyEIIQTCRQOghda7gchAooWSsZpSA0BwmUDsKsxKjZgRLTeV6PQahlM8pT0UeZlpZGZmYmffv2ZdSoUfzwww9A455KIXSRQOkgzG2MWqDl3Fat0WqWc38pRn5+PmlpaS16/eZSO1C2ho8SYPny5axbt46BAwfypz/9CTA9lYmJiWzbto1NmzaxYMECEe46AAmUDqKuvNcI2O8dzLo5TvZR1mbkyJF89913x/VUPvHEE2RkZJCRkcGTTz4JmAq43r17c/3115Oens6oUaMoKysD4LvvvuPCCy+0Tenbt29v3i9BaDFkHaWDeHzZVjbsKyScCgyvC7fbTUW1QlOKCI8LhaKqsgJP2EE0vX6XYm36dInjoTHpjZ5zqvkoa7N06VIyMzMb9VSuXr2al19+ma+++gqlFEOGDOHcc88lISGBbdu28dprrzF//nyuuuoq3nrrLX71q18xefJk7r33XsaOHUt5eXmAdUkILpJROgjrl2mPjbzTGQAAIABJREFUUCpVM/WtsH9u6dVBp6qP8vzzzyc7O5vCwkLuu+++49537NixREdHExMTw7hx4/j8888B88vF+qKx3ndRURH79u1j7NixgPmlEBUV1eg9hNZDMkoHcd/oPmwvraAT+/EWRxITHcuRSh1vhZcz4iNRERpHdu8iPiWVyNi4Vm2Lk32UFsuXLyc5Odl+3Jinsjn3LSsrk7Wu7QzJKB2Ebo9R+vZ4G+ZkjgLfGGXrVWKsjZN9lA3RmKdy5MiRvP3225SWllJSUsLixYsZMWJEg9eKi4uja9euvP322wBUVFRQWlraIu0Umo8ESgfhsspA+H4yMxutntrerR8one6jbIiGPJX9+/dn6tSpDB48mCFDhjBt2jT69evX6LVeffVVnnrqKbKyssjJybGXIAnBR3yUIY6/D7DKMNhcXE4Sh3GXuPB4IilzRVJYUsVZ0eG4OoRzcMd3xCQkEpOYdJwrC8LxER+lEHLY/kl0QGEYqqZkra/rLWIMQWg+MpnjIDQwy0AoHU0zbNWagbJnui0xhtA0xEcpgARKR6FpWo3lHK+9MwfMiR0Q1Vpzke2HAkjX23HofgYhpYyamXC/AmOG2IMEoVlIoHQYLttJSYDlvCaj1MQeJAjNRAKlw9D9DEIK/wJj5vPBrO0tCE5BAqXDsJyUWE5KX9fbG1CJUTJKQWgOEigdhkvTzAXnmhUYzeOGVWBM11o8ozwVfZR/+9vfyMzMJCsri4yMDNtcNHXq1OMq3c477zxOlXW+TkFmvR2GOeut1V8OAlqlEqMlxWgvWIFyxowZQMv7KPfu3cucOXNYs2YN8fHxFBcXc/jw4Ra7vtD+kIzSYZgZZU05CGsBpb3ovBUyyvpwso/y0KFDxMbG2pltTEwMPXr0qHPeww8/zKBBg8jIyOCGG24IWL/6j3/8g5ycHDIyMpq0311oWySjdBLL7iX5wDriDUWEqiDScOH2eDij0iAMIMxFlLea8OoqVEQkGnWNPHXolAmXPNroKaeaj7Jv376kpqbSo0cPLrjgAsaNG8eYMWPqnHfzzTfz4IMPAqYebunSpfZ5JSUl5Obm8tlnn3HdddexcePGRj8boW2RjNJhaLV+sBPLVrznqeajdLlcvPvuu7z55pucddZZ3HHHHcyaNavOecuXL2fIkCFkZmby8ccfs2nTJvs56/2MHDmSwsJCjh071mi7hbZFMkonccmjFFZWsa+8im5qH+Ul0SQkJLDrp0o6KI0uHaOpKC+i8MhhOnbvgcvder9+p/soNU1j8ODBDB48mIsuuohrr702IFiWl5czY8YM8vLy6NatG7NmzQpoW+33U9/7E9oPklE6DGvLYmCBMTBQtVRrrbtEyMk+yv3797NmzRr7cX5+Pt27dw84xwqKycnJFBcX15lMWrRoEWBmuPHx8cTHxzerDUJwkYzSYVjVWmrkvYZP3qvMSoxBKjA2Y8YMpk+fTmZmJm63u14fZUZGRpN9lNdccw3z5s1j/Pjx9vHJkyczZswYBg4cSHZ2dr0+yksuuYSbbrrpuO1qDlVVVcycOZP9+/cTERFBx44def755wPO6dChA9dffz2ZmZmkpaUxaNCggOcTEhLIycmhsLCQv/3tb826vxB8xEcZ4tT2ARZVe9nhXw4iJo4fSsFdbdA9MYpqKjl6YD+Jp3UlLCKyDVsuOAHxUQohiVWyVgVklL7JnICStc77ghSE1kK63g5DtwKk7ydlGOi6Cy+YXW/fVh3Z7900xEcpgARKx2FllAY6bs1nOdc1qsCczAnSGKVTEB+lANL1dhz2rLevHITppDTLQShF0Ga9BcFJSKB0GNYv1Nzvbcow7EqMQS5ZKwhOQQKlw9A0zbduUkdDoQz/dZQEtWStIDgFCZQOxK6b47Ocm6IM03KuaZpZYExmvQWhyUigdCCWQQhMy7ldYMyqm9PClnPxUdb4KBcsWMD+/ftb9F5C2yOz3g7E7nprylc3xzxu181p4YxSfJQ1PsoFCxaQkZFBly5dWux+QtsjGaUDMWe5LXlvAxllK0/mnIo+yjfffJO8vDwmT55MdnY2ZWVlfPTRR/Tr14/MzEyuu+46e01mWloaDz30kN32rVu3AqZ+7brrrmPQoEH069fPfk9C2yIZpYP489d/ZutPWyk3DAxlEGZUYygd3e2hospLhKbhCnNRVVGBpoE77Ph7nHsl9uKewfc0eo74KE0f5YQJE3jmmWeYO3cuAwcOpLy8nKlTp/LRRx9x1llnMWXKFJ577jluv/12wBRmrFmzhr/+9a/MnTuX//3f/2XOnDn8/Oc/529/+xvHjh1j8ODBXHjhhURHRzf62Qmti2SUDsXcwhhYDsLqbGsaLSqoFB9l/T7Kb775hh49enDWWWcB8Jvf/IbPPvvMfn7cuHF13uv777/Po48+SnZ2Nueddx7l5eXs3r270fcktD6SUToIK/PbW17J0coKulUfpLQ8iriEJHb8WMZpmk7SaXEcPbAfw1tNUtfTW60tp7qP8nj38r+f/72UUrz11lvNVr8JrYtklA7EnO+uKVlrYShqKjG28vKgU9VH6X/vXr16sWvXLr777jvA3CN+7rnnNnrtiy++mKefftoOsmvXrm1W24TWQQKlAzGXB2k1tb3xKzDmc1K29oLzGTNm4PV6yczMZOLEifX6KGfOnNlkH+Wzzz7LoEGDKCgosI9PnjyZvLw8Bg4cyMKFC+v1Ud51111NaldzsHyUvXr1Ijs7m0WLFjFv3jzALFc7ffp0srOzUUrx8ssvc+WVV5KZmYmu60yfPr3Raz/wwANUVVXZy478J6+EtkN8lCFOfT7Aw5VV7C+v4nS1j7KSaDokJPDdjxWkoJPaOYaioz9SXlxISlrPNmq14BTERymELJZqzf4KDChZS1C63oLgJGQyx4HYqrWAujlazRilpqOUaRaytGtC/YiPUgAJlI7EXmDue2xZzg1Vq8CYodBcDVxEAMRHKZhIOuFArNin0ED55L0+J6U5mSOqNUFoDhIoHUhNRqmDVlOJ0QyUwStZKwhOQQKlA9H9ykGYTkrDlvcqI3glawXBKUigdCAu/wJjmmkNMo1C+DJKKTAmCM1BAqUDsTJKZVVi9C8HoWjxkrWnmo9y3rx5ttgC4Le//S0XXnih/fjpp5/m1ltvPen7zJo1i7lz5570dYSTRwKlA9E1X70c3+4cpQx7MscKmtByGaW/FCM/P5+0tLQWue6JUjtQtrSPMicnh9zcXPtxfn4+BQUFeL1eAHJzcxk+fHiL3U9oeyRQOhS7HATKDo4KZS44b+GMsj6c7KPs168f3377LWVlZRQUFBAVFUV2djYbNmwAzECZk5MDwBNPPEFGRgYZGRk8+eST9jUaOj5nzhzOPvtsLrzwQr755psT+/CFFkfWUTqIH/70Jyq2mAJY3WtQSTUFhgFKB5cbrdpgr0sHt0ZVeTmlHg8uV+P/C4T37kWn3/++0XNONR+l2+0mOzubVatWUVZWxpAhQzjzzDPJzc0lJSUFpRTdunVj9erVvPzyy3z11VcopRgyZAjnnnsuhmE0ePz1119n7dq1VFdX079/fwYMGNDoZyYEBwmUDsVUTmpomJM4loJSge8oLeakrK8UxIoVK7jllluAE/dRfvDBB4Dpo3zrrbcA00d5zz2mTs7yUX722Wfout5kH2VD7bJ8lIDto6wvUIJpNMrNzaWsrIxhw4Zx5pln8qc//YmOHTva2eSKFSsYO3asLd0dN24cn3/+OUqpeo8bhsHYsWOJiooC4PLLL2/0vQjBQwKlg/DP/LaVlGN4i+lQXkxldRhhcUnsP1ZG54gwwpMiObjjO6ITEolNTGqVtjjdR5mTk8MLL7xAeXk5N910Ex07dmTz5s107NjRHp9s6F6NtaG+9ym0PTJG6VB0zTfr7VOt2WsrDfMfY2vXzXGyjxLMQPnll19y+PBhUlJS0DSNjh078s4779gZ5ciRI3n77bcpLS2lpKSExYsXM2LEiEaPL168mLKyMoqKiliyZEmz2yW0DpJROhSXplGBjqYHloNorZK1tZkxYwbTp08nMzMTt9tdr48yIyOjyT7Ka665hnnz5jF+/Hj7+OTJkxkzZgwDBw4kOzu7Xh/lJZdcwk033XTcdjWXhIQEOnbsSHp6un1s2LBhrFy5kr59+wLQv39/pk6dyuDBgwGYNm0a/fr1A2jw+MSJE8nOzqZ79+6MGDGi2e0SWgfxUYY49fkAAXaXVVBUVUGXqsOUVUQSHZ/E90fLSHO7iesUw5Hdu3CHR9AhtVMbtFpwCuKjFEIac293Tdfbsp3bK4JaOaMUBCchXW+H4gK/2t41XW+v1fXWWr+2txMQH6UAEigdi2kQ0lC+CGnXzfEFSl3XAmafhfoRH6UA0vV2LPZ+bytQqpqut205l0ApCE1CAqVD0W2DUCDK9x9N18RHKQhNRAKlQ3HVyijxTdzYqjXJKAWhyUigdCi16+bYBcbAl1FKoBSEpiKB0qEE1M1B1RQYQ5lFxTTNV4nx5Lvfp5qPUjj1kFlvhxJQNwfLcq5jUKsSo1Invb+4PilGW2IFyhkzZgAt76MUTj0kUDqIz9/4liN7igEzcyz1GnioQvNq6LpOlaGjKcVajwtDeamuqsITXthooEzuFsOIq85qdlvKy8u58cYbycvLw+1288QTT3D++eezYMEC8vLyeOaZZwDTRzlz5kzOO+88YmJiuO2221i6dCmRkZG88847pKamsnPnTq655hqqq6v5xS9+Yd+juLiYK664gqNHj1JVVcUjjzzCFVdcEeCjvOiii7jpppu47LLL2LhxY6Pt+ve//01paSnbt29n7NixPPbYYw2+v4baumTJEh555BEqKytJSkpi4cKFpKamMmvWLHbv3s2OHTvYvXs3t99+e4tY0IXgIF1vh2Kr1Hwo069Wj1nt5Lvelo8yOzubsWPHAoHex9dee43f/OY3xzX7WD7KdevWMXLkSObPnw9g+yhXrVpFp041Wy4tH+WaNWtYvnw5d955J0opHn30UXr27El+fj7/8z//E3CPxtqVn5/PokWL2LBhA4sWLWLPnj3Nbus555zDl19+ydq1a7n66qsDgu3WrVt57733+Prrr5k9ezZVVVVN+nyFtkcySgfhn/kZSrGhqIwEfiS8BHRXJMVaBFUVXnomRFGlKjh28ABJXU/HcwJSCH9ORR9lQ23du3cvEydO5MCBA1RWVtKjRw/7NaNHjyY8PJzw8HBSUlI4ePAgXbt2bbS9QvtAMkqHYq8K8lViVMqs7W2uo2z9SozB9lHm5+eTmpoaNB9lQ2295ZZbuPnmm9mwYYPtqzyR6wvtCwmUDkXTNN8stw5+dXNaqxJjbZzuo2yIgoICTjvtNABeeeWVFruu0LZIoHQwLq1GjOG/jlIFIaOcMWMGXq+XzMxMJk6cWK+PcubMmU32UT777LMMGjSIgoIC+/jkyZPJy8tj4MCBLFy4sF4f5V133dWkdrUUs2bN4sorr2TEiBEkJye32HWFtkV8lCFOQz5KgK3FZbiNIjqUl1FVHQbRHThSVEHvmEiI0jiy53viU1KJjI0LcqsFpyA+SiHkCeh6o3D5xihVrXWUgiA0jsx6OxiXplHlJ++15kMMwwyagGxjPA7ioxRAAqWj0dEaKDDmn1FKoGwM8VEKIF1vR2N1veuznGuaZu73bqVZb0FwEhIoHYw5Jlm3HIQVG1u7EqMgOAUJlA5GxzeZYwdKn+XcsMYsJaMUhKYggdLB6L6MkjoZZXBqewuCU5BA6WCsX27dAmO+xy1kOW9NH2Ww3JLnnXceja29TUtLY8SIEQHHsrOzycjIaPS6/u394IMPGDBgAJmZmQwYMICPP/745BsuBAWZ9XYw9hIgv53fYCrYoOXq5rSmj7I9uSWLiorYs2cP3bp1Y8uWLU16jX97k5OTWbJkCV26dGHjxo1cfPHF7Nu3rzWbLLQQEigdxPIFL3Lo+x3242pDUW4oNlCB8rpwuV2UVys8aISFuaiurEShGrUHpXQ/g/On3tDstpyId/LgwYNMnz6dHTvM9/Dcc8/x1FNPtahb0tK1lZWVMWHCBGbPnt3k93TVVVexaNEiZs6cyWuvvcakSZN49dVXAdi1axe//vWvKSkpAeCZZ54hJyeHXbt22e3t16+ffa309HTKy8upqKho0S2UQusgXe9TidrJo1bPsROgPh9lYzTkcrz11ls599xzWbduHWvWrCE9Pb3F3ZJz5swhLy+P9evX8+mnn7J+/fomv88JEybwr3/9C4AlS5YwZswY+7mUlBQ++OAD1qxZw6JFi44r5X3rrbfo16+fBMkQQTJKB1E78yuu9rK9tIJO7MdbFEl0TCx7ihUdlMZpnWMo/PEwlWWldOzeo4ErNo3mdr0bcjl+/PHH/P3vfwfMcc/4+HiOHj3a4HVOxC35xhtv8OKLL1JdXc2BAwfYvHkzWVlZTWp3YmIiCQkJvP766/Tu3ZuoqCj7uaqqKm6++Wby8/NxuVyNujc3bdrEPffcw/vvv9+k+wptjwRKB2PtxDFnvs3tijqar26ONUbZOrPeJ+qdbA7NdUvu3LmTuXPnsmrVKhISEpg6depx/ZW1mThxIjfddBMLFiwIOP6Xv/yF1NRU1q1bh2EYRERE1Pv6vXv3MnbsWP7+97/Ts2fPZt1baDuk6+1gdGoKjGngq8ToJ8bQ9FZbR3ki3skLLriA5557DgCv10thYWGLuiULCwuJjo4mPj6egwcPsmzZsma/r7Fjx3L33Xdz8cUXBxwvKCigc+fO6LrOq6++itfrrfPaY8eOMXr0aP77v/+b4cOHN/veQtshgdLBuKy93b5tjIZSvm2NgGGto2yZkrW1OVHv5PLly+3lM5s2bWpRt2Tfvn3p168f6enpXHfddScUrGJjY7nnnnsICwur05ZXXnmFoUOH8u233xIdHW0/Z2XPzzzzDN999x1//OMf7THdQ4cONbsNQvARH2WI05iP0qsUG4vKSORHwko0dFcEBSocrcqgR3I0ZeVFFP14hJS0M9BdrnqvIZwcq1ev5ne/+x2ffvppWzelVRAfpRDyWL9cw7c7x99yHljbW3bntAZ5eXlMmjSJ2267ra2bIpwkMpnjYOy6OUpHo9osMKZrVGGWr23tujmhSEv6JwcOHHjcypNCaCCB0uGYfnOzEqNhKFy6NUbZ+nVzQhHxTwr1IV1vh6P7CoxZ5SBqut6SUQpCU5FA6XDMwKjXVGLUNRQqKLW9BcEpSKB0OC67HIT52KrtHThGKYFSEBpDAqXDaagcRGDdHOl6C0JjSKB0OOZOnAYKjLVQJUbxUZpLgY4nwgC47rrrSElJOa7HUmhfSKB0OGY5CK1OOQhvC66jtKQY1p+0tLSTup4/tQNle/BRAnV8lAMHDuSpp5467jWmTp3Ku+++2yrtE1oPWR7kII4t2U7l/pKAY7phEK0UZaoSw9CpcB0ktlpRqGuUeHSqy8sodBdS4t5b7zXDukTTYUzz5Q2nmo/yk08+Ye7cuSxdupRZs2axe/duduzYwe7du7n99tvtbHPkyJEtmnELwUEyylOG+sYhNbQWkFKKj7IuW7du5b333uPrr79m9uzZVFVVNfk+QvtDMkoHUV/md7Ciih8rquhu7KW0NIao2A4cKaikg9tNQqcYDu3aQUR0DHEdU074vuKjrMvo0aMJDw8nPDyclJQUDh48SNeuXZt0H6H9IYHS4dhOSmu62y4w1rqVGE81H2VT7i+ELtL1dji2k9IXmGpK1pp/t1Zt71PNRyk4GwmUDsdyUtaEQjPL888ojVbIKE81H2VTmTRpEsOGDeObb76ha9euvPTSSyd0HSG4iI8yxGnMRwlQWO1lZ2kFndlHdXE00TExfF9o0BGdTqfFcvTAPpRSJJ3WLYitFpyC+CgFR2D9gpUlxjDMn6wc0rKcC4LQMDKZ43B0/3IQ+AqMabo5RmlYdXNkr7dFS/ooBecggdLhuHzTN8q3YtKum6NavxJjKCI+SqE+pOvtcAIySs3segerEqMgOAUJlA5Ht8QXVtdbGYHyXt3sess4pSA0jARKh+NfYKxuyVo/g5AESkFoEAmUDscuMOarnhNYiRGpxCgITUAC5SmAf4ExqxyEaTmvySg5iXFK8VE2zUe5Z88ezj//fHr37k16ejrz5s07+YYLQUFmvU8BzFlunyWogYzSUAauE7x+c6UYzcEKlDNmzADah4+yW7du9fooBw5sfI212+3m8ccfp3///hQVFTFgwAAuuugi+vTp05rNFloACZQOYtmyZfzwww91jpcZBigvbsOLUjrobqq9Bl+5XRiaorqyAk9YuB00/enUqROXXHJJs9siPsq6PsrOnTvTuXNnwNwK2bt3b/bt2yeBMgSQrvcpg2YLMcDc+60g4NiJIj7KuhzPR7lr1y7Wrl3LkCFDmnx/oe2QjNJBNJT57Sgtp7K6jJTKo5RXRuCKTuBgUQW9YyMhXPHjvj106NSZiOiYE7qv+Cjr0piPsri4mPHjx/Pkk08SFxfXpHsLbYsEylMA/9regF261jAULr11lgeJj7J+H2VVVRXjx49n8uTJjBs3rln3FtqO/8/emcc3VaV/+Dk3paWlBcGCsspqZWkpWymrgLgOIgiKOwVGEQW3GR13GGb4qYwro47iAm6DguPGzKAzKDgiKiKLYkXWslP20r1Ncn5/3OQmaZM2KS1tk/f5fCrNTXLvacC359xzzvOVoXcEYABaKf+RtTWU7S0+yvJorZkyZQpdu3blnnvuCfn9Qu0hhTICMJTCqY1ySYxOXXPZ3uKjLM/XX3/NW2+9xRdffGHd0/33v/8d8nmE04/4KOs5lfkoAQ4UlXCopJT2zn3kF8QTk3AG+3JK6BDdgPjEWLJ3bie+2ZnEN212mlothAvioxTCBnO/t7Is50q7s70xb1gq2ZkjCBUhkzkRQNmAMU9ujrkzRwxCHsRHKfhDCmUE4A4Y8xRKP0mMIu8FxEcp+EeG3hGAzXJSunEXSvORoZTYgwShAqRQRgAeJyWgzf3e4OlRUkPZ3oIQLkihjAA8TkoDV3CO+djK9pahtyBUhBTKCMATB+HKzXGavnNLtWbI0FsQKkIKZQTgCRjzTmIEJ2bAmHGKPUrxUVbfdYS6iRTKCMA7YAzlsZy771meao/SLcVwf7Vv3746mg2UL5R1wUcJlPNRCuGNLA8KI7Zs+RO5eeX/B9Zo8u1O8ilFORUKhR0DpeFItg2HvRSnw8HuI7Hl3psQ35Vzz30k5LZEmo8yUFsKCwuZNGkSmZmZdO3alcLCwpA/S6H2kR5lBOBeYG5me2trh462/qM4lVuU4qMM3Ja//e1vxMXF8eOPP/LQQw/xww8/BH09oe4gPcowoqKe34+5BTTWJ4grsOMkhgIVh6PUSafmjSjIzyHv+DHO6tjZk6ETAuKjDNyW//3vf1aWTkpKStDXEuoWUigjBAN3ZK1rMsemKIVySYxKVTU5x5dI81FW1Jaq/PIR6hYy9I4QbK7IWlUustbbSVl9S4QizUcZqC3exzdt2hTSUF+oO0ihjBDMWW6Pk9I9662doCzLefUtOo80H2WgtkybNo28vDxSUlKYO3cuaWlpIV9TqH3ER1nPCcZHCbA1vwinI5/E4pMUl8ZA7Bkcyy+h6xlxlFLCiewDnNmmHQ0qKDSCUBbxUQphhU25Fpy7DUIK186cmulRCkI4IZM5EYJ5T9KTm2MtQndqVFT136Osr4iPUvCHFMoIwcDdoywfMGaTHqWF+CgFf8jQO0JwR9ZSNrJW11wSoyCEC1IoIwSzRxkgsraGkhgFIVyQQhkhmMuBPBsYfSJr3WJf6VEKgl+kUEYI7jgIbW0ScRVKp9fOHCmUguAXKZQRghUw5nrsGoF7JTFWXbUWCT7K119/neTkZFJSUujRowcff/xxtbdBqLvIrHeEUD6y1k8SYxVnvUOVYoSCu1DedtttQO34KPfu3cucOXNYt24dTZo0IS8vj8OHD5/WNgi1ixTKMOKRrXvZlOffd+hwaoqcmmiK0Y5SDNtJih2aaBQNDtooLSrCMIqw7cnxeV+P+Fj+1KVNyG0JJx/loUOHSEhIID4+HoD4+Hjr++3bt3P77bdz+PBh4uLieOWVV2jZsiU9e/Zkx44dGIZBQUEBSUlJ7NixgwYNGoT8WQq1jwy9I4WyBhtXT9Iz2PY2VYZGuPsoe/bsyVlnnUWHDh2YNGkSS5cutZ675ZZb+Otf/8oPP/zAk08+yW233UaTJk3o2bMnX375JWC6Ky+++GIpkvUY6VGGERX1/PIdDrblF3MWB9B5DYlpGMfeAkWiMmjVujFH9+7BsBk0bdk65OuGu4/SZrPx6aef8v333/P5559z991388MPP/D73/+e1atXc9VVV1mvde/qmTBhAu+99x7Dhw/n3XfftW4dCPUTKZQRgidgTHkFjEXh1F5JjNW4hTHcfJRKKdLS0khLS+PCCy9k0qRJ3HPPPZxxxhl+f0mMHj2aBx54gGPHjvHDDz8wYsSI0H9Aoc4gQ+8IwTdgzCwypszXvVOn6pM5/ggnH+X+/ftZt26d9XjDhg2cc845NG7cmA4dOrBkyRLA/Ew3btwImPcx09LSuPPOOxk1ahQ2W/UIkYXaQQplhOBeHuSW9zq95b3O6s/2DicfZWlpKb///e8577zzSE1N5b333uO5554D4J133uG1116jZ8+edO/e3WfZ0IQJE3j77beZMGFCUNcR6i7io6znBOujdGrNT7mFNOUoDQtAqxhynA2Jcmg6nJ3AyWOHKSksoPk5HU5Dq4VwQXyUQlihXP9x4snHcfcorXuUYg8SBL/IZE6EoJTyUq050E6NYYAdzIAxZYiPEvFRCv6RQhlB2BQ4tQJ3wJjh2p/jMghprV1JjJE70BAfpeCPyP0/IgIxp3E88l6blcRIjSQxCkK4IIUygvDEQZjBfjFIAAAgAElEQVSPPbk5WnJzBKECpFBGEIbyjYMwlGsdpfQoBaFCpFBGEAYKJzawFpm7s71PrUcZ7pq1/v37k5qaSrt27WjevHmN/JxC3UYmcyIImwKtvOIgvJMYbVXvUYa7Zs09wVPWiCREDtKjjCDMLYveQ2+vOAjDs76yOli4cCHTp0+3Ho8aNYqVK1cC5va+hx56iJ49e5Kenk52djYA2dnZjB07lp49e9KzZ09Wr17N/fffb2nW7r33XrKysujRowdg7h+fNGkSycnJ9OrVixUrVljXvvLKK7nkkkvo0qUL9913n9WOadOm0bdvX7p3787MmTNP6Wd8+eWXfXYL/e1vf+O+++5j27ZtdO/enRtvvJHk5GSuvvpqCgv96++E+oH0KMOIPy79mcz9JwM+X+LUlGonDXUJDqcNZbNRYtfE2gyUDUqLi4lqkI3htS+5W6vGzLy8e4XXdWvWADp06MCHH35Y4evdmrU5c+Zw33338corr/Dwww9bmrUPP/wQh8NBXl4ejz/+OJs2bbJ6rN7DXW/N2ubNm7nooosse9CGDRtYv349MTExJCUlMWPGDNq2bcucOXNo1qwZDoeDCy64gB9//LFSe1AgrrvuOlJTU3nssceIiopiwYIFLFy4EIDMzExee+010tPTuemmm3j55Ze56667qnQdofaRHmUkofwfNnexBngyCNxD7w0bNlRaJKG8Zs1d/L744gumTZsGeDRrFbFq1SpuvPFGILBmrWHDhpZmDWDx4sX07t2bXr168fPPP5OZmVmlnxkgISGBoUOHsmzZMn7++WdsNhvdunUDzF8Y6enpANxwww2sWrWqytcRah/pUYYRlfX8sotLOVhcyjl6DwX5CUQ3asz+XDvnxEQT3yyaw1k7aZzYnLgmZ5xyW8JNsxaI3/72tzz99NO0b9+eSZMmWcdVGVFy2cdC/UJ6lBGElZvjeuwbWVu99yjDSbNWEYMGDWL79u0sWbLExxK0c+dOvv/+ewAWLVrE4MGDT/laQu0hhTKCsJIY3Z0b7S/bu3rWUYaTZq0yxo8fz9ChQ31uFXTv3p1XXnmFlJQU8vPzueWWW6rlWkLtIJq1ek6wmjWAE6V2dhWW0Jq9lOTG07BRI/bmaVraomjeMp7snduJS2hMQmLzGm51eHHJJZfwwAMPcP755wOwbds2xo8fX2NLpuoSolk7TSilXldKHVJKbQrw/DClVI5SaoPr69HT3cZwwXD3Gt0H3D1K7wXoYfiLs6Y4evQo5557Lk2bNrWKpBCe1IXJnIXA88CbFbzmK631qNPTnPDF/VvRXEuJq1Aqn2xv7wmYSCQUzdqZZ55pzbJ707lz54joTUYStV4otdb/U0q1r+12RAKeyRyXas3pRGHDfVuyunNz6iOiWRP8UetD7yAZoJTaqJRappSqeA2MEBDf3BxXwJhSnkJZzUmMghAu1HqPMgjWAedorfOUUpcBHwFdyr5IKXULcAtAu3btTm8L6wk2q0dpbmN0eiUxatc2Rh3hQ29B8Eed71FqrU9qrfNc3/8baKCUSvTzuvla675a677Nm8usrT/ckzlO9zIhV49SA2jzeelRCkJ56nyhVEqdrVyL/JRSaZhtPlq7raqf+E7maFfAGC7LuTsOQnqUglCWWi+USqlFwDdAklJqr1JqilLqVqXUra6XjAc2KaU2AvOAa7SsYakSSpl2c43yvUcJXgFj4qMUhLLU+j1KrfW1lTz/PObyIaEacKvWsJIYFaV45L1V6VGGu49SEGq9RymcXsx7koa1z9sz9PZE1lZHhz3cfJSB2rx06VL69+9Pr169GDlypHV81qxZTJ48mWHDhtGxY0fmzZtX9Q9TqHVqvUcpVCPL7oeDP1X4kvZOB2gHDZxOnFqhVRQOp0Y1sBHrtBNtL4WGsVjatbOT4dLHKzxnJPgoA7V58ODBfPvttyilePXVV5k7dy5PPfUUAJs3b2bFihXk5uaSlJTEtGnTaNCgQaXXEuoeUigjDuX6MnfleORf2pMNoQlJTxnq0Lusj/K///0vYPoo33zT3KDl9lEeP3484HlWrVrFjBkzgMA+SsDyUbZt25bFixczf/587HY7Bw4cIDMzM6hCGajNe/fuZcKECRw4cICSkhI6dOhgvec3v/kNMTExxMTE0KJFC7Kzs2nTpk3Qn5NQd5BCGU5U0vMDOFBQRKm9gBYlJygqaYiOacKxwlK6NW1EibOQnEPZJLZrT9Qp9nzCzUcZqM0zZszgnnvuYfTo0axcuZJZs2ZV2AahfiL3KCMMA4VWhtV5NCNsXdneVmTtqS8RihQfZU5ODq1btwbgjTfeOOXzCXUTKZQRhjl54wkY80liPIXI2rJEio9y1qxZXHXVVQwZMoTExHL7IIQwQXyU9ZxQfJQAe4pKyCktpo09m4LCOGxxTcjOK6VLfENsMZpj+/fStGVrYuLiarDVQrggPkohLDEApzZQyuw1WnEQ1dyjFIRwQiZzIgyba2cO7qG367gZB2HG1EayGCMUH6UQOUihjDCs3BzXY0+P0hT3QsWzyeGO+CgFf8jQO8Kw5L3WOkl/AWOR26MUBH9IoYwwPKo1zDXm3oXSqN7IWkEIF6RQRhjuv/ByAWPabRcSJ6UglEUKZYRRVt7rne0NiJNSEPwghTLCsLn+1K4kRndR9ASMhd6jzM7O5rrrrqNjx4706dOHAQMGVCrGqCkWLlxI8+bNLTfmTTfdBMCjjz7K8uXLK3zvJ598wuOPB94GWtaIVBnt27cnOTmZlJQUzj//fHbt2mU9V5MOT6H6kVnvCMM9meN0q9acpsbXp0cZwmSO1poxY8YwceJE/v73vwOwa9cuPvnkk6De73A4sNlslb8wBCZMmMDzz/sqTGfPnl3p+0aPHs3o0aOrtS0rVqwgMTGRmTNn8uc//5lXXnkFqFmHp1D9SI8ywvAsD1KgzCG35aQk9MjaL774gujoaG699Vbr2DnnnMOMGTPIyspiyJAh9O7dm969e7N69WoAVq5cyfDhw7nuuuus9YljxoyhT58+dO/enfnz51vneu211zj33HMZNmwYN998s9WjO3z4MOPGjaNfv37069ePr7/+usJ2ZmRkWMLf9u3bM3PmTHr37k1ycjKbN28GfHuMS5YsoUePHvTs2ZOhQ4da59m/f79fz2VlDBgwgH379lX4moo8mkLtIj3KMOKJNU+w+djmCl+jgXyHkwaUoBwKMLBrhQE0/CkKe4m52Doq2twzfV6z8/hD2h8Cnu/nn38OuI+7RYsW/Pe//6Vhw4Zs3bqVa6+91opbWLNmDZs2bbK0ZK+//jrNmjWjsLCQfv36MW7cOIqLi/nTn/7EunXrSEhIYMSIEfTs2ROAO++8k7vvvpvBgweze/duLr74Yn755RcA3nvvPVatWmW9btKkSeXalpiYyLp163jxxRd58sknefXVV32enz17Np999hmtW7fmxIkT1vFAnsvK+PTTTxkzZoz1OJDDs6rnF2oWKZQRiys3Bw3KsCZ1QJ3SgvPbb7+dVatWER0dzfLly5k+fTobNmzAZrNZrkiAtLQ0H3fjvHnzrGKxZ88etm7dysGDBzn//PNp1qwZAFdddZV1juXLl5OZmWm9/+TJk5ZlyN/QuyxXXnklYLolP/jgg3LPDxo0iIyMDK6++mrrtRDYcxmI4cOHk52dTYsWLfjzn/9sHQ809A71/MLpQQplGFFRz8+N1pofcws5g2PEFmgcOoo8GmE4NB1bJpBzJBt7SQmJbc8J6prdu3fnH//4h/X4hRde4MiRI/Tt25dnnnmGs846i40bN+J0OmnYsKH1ukaNGlnfr1y5kuXLl/PNN98QFxfHsGHDKCoqqrBgO51OvvnmG2JjY4NqZ1nclqFAnsiXXnqJ7777jn/961+kpqZaRS1Ux+SKFSto1KgRGRkZPProozz99NNBtSvY8wunB7lHGWF4khjdkbWnlsQ4YsQIioqKLI8kQEFBAWC6Glu2bIlhGLz11ls4HA6/58jJyaFp06bExcWxefNmvv32W8DsdX755ZccP34cu93uU5Avuugin15jdU+MbN++nf79+zN79mwSExPZs2dPlc8VGxvLs88+y5tvvsmxY8eqsZXC6UIKZQRiJjGqcgFjugrZ3kopPvroI7788ks6dOhAWloaEydO5IknnuC2227jjTfeID09nS1btvj0Ir255JJLsNvtpKSk8Mgjj5Ceng5A69atefDBB+nfvz8jR46kW7du1rB03rx5rF27lpSUFLp168ZLL710Sp9JWe69916Sk5Pp0aMHQ4cOte6NVpWWLVty7bXXWjk/Qv1CfJT1nFB9lAC/5BUS7TxJk6IiSuwNKI1qTGGJg3NbxJOfe5yCnBOc1bFzDbU4NPLy8oiPj8dutzN27FgmT57M2LFja7tZggvxUQphi9mDLN+jxOpR6jqzO2fWrFmkpqbSo0cPOnTo4DNzLAinC5nMiUDMpea+cRBONHir1pwaVb3rwKvEk08+WdtNqBRxWIY/UigjEENBqSpbKN0BY16qtWreMROuiMMy/JGhdwTi7lG6LefWtkanFnmvIPhBCmUEUi6J0XXc6azeyFpBCBekUEYgNqXQqPKRtVpjSMCYIJRDCmUEojCTGPGX7e3qUTpF3isIFlIoI5DySYz+AsaC71GKj9I/8fHxVXr/vffeS/fu3bn33nuDvpZQs8isdwTiUa25eo1elvNQA8bER1n9vPzyyxw+fNhn37dQu0iPMgLxSWL0ChhzVGHWW3yUVSMjI4M77riDgQMH0rFjR6tto0ePJj8/n/79+/Pee+9V+fxC9SI9yjDi4P/9H8W/VOyjBCjVmiin5ihFOB1RGIaBzQHHDMXJBjZKiwopiGqALSqKmK7ncfaDDwY8l/goq+6LPHDgAKtWrWLz5s2MHj2a8ePH88knnxAfHy/28zqGFMoIRAU4rrXnOU3VJnPER1kx7lsbYPaiDcOgW7duZGdnB30O4fQjhTKMqKjn581Ju4OdBcU0Zy+O3HgaNGzI/kKDBJuNs1smcChrOw3jG9M4sXml5xIfZWBfZGxsLCUlJURHRwNw7NgxEhMTy7UBZIF/XUfuUUYg3kmM5lqhskmMwTspxUcZmPPPP5+3334bMKMfFi9ezPDhw6u1ncLpQQplBFI2iVFrjYEyxRiElu0tPsrAPPfcc3zwwQekpqaSnp7OVVdd5TMxJNQfxEdZz6mKj7LY4WRzfhHNycaWH40yGnDEHkM8inatG3N0724MWxRNW7aqoVYHj/go6zbioxTClrI9SiuyVofeo6xpxEcp1AVkMicCMdyLyl3Z3lo7raG3qVozcAa4n3i6ER+lUBeQQhmBuIcRPvcoLcs5KEOh7XWjR1kfEB9l+CND7wjEO4nR46R07c9x9ShFsyYIHqRQRiiBkhitHmUduUcpCHUBKZQRiumk9BMH4XT3KMNvNYQgVBUplBGK23Lu3rNoKOXVo6xbSYyCUNtIoYxQAiYxau84iOB6leKjFMIdmfWOUMolMQIa19Db8HJSVuKKFB+lEAlIjzJCKdujrGoSo/goAxMfH89DDz1Ez549SU9PtwxBS5cupX///vTq1YuRI0dax2fNmsXkyZMZNmwYHTt2ZN68eZV8+sLpQnqUYcRXi7dwZE9eUK8tcjpxaCcxuhSn0wAjilKHZmuUuWTIXlJCg+gcmp/TmCFXnxvwPOKjDOyjzM/PJz09nTlz5nDffffxyiuv8PDDDzN48GC+/fZblFK8+uqrzJ07l6eeegqAzZs3s2LFCnJzc0lKSmLatGk0aNAg4OcvnB6kUEYoyuu/gQl95lt8lB6io6MZNWqUdZ3//ve/AOzdu5cJEyZw4MABSkpKfD6H3/zmN8TExBATE0OLFi3Izs6mTZs2Ff4sQs0jhTKMqKjnV5Z9RSUcLSnlHOc+8gviiYpN4GC+g46NYoiOVRzbt4emZ7ciJoDxx434KAP7KBs0aGCJer1fO2PGDO655x5Gjx7NypUrmTVrVrl2BXN+4fQh9ygjFHcSo1LuJUCeJEZ3trcziOVB4qMMnZycHFq3bg3AG2+8UW3nFWoOKZQRiieJEZ+AMafP8qDKC6X4KENn1qxZXHXVVQwZMsTHeC7UXcRHWc+pio8S4EhJKfuKSmnHbgpzG9MgNo4DBdAmpgFnNGvIoawdJCQ2p1GTM2qg1cEjPsq6jfgohbDG3aM0+4yePd9OZ+jZ3jWJ+CiFuoBM5kQoVra3e+Zba0Dh0JjbdFyeytpGfJRCXUAKZYTilveaBiF3UTRc9ygVhogxgkZ8lOGPDL0jFPdfvLU7R5t9SyuJ0RAnpSC4kUIZoZRLYnRql0HILcmoO7k5glDbSKGMUGzW8iDzfqRTa1Pma/UolQy9BcGFFMoIxW+2t1LWbhjpUQqCBymUEYrfJEYFDkxrUCg9yrrio1ywYIHloYyOjiY5OZnU1FTuv//+03L9v/zlL8TFxVl7zgHWrVvHp59+aj3+4osvrJ1Hgdi2bRupqamVXi/Y1wmnjhTKCKVsEqN5TFkajGB7lG4f5dChQ9mxYwc//PAD7777Lnv37g2qHYG2NVaFSZMmsWHDBjZs2ECrVq1YsWIFGzZsqFDGW50sWrSIPn368PHHH1vHqlIohbqHLA8KI1YsnM+hXTuCfn2+3YkNOzanRmuFU9nQGn6ItmEvLUU7HbRO6sbwjFsCnqMyH+WNN95Ifn4+AM8//zwDBw5k5cqV/PGPf6Rly5Zs2LCBzMxMxowZw549eygqKuLOO+/kllvMa7722ms88cQTtGrVii5duhATE8Pzzz/P4cOHufXWW9m9ezcAzz77LIMGDfLbRofDQVJSEmvWrKFZs2Y4HA66dOnC2rVrueOOO0hISGDTpk1kZ2fz3HPPcemll2K327nvvvtYtWoVRUVF3HHHHfz2t78N+Dn8+uuvOBwOZs2axdNPP80NN9xAYWEhs2fPprCwkJUrV3Lttdfy6quvYrPZWLhwIS+++CIdO3Zk6tSp7Ny5E6UU8+fP58wzz8RutzNlyhS+/fZb2rVrx4cffkjDhg35/vvvmTJlCo0aNQr48wrVjxRKwQdPjzI4yVpd9FGWxWazce211/L3v/+d6dOn89lnn9GvXz9L37Znzx6+/PJLtm7dysiRI9m2bRuvvfYaLVq0YM2aNRQXF5Oens5FF11Eu3bt/F5j0aJFXHPNNQwfPpxJkyZx9OhRzjzzTB599FE2bdrEs88+C0Bubi6JiYncddddAIwbN44LL7yQ6dOnY7fbKSgo4NChQ/z6668sWrSI5ORkrrzySj766COuueYaMjIymD9/PoMGDeLuu+8O4m9IqA6kUIYRFfX8/JGZV0iM8yRnFBVSbI/GHpVAXomT886KJ//kcQpyTnBWx84hnbO2fZQJCQl+2zVlyhSuuuoqpk+fzuuvv+7TO7z66qsxDIOkpCTatm3L1q1b+c9//sMvv/zCu+++C5jGn61btwYslO+++y7Lli3DMAzGjBnD+++/z9SpUyv9vFauXGldIyoqisaNG3Po0CE6d+5s7ezp06cPWVlZHDlyhMLCQqsneeONN7JixYpKryGcOlIoIxh3EqNrXscrYMw3idFtE/JHffFRtm/fnqZNm7JixQrWr1/PRRddZD3n3tvu/VhrzYsvvsgFF1xQ6bnXrVvHzp07GT58OADFxcX8+OOPQRVKf9eHwF5Kf68Vah6ZzIlg3Lk5eOXmaABn8EmM9clHOWXKFK6//nquueYaDMPzT3/JkiVordmyZQt79uyhS5cuXHzxxbz44otWgfr1118pLCz0e95Fixbx5z//maysLLKysti/fz87duxg3759JCQk+MyCl308fPhwSxHncDg4efJkwPYnJibSsGFDvvnmGwDeeeedSn9moXqQQhnB2BRoP0mMTu8kxkpmvuuTj3Ls2LHk5OSQkZHhc7xz584MHTqUyy+/nPnz5xMdHc3UqVPp0qWLZS6aNm2aX9u41pr33nvPR/2mlGLMmDG8++67jBgxgo0bN9KrVy/ef/99rrjiChYvXkyvXr1YvXo1zz//PJ999hnJycn07dvXCjoLxIIFC5g6dSoDBgwgPj6+0p9ZqCbM4VV4ffXp00dHCpmZmVV+7478Ip158oQ+fnSb3rdvn95/5KTeuOe4Ls4t1gW5J/WBbVt0aXFxNbY2dHJzc7XWWpeWlupRo0bpDz74oMrn+uabb/SwYcN8jl1//fX6ww8/PKU2RjL+/v0Ba3UdqAPV+SU9ygjGsOIg3LtxzONugxDUvpOyunyUc+bMYcKECfzf//1fNbdQiATEcF7PqarhHGBPYTE59hLaOg6QXxCPrWEC2QUOOiU0JCrayfH9+2jWqjXRsXHV3Or6yYYNG8oN2+Pi4qy88kgkUgznMusdwRhKeTRr4GM5N1yTOU4RY1h4pzEKkYUMvSMYQ7ltQdqTMoaZxKhcs8IixhAEKZQRjZmbo9Ble5R16B6lINQFpFBGMB7VGngWB4FDa0+PUobegiCFMpLxyfYGV8CYWRyDXUcpCJGAFMoIxuYj78UqlE5tataUUkENvSPdR7lt2zZiY2NJTU2lZ8+eDBo0iK1bt9boNUPl6aefpqioqLabUW+RQhnB+Mh7AVy9R6fbcm5U7qTU4qMEICkpiQ0bNrBx40auu+660+bADBYplKeGLA8KI04s3U7J/vygX+/QmsZOJyWUoB05OJWisdPAoeBQdBSlxYXos/JpPKFFwHOIj7I8J0+epGnTpgBs376djIwM8vLyMAyDF198kf79+7Nv3z4mTJhAXl4edrud+fPnk5aWRmJiIjfffDOff/45zZs3Z/bs2dx3333s2bOH559/nssuuyxg25YvX85jjz1GkyZN+Pnnn+nfvz9vvvkmzzzzDIcOHWLIkCGcddZZLF++PKifQ/AghTKCce/E0eAaepvfeKZvlDUcD4T4KE1+/fVXUlNTOXnyJMXFxVbWd8uWLa3PYPPmzUycOJHvvvuOt99+m8svv5w//OEPOBwOS7iRk5PDRRddxF/+8hcuv/xyZs2axeeff87GjRuZOnUql112GfPnz/fbNjBNRpmZmbRo0YL09HS+/fZb7r77bp566im++uorzjjjjAr/PgX/SKEMI864vFNIry9yOMnOL6I52UTlR+PARpEzllgULVo35ui+PT6WnWCIVB+le+gNptXn1ltv5Z///CfFxcVMnz6djRs3EhUVxfbt2wHo168fU6dOpaioiDFjxtCzZ0/sdjuxsbFceOGFACQnJ9OkSROioqJITk4mKysLIGDbANLT02nZsiVgLpDPysqyJCNC1ZFCGcEYVo/SAK8kRoeVxFh5wJj4KMszevRopk2bBsBTTz1F27ZtefvttyktLbWMPyNGjGDlypX861//4vrrr+eBBx5gwoQJREdHW+cxDMPyUhqGYdmLArVt+fLlAT2WwqkhkzkRjHt5kBPlGoZrr9065mSOs5LJHPFRlmfVqlV06tTJ5zNQSvHGG29YxX/Xrl2cffbZ3HLLLWRkZLB+/fqgzg1UqW1lPZhCaEiPMoIp26N0H7Mia5VR6fIgt4/y7rvvZu7cuTRv3pxGjRrxxBNP0Lt3b8aNG8eSJUsYPnx4hT7Kl156iZSUFJKSkvz6KFu1alXOR3n77beTkpKC3W5n6NChlTopx44dy+TJkwP6KA8dOuTjo9y9e7cVB9uiRQufdMWyuO9Raq2JiYlh/vz5AEyfPp3x48ezaNEiRo4cafX4Pv/8c55++mkaNGhAfHw8b7/9doVt9ybUtgHccsstjBw5krZt28pkTlWobc9bTXyJjzJ4Np7M1ztz9urDB3fpffv26R0HTurNe05op8OpTxw6qLOztldTS6uG+CjrNuKjFCICA3PBuXeP0gmgTYNQbW9hFB+lUBcQH2U951R8lOBOYszhjKIiK4kxt8RJ17PiKcg9Qd7xY5zVsbOEWiE+Sn+Ij1KICMy+pM1aU2n4JDG693trKZSIjzKSkaF3hGMohVMZKOXrENI+SYwixhAiGymUEY6Zm2OUz81xapH3CoILKZQRjpntrXB7MXwKZZDZ3oIQ7kihjHBsyqVZs4beLnmvt5NSht5ChCOFMsIxlwcpsOIgTJw6+KG3+Cir5qO8+OKLyc3NxW63W7KKHTt2WHu4wdyWWNUlUUL1IYUywjGTGF3Z3hrrXmWwQ28tPkqgaj7Kzz77rJzEo2yhFOoGsjwojFi2bBkHDx4M6T0lTk2p1jSkCIcjCmUYlDjgO5uBzaZoEhfLZb/5TcD3i4+yPN4+yldffZVNmzbx7LPPAuZ2zYcffpjBgwfTpk0bNm3aZIkyAO6//362bt1KamoqkydPplu3btZzDz/8MAcOHGDbtm3s2bOH3/3ud9x+++1BtUk4NaRQRjjKkynmi/YMwyu6Ryk+SpNAPspQefzxx3n++ef56KOPAMrty96yZQuff/45J06coGvXrtx6663YbLYqXUsIHimUYcSll14a8nsOl5Syv6iUdmRRmNuMBg2jOVBo0DK6AWcmxnJo5/aQlgeJj9LXR1ndjBo1iujoaFq0aEGzZs04fPgwZ599drVfR/BFCmWE40lidKuE/GV7B75HKT7K8nj7KKOionB69chPNbdGfJO1g0zmRDiebG9lLg2yAsbMgqGMilVr4qMsj7ePsn379qxfvx6tNVlZWfzwww8Vvle8kXUT6VFGOO5yoV19S7dSypPEqCqU94qP0iSQj/L888+ndevWJCcn06NHD+t8gejVqxcOh4OePXsyZcoUn8kcoRapbc9bTXyJjzJ4ckvtekNOvj6Ys1Uf3LdPHzhwUG/ae0Lv3ndSa631oV079fGDB6qjqVVrn/go6zTioxQiAo/l3GMKsuEyCBFctndNIj5KoS4gPsp6zqn6KIscTn71SmK0a0Uu8URr6NC6Mcf27QUFzVq1qcZW1+OKszQAACAASURBVE/ER1ke8VEKEUHZHiVQ/h6lQ/Z6g/goIxkZekc4niRGj2rNioMAM2BMNGtChCOFMsLxTmJUVm6OwonLbF7J8iBBiASkUEY4hjIzvX0MQlbAmDn0Dsf72IIQClIoBVdujtfQG1ehdAaX7S0I4Y4USsE11PaNg3CiPUNvrSu8TxnpPkqAzZs3c+mll9KlSxe6du3KNddcw6FDh4J+v91ux2azWUuhJkyYEPROIDC3dJ4OnVykIoVSMHuUyrB0QWaOjrntuzInpRYfJYWFhYwaNYoZM2awdetWfvnlF26++WaOHj0a1Pvd2yQTEhLYsGEDP/30EwCvvPJKUO/XWmO326VQ1iCyPCiM2LLlT+Tm+VeNVUShwwk4OOq043QaYNgodcL6gzZiG3bizNiJ5vDbj85LfJTw1ltvMXToUC677DLrmFumsX37djIyMsjLy8MwDF588UX69+/P8uXLefzxx0lMTOTnn39m/fr11nuVUgwZMsQyJc2dO5c333wTgKlTpzJjxgy2bdvGmDFjGDx4MN999x2pqank5uaSmppKSkqK9XqhepBCKQABlJTeBqEAQ2/xUcKmTZvo06eP32u3bNnS+gw2b97MxIkTLVflt99+S2ZmJu3atfOxAJWWlvLpp59yxRVXsGbNGt555x3WrFmDw+EgLS2N888/n7i4ODIzM1mwYAEvvfQSdrudDz/8UNZ51hBSKMOIc899pErv21FQTImjkJb2I+QXxmFEx3OoyEmXJrEYhp3jB/fjDDKJMVJ9lIEoLi5m+vTpbNy4kaioKLZv3249N2DAAJ/zuXuEYMo0MjIymDdvHuPGjSMuLg6AMWPGsGrVKi666CI6depEv379QmqPUDWkUAquBeaG1/IgTxKjLariHqX4KM3PIJDR/KmnnqJt27a8/fbblJaW+sQ+lLUpue9RelPRZxDIxiRUPzKZI7iWBymrQLpxajxJjAGWCImPEm688Ua+/PJLPv30U+vYv//9bzIzM63PQCnFG2+8EfKa1KFDh/Lhhx9SWFhIXl4eH3/8MUOGDCn3uqgos88jIt+aQQqlgE0pc8E5TtDK2qHjm8Tov1C6fZRffvklHTp0IC0tjYkTJ/LEE09w22238cYbb5Cens6WLVsq9FHa7XZSUlJ45JFH/PooR44cWc5HuXbtWlJSUujWrVulLkowfZQ5OTkBfZSXX365j4+yS5cu1nKdadOmBSxCcXFxLF26lGeeeYYuXbrQrVs33n77bZo3b8706dN59dVXSU9PZ9euXT6G8mBIS0vj2muvpV+/fqSnpzNt2jSSk5P9vnbKlCmkpKRw0003hXQNIQhq2/NWE1/iowyN/YXFekNOns7JydT79u7X2UeO6Y17juujh/O1vbRUH9i2RefnnKiG1oaO+CjrNuKjFCIGQylAoXECCuUaHjq0Rhnu3Jza2Z0jPkqhLiCTOYKvak2b34E7N6fioXdN8+STT1bLeR566CEeeuihcsfffvvtoM8hPsrIRQql4KVaU66cbydgs5IYTTGG7PcWH2XkIkNvoUwSo5+AMWVUGFkrCOGOFErBJ4kRtDnbjcJdG5Vh+GRTC0KkIYVScE3muIfepinIUN5xEGI5FyIbKZQCNh/LuffQ2zxuiJNSiHCkUAo+kzko7zgIT8BYRfcoI91HuW3bNmJjY+nVqxddu3alf//+vPXWW5W+b926dT67efxht9s544wzKj1XsK8TqobMegt+cnOUOfR2dSLNgDH/u1K0y0c5ceJE/v73vwOwa9cuPvnkk6Cu7XA4sPnRt1WFSZMmMWnSJMDc271ixQoSExOr5dyVkZSUZKnStm3bxtixYwFze2Mg1q1bx6ZNm7jkkktOSxuFqiOFMox4ZOteNuUFb8V2ozUUOJxEUYrhBK0VTk6igX6Fufy+SYOAQ2/xUZanc+fOPPXUUzz00EPceOON5OXlMX36dDIzMyktLWX27NmMHDmS2bNnU1hYyMqVK3n44Ye5+OKLmT59OuvWrUMpxezZsxk1ahQA999/P8uWLSMuLo6PP/6YFi1asH37dq677jqcTicXX3xxUG0TqkaVCqVSqinQVmv9YzW3R6gFVICDWrss5xVM5oiP0j+9e/dm8+bNAMyePZtLLrmEhQsXcvz4cfr378+PP/7Io48+yqZNm3j22WcB+N3vfkfz5s356aef0Fpz4sQJwJSGnH/++Tz++OPcc889vP7669x///3MmDGDO++8k+uuu47nnnsuqHYJVSPoQqmUWgmMdr1nA3BYKfWl1vqeGmqbECJ/6tKmSu/TWvNjXiFN9HESCh0UO6Kw2+LJLdV0a5lA3oljaKf2EfkGQnyUns/UzX/+8x+WLVtmRTUUFRVZPWFvli9fzkcffQSYspGmTZtit9uJjY3l0ksvBaBPnz589dVXAHzzzTcsXboUMIf4M2fODKptQuiE0qNsorU+qZT6LbBAaz1TKSU9yjBAKWUlMaLsrmNmwBhWj9LdvfQtlOKj9M/69evp2rUrYBbNjz76iE6dOvm85n//+5/P40C/iKKjo63vbTabZTFSSlX6i0uoHkKZ9Y5SSrUErgb+WUPtEWoJW9kkRtwBY9pyUjr9DL/FR1meHTt2cO+99zJjxgwALr74YubNm2c97570SUhIIDc31+/PpLXm+PHjFV4nPT2dxYsXA/DOO+8E1TahaoRSKP8IfAZs01p/r5TqCGytmWYJpxt3EqN3ZC2Aw6GtouJviZD4KE1+/fVXevXqxXnnncc111zD7373O2vGe+bMmRQUFJCcnEz37t2ZNWsWYP6S2bhxI7169eL9999n5syZZGdn06NHD1JTU60hdiDmzZvHM888Q1paGnl5eZX+/MIpEKyPDRgUzLG68CU+ytD5Na9Qbzl5RB8/sl3v27dP7zucozfuOa6L8kt0Ye5JfWDbFl1SVFQt1woF8VHWbcRHWZ6/BnlMqIcYUGbo7WU5d/coa2Ebo/gohbpApZM5SqkBwECguVLKe4a7MVA9K4WFWsdQYPcOGHMddzg1NlvgoXdNIz5KoS4QzKx3NBDveq332ouTwPiaaJRw+jFcuTlKmb1Gvz3KCN/vLT7KyKXSQqm1/hL4Uim1UGu96zS0SagF3EmMKG0GjClzK2NtD70FoS4QyjrKGKXUfKC99/u01iOqu1HC6cfwTmJ0b8tBmbk5tRwHIQi1TSiFcgnwEvAq4H9BnFBvsQFObfYoXXFiQO1P5ghCXSCUWW+71vpvWus1Wusf3F811jLhtOJJYjSH3mjvgDF3EqP/yZxI16wJ4U8oPcqlSqnbgA+BYvdBrfWxam+VcNrxqNZcWIXSHTDmPw5Ci2ZNiABC6VFOBO4FVgM/uL7W1kSjhNOPR95reOIgUFYchBHAIFSZZm3IkCH07t2b3r17W8toVq5cyfDhw7nuuutITk4GYMyYMfTp04fu3bszf/5861yvvfYa5557LsOGDePmm29m+vTpABw+fJhx48bRr18/+vXrx9dffx3wZ3M4HHTu3Jljx45Zjzt27MixY8e44YYbmDZtGkOGDOHcc89l2bJlgCnCveeee0hLSyMlJYVXX3014PmXL1/OBRdcwJVXXklSUhI33XST9dzMmTPp168fPXr04NZbb7X2rw8ePJj777+ftLQ0kpKSZIlRHSfoHqXWukPlrxJqkz8u/ZnM/Ser9F671hQ7NdEUoR2HXMcMklok8OQ1qa4kxvKFUjRrJuvWrSMzM5MWLVqQnp7Ot99+S3p6OnfeeSd//OMf0Vpz3XXX8emnn1omIK01a9as4ZNPPmH27NmV2s6F2iMUzVoccA/QTmt9i1KqC5CktRZBRhigvL5zCzFQmPcsqTwOwk2katbS09Np2bIlYK63zMrKIj09nc8//5y//OUvFBUVceTIEfr06WMVyiuvvBIw1WlZWVmVfrZC7RHKPcoFmMPtga7HezFnwqVQ1hFmXt69yu/NtTvYUVDM2exD5cVjR5OnGxHlKqGB5L2iWTOJiYmxvner0AoKCixjeevWrXn44YcpKioq9x5vdZpQNwnlHmUnrfVcoBRAa11IADm2UP/wTmJ0T+n4BIwFGHqLZi0whYWFGIZBYmIiubm5Pu0X6heh9ChLlFKxuEdlSnXCa/ZbqN94T+bYXLtzDAV2d8CYYaBLA2vW7r77bubOnUvz5s1p1KgRTzzxBL1792bcuHEsWbKE4cOHV6hZe+mll0hJSSEpKcmvZq1Vq1blNGu33347KSkp2O12hg4dWqlqbezYsUyePDmgZu3QoUM+mrXdu3eTmpoKmPdbP/7446A/T4AzzzyTiRMn0qNHD8455xz69+8f0vuFOkSwmiHgQuBL4DDwDpAFDKtt/ZG/L9GshU6xw6E35OTrvTk79aEDe/S+ffv0jv05+pc9J7TWWp84dFAfytpRLdcKBdGs1W0iRbMWyqz3f5VS64B0zCH3nVrrI9VduIXawWd5kDX0Njc0atc2Rn/rKGuaWbNmsXz5coqKirjoootOSbM2f/58a3JGEEJB6QpumAMopc7TWm9WSvldA6K1XlcjLTsF+vbtq93LUMKdX375xcpmORWcWvNTbiFNOUZcoabEYcNui+dkqZPurZqQf+IYecePcVbHzhGb0yKatfL4+/enlPpBa923lppUIwTTo7wHuAV4ys9zGhApRhhgKGUGimmFwuk6ZvYo0Rrl2rqjdeVJjOGKaNYil2A0a7e4/hxe880RahN3EqMy7ODwLGlwOsoYhIxQFksIQv0n6H/xSqnblVJneD1u6tr7LYQJhlI4la18wJgYhIQIJ5Suwc1a6xPuB1rr48DN1d8kobawepQV5ebUQhyEINQ2oRRKQ3ndnFJK2TBjIoQwwVDKt1C6/radTpH3CpFNKIXyM2CxUuoCpdQIYBEgu/jDCHPyxoAyuTmVDb3j4+N9Hi9cuNCy/ITKhg0b+Pe//209/uSTT3j88cerdK5A2O12HnzwQSu3OzU1lTlz5pzS+RITE3nggQeqsZVCXSKUQvkH4AtgGnA78DlwX000SqgdzBWUrtwcr92pTqfGcM161/RayrKFcvTo0dUu3n344YfZv38/P/30Exs2bOCrr76itLS0yuf7z3/+Q1JSEosXL65wf7pQfwm6UGqtndo0nI/XWo/TWr+stZZIiDDCptw+Sj89yioOvQN5I9esWcPAgQPp1asXAwcO5Ndff6WkpIRHH32U9957j9TUVN577z2f3mlGRgZ33HEHAwcOpGPHjrz//vuAWbxvu+02unfvzqhRo7jsssus58pSUFDAK6+8wl//+ldL0pGQkMCsWbOs1/hzYzocDjIyMujRowfJyck888wz1usXLVrEnXfeSbt27ax96kJ4EUyu92Kt9dVKqZ/wEmC70Vqn1EjLhNBZdj8c/KnKbz/L6cShNTEUE+OIMs3mTbvjHDGnwsmcwsJCa080wLFjxxg9ejQQ2Bt53nnn8b///Y+oqCiWL1/Ogw8+yD/+8Q9mz57N2rVrLeHFwoULfa514MABVq1axebNmxk9ejTjx4/ngw8+ICsri59++olDhw7RtWtXJk+e7Pdn3LZtG+3atQuoYwP/bsysrCz27dvHpk2bADhx4oT1s3/++ee8/PLLnDhxgkWLFjFgwIAgP3GhvhDMgvO7XH+OqsmGCLWP5aEs8/vQN2Cs/CAiNjbWZyH2woULLUFvIG9kTk4OEydOZOvWrSilgh76jhkzBsMw6NatG9nZ2QCsWrWKq666CsMwOPvssxk+PPglvwsWLOC5557j6NGjrF69mrZt2/p1YyYlJbFjxw5mzJjBb37zG0vT9s9//pPhw4cTFxfHuHHj+NOf/sQzzzxTbfEWQt0gmEL5T6A38Get9Y013B7hVLj01CY9jhWXcKi4lA5kkX8yEVuMwcHiBrRw5+ao4OS93gTyRs6YMYPhw4fz4YcfkpWVxbBhw4I6n7f30X0/MJT7gp07d2b37t2W5Neds9OjRw8cDkdAN2bTpk3ZuHEjn332GS+88AKLFy/m9ddfZ9GiRXz99de0b98egKNHj7JixQpGjhwZdJuEuk8w9yijlVITgYFKqSvLftV0A4XThynGUJbVHG1G17prozL8OykrIpA3Micnh9atWwO+w+uEhARyc3NDusbgwYP5xz/+gdPpJDs7m5UrVwZ8bVxcHFOmTGH69OmWRNfhcFBSUmK1y58b88iRIzidTqvXuG7dOk6ePMmqVavYvXs3WVlZZGVl8cILL7Bo0aKQ2i/UfYIplLdiGoPOAC4v8yXD8TDCncToxDPDbaBwaHccROiFct68eaxdu5aUlBS6detmOSPvu+8+HnjgAQYNGuQj9B0+fDiZmZnWZE4wjBs3jjZt2tCjRw+mTp1K//79LW+lP+bMmUPLli3p0aMHvXr1YsiQIUycOJFWrVpxySWXYLfbSUlJ4ZFHHrHcmPv27WPYsGGkpqaSkZHBY489xgcffMCIESN8erlXXHEFn3zyCcXFomoNJ4KxB12ltV6ilLpFaz2/whfXEcQeVDWOltjZW1RCW3ZRktsMbTg54YwjzjA4p2UCR/buxhYVRdOzW1XL9aqTvLw84uPjOXr0KGlpaXz99decffbZtd2ssEfsQR4ewMzGuRWoF4VSqBo2q0dprqh0ajMYwoqsDRAHURcYNWoUJ06coKSkhEceeUSKpFCtBFMojyqlVgAdlFLlUu211qOrv1lCbWC49ix6Fp27RBnWPUqF01E3C6W/+5Jjx45l586dPseeeOIJLr744tPUKiFcCKZQ/gZz1vst/DsphTDBfcPax3IOnoAxw0DXo7RA9xIfQThVgvFRlgDfKqUGaq0PK6Uaaa3zT0PbhNOMYSUxmsFiaFOM4R5tB0piFIRwJ5S93p2VUpnALwBKqZ5KqRdrpllCbeCdm4NPj9JEGbWTmyMItU0ohfJZ4GLgKIDWeiMwtCYaJdQOnh6lr2rNHTBmuJYHifhBiDRCcvprrfeUOSRSjDDCptw9SmUVSlO9ps1huFdujiBEEqEUyj1KqYGAVkpFK6V+j2sYLoQH7n8M2mvo7d7/rZ0a09Vc3iAUqT7KjRs3+shAFi1aRFxcnLVv/aeffiIlRZwx4UAohfJWTA9la2AfkOp6LIQJyp3EiIEy/OXmuHqUNXifsj75KJOTk9m1a5e15XL16tWcd955rF+/3no8aNCgam27UDuE4qM8orW+Xmt9lta6udb6Bq310ZpsnHD6MQCtvO9R+snNCSFgLJx9lIZh0K9fP7777jsAfvjhB26//XYr53v16tUMHDgw6M9KqLsEs44SAKVUG+CvwCDM0dgq4E6t9d4aapsQIk+seYLNxzaf0jkKHE4UDho4HTi1QacmSYzvdAcOhxNbAHlvJPsoBw4cyOrVqxkwYACGYTBs2DAeeOAB7rrrLlavXs3MmTND+PSFukrQhRJYAPwduMr1+AbXsQuru1FCHcAjpwTMHmVUA//y3kj2UQ4aNIinnnqKIUOG0K9fPzp16sS2bds4fPgweXl5dOzYMei2CHWXUAplc631Aq/HC5VSdwV8tXDa+UPaH075HFvyi1DOAlrYj5NfGIctOpbsIndujlkonSEMvcPdR5mens7333/PqlWrLLN5mzZtePfdd2XYHUaEMplzRCl1g1LK5vq6AdeaSiF8sHklMboXCwE4dNUmc8LZR+lub9u2bVm4cKFVKAcMGMCzzz4rhTKMCKVQTgauBg4CB4DxwKSaaJRQe5i7vN0BY8ra813VbO9w9lG6GTRoEMXFxbRt2xYwC+WOHTukUIYRlfoorRcq9QZwl9b6uOtxM+BJrbX/u+a1iPgoq86uwmLy7SW0ce4jP68ptmgbB4ujOLthNM3PjCV7xzbimzYjvtmZ1XbN6kB8lLWD+CjLk+IukgBa62NKqV410CahFjFQpuFcuXuNrh6ld25OCPcoTxfioxRqklAKpaGUalqmRxnK+4V6gKFcPkqcoJUnN8fpHQdR97Ywio9SqElCKXRPAauVUu9jdjOuBkLf9yXUaQwFTu1ZG+TOzXF65ebUF4OQ+CiF6iLoQqm1flMptRYYgbnK7kqtdWYlbxPqGTY8lnOFyxoEOLyTGOvg0FsQapKQhs6uwijFMYzxJDGaYgyttauXWfdzcwShpghJsyaEP4Y/1Rq+8l4plEKkIYVS8MFbteY2B5kBY+57lKpOTuYIQk0ihVLwwSPv9YqDUF49SmWU28IYqT5KIXKQQin44OlReobeCrwia2t26F2XfJRa63ozwy/ULFIoBR8Mrx6l8tOjDDU3p775KLOysujatSu33XYbvXv3Zs+ePUybNo2+ffvSvXt3S5u2bNkyrr76auu8K1eu5PLLLw/yUxbqG7JgPIw4+H//R/Evp+ajdAJRDie5lGA4FKpTZ6Lv/ANO1wy4t7zXHQ0Rbj7KX3/9lQULFvDii2bI6Jw5c2jWrBkOh4MLLriAH3/8kQsvvJCpU6eSn59Po0aNeO+995gwYUKVPnOh7iOFUgiM8vyhKWM5d2prPBJOPkqAc845x5JhACxevJj58+djt9s5cOAAmZmZpKSkcMkll7B06VLGjx/Pv/71L+bOnRv0dYX6hRTKMOLsBx885XM4tGZTbiEJHKVRoabYEQXuOAiHud8bgjcI1TcfJUCjRo2s1+/cuZMnn3yS77//nqZNm5KRkWHp2SZMmMALL7xAs2bN6NevX4W9VKF+I/coBR98lgcZnskccAeMhaZaq28+yrKcPHmSRo0a0aRJE7Kzs1m2bJn13LBhw1i3bh2vvPKKDLvDHCmUgg9KKVOM4TdgzGkVymAt5/XNR1mWnj170qtXL7p3787kyZN9UhVtNhujRo1i2bJljBo1Kqi2CvWToH2U9QnxUZ4aP+cVEqtzaVaST0FxDEaDOA4VQ4czYmnYQHN07x7OOLslDRvFV36y04T4KGsH8VEKEYu51Nzdo3RbzlWVLeenA/FRCjWJFEqhHOaWRXdujqdQOsrOetchxEcp1CRSKIVy2HAtOFfuYuiVm2Oto3T4f3MdQnyUQnUhkzlCOcydOK44CB+JL9byIGcd61EKQk0ihVIoh6HMJEZ3gcQ14edw5eYYoloTIgwplEI5TP+kp0eptekS8o6DkEIpRBJSKIVyGMqVxOhSYZhxEKpMwJgUSiFykEIplMOGW6umXT1K7epRms8rw9dJWd98lCUlJdx111106tSJLl26cMUVV7B3714ATpw4YckwwJxN97eY/OOPP2bMmDHW48cee4zOnTtbj5cuXWqJQYT6jxRKoRymak1ZTkp3bo7jNAy9T4eP8sEHHyQ3N5ctW7awdetWxowZw5VXXonWulyhDMTAgQP55ptvrMfffPMNjRs35tChQwCsXr3aZxePUL+RQimUwx0w5k5iBN/cnFACxuqij3LBggU888wz2GymJm7SpEnExMTwxRdfcP/997N9+3ZSU1O59957AXPXz/jx4znvvPO4/vrr0VrTvHlzmjRpwrZt2wDYt28f48aNswxEq1evZuDAgcF+5EIdR9ZRhhFfLd7CkT15p3yeUq0pdjrZQjGNE0tJvqQFSqmAPcr66KNs3Lixz/G+ffvy888/8/jjj7Np0yZL3rFy5UrWr1/Pzz//TKtWrRg0aBBff/01gwcPZuDAgaxevRqHw0GXLl1IT0/ns88+Y9SoUfz444/069fv1P4ihDqDFEqhHO5epPb63gDcxkhlGD4RCfXJR6m1RxUXzHGAtLQ02rRpA0BqaipZWVkMHjyYQYMGWYVywIABpKWlMXv2bNavX09SUpJlUBfqP1Iow4ghV59bLec5WWpnZ2EJrdgLeQmU4sr2dtVG7ziIQMXFTV30Ue7atcvyUbpZt25dwCgH72vabDbsdjtg3qf861//isPh4OabbyYhIYGioiJWrlwp9yfDDLlHKZTDOzcHS7UGTjyRtRBcgaprPspGjRoxceJE7rnnHkvv9uabb1JQUMCIESNCun63bt3Yv38/X331Fb169QLMHudLL70k9yfDDCmUQjl8J3NcAWNonLiGqIY5CRLMhE5d9FE+9thjNGzYkHPPPZcuXbqwZMkSPvzwQ5RSnHnmmQwaNIgePXpYkzmBUErRv39/EhMTadCgAQADBgxgx44dUijDDPFR1nNqwkdZ5HDya34RzckmuqABJU4DbWvE0VJN95aNKSnII+dQNoltzyEqOrpar11VxEdZO4iPUohYPD1Kb8u5eawqcRCnA/FRCjWJFEqhHDbrHqXyFErXcw6HxhZiHMTpQHyUQk0ihVIohydgzIZS5gyvJzdHE2Wrez1Kf4iPUqguZDJHKIdSypzlVu4kRs+kjtPprLNxEIJQU0ihFPxi2ihtoLRr2O1yUtbRe5SCUJNIoRT8YqrWDJRy4t2j9C6UTimUQoQghVLwi02Zs94o90ZGr9wcpUCBrkOTOYJQk0ihFPxioFzLg0yFrxUH4SwfB1GffJTV4ZEcNmyYtZe9ffv2HDlypNraJ9RNpFAKfrECxvAEjCm85L3KqJHI2pr2UYpHUqgKUigFvxgoz15vbd6PrEocRF3zUYbikZw2bRp9+/ale/fuzJw58xQ+TaG+I+sow4gVC+dzaNeOajlXsUPj0E5atW1GymW/BVcchLeT0j2ZU598lEDQHsk5c+bQrFkzHA4HF1xwAT/++CMpKSnV8vkK9QsplIJ/FGj3kFubCYyeLB3fHmV98lECQXskFy9ezPz587Hb7Rw4cIDMzEwplBGKFMowYnjGLdV2rv1FJRwpLaW93kFBjnnMUJ7IWkMZ2J32Ss9T13yUEJxHcufOnTz55JN8//33NG3alIyMDIqKikK6jhA+yD1KwS+GUq4eJT77vR1+epQVUdd8lBCcR/LkyZM0atSIJk2akJ2dzbJly0JqkxBeSKEU/OL+h+H0+idi+Mh7jaAWnNdFH2UwHsmePXvS5Y9SjwAAIABJREFUq1cvunfvzuTJk2UmPMIRH2U9pyZ8lABHSkrZV1RKW7Kw556JXTkoVfHkOzTd2jQh79hR8o4f46yOnSuNgzgdiI+ydhAf5WlCKfU6MAo4pLXu4ed5BTwHXAYUABla63Wnt5WRh1u15u2kNC3nnh4lmLtzlLLVTiO9EB+lUJPUeqEEFgLPA28GeP5SoIvrqz/wN9efQg3iO/T2zs1xx0G4xRi6TtzAER+lUJPUeqHUWv9PKdW+gpdcAbypzXsE3yqlzlBKtdRaHzgtDYxQDC95r8216NyynGtdL1Rr4qMUqos60BeolNbAHq/He13HfFBK3aKUWquUWnv48OHT1rhwxeYVB0EZy7nTIao1IbKoD4XS30xBuRkorfV8rXVfrXXf5s2bn4ZmhTcGnshaZQ29PWIMQ1RrQgRRHwrlXqCt1+M2wP5aakvE4DdgzPWcT49SVGtCBFAfCuUnwE3KJB3IkfuTNY9Pj9IdB+HVo5ShtxBJ1HqhVEotAr4BkpRSe5VSU5RStyqlbnW95N/ADmAb8ApwWy01NaKw7lEqG8oVB+GxnDtRri6n0+msVz5KQagKdWHW+9pKntfA7aepOYILd8CYxgDDjIPwtpwbNdSj3LBhA2vXruWyyy4DTB9lZSJdQahpar1QCtXHiaXbKdmfX23na+xwwFkNUCPiUGVzc5SBUqrSQnn48GFuvfVWdu/eDcCzzz7LoEGDWLNmDXfddReFhYXExsayYMECOnTowKOPPkphYSGrVq3igQceoLCw0NKuZWRk0LhxY9auXcvBgweZO3cu48ePx+l0Mn36dL788ks6dOiA0+lk8uTJjB8/3m+b2rdvz8SJE1m6dCmlpaUsWbKE8847z2+bkpKSWLhwIZ988gkFBQVs376dsWPHMnfu3Gr7nIW6jxRKoUI0ClwBY+44CHdtdIsx6puPEiAxMZF169bx4osv8uSTT/Lqq68GbBOYPd3169cTExNDUlISM2bMoG3bthVeQwgfpFCGEWdc3qlaz/drfiE2ZyHKeRQ0aHMg7lGtucQY9c1HCXDllVcC0KdPHz744AOACtt0wQUXWKKNbt26sWvXLimUEYQUSiEgVhwEZo9Sa3NVpbflvLKhd130UXqfx2azYbebXs1HHnkkYJu8r+v9HiEyqPVZb6HuYliRtU6v3BxCys35f/bOOzyqKn/c77l3YgKEYiCUEHovIQkdlKZSXCNWBGERzMIKoghWdH8Itv2qi4CsiiJrUEBBwVVRsQTpoKEKiNQYkAUCCEIgdeae3x937s1MSO+ZnPd5eMi0e84M4TOf095PefRR5kROfVIoVKBU5IguhEclRjNzM+vmmI9r+XBSlkcfZU7k1CeFQvkoKzgl5aMEOJ6SRpIzg0YygeSL9TH0dJJlNRCCliE1uHjmNOmpqQQ3bloi7RcE5aMsG5SPUlHp0eyMMvPLVAOs2Tlz6F0+Mi/lo1SUJCpQKnLEq+oiVlGxzFVvoekYhmH6KcvYcq58lIqSRAVKRY6YTkrh3ktp3udZYEwzV3vKRaDMDuWjVBQXajFHkSOWQcjwEGJ4FxgzS0AoMYbC11GBUpEjlkFIejkpzRlLo4JYzhWK4kAFSkWO6HZGmWk5t2vpKHmvohKhAqUiRzzr5tjyXs+6OcpJqagkqECpyBHrl8Nr6G2p1jws57Xr1vV6XVF8lAXlyy+/JDIykvDwcNq3b88777wDwGeffeZ1xjwn+vfvT2H23L7xxhu0bGnWND937lyOz9N1nYiICCIiIrx0cU2bNvV63bp164iKisqz3Xnz5tGuXTtGjRpV4D4rCo9a9VbkiOY19DZAapkZpSHxc5Tt92xGRgZ///vfiYuLIzQ0lLS0NBISEgAzUEZFRdG+ffsSafu6664jKioqzzPqWYUhReWtt95i9erVNGvWrNiuqcgbFSh9iNWrV3P69Oliu54E/ILqcOOAMIQmEYa1+Vy4C4zpeV7j2LFjREdHc/bsWYKDg4mJiaFx48aMHTuWqKgo2xkZGBjI5cuXOXXqFMOHD+fSpUs4nU7mz59Pnz59+O6775gxYwZpaWm0aNGCmJgY0tPTcTqd1K5dG8BWoG3ZsoUvvviC9evX8+KLL7Jy5UqGDRvGzp07ATh8+DAjRoxgx44dXn3Nro2s9naLyMjIQn+ueTFz5kyOHz9OfHw8x48fZ8qUKUyePJkJEyYQHx/P0KFDiY6OZurUqSXWB4U3auityBPDLjDmIe91GfbQ2/JRWn+effZZ+7UPPfQQ9913H3v27GHUqFFMnjw517Y+/PBDBg8ezO7du/n555+JiIjg3LlzvPjii8TGxrJz5066du3K7NmzCQoKYujQoTRp0oR7772XpUuXYhgGvXv3ZujQofzrX/9i9+7dtGjRgpo1a9qZXUxMDGPHjvVqN6c2ikpqaipdu3alZ8+efPbZZ/l+3YEDB/j222+Ji4vjueeeIyMjg7fffpuQkBDWrl2rgmQpozJKH+Lmm28u1uu5pGRfUgpSXEBoTvem8sxyEEIIhCaoUiUgRx/l1q1bbd/j6NGjefLJJ3Nts1u3bkRHR5ORkcHtt99OREQE69evZ//+/Vx33XUApKen06tXLwAWLlzI3r17iY2NZdasWXz//ffZmn/GjRtHTEwMs2fPZvny5cTFxXk9/uOPP+bYRlE4fvw4ISEhxMfHc8MNNxAWFkaLFi2y3aDved8tt9yCv78//v7+1K1bl8TEREJDQ4vcH0XhUIFSkSNeizlu1ZrAAHRcHqq1q6us54wVDBwOh72tSEpJeno6AH379mXDhg189dVXjB49mieeeIJrr72WgQMH8tFHH2V7zbCwMMLCwhg9ejTNmjXLNlDeddddPPfcc9xwww106dLFHq5bSClzbaOwhISEANC8eXP69+/Prl27aNGiBbVr1+bChQvUqVMHMK3w1s+g/JflDTX0VuSIEMJ9Eke3y0FI80Cj7aTURO7zlL1792bZsmUALF26lOuvvx4wV32tOcLPP//ctokfO3aMunXrMn78eP72t7+xc+dOevbsyebNmzly5AgAycnJHDp0iMuXL3ud8d69ezdNmjQBrvZaBgQEMHjwYCZOnMj9999/VT9zaqMoXLhwgbS0NMAc2m/evNleXOrfvz+LFy8GwOVysWTJknyZ2RVlgwqUilzRhHDLe805StNJKezz3tY8ZU7MmzePmJgYOnXqxOLFi3n99dcBGD9+POvXr6d79+789NNPVKtWDTC3yURERBAZGcnKlSt55JFHCA4OZtGiRdx777106tSJnj17cuDAAaSUvPrqq7Rp04aIiAhmzJhhZ5MjRozgX//6F5GRkRw9ehSAUaNGIYRg0KBBV/UzpzZye1+hoaGcOHGCTp06MW7cOAC2b99u//zrr7/StWtXwsPDGTBgANOmTbMD5fTp0zly5Ajh4eFERkbSsmVL/vrXv+bvH0VR6igfZQWnJH2UAL9eTuEamUKwcY7ky9eC7uKCswrV/HQa1wvk/Mn/IaVB7Yblv37MrFmzuHjxIi+88EJZd8VnUD5KhQLTci6lu26OFHY5CJdHgTFnRvmfP7vjjjs4evQoP/zwQ1l3RVEBUYFSkStmiBT2WW8wPZUFKTBWHiisck05LRWgAqUiDzQhcJKZUYJ53ttzjlLK8h8oC4tyWipALeYo8kAXZBYYs+4jS21vl1GokrEKRUVBBUpFrmjg3hBkS87NjNL62TIIqUCp8GFUoFTkillgzMK7bo5UqjVFJUEFSkWumEHRmpu0VGtmyJQSFSgVlQIVKBW5YpWDyFo3B8yVb03TaNEpwstyXpl9lImJiURFRdn9+ctf/gJAQkICH374YYHbUZQPVKBU5IpVDkK6ZyshM7M0xRhWRln69b0tH+WqVav4+eef2bVrl+2HzG+gLCzXXXcdsbGx9pFJi2effZaBAwfy888/s3//fl5++WWgcIFSne8uP6jtQT7EoUMvkHT512K9pl/VNhDyhIdqLXNRxyUlfnkMvSubj/LUqVNeRyQ7deoEwLRp0/j111+JiIhgzJgxTJw4kYkTJ7J9+3YcDgezZ89mwIABLFq0iK+++orU1FSuXLlCw4YNufvuu7ntttsA8xjm8OHDvWzpipJHZZSKXLGCouemc9ty7jKH3qmpqXTv3Vv5KIFJkybxt7/9jQEDBvDSSy9x8uRJAF5++WX69OnD7t27mTp1Km+++SYAe/fu5aOPPmLMmDGkpqYCppru/fff54cffrD1cAAXL15ky5Yt9nBeUXqojNKHaN16erFfM8npIj45DYmGJgyEtOrnCHPorekEBASwdcMGqtW6FqjcPsrBgwcTHx/PN998w+rVq4mMjGTfvn1XPW/Tpk08/PDDALRt25YmTZrYtqKBAwcSFBQEQL9+/Zg0aRJnzpzh008/5a677sLhUP9tSxv1iStyxS5Pi4ZDSHc6mVkOoqCr3pXBRxkUFMTIkSMZOXIkUVFRbNiwIdv2csIyKVmMHj2apUuXsmzZMt57770i909RcNTQW5Erul2yVjPr5mQtB2E9nkOgrGw+yh9++IHk5GQAkpKSOHr0KI0bN76qP3379mXp0qUAHDp0iOPHj9OmTZtsrzl27Fjmzp0LQIcOHQrcJ0XRUYFSkSvWViApdNNybt4CMuW9kHNGWdl8lDt27KBr16506tSJXr16MW7cOLp160anTp1wOByEh4czZ84cHnzwQVwuF2FhYQwfPpxFixZ5Wc09qVevHu3atcs2wCtKB+WjrOCUtI/SaUh+uZxCbXGBwPQM0tKroulwOv0aalfxI6R2Nc79fhzdz8G19UNKrB/FQUX1USYnJxMWFsbOnTupWbNmWXfHC+WjVCjI3EdpDr3dTkppoCOwkkitAqjWKqqPMjY2lujoaB599NFyFyQrEypQKnJFCIEQINHt7UFmOQi8CowZrvK9Obqi+ihvuukmjh8/XiptKXJGBUpFnugIDGFVYhSZgdLLcl6+M8rConyUClCLOYp8oAnrCKNZstYKlEYFs5wrFIVFBUpFnpiqNbMSo3SbhMxAaT4udA3DcCknpcJnUYFSkSc6V1vONeE99LYyTYXCF1GBUpEnppPSu26OeZ/5uNB0QDkpFb6LCpSKPNGEcJeDkJnlIAAD03JeN6QhkBkoK4OPctSoUbRp04aOHTvaZ9OzQ9d1Wxbiafzp37+/vVE+IiKCFStWAOZJJkX5Q616K/Ikc+iNvUXIXODJzCoBjFJ2Ulo+yri4OEJDQ0lLSyMhIQEwA2VUVBTt27cvkbZHjRrFkiVLABg5ciQLFy5k4sSJVz2vSpUqtrUoK0uXLqVrV+992Vu2bCn+ziqKjAqUPsT0wyfYdzmlWK/ZMbAKExrVtWcnBd6qNUPmfozRV32Unqqz7t27c+LEiYJ/uNlgfQ7r1q1j5syZ1KlTh3379tGlSxeWLFlin61XlC5q6K3IE++6OeZ9thjDkKSkpHDTrUPp1rNXpfNRZmRksHjxYoYMGZLt46mpqXTt2pWePXvy2WefeT02atQoe+j9xx9/XPXaXbt2MXfuXPbv3098fDybN2/Osz+KkkFllD7EC61CS+S6Z9LM+bds5b2GpEqVKsSu+oIawXWpWqNmpfJRPvjgg/Tt25c+ffpk+/jx48cJCQkhPj6eG264gbCwMFq0aAFkP/T2pHv37oSGmv+mERERJCQk2PYlRemiAqUiTzTP897uQbgt7/Uaeuc9R+lLPsrnnnuOs2fP2gtI2RESYopCmjdvTv/+/dm1a5cdKPPC0yak67qqoVOGqKG3Ik8sJ6V0bzoXHhUZXS5rj1D2Tkpf9VEuXLiQb7/9lo8++sjcR5oNFy5cIC0tDTCH9ps3by6xxSVFyaICpSJPPC3nQhgghNccJYCm6dku5viqj3LChAkkJibSq5c5L/v8888DsH37dttN+euvv9K1a1fCw8MZMGAA06ZNU4GygqJ8lBWckvZRQmbdnPr8D7+UqqQbDnRd51S6g/rV/albswpnjydwTUAANevWL9G+FIWK6qMszygfpULhxrO2t9AMhJG5+dwaepuqtfJ7Mqei+igV5QMVKBV5omHVzdHdc5OZqjXDyDzvXZ6PMFZUH6WifKACpSJP7IxS6CDMlW0zUApbjCE0DVeG763KKh+lAtRijiIfaPaqt47QrG1BhpflXNM0pCy/GaVCURRURqnIk8xVbx0sy7lZHAKXh0EoP/soFYqKiMooFXkihDAlGHY5CPP+rHOUhstQTkqFT6ICpSJf2JZzpFeg9JyjBOWkVPgmKlAq8oUZIjUQRmY5CGE6zwMDA70CpfJRZpKTj7Jp06acO3fOvr1u3TqioqLybHfevHm0a9eOUaNGFbjPisKj5igV+UIXwsNybt6nCe+hN2R/jLGkqOg+ysLw1ltvsXr1apo1a1Zs11TkjQqUPsRzq35h/8lLxXrN9iE1mHFrBw/VmoFw76s0t52b5DT0Vj7KgjNz5kyOHz9OfHw8x48fZ8qUKUyePJkJEyYQHx/P0KFDiY6OZurUqcXWpiJ31NBbkS9Mb5BnaMy0CqWkpNC9V2/TSdmjh/JRepCbjzI3Dhw4wLfffktcXBzPPfccGRkZvP3224SEhLB27VoVJEsZlVH6EDNu7VBi19Y95b225dz8u0qVKuzYto1zvx+jZt16LF/5qfJRusnJR5mdqdzzvltuuQV/f3/8/f2pW7cuiYmJtptSUfqoQKnIF+aqt3fdHM//6vld9VY+StNHWbt2bS5cuECdOnUAOH/+vP0zKBdleUMNvRX5wrPAmBDeGSXkvJijfJTZ+yj79+/P4sWLAXC5XCxZsoQBAwbk2J6ibFGBUpEvNCGw9pILYQCZTkowM0ohxFUZpfJRZu+jnD59OkeOHCE8PJzIyEhatmzJX//61wL+qyhKC+WjrOCUho8SzLo5p9IyaMJvOC/XIUNIHLofp9J1QmpWoU51f84c+w3/qlWpGVyvxPtTGJSPsvhRPkqFwgOrHIRZYMxAoHlYzs0sUtM0ZDl1UiofpaIoqECpyBdeBcbcdXMsea/hKe8tp0cYlY9SURRUoFTkC88CY5pmgGFWYNQRXnVzDJdvrc4qH6UC1GKOIp94FxgzM0nLcu4pxiivGaVCURRUoFTkCzujFA6E5nZSWoHSyij18l0OQqEoLCpQKvJF5hxlZt0cQxrm/koPea/hciknpcLnUIFSkS90MstBkEXeG96igfmze+N1TExMpdWsJSYmEhUVZffHkmckJCTw4YcfFrgdRflABUpFvrAzSqG7LeduJyVXG4QopYzS0qytWrWKn3/+mV27dtG/f38g/4GysIwaNYoDBw6wd+9eUlJSWLhwIQDPPvssAwcO5Oeff2b//v28/PLLQOECpTq2WH5QgVKRLzQhEMKS90o7FmoeB741TQe8z3sfO3aMG2+8kU6dOnHjjTdy/PhxAMaOHcuKFSvs51k6s1OnTtG3b18iIiLo2LEjGzduBEwFWq9evejcuTPDhg3j8uXLJCUl5apZe+KJJ4iIiODo0aN07tzZbuvw4cN06dLlqveYXRs58Ze//AUhBEIIL83aqVOnvOQVnTp1AmDatGls3LiRiIgI5syZQ2pqKvfffz9hYWFERkaydu1aABYtWsSwYcO49dZbGTRoEKNHj+bzzz+3rzdq1Ci++OKLHPulKBnU9iBfYvU0OL23eK9ZPwxuNrMiU9ureQ29BZCWmkJERATSMHA5M7iUdJmht90GZGrWxowZw3vvvcfkyZNz1Y1ZmrV//OMfuFwukpOTvRRo1apV45VXXmH27Nk8++yztmbtxhtvJCoqinvvvdfWrHn6Li3NWkRERJ6ataxt5IalWbOOZk6aNInhw4fzxhtvcNNNN3H//fcTEhLCyy+/zKxZs/jyyy8BeO211wDYu3cvBw4cYNCgQfbZ8q1bt7Jnzx6CgoJYv349c+bM4bbbbuPixYts2bKF999/P9//fIriQQVKRb7R7bo5HkNvAf4BVdi+YyfSmc4fJ35nVewaft67D6h8mrXBgwcTHx/PN998w+rVq4mMjGTfvn1XvW7Tpk08/PDDALRt25YmTZrYgXLgwIEEBQUB0K9fPyZNmsSZM2f49NNPueuuu3A41H/b0kZ94r6EO/MrKTQB0ioHkcUk5JIS3VKt5TJHWRk0a0FBQYwcOZKRI0cSFRXFhg0bsm0vJyxBiMXo0aNZunQpy5Yt47333st3/xTFh5qjVOQb3bace696g1k7J3OOMjMIVDbN2g8//EBycjIASUlJHD16lMaNG1/Vn759+7J06VIADh06xPHjx2nTpk22bY0dO5a5c+cC0KFDycmZFTmjAqUi32SWrAVLxm397TJktvLeyqZZ27FjB127dqVTp0706tWLcePG0a1bNzp16oTD4SA8PJw5c+bw4IMP4nK5CAsLY/jw4SxatMhL1utJvXr1aNeuXbYBXlE6KM1aBae0NGsAx1LSSHZm0FD+RuqfoTgdKfjpVTiVIWhSuxo1q/iR+NtRqlSvQY06waXSp4JQUTVrycnJhIWFsXPnTmrWrFnW3fFCadYUiixkFhgz5b3CLjbmKcYon8cYK6pmLTY2lujoaB599NFyFyQrEypQKvKNLsBl180x3PsILSelJcbQMQxXWXUxRyqqZu2mm26y954qyg4VKBX5xrschHRPUGaR9/qYGENp1hSgFnMUBUD3kPfiHnpLKdEBl6e8t5xazhWKwqICpSLfaFjlIDSE5g6USLSr5ijL39BboSgKKlAq8o1nRik0cxFHulVr3nOUKqNU+BYqUCryjeZRYCzTSZlF3ute9fbFbWeKyosKlIp8o7v/tiznVjmI8DYNPcpB6Cxf+anP+yhzanfmzJk0bNiQiIgIIiIimDZtWoGvrSh/qFVvRb7RRKa819NJCWDIzHIQkPtZ5uLC8lHGxcURGhpKWloaCQkJgBkoo6KiaN++fam2CzB16lQef/zxYm83Ky6XC13X836iosiojFKRb3SPchAIA+kRKF12OQhL3mvOU/qijzKndnNjzZo1REZGEhYWRnR0NGlpaaxZs4Y77rjDfs7333/PnXfemWtfmjZtyvPPP8/111/PJ598kmubiuJDZZQ+xCtxr3DgfM7nkwtD26C2PNX9KSBz1VsKd90cc5qS1NQU7h50PQF+OtIwOHfuHEOHDgV800cZFBSUbbuWHGPOnDksWbIEMDen9+vXj7Fjx7JmzRpat27Nfffdx/z583nkkUeYNGkSZ8+eJTg4mJiYGO6///48+xIQEMCmTZsK/4+qKDAqo1TkG8tmnrVuTkBAFT7+diPbduxk+7ZtPDnlEXvovXXrVkaOHAmYurC8/oN369aNmJgYZs6cyd69e6levbqXKzIiIoL333+fY8eOAabFZ82aNXTv3p1Zs2YRHR2d7XUtH6XL5WL58uV2nyxyayM7cmt36tSp7N69m927dzN48GAOHjxIs2bNaN26NQBjxoxhw4YNCCEYPXo0S5Ys4c8//2Tr1q3cfPPNefZl+PDhuX6GiuJHZZQ+hJX5lRRWOQhrw7k0hNdXrcuQ+OVRN8eXfJT5ade6dk7cf//93HrrrQQEBDBs2DAcDkeefcnqq1SUPCqjVBQI00lpWc69HzMMiciymOOLPsrc2s2Otm3bkpCQYF978eLF9OvXD4CQkBBCQkJ48cUX7emAgroxFSWPCpSKAqFZGaWH5dzCJSVCWIHSzBZ90UeZW7vZERAQQExMDMOGDSMsLAxN05gwYYL9+KhRo2jUqJG9Ql9QN6ai5FE+ygpOafooAQ5dSUWT6dQ1jpF6oTFOvytcowVw0qnROKgqtapew5mEeAKqBVIjuG6p9Ss/lFcf5UMPPURkZCR/+9vfyrorBUb5KBWKbDDr5liqNfcmcw/LOZhla8ubaq28+ii7dOlCtWrV7KqMivKJCpSKAqEhyPCQ92pCI1O15g6cevkzCJVXH6U1T6so36hAqSgQuoA0MjNK4XZSCrzPexuu8pVRFhblo1SAWsxRFBBdiMxTOMIAYarWdC/VmjIIKXwLFSgVBcJc7/YoB4HAQHqr1nTlpFT4FipQKgqE7i4HIcHLcu6tWtMxXEq1pvAdVKBUFIjMY4wammY5Ka+W9wI+VTtHUblRgVJRILzKQQhz03nLli3NjNKdQS5d9hHPzHyuVLYIlUcf5axZs/J1jYSEBDp27FjgthWlj1r1VhQIz3IQDk2C07ztOfS2znNLlwF+JdeX8uqjVPgeKqNUFAjdCoJuy7kl79Uw5b3S4xijYbiUjxJ46qmneOutt+zbM2fOVBvMKxgqo/QhTv/zn6T9Wrxngv3btaX+M8/Yt61vVgOHeTJHQmpqKoMHX48LCPDTOX/+D27q1w/DMCqljzIrI0aMYMqUKTz44IMAfPzxx3zzzTdqC1UFQgVKRYHIzCh1hEgHKQgICGDNdxs5I6Ft/RoseT+GTevWIl0utm7dyqeffgqYPsonn3wy1+t369aN6OhoMjIyuP3224mIiGD9+vW2nxEgPT2dXr16AaYXcu/evcTGxjJr1iy+//77bAUVlo9y9uzZLF++nLi4OK/HPR2QWdvIjvy2CxAZGcmZM2c4efIkZ8+e5dprr6Vx48ZquF6BUIHSh/DM/EqKq8pBuJMi9wEdXIa05yizy5gqo48S4O6772bFihWcPn2aESNG5LsNRflAzVEqCoRngTGzbo55vyVcc0lp1s0RAulyKR+lmxEjRrBs2TJWrFhhTwcoKg4qUCoKROYcpSXvdS/miCxiDCEwDKNS+ihffPFFQkND7T8AHTp0ICkpiYYNG9KgQYNCf/6KskH5KCs4pe2jBNiXlEx1kcq1xmlSL4Ti9LuMvxbA/5waoddWJajaNZz7/TgOPwe16oeUat9yo7z6KCsyykepUOSAJjzKQQAgbDelfYxR18rVqm559VEqKgYqUCq/9aHaAAAgAElEQVQKjC7AkJaHUqJpApGNas2Z4SzLbnpRXn2UioqBCpSKAqMhPAxC0hRjINEQuNxZpNB0pJFWhr0sHpSPUgFqMUdRCHSRGSiFMBBCwwAvMYY59FaqNYVvoAKlosDoIouTUgik2yDk9DAISUMqg5DCJ1CBUlFgNCFwycyM0lStXe2khOw3nSsUFQ0VKBUFRidzvdtT3pvVcg4o07nCJ1CBUlFgMi3nAk2TNGnW2F03xwyUixYt4tHHHwco8WqMZeWjVFQu1Kq3osBo9nlvYarWrPsxjzBKaa6EAyW6oFNWPkpF5UNllIoCo3tazrXMk1328UYp7Wh6LCHB53yUYJ5VnzFjBp07dyYsLMw+7hgXF0fv3r2JjIykd+/eHDx4EIBFixZx5513MmTIEFq1apWnRUlRvlAZpQ+x8eNDnPs95//chaFOo0D63NPa6z7LIGTJe1NTUxg4cCACDSdwJekiQ2+9FYCpjz3ucz5K+7OpU4edO3fy1ltvMWvWLBYuXEjbtm3ZsGEDDoeD2NhYnnnmGVauXAmY8oxdu3bZAf3hhx+mUaNGBfwXUZQFKlAqCkymQcgBwiAgoArff/89DqMqp4Gt36xkz66dAPy0bRufr1oF+JaPEuDOO+8EoEuXLrZz8+LFi4wZM4bDhw8jhLCtSAA33ngjNWvWBKB9+/YcO3ZMBcoKggqUPkTWzK+ksJ2UwoEQmYFACAnSXOgRQqDp+lWv9SUfpb+/v/l56DpOp3lcc/r06QwYMID//ve/JCQk0L9//6uen/U1ivKPmqNUFBirEqPlpLTvtwKox17K7l27+JyPMjcuXrxIw4YNAXIV+SoqFipQKgpMVsu5hbAMQjJzL+X/Pf+8z/koc+PJJ5/k6aef5rrrrsPlUntIfQXlo6zglIWP0pCSvUkp1NGvUN31B6kXQnD6XaaK5s/vTp16NQKoVyOAC6dOYric1A5tXKr9ywnloyx+lI9SocgBgVkjx/A4o6NpmodBKFOM4UwvH1mV8lEqioIKlIoCI4Rwa3sz5b1CCK/TOWDOUZaXs97KR6koCipQKgqFblvOTXmvEBqGQZbz3jrSMJCGYRYcq4AoH6UC1GKOopBoQnir1hBId+jMHHorg5DCN1CBUlEodMCQnvLeqw1CmjuLNNTqr6KCowKlolDoWTJKEBjuOUqnx9AblGpNUfFRgVJRKDQBLs9yELaT0lz1llJmyntLWLWmUJQ0KlAqCoUuBO7Ekaatm4M1DEcikRjS3B4EhVOtzZw5k1mzZtm3Z82aRdu2benYsSPh4eF88MEHher3unXr2LJlS6FeWxAsG5LFokWLeOihh4Cr35vFSy+9RIcOHejUqRMRERH89NNPJd5PRf5Qq96KQuFlOQdwZ5fWN6/LkDjsjLJoQ++3336b77//nri4OGrUqMHFixdzNRDlxrp16wgMDKR3795F6hOAy+VCz+Y8e2HYunUrX375JTt37sTf359z587Z598VZY/KKBWFQvOwnAPmLiFAc//wxRdf0KtXLwYOvY1bb7+dxMREwMymoqOj6d+/P82bN2fevHn2NV966SXatGnDTTfdZHscAf75z3/y1ltvUaNGDcBUpo0ZMwaANWvWEBkZSVhYGNHR0aSlmSVys/NFJiQk8PbbbzNnzhwiIiLYuHEjx44dK5Avc926dQwYMICRI0cSFhZWbJ/nqVOnqFOnji3OqFOnDiEhIcV2fUXRUBmlD7F20QLOHIsv1mvWbdKcAWP/ftX9mee9NTOZtIfeJj16XcePP/7IueMJLFv5X1599VVee+01AA4cOMDatWtJSkqiTZs2TJw4kT179rBs2TJ27dqF0+mkc+fOdOnShaSkJJKSkmjRosVVfUhNTWXs2LGsWbOG1q1bc9999zF//nymTJkCZO+LnDBhAoGBgTzuLlVx6623FsiXCaacd9++fTRr1izH56SkpBAREWHfPn/+PEOHDs3x+YMGDeL555+ndevW3HTTTQwfPpx+/frl2g9F6aEySkWhsJyUhjC/a6Vh3W9mlL+f+J3BgwfTb/DNzHvrTX755Rf7tbfccgv+/v7UqVOHunXrkpiYyMaNG7njjjuoWrUqNWrUsIOKlNJWs2Xl4MGDNGvWjNatTb3cmDFj2LBhg/24py/SKhGRla1btzJy5EjA9GVu2rQpz/fevXv3XIMkQJUqVdi9e7f95/nnn8/1+YGBgezYsYMFCxYQHBzM8OHDlX2oHKEySh8iu8yvpLBm5qT1KyQBkemkfPLRKTz5xONc37UzGzdvZc6bb9qvzcnLmF1ArFGjBtWqVSM+Pp7mzZt7PZaX0CU7X2Re5OXLBGzTUXGj6zr9+/enf//+hIWF8f77719lYleUDSqjVBQK3bKcZ8korVh38dIlGjZsiKbpLPvk4zyv17dvX/773/+SkpJCUlISq9xWdICnn36aSZMmcenSJQAuXbrEggULaNu2LQkJCbZDcvHixXkOV7N6Knv37l0gX2ZJcfDgQQ4fPmzf9nRrKsoelVEqCoWnkzI5OYXw7u2QmCd0Ro2bxKNPPcOwYcOoX7cukZ3COHXmbK7X69y5M8OHDyciIoImTZrQp08f+7GJEydy+fJlunXrhp+fH35+fjz22GMEBAQQExPDsGHDcDqddOvWjQkTJuTazq233srdd9/N559/zr///W/mzZtHdHQ0//rXvwgODiYmJgYwfZm33XYb3bt358Ybbyz2LPLFF19k7ty59u3PP/+chx9+mD///BOHw0HLli1ZsGBBsbapKDzKR1nBKQsfJUCaYXDgcir19CSquS6ScqE+hn8y1+DgtNOP6lX9aBRUlaTz57jy5wXqNWuZ41yjouJSWXyUauitKBSeJWvB3CcphOmk1IW3ag0JUokxFBUYNfRWFArPobe05L1CYEiJLrMzCLmyLTZWkfnjjz+48cYbr7p/zZo1VxUvU1RsVKBUFAohBJrIKu/VMHCiA+mWGMPzvLdfGXW2hKhduza7d+8u624oSgE19FYUmkx5L6aT0sooAZd7qG2d91YGIUVFRgVKRaHRBbZqzTIIGdLIwSCkAqWi4qICpaLQaAhbtWY5KcHcjC4x5ymFbRBSizmKiosKlIpCY6rWMjPKTIOQu763kZlRSpVRKiowKlAqCo3ulveGhPRAaNIWY1i/VE7DPKet6XqBh96+5qNUVGxUoFQUGk95r9Cll7wXvLcIFUbea+Hpo9y3bx8bNmzI85x3ThRnoHSpLLnSoLYH+RB/rjpK+skrxXrNa0KqUevWqxVn4C5Na3kohYE1DakhWff9N7z/1hwMZwY1AgN554151KrXgJkzZ3L8+HHi4+M5fvw4U6ZMYfLkyYDpo/zggw9o1KgRwcHBdOnSBTB9lGvXrs3RR/n444/bRxjnz5+Pv78/TZs2ZcyYMaxatYqMjAw++eQTAgICePvtt9F1nSVLlvDvf/+bxo0bEx0dzdmzZ+0jjI0bN2bs2LFERUVx9913A2aGePnyZdatW8dzzz1HgwYN2L17N/v378/3Z3ns2LGr2mrYsCGtWrXi6NGjXLx4kaCgINatW0ffvn3p06cPMTExtGzZsqD/bIpiRmWUikKjeRxJFJphy3uFkHTu1ovVazawa9cu7rr9Nua9+Zb93AMHDvDtt98SFxfHc889R0ZGBjt27LB9lJ9++inbtm0DyJePcvny5ezduxen08n8+fPtxy0f5cSJE5k1axZNmzZlwoQJTJ06ld27d9OnTx8eeugh7rvvPvbs2cOoUaPsoJ0bcXFxvPTSSwUKkkC2bem6TuvWrdm/fz+bNm2iS5cubNy4kbS0NE6cOKGCZDlBZZQ+RE6ZX0mh23FSgGYgDWthR5J46n/8Y/I4zp9NJDUlhUYNM23dlo/S398/Wx8lUGgf5ZtvvmmLez19lJ9++mm219i6dav92OjRo3nyySfzfN/58VEWpK0+ffqwYcMGfvvtN55++mneffdd+vXrR7du3QrchqJkUBmlotDonhmlkEj3nKQh4JVnn2LMuAfYu3cvr7/2Gqlpafa8YmF9lFmp6D5Kq60+ffqwceNG4uLi+Mtf/sKff/5pD78V5QMVKBWFxnZSAggX0gBNM8UYl5MuUbdeAwA++nh5nmKMyuCjzKmtHj16sGXLFjRNIyAggIiICN555x0v1ZyibFFDb0WhsRQXKckptGvfG2noICQT/z6ByY8+zYPRo3mlcShdO0dy9MjRXLcI+ZqPMjk5mdDQUPv2o48+mmNb/v7+NGrUiJ49ewJmhvnRRx8Va/EyRdFQPsoKTln5KAFSXQYHr6RSX79EVdclUi7UR1RNA5fksiuADIegdb3qpCVf4cKpkwQ1DOWagCpl0ldFyaB8lApFHniq1ryclLYYw8NJiTrvrai4qKG3otDYlRjRvJyUTiSaNE/mSCkznZQ+FiiVj7LyoAKlotBoAALM/NGw75XSzDGllBgShDtQ+ppqTfkoKw9q6K0oNEII96Dbw0npPsBoLfS4DANN00xXpUsZhBQVExUoFUXClPd6Oykh0yDk9Dzv7WNDb0XlQQVKRZHIajm3sH6xXCpQKnwAFSgVRcLLcq4ZORqERBENQgpFWaICpaJI6Agi6tUFQNM9nZTeQ29dK1hGWZl8lGPHjmXFihUl2BtFUVGBUlEkNGFLgxCagXV+wcoonS6PjLKQQ2/lo1SUNWp7kA+xevVqTp8+XazXrF+/PjfffHOOj+tZVGtGhnn769jV/PP1ORiuDOrXDWbBm29Q1aEzY8YMfv/990rno7x48SLh4eHEx8ejaRrJycm0adMmW9mHovyhMkpFkfBUrQlh2AahHt178PGqWL5au4URI0Yw9403AHNvZWX0UdasWZPw8HDWr18PwKpVqxg8eDB+fj5W7NxHURmlD5Fb5ldS2AYh4UBo0jQIOTT+d/ok0yb+nbNnE8HlpEnjxubzDKPS+iiHDx/O8uXLGTBgAMuWLePBBx8s8DUUZYPKKBVFQsMKlH7gzig1TeOp//c0Y+7/O1/88CPvvPMOaWlp5vOkrLQ+yqFDh7J69WrOnz/Pjh07uOGGGwp1HUXpowKlokg4bDGGAyFc5tluTePSpUuE1G+Ay5C8//77YGWeMvvTOZXBRxkYGEj37t155JFHiIqKQtf1vF+kKBeoobeiSGhCkJqcTMc2PRFIpKEz8cGJPP7YYzz4wBhq12vADX2vy8wIjeyzwMrgo3z00UcZPnw4w4YNY926dQW6nqJsUT7KCk5Z+igBkl0uDl9JI0S/RIDrIikXGuBX3UVqaiqaUY1EDNo3qIGuCRLjjxB4bRCBQcqs4ysoH6VCkQ+sxRxD6CDcTkrMchDWwNJpmAsy6hijoqKiht6KIqFjOSn1zPlHYZ3OMfFVMYbyUVYeVKBUFIlMy7mGeUZHItwh0jrG6HLr1XwtUCofZeVBDb0VRUIIgSYynZRCc+V43tvXAqWi8qACpaLI6EJgSEu1Jq8yCGUGSgeGkb/9jApFeUIFSkWR0YXAZQl7NcO2ZEgh0YXIklEaOe6lVCjKKypQKoqMl5PSQ7UmMQOl5xwl+F6RMYXvowKlosiE162D26aGphlYCaOBxCG85yghf4GyovsoFb6FCpSKYsEKfUIzMNznvaWQ6IhCBUpPlI9SUdao7UE+xKFDL5B0+ddivWb1wHa0bj09z+dZGaXQTDHG9z98z9w5c0hNd1Hj2mv5bMVyagddy6zX53Hmwp8c//13n/VRJiQkcPPNN3P99dezZcsWGjZsyOeff06VKlV49913WbBgAenp6bRs2ZLFixdTtWpVxo4dS40aNdi+fTunT5/m1VdftdtWlD0qo1QUC1ICwgHCzCh79erF6i+/5vvvNzF46F288soraLr5vXzo4EGf91EePnyYSZMm8csvv1CrVi1WrlwJmOq3bdu28fPPP9OuXTv+85//2K85deoUmzZt4ssvv2TatGn5+twVpYPKKH2I/GR+JYkUDjS3au306dOM+8c/SDxzjpT0dNq0bG4qzIRg0MCbfN5H2axZMyIiIuz2ExISANi3bx//7//9P/78808uX77M4MGD7dfcfvvtaJpG+/btSUxMzLMfitJDZZSKImOFMQM/u2TttGnTGHv//WyM/ZHpL88hJTUVIQRC07jGw+rtqz7KnJybY8eO5Y033mDv3r3MmDGD1NTUbF/ji7KaiowKlIpiwxAOrGWdS5cuUb9+fTQp+eKTjzKLjglhByBPKoOPEsyphAYNGpCRkcHSpUuL5ZqKkkcNvRVFJjk5mUHtWqEjERg8ED2Zp6c9wwMPPEDDeg1p3bkLf5w+AYAQGjKbQOlrPsqceOGFF+jRowdNmjQhLCzMK2gryi/KR1nBKWsfJUCqy+DglVQaOK5QxXWWlPOhBNTSuHzlEtWNavyGpGGtKtQO9OfimUTSU5IJblLwmjOK8ofyUSoU+STTIKQjpeWk9D7v7coixvDFL2iF76KG3ooiY8t7re9dYYC0frUkutC8Np1LKZGGgajgNWOUj7LyoAKloshoQiCEmVECCJEpxjDc570z7PPe5q+c4XLZJ3UqKspHWXlQQ29FseBpEBKakSnGEBKHVvRjjApFWaICpaJY0Mk0CGl6FjEG4HRlDZTKS6moOKhAqSgWdCFwWcJeTdpiDENIHELgNJRqTVFxUYFSUSxkHXobLrdByF2N0WVIDClVoFRUSFSgVBSZwMBAHAKMLAYhTdPMobf7fqcr/2VrPX2UP/74Iz169CAiIoJ27doxc+ZMABYtWsRDDz1UUm/LJiEhASEE06dnnqU/d+4cfn5+BW4/MDCwuLunKAVUoFQUC+bQWyKEZjspdV03V72tQGl4VmPM/xzlmDFjWLBgAbt372bfvn3cc889JfEWcqV58+Z8+eWX9u1PPvmEDh06lHo/FGWD2h7kQ0w/fIJ9l1OK9ZodA6vwQqvQPJ+nCbeTUugIYfDNd1/z+vx/kZqaSp1rg5kx7x2a1q7GzJkzOXzgAAnHjnEqMTFfPsozZ87QoEEDwBRMtG/f/qr2jx07lqNXMiAggF9++YXExERmz55NVFQULpeLadOmsW7dOtLS0pg0aRIPPPBAju+vSpUqtGvXju3bt9O1a1eWL1/OPffcw8mTJ3Nt/7fffmPkyJE4nU6GDBlS0I9fUU5QGaWiWHAIazuQ6aTs0a0na9b8wHfffcfdQ+8iZv48e4vQkaNHWf7Bonz5KAGmTp1KmzZtuOOOO3jnnXe8jDsWuXklExISWL9+PV999RUTJkwgNTWV//znP9SsWZNt27axbds23n33XX777bdc3+OIESNYtmwZJ06cQNd1QkJC8mz/kUceYeLEiWzbto369esX/gNWlCkqo/Qh8pP5lRSaeyHHEH7oIoOTp04zYUo0p06dxJXuJLhRE3voPXjQQPx0ndq1a+fpowR49tlnGTVqFN999x0ffvghH330EevWrfNqPzev5D333IOmabRq1YrmzZtz4MABvvvuO/bs2cOKFSsAuHjxIocPH87VMzlkyBCmT59OvXr1GD58eL7a37x5sy3tHT16NE899VTBPlhFuUAFSkWx4HCf95bCAaTxjxlPMnnqw/Tpcz17Nu9k+uyX7b2UAQEB9jHGvHyUFi1atGDixImMHz+e4OBg/vjjj1z743mtrNcVQiCl5N///reXODcvrrnmGrp06cJrr73GL7/84qWCK0j7ioqHGnorioXM896mk/JS0kVC6jcEYMknHyKEsAOl0K7eIpSbj/Krr76yJRqHDx9G13Vq1arl1X5OXkkwF14Mw+Do0aPEx8fTpk0bBg8ezPz5823P5KFDh7hy5Uqe7/Oxxx7jlVdeueosd07tX3fddV73KyomKqNUFJnk5GTaNG2CU0p0JA9NGsXjU57mvjF/pW69uvSI7I448bs99BaaO6h6rHzn5qNcvHgxU6dOpWrVqjgcDpYuXYqe5Zx4Tl5JgDZt2tCvXz8SExN5++23CQgIYNy4cSQkJNC5c2eklAQHB/PZZ5/l+V47dOiQ7Wp3Tu2//vrrjBw5ktdff5277rqrAJ+qojyhfJQVnPLgowRINwx+vZxKfUcaVZwnSL3QmGq1/Ll45TzVpD8X/PxJl5LW9arjTE/n3O/HqFmvPlUCq5dov7JWU1QUL8pHqVAUgKyqNXPTOe77JA6RzXnvfNaxUSjKGjX0VhQLGiAEuNyB0hRjuE/nuCQ65nlvKSVC08zaOaVwjHHRokX5fu7evXsZPXq0133+/v789NNPxdwrRUVDBUpFsSCEQBciM6PUM8UY0iXtXzSnIfHTtQKfzikNwsLClF9SkS1q6K0oNnSwxRiahxjDEBJr6cVpCXwdDlxKjKGoIKhAqSg2HF6qtRzEGJ61c5wqUCoqBipQKooNU7Vm4inGkBiZYgz3go6uO8rd0FuhyAkVKBXFhm6JMRDuQInbSQm6O5P0NgipaoyKioEKlIoiYzkWHbZqTQfhArd2DUAaBpoQZFhbhBxWkbHss8ry5KM0DIPJkyfTsWNHwsLC6NatW54CDYVvoVa9FcWGLoQp79UcWJsorXPOUho4HFo2tXNc6A6/XK87ZswYPv74Y8LDw3G5XBw8eLDk3kQ2LF++nJMnT7Jnzx40TePEiRNUq1atVPugKFtUoPQhnlv1C/tPXirWa7YPqcGMW/MnqNVtMYYf33zzLS//811cMoMaNWrw/ryF+DUMYdbLL3L5j9McPXKEhITfeOThyTz6xBNA+fVRnjp1igYNGqBpZnYcGpppafruu++YMWMGaWlptGjRgpiYGDZu3EhMTAwff/wxAOvWreO1117LVaKhKN+oobei2LCclIbwo2fPSFZ/toYft/zEbbfdxpz583AIDUNKDhw4wOqvv+brlSt46f/+r9z7KO+55x5WrVpFREQEjz32GLt27QLMchAvvvgisbGx7Ny5k65duzJ79mwGDhzIjz/+aEs2li9ffpWWTVGxUBmlD5HfzK+k8DQInTx5kmlPTuPcH2dISU2hWaOmZl0dQ3LLLbdQpWpVagcFUadOnXLvowwNDeXgwYP88MMP/PDDD9x444188sknpKSksH//fq677joA0tPT6dWrFw6HgyFDhrBq1SruvvtuvvrqK1599dXi+6AVpY4KlIpiwxp6G+g88cRLjB/zGHffcydff/cFc1+bg0MIJOB3zTUITUPTNXRdqxA+Sn9/f26++WZuvvlm6tWrx2effcagQYMYOHAgH3300VXPHz58OG+++SZBQUF069aN6tVLVv6hKFnU0FtRbNgZpdC5ePEyDRrURxqSFStWIMn8VjbsTecOrN1B5dlHuXPnTrs2jmEY7NmzhyZNmtCzZ082b97MkSNHAFM3d+jQIQD69+/Pzp07effdd9Ww2wdQGaWiyCQnJ9sLHBlSMnHyJJ5+eiLjHxxLSEhDIjtH8Pux4/Yvm3vhG013AOaN8uyjPHPmDOPHjyctLQ2A7t2789BDDxEQEMCiRYu499577cdefPFFWrduja7rREVFsWjRIt5///2ifsSKMqbMfZRCiCHA65hHhRdKKV/O8vhY4F/A/9x3vSGlXJjbNZWPsmyQUrLncgp1HAbVM37DmRyCrlfFqSXjSsugmn8Njqak0zioKrWqXsPFM6dJT0khuEnOdWqKivJRliyVxUdZphmlEEIH3gQGAieAbUKIL6SU+7M8dbmUsuR3FiuKhBACHYFLZlGtOTQyPM5725vOdYd9OkfVlVGUZ8p66N0dOCKljAcQQiwDbgOyBkpFBcEhwCBTjOHKkDh0HQOJZpjB1DrGqDscSCndm85L5ldR+SgVxUFZB8qGwO8et08APbJ53l1CiL7AIWCqlPL3bJ6jKAeYYgyriJiBYRj2Rm3DMPDThVdGCeYxxpIKlAVB+SgVOVHWq97ZjbeyTpquAppKKTsBsUC2M+NCiL8LIbYLIbafPXu2mLupyC96FtWa5aQEM1A6NM3LSQngUro1RTmnrAPlCaCRx+1Q4KTnE6SUf0gp09w33wW6ZHchKeUCKWVXKWXX4ODgEumsIm8cApyeYgxAE9lnlLp93lvp1hTlm7IOlNuAVkKIZkKIa4ARwBeeTxBCNPC4ORT4tRT7pyggum0QciCEu7oY1okdA4fHHKVtEFJFxhTlnDKdGJJSOoUQDwHfYm4Pek9K+YsQ4nlgu5TyC2CyEGIo4ATOA2PLrMOKPMk0COkgzYxSuL+PDSFxaAKXYdXTEWi6jksFSkU5p6wzSqSUX0spW0spW0gpX3Lf96w7SCKlfFpK2UFKGS6lHCClPFC2PVZkxfJRgjn0BtMghOU7d886m2VrzSdkeKx8Zzf0Lk8+SoWi7JcaFT5FphjDDw3zSKDhUbbWMk86XRJ/h7ny7cpjjrKsfZQKhQqUvsTqaXB6b/Fes34Y3Pxy3s9zYwXKr7+O5bWX/0VaCtQJrsO/35hHvWuDefn/XmT/bwmcO/U7J0/8zoRxf2PsyHuB8uujXLduHTNnzqROnTrs27ePLl26sGTJEoQQPP/886xatYqUlBR69+7NO++8gxCC/v3706NHD9auXcuff/7Jf/7zH69jmYqKRZkPvRW+hTX07t77OtasWcIPqzdy1x3DeOutt5BINCDh6CGWf7qKuLg4Xp71GmmpqWzftq3c+igBdu3axdy5c9m/fz/x8fFs3rzZbnfbtm3s27ePlJQUvvzyS/s1TqeTuLg45s6dy3PPPVfET1ZRlqiM0pcoQOZXUlgZ5Yn/nWb8U49z+tSfZGQ4adykEYaQaBL63DgYzc+POjVrUDc4mLPn/mD9T3Hl1kcJpgjDEn9ERESQkJDA9ddfz9q1a3n11VdJTk7m/PnzdOjQgVtvvRWAO++8E4AuXbqQkJBQxE9WUZaoQKkoVnT3VqCnH3uKyQ+OYPCAv7J5cxyvzHkJA4mUkioBAV57KV0uJ9Iwyr2P0n6Puo7T6cdiT5YAACAASURBVCQ1NZUHH3yQ7du306hRI2bOnOmV7VqvsZ6vqLioobeiWNEFIODSxUs0aFAPoRt8uHyp/biUEl0TZLhP5+AOYtf16llufZQ5YQXFOnXqcPnyZTs7VfgeKqNUFBlPHyXAyEkPM/Uf/2DMmEcJaRBK5/AenDh1DDADpSaEfYzRyvY6hYWVWx9lTtSqVYvx48cTFhZG06ZN6datW8E+OEWFocx9lCWB8lGWLQcup+CvCWo7DyOMYFIvVaNGvWv4448/qElVLlXz54/L6XQIqQHAmd+OUrVmLarXrlPsfVE+ypKlsvgo1dBbUew4hLAt5gjDyzdpGAZ+mlmN0WWY92sOhzqdoyjXqKG3othxaIJUw0AIHSGyOcboXlvJcEkcOuh69qdzigPlo1QUB5U2UKanp3PkyBHq1q1LUFCQrQJTFB3dnVEK4bDPeyOtEzuSa6xjjC6DKuhoDgcZaVfvjSxtlI9SkROVNlCeOXOGjz/+GACHw0G9evVo0KABoaGhNGrUiKCgIFWeoJA4cKvWNB3pPu/teYzR+qWzVr51h4O0K05VEkJRbqm0gbJ+/fr8/e9/JzExkTNnznDq1Cn27t2LtQhUvXp1mjVrRsuWLWnZsqW9EVqRN7oQIN1iDGlmitIl0TUNw2XgAAQeXspSKAmhUBSFSvtb6XA4CAkJISQkxL7PMAzOnj3L8ePHSUhI4MiRI+zZswchBE2aNKFDhw60b9+eatWqlWHPyz8Ozarv7YeQl82fDQNN13FmZIABDj1zL6Wnl1IFSkV5RE3MeaBpGvXq1aNbt24MGzaMxx9/nHHjxtGnTx+SkpL46quveO2111i+fDlHjhzBF7dWFQZPzRpgq9QMHEj3HKXhkui6zlfffM0vv+zHT9fsQPn6v//N9YMGE9G5M+Hh4XzwwQeF6kdaWho33XQTERERLF++nHHjxrF/f+Hq1H3wwQd07NjR/nK0lG85sW7dOqKiogBzASk4OJjIyEhatWrF4MGD2bJli/3cnLRxivKL+vrOBU3TCA0NJTQ0lAEDBpCYmMju3bvZs2cPv/76K8HBwfTq1YtOnTrhUJmQjbUV3MCBBmgOaQZKf51vvv2GqloAvVq1IjXD4O2332bND2tZvXIlIc2a4UQUeOO3xa5du8jIyLAXZIYPH16o66xevZq5c+fy3XffERISQmpqKosXLy7QNYYPH84bb7wBwNq1a7nzzjtZu3Yt7dq1U9q4ioiU0uf+dOnSRZYkGRkZcteuXXL+/PlyxowZcvbs2XL79u3S5XKVaLvZsX///lJvMyvVqlXzup3qcsmv9/4q+/TvKzt0aCX7XN9X7tlxQMbGxspatWrJJo2ayPYdw+TXm3bJRo0aycOHD8vTRw/LS+fOel0nNjZWRkREyI4dO8r7779fpqamSimlbNKkiXz22WdlZGSk7Nixo/z1119lYmKibNGihaxRo4YMDw+XR44ckf369ZPbtm2TUkq5cOFC2apVK9mvXz85btw4OWnSpBzfT58+feSaNWuyfczzmmfPnpVNmjSRUkq5du1aecstt0gppYyJibnq+tOnT5dTpkyRUkpZq1YtmZiYeNW1f/rpJ9mrVy8ZEREhe/XqJQ8cOCCllNLpdMrHHntMduzYUYaFhcl58+bl2PfSJrvfP8zqBGUeB4rzj0qDCoHD4SAiIoLw8HCOHDnCunXrWLVqFT/99BNDhgyhefPmZdKvV+Je4cD54hXAtw1qy1PdnyrQaxxC8PITj3L3qJGMvac3H8Ss5alnHufTzz5h4MCB3DYoikHDh3Pkf2dISkqiZcuWnD2egMuZYV8jNTWVsWPHsmbNGlq3bs19993H/PnzmTJlCmCer965cydvvfUWs2bNYuHChSxcuJBZs2Z5qc4ATp48yQsvvMDOnTupXr06N9xwA+Hh4Tn233JOFiedO3fmnXfeATK1cf3792fIkCGMGTOGgIAA2rZty4YNG3A4HMTGxvLMM8+wcuVKFixYwG+//cauXbtwOBycP3++WPumyBs1R1kEhBC0atWKcePGMWzYMNLT0/nggw/49NNPCyxY8CU0YE9cHLfdMwKAe0fczU9xW+y9qtKQ+OnCazuQnuV0zsGDB2nWrBmtW7cGTMv5hg0b7McLojCLi4ujX79+BAUF4efnx7Bhw4rrreYb6TGf/eyzz7J9+3YGDRrEhx9+yJAhQwBT9TZs2DA6duzI1KlT+eWXXwCIjY1lwoQJ9vROUFBQqfe/slNpM8r/pVzhkf1HaV+9Fm0Cq9E+sAptqwVQRS/4d4cQgg4dOtC6dWs2btzIpk2bOHLkCLfffrv9H700KGjmV1JYwc/l/h4WmqlQs8vTSgM/XSOweg2qVq1GfHw8tQOrkp6SYl/DM7BkR0EUZnldKysdOnRgx44d3HDDDVc95nA4MNz1frITCOfErl27vM5EZ6eNmz59OgMGDOC///0vCQkJ9O/f3+6/2l9atlTajPLUpd84cfEwi/53iscO/s7NOw7RcsPP9Nu6ncf2H2bF6fP8LzW9QNf08/Pjhhtu4IEHHqB69ep8+OGHrF69GpfLVULvovwS2bMHn338CQAfr/iE7l17IRAEBgaSdCXJPsb4yGNPMGnSJC4np+JyObl48SILFiygbdu29hYtMA1C/fr1K1Rfunfvzvr167lw4QJOp5OVK1fm+vynn36aJ598ktOnTwPmavq8efMAaNq0KTt27ADIt1Zt/fr1LFiwgPHjxwM5a+MuXrxIw4YNAe+jl4MGDeLtt9+2vxDU0Lv0qbQZZXhQKJ+1jyfpymaOJiWy/3IqB9OrczSlJf9NacXSRHPo3OSaDG6ofS1D6tajd61A/LS8v9nr1avHuHHjiI2N5aeffuL06dMMGzbsqm00vkJWzdqjjz7Ks7Nm8/SEB3hv7svUqVOX2f98B8OQ3H7b7TzxxBMs/CCGF994j9HR45EZqQwYOBBNCKpUrcpjjz9OQEAAMTExDBs2DKfTSbdu3ZgwYUKh+tewYUOeeeYZevToQUhICO3bt6dmzZo5Pv8vf/kLiYmJ3HTTTXY2Fx0dDcDjjz/OPffcw+LFi7PNOC2WL1/Opk2bSE5OplmzZqxcudLOKHPSxj355JOMGTOG2bNne1173LhxHDp0iE6dOuHn58f48eNV9clSRmnWPHA6L5OU9AsXLu5m9/lj/HjJxc9GG36lA+nCn+paBkOCqjI8tDG9awWi5WM4tGfPHr744guqVavGX//6V4KDgwvzlnKkPGrWABJS0kh1GYRyHAgg+Y9rqVWvKn9ePI9wSmrXrs2BC8nU8HcQGlSVtCtXuHD6JEENQ7kmoEqx9+fy5csEBgbidDq54447iI6O5o477ij2dioblUWzVmkzyuxwOAK59toeXHttD5o3hTuki6SkXzh5bguxZ46xLrkeq8724JNzRwlxpDEmtD5/bdiQ2tfk/DF26tSJ2rVr8+GHH/Lee+8xatQor+zLV7HFGJrHpnNDousaTqcTaUiu0QXp2ZzOKQlmzpxJbGwsqampDBo0iNtvv71E2lH4JipQ5oIQOjVqdKJGjU60bQ7j0xI5dno1n//vCF+ntuX/EvyZnXCW4fUCmNy8NaEB12R7nYYNGxIdHc3ixYt5//33GT16NI0bNy7ld1O6OAQ4kQjhQMo0wH3eW9dJJwNcEj9dIzUjU4wBlJiXMruTNS+99BKffPKJ133Dhg3jH//4R4n0QVFxUUPvQpKU9Mv/Z++846Oq0j/8nNumJZMeMHSQ3iIdREVEERBWXRSVKiqrqLC6dt1d3HX9qairWFZFxQaCvQuiYAdBLCg1tFAMSUifTL3l98ckA6EmkACB+3w+MTD3zr1nxuE77znnfb8v32z5mJd3OfjWOh1JCK5I17i1dXtSDxBhlpaW8tJLL+Hz+Rg3blytRJbH69Q7Pxzhj2CEU9UiTL2UQGEjPAkODCmEr8xHelwK+ZZJQfkeTudbNuGK9+JNrd3lCZu642SZep+0u95HSnx8R4Z2vo1XTx/Lm41/pL/4lldyw/T+/iee2bwR3dz3C8jr9TJ+/Hg8Hg+vvfYaeXl5x2DkRwc5Vu+tYlkGQhKxem8EGLqBqkSdzvUKp3NZUeps6m1jcyTYQnmEaFoy/dpcyyunT+C1jB9oZa1h2pYyBi75nlWlZfucn5CQwPjx42O7nWVl+55zIlBpjGEIBbCi9d6mGUs6Nw0DrSJndU9fyj2rc2xsjhdsoawlVDWBc9pN5r3eZ3NP/GfsDEUYvGIdD2f9jrnX8kZiYiJXXHEFfr+fOXPmEA7XLF+zPiBXJASYFRYZsmzujigBw4wmnQOE9UqhVO3eOTbHJbZQ1jJud1Nu6HEbH7YL0UP8ysPbdUYu+5aCcNVIKSMjg0suuYScnBw++uijE86yLRZRVgilqBDKWERpGqjK7pYQEN35Ng0jVvliY3O8YAtlHdE2YzDz+l7Ije7FLCt3cvaSH1hVWlzlnDZt2jBgwABWrlxJfW6vu79E+t2elNGPmCRFhfLDDz9k/fr1mJaJLASyEMx47L+0a9eO3qf355wLhvPSS7P2uV51qC0/ymnTptGoUSMyMzPJzMzkjjvuAGDAgAG0bduWrl270rNnz5idm9/vZ9iwYbRr146OHTvGzrc5cbCFsg5xOFK4q9dUZjZeR9gIM3zFWj7Lza5yzplnnknr1q359NNP+eOPP47RSGsfCRAC9D3qvS3L4v333icrKwsTC0yLt2bP4qtFX7Bs2TJ+/mkF774+B1M/vJLPPf0oR40axfPPP0+HDh0O61o33XQTv/zyC7/88gsPPPBA7PHZs2fz66+/MnnyZG699dbY47fccgtr167l559/5rvvvuPTTz89rPvaHJ/YQlnHCCExpM0Y3mwnkUoeV67exZvbdluhSZLERRddhMfj4Z133iESOTE2M7Zu3cqk4cMY1LMnw4dfzbYd2Sxf8QMffPgB//73vznzvLPZsH4Dz814hH888AherxdZUfHGx3PFqEsB+OKLLzjttNPo3LkzEydOJBSK5mM2b96cf/7zn3Tr1o3OnTuzdu1a8vLyGDNmDL/88guZmZls3LiRAQMGxCL1F154IRbB10YJYN++fdmxYwcAbrebs88+GwBN0+jWrRvbt28/ouvbHF/YCedHic4ZA3jPs4bLf17FlA0tCRm/MaZ5ZyD6D+3CCy/k1VdfZeHChQwdOvSw7rHz/vsJraldP0pH+3Y0vOuuGj/vhhtu4KIrRnPJmLHMf+khbrvtHl546k0uGDacs88+i6GDh+JUHZT7fJzSpDkAkiwjhMDQ9WPqRwnw3//+l9deew2ABx98kMGDB1c5Pn/+/P1W9xQXF/Phhx8yderUGr9nNscvdkR5FMlIaM9b3bvRUWRx6+YIb2T/HjvWqlUrevfuzbJly9i0adMxHGXtsGTJEi4cNQrdsrj88ov4fslyIGoZJkkyJhamEbVfM0wLwzRjuZSGHjnmfpR7Tr33FMnKEtQHH3yQG2+8scpzdF3n8ssvZ8qUKcfMvNmmbrAjyqNMWnxL3uihcfGPP3DTpmYkqOsZnBEVg0GDBrF+/Xo++ugjrrvuOlRVrdG1Dyfyq0sUIQhZ0TLGmH+IFV1usIRFvCcOt8fD9uwttE7vjEsDSY2mCB1LP8qDMXv2bLp27codd9zB9ddfzzvvvBM7NmnSJFq3bh2Lem1OHOyI8hiQHNeYud1OozE5/GVdET8VRtezVFXlggsuoLCwkG+++eYYj/LI6NevHx+/9Sa6ZTFv3kf07RttreDxxFHuj1rYGbrBrbfdxv333EJBUTQjoDwQYNbLrxxTP8pDoaoq9913H0uXLmXNmjUA3HPPPZSUlPDYY48d0bVtjk9soTxGNPS2ZE6XlrgpZ9zKDewMRCt0WrVqRZcuXfj222/Jz88/xqOsHpV+lJU/jz76KDNmzOCNV15hZN9evD73PR588HaEEPz5opE8/vjjnHfeeWRtyGLy5Mn07HcGA/r3pVOnTlxw4cU4HQ40TYv5UXbu3BlJkmrFj3LQoEGH9KOsDi6Xi7/97W88/PDDbN++nf/85z+sXr2abt26kZmZyfPPP39E17c5vrBNMY4xX277knFZLk5Vi/i03yAcskJ5eTkzZsygSZMmjBkz5qDPP15NMQAKwzrbgmFaauVY4Z1EfM1RVAV3okJ+fj7xkpu4Bgn8/kcpqXEapyS4CJSVUpKXS2qTZija/t2YDgfbj7JusE0xTnBCusGanFKCkWPbpmFAkwH8o+F2VusNufPXhQB4PB7OOussNmzYQFZW1jEd35GgSJVJ59GlcEmObuDs2TtHCIEmS1XKGIFar/meNm0amZmZdOrUiRYtWth+lDY14qTdzNmwsYhFs36lCAvLreBOcpKS7qFx0wTan5pM41TPUWvodFX7i1lW8jpzSjpwRvYKLmrWnV69evHjjz+yYMECWrZsGROX+kRlXxwDBQWQZBM9JCFJEgKBYRpYloWm7CGUat34Utp+lDZHwkkrlA0VmeEOJ3LIRPgBfwR2FMPPxUA2vwuLgENCTnSSdEocaY3jUVJcKMlOlGQnQqm9YFwIwaPdh/Hb999w66ZEuifn0TQ+nXPPPZd58+bx888/06NH/ZvJKHt0Y4wKpYFpRAVfkiRMwwILNFngD1fUe8tKNGXoKCTe33333bYo2lSLk1YoU1olkXLv6ViGhVkewfCFCRYHydlWSn6Oj7J8P2ZpiISdPhJ2+in5eY+NFQFyggMl1YWS6kJNc6Gku1HS3MgJ2mFFonFaAs92bM4Fv5dz48qlvNdvOO3ataNx48Z8/fXXdO3atcbpQscaeT/GGJZlRVtCSBKmYWAZJpoiY5hhdMNEkSWkvXp829gca05aoaxEyALZqyF7NbSMOLwdUmm7x/EdxQG+y8pn+ao8tmwsIjFicqqk0kNWaVkaQttWhhXavc4pNBkl3YWa7kZJd8d+K8lOxCE6OHZJ68T1ye/y38IWPL9+Mde0HcjAgQN55ZVXWLFiBX369Kmjd6FukIVAEmAQfd1CVPTOqVin1CM6GNGpN0C4QiijdmsnRimnzYnBSS+Uh6JRootLezbl0p5NCekG323YxYe/5nDD7zsJRAzapsdzTY8mnNcgAakoiJ4XIJLnJ7ShGP9PeziYy2J39JnmjkWjSrITKU6NRaF/63QBn333Cf/3RxqDM3Jp2bIlzZs355tvvqFbt25oNdwJtsyo+UTlbyyI/keAiI5LyNIhRfxwUYRAt6I178SEMupLaWJhGRaautuX0q1F1ylD5eV1Mh4bm8PBFsoa4FBkBrZrwMB2Dfj3hTof/foHLy/J5pZPVpPi0bjmzJaMGdyMREdFR8GgTiTPj57nJ5IXQM/3E9npJ7C6APawXBSqhJzkQE5wIHsdPKs04dniIG/mrmBy514MbN2Xjzd9wsrPltOla5eo6BkmZtjEDBsYvvBuMTT2+G1YUM30L6FKCIeM5FIQmlxrG1lRoYxW51QRSqWiJYRhoLmi71fY2L3zHfWlNJCk+reJZXPiYedRHiGWZfHD5kKe/nIjX6/PJ8Wj8ddBrbmsV9OYg/c+zzFM9MIgemEQY1cAvSiEURRELw1jlIQwfZFo9FcNikbE0bbpqdG/CBGNECWBkEUsWkSKPhbzPqukQlytiIkVNrDCZlRYZQk5XkXyqNUSzLi4OHw+336PbfaHCFsmjfkDELw5eymdMzvQsUs7CgsLSXIm8NSL/+PpZ55DVRWcmsqU6ycz7JyBpDRuilpRqlgdQqEQw4YNY9euXdx5550sXLiQm2++ucZWa9OmTWPmzJmxHuznn38+DzzwAAMGDCAnJwen04mmacycOZPMzMzYOTk5Oei6zhlnnMFTTz1VLzMVasrJkkdpR5RHiBCCPi1T6NMyhZ+2FvHgp2v5+/ureHlJNvdf1JleLZL3fY4soaa5UdPcVFkQrcCyLMpKyrjku+Uk6AYvdDyN/B15LPp8Eb1796Z1m9ZR8dMkSku3oTb07BbDI8AyLcygjumLYBSHMHwRlEQHkvPwPyaKAL8Z7e9tmiHmL/wYRYPOmVHxeu6F51j4+ULeXvAlCV4vKZrBWxUpO0YkUiOh3NOPEmDUqFGHPe6bbrqJW265ZZ/HZ8+eTY8ePZg1axa33norCxdGc1/feOMNvF4vlmUxcuRI3nzzTS677LLDvr/N8cVJm3BeF3RrmsTcSX14bmx3AmGDS59dwp3vrMQXqtkOrhACb6KXSR2cfO1N5yn/97Q4swNGY43Fm5ainZqA89REHE290fVFpXbWGIUkkN0qSlp0/VQA+q4AenGoxsYS2dnZnHPOOQzu1YOJFwxh+/Zcli5dzoKFn3L3P++iV69ebNmyhYcfe4Snn36alKREwrpJQkICV155JQCff/F5vfCjhGiHTYg6CIXD4aOWg2tzdLAjylpGCMF5HRvSv3Uqj32excxvNvHdhgL+OyqT7s2SanSti5v34/ntH/JCQTJXBQro378/b775JmvWrKFjx477nP/NG+vZtW3/U+DDJTnNRd+BjbF0s1o795XccMMNjBs3jqGXX8HTM5/nb3/7J7NnP8T5g4dw/rlDGXfVFWRlZVFW7qNVq1bsLAlSYpiYloUky4R1nWsnX8+ixYvrjR/l4MGDWbZsGUOGDGHkyJHVep9s6gd2RFlHuDWFu4a2Z96kvhimxahnlzDru801isyEEExr04Yy4nlg1SLat29PUlISS5curcORV0VyysiJDqygjr4rEN0oqgZLlizhiiuuQBGCYZddwfdLlgHRJVKzoo93tD5HxKpzLCBSUaGzOXsrTZs0qVd+lAsWLCAnJ4dQKMSiRYsOeQ+b+oMdUdYxvVok8+lfz+Dmeb9y74er+W17Cf/35844lOot9Pdu0I5zN7/PG77mXFeymV69erFgwQL++OMPMjIyqpx7xqVt6uIlANF8U70giF4QiE7Lqzm1rKzOEbHfVkxsExIScbvdbNq4iQZNmgHRnW+HKiPJMpZ14G6Mx6MfJYDT6WTEiBG8//77nHvuubV2X5tjix1RHgW8TpXnxnbn5nPb8M7POxj/4jJKAtVPqJ7WoTcmEg+s+5HTTjsNVVX54Ycf6nDE+yK5VOQkJ1bIwCgKHlJ8+vXrx9y5c1GE4JM35tK7X18A4uM9lJWVYVVU59x4ww1cf8P1hMqjSwYFhcU899xztGvfnq3btsdMQY5nP0qfz0dOTg4QXaP85JNPaNeu3RHdw+b4whbKo4QkCaac05rHRmWyIruIUc8uYZcvVK3ntvI2ZLgnh0/9LdnqzyYzM5Pff//9gCk5dYXsUZG9GqY/ujNeyYH8KGfNmkWv0zL5eO7r3PfwIwCMvORCnn5uBt26dWPrtm2MHTeOAWecRb++vbl4UD8uOH8QbrebuLh4Hnvw/+qFH2V5eTkjRoygS5cudO3alfT09MMeq83xyUmbR1mc52fhC6twuBUcbhWHR8XpVnDFazjjVNxeDbdXw5PgwOFRanUX89usXVz9ynKaJLmZc00f0uIPnQKzuSyP/j9uZagzi/9rfS5PPvkkZ599Nunp6UfVj9KyLPSCIFZIR0lzI2kHX0IwLYvfygKkazKe8HoUKR1fgYukhm7C4SAlZaWkxiejxTtZt7MMpyrRLMVDOOCn8I8dJJ2SgcPtOeJx236UdYOdR3miY4EzTiXk1ykrDBHyRwiW6/vdrJBVCU+ig/hkB/EpLrwpThLSXCSku0lq4I5VllSX/q1TmTWhFxNfWs4VM5cy7y99SfYcvDSxRXw6w+OW8WHZqdwq8mnRogU//fTTPruxdY0QAiXJQSTPwCgMItLdB90Jl4RArqz3FqJqdU7MUi36mEORCMXs1ip9KWvHHGPatGl8/vnnBINBzjvvPNuP0qZGnLRCmdjAzfAbM6s8ZpkWoYBOoCxMoCxMeUkYf0kYX3EIX1EQX2GQbasKKC8JV3meJ9FBSoaHlEZxpDSOI71ZPImHEJC+rVJ4cUJPJsxaxsSXljPnmt64tYP/77ijXR8+/HELj2X9zOTu3XnrrbcOuZFRFwhZQklyou8KYJSFURIOHhGrkiBiWkhCBREdr2FYqKpc8efoYw5FwheKNharbbs124/S5kg4aYVyfwhJ4PSoOD0qSQ0PPN3TwwYluwKU5AYoyi2nKMdPYU45Kxdvx6iIiDSnTIMWXhq2SqRR60QatPSiqFWnqX1bpfD4ZacxefYKbpjzM8+N7Y5ygLJHgObxqZzrXsrH5S25s60Lt9tNOBw+4Pl1ieRUkNwqZlkY060gqQeegu9Z721ZUVGMGmNURJRGNKLUFAnTsohUWK/JqlqnvpS2H6VNdbGF8jBQNJmUjDhSMuKAtNjjpmFStNNPXnYpuVvK2LmxhOUfb2a5BbIikdE6gSYdUmjRJZXEBm4Azu/UkHv/1Im/v/c7D85fy93DDl6X/NfWXZn/awHPbvyBMzMziUQiGIZxTOqK5QQNM6hjFIUQaQdOGVKFwGeaCEnFNINIssDUzT2czqNfLo4KsQ3pFUKpqOhHwcDXxuZQ2EJZi0iyFJ1+N4qjfb/oYyF/hJwNJWxbW8i2NUV8//YGvn97A0kN3bTqls6p3dMZ26cZG3LLmPnNZjpmJHDhaY0OeI/M5Cb00n7jjbJGTOqcwY4tu/D7/cTHxx+lV7kbIUvICQ6MoiBmQEd2799YOBZRSgqWEUGSK9zNAVlIGBX5ko4KX8qQbhIPKKpKOOjHsiy7JNDmmGILZR3jcKs075JK8y6pAJQWBNiysoBNv+Sx4tMt/PjJFlKbxDGid0NWN0vi9rdX0qZBPB0yvAe85o0tWzF2bYC3836iv9ISv99PXFzcMRETya1g+iSM0nDUom0/Y1CEiLq9CRXLMpFl0Cs3baRowrhlWSiSQBJid/8cTYsadRh6rOmYjc2xwM6jPMp4U1x0ObsxF97UjfEPIxwPkgAAIABJREFUnE7/S1sjhGDpWxvoszqEE8HkV38kED5wd8hBDdvQUs5nbqEbVVUxDIPIMZqiCiGQvQ7QTczy/Y9BlSpbQkTFTlLM3RGlLGFiYhnRqHF/O9962J5+2xxbbKE8hngSHHQd2IRL7+rJqHt60r1vBkPLVbYUBrjq/75m+7qi/VbACCEY3zCebJpgSFFB9fv9R3v4MbxpiQhNxigL7ze9aneTMYWPPlrEuqxV0UjRjG7o/O/ZZ+jQsT2dOnXiT+f0Y96cqBmFUpkiVM0vgVAoxKBBg8jMzGTevHlcffXVrF69usavZ9q0afvdJa8O/fr12+/jEyZM4K233jqsa9oce+yp93FCauN4BlzRlj5/akno5Z94Z+suHn7iR85snkyfP7Uio3VilfPHtOzJQzt+oNxQcTqdBAIBEhISjtlanpygoecHMMsjyPFVc0LVPZqMffzxIqzzvTRt2AHTMJn18iy+/vprvln0NWmNGpC1LZd5b71T4SKkICSBHqnezn5t+lEeLt9///1Rv6dN3WNHlMcZTo/Kg3/pSfuG8XydYpKb7+fdR37i46d+pWjn7j4yHkXmokQ/IVRUp4RlWQSDwWM2bsmhIBwyhi+CZVkxP8ouXbow9Lxzydm2jSVLfuCTT77k7r//k4FD+pOVtYGHH3mY+++/H09F9U1aShIjLrmcsG6yaNEiBg3/E33OOPOY+VEOGDCA22+/nV69etGmTRu++eYbAFatWkWvXr3IzMykS5cusZr0uLg4IFrBdMMNN9ChQweGDRtGXt7u/kkrVqzgrLPOonv37gwePDhWJ25z/GJHlMchqiwx/ZKu/Omp79jQLZ4rEhL5aX42c/+1jC4DG9PzghZoToXrTu3Bpg3ZlFg6iiSx+OWZlOX+UatjSW/WkrMnTKrWuXKchl4QwPTrMT/K8ePH88ILL/DQ7bfw2ltvM3ToAIYNHcag/pchtAhlPh/NmzevkksJUOLzM2HCBN6d9zrNGjfmb/f846j6Ue6JrussW7aMTz75hHvvvZfPP/+cZ555hqlTpzJ69GjC4XBs/JW8++67rFu3jt9++43c3Fw6dOjAxIkTiUQi3Hjjjbz//vukpaUxb9487r77bl588cVqj8fm6GNHlMcpnRolcO1ZLXn75x0EWsUx+l99adu3Ib98sY3X7/2BLb/tolV8IpowKDE0XC5H9B/rMSzdF04ZoUqYvnDMjxJg3Lhx/LxkCbplARJWRWc1XTdjSwWVQlOZIrR6zRpatGhBu3btMfQI48aNPap+lHuyv3v17duX+++/nwcffJDs7GxcLleV53z99ddcfvnlyLJMRkYGAwcOBGDdunX8/vvvnHvuuWRmZnLfffexffv2Go3H5uhjR5THMTcObM1HK3P4xwe/M3/qmQwc254Op2ew+LW1fPzUStr2bkhidxkdhYgSpvtFo0hMjHo8HguEEEhxGkbRvksAIladIwEGQhLEuePxeDxsz95Gq+YtAZAlCUWWCEeiwimrKlhg6lUjtqPpR7m/e11xxRX07t2bjz/+mMGDB/P888/HxHDP17y/sXTs2JElS5Yc0Zhsji52RHkc41Rl/jm8A5vyy5n13WYAGrZM4NK7etJjWHPWL8/F8llImBSbAlmWj+nuN0TzKpEFfXr0Zu7cuUDU7LZnv35ELIv4+HhKS0uj1TmGyZ133smdd99FcWkJAKWlpbw75yWatGjNli1b2Lx1GwCvvnb8+FECbNq0iZYtWzJlyhRGjBjBypUrqxw/88wzmTt3LoZhkJOTw+LFiwFo27Yt+fn5MaGMRCKsWrXqiMdjU7fYEeVxzsB2DRjUPp3Hv8jiT5mNaJjgRFYkeg9vSfPOqWzL2YQ7EsanOklx+Qn4wke9pLHSj7KSqZOn8Og/H+Tau6Ywffp00tLS+M//nkU34ZJLLmTy5L/xv6fnMuu517juuuvI25nLeUMH43K5UFWVK6+9AUtRefHFFxk9ZgxBv5+ePXvUih9lRkZGrfhRzps3j9deew1VVWnYsCH/+Mc/qhy/6KKLWLRoEZ07d6ZNmzYxkdc0jbfeeospU6ZQUlKCruv89a9/3W8PJJvjh5PWj7I+sbXAz6BHv+LC0zJ4aGTVTYjVq1eT3qAZO1RBklmG4jdISEjA4zlyD8fDxdJNIjvLkeK1mLPQzlCY3JBOW4ePcCgXwq0IBw1SG8dTWlCCL1ROw/QGSIrMLl+IP4oDtD/FiypL5G3ZhMPjISGtwWGPyfajrBtOFj9Ke+pdD2ia4mZs32a8tWI763PLqhwTQpCa4sFBBJ/kQCDwlx/b6bdQJCSXglkeiSWgVyadm3tV51iWhVzRP0gPR9f/nJU137F1Su2IXYSmTZtGZmYmnTp1okWLFrYfpU2NsKfe9YQbzj6VN37cxoOfruWFCT33OZ6sKeSEBah+IuEIAV8IV9yhndPrCsmjYgZ0zGDULCOWdC6iHzlJNgCBaVgoFQa+ekRHwxFzEQrqJnFEK3RC/vL93aba2H6UNkeCLZT1hCSPxnUDWvHQ/HUs21xIrxbJVY4nay5ywgFCThUtEqGkyIdpgNurHZNqHeGQQZGilTputUoZowQIyQAUTMOMCWWlm7kiCWRJEKyIKBVNI1BWimkYSLW49mr7UdpUF3vqXY+YeHoL0uMd/Hfh+n2OKZJEnGRQZjlRFIFQDMqLQ/iKQrXarrW6CCGQ3QpWyMDSTZSYMUbFR66iJYShW0iKjLBAr8ilFELgVGRCkWi+paJGSyL1Y2RSbGNjC2U9wqnKXHtWK5ZsKuCHTQX7HE/RNExkTKfAsHSc8SqBsjCluwL7Nauoa6QKf0rTH4lNvXVLAgRC7HY6F0IgCQnD3J0r6VAlgroRXcPUKoSymjXfNja1jS2U9YwrejclLd7B419k7XPMqzqQhIlPKICF7DCJS3IQ8uuU5Acwj7JYCkWK1n/7dQQgCYhUGviig4jmUgLIQq4ilE5VxjAtdNNCVqI+l3ZEaXOssIWynlEZVX6/sYBlmwurHJOEwCuZ+C0nsgrBYBC310F8ipNwUKc41x8TpqOF5FZBN7HCBqoQ6BVNxkwrgiwLDL3Cl1KSMDBjywSOPXa+hRAomoZhR5Q2xwhbKOsho3s3JcWj8b8vN+xzLFlzYiFhOGRCoRCmaeKK00hIc6GHDYrzArUulpWOOftDcinRyNGvo0qCTz54n7VrN2OZOpISjSgffvhhevfvy8CBA8nsmskrr7yCc4+db4iuUx4qoqwtP0obm72xd73rIU5VZkK/5jyycD039ayaWB6nqMgiTLml4CFEKBTC5XLhcKskpEHJriDFuQESG7iQDtLxsbYQkkByyZgBHc2p8tmHH5I47AzatDkFWZZ4buZzLPxyIV9/8SWmAook8+mC+bGd71gupaZh+MoOuvN9PPhR2pyY2BFlPWVMn2a4VBlfsKohhBCCBNnCjwtJMat4VDrcKolpLnTdpDi39iPLPdnTj3Lwn4exddtWfvvuexZ/8jF33fUfTj/9z2zZtpHHnnyYp556iuSUaLpTnNvD+PHjEULw0/ffcO6ZfencuTPXT51KKBRGj0Tq3I9ywoQJTJkyhX79+tGyZcuYM7nP5+Occ86J3ff9998HYMuWLbRv355rrrmGjh07ct555xEIBOrsvbU5+tgRZT0lyaMxqmcT/JEQYd1EUySKP9xI+I9yVMvCa5ro6OgGRDQN2J1LaZkWgYhBUAgUTapybG+0DA+Jw1vVeHx7+1He/M/beP21Nzhr6FBGDT+P4UNPI+BLwecro0WzlmBVWq9FI8hgMMjtU69l5tz3Gdq/G2PHjuXlOXO49fbbgbr3o8zJyeHbb79l7dq1jBgxgpEjR+J0Onn33Xfxer3s2rWLPn36MGLECACysrJ4/fXXmTlzJpdeeilvv/02Y8aMqfH7ZnN8YkeU9Zir+rcACwrKQ1UelwQIrFjO4t55lEISKKqMZVnoYZO6MLHc24/y++VLUUImAjArP3ZSBCEEhmFW5FIKDCMaIa9bt45mzZvTpEUrIobFhAkT+GH58tg6ZV37UV544YVIkkSHDh3Izc0Fou/jXXfdRZcuXRg0aBA7duyIHWvRogWZmZnVHpNN/cKOKOsxTZLdZKsyheVhGsQ7q0R+2/0+CnRBg2ABTkfCft1yQgGdknw/iiqT2MCNJNVdBY+QBMKyUEwwia4xJiR4cLs9bMzaSPvObZGFFEs6tywLqSL3Mlix8y0kKSaUde1HWXn9PZ8/e/Zs8vPzWbFiBaqq0rx589jSxp7ny7JsT71PMOyIsp4T54zmGxYHqu4IJ2lOQKA7FILB4H7FwuFSSEiN7oaX5PlrNc+yX79+Vfwo+/fvD5IgyR1HSVlF3bbQmTL5JqbePIXS0lJkIVFcUsJzzz1Hu3bt2LY1m62bNxGIGLz66qv0P/30w0o6ry0/ypKSEtLT01FVlcWLF5OdnX1Y17Gpf9gRZT3HocjIqswuX5gk9+66brcsowiTABqSEULXddSK9q9Vnu9W8aZC6a4AJfl+EtPciBpGlnv7Ud58883MmDGDiRMnxvwoZ82aheRSuGL4n7n2zim88CTMmTOTieOuIRQJ0rNnTyQhISsyt99+O06nk1mzZnHj5AmYhkG/Pr255uqriJTX3ByjtvwoR48ezfDhw+nRoweZmZm0a9euxtewqZ/YfpT1nDVr1pDetCU7igK0SovD49j93bfN76NIF6QFCoj3JB803zHoi1BaEEBzKSSkuerESMMM6ui7AhTFK8QpOxBCJliSjiQJEhu48e0qpTTsIy0tLSbq2QXlBCIG7Rp6CZb7KN6ZQ3KjJmhOZ43ubftR1g22H6VNvSHJpSFLggLfvtNvC4HhkA/ZytYZpxKf7CQc0KO14XXwBSocMqYALWwihIppRpCV6GYOgFLpSxnZveboVGXCuolhWihadB1QD4f2vfghsP0obY4Ee+p9AiBJgiS3RkF5GN0wUSoSyT2yjCxMApKGFI5W6UjSgb8bXfEalgW+oiBlBUHiU5y1GlkKIQhqEs6wiYhTMaxyJFnCDETNLxRNBX9VoXRVVuhEDNyaUrGhU3OhtP0obY4EWyhPEJI9Grt8IYr8EdLio5GXEAKvDMW6i3g5EK39PkSHRrdXw7IsyotDCAFxybUrlhGHjDtkYukOLMtAUaK7ypa5227N2CuihKhQehwKinboUsbqYvtR2lQXe+p9guBUZdyaQmF5uMq0OVF1RGu/NUEoVL1IzO3VcHs1Ar5IrftZWpqEKYBwdA1SyLt9KYUiISOhG7uFUpWjpYyBilJGVXOgh4+Nx6bNyYstlCcQyR6NkG7gD++2K4tTFCRhEpS1A6YJ7Y0QAk+iA1e8RqAsTHlJ7bn2KLKETxYQikapkhQdq2mY0brwvXwphRA4VZlgpYmvpmEaJqZx8NxJG5vaxBbKE4hEl4osBIXlu4VNEoI4ycKPGyEiRKrZpEsIQVySA6dHxV8Soryk5uuC+0MVAp8qECYIQwMpKniVdmuKkDEss4qgu1SZYMTAtPbc0LEt12yOHrZQnkBIkiDBrVISiGCYVaffJjJmhUdldRFCEJ/ixOFWKS8O4S89cnFShcBXsTIu626oNPCtsFOT5Yr+OcbuqNKtyZiWRShioFS4nUequYxgY1Mb2EJ5gpHk1jAti5LA7sjRq6qARUjVqr1OWYkQAm+qE4dbwVcUxF+2r1geLD9zbz754H3Wr1uLrkpIupulS5cy5E8D6XtmL9q3b88DjzwIRNN5KneqK3e+/ZGoxZqsKHUWUe553wkTJsRquLt27coXX3wRO2/06NG0bduWTp06MXHixGpH6jb1E1soTzDcmoxDkSny7xYSWQg8kokfF7oerBKtVYeoWLrQXAq+wiCB/Yhldfng/ffZvG4tQVVCMjSuufomHnvkCb5auITff/+dS0dGDSv2tIDTFCm6oROu7MroOKwUocNh+vTp/PLLLzz22GNce+21scdHjx7N2rVr+e233wgEAjz//PNHZTw2xwY7PegE4tNPP2Xnzp1EDJOwbkbzDisyeyKWSdgE1YygyOpB8yn3pGHDhgwZMiTqc5nqoiQ/QFlhEAS44rQDPi87O5uJEyeSn58fK2Hcvn07H3zwAZ9/+SUvTn+It55+hfz8AhplZGDoJrIs06lTZ/IK8zBNg9WrVzNgwAC2bt3KmKuvY+xVUaEae9XVbNu6Fd2ymDp1KpMmTQKike1f/vIXFi9eTFJSEnPnziUtLY2NGzdy/fXXk5+fj9vtZubMmTUuP+zbty87duyI/X3o0KGxP/fq1Yvt27fX6Ho29Qs7ojwBUSpEUDd3R2WKqLBckwSmeXiGvUISJKS50JwKZQVBAr4DR5aVfpQrV65k9OjRMSPcESNGcOd//o93liylZavm3DjpL3Tv05XxV1/B//73DCE9hISEaVqsXbuWBQsWsGzZMp58+AHK/CFM0+K5Z55hwfvvsuTbb5kxYwYFBdGOlOXl5XTr1o2ffvqJs846i3vvvReASZMm8cQTT7BixQoefvhhJk+eXOPXPn/+/P1W80QiEV599VXOP//8Gl/Tpv5gR5QnEEOGDIn9eVO+j7Bh0rZBfCxhfG1ZGVhhEv0hGjQ45bASySvFsiQ/QFnBgTeGlixZwjvvvAPA2LFjue2222LHFCGImBZoFndPuZvLxoznow/m8/rrc5g3by5vzX4D0zQZNmwYDocDh8NBWnoaBbtyCWYk8szMmbz91ptIisK2bdvIysoiJSUFSZJi7R/GjBnDxRdfjM/n4/vvv6/iQVmTddpbb72V2267jby8PJYuXbrP8cmTJ3PmmWdyxhlnVPuaNvUPWyhPUBLdGtuL/AQqSv8AvIpMfsQJchmRSARNO/DU+WBUVyyrPGcPUZYFGBbgEIiAoFXzpkwYezWTp1xHk2YZlBaVYGFVGZ+iKOi6wcJFi1i0eDEfv/MWyanpjLjk0gPu5AsRjZ4TExNjfXRqyvTp07n44ouZMWMG48ePZ8WKFbFj9957L/n5+Tz77LOHdW2b+oM99T5BSXBFe2EX+3fvxiaqDkAQUeUa737vzZ7TcMtinw2e/fpRAvHx8QTLfQDomsoni+ZDxSbN+nXrkWWZ5KRo/5w9lwgEoEiCXQXFJCUl4U1IZPWqVVWiPNM0Y/1t5syZQ//+/fF6vbRo0SJW021ZFr/++muNXqskSUydOhXTNFmwYAEAzz//PAsWLOD111+v9nqvTf3F/j98giJLEl6nQrE/EkvedskSsjAJSg6CwSN34K4Uy0DAT6s2LWjUqDGNGzfm0UcfZcaMGcyaNYsuXbrw6quv8vjjjwNw2WWX8eSjjzKqf1+yNmUz+53ZdOrVnYFD+3P1NROZPXt2zC1877VUlyrT64yB6LrOmecO5j8PPUSfPn1ixz0eD6tWraJ79+4sWrSIf/zjH0BUqF944QW6du1Kx44dY03BavRaheCee+7hoYceAuDaa68lNzeXvn37kpmZyb/+9a/Deg9t6ge2H2U9Z39+gJWUBMJkF/hpkeoh3lnh7+gvp0S3aBAoIC2tSa1EQ5ZlUborQMiv40l04ElwHPT8sGmyxhekkVPFVfYHaiAZvyRAlkhs4CZSEiS/vJD4+Hji4+Njz8stDZJbGqRDhpeIv5yS3J2kNG6KWiGscXFx+Hy+I349NtXH9qO0qffEO1Rkae/pt4aFhK7WbFPjYFTmWTo90QoeX9HBa8pVIUBAxLJAjY5NlQRGRXWOpMhIlkAPV03i9mjRxPNA2EA9Am9KG5uaYm/mnMBIksDrVCkNRDBNC0kSxCkKgjBhRSMUCuJyuWrlXpXljgjwl4axLIhLcux3Z10IgVqx8y0UGUvWkS0Vw4jWeMdchPZqGubSFASC8pBBnNeBkCQioRCuiqCzJtGk7UVpUxNsoTzBSXSrFPnD+EI63grTDLdsETBceMI+LCux1vwmhRDEJzuRhMBfFsYyrQOa/2pCEDYthKRiKH6UkBdhgWlYSIpARiJk6FHhrHi+LAmcqoQ/rCOEE1VzEAlVv3Z9T2wvSpuaYE+9T3A8DgVFEhTvWfutqETQMCX9kK1ea4oQAk+SA0+ig2B5hNL8ANZ+ujuqkiBsWUiShilHG4Ypguj0WxLIyFhY+2zoeBwK/nCFI7rD9qa0OTrYQnmCIwmB17V7+g2QUNG4K6wqtbZOuSdCCDwJDuKSnBW9wwP7tMLVhCBiWdHeOUoIRDQR3dBNhBAockX/nL2EvNJJKBAxUB0OLNOyLdds6pyTdupdWlrKt99+i6ZpsZ/KKhCHw4HT6cTpdOJyuXA6nfU6Vy7BpVJYHqYsFCHBpeGQJDRhEsBJKOSvkftPTXB7NYQEZQVBinP9JKa7kCr6+aiSAAsMUZFUroEaIrahoygKhKNCWZkuBMSS5/1hg0RHtBNjJBSM7Xzb2NQFJ61QlpeXs3LlSsLh8CFrn4UQuFwu3G43cXFxsR+v14vX6yUhIYGEhATi4uKOS0GNcygokkSxPyqUAF5FYlfEhW7mH7Lp2JHgitOQJEHJriBFuX4S093IioRWcT+j8iOoGUghmUiFk7msKhDaN6LUFAlVlvCHdFI8biRZqlinrHmfbhub6nLSCuUpp5zCHXfcAUT/MYbDYUKhUOwnGAwSCAQIBAL4/f7YT3l5OTt27MDn8+3jQSjLMklJSaSkpJCSkkJaWhrp6emkpaUddrlgbSCEwOuKJp9X7n57VQe7IiF0RRAOh3HWsE/2nhwqf9HhVklMF5TkBZjz8jy69uhM604dAPj+h+Xc9dfriIQtQoEIF4/4M/c/cj//evA+hCwxZeqN+1zPo8mUV1quOZxEgrW3fDBt2jTi4uK45ZZbmDBhAl999RUJCQlYlsWjjz7KOeecA8BVV13Fjz/+iGVZtGnThpdeeqnOInObY89JK5R7oigKiqIcskPhnliWRSgUorS0lOLiYkpKSigqKqKwsJCCggI2bNgQ830UQpCSkkJGRgaNGjWiSZMmNGjQALliHe5osHv6rZPgUvHIEkJYhGQHoVDwiISyOmhOhcQGbj6e/xERXadtRZLy5IkTefXlh8jM7I6Z72XthvVAtOpHQhDZz2aT26FQHIgQNkw0hxOfv7DOouLp06czcuRIFi9ezKRJk8jKygLgv//9L16vF4Cbb76ZJ598MvbFa3PiYQvlYSKEiK1jpqen73PcMAyKiorIy8sjNzeXnJwcNm3axMqVKwHQNI1mzZrRokULTj31VNLS0o44TWf9+n9T5ltzgKMW5SED3w6Bo7IFrGFQbFnkWQaquv81vvi49rRp8/caj+VAfpSfffEpS374jkdnTOehOXPYlZ9Pw4YZmGYY2anSqU27aFsISZC1fj0XX3wxO3fu5K9//StTpkwB4MorLmXjlmzMSJgbJ1/Hn4eejx4KkZyWdtT8KCtF0rIsAoFArbb0tTn+sIWyjpBlmdTUVFJTU+nQITrNtCyLkpIStm3bRnZ2Nps3byYrK4vPPvuMhIQE2rdvT/v27WnSpHZKC6siUCSBblo4sACBLAS6BRZmlXzF2qDSj3L8+PG8+OKLTJkyhffee48RI0Yw5PyhnHPGUHLdggnX30i3bufRv38PBp9zIeMuGIUUcICArA0bmPvmPBwOB507d+a6665DVVVemvUiuSEVxYpw8eCzOLtvb+JTUmN+lI888gj/+te/uPfee3nyySeZNGkSzzzzDK1bt+aHH35g8uTJLFq0qEavZ39+lFdeeSWffPIJHTp04JFHHqm1987m+MMWyqOIEILExEQSExPp3LkzACUlJWRlZbFu3TqWL1/O0qVLSUhIoEuXLpx22mkkJydX+/qHivxKAxG2FJTTPNWD16nGaq4TrULS1OQaLT0cioP5UcqKRFIDN4WlAa655XauHjWUzxZ+xBtvv8Eb8+ax8IMFCCEYMuh8HA4HiYmJpKenk5ubS+PGjXniiSd44623MS3I2b6N7O07yGjc5Kj7Uc6aNQvDMLjxxhuZN28eV1555ZG8ZTbHMbZQHmMSEhLo0aMHPXr0IBgMsm7dOn777Te+/fZbvvnmG1q2bEmfPn049dRTjzjKjHMqyEJQ6o/gdapokoRDMgmaLkIhX60K5d7sHa3KioTHpVIQ0WmU2oyrrx7F5Ml3cEqDJhTk5gPEyisjkQiyLKPrOl9++SWff/45CxZ9TVFYcMPoP2GY1n4rdOrajxKiM4dRo0Yxffp0WyhPYI6/XJaTGKfTSdeuXRkzZgw33XQTZ599Nvn5+cyZM4enn36aX3/99bDbOEA0+TzepVIa3G295lUUgjiJmOW1WuFyMD/KsrIyAByy4KsF8zGkaEbAmlW/I8kSSfEJWKaFkAWyVbXmu6SkhKSkJNKTvGzesJ5lP/yArGkYun7U/Cgty2LDhg2x63344Yc1XvO0qV/YEeVxitfr5ayzzqJ///6sWrWK7777jnfffZevv/6agQMH0qFDh8NaU0xwKhT7w1FjCaeCV1HJD5tEZOmwXc/9fj+NGzeO/f3mm29mxowZTJw4kenTp8c2cyDqR3nNNddE/SrnzuPjua/z+F2343bJSMLBc0++gCzLWLoJToGCVCUN6/zzz+eZZ56hZ/fTOKVZS07r3jPW63tPP8qEhATmzZsHRIX6uuuu47777iMSiXDZZZfRtWvXGr3GPf0ozz33XMaPH09paSmWZdG1a1f+97//1fh9s6k/2H6U9QTTNFm3bh2LFy8mLy+PJk2aMGTIEEpKSg7oR7k/DNNiTU4pyR6NjEQXlmXxu8+P2yojXbiIjz96idshw2RteZAmTg0ltA4zHEfYl4RXFshOBTlepXRXCX4RomHDhvssPWwt9OML6rRvGEfelk206twVX3n5URu/je1HaXOcIUkS7du359prr2X48OEUFhYyc+ZMAoFAjabjsiSIcyiUBiKxne44GQK4iUTK6vAV7IupjjtRAAAgAElEQVQqRSPisGUiSRqq00KSBRELrJCBkCUUq6Kj5H7yKeMcCrppEtQt1DrOA7U5ubGn3vUMSZLo3r07HTp0YNGiRYRCIfLz80lMTKxSE30wvBXrlJWNx7yKRqkeIUykTssZ90YSIuoiZFpIQsM0Q8QlOgkUBnBYEAnqyFI053N/ywLxzujHtywUwe10sWHlL5imgSQdOpHf9qO0qQm2UNZTXC4Xw4YN47fffgOgoKCAuLg44uPjD7l26XVGDXBLA5EKoZSBCGFFJRwO4XTWjplvddAkQci0kGQN3ShDcUoVuZ0QLg5Fk+ON/UeUqizhVGV8QZ0ET4VBRjCEoxq797YfpU1NsKfe9RxFUUhLS8PtduPz+SgoKIiVTh7wObKExyFTEoiKj1qRJuTHRSh0dKffDrHblxLLQkgGFmBJAgWIRMx9NnT2JN6pUB42kLVKoTzypmk2Nntz0kaUO8tymLHsMRokZnBK3ClkxGXQOK4xjeIaocrqsR5ejZAkicTERDRNo6SkhPz8fJKTkw+6g+11qfxRHCAUMXCocsXutyBi5B3FkYMmSegRAyrs1iwrgqxIGEKgYhGxQLbk/dZ8A8Q7FPLLQvgjJorDQfgAPb5tbI6Ek1Yot25eQ8pzq4jwOxsVizWqSUg1Casmwq3h8iaQkJhKenoTmmW0oXXTTiSnZ1RrWnescLvdqKpKYWEhu3btIikp6YA9cbxOhT+A0mCENFUmQVHIDxuE5GjeoqIcnY+GVrGhoxP9cjLNMLLiIGKYqIDTpWCFJEJmBMMw9jEScTsUJCEoC+rEO50EyspqvRzTxuakFcqOTU9DjJlIyO+juLSA4tJdlJUUEfCVEikth9wyCJeRx2by+JrlFc+znAru1GQaNm5JRrPWpDRpSnqzFnjTGhwX/zhVVSU1NZXCwkKKioowTROPx7PPeZoi41JlSgM6afHgliUkYRHEQShUjqIcnTShSqGMoCAAywojqy6CIQMUgRDRiBIBpYXlJKZWXYOVRHQXvywUIdnjwl9Sgh4K2bvgNrXKSSuUnsQkeg6/+KDnBEMBsrb/zrqtK8nesZ6dOVvw5efjKi8j/5c/2LxH7a/m9nDKqW3IaNOOxu07cUqbdrGWqkcbWZZJSUmhqKiIkpISTNOs0h+7Eq9LJbc0GI3eZIl4WeDT3YQjJXhqYIRbk37a7733Hm3atIkZhfz8wzKumzIFKxImEvQxcuQI7rnjQR569H5Sk7zcNOlGlIql9FAwTKA0jHuvvuHxToXS4giWGo32w8HAYQvltGnTmDlzJmlpaYTDYf7+979z+eWXH9a1bE4cTlqhrA5Oh4vOrXrSuVXP2GMRM8L6wvX8mPsjy7ctZcOGX/EUWaSVllO2I0z2b7+AZSGrKo3bd6LlaT04tVdfvKn7WrHVJZIkkZycTHFxcaxkcG+x9DoVckuhLKiT7NHwKholOoSJ1Nn09b333uOCCy6ICeVVV07gP7Nepm+3bsSHs1m/fiOyGhVGSwCGhSRH+3xLCviKQ8iqhMO9ex053qkCAXwRE0XTCAf8eBKTDnuMN910E7fccgtZWVl0796dkSNHoqr1a93apnaxhbKGqJJKx9SOdEztyPiO4wkbYX7K+4mvtn3F4m2LySv6g4xiDz1CLVF3ZpP98s8sfnkmDU9tQ4czzqbd6WfhivfWydj+nrWd33177fpa0dQa0yxClmVkRa5yzB82kP6ItoG1LPAbJgoRNGk9QpLoFOfi360bU1MO5Ef5wQcf8NVXX3Hffffx9ttvk5eXR8YppxAyLZJVN23bNkWpEMrV69dy7iVD2fbHdq6++mqu/ss1KJrMhRddRG5+DqFQkKlTpzJp0iRcqkyLhqlMHDeWr775mrQGDY/Yj7J169a43W6KiopIT08/4HU2btzI6NGjMQyDIUOG8Oijj9aox7jN8Y+dHnSEaLJGn1P6cHuv2/n04k955cI5nHnWRSw+dTvP9PiZxecFUAe0JRQKsGjWszx77Tg+efIRcjasOzoDFNEUIkmSMAyjauqQAEUWGGY0cVEIkAQYyJjmwVOMDkWlH+XKlSsZPXo0U6ZMoV+/fowYMYLp06fzyy+/0KpVK2666SaGdMtk0qhLeOGF1wkE/CBMhATr1q/j47nv8+3HX/Lwo48QCATwpjp5/OGnWPjRVyxbtpwZM2ZQUFCA16Xi95eT2b07n73/Hv1P78e9994LwKRJk3jiiSdYsWIFDz/8MJMnT67Wa/jpp59o3bp1zJj5QNeZOnUqU6dOZfny5WRkZBzR+2ZzfGLXetcRETPCN9u/4Y31b/Ddju9QJZULvYPokpPKtqXLCQcCNOnQmd4XjaJp566HPc3dX63t/rAsi8LCQkKhEImJiTFLNV8wwqZd5TRL8ZDgUskJhskLR2hs5pKS2LJaY9jfGmVqaio5OTmoqkokEuGUU05h165dTJgwgQsuuICRI0fGzl2yeg0fLfiMb959E8sKsXjxV9x9x30oisq9d9yF6QvT+ezuzH59Dl27duVf9/6bd95+FyEJtm3PZsGCBXTt3pM4p8aOXcVQuJP80jLGXjmRb7/9lrS0NNq2bRu7XygUYs2a/TvBV65RxsXFsWnTJubPn88555yDz+c74HVSUlLIzc1FURRKS0vJyMg4aSLKk6XW25561xGqpDKw6UAGNh1Idmk2r65+lfc2vMfb7ggXjBvC2UXt2LhwEW/95x6adOjMGaMncMqpbQ994cNECEFSUhKFhYUUFxcjyzIOhwO3Q0GWBGWBCAkuFa8ikxfWCQppv+k4R3L/A9G2dWvcjZtx+3VX0bRhBvn5OUiShCprSE4Z0weKpGAYBl988QWLv1zEl4u+xgrLjBwznGAwiFOJTo7KQiZJmkYkFDxsP8rKNcp33nmHcePGsXHjxiP2tbSp39hT76NAM28z7ulzD/P/PJ+x7cey4I/PudX/OCXjO9B3zDh2bd/KnLv/xvynH8NfUlxn46jc4FEUhcLCQnRdj3pUOlRKgzqWZeGWJWRhERBHVqVTHT9KgI8//hitQkPXrt+MLMt4vU4kWQAWlixF1wQqKCwsJCkpidSGiWRv38iyZT+gh42YKL77ztsoTjdvvPkmp59++hH5UV588cX06NGDl19++aDX6dOnD2+//TZA7DXbnFjYQnkUSXWlckvPW/jk4k/4U6s/8dr6Odxe/gQNpo6g54g/s+bbxbx4019Y9dUXtWqiuyeVYimEoKCgANM08bqiLjz+CsGJlyUCuAlHSqt1zUo/ysqfRx99NOo3OWsWXbp04dVXX/1/9s47zKry2sPvt9vZp5/pfZihi9JUFBUrEgQR7Iol9qtGo0aj4ZrEaxITiXqNJpqYRBO7RhN77KKiQURAQQQRYWaAYfqZ08uu948DowgI2G4i8z7PeWBmzq6zz2/W931r/Ra33norUPCjvPHGGxk7diyrV6/mvvvuY88RIzhxwnj+66wzueuuGxHC3iiUYFsOQis8prKQOPjgg7Esi9GjR/Pr3/ySvfYcRzpu4NiFfNGPV67g4Enf4c158/nvje0nHnjgAe666y5Gjx7N7rvvzpNPPrnD9+uaa67h5ptvxnGcbe7nlltu4eabb2afffahra2NcLi/x/i3jV12jjKej/Nyy8v4VT8+1YdP8RHQAgTUAEEtSEAN9DnXfF18GP2QX739K97tfJe9K/bmh40XsvSBv7Nh5XIG7T2e75z/fXyhz//Q7egc5WfJ5/P09PTg8XgIR4pY0ZakNKhRFfbSa1qszRpUOu2Uhxu/kUT6D1JZQrJEiduG45p4PYPoaU0RKNLxCLDjedJSnrxkUVHxSXK/mbfobc/g8SnUNJbzzqoNeFUJPd5GoKiYQHHJ137umUwGr9eLEIKHH36Yhx56aKfE+D+Z/jnKbznrk+u59q1rt/lzgSDkCRHxRCjyFFHqLaXEW0K5r5xKfyVV/kJ9eIWvAkX6YrdxePFw7j7ibh5b9Rg3L7yZs3ou5oqZl3Pw6v148+F7ue9Hl3DkJVdSu9seX/Aqt43H4yEUCpFIJMhm0vg9hSqdqjAEZRlwyUkeTDOHpn39bkIesclFyINlppBkgZAEtukgQirEQXMUshjYtt1XYql6FPwRD+lYoWFY2KvSkzbw617y2QwBvn6hXLRoERdffDGu6xKJRPjLX/7ytR+zn2+WXTaiNB2TnmwPGTND2kyTsTKkjBRJM0kinyBhJIjlY8TyMXpzvfRke+jKdpEwNh+OKkKhJlhDY6iRgZGBDI4MZnjxcBrDjTsloO3pdq751zW81fYWh9cfziW1Z/Hq7bcR62jn4NPOYc+p07ca2X3RiBIK82yxWIxsNovkC9OZMhlWEcSjynyUSmM7BrUiRzBY9YX2vzOsy+aJWw5DPRlyuQ0EAsOIdxYcgyIVPsy2NI7jEJVSW9Swu65LrDODlXfQSz00RTOUaQ4i2UN5w0CkrSxI9ftRfjX0R5TfclRJpdJfudPb5awcHZkO2tPttKZaWZdcR0uihaZ4E29ueBPLKbjc6LLOiJIRjC4bzd6VezO2fCxBbcsywk1U+iv546Q/cu/ye7ll0S2siK7gxitns+ahZ3jt3j/Ts76FiedciKx8dRUiQgjC4TCmaWJmU4CHRM4qmGSoGu15gWFHv7LjfR4eScJ2bVxRKE90HANZlclnCvdT8siQLfxRN01zM6EUQhAq8RJtS2PGTTRZIuNK+AEjm0EPbHnf+/0o+9kZdlmh/KLois6A0AAGhAZs8TPLsWiON7MiuoLlPct5v/t97l9xP3/94K/IQmZU2Sgm1EzgsLrDGBQZtEWEKITgjN3PYEz5GK547QrOee2/+PmxP2efmjoWPPEoyZ5upl9+9Vdq+CBJEkVFRXR1daGKQu/vsqCHkCLTnjfJSQq2bSHLX++j4ukzxyjYrTlOHkUNknNMHNtFeBXIWuhC3ao3paxIBIt1Et1ZfF6JuGHjlxXy2xDKfvrZGXbZofc3Rc7KsbRrKfPb5jNvwzw+6PkAgIZQA1Map3DkwCO3Krrd2W4ue/UylnQt4dI9L2Wfzjpe/vPtVAwazLGzru0rg/wyQ+9Pk06naYtlyLgqI6pCyJJgeSqD5maoUQQ+X+mXPsbnkbMdVm5sNKbmV6KqRUiUE+vIEC73oXlkzA0pDMkmJeU2W9D5NPGuLOmsSbfkUCSZ6GaK0vqGfwtnp28ju8rQuz896GtGV3T2qdqHS/a8hIenPcycE+bw0/E/pdxXzh1L7mDa49M4+4Wzea7pOUznk0ip1FvKXyb/hSmNU7h18a085X+HI3/wI7pamnj0Fz8mm9yx1J0dxefzEdAKc3mxdL4wLFcUsngxvoGmY5okQEB+Y6OxQkS5sbGYYSOkQn2l4kg4jrNNF/dgsQdNEqgIMkLFtizsbbij99PPjtIvlN8wZb4yThx2IndNvosXj3+RS/e8lA2pDVw19yqm/GMKf132V9JmoeWqJmvMPnA2Z+5+Jn9b+Tfuyj/JUVdcTXTD+q9cLIUQlBWHkXCJprK4rktIVXCRyOF+bXmdm5CEQNu08i15cJw8kiwhyRKWUegyKVQJCYHkim22hpDkwhBcdyBvgy1k8pn+Frb9fDn6hfL/kUp/JeeOPJdnj32W2yfezoDQAG5edDNH/OMI7nz/TjJmBklIXLH3FVy252U81/Qct8cfYtrl/010w3oeu/5/cN0db1W7PWRZJqgr5B2JRDJJQJYQuGSEF8PYdlQZCAR2+BhPPPEEy5cv7/t6/vz57LvvvowZM4aj9h7LTb/4OZKk88tf3sqNN96Aom1sFQFI3sI8qYayTaEE8PhUwl4VAeRVf79Q9vOl6RfKfwMkIXFQ7UHcNfkuHpz6ICNLR3Lr4ls56vGjeHzV49iOzTkjz+GqcVfxUstL/D7xMFMvuZKOptVk4gncnejrvT2KAzou0JvMYlsWgY1VOvn8V1Na+VmhPOOMM/jTn/7Ee++9x2uL3+WwY45FSIWVb9e1UFQJ23RwHRexsT2th60v6HyaSLGOB0FWaBi5HM52Gq7108/n0b/q/W/GyLKR/P7w3/Nu57vc9M5NXDPvGh5Z+Qg/2e8nnD7idABueOcGvIO8zLzgEtKmQbyrg3B5JT9/ZjnLN3z54XjasJBwUQQMqy3i5MMGkXeNnTLz3Rk/yqqqQp6mX1UZNGw3zI1/v5cv/4Cp0yfT0tLCZZddxmU/uBQEzDz7NJrb1mLbdp8fJRQi2/PPP59XX32VoqIibv/jX7H1EKvWbuD08y8gGu3drh/lmWeeSSgUYuHChbS3t3PDDTdw/PHHk0qlmDFjBr29vZimyXXXXceMGTNobm5mypQpTJgwgXnz5lFTU8OTTz65zV5F/fxn0h9R/psytnws90+9n+sPvJ62dBszn5nJje/cyAlDT+CiMRfx1OqneMa7EI/fTy6VItX71eU7KpLARRSEcaMvZVboWNaOt4LdGT/KYcOGccwxx3D/nX8mn8thIAOCDz9cyXPPPsfzT77Kddf9AsuyEIrEn266nVeee4n58+f3+VFCYeV+zz33ZPHixRx88MH87je/RkZw1Y9/zOyf/WyH/Sjb2tp48803eeaZZ5g1axYAuq7z+OOPs3jxYl599VWuuOKKvnnbVatWcdFFF/HBBx8QiUT6DDL6+fawy0aUdjJJau5c5EgEORJBKS5GLipC+jdqSiWEYNrAaRxUexC3LLqFe5ffy9z1c/nlhF+SMBLct/w+Jo6ZiDcYIt0b5apD6r4S9/TejMG6aIYyjwNWnoRwSbs+8vleVHXHulC+9dZbPPbYYwCcfvrpXLXRoOKzXHPNNZx66qm8+OKLPPzww9zz4EM8/fLLCCFzxBGH4At4KSkppbS0jI6ODioDpdz+1zt4/PmnkRSJdevWsWrVKkpKSpAkiZNOOgmA0047jWOPPZYfYPDuooV899zzUDe2783n85977kcffTSSJDFixAg6OjqAQvXP1Vdfzdy5c5EkidbW1r6fNTY2MmbMGAD22msvmpubd+ge9fOfwy4rlEZzCxuu+OEW35cCAZTSUpSyMpSKCtSqSpSqKrSaGtS6OtTaWqTP6Zf9dRDSQlyz3zV8p+E7XPOvazjjuTO4aOxFfGfAdwollSEPmuUl0dWJrGpoX1Lsgx4FgcBVdYSVR7MtEpKOYfd84X1+3pB90KBBXHjhhZx33nmUlJXR1tmNEAqqKiGEQFElJKnQRvf1t95kzpuv8fqTL6GU+5gxYwa5bfTyFkIQ8SkEw2Geeu5lKkJBAkXbTz73eD5pXrYpanzggQfo6upi0aJFqKpKQ0ND33E//X5Zlslmdzzy7uc/g11WKD1DhzDwmaex43HsWAwrGsXu6cHqiWJ1d2F1dpF9910SL3TCpxcOJAm1uhptYCOeQYPxDBmCZ9hQPEOGfO0COr5qPI8e9Sg/f+vn3Lr4Vvav3h9N1mhNb6ChZAB2RxfxjjaKa+qQv0RfbkWW8HtkUnmb6mAQM5UCn0pWaNi2gSxv/zo3+VGefvrp2/WjnDp1KkIIVq1ahSzLaKEQQsi4ro3r2oV8SrcgWolMkkg4QtAbYPGyZcz/VCdMx3H4+9//zsknn8yDDz7IhAkTKC0uor5+AE88+wKnTZmMN+Rn2bL3GT169E7dk3g8Tnl5Oaqq8uqrr9LS0rJT2/fzn80uK5SSx4Nn8ODtvs91HKyubszWVsx1azFa1mI0N5NvaiLz9gLcTcM4VUUfMgR91Eh8Y8fi3Wsv1Jqar7wiJOwJc9PBN/HoR49y/YLrmVk8E1nIrEuvp76slkR7G/HOdoqqvtyxQ7rKhngWxRNEz2SQXZu08JHPR/H5Nq+R3+RHuYnLL7+c3/72t5x99tnceOONfYs5UPCjPO+88/jtb3/L3//+d+677z5+8IMf4PP5UBSF3999D7aQQCiAWUg89yi4gGO7TDlyCnfc9nv2mrQfAwcNZPz48X3H9fv9fPDBB+y1116Ew2H+9re/AfDXe+/jwgu/x52/vRHXcTnl1Jk7LZSnnnoqRx11FHvvvTdjxozZoeZk/Xx72GVLGDOZJpYsPR9VLUJVI2haCZpagqaVonnK8XjK0T1VaFo50jZcgFzbxli7lvzKleQ+WE522fvk3l+Gs7FfilJdhX/f8fgPOAD/AfujFH3xFqpbY2nXUqItUcobC82vdEWnUiol0dmBP1JEsOSLlx0als2H7Umqwl5CGrSk0mRVDwPYgNdXj0vhuZGFjCIpX9kfhLhp0Zw1GOSVcLKr0PVahBuktz1DuMyLx6didmRwLBvLtVHLfWgbI/lt9Rd3XZcP2+JgmZQ4MkVVYVTP1+s1uquwq5Qw7rIRJQgC/qEYZpRsdi2JxBJMM4rrfjbfTsLjqcDrrcOr1+HzNeD1NeL3DcLna8DT2IinsZHQEUcABfHMf/wxmYULyby9gNScOcQffxwkCe+eYwlOPJzQEZNRq768ddmoslEs61yGLutkrSxZM0tcT+MLhUjHetG8Xjw+/xfat6bI6KpMbyaPK+dQXYcsOllk2uOrsN1PhFEIga7o+BQfIS2EV/F+YeHU5UIiRt6VURE4Th6PFgHANGw8PhWhSmDZKMiYeaNPKLeFEILSgIe2BJhunmQ0R1Glr7/+u58dZpeNKLeG6zqYZi95o4t8vp18rp1cfgO5XCvZ7Hqy2bUYRmff+4VQ8PkaCQZGEAzuTjA4klBoD2T5k5Vh17bJLVtG6vXXSb4yh/zKQpta7557Ej56BqGpU5F3orLls6xYsYJhw4fRmmolkS/kUNb4q7G6kzi2RUlt/U7PV+atPL35XnpSNrbpR9bb0GUPaakcn0hTKhlIasF53XIsDMcoCLVVKH3UZI0SbwkRTwRJ7FwGmuu6vJ/KUqIqhK1mJEnD52sguiGNJAsiFT7slIG90ag377EJlkW2u1/bcVmxIY7HNQg7Or+/6xaeeOqxzd7T70e58+wqEWW/UO4klpUmm20mnf6YVHoVqdSHpFIryOfbARBCJhDYjUh4b4qK9iUS2RdV/aSdg9HcTOL554k/9TTGmjUIXSd81DSKZs5EHzFip89n04Pqui7t6XaiuUI+ZaO/nmRbB6rupaiqervRk+u6pM003dlu0mYagcArh0mm/VRHdEoDOqsTKdJAvbuBSHjoFvuwHZuEkaA310vWyvZ5foY8O5ey9FE6hyygWnRi2xkCgeEkurPksxaltQFc08HqzOAIF0s4BKq3L5QAaztixE2olATYEiXVfiS5P5X4y7CrCOUuPPT+YiiKf2P0uPtm388b3SQT7xNPvEs8tojWDQ+zbv3dgEQ4NJqSkoMpLT2cwIDhlF5wASXnn0/u/ffpfeQR4k8/Q+zRv+Pbd19Kzjkb/4EH7vSwUAhBpb8SSUh0Z7tpyaynvqSSZHc3mXgv/kjxVrdzXZeUmaIz00nOyqFICuW+ciJ6BEUorMwlSeVsSgNQ6tVJZQ1yQsZxTCRpcxNhWZIp0ouIeCJ9+1yXXEfICFHlr9phx3evJIhbNpKmY5pxHMdG8cjk0gVvSkmVQIAjgWrveFvdsrCPWHeGjGTjNQWpWJ5QSX8FTT/bp18ovyI8Wime0kMpLT0UKBjPxhNL6Y3+i56e11nTdCtrmm5B1+uoqDiSioqjCI4ahXfUKCquuorYo38neu+9rPuv89F3353Siy8icMghOyWYQggq/BXYrk1vrpf1difl/iCpaBTN60f9VL4fQMbM0JHpIGNmUGWV6kA1YU94s+FyaGMPGttxCSpywSQDP+l0F8Fg9TbPI6gF8av+vhYaWTNLXagOr7J9YfLKElHTxhbejfcyh6oVckNNw0b3qQhFRrgWAoGZziOHtp8I79U1fCRIWAqRoEouaeINaP0LO/1sl/5xx9eEJHkoioxj4MDLGDfucSYc8BbDh/0Sn6+BtWv/zIIFR/L2gqNYt+5uHK9LyTlnM/ilF6m67hfY8TjrL/weLaedTva993b62NWBagJaAMuxiGoZhCQR72zvM88wbIN1yXU0xZswbIOqQBWDI4Mp0ou2mFMMedVC1JkzCz3AZZk0fiwrgbMdMw5JSJT5ymgMNwLQFG8ino9v9/y9UuEcjD638+wn3pT5wmKb0CQkW+Di4mStHb43Jf5CqlGaQkvcZDT3tVvI9fOfT39E+Q3h8ZRRU3MyNTUnYxhROjr/SVvbP/ho1S/4ePWNVFZMp67+bCLHH094xgxijz1O122/o/nkmYSmTaP8yitRK8p3+Hh1wTpW9a4i5+RR/DqehEEy2kPO59Kd7QYK3pglesnntuX1azKKJBHPWQQ8MiEcDNMi53gxuzsRQsZxHFz3E89KIQRCCCRJQpZlZFmmSquix+hhfXJ9oVuhvu15Ra9cGFpnXUFAKNh2Dk0TKJr8iTelJiPSJqbkIFvSDht2hIIBPMkovVlojPhI9eTIpUy8wW+22qqf/yz6I8r/BzStmLra09ln3BPsM+4pKitn0N7xFG+/fQTvLTmXRGYZRSedyODnn6fkwgtIvvACa6ZOJXr/AztsqSYJifpQPQApkcP0QCYeozfZTUgLMSQyhHJf+TZF0nVdTNMknU4TlvKIbJyOjg7y8RjBfBbHVLFtqzA3WlnJpZdeis/n60sc33333Tn11FOxLIvm5maOOfoYpk6YyjH7H8MxRx5DV6wLwzC45JJL2GOPPRg5ciTjxo2jqakJSQh0SZC1XWRZx3EKJYGKJnHBxefx6KOPIrTCo+sqBSNfJ2+zYMECDjroIIYNG8bw4cM599xzyWQy3H333Vx88cUAyIpCWLGxXUi7DqpHJhXL49hfzqruiCOOYPTo0ey+++5ccMEFfQ7s0WiUSZMmMWTIECZNmkRvb+9Wt7/nnnsYMmQIQ4YM4Z577vlS59LPV0+/UP4/Ewzuzm7Df8UB+7/BwMbLSCSWsHDR8bz33lmknWbKL72Ugc88jXfMGDquu7gkzZEAACAASURBVI6W004n39S0Q/v2Kl5KvIW+1kmPgSO5FOV0qv3VqPKW3RwdxyGTydDb20tHRwddXV0kEgkkbCwkNK+f4uJi8sEw0UAAXU+iqip+v5+PPvoITdMIh8O888471NbWomka5eXl3H777UydOpVFixaxYMECrv7x1ZgZkzvvvJOmpibmzZvHe++9x+OPP04kUog0vZJE1nGQJB3bzuO6Dqomg1uo0BGKBEIgJAkXaG1axwknnMCvf/1rVq5cyYoVKzjiiCM2K5fcRCjgQ3MMupJ5fEUeXMclHTe++C8ReOSRR1iyZAnLli2jq6urrxXu7NmzmThxIqtWrWLixInMnj17i22j0Sg/+9nPePvtt1mwYAE/+9nPtimo/fz/0D/0/jdB04ppbPw+dXVn09p6Py1r/8yCd6ZTWTGDQYOupO7OPxN/8kk6rp9N07HHUTFrFpETT9h8J8/Ngvb3AXBxMR2ToG2yyQZCuKA4AkdWkFSt732O4+DYDo7rIAM+ICAVjChE1SiY8mtWbEigolKq60Rki0TWwJQVnEwhd3PKlCn885//5Pjjj+ehhx5i5syZvPHGGwC0t7czefJkvF4vXq+Xww45jJZ4C+u711NeXk4qlSKVShEIBPD7Cwnyu5WVMG9DJ47k5YknXuCVV37NXX++G4CXXnqJ227/He0b2rnh2uuZdOhE7vjTHXz3u99lv/32K1yrEBx//PFb3Oenn36a637xC9LpNIHiMu68+x4GRCp45aU5/M+vrkaIwrZz584llUpx0kknkUgksCyLP/zhDxx44IFb/f2FQoUUKMuyMAyjbxrgySef5LXXXgMKJsWHHHIIv/71rzfb9oUXXmDSpEkUFxcyEyZNmsTzzz/PzJkzt/PU9PNN0R9R/puhKH4GDDif/cbPYcCAC+jseo635k+ipeWPhKZPZeBTT+IbO4b2//kfWi+5ZIuh+CaBzFpZTNtEkRR0RQcBQpJwBDi2jWWZmKaJYRhYloWLiyzLqKqKpmmoioosyUhCKizi6CqJrFXopbNx9TtNAEUpJH6ffPLJPPzww+RyOZYuXcq+++7bd04XXXQR55xzDoceeii//OUv6WjvoD5cz5Rjp/D8S89zxJQj+NWvfsWCBQvo6uoiGo2yabbRYJPbuY28MS2oubmZ119/naceeYLvX3UpKTPD8g9XsOfoPbd7fydMmMD8t9/mzTkvM+2oGdx0w43oYY0//Pl3zP7FTbz77ru88cYbeL1eHnzwQSZPnsx7773HkiVL+qzUtsXkyZMpLy8nGAz2iXRHR0efMXFVVRWdnZ1bbNfa2kpdXV3f17W1tbS2tm73Wvr55thlI8pEzuTl5R2UBjyUBjxUhDwU+7V/m7I2VQ0xeNCV1FSfzEerrmP1mhvp6HiK3XabTd2ddxL969103nwz1nHH4WSzSF4vqYk/oSPTQc7K4VN9VPoq8aiFFJtsupveVC8+fAgLEAJFUfB6vei6jqpuORT/NCGvQixrkM7bBHSFgCyRsgMUqzFc12W33XajubmZhx56iKlTp2627eTJk1mzZg3PP/88zz33HGPHjmXZsmWMGz6OZ+c/y6J5i1g+fzknn3wyd999N+PGjetbGMo6hSG269obF4gEM448BkmSGLrbUBrrG1jVvBoA19h+u4f169dz0kknsaG1lWw2S2X9QKIZgwkHTuDH18xi+cpTOWnmCdTW1jJu3DjOPvtsTNPk6KOP3q5QvvDCC+RyOU499VTmzJnDpEmTtns+wFZX3f9dnsN+CuyyQtnUlebyR5Zs9j1NlqgM61RHdOqKfAwo8dFYGmBgmZ+BZX48yjefb+f11jF61B/p6n6FlSuv4Z2FxzFgwPkMPOsSvGPH8nE8TnLdGnpLPKTdPKqkUhusJaSF+uYcs9ksRt7Ajx9b2CiqjEhlsbwSgUDZDn0og7qKJATxnElAVyjSVJJZl5woPEKJRILp06fzwx/+kNdee63PdXwTxcXFnHLKKZxyyilMmzaNuXPnctxxx9FQ0oByiMKUKVOorKzkpZde4sgjjyysmDs2saxJPmfjuoUUIEkWODY4jou0Mf9RkWWGDR3KoncWcszM4z73er7//e9z+eWXc9RRR/HE3x5i9q23053MM+sn/82hBx7Oiy+/wPjx43n55Zc56KCDmDt3Lv/85z85/fTTufLKK/nud7/7ufdJ13WmT5/Ok08+yaRJk6ioqKCtrY2qqira2tooL98yc6G2trZveA4FMT/kkEO2+zvp55tjlxXK3TzdzKn7C91SKV2imE6KaHcitJkBWuNeXmtT6cp+8oGTJUFjqZ8RVSFG1oQZUx9hZE0YXf1mxLOsdCJFkX1YtepXtLT8gWjPXHx1l5NCZX0xSHaeUnyUROrI5/NEU9E+J29ZlgkEAsiaTEuqhaAniGYrSBmD9lgrlZHtW7LJkiCoKySyJtVh/VPD7yBCFFzDTz31VMLhMCNHjtzsgz9nzhzGjx+Pz+cjmUyyevVq6uvrWbx4MZWVlRSHiulOd7P4vcXsOWZPZFmmoqKC9tWrKRs0iKeeeplgyItlWUiy4Olnn+C/vncuG9rX0bSumREDhxM56yyOmDaFacdNZ78J+wNw//33c/jhh292HfF4nJqN9nf/ePJpJNfGcV3eWbqCffbfi6GDdmPxknf48MMP8Xq91NTUcN5555FOp1m8ePFWhTKVSpFMJqmqqsKyLJ599tm+uczp06dzzz33MGvWLO655x5mzJixxfaTJ0/m6quv7lvAefHFF7n++ut3/OHo52tnlxVKTXIZGHIZmF8D+fcgl4BsL3yqL0zG46HJrWS1W81H0mA+TA7ind4anlpSWHBQJRhd5WP/oVVMGFrOnvURlK+xdlhRguy22/XEtKH8/t1bWLzi+9wy4lYGaiXoKRvDtOjs6AAhkGUZv9+P1+tFVdU+ISx1SunKdFFXVEsm34UTz9ChdlDhr9iuWIa9KvGsScaw8XsUQrJM0g4ALpIkCIfDXHLJJUBhOOk6Dk4mwzvz5nHx976Hoig4jsM5Z57J3nvvzQsvvMB5551HPp/HsA1G7jmSC753AVBYLT7vhOMoqalh7IjBZFI9dHe34zgOgwYOYeLhh9Ld08Xt//s7vJKHkqoy7r39L1x51ZV09XQjSRIHHXQQxx577GbXcO2113LCCSdQU1PDPuPGsWbNGoIqXP/bW1nyzjwkJAYPGsqkw7/D3//xKDfeeCOqqhIIBLj33nu3el/S6TTTp08nn89j2zaHHXYYF1xQuI5Zs2Zx4oknctddd1FfX9+3Gr5w4ULuuOMO7rzzToqLi/npT3/KuHHjgEJ7jE0LO/38e9BvivFZzBxko5DpgVRn4ZVsK7zirRBfS1dPlPdyFSx0hjLfGcH77kAcJMKKyaHVDlPGDODgvUahez5/3m9ncFyHeRvmcf/y+3l7/dvUGDWMsSuZtudZ1NcXKl9kIVAzGTyShLeuDmkrrkGO67AmvgbbsanXqol3tJPRbQJFxZT7Pj+h3XZclrclKPFrVEe8JCybpkyectoIun5y0SwBWUEYeZx8Hj7v2RICyeNBeL3IgQCGLtOUXEvIE6I2WDABztoOH6Vz1HpAza/GsiLkcjKq7UfRFIoq/H1OQmmfhZoVaJKKuhMWaj3r12G50CWFCOoKtWEv0Q1pVI9MuPyL28XtKvSbYuyqqDqo1RDaeh0zQJnrMinTw6TuVdC9ksSG+fyrKcnLXSFeWbsHT6xtJ/j0Go4qbuXEkRHG7HsoFA34QqfTm+vl8RWP88rSV7CjNlVGFTNyM8AFVVWRJAmPJ4WmKfj9dbipLMa6dRhrmtAaBmzRnkISEtX+apriTcRI4Q0EIJ0imupGICjzlW3zXGRJEPQoJLN5XJ9LIJ+hNhZHz+aRzCx+wBUgdA0l7Ed4dITmBaVg7OsC2DauaeIaBk4uhxOPY/f2ghDUeVV6tBhJLUzQE0SXBJKArCujCoHPp6CqAdIxAzcnME0TeeM8pSYp5Mih2TKu4SB2sH7bGwqR6OqkuKiI7oxJacCDP6KR6s1jZC08vq/uj10//7n0R5RfJY6D2bmKtxYv5onlCZ7tLieHxmixmrOKlnDkXoNQ9zgaKrZtp5bL5Vjftp55K+fxYdOH5KN5gmYhE1IIQXV1NQMHDqSxsZG6ujo+/vhjBg+uIptbjyQUvN4GyNmYa9eCJKE1NCB9xgwDoC3dRjQbpSEwgHR7B7YEUV+OSn9lX5J6H7YB+STkU9j5FORN7JyEbRamGSTZRdIcJBUk2WGLIEzRQQuAJ1h4faoayHVd3GwWO5HAjsdxTRNbEnjKylCKS1iTN7FcqKWQLuP3DyIZy5CNW9hqlqKiEFKvDbpETzZGiRtECqgokR1rsOY4Nl0tzXj8AdpsL4okGFQWoLc9DS4UV/kR0icXtO+++27RxfG+++5j5MiRO3S8bxu7SkTZL5RfI8msweNvLuGet9ezOqVRKzq5UH6K46uj5HY7kWjVQUTTBt3d3XR0ddDa1ko+/cmHMC/n8ZZ42WPQHowZMqav2uXTbHpQbTtDJtMCOHi9A5AsBaO5GVy3IJbezV17bMfm49jHyJJMtVRGoqsTO6AQkzNU+asoVv2FOdtcDMwsrguOpWDlZBzTwRUCtSiMGQqzypEopYtSj49YzEUWLiWREMI2wMyCmQEjBa4DSKCHwFcMnhCfVlXXdcnEusl3deA1QMgymaJi1vsCDFYTWFaUYGAEju3S05oC1cIiR7EUREYiKlIEHB3VlVGr/Ds8bE50dZJNJlDLa1kfy1Fb5CMgScQ6M/jDHvyRLf/Q9FNgVxHK/qH3V4hlWWSzWTKZDOl0mnQ6zZCAxY/28bOqtZum1hiLjH1Y2ZFH6uwC/gGAKxwSapK4GscoNRhYO5CDdjuIgwcfvNVSw60hyz78/kFkMs1kMs14vbVojY0Yzc0YTc2FYbjvEysyWZKp8lexLrmOtM9A8/mwMlkqgyp6bG3f/KKr+nCUUqx4FidvIDSFRDhEr+xlWFUIBVBTWZJuiJDRSTBYTywWI2cLvN4IeDeaX7gOGGnIxgrim4uBrIG/DHwlIMkIIfAXlRFTDHpTMWpyHrzdXQxQY1hlEVAcHCeHrHiRFQlZ0VEUyGbz+F0PHr+HbDaP6nhxczbCu4P+l6EQmUQcj53Dpym0x3MMrQzg8amkEwa6Xy0ku/ezy7LLCmV0eQc9j3yEUARCFghVFHqxyOAKFwcXR7jYOH0vCxvTtTEdC8M1ydsGWStP1sqRNrNkrRyGsMhjYeOwqbxElmV0v05lsaDHhJWxInqNEEGpkx9Jj7IXq1hdMgLroCsZOmz6DhvcfpZC24RBZLMtZLPr0PWqT8SyuRl1wABk/yc9dEKeEKF8iHimkwaPD8lJIhlgSDLtEni1UvRYHicdR2gaam0tcjiMJ2tiRDN9yefFqkKHoWO4LmHNQVEUkskkuq5/EtUJ6ZOht1sDuTikuyHRCqkO8JcXRFOSKPeV87GRoMuvUllSj7mhDXVDF44fbC2NLHtRdZl82qK0rogUSciAbVgYrgVC4GRMpB0UStWjo3p0sok41RU1fNyZojORp6LIg5G1SPbmiJRv3++yn28vu6xQrlmxDNNIIBkCyZUQCKS+l4SEQHYL/1ORkJFQ0BDs2HDOFg451SApp+lyo0SzCXrNBEE1zT51JkmnlDfXjuQMYw++19jKBV3XoT5yDoyeCYf95HMXkz4PSZLx+RrIZteRy7Xh8TgFsWxqxmhpQdsklq4LRooaM49kGLgY2Iqf3oyDt6gCT6wTrSuKLUtoVVXIRUWIjT6RfcnnWaMglJpCh2GRIoTXiBIIlBaiylwOr3crRr1CAm9R4ZVPQaodkhsg3QWhalRvEaXeUjoznRSHi2mvqaMo3ksg1ovd3Ild60XTNXIpE8twCBQFMbIpZEuAAEtxUXI2ruNuNr/4efjCYeKdHciWQYnfQ08qT5FPxRfWSMfy5DNm/8LOLswuK5SNExpZJD1KPpfFyOYx8hZmzsUydJxcGCtbjJ0uwbUKoiJwkIVJUHEJaTJq0MAIxegKtLFWtNJudqFaEj7HS8D2EnT8lItSKiil0q1giDkQT1pBGC5sLPc9F3CBzjUB3tDuZ3C4jaLFL6IsORd1v6nIh56HUHd+fkwICa+3nlxuPfl8B65mozU2FCLLlhY81cVIZhzMDJKkkNHDrLMzVATLUNJRaN2A7kI2qNHhMyn1OpRJnww9ZUkQ0hXiWYvqiItHlplx8kyu++MdFFlr0fUyRo0axdixY3nxxRfp7OzknHPOYd26dZimSUNDA88++yyO43DZlVczZ84cBC66KvHIH66ncfBQSkK19Eoq7el2fHod7eEiBvqyXHDOD5ly4EEcP/MUwIeZt1A9HiSPwrIFi/jhz2fR3dWNLCQmTJjA735/G4888ggLFy7ktttu2+Y90/0BknI3mUSMirJK4lmT1liOgaV+cmmTZG8eVVeQtiO806dPZ82aNSxbtgwoOAOddNJJNDc309DQwCOPPELRVtoW33PPPVx33XUA/OQnP+GMM87Y6d97P18fu6xQrs3EubV7Lo7k4EgOts/G8puokolfzhJW8pTJDpWOn/JcJd5sGWaimlysjvWxOpx2H7RXoEiljJLHcLAHfFUWnkEawSFlVA1qwK9v2SrWMWzseB47Xsj/s3pz9K7pxWqJkeiqQuLMwhvngHjtNbSqAOqgKrTaIFp9CGUHFxaEEOh6LQgJw+gG1cVTGcaNtyFl2nAlFRGuBW8JXiHwda+Glg1oposlSTiREEXVNWRSrXRmOnFch3Jfed9QOuzTiGXTpPIWfr+fNSuWk86aZL06rz3/BNXV1biuSzab5ZprrmHSpElceumlACxduhSAv/3tb2zYsIGlS5ciSRLr163DL/IFAe/+iDpvmCYrg9fN47gypjeM43WR/H7snm58ihfTUw4hD13xbk79r+/ypzv/zMiRI4m4fp564emt2qxt9X5JEt5QmHRvlGCJTXVEZ200QzRjECrWiXVkyCTyBD5nNf2xxx4j8JmOmpts1mbNmsXs2bOZPXv2Fu5Bm2zWFi5ciBCCvfbai+nTp29VUPv5/2GXFUpJkcjreRRXQXZlNFtDsv0IWyBMQcZWaHUVOh0V1VHRHZkSvZXKxjWUhlLoOFiJYjJdQ+npHE57qhRWKfjXWNS82oURiFGxezH68GK8u5X0zZdJmoxU5kMt+2TOaxQDKIllufjBxaxeG+fS0bWcoHdgL3kHY0MVqQ0WuIWITg5raI1hPAPD6IM+v/ugEAJdq0TJZZFjHQgX0HTMFFg5Fy3oQwKsjk5Ku/PYkku61I+uhcj2RvFk0tQEavoaltmuTZW/qtATR1eQJUEsYwJw5JQpvPnC8xTNmMxDDz3MKaecwiuvvEIymaStrY3vfOc7n1zvqFEAfTXQ0sZotXaTg45tEQiHSX30JoOExF1/u5vnX3qbP955BwiYs2gBt919Nx1tbcy+8kccc/aZ/OEvf+S042dy8P4HEk3FyGNy7OQZSJ8RtqeffprrrrsOwzAoKSnhgQceoKKigtdff51LL7kEyzSQZIU3//UvMrFuJh//XaxsBtMwmf3z/2Xq0ZNQtlK2mkqluPnmm/nTn/7EiSee2Pf9fpu1bwe7rFCOrhvNy6e/vNWfua5LPp8nk8n0vTZ5Jm6q6+1MJslk2pCDq4nUvUlANTCiQ0i1juGjzmF8lHcIzGulYUEHtbpEYEiE4N6VeEeUILbyQauJeHnk/P34+dPL+cX8FuYNL+e3P/geRXOuxn3vKsyyqRjDf0S+Uya/Okb2vS4A7KODWL05JF3hhqU3sbJ35aarANssvHALCxwCkBQkoeHkcrDCKaTnuC5CUbAUgbHaRFd0HMOC9132qBrFj/aZhSzJdGe6sRyLmkANsiQT9qp9Qjlz5kz++3+u5cAjjmDZBys555zzeP3117Ftm3POOYczzjiD2267jcMPP5yzzjqL6upqTjzxRCZMmMAbb7zBxIkTOe200xg7dizICiCgqBE11kKpkUPGJe0U7ltzcxNz//Uvli9awuSjpnDY/uNZtnQJpx57KrJVaEPhahJuDrLR1Ga9fSZMmMD8+fMRQnDnnXdyww038L//+7/cdNNN3P7737P74EFEuzrRPR5ef+5xDjh4IpdfOYvqoEbrmi6SPTkiFVtW/vz0pz/liiuuwOfbfNGn32bt28EuK5SfR2HYqqPr+nZrbm3bJplM0tvbS3f3IhKDXyef/QeZ1kZizQewrLeBD/ImVUs62e3DGLrqkqoFz15lVO8xoM+oFkCVJX5x9B4MrQhw7dPLOeV+g7+e+VuKBx+O9swP0Ba9SeD4u3BnHorVlSX/cYyY3I2TsXDSJk7axDVtwEa4mwRSLqThSDI4Bq5r4riFoabrOAWR1DSEqqIClmtj2Aa66sE08hjZbKG7o68CVai0pdtoTjRTH6wn4tOIpgvO4KNGjaJ93Vqe+/ujHDRpEqYZR5ZlFEVh/PjxrF69mhdeeGEzm7Xa2lpWrlzJnDlzmDNnDhMnTuTRRx9l4sSJhRvijSA0H6b0MqptkLEdQOaYYyYjSRIj9hxNff1AVrasxclmwc7hGg6apmFaJmgeVEMmlUr1WZltsllra2vDMAwaGwvlnwcccACXX345J590EgfvM47yTJr99t2XM848C9M0mXnCcYzbYzTJaI5cutC9cRPvvfceH3/8Mb/5zW9obm7e6eet32bt359dViiNXJb2j1ehBwLogSDeYBDVs2PVHJ9GlmUikQiRSGTjh+54XNclmVzGhrZ/0LTiH/SsHEdby35sMEwiXoMxTV68a3tY8uRKmot68Q0ppr6+noaGBoqKijh9vwYqw14uenAxJ9wxjwfOnUbleWPgb6fB/cchJv0Cdb+LUMt9yCviqFV+3HSaq0ZcgGOpQCHykjwC4fcg6QpCEoVIOboWtzMJNshFRTjpNFgW6oAGZL+PlJGiJdFCqbcUf14h1Rsll06h+wMUe4tRZZX1yfWsia+hPliPKkt9Jd0zpk/nNz/5MXc980+c3uW4rk0wGKS3t5dgMLhVmzWPx8OUKVOYMmUKFRUVPPHEE0ycOLFPKISskZFDuNi4CBxXAAau6yLJEkISGP4ydh8xgkUL5zPt4Il4KorI5/NIIQXXcJDsQsWT4zh9NmvTp0/ntdde49prrwUK5hVHHnkkzz77LNNOPJFH77uXAw+dyJtvzOXuhx/j3LPPZNZVV3LMlBNI9ebxeBWkjQYob731FosWLaKhoQHLsujs7OSQQw7htdde67dZ+5awywrl/Oa1nNUcQ8+14c1n8WZTBPMZilybEgkqVIUar4eGcJCq0jLC5RVEKqrQPzNZvzWEEIRCIwmFRjJkcI6OfZ9hzUd/Yd279cQ+PozXczZ1NRLDcyXU95TSEY+zYPFcnpKeIhwOM3DgQAYPHsxdp43hwoeWMvPP83novPFUnvcKPH4BvPhjiK6Gw38GRhrRsxJhZpGEjBsswVVLcAxwMhZEc9iSQGjgpHpwU0nQZKwyCxGQ0MobMZqaMFuaYUADAX+AiCdCT7aHULgRJZ0m0d2FpnuRZJmgFqQx3MjaxFqaEk34tGpcwLIdzj77bLRAgEF7jGL1G2twHANd15k/fz5jx46loaGBVCq1hc1adXU1juOwdOnSvvnLiooKVqxYwbBhw3j+medxPQJwsZB5/PEXOff0c1nbkaFlXQsN9YO5+Eez2G/ffTjioIPYd/8JoMrc8/B9fGfcYXgkDcdx6Onp6bNZAzZr4rV69WpGjhzJyJEj+debb/LRqlWUVFQyePhuXHHJ94inUrw5/x1OP/V0etvSpHrzhEoLqU8XXnghF154IVBwX582bVqf8PXbrH072GWFMhmMkPZnSftD232vyNioq3rwfNCK38hR7FrUKoLBAR8DysporK6m0u+jTFOIKPJmwyZZ1qmuOp6qyuPoHTmPj5b/hdXzq1i7+mDaPII9R1VQtUFhWipMrlJieVE7HyxfzrvvvoskSZxYMZAHNxRz0h/n8egF+1N+/N3w5EWw8C+w+F6Y9CC4fgjXgrcYIckIQPKBG3ZxcxZWdw9WRxRcF8lfhFJSilB6CqvhmtiYZ7lJLAdQ4a8gaSZpS7dRW1ZN74b1JLu7CFdUAqArOo2RRtYn15MyOgDozRjU1tZy1Q9+wPJUlpQbwHUtXNfkww8/ZNasWX3ll+eeey7jxo3j+eef77NZA9hnn336uiXOnj2badOmUVdXxx577EE0HkW4eSyhMmRIA4ccfiRd3TFuv+12dF0nFPHy0COPcOWll9H14x8jJInxBxzASUcdj7DBo3owTZPLL7+8z2Zt/PjxNG1s1HbLLbfw6quvIssyI0aMYPKkSTz25FMcf/JMVFXF4/VxzU2/J2na+EIamY0VO9p2ktr7bda+Heyytd49hsUL3XHyrkvedjBcl5zjkLYcei2LXtMmZlrEDJO4aZGyHfIu2MCWrg+foOJSqanUeD3U6Bp1uka9V6PR62GQ10OZphCLvc37C+9i9dyxZLqGU94osf/u9fBOO65p4z+gmsQwiVXNq/nwww/5oDPHh2aEE7S3OCPwDv50C0gKOBYrpj7BbnsfWPj6MziZDGZbW6FVhD+AXFSGa4hCywQBrmZiKt2o3jCqXIzR1IxrmWj1A0ipFuuT66nwV6BnBane6BYRteM6dGY66YrLCARDK0JoskZ73qAjb1HDOgJaCI+nku7ubhzHoby8/AvNv7muy4pEB6YIMUC0ojk2vnQW1xOmO1OGx6sQKvVixXLYPVGcbBRTVfE3DsTuyiH5VAyPQywWQ9d1ioqKPvc8cukUsfY2wuUVeIMhXNdlTXeanGEzuDxAqrPgW1pU5d9ubuW3mV2l1nuXFcqOjvdZtHgWsqwiyyqKoqEoHlTFg6rpeDQv8CfK9AAAIABJREFUiuJBklSEpCEJBSGpOKhELYkWQ2Jl0mVVRqLF0UkKPzl0bFOg5C2EpGHrAWKyh093cgkpEsP9Xob7dQa4a7DfnYs0b08UVCYcM4DqqCCzsAM5rFF8mIUnNw/z/cdQu5cDsMQZxFp5INbQaexXnqHXN5jdBg+AkkF9YulaFmZHB3ZvL0JRUCorkcPhPmFwDLuw+JOxwHVxZAPhA9UbwmhpxjUMtPp6Wt1eUmaKQeFBpNo7sS2Tktp65I0+l5vMedticeIZB0XtpcQbRpe9rM8aeNwcYZFA16uxbIdkstBp0evzbZxflPtSg3aEjmyMdlOjVEoQcnsJiBJEopW4U43p+iipDeDmbKyeLI6cwe3pAl1HKarGzTmoVX7SmTSJRAKfz0f4U/fks7iuS8/6dYBLSW09QggMy+ajjhReTaY2oBPvzOALaQSKdn5u+9tCv1D+B7MjQtmy9m2WvX9ZYXiIDbgI4SDEpn8dJMlFklyEsBHC+dz9bQvDVei0K+kW9fRoA2mTB7DOraTJLiXtFoaiKhY16V4au9IcJP8fe+8dL1lR5v+/q+rEjrdvnhwYGOIKwjCARMlBXAQRVqIi6xpQMKy7P3XdddV11YUV0DWn1S+wKF+iAZRkGhAFBQnDMHluDp37hKr6/XF6AkGXTd9VZj7zqqk6p0/fPt237qefqud5Ps8znC2fomfNXSibhQDZeQcj9n4VP+AQLr11goMG4aD4UTqdNq866ThevrCQSZn17oaerZKOT2CtwentxRkcRKgX1ma0xmJaCWm9hdAKpEWGLnp6BNNpI+cOsz6ZIBAeFVmkWZ1FSoVyHLTWGJ1mqUX/BQgpkMpBKgflKJTjolwX5bg4rovsallCZsH+pt7AI2EeW8jnd0clEZ2pSWrpAJV+hRPmSEYayLxLtTpGrl5HBiHC70f1hKiiR61W21Yed2uZ2RdCp1FndmyU8tAwYSGTujtwxcE0W20cJZHWYjR8/Rtf5+UH/v7CYy9V7CLKP2L8R2XWrLV0Oh0ajQbNZpN6vU6tVqNWq1GtVpmZmWFmZpok6SClQQhNGLoMDVUYGKgwONjDwECZMFRo3SHVTdY1ZniiOsma2iQREXnboL89zrCu0l8QOF7K5tTjGTufNezOUyznGXZDCxdlU5bxNAfqJ1hR28weiaJn770pDC/j1kcFn/pRkzcftR+vHI7pdDosndtPOa6RthXWgCwUcIeHkcGL12Rsz44hOg6OCbFAYjp00iapTbZdJ0TmOXc8D9cPkI5Cysz7O9WISLSmnEtoxjWkUCAKBLZNSAdHlTBaE8cxSjlZ6VybiQppazEGtAFjzLOE0YUUOK6P43k4vs+YTmkon0ViPWEwjOf1Y6IWk6MpOTVLob+HpOFCamj4MaZWI9doIBwPWRzCnZMRXrVapdVqUSqVnpdNs+O8mNq0AWCbVWmtZcN0i1o7ZWl/jvZkhJDQO5x/0XnlLyXsIso/YvxP6FFaa2k0GkxOTjI+Ps74+DgjIyOMjY2hdba4LpfLLF68mN12242lS5dSKBSwacSaLU/xwFMPMbL5URa21rNPbTW7tzfgotESkmIf8bx9qA7twfcaHvc3lvNEeZgNfuY86bcTrODnHMJP2I3VCKAWFyiESxjKvYvdy/0Qa4SykBO0i3MoFEvPK0FrrcWkKUkckcYxaRSRxhFpsp0MlVAEbgFPBAgERkfEoknVrzMn10dSr4FO8AMfaTWYNGu/x7S0AEIghEIbiyULqxLZTWVXWAM2I0mDQBtJarc2RWoyYgUyVSZH4HmCXHEILwipTrQxcUyfsx4dLEa3HNIeyWytSq8fYEdHENLFnb8QVQyw1jIzM0On06Gnp+d5geJb0Wk0mB0boTwwRNi1PlNtWD3eQAhYVAypT7bJlb3fm974UsUuovwjxoshykdXT3H5N36JJyW+kgRKEriSnKvIeQ7FwKEYupTzLj0Fn/6yT39PwFB/jp6ihxQC4iZpbYzpjU8ys/EJGltWE02tI5/OUKZOn2pS0FUE25ftnfwQjwWL+VluKb8NlpCOdjixPMjxf3ompf4sxu6Zx1Zx5xdHqDku9k99fp1bzL0zdRIhmKsjTiqOssft32Tfe57A+5srWTZvCNNjMTkQ1iKMoJMGSHxcFWKNzYgxjjB6+70o18V1XRxH4ihwhAFdBR0jrYPVBbQtAS6CBClmkKKeWYJIVJBDKBeki5UOW2oJjuMy1JPHCsV43GBMB5RNlR45Qz6/Bzq1TE1NvbAlZy1ZLdoUTDerSEeQRNi0jUkiYqMYcfoJkjYy1RkLC1COj9EuPWEHTzdI7GJEyWWiMU0+n6cgBPGGjQjHw1+2BOE4mcU4NUUcx/T29hK8gAVurWV68yaMTulfsGibglIzSnlmokkpdKhYSaeZUBnO477IEhQvFewsRLnThgelrRp7mCeQOkYmMcpGOES4RAQiIiTCFx0UHaxo06FFTbQQNIlEk17q+CLBAQa7DcCgiIMB2l6FkWSQkbbLFD04g8tZdODx7Ln/Sg70ffx6i4dXb+T2OS2+l8Qc9PX/w5sGSxzzp69l6T4rOfO9W7j5ylVE35K89YzbufrwP+f2Ox9j7M7vsuKBOxmYnWbd4DxKQR7mL4K0jelEpFEHk2ZLWYiIiRAi8/O4nsCVAlcYXJsgTSMjmqTbACtdjFBomSK8GNdL0fUIHbtYNUjKAASS2eoYoV+i1JPV2BGA0m3G6xEVGeI5kiE3oNZoUbNlephhvL6aMJiL7/vU63VyudyznTlCZOmLygGeTVoCUMYQJi1ElDJNngWsx2tY0tQlshJrImaa4KgQT1Tx6wF+4NPpdCgNDuIMzSUd20K0dh3+0iUIpejt7WVqaorp6Wn6+vrwn1M2QwhBobePmZHNtGo18j1Zfn3edxgq+YzWOuTLIbIjqU21d9ol+EsdO61FyZaH4fNH/d5LjPTQTp5Y5ohEnhY5arbArMkxleYZ1wVGdJ5NusRGyozbHqYoY5BIC71SMugr+r0OvpkgjCcZ8A0HHvAyDj74YPr7+1nd7PDRJ9fz3WqbXKvOKx/5MW87+ABe9soTqM+2+PbHb8V75il2i++m9PQkQmtmegf4/oqVXHfMyVy9qI85S5YStpvkoyaesDikKJFmtWwcg3VAK4HZ4Q9YWYUSLkrmcJwCwglAuSBktmcbbSGJp3HdCkEwD12rkYxOIPwS0sn2MSPdwu8t4BezNMwo1Tw5WmeoFDBUyoiunmqeaUVU9AxlNc1IIvDIEUTBv+tM+V2YTVLWt2OG2UJZugSxhajKVDyMtSlCxSRxmr1PJUmUS//QEK7rEW+YxNTHkWGAt3gxQim01tvCl/r7+5+3ZQEwvWUzaRzRv2ARsuscs9aybqpFI0pZVAroTEeERY9i786zBN9ZLMqdlyg7Ndj0QOYt3rF5OXDDrBjWiyzDANBsxazZWGPN5jprR+usm2iyodpmSztmUqdIUjyT4NuIAVujz9ZYFAgOXDLIgl6XTZPjPLJpE2mjTrE+y5ypWQan6syZbRImKYmUbK4U2DxQxClbyk6Lkh9RvOAqevfYi6aTR2Cp6AYDpk2QtrJla9iLDnpotmOanQjlaMJQImWM1m227i0KGWJFjtTmiLVHasATUwRylsjkaKSDOGlMeWYUFwdbHECRfT7BghKv/7PX86/f/FeemWjQ6sQc8/LlrFy5kltvvZWfr13P+97yZqY2ryVNDEML5vK1b3wNlSo+8rGP8ON7f7wtv/6GG27Yln/9Qrjooos45dRT2fOkUwltnSFZpVTYA4zm3jvv5n0f/BumJzcjhOTggw7h7z7wPm6+/bs88uijXP3pf8ZXOfRkFdOcRIYh3uJFCKVI05TJyUkA+vv7cZ5T6jeJIqY2bSBf7qHYv71S5Y77lXMcl7PPPZNNWzbw2GOZHuWHPvQhvvCFLzAwkD3nox/9KKeccsrz3tf3vvc93vGOd6C15pJLLuF973vfi557/5vYWYhyp116t5pNnvn1WozRWG2wRmceV6OzsdZYnfVbm9ZpNk5TdJqidbdPNWmSonXW96WacqrZOzEkqSFOf/eX0YZnYAPgm4SXtRoM1NtUZju4scUKMH0WtUThDDns6QgOGN5A39JXosLF1B8xbDQ+XiSJo5h8OWRGFJm2RcqhYrAzSq49jQorlHoHCJOEaq1GvR6ByGOcXlJSFC0Cp4OnpnCYQiKIbECkc2jZQ96ZRTojzJghRir9VJoTFGe30HJDVFAgn8vz6CO/YWLdJOVKnu9974fMmZMptAsh+MLHPsrKY47lLZeeR6+a5OmnWxSLRa772nWsX7+eW+6/haHCEDNjM88SCfldkELQ4yhmkzzGTGCtZnxikgsufSOfufJLHHP0SkIzxvXfvh8nlkgns6TrU1M05AyByhGU+jG1yW2q747j0NfXx+TkJFNTU/T396N2CKtyfX9bbZ2wVMbpZhk5SrKwN8czk02+9b2bKOQLmNRgtNmWC3755Zfz7ne/+3e+H601b33rW7nzzjuZP38+K1as4PTTT2fvvX93tc5d+H+LnZYo60//iu9ff/OLvl5ikMKixNY+G2/vDY40BMJmjhFH4AYCVwk8L1PR8QMXz3PxwxClFa1NTarrZpBjTQozLaS1xEqxbmgxj+25Hze94hVESnDRml/y2jMv5gdfWkOyfoZle32elYe9i+JRS+GRR+nTHnVhSBoJe/XnmExSJuOUqjNAMVdgsDpCs2WoJYpO4uAAeRHjJG0coVB+H8rzcJRFiTbWNHF0ndBMZe9durhEDOVGyIWLmSzC2MwEg7WItBVjsbzy6GO48/vf5cxT/5R7b/4OJ73qNTz68AMATI6NcuDRx1BTRXqYYfnyfnK5XmamZ5gzOIfUpKyrriNfzOPlPKy1FItFGo0GADfeeCO33XYbX/3qVwG46667+KerrmLz6Bgf/uhfctarh7n22mu58MILOfTQQ+lEkJuzjLPOHsY0LZ4wWOVQDhL+712r+MSVV5HECf29fXzl7/+eOcDP1q3jnVdcgbUWrTW33HILvu9z7rnnUqvVSNOUa665hr0WLaA+PUlleHupjrzvUFQpn//Mp7nyqmt588XnU5+OKPW/uCX4Aw88wLJly1i6dCkA55xzDjfffPMuovwDwk5LlDNz+9h86AY818N3Anw3R+CFhH6e0C2QD0vk/RLlXIVSUKEn7KMn7KPg9yCUA9LNlubSzZwP0gXHf1bNagBdqxGvX0/8zDNET68heuopOo8/Tjw+jgB6XBdv770Y3b+f+43Hw73L2STmsTmV2N8AoeLKoSP5+fXX864jV/DwDyusu+c8pHsRB634Iirv4ucchloJo1d9grVr1yAFeDaLT2xLWI9AWQ1WEkiBFAItILWGNM1Cm1pSohy1rSZQZgMb3D2W0HPFRUCCNQnN5mqKTpG4GLDJj1nYziGA004+lk9/9nOccMqpPPH4Y7zxdefz8KqfsX68wUVvvJQ3XHgeu//LZ3nF4UdwzrlnMDinypGnnMVnX30CPz3yQVYefhSnnPFa9tqvhBQR1sLaySauymrzxNoQJdm9rlu3jvvvvZfv//ox3nDaSRx3zEoeffRRLrzwQoK8Q2MmIk00Mh9imi0crwxYEuFw0oqlnHn392jUUr7yza/ziW98jU9efgWf+MhHuObqqzn8iCOYnJyk1WrxpS99iRNOOIH3v//9aK1ptVpInVKfmtymqLQVV/3Dh3nL295JW7ggIWolRM1sLlxzzTV8/etf56CDDuJTn/rU85TLX0iPctWqVf9tc30X/uvYaYmyp3cR8w6/kEbSoBE3GIvrVKMqtbjKbHs99Wod+wKxgZ70GAj7mecOsIAK89MSc+IcAx2XSlOQn+1gxiZItmwh2bQJXa1uf7Lr4i9eTP7QQ/D32otgvz8h2GM5wvFZnBqWjo8x+MMfsGHLQ/QMzaeu9uRHoy3Wrmuxir05/8FZzlB1FszMY9NPzgX7eso9X2RSGzwh8BCk2mQRMwIUAmmyqpKpkNhuhosi8yILIfFciTYarQ06NjhKIbfGOCJRKiSf3w1jEpJkljgeJ03rlAUUfJj1WiDgT/bck7Xr13Pjv36OE084jgYWBxiOLYcdeCR33P8w991zJz++5y5OPfYUbrv7XhYsXMCdP/kFP7n3Rzz0wM/583PP4OovfomXH3YoFkszbiPIxIGbnZQnx+rMtGKOPul0xhsxe+2+B/MWL+GpJ5/apuno51waMxFRMyVX9rLKmlqipKItCoyNjPHuv3gfI+MzRHHKggXzaXkOh+y3H5e//e2cd/HFnHnmmfT397Pvvvvy7ne/myRJOOOMM9h///2z8hb1GvWpSbww89o//PDDrFmzhn/6pyu596HH0BbwJPXpiDddcikf+MAHEEJsE/f98pe//Kw5tUuP8g8fOy1Ruqs7HH9tCtoBU4DUQ5gypDFCJ9g0ItUtTNpC6xYkbUTSQcURbrQRx2x43s9MgVEPZguKViEkndeD3H038vlF9PvLKeYWIYUitaCfgPYTTeCXz/oZx7M7T6kCq6ZWI9jCX5ndyZlhPtxreTxxuM51OSjocMzmvXnswddw6AkzzCYthPXoe8sVjHcJajHy2fUiRZ0ZL2HC6yMSkkJqGYgsgc7Uz7UyNGyHxKYoqSgVigS5cFuoi5Quvj+A5/XR6WwmSWZRQhKSBZsHQ4aTTz6av/rHf+Rfv/UdRhtNWkANSwXBAZU+FpzxOk45/8+47HWv4elffZcDXnMWrudTOfYIXnPcoSyrSB64/Xpef+jizCp2R0mEAD1K6CTMLwh8R4IQTDZirAAQdESBhbst4WerHuRVp5+O6ys6zYRc2UPmXGxqUcpBW3j7317Nu974Z7zquMP54U838OErP0k4PIe3ve0tnHTkkXzvZz/lkEMO4a677uLEE09k8eLF3HfffZx//vm85z3v4YILLqDUP8D0ls00Z6cp9vZv06NcunQJaZoyNj7OaWeezK3X30GoykiR6Wa+6U1v4rTTTnvevJk/fz4bN27cdrxp0ybmzv3PVeHchf8Z7LREGa/djPuzrz7nbDeOT3rgeLjKy5bTygenBH7Y9YiH4ObBy6P9HI2cYiKfsqXcYXNulhFvnBF3nBFvjEiOAr8GbqWS9rAgns/CeCELooUsihbTYypYK7M64kDbWEbMXNKkTNt5gp+6TzIqx1g7uwTbmyNdXuEXm5r0r03Yb91htKIi8/waqlXBGIdeKRgXhglr8UyWAqikQKoyFT1Bf3MNY+FCphyftY6giGDQCAJt6UkVURrT1BEztVncap2Ck8MNPKSvEL7qVnhcgOMU6XSycgUW6KSCCy8+nUpfjhUr53HfPQ9jsejWFHev+imHHHoMQ0ERd6zKxrXr6JuzjAce+hlLShV6hnajqn1+9eQGDviTfZHlBQwPDZKsn2Z4yVx+cMddhIU8s8kGXF3nvtv/jXe/+UIeX7ORzevWMrj7/lx4cR+nnXgGLzvsGI48aCVuavjqV7/OSccfD2TWNcDMbJV5+xyKCEr8641fBQtelLLZwF4HHci+e+zBql/9it888ghhGLJkyRKGh4dpt9s89NBDXHDBBXhhjrBYpDU7S5AvPk+P8pRTT+NL/3YbdQdaGzbj+gsp9gbcdNNN7Lvvvs+biytWrGD16tWsXbuWefPmcd111/Gtb33rf2La78J/EjstUQ6fuoL+V9yLcF2E6yF9D3YQYPjvgLWW0eYoT88+zerZ1ayeWc3jU49zR+37GJvtt+VkhaLYDREtpjYzn/GpAaztKpQnyzisOMtu8RrOyz3JfoeeyNw9lnDZU+v47h4dFnx3Ek9XmGr1I1VCTyCo5ALatTYz2rDIcxCewhiL0Za67qNIhzmt9Xh6HlU/pBEI6lKQs9DrKfL5PL2qQKfTptFuMqPr+A2XXN1HCYkMFTJ0cfwyRvmM15pYKxlpDFAaqPDnf74cYWuEfoPA6WB7Bb98/Ddc/rcfxHUdtJFc+roLWbnvMdxz/3VcdtnfkSSZ5/eAAw7grVf8JYQh//DxT3D66y5mwYIF7LPPPszUpkmVS1MIhpfO48gjD2dyaparrrkGFRTpm1/hi1/7Gh96/wf46/FxXCE5aOVhHHLsKRglsKklCIJMj/LsszM9ypfvx9oNY5jI5Z8//vfcs+ohpIU9Fy3imOXLufG2W7jms59DSkkYhnz+85/HWpsVV+sbIGq1qE2M0TtvwbPmjRRZDaRNMy3+5uMf5IlHf4PjSJYsXcLnPvc5ALZs2cIll1zCHXfcgeM4XHPNNZx44olorXnDG97APvvs8982D3fhv46dN47yvwHWWqLUUGsn1DoJ1XbCbCthuhkz04qZasZMNWImGxET9YixWsRUM8KSIP0RVLgRFW7EzW0CN4vhc0TA0sK+rBg+mFOWHcV+A3sxMjLCDTfcQK1W45RTTmHZy/bn4oef5vHRJl+rCBYvXMqMkxAbFwVUAo+ZKMGzsDjn41T8bX/IVicw8RRgiQu7EWvJtNFUVWYZ+oml2DZ4BhxPoGVMnHYAyKmAQLs0jWUSS4TFVZJKqPHEFhxhkE4R3fSJOzWcokLKdpaanVrcVCBin7btwRWZOrgOprCdBmknoJXLUbSa0HURQYAMQnC9Z6kHzUazTLQmSE1KwRgGjeWZ3BJcIkqyw3BhfpbHPdEmaafMeBBqyxwkk64G3aJSqRCG2evbVpVkGgQdHDWG6F1C0ohJR0ZIpSQuFSgPDtNst2k2m+TzeUqlEkKIbepChd4+CpXnC+2OVrNMpR6pCFOozMm9YAXHP2bsLHGUOy1RzjRj7n5ynDg1JNoQpTs2TZQYOommnWjacda3Yk0zSmnGKY1OSiNKSfTv/vw8JenNewwUfQaKPkMln8FiwJxywHA5YH4lZG5PSM5zmGxP8suxX/Lg6IM8OPoga6prAOgP+zli3hEcNngYYz8bY+0zaznooIM49sQTueLJjbyuXWPvwaUoLybxZ5nt9BAbB4nAYBlC0pfzUDuQJXELJldnwfV9u4GQpMYyGSdMJCnGQmih2LGojsZiMSoiEoaW9UiReAL6rKSEQKoE7dSYdhrklUVYSzLrkaSKalCm7NXJe02sEEhj8WLDhJxDLs2TTy1GdWh16sTKxUpBuVpFdOelECAcgXQVIvCQYQhhgWnTZqI9hbEGX5ZpOr30mE0sKC9DCkncSZkda1HsDTCuwJnqUMfSoUEqFE5YpJLzyHkKU2uh6wYlRlGiAcU5pIlHsmULqVK0fZfiwBCxMTSbTQqFAsViESEEs6MjRK0mvfMW4D4n/dFay8bpFrPthIqV5JSkMvzSEvrdRZR/xHgxRPnQExNc9dlnO1K2Tl8lBY4UOFLiKIEjBI6SuErgKomrJF537CmJ52S972TjwFX4Tnbd8/BCH/cL/N20kxYjrRG2NLYw0hwhNjFKKBbqBai6Q6lcZPnyPQmXxsyfuxRXW6QTI5w22gS0Ex/dfbECgsAVCJ9sJ9RahI6QURvjeBjXg+55C9SUS9XxMQgCnVKIYnSsSLoOIlekOBgcDD4gCABJKiNask6PMQhjaWqPtvDpyIA5eZAyJjVNrE0QQlGlgJsG9GaFHJkWGksHR0n6HI2NY0wUY5MUm2yXX8vI00CgmM45TEuDdhcQ0GRAQSU/lIlZjDQRQtA7J08608G0EqpBShy1mbU5tAXfUVRyLj1tnRVZYz1CaPBLpJRJRkbRjkPTVQSlEseefCrtdhspZTcg3XLVx/+Bfffem955C54nRGyMZe1kk1asqRhBsavE/lLxau8iyj9ivBiivPepCd6wZiNi6x+f7YbMWBBYhAVp2d6TKfPI55yXFpS1SLPDscnOZT0ok40dk41dA46xOBocDW537GqLl1pcDZ62eBrcxKK0xRiNtimp0QgrkDZzTxx8Xj+L5+/23/4ZWqDlS5qBQEuBMpZCxxJG5oV4/b8EAeSlwBGQWGiZ7YFZQmZ1uoXs/g5MitAJpAlCpwijsTJlsq9Awy0RpJuYq0KKpQW06jGNmYjKnDxKQDrWwhYVU81ZCsUiqfSZacY04xQfkUUKqATXrM+qfSiPVA2QjI5jPY+6Eji+j8wVaHc625bhcbvFzMgWcuUeSjukN25Fqg1rJpok2tCbCso9Pvmy/7zr/hixsxDlTuvMmStGOLr6C5ASI7KQEyMkSIkWEisVRkqskBihsN3rjJRooTBSoUV2Lu2ONZJYSFIEiRWkVhBbQcr2sf1P0IxEExIT0ia0MY5tIdMO+Qj2C8pUyw0UAjdVgMETTVyV4BhNkri0TYCwAmPBs4KSauIrixAS0gisBr8Mjoftfhat2JBGBtUU4Lu0PJ9qzqWes5RsnSJNXHw6HQ+ZdijRQGBJRImm8JlUVQoiT19QYXS6QUf6DIRTOFIjhINSBYRw6CQNpEnoWImnQ9w0T1FBzaa0cCgFCqcrmGsNGOGicbAiwDrbv+SLDSigMXKYWdGh3thE2XWAPO1anFlxnoK2xXVdOu02AwMFevMeUaKZbsVMNRL6tcu0mEeFLQgd45gRGBokGZuiFATU4wSdZt7uZrOJtZZyuUyu3EOrOosXhs8KRIcszXFJf441E01mHIuYjXBciZ978VoCu/C/i52WKPvHf8KHah96wcfECxjZ2Tn7gtc8f9ylQ7vdUqV7PsUhwSXGI5YukfCJhUdH+ETCoyN9OiKgJUPaW3sV0JQ5mipHXeVpqBw1VaDu5EmFS0uWAQHe1rvoKvJIwMnSL5XVOFaTmJTIKpzusaO646SNGzdQNkUChW4jK6ONjaGpQibdCrNOmVnKFNMmfVQp0gEcLA6etbhWU9YVLB4mSpnr5GhZi+30kSuCFg0SJsAaXNdlkl5KdgZDg1hX8drDlI0L0rApirv7u2GmAboDMm++waQWnWim2020dvFSF2FDmpmAEJ1mgo3buJ7O+r7RAAAgAElEQVSPjA1hEFBr1UmSBM/z8F3FnHKIKQZEEy0KSch6O5+5YhSPBBNP4vaXSSbrlAKfpuMQ12bx8wVaW8myt4+k06E2PoYz38d5jgKR5yiW9OdZM9FgRlnEZJu+YYnrvbScOy9V7LREGYRQGs28uZkC99ZHdkzh235qR560O1639UKbjYUFa7tPsGCNAEPWNKAzRXBr22BF9nyTUauVAuEo8ByE7yHCHCpfwin3IfMVhJ8HNwdeHuv4oBweFwMsbI2SWjCeQseZnmTbFXiygxWSVObpaLCOIhYOLQRayB3f9LNhM4eM7L4pYQRdtV5EbHGjBAs08GmIIYRjCW1M0bTI2xYBVVyZCQQbKzF45EUINoB6gKKMogyOxTgRvbLFJjWXgqjTq6aI8htx2kP0pAGhEqyvRzQjzcLeHJ6zfQ9QSoGUClwgdKj4TZ6OBNJadmtvYQKJNjnySYkokdtSIFUNpPRo1Jr09nvP+nlBf0gy1mKeDJn2lxC2N1MWTVq6QVrO4dZa5C1EhTztZgPPD2i3WhhjKA8OMb1lI9WxESpz5z9vvzJwFYv78qydbDIjLXK8Rd9wHuW8+AJru/C/g52WKCfmH8Z3Xn4OSJntgQkJSITKeiklSAcpJUJKhHSQUiGUgxIO0nGQ0kNJFykdhHBR0sORHkp6KOXjSC8TkVUBjgrwnDyuyuHFBlGvI2ZnEDPTyMkpxMQ4jI4iNm8i3bKFZMsIJAkwBUwhi0X83XfH32OAYK+9CfbZh2D5HsinnyY3sJjp6WmSKKFYKNOeyfYxZ32fIUaxuk2SDNNpWXpyDkmkiXVKLNiay5h9GUi6JRsAKTBCZPqULvxOUiXz7jaNQ9OGYPuy+uHW4KLxbULetsnZDoomyhoECukUsCaEyCdnA3YX0FQlWo5Pzh0hzY1iozJ+VGGZclifaJ4erzO/N0cpeOEla+iVqURbmBZ91MrzWNAaZzaZoqlcHO0hzCg5USYhRGqPtAlTcZ2w4BPks9o/Qkmc3oB0sk2/5yCGl9GYnSDXGcHIhKl8mVKziV/PirfVqzM4jkvUtswYQ7F/kOrYKLWJccqDQ89z2uR9h8V9OdZOtZgyBjnRonfopeUJfylipyXKdbUis7+Y8x98lu62COjWdpESI0R37zLbw0ylQkuJ7vapVFlTilQ5JN1xrBwSxyVWIfHwMuL5exEd4mI8D+H5eFLga42fxPidNn6jgVedJdg4Tfj0nYTJHZx38lFsma5hVUBkXGqNCFyF1hbdEmxhuHvvmYU324ozj72w5KygaS1p14M9QEQJg8Aj84+naJOgiTEyBQeEA1oqUqFIhUOCw6LKAk4/+2w+8oUvoxGkacrxeyxj34MO4uobvs3a8RYfetvljG3eRJqkzFm4kGtvvAlrUj7xwffywP33IYTA9wM+/aWvs2jhEoRIEF4H6TZxkzwVq6gJOP/CCznl1NO45LxzUc8hlwcffIjLr3g7W8amQQheecQRXP037+CGb3+ZBx5ey19//K9pmSnm6nloIqoIZCppzFgaMxDkXcKShxs4qJKHrsUoR1LoHcImZcTUGobEDFWRw2kkMDFJcWiIZm2as856HWMTk4RhiBTwrS99MSvClstzwQUX8NBDD9HX18f111/P4sWLWdSbY/1Ui4lUIyfarPrlvbzzne/8o9Oj3Fmw0xLlPsND1E4/A4vFmmyJae3W3mBNdpyNu8ddzcpMt9Jk2pTd3hqD1hqd7qBZue04RScdTJSikwSdJtg0/Xfv0SgH7XrEyqMjPFo4NI1LI8kxnhRpxIqztGImErC1erhQIMBxNIEwOEJnDY1jUxyrcW2KIzXKGhQa9ftqzu64KjRA/PxL8rmQDY89wuLJX+OHIbf9aBVzhwdxbIqrEz774b/j0MOP4vVvegsWWP3bRwnaTb5783eYGd3CD+77EUjJpi2jeMUCbcdicbHCwwhgBwexdSR1LI822igBrpT4UlAdH+fM176Wr3zjK+y7/yJG7TCrbr+FutuPp0CgKSYVpsI2s0mDii7hmFlSJeljnI4p0GmW6DQTvECRK/nI0EHXYnAkKheghvaC2Y2UmCYtSuK6wI6MoCsDCCG49lOfYP8DDwLHxcPSmJ7iuq9+lUqlwtNPP811113HX/7lX3L99ddTCl0W9uXYMNVkS7vDW/7irdz1wztZsGDBLj3KP0DstEQZ12v86vZbEEJky+tuv7U993hr3Nxzx063V0plLfS3j5/TNIJIQzu1tGJDI0pptBMaUUKzE9PqxLSimE4Uk6YpbqoJ4pSQiEA0KAmNI3bwm3uQE4vol00kFmU1Cp2VgjAZOWZEmIX0GAQpW61Bl3ibZShJRdcKpuvdJ1Mbeq6XPgudMt3Xy5xEFsFRx72S79z9C1576rF8+6bbOP/Vx3H/ql+yd/sZOiNr2e/IAxiWM7SMx957LCdKFFs2jdNf6SeXSioFhyXze7DtGURzmsLur2Bk3eMo43DTzXdx+49u49pP/wv51PLru3/Itz5zLdOT4/z1x/6RI08+mc985jOccs6fMbD/IYxhkcJwyGmvxvoOJj+AK38LRvCbu37DRz/xUYgM5UoPn77manKFAr/49c+44gMfxFiJxeHmG75LlHR405svoF6vozF89l8+yxFHHIEIe3Cn16JKEVHdozw9jrYS43gQR2AMHcfF9Xxuvukm/vbvPgzAWWedxdve9rZtaZDlLlne+oN7mLdoCf2Vubiuu0uP8g8QOy1R5lXKq3aXmc/FaDJjUmclXc12yzKrM51ZlMZodGIyVXOjM4+r1plKercJq8HqLJfbbl2qa8AgMRTQ9LCd0FzSbb1HgkuCR4LvxHjEuFstxS4M0CagToE6BZocRc620Sge+v40M2MvYPJ1kRUszBQnBSKzpoVFIhFGZEpBjkBrgzIRBkE4XOKIk0OU52MLwxght/qpMNaSdDXdTjj3DXz6Yx/hZa8+j188tZ7jL3ozzV88ziZ/kPPfeBGXXnoZB3zpGxx3xMFc/LpX0ztnHuedfgynnPF5jjr2QVYedTSnnXMO+x+wP2WdCfYWk2k0AuPMdAnc4JAyunaM+79zE79Yu4mzzzqdXz36WyZWP8m555/PotCjkTRppikdQsailM2xpublaYSW/fY7mIdWPUhztsG3vvJNPvOZa/nbD3yAT/7zZ7jyg3/HUSccRXNmM1Y0uOqLN3DEEa/kPZe9F89o9Fbx9aAMQ/sgJ1cTlCLiuoOnE65417uRSnHaicdz+WVvJ3F9RsbGKPkOSRTh+j7lcnmbgjpAOfQwzWkG585jSzNGCLFLj/IPEDstUUbVEfZ/4h//n76mRWCki5EeVrngBAjlYZ0AI/MkOMRa0TGS2VTQigXNyNDoGFqJoGV9OtajQ4gOK9hCPytkjtQrIYREOHWETLe56O1zQ5q6d2G3/Z89bDDZg1srMgJpd2o4VlPXeWQkkeksVgi0yGJDE7oOH2DxHruzcf16brvheo4+4UQcJ3NymbDCylNP5+5HDubeH97Nfd//EVed+Gesuuc29plbZM19N/LDnzzI93/8Sy599alc+eWvsOKVx2OEZHVuEX3xDH6aEJo2wtZoKsmrzngVrsmxYreFLFyyiJ899lvqiaaWaiJjyTk+xXQjszZkVszBFQIpoB64rK6N88YT3sjM+BhpJ2LBggUYqXj5EYfw3r//e8751a94zZlnsnjvYY46eBlvuOx9pEnCySecxsv3fRlpK8HJdUWbB/dCzG7AE9N89VMfZU7vHGaE4Oy/eCvzv/0dzn7NGd0vYJgZ2Uzv3PnZ7+E5Dp7QVRR9h1TApkZEq5m8ZDJ3XirYaYly0l/EpfpjeErhOgLfUbhK4LsKTykCV+I7isBT+K4i9BxC18l6T5HzPHK+Qz7wyAcu+cClEAb4ShInmiSKiKKEqBMRtSParTadZpN2o067VqVVq9KqztKqztKYmSHptJ91f0JICn19lAeH6Fk6l8GhYXrnzqcyZy49w3O31Wx5/PHH6R/IiuUef0H/tuebWGOaCWkzQZK5n2JloDmFH0ekjkPkBzj5kFgnGG22qZtDtsQGmwXUC0UqspAnYQzSWlws3tbYKGvpaTc55bhj+eT/91fceOONzM7OItOEXKuVqe2UhrjgjDO54IyTOffcN/Djh35OeNppxF6eFcefwjHHHsOCwTIP3Xo9r1u5B5IsO2k2N4dn6CGSLj1pjVLaxDFNWm6bXBLiG0tJNNltzz154BcPcdCJWeGuPor0iDpVDIm1pAZySvLJd7+LC/7ibbzi9Ffx4P338fmPfYSO6/Gmt7yFI04+mgdvv4/DTz2VO778FV5xxEncd+/B3HbTjbz9ikv4i0sv5+wzzyUXdsj3FzILvLII4eVZshskLUMlklx46omseuIpzhSKOUNDbBodZ87wMOMb1lGtVuntfbaAxvz58xkd2cyS/jzrJps8vm49lcogu/CHg52WKBf1BHz4lbtjti2h0+1FxNKk28eZ86WZoKsJOklIk5h2HFOPY9I4Ioki0igiiTrEnc7zCO95EIKwUCQslsj19DCweDeW7F8hX+ml2NdPobePUv8ghd4+lPMf+/VYYzGtBNNKsXGW6V3H0iJLiwxzFvoWsqneoHdmmmK9hqhXyZV7SHp7mUm6JRSsRFqNMhphDW5XEi4zUCVKKHK+h6scVHc/t7fQw6UXX0J/Xz8vf9kB3HPfvZlOZZzwkx/fz4EHHkguDGk0Up55ZjP9/Xvw6weeZqC/H3dokGmd4+ePbWCffZZjrGR4oELr0XtYuPtyfnzLdwhyBTbaASLhcMttd3Dpa0/ktxtbrFu/gRXz92LpG4scc9xZvP7w4zlg5UrI9XHd//kWBx51NK7MaD+1llq9xu49w+Q6hlv/zzcBqPs5Nm0eYdHeK5m/95/w00ce4cmNGwgedJi/eDFvevM7aBuHp556CFecQ6sN0aYqxZLFK5dJ/R5madBXqKFNxB0/vJtjD13JUMnlxJNO5N9u/DcOOmB/brn9Ng5beTBpEuN62z1UW/UoJ0Y2smBgiO/e8m0+fvUXmJxq098X/ofmwC78z2CnJcr61CTf/5d//r3XKMdBuS7KcbPedXFcD+W6uL6PF+bI91RwPB83CPCCADcI8cMcXpjDy+Xwc3mCfAE/XyAsFvHz+SxI+gVgrSXRlk6qmWqndOKYVpJm6kVxV70ozo6bsaYVpayoJExMtvATg6/t1kQamljaWKQQuFZQEEDTYlp1BoSDyg+iQ4OyIBEEM5o5OGyfElmsogU0Bo0mFYYUTWpTGlHyrPtu1BsMlPt563mX4nQUBe3jWcWgKfDMr5/iQ+//IEopjNFccM7rOfhlB/CDu3/Ee977XuI4AgT7778/F150CVUR8N73vZ9TLrycBXOH+JPlS2k02/SqJgbJwkXLOOI1lzI5Ocm1//ABQrfIfH93vvjlT/KeD/410xNTKCk57BUHcNppx1ERGk8KYmP5qw9+kDe99SKGh+Zw8EH7MCUEC1qaf7z2alb95McopVi6fE+WnHk2//fGf+PLl12GJxWlYoFvfPOb9Mwt0B5v0Eoks1Xw6+MI1eLE088iiRN00ubYQw/m4jPPIh2d4bLXnsR5q37OYUcfQ09PD//yz1cytWkjtdTyzne+4wX1KC+48CKW77k3I+2IdMIw1J/btRT/X8ZOK4oxMVPnzgefxIquY0NkLg7bzVgx3XAhYy3aGIy2GGvQJnP2aG2y80ajU0NqLMYYUp3Vn0l1dn5b2dvEgDbY1GC1QRiDSDOVHamzpozFtQJPgG8FvhAESHwkvpDZWCgCoSjLgJIMiP60l+ULl72oz8ViM9eSIPNwi2yM1UidInWCMOn2PUzbXX5nu6tsXYobK0mVxLoKZNc5ZOyzUzmxOMKgJLgIHCRYB2EUwjoI+xyVHZmiZUwqElJ0dp9GYq1EocnRIaSNg0EjqKscaQw5GxMoRWrnYoUhzo0yqXrQaYnetI0TjOB0erGiwkiQxY0OuQ5yvEMgG5SCOqlcSLPToiYT0mKJmtnq9jLYbnyUsJYwjij5Hj35HHI2ot1M6djss8mracK8QuR6oTmJac6SND1ManFCjQ1dqrGP1iYL8LeWNFemv7+P4AU0KlNteGasQccYehyHBUP5P0iy3CWK8RLH2odWs88da6BLlBm2jrq+4W2PZcdC7DDuHme97J6RCKEQuAiRLVH/XQh4liH3AjDWoG2CsQYpJEq4SCEx1tDB0kHTInOwKFICkSKFxZKVv207Dk3HIRHZiyij8dOIEEvcjkEbQtHB0RZXKIhjhBTIfIgV4GBIVQqOQSmLKwy5bgD7s+7TCFLtoLWDMQ6JcUi0pEOXbmUL66QYlQAWxzo41sUxLq7J+sDktv02rDBokRDLmEgEtKyPYwwhHcq6iVCQCEXLEVi5CWUKOHGJIWeaNlXGGKTP+vj+LG6zyIJIsCknGSMlKCl6agWCqIqbmyKgTNsmBO0m8/v62NRuUsu+RQhkps/ZVIpRJKPNDoEPQ8qh2EhpG0VD99OpRxRbG3BdhcyX8WSVpOWRthVSW3rDOnUR0kkz4RGnVWV8JEbkKwyWA3xn+3xxlGTZcJH14w1m05R4pM7iwQLOrnTH/xXstEQZqCaR7XTzrLebQlvH23zDFhDd4PPuP3bsdzi/9Z/Zan3RFb3d6hjZdmywwnabwQiTvYa0WCm6ZRItVht0nGI7GhEZBBLpOLjlEFUJ2ZBa9jfHMJk28RxJ3s/S8CKr6FhLZMgCtq0hZzS+NdiGJhdKWnEbpRSBl1A1Ponj44o2RsQoZREiARFvIy0H0LZLglqSGsCYzOljLQKLgszJQ4Kw2dLcILcHQ2mFNR4qpfucbFHfEgmGCCsMiZOpGvm4+MYlsD5hWtx2HwmGWMY0RIJvW+Rsi0ISYxBEsk7qW6wCF8M8dzNGS4Q0tHJbsGmZ/tgn1B4TvmSyrIhb8xGmTs7pkEs8GkmHVr3Ognye2GrWtWp0TEgHQW/oc8bhr6Dd6XSVpATS8v+zd95xn1TV/X/fMuVbnrqFLbA0QRQUBMGCIEgzlKUooBgTjSZqxB9q1MSGihV7QSG2qL/YUQFjRVCRokgUQYgFCXX7Pv1bZubee35/3HnaskQj+iOvLGdfszNzZ56Z+c7cOXPuOZ/zOXzq/R9l3733pSc5424XmszQrDahlSZpVWirqbqGymcMDHhSVTLtcgRIqx5hxnFHv0272WTZQD6Xz661Yred2qzf2mNLv+S3G6bZdWmLZr7DvrYPmuywd9yvSfhQ6+PAgizmbbwQ2w50FrMKqXkuy+3so0TdZ11tuz7LLiREHKMDvaDdiEYHhRGFsZr4T8EYyCbQAexTDqUZulBAUcxfR8oCMqFtflO/igk3Qqwc2aJE1QSbqj65VhqtBINgVIigdbX9bKIgiqgyFbNslfNAdc8s5kjV7RWWSkUGpYDFIDSoyKWiIQWGQCUGRyQL7mhNwIA0sJKRhZQmOTBAoRzTuksu0xFC1IM+KV2bo22F0ZHAI9UVpFsgjUTGLUnp6pSinWCrhJ5JyMVgfMlMp8tEr0cqwrLE0tHjTOmEsWqAT/7gRyw1mpEtm3GdGWYGh5kZGKTXEwYroSOKrmvTN20G0hmyahM282gTqLoJ5YSQDA2wpNFnsg9VMGhXMRgmKaXitm7BcCtn2UBGYmKgbNXSJo0pw71TPX63ZYaVAzlLh/LtPouH5M8jO6yiDPkyxvc9FGDBMHqWH0KhlMy315NG5pcVtTKJf6RVtAAMOrLaKLA6lik1Ss0ynpFWirwnJD0h7QVMN0DHxZRH8Tjt8U2haniqLFBZhwuOsU6PjTMFM86hlSfRHi2ewxOhk9es5SxQvMwrXIiKWQOGQKIES8Co2o+o4vJCF5gIeFF40VRB08dGhbhwIjIjzRIaq/oatNRMQ8iCj8k8blPhSJUjpYe3BpcmVDZlSg8yJYIVRyYlGQUNVWIQtBJQ05G8Q5iD6RsS2iFDyRL6KIKaIlMzjLopvNNMmAFmbJNM+uiiQumAtmASoaW6tPCzcSskVSTBUnlFqXK6toEKGl3mDIYeXXsvzg6zWdpsHR5lyeAgI5s2Mjy+FTcyylRriGymhxZHUTkmyoAyoxjl0aFE5QHloOz0MFozODpEWUwx01cQAlnZIUkcvY7jN52C0XbGsnaGNZqRwYw8Ndy5pcO66T4zfccuy5oY/dBQ/P+H7LCK8rZrruOwK7O5ofY8GFvmlA4EZNY2ktl8lDn+tLl53B7XtdRKS2putbm5r4/nQeK8qNvYJvtGE9ObF3JgLwO2F7LJD1W0evMvi0IwWjC6VoBa5hShJSxShkGgQtNHU6GoRFNiqJQhKIUoR6jp0rKgyQrISrAhYIJEhbgdEajTH6kp6uZTIbeJ4aB9IO0WpBSRUCRJqNKUjmnRkSZpWZIVBYmvYhq7FpQRtPFk2qNMj9p1jBVi6d+QUEmCKM+oTDLqJ+mTszkbYcbmJGVJOlNiJaC0RuuMMncoU5HqklSXtJiixRRokFxRkdAgpZIZun6KUo2wWTcYW7aC0ZlJku4Mvh9rmQO131oTvCKgUSZH4wgq1GxMMDM5jUaRpAneVfgg6LJP0zoaScb0jGPrTMmSdsqydkYjt+y1coB7NnWYrBy/Xj/NmtEm7cZDBMB/btlhFWU+PUgrefKDfRlzIsqB8qAdSjmUrjC6QJkSpSu0KVG2IDPTtMxWcjVBg0myNDDULNESh8eGxaUavChKErpYChIKSSh0SmFSvBaU6qHoomQ29dFjvSevhKyEvIK0oo56R3Ea+gk4q/BGCAacUfQ1aGtp2Aa6SpBKkQRDjokdzdd+WgkE8XjxBAlzbVEClH201oixlGlKmWUQAtpVGFehRVBBqKoEUDRURYJD64DRAW2KqFTrGyEB0lCwutqAoPA6o8hSysSD8ihmSAWUg6AEj8GJQdUxb41gtSdRHRpKGDQgoU9vJkP6CiVCmSZohHa3H90jQ6MY24Yi0BXBCTitMEstI9UGpDdNWVpKr3GFzGU4ASjnUN7RTjyYkskFCnNpO2PNijZj4302dEtu39phJE9YNdq8D5vSQ/Knkx1WUS5ZPc3gijfPs/bWZRqU1DkpEtsABI2Imp8QIjdvHNtKMDUJr441uUUjwcTlYOaWpV4mGIK3GC9opyFotBesD1jnSX1J5gtaoUtTZmgxTVNN09STJJSLDND/kJeQhoAXi8NSSIIniYEXiWUTgopBnaCjlzALgYYPGAl4UgSNDl2SqiCvArY+vihFZRUzuVCk0EuhqnuMFU0qkIumpRVJzWUpCCr0ENud612C4ObcGLWfdY44WBHE4LzGO6GqQl1/KGBDgS5LlLKItYQ0IyQpKlQYSkxeopOAJIFqzlKN91M5MHW9IS2glaBN9MFaeqRlj9BTiFicSimtpcwCoh1aBbQWNCG6YLbRP1XX4roWBGzmcM2ESTtCRcrESJ+RME5ebqF0Wwk6I6FN6jN6QSMTFZvsUhhYymi5jpGiR9VLcZXgrcInMRiHgC77oGDQVihrmZ7J2DpTMtJKWTaY0m4m3LOly3i/Ynr9FKtHGgw1t+eZfkgeqOywitKndzMytmEugKMW+Phg1tc362tbsB6YU4gSNEFMHO6JicshtlNPJghpcKS+IvclDV+S+4JG6P+X9GZBKaZNmwnarJMhNoU9uNePslFG2CijbJAR1oclnCdLsGEVGlBGzdX6Qam60Jlgg6/nNewpRDuJuf8zoIUk0LORcl1U7RII0dJtpI5WWlDpilIUpXh6QdElsN+y/Tjp9BP54IXnkyqFLgP77XsYjznw0fzL5y9kw6YtvOqc17N+3XqqyrF6zSo+8cULUSHwlteez7VXXY9WkOUZn/iXd7H7bjtj1HyQTATEK1yV8OKXvJajjjqGE088EZxH96toMir4+U2/4Ly3nc/mLVtQSnHIYw/ivDe8jsu+8W1uvulm3v7Gc0lUIDOBVLlofeoKqypyIPQgBIuTlC4NxnSTjrZAdGUkVLTKDtoHQqpRTcAKueqzrLydl77iXVxz9U9R2vCy17+aM08+FF1M8JwXvJgbb7yVkZFhPnHh+1m9ck/CRM7WZDn9tmY028p1376cl7/hHXjvee7pp/Gys5/PjKSIKHRVQlUyYAqUMXRnMn49kzPcTFm5rElnqmRzv+LOsS7t6ZLVow2y/2X1wx9s2WEV5Razhif/cupPciyvap/cnH6MZBHegFOGPikdlbNFN5mUpWwNw2wKI4yHQaalGSeaTEqLSVqMyQBTNGNxs8xAwyD5gqlhkWacVw1Nd+j3pblpsrKk1e/S6nVpFLEEhteGfmoo0gyfNjBaYUJAVwq8QZGCjtHVUMRJIaTakyhHQ3uC9jSbTX73q99RFV2SZs4VV13FipXLUVrIreOCd36QY496HH//or8E4Je//A3LrXDxxd9ibMMmfnLdxaAtd9+ziWariZcU52NATavo4dRGSGyJTSqyRpck7VNVOcFaNBUb1m/ib198Dhe9/3089sDHICJ849vfoTvdRc/FkRSlGMqQgMkQbbEI7dAlDREKZYzDakdOlxHZiniFl4TCJfQCODSTySCFymBBtuqF73kbgyMrueLHP6LSik2TfTawnK985hIGhke48cZvcPHF3+K8d7yTT33qXcAUmVO0Sk1ZGl587tv4/Bc/yyOWjPLkE0/j+MOPZN9H7o03gWlJ4mjHe/Cepqpomi6hY/nPbptGlrJiKGd6umCqcvxm4zQjjZSdhvPtl0x+SP7bssMqyrvHV/E0/yZEovct1nZRkc4LjZc4OepMFAwBTVUzPFZE+IqLoEcAMlMwlE4xlE0xkk0wmo8zmo+ztLGV5Y2tLGtuweouw2xiGJgsBtnSXcZYdymT/SUM9UexdGnYcXKTgm4ykY0wlgwwnqZMpIbxNNCzBXqWpk2W0golVpXpjc4AACAASURBVAQbIpxHEbC+wJYFpnCYwqFC1BY+UXQHoMpA5Z5MeVJVAFOEoBCvUAk1plNqC9RAsBAikFx8ggSLdlntnlAcfdhfcNWlt7D2pOO57EtXctrJp/Dj63+CD4Z1GzbzxCOeyKTXQMKKvfdnY6m57d4phnZaxSaGEB+wK9ZQENjk4eA1B3D9HT8DrfnuZd/mh9/9AW+94B10guGbV17Phz7yObZu3sIbzj2XY44+lk//6+c4/fTTOfAJTyDUwbQTTjwBFRYD47975ZW8/8MXUlUlI8PDfPh972XpipVce/2tvOEN58ZCbAp+8OWPMz3T46yz/5GpmQ7Oey58+6t50sEHsjx08T6lIqOrG3S15ZIvfpZ/+8F19Po5AcVwkiN9z/e++R1e+OrXcpfsypPWPoNXvvLtBImppdoK2np+dvPPeNieO7Pvw4fwlbD2tOP56rVXsec+D8eWMJwKlYnQI5kL+3s0niWqRHnNVD+jsAOM2IRe3zHWK5noVYy2UpYPxsj5Q/LHyw6rKDM9zmRzfngyy5ajIt6F2cFpyqyPanF0fC6XZw5cuaANoVc1WFflrJteXG5CZo+h5mGbMusnJcJu4pV4RDoI3ajMRaNEMYxmqF4PopGjR6i6jhK49aufY3rdXdwnGP0AfPwDq9bwyFOfBbVlp1T022lboVQ5R+l21Kkn8JH3vZdDjzmKX976a8542nO57robUVM789yzzuaFZz+Pf/nnL/OEw5/IKWetZcmqJRx98lN49ol/xU+v/QmHHvZ4Tj19LY961H4o0ShgwGfgFC2fkQbLSNEi9ZZNd27gkou/xh133MHpp5/OYVcfzq9+9StOP+N00CbmpWvB12D+fhZwVijSwEGHHMg3jvwSSin+9Utf4kMf/SivP/cfueiij/CWt5zHIYc8gU6nQzcRPvPZj3P4YYdx9ovOJqcH/ek5AHtieqSqR4sJxsZmsHg+885z+eFPbmDNrrvxxre9naGly9ly773svHQ5Xgy9dCnNwRF+sW6AnYbb5KYgtSV3r9vC6tU7oTTYLLBmt6XccMPNsKKirDT9MqD6kHuNMoq+LAz+COI8OV0aVQ8xGqVbtEyDyglbOgVbOyWjzYRlgxmpfWhI/sfIDqsogzJ0fE38IPeBlsf22VVRi9rnmudSHuPOi/WTWrBfXFfb8UlGGGYMHERIj8doX7cJoR7PSzCooLG14tbOk/X72BDIygoVhMR7dAhzp5ZZjTyXbbS9Xzm/Yf42LPyd8dpQniDR8haZD8SIaATFbvscyN1338uXLr2UJxx5DBNGKJSwyXj2P/Iovnv1jVzzgyu46vuXc9pRT+fy717Hw5buz5U/uJqrr/sh1177I571tL/hgx/7IIc/6XAAbEgii7oP6BAw3qFC4KTjn0oqjkfstSu7776Gu9b9Fm0FZR3BBrQzpMFQiibPZsiTQLCW6dYQv73jbt59zsvZunETVVmxZuedSSrNIY85kPPe9GZOOWUtTz3heJqr1vCIRz+Wl7/yVfRc4MSnHsEh++6NKI/RMWPLuxjcqyrPPes3cvjj9uMD553De/75X3nvG1/Np953PjY41hTT7NSZZCq1sQ80LEWriSszVBEY6w7TqVps7C4jMwVVsMwWeTNpwKQB2jW21WlMobGlxldE98zCx+g8baZQ1RTBGLq6TUHK1m7J1m7JQGpZOpjRzuz/yNzx/6mywyrKPJ8hXfGV7aTfLLYN5+ezWEC1QKHobfarc8NngX0odG0JamrQt4BBoYPCisIEsEFhAyTeoNEY0SxrzrCqPcHKwa2MtiYBKMcz9C0Nhq7xDN0Z03DK4/ZleTGBTwyHPW0tPjEIGh8iIFxLBMEriZbErMrc3iuittta/1XQi9fnomAehbBCVZxw7LG87y2v50tfvoSx8XEShEZEjjI0Msxxpz6dY059Oi9+zpl8/6fXcPTxayEd4ZAnn8ITDj+VJSO7c/k3ruaJjz8BpRQFGq0CnaKIV1wD+xta0dKRYEQFISkDj9hrL2698RZOfupxSOJx3tR14JqEokkjCDtLn7/7p3N57t+fzRF/sZZbrrqS957/LgbzZbzgH/6Jw487nqu+9x1OOelUvviZT/GEgw7kq1/+Et/7wQ/5+5e+hrP//q95+hknUZGQBSEznhTP0mWDNBs5pxx3JN4pTnvqsXzy85dgTMkuq5ayeeOv2HPnjGza052c4HHSoTsZGMta9BoZq/bYmc1fvBcjiqJsctsd0zRHHsa6zk7kpiQzBbkt4kc0CegkQsCsQHAKXxpCpQlV3UfrPqycp8UkbQVBm1gbvsiZ3uJItGK4mTLaSh8K/PwBssMqyscs253jrjj3wb6MORERpJgmdDYhMxvw0xsI0xCmOri0S3//QO/AkvKJBZ0ngduwhMG7H49ujJC2dp4/UHX/5/iTXSvU2exxrlCkkvHsM5/D0MAo++9zAFdf9yM0mhzLj675IQcc+FhajSZTM9Pce+cdrFm1C7+9+ReMLlvO8hUr8eL51a9+yd777EsXw+jS5dz067vYbc+9+Pq3r6TVarOFQbqkfO4bl/OE05/PvXfdyW133ku264Gc8OyH8ayTjmL/I07i0Y+JxDXf+OoXOfKwmH1VecNYv8n45AwDI6vodR2f+twX6SHcIYp7bruH3fY5iD32OYgbfvYLbrv9dmxziCWr1nDmM/+Sbq/Hz2+6nVNOG8WECCZHGbzSeIHjjz6Wy6/9FUc+6bFc+eOf8oi990BpOOnoJ/OpL/0bhxxwABd/83s85dCDybMeOT1G3RihgL32HOE1v/stU7+7haUrd+W7l32Vj37ww4x6TxEsnTJjEo1oITUluSnIbEGqK3Qi6MTVnz5BvCJUGlcavDMoL3OBoLaPBUSCNvR1xvh0xuaZgsxohhoJQ62U3OqHLM3tyA6rKG+5d5xP/wl+/vYAPrLtNgnkZYdGMU2zP02zP0W7P0GrN8lAb5zB7lYGu2Okbj5Z22nDWHsnti7Zg01Dh7LZLWfjbUthS5ddd/o5e6y4ieLgbzCSr2WieS99l+DqIfG2XtNZTKig69SYaG1GK3N2KL14gD6fg7TYYbC93xuAuwiwciUnPe8FbCAwjlAgjBH46c0/47WvfwXWWCQEznjGWRx4wCP44fev5A2vegllGcHu++//GP72r55NM3R57T+9mnOecyYrV61m74c/kplOB1vny++2x178zdNPYOuWzbzu7e8hy3OyPOedH/44733L6xnfshmlNY993OM55oRj6aEJCjLleOU/vIxXveivWb5iBfsfeBDr7gloVfCZT3yIn1x3LVYr9tlzD5505NF8+ZuX8y8X/Q0mSWg1W7zrAx+KNYMM9IG+KCo0KHj+a97Ma895IdNvmmR0yVLe/p4P87uwmmOecTbffumL2PPwUxkZHuKij1zApjDE5g3reNmrXsOln7kQm3kueMurOOOvnosPgeeeuZZDHj6C91vxYhCxIDYWFdaaSuf0VItxBcp6UlOS1ZanrrOWTO7jcxUBr3BO40qLOIWWQNN1adIFpahUwkyZMj5lEZ3QyhIGmwnt3D4UNa9lh+Wj/MKXr+BfbxiPvi+J5Q20BEzw6OCxEucmBGxwWO9IgsP6isTHeepLEl+S+oKsKsh8n9wV5FWfZtWjWfZolT2aZZ/tdbeZJGNrs83m9hCbmoOsHxhmXWuEeweXsKExglcx0h47fJ0jIgYkAtx3H7qXtx39aB758BEUQt9ldMpBStdgdvCvt5nHSS1Ynt0+51hYNGduPS7DvCpeOF/8t4vPdZ89ZP6Is8bLth7dBWp+bgrU+ecoStGURGq52UTQoGL9n6DivoF5kg7q39hUFRmR3KPA0pME/0CiXX8i0QgZJTkVuSrnltMFRCQiUBJrqVdicTV2N4glSExFEi1gA9qWJLYgNfMMUMKsj1khDkKp8VXtoligBpyyVDrBKUvQBpuktPOU0XZGsg3N20N8lP+fRCn1VOADRHKxj4vIO7bZngGfAQ4CtgJnisgdD/S8xa1X8P6bJ+/vomYXttMW22f5KuN/GVXSososlaknm1Aay5hJ2GATKptQ1PO4LQGtiYAZWIZiFfA4IBFFEiBFkaBIRWFRpGgSiSS4FoOd3Jepqk0+vQs+maaRTZE3N6NCgi2GMVWLP1y2VRb39wH97yqVCFzXeMBFVIH2tXq775xt8tF/79FFRdwlGU7SOIWMWcLdiDLwKFWhVIUX8JE1pFZKNR2caEoff7PREbcY6uwrqVEPiz3Ws8G5bT4Wc3n+C6mlVP2bFn8cYj6DqssIRwKSDg1mpIEmYGOOFVmdopngMOJJlaOpCrRe/IwqMbFAXbBUpcUVOYU068yyWH5Ya8HaCmMqTKMiaZUoFfDO4J2Oaaeuwno33wVK8F3NeL/F8hUr/vCH879IHlRFqZQywIeBY4B7gJ8qpS4TkVsX7PY8YFxEHqaUegZwPnDmAz33Hi1Ne//Hb3tFLA7gLG6ThXbZnFUUX5fGAtttdj+ReStKFtl1UIeQ69UFwZFtFNTc8LeGECl0pGJDz71xShS2HMCWLXzSwWXTVI3NuGyMpGhiXcKswlqsmOo2Fbi/AM+2EmQ2Dq7nX8DZ3yazv3F+EgwoQ/wOpihMTTm3/bNFtFGIpYPrX7/QBRDTS6PluLieZPzkKG1IZhURkXU+stKnKJXHIAgLtusS0RVaCzkBpRRCNQcWMAq0LdCmwmvNscecQb9wNTN+zNh674c+xKP23js+ZRUtWdGGhIrM97C+wgdNFQweHVn0dUznFKXQSmOVgm1ztet7XZFT1pa01wqnNEoLqXLkFORSxlGORGxtqqr4hLYzjHGicc7EqeYJ9ZgIS5vt07p+XMz7oRGhCt37HnAHkQfbojwEuE1EbgdQSn0BOBlYqChPBt5YL18MXKCUUvIAfQZ3bp7gt8Uvt7PlwXVFzJ9dLWrY9qpm1/eXk5kMCzKMCqAYmDtEv947vgS/h0r9zyKzDEoVDwjQuc3f3vdIkfdSwn02gGK7VqoCTE0EJYrof6wVpA6gZqv4ugxfczl996v/tv3Lc9s+oQAYKtoxvlZ/O+6juxZ+I7cj8vt2IIlkJ9vZMv+J3naK2+JnLKrC+9weue9q3z34LooHSx5sRbkauHvB+j3E0ed29xERp5SaBJYAWx7IiSf6I5zSOPaBHOJ/hIyrBkN66PfvuOP28T9YRKAUR1+VVNqDggRDLgkp9n6t4B1F+uF+XFU7gDzYinJ7Pe8PySu5zydWKfV3wN8BrFmz5veeuJ/NcPn01Q+y/bgYgfmH7bdY9pUjmPIzf6Jr+GMP8Mce4b53/39CbFEBiYrBoUr7qDSFmpx4fv7gy8IwzX/3b+LfzWKCIxvW/PLcURe0a72YN3VHkgdbUd4D7LJgfWdg3f3sc49SygJDwNi2BxKRjwIfhRj1/n0nHn3qat5796f/yMt+8ERLLC+r6+Xz04PZ3Iq3475e1XkX6GxbwwhNI2gFhVd0S40EhQ6CDoAsLKSma6+oYBFMBKjMlYbQdUbR9mgQZxnSQ01RF6gZliJ5GSICIgSJxCFeWWAWmE8NWdoW0C+oekystEcpD9rXWmvWh6nnosASof1A9K06UTjRBBGMBPJQkEhFZTOUjoPSUgyiFFYCDV/QKErQmspaKpPitcETwd4WwagqXodoJNg5mr15WeBbVaEODsXKSfHa/VxaqFJhjgREEIKar9I0S+88O/cSGaZmPc7b6/BaYvEQExS2Xo4JEJr5XrQ9mfX9zqbUxueX6e35NXYMebAV5U+BvZRSuwP3As8Aztpmn8uAvwauA54OXPlA/ZMA+2aBd27cPNeJI5Sk9lXVEc95yEmM13oFTimcUlR1pLJSUCnmcm8XXphBSANkImQSSAUSEbIgKB8P7ERRGEU/VahEgVGUyjKpBphWDTQlAnjRc5AXDdggpCHQCIrBulrgXJR2LsBRv6Rq3hXW8dAJipYRWkZIG54Zr5jys9S8//Wt1UidXaSjj0s0icwqjjg3Ql0qIypzo6S+W+X9Gp9SBy6kBirNXsVcDr4KkeZu4d8AEuL+Qc3+XSA+nfmQk5qNpC8EMSw6Umf7FxXjT4vOV2EpyChUSp8k7iBgcCS6IqFHZAaNJdVmEwxn+1A124eY7UtR0Tk138fmPYbbXDSxTxmJWV1piIpPS4y+KJkNJOo63L/tzQ5oHVA6KmwnghdLKZYqmDk4lSIWiYslQ+rScOJZSN68o8mDqihrn+PZwHeI3fKTInKLUuo84AYRuQz4BPB/lVK3ES3JZ/wpzu1n+gxuOnG+4T5jqcVQ6znVo+ad5NvuN9+9F29fBOVW1BZFDBYYD9YJSCTpqVKFsgFbK+geTTwBJVUsz4CKEBdidcMspLRdq06PjApztjLk/NXNvzCzOMagNKUWbN5lIO3T1uCKBq7M50iJZYHijR+MaA3FecAhVCqw24qHc9LTT+LtF74dAOccR+53JI868FF85HMfYcumLZz70nPZeO8GnHPssvNq/vVzF6G95w2vfwdXX/MTlFLkWcpnL3onu69ZvV2rWFD8zcvO5YSjDuNpJx47tzEGZQLX//wm/vEt72Pj5q0opTj0kAN4z5tfxcWXXc6/33Qr737bq+c+fLq0MUCR1TXE1QLLj8UfF2obr+YmRlOgKeIHIVg6U31OPuVZc/d5/fr1nHbaaZx33nn0ix7nnPNSbr75ZoZHhvngP3+Q1WtWz/UpLfG4V3/vKt507lsJ3vPMZ57B/3nJi5hLEliAcr1/R0l8TtEqrdAmMr6rusJn6XMKl1P4BoXMM6FaIFOeNhVGKlTw0dLXoJTDhIq09FgHeuVO93Pu//3yYFuUiMg3gW9u03buguU+cPqf+ry+PISfdxdiYreNqi5Yvx/SjO3uC/+1UbYAurJ4ZwEHUtSvp5rdoy5lO9cmUA/hUIElIafn2wsuSbZzDrb5EMwvV/0BdNUgy2ZI8i4m7VMWLZzLFg3Z5yF7ss0RoNlscvt/3E4+0aDRyLniyitZudNKkpAwVA7x9re9nSOfdCTPe/7zQMGtt9yKD22+esml3LNxnG9e8T201mxYt4Gq0WAzI8zWTt/2vvXIGdMD3G2G8MrPTZs2b+KZL3oV7/rouzjg4AMQES7/+uXcVJRssoYprbkziSQorb4hrzRTLY8YjVVSF1qT2vqN1rAifr113dZ3OV3XoOsSjC6xpo/YCj+a8qUffnnunpxx1Bk85aSnUJo+n//i5xkeHuLaa67h0ksv5d1vfjcXXXTRoi7hvefc17yZz3/+86xcuZLjjz+eY489gb333ntx/1CxbwQUQcCoWOPH2gqTzFt7wVu8b9Avm/RCQq9mpFJARmCAAuNLjC9RSuONQrRDSUVeerIydvl+qgjNDIZHSJqD5Pb38Z7+75UHXVE+WHLX725kbHn/wb6MByxB70Kw2wOHzAdGtlXzi530KpaQKEewoaKRTJM3pnGhT8cNUIZ03rKrDxhB1dTZTHHrU444iiuvuJITTzieyy75GqeuXctPrr+exAU2b9zIEYcdhg0KUYpHPWI/EMWWjVtYsWwnUrEoJ+yyfEXMqhHPw/d+JDfdfhMB4ZuXfY/vX/593vKB91Fox5XXXMnHPvExtm7eyivf/EqOOPYIvvDJL3DymSdzwMEHxGtUimPXbotqEK765g/42Hs/SuEqBpcM8Y4L38HS5Uv592uu5x2vPT/ab0rxrW/9CzOdLs99ziuZmu7gnOdN734d+z/+sZGcmQh4ikmgmqTGkd51252Mbx7j2AMOQnnP9779XV7xipehlHDCCSfw2te+DhFZkE8d+MUvbmD33dewx54rgMApp57Aty7/FrvssxelGHywCJpcl7RMl5btYUyYe5jiE3TZpKradHzKNFDVTzdFIl+p75GECqUU3mi88QRd0ugLjU79IUig3zC4pQM0W8OMJk20eiiFEXZgRdnLNLf5G+aGrBE8rGoFoObaZycTYrsNxDrbgegkDwrr62waF5mAEgdJHR3VEitea1mAVZstOxFmFU9UWrpWQAGh0orKaEobp15iCNYixqKUxiqNVhYVAioERGvCVVtgc600pfbv1UETNWdobgcgt0BKoDQgVrBqjGyJof2YFqqc38ebjDIdxNnm3G951lFH8Y4LL+BpBx/Ir2+5heeuXcsN111H3u3yt2eeyfNe/nI+9clPctihT+TMp5/Gsp1XccKJT+Vpp53OT66/nic96Uk87WlPY9/99o0+CWLwIyhPML1YcC0k4HPW3bGRL3z5a6y783c844yzOOInx3Pbr+7mhLP+EpfuiglC5kryqsIEIXdtrM/J+st48qOewClfOZaBLOEzX/giX/nQ/+Wtb3sFr7jwU7z/Pa/hcYcczNaJkp7K+OTnv8ZjjziUF7z8BXjvqXo9MiUkSpGiICSULouKTDRahG995busXXsyZRKxrOs2bGLZijUxSJQYBgYHWTc+wdDoskgSLYrf3DvJyIrd2FiOANBavic3//zfcZLQTro0bD/WJa+flziLdk2Ma+F8zjQwRaiRqkIqwmAoSX0HLR6UxlmoMo+tPO0epC7e414K40MGOzTEQGOYYdt4iBRjO7LDKspdBqf5RfPOP9vxdRAyB5kTcgd5FeeNurJhUimsS1A+RUsG5BjJUapBQpNMGiiTI8aCTQjJ4qJRhQiq9NF35QXtKnAe8VExz1t/bIsI+f3iQQUVozIG3ApBdUE6DUo1TDApSgJpMUlazYAIj95rD+68916+/I1vcNxhh2FCDL4kLnDcoYfy0yu/w3euvZorrvoRR598Cl/70SWM7j7K13/8dX561fVc/6OfcsaZZ3DRRRdyWM1H2XQxBbNVtUjEMoInJXD8iSdTqAGW7v4Ydl6zB//5m/WYoBjsOVZt7aCD4EyGU01QgTRYUjEMVY5frl/Hi196Pps2b6SsSnbddTV9EfZ/7MG88tXv4finHc/RJx7NilUrePSBj+C155xL6j0nHHc8++1zEL5jEeVxajHBswoVWuCySy/lfR/4IEWIQT4vwpRYGnVaZRBFT3JyMRjlY3VHXZDoiqF0ksyWjGYTDGYdVrY2xkfmDarfxPgWxjcJKCbxTAJFHWCJytGT+c5cRU1nFT4FXTna3UDq4zOpDIwNKGSwxVBrCcNJ6yHL8ffIDqsoH73fUXz4az+uIRYS08OUxhPZo+eikrPRbomRyUrVkUuEUikqoFTREiuAUgmljvNKCaUOVDpQpYFu7qmMo9Iepysq7fC6x6LiKwvEekvmMrIyp9FPaXctQzOKncYq1mzusOtYn8bRRzI8XQOBD0iImePzIkoRtAEErzWFSvBKE5QmVYaG0iR1He9ppZiqw8NZXWVR4XG9GUxjGtXsYUqQ/hDBZ3SzFr20gSjFVHsFRx+3lle/93189QtfZ/PURorUcvdyi9MVLBvkKbsdz7HPOIUXPeuF3HLVr1n7F6diQsLJT9yTk5/4TFaP7Mbl3/oBT3n8CSilMVUTUYGy72oXawxONIxnVPcQIZbECD322Wsvbr755/zFscfV34WYoKcQRFeIKfDS4XVvPo8Xn/OXHHHsMXz/qp/ygXddwKYQePY5z+WJxxzKtVdcxbP/4iwu+crHOeGxT2T/L36Fb33/h7zoxa/gRS98IaeffjpKAknlsM6hcVRJoJfATf/xH5ShZNeDd6UTSsQ3Wb5iZ9av28iKVavBzTAzPcmyoQY+KJxKwfRZvfMomzbcxXA+hQI2rF/PyuUrMb0RrGujxBAkMINnUjm6dSzdAgMB8tBHhS6IR7TC5wpjU7KpPlnXkfioTGdymGlpGoOjLMtHSc1DFRv/UNlhFeXd48KVHLrYwvojQUeziYFNgBBT9pQEqIfFKgS09xjvMM6TlBVpWdHo98mKLmk1TVAFpSro24pe4phqwWQzMNEqmWjPMDYAt69SVNvUuv/AINy1XGO8ql0CGh0Uia+T1BSITaPCrGreQglYFcgHWqTtNgSFmnG0+54m84o/BEGJxvVHcMUQNpvC5NNk6QZCleP6g3iXA7GQ2qlnnUYyalh20DD/ec1v8MqhUNz4g19wyIEHM9ho0u1Mcc+dd7LHzsu49Zc/ZsWKJazeZRShxa9u+yX7PeIRNNIey5cu4fbf/oxVe+/DZZd/m8bgIFvaQ1Ta8vWv/xtnrV3L3XfexV133snue+zJXz33Oaw98XiOOuoIDjzoICBwySVf4ilPeSw2nyEETy/zjM9MY5etYDwELv7i19DAiBHW33kXT37UXjzh4fvx85/cyk23bAa9EytWrOAZz3oWU90eN994E8879liM93QzzVgL+mkd3BLNVy+5gqeufTqhmC//ccQxT+Wyiz/P/gcdwrf/7XIe98TDyJOKLJ0itY5UCSse9zBe/ru7uOu3E+y6bHe+9uUr+fSHPo4UOTMUTGtNT8V6TQrIBRoCie8joQMEgoWk3SCRBD8+SVbNYEIgaMV4G7oty3B7KWuyEYx+iKj3vys7rKLMtk7QWLcVxAEBkQodYuRPBcGE2q8YpKZi82gvGO+x3sca3N5H+rUqLhsfMLWfE9GEECE8pUmotKWo532b4m1CNx+gyJZih3OSZoN8oMHwQIOBoRatwRzJc3yW4fOcKklwITC+4Q7uueEH3LiyzdbWOowYNAZnhVJF23ihGDFocRgxGGMwwWB8ihZNpyN0ZzpEzvXFfqn5gZggVAiBoszAJSRpjyTtkQ5swgUNKjDR2Mjgbk3+6oXPpmVbLMuX0bIZq5pw+3/cwJtf93qMtgQfePYzn8nhTz6K71/1I/76+X9Pr98B8Rx40GP465e9hK1pi7Pf/FbO+Nvns9POO7PPIx9JOT3Nst4MJvTZfY9VrH3mmWzdPMYFb3oLu3iYGFzK+Rd8gje+5Q1MbN2ENvC4JxzEEScdzoRTFEboZp7/88qzeeXf/AMrVy3nd7pgggAAIABJREFUkIMfxea77wHV5kMXXMx1V1+L0Zq9996bo484mksuvZSPfPQCUmMYzBt84q1vpZd4xkcUpdEQcqRsICEBNN+57Jt85NNfwhDIfEVelbzg5BN5ySuv5JQn7c/wkkE++cl3sqQ1xfr1m3jJ2edxyWc/S+qavO+893Hq6c/FecdZT386K3Zfyibt6JHi0RigHRRNARX6iExH0H6iaI0sxQRFb/NmbDlJFoRgFFsHodsyLGkuY0U+8tDw+gHIDstHedmnL0P/+yCLgDpSA4OVpyL6oUoJVAgFij6KHtBTwowSOgh95ejjQTkSqUikokFFUwcGlGfICMMJDCeKJblmJDcsb6W02zm62UQ1m5hWC9VoopoNTJ6jGg2UMUhVIc4hZRWXy5Ky12fdXRv42I3nc/leE3zgUZ/gMXu0UaXQn46+Q2cSJFE4I1S6htDobRWoxYR6EoMJCTakaDERkacrgq7mIk0x2CWI9lTWU5iSRFW0jZCqiHG0dojEtvF+hqqajPfWa6qeIVSWvDVEc3AIm6aICEUQOj7Q8Z4Z56jqCLpVgQFr42QMtk79cUXJ9PhmMt/AShIDYHTxqkdhCrqpo0DoS8SaAijRZEFoaGgkoa53o+iHHF8miKtrx4hA8BBCpMGzPVLXZ2QKEg/9TFEuGYR0gLKwzBSeajYYB9gQaJddhstpbPAEq+gPJKhGwNrZQIzCuCbaNRDXoBBNgeCUA9XDlgX4QDdp0dUNAopE4kglDxqkIIQplAR0ahhcshNaazrr15P0+tGCNJqxttBpapY0ljKaj/5ZLciH+Cj/l8tOg57VzfvrQH8C5nMJSPCIK6FfgOuDKxDfx7kZJtwWpOohVQ+qHuJ6SNmJUzUT58U0BHefY//ncsWPz1rCiTfsSbaXYrq7irwo0caAsiQo8JB6AEGLR+MQ7fDG4Yyj1J5CV5RmMURKi8ZgovIUgw6RwbvSFZWu8CoqXOsN4nMKlyAmwebTeCbwbgKItVyqboJWLVoDQ9hWi77AVh/odAu63s8pM6OgbS0trUjDGKrajPIJuV2J7lt8tyCUCgmWFoMEekzpGTq2wuuKUtXHCjVUR1JygVw7GmkVqc9EUVYZ/SohhIgcoCZrVkqoMk9PlVTiSDwsmQLrEnpJyvSSEXpi6HUD0hXAkRBo4WhVPdpFHxN8rLU+lOBSEOVIKFAhQZeD4FMEA7bC5z0wMyhnMZUhlArVL+jrjE7WxhOj6k0R8qDxlDiZjCMWaxhcupK00aCzcSNMTJD7gBjN2JBmsiEM5yM8rLkcq3fY1/tPLjvsndxzr5xf5uegRDHLHj67rGYZxUVDZZAqwTuDcwmFt5Q+o3A5fdekdE2kHMIUw2TlIE3XpOGb5CHDoDEqx9oGxgqpDqQqpobFLGSFUSa+tPcjzldUvqL0njIIXTRGZ/zjLYq+QBoseaURZTHaodIeNigqZylNIHWagdE2kjQoy4rQd+hSaARLczarOK2QpKJwHSqpCMZTaBdt7QXfEoUiEYutrVCFwltFIYGy30YhWFtikwJrHdlghZNpxsXT6fTp0yBgYh61BBoIDQUZCu09ygvaGShHEQeVlHRVRakqKl1S2IpSOZyat46NElI0qRgSBJNBOhsc8+D6BlellKqBVpHXs8JQBEOJBeVBApSCkgF0iAmQ9zbmn4kqwYonJ3D62qNxZVED8KOp/bF/Pp9HPHr3eEIUymUE3yCoFFSXNK9otXZGRFEUBf1+n343BqMInsoJM0lktE9EGBTIROFURaknsVXAKkV7yRKaQ8OUnQ4zv72NpKpAKaaHErbkjkbaZI/WSnKb/wnflIcEdmBFmaw4FP+Ld2OTCp1W6KTEpAUmKdFJH216YLqI7hCYxssUzk+Sui14N1Y70RdLKU1mQpu7XcamAjZ3DZPTOcXUCtq95QwVSxnq7cRQfylJmI04RmvPKEi1IkORaUgVZEqRak2mcpq5pm0UIxKwCzKFxjUMGxVJICRiM50OeKPJQsxVH5/socTUNXJszNFWniwR8tyg0gbKDlCoISZ640wVU3MOiURZ8qSBUQYnjtKX9H2xOOtnTgyiDEgKLsMSIjO3miFnhgbEMgR1ydtCorKXmgU3pg7Getw+XewqUIAVQ6JijnqihEQURhNJKfDxikqN7ipKadaM5ULIGmRpSpIldOnTK7r4ECtTGpKYIOgMxsVyv0ZrjEmZTCZpYBgOLXQQEjzXfvvriC0ItoeoWdB3QuUMvSCUWjPYGGYwaVD07kQkAXZibGyCophNDBC0REbxaRUZ7y3CUICGGCpdUNopbAW6CuStFgNLlqG1pnv33aipaRKgbGesb5VghFXN1QxlQw9hIP9MssMqyl/dNs1N14CSBC0JJsas/0uROlTiAbEFJp/ENiZIG+OkzTHy1lay1hZ2bW/iYe2tqEGBFRDCb+h0RtjcHeSewvKTquLuUNEoRxjqL2O4t5zlvV0YLpbT6A+iKzN3RoAkM7QGEtKW4capfydLFU9ZfShs2EgjOPqhQptYgMqEpC4jwVz+4WzOciCgcShfIq7AdwsmOo5uJnTTGLkGyINisDDkBeACwfRxWQa5hrSJSiORgkOB1CUeJGDEYYKPEX/xCIFSCf375AZFeg9UnTuvFvOiJyiMUhjisNyqgNVyH1KGEDTeabwz9FQs0Zt6izIG5R1KSpKBQXxTMVFNUJYlSinaWZu2TbBhClyJGUvRfQHbgGwAYxKCgqV+mGD7hHSKYHs4Hd0gSgxKN+mJMFb18OIZSJuM5CPkKqcs+0yOb8G5wZoFaQaFQotB+ZQghhnj6JpILDIQhKYYvO4T0ml8SEj7gjaawZ1WkrVauMkJinXr0SHg04TNw9DVJSP5CMsfGmb/2WWHvbvezZD+HjKUOXbpWtmo6GWKo1GfQmcZamY5FRFPOW9jBkzWJxveQD5yD/nwPaRD97DbknvZw3Y5HJCQ0i8tm4uE24s+v61+zQbZzNYwTu7aDPWXMlwuY6XbhSVuJwaqUarxQLM3yBAj/GLTOoIXDsHQExMZtufywz2pDRjbJ7VNEtPCFY6IQ05QOkUl7bl6PU3lCd4jLiASY9x9oxgb0BSJxVmLLIiD21CRFSUDVUlWleSuJJPaR2oMyqSIahI5aOIdi38/nyeuCIgpEe3BemSOdijuEUKgqgJe9WslGRVjCIbg43AaEkRFyrY57nYV/cPalTgLW/UW6EOqUpaYJbRUhqq6qL7DVMMoZ1FJAqmKhB+mT2Wn8ab3/9g773i7qjL9f9fa/bR7zu03Nz0k0os0BaSHohFEKVaaOurY2zjjoAOWUQQVR1RUxgEZlZGxoD9EkBJABJEuGIRACkluyW2n77bW+v2xT24IwTLozOgkz+ezss/OPbucs/d59lrvet/nAWtrNYxMbRy7h8T2mIxqNKMWUkgKVokcOUxsaDQb1E19y7eExEYoG6GzMIV0UsJAMhNpDJKciijggwxxZJXUL5G0bNwkwi8UKfX2QZoSrVmDabUwQtCs5Bj32riWy8LCQvLOf8UXaSeeL3ZYoizIScLq1zsF0YZZKzqjnrH+R0L4CFlAyC6ELCFkBZWUSdu9tMYWz77N6AaW9zh+zyPk+tZQGBxlfmUd84FDmja1dQUm13eztpVnqithurieVYUnqPkhbedZky5a4qUOe7qfYcbf1KnAESBkFnMzOpONUzW0JiOgTujK1hJfuTjGxTYetnCxcLAR2J23FTudPpOAkhojMmMwoRTCqI6Sjg2Oi3Eymnq2zMcWZxutNVoIhGdjexa2b2E5FqIzm220IU0UcZgQNxKS2NCyY/K5CTypSZRENxwsq4iwLbROs0fYljihyax3DRDZLdw4AQO24zMnKuMYJ4sXz/ZsS53jJhkxunWMEyGsrXWaOrHRsY82Hi0NytJEpoZCIY0kUAGe9hAIIiIEFmi7o/eY+QJJO0a4bRxPElk+4w1BGhlcHVNUIY7lUgqqxKlhyhTwag0sIegaGMTP5Uk3bybdPIHBEHsuE2VDJNr0BD305/p3pvv8D2KHJcoX7LcfPRf80+y66DgxCQDREa4VAjnrp5ot5R+IAW0V2cnkzuJQUR2PmRoNmRxxmBjbk5nJJUzNxJg1EbneKYr9j5PvWk3lBevo3X2GxYnL5MR8xsd3oT0zTN7zeNrayKPe4+ztLMadHGNtYyPtXEcXUspZqy3QHd8Xia00UkoQEo2LFh5K+sSuQ/iMlBGBwdYaF41PQqAS/DSrDBLGwdZb3KYswHmGOtGzP3dHlG1LYbnsjKuNxChQYUoSppjq9ttvQYpG+A167RCjXJKohK3yaJEl8JOAh9cZygqk3npdJJIkltSTaQK7QKALKKExUmNElJX7JTGSBtpTGJ9ZVSWjbJI4h0odlLLZIq0WWRGRE6KFxjIW+bSIlwaIjgiGpJOfakuM3QRRQ1gx0k4xdomWshhtClSaOW52pXV8HVMuuHiVLuIUauNj+GkT43r0Dg4hwpDoidWYJCaxJK28z1QQ4joei/LzyDl/OEy0E39e7LBE6QUBw8t2/b3v0VqTpuk2LUmS2RbH8TZty4xmFEW0223CMKTVatFut2m1WiilsuBhz9ZjNEPJ5FMLkGoZed9n7pJJeoefYGjOvQwOrcb351LoOZ533buSoxadwkf2/gDXn34K5WIPp5/3CRq2x+KepYxPThEbQ1DqIjSGmVQRm20LvYUweEKSA1xlkJHCTTQ+Oks5NxbgdRrQEcE1QmWJAZZEeB6WF2QPFm0wSmMXPF5z6hn8679c1ukdpizZfzcO3PcAfnDldxnfvJm3fPDtbNi0kSRNWDB3Pj/65vfQWvP+8z/Eyjtv7+hRenzrK1eyaP5CiDMfIG/27DNif9N73spLjz2BU1a8AkXSKQdVaAwPPvAAHz3/n9g8OYFlORxw8L588J8/wM+uvYHHHvwNn7/oHyDfGTtoiYpzmMQlqNUQqcL3DFZfFyYXMBVNMxNPo4zCtwKEEmhj6Cv14douQoKUAkPM1NTTHHXMy4Gsl7tx0zgnnXYSH7jgI/zwW9fzuU+ez8DgEJZRnPuG1/Ou974L2y+gkoSpsaexleL+J9fwofe9j1ajwfGHHspF//iPtD2HZk7Q9EJ6gl76cn07e5H/S9hhiXJsbIzvXvPdzM5UKYw2KKW2ac8nGd9xHHzfx/d9giCgUqkwPDxMLpcjn8+Ty+UoFosUCgWKxSJBEFCfDFnz8ARPPbCZTXfOMGZezMCSN7PooCfAu4mJjf/KPw6CdNfz7f/4NNcdehR64VLu1UX+JmoSN0Lwsl7GTJwiRWYMq43BTxWldojnWDipRJpn5Y6KLC9QkvleQwKOjbYdUsslMjZJYkjTFJMqSBNoNrBtG8dxcF2XfD7PqtW/hbJHLgi4/vrrGZ43FxnYOHPyfPyC97P8pcfxrne8E2PgoYceQpdtrv6Pq9k4McLtK1di2y1GR5/GzyXUrDpaW0hL4nkerufiOA5IQeobJt0ZVnvrAfCVh5900arVOPdv3sRXPv95Dj1qD6SjuPban1GImhSsLI2IGNpYJAQEYRFlHPKtUaR0kD0DiP4ik+Ek0/VNaKMpukV6g15yTo5EJTxZfZJN8QYWesMo1SSKZ0hViHYEP77tPwm1IDWZHuUxx78SnfQgkJy84hVcdMGHUVrTPTwX2/OJ2y2mx0Yw2tAqlvmnv/97vvjhD3PQ3ntz8tv/lh/e/QtedMKhkHNZVJhHsANrQf4lYIclyscmH+PO5M6t/7F1ynUbbCnt25J28cz1Ld4yUnS8ZYREiC25kQIrthCJwKpbWNLCFvbsUkoLISwQDgYbI2zUoE3SZ9EMBb+KJeEjDol9IkFwMHsV72VPfQ9zFrU5ac4cHqo1mHgwxd1tKd2tJraQmQ2DzuTangkjBGmckNgahMoS0E0Wa8w8WW0ULoly0SKPZzvYbkaoNmDb2Zey5aGilEJrTRiGtNttjDEcdthhfOtb3+Lkk0/miiuu4OSTT+buu+9mamqadevXc9DBBzM2sRmAoblzmKpNs2b9Wnr6yti5cYQw9A7NA1liXI1zwKL9aTay6bHvfPc7/PBHP+SCL1xAK2lxx8o7+OaXr2BicoaPffRjvGzF0Xzhkks47ZRTeNEhe2O5meDYKSuOQzRBVoFIMiN9/t+NN/LVz34dHWnK3V187UtfZE7/XH72wC2c93fnZbFNy+bWlbeSNlNOWHECtVqVJEn43CUXsN+BuzLZeJLYCGIjifTWkE3eybP+t2NMbp5hnwNeQlFDr52yzlakSlEeGMTxfFq1KrWJzShp0c7lkaufoDY9zYtf/GJqQvPKU07hx7feyHGvPpmeoGdnL/IvADssUfb6fexa+/1Db/gDOhnmD77j925sMo0ioJMn2GkB0PeslJo2i7hPzqN/4CmGh1dxXN9/0G7/BIcvYukYg+C2X/yCycnJ53k+z3WGUOnp5YAXv2TWTwWzxVqikzLfOc0TT3oll17yWY486mgeeeQRTjvtNO68806SJOGcc87hLW95C1deeSVHHH4kZ5z6WgZ6+zjt1GN52YpXc9hh93DU0ct5/Zmvp7QoxHEya9jpcJqZaIbx5iiJiigpRc4YNq/fyPU/vZJ169eyYsUbWf7S63j0kd9y+itfgZM36EaAMxNnqTS+hSlYCKnp2xxx8qL9eNX3b8ZY8LX//BIXXnYJH/zYB7n0kkv56IUf4cWHHETYbFJXm/nGv17JQS/Zl7e+/2+IU0WzFbE53Upage1Ttg2ObpGT/YzUHL7z3a/x0pe/knmOpqunhHAdrv3xdfzi7nvYdbfd+PhHzqNSyKM8HxQMT4xz/6ZNzJk3jymRgoa+JXOo/axBX67vz3Ytd+JPww5LlGn3fC47/BV/9v2KLbW5xpAzkNOGnIKCMhRSQy4xlGJNITIUI0Mx1OTSCD9pY6sWlm7Rki0aVpOq3WDMmaRm16mLFhOyxYysUp9I2T3nsrzUZNiJ8PNTNJVAWSFGJB1fn46x1+8IH2xROe+cdZbZaGTHJTHTTQSIjUXTZCrnltBIaRBii0OfnHXpm7vbfqx7eiPf/uH1HHr0ccTSAgm2G7L82AO471crueXmO7jplpUcc/xLuOvu7zNvQYW7fnUjv7zzfm6//U6OP/Y4vn7FxRx91KGAhmgD3QIqliaQhoKvETac/MoTEPgsmLsHCxbMZdUj67JkeyTWCFgqxLguVn8/Tppi1UFEIIKAtWNT/MOZr2JsYhNxnDA8fxhfGg46eF8+85ELWXHqyzh2xbEMzBnkBfvswXnvPo84NZz48hPZb78X4lkeqU4Zb43jkVJJQqphH2tMFk296Uff49+/+U0qgz2kccxLDjqAB395FwMLFnLJRRfz5re+je/95zUU6k3cNMEUC8yUXVKVkaTfV6G/a2inws9fGHZYoux96re8/fEUy3RcBTspfJmCOdjGZM6CnXVHZ+uOBlebzhI8bfBU9n++ypTNt+ghZjIaMVKEs5IaQrSRtGaXUjSRNBCigaSBFHUsVUOmNT5W8PhZKccPxuusu63MffPmM7f/hUyzmMifodWzCtUvkAK6bMMJx+xDYjIr0y2eL5m1wTPVgDJoIwiVTzvNWqqzW0EKjWfFuDLBlUnmLCg2YonfkS4lJFJo5hXGWfGyI/n8J8/jW9//PmOb68TaYqRdwZEJubLLK199HK9+3VGcfvo7+MWd93Lyycsp+Jqjj9mXo47el0pPnhuuu4Ujjjw4my1PHawwJZmKMbHEGhGYtoUKe1BREbcwjjES1XbYbZelrLr/Qewjj8YeGgKtSUfHUGkCto0slaj3dfHe17+GM992JseduJxHVv6aL3zhiyyu7M6nz7+Y173q11x//Q28/sSzuOmmm3jDy8/kmP2P5brrruPv3vYhPvjBD3LmmWeCSilGDaKmZr2eRywEPjC1YTVgOOhFB2O0ZmZ8lJ7uHipzhpkZGeGMU07m/E9+ktL0DKllMVm2qXst5hS7GRkdo3toGC/IsXHjRubMmfPfc+PvxPPCjkuUAwXOKX0nS9IWW3pJphOzA2TH1KvjuWxk9jcjDEjdWc9eI02WgiIzV0Ijs/d3/As7NrI6e41GZ/LAaBSaFG1StEnQRqGMjdZ5Um1xqAp5iQhZNd+BA5rsxipgFcVnfA5LvGkb92u3U40jsNDSQRlDYgyxViRKEKuAVPmkKkuqFMLgO4ayJ+nyPXKuu00ZnDGGqJnSqseoOMESKZ5ncB2FLRNEEoKBSqvJO191PAO+z0t2eQE3j96PawRdCG6782523Xd//GCQuDXNE09tomfuntz+wDoKPQX6BvvQWnPfr1ez9+57Q6uP/u5e1t75OEuW7MIPbriDLt/DKAstfX780+9x9ptPYMNjTdateZolixbzjte9jiPPOIMVp57K/oCJY66+4UaOOPmlNIo2VdVgPByjUWuw56I9WRDO4ZPf+wRCCoSQPPXUGvbZZz/22Wc/7r77bh577DGCIGB4eJg3v/nNNBsN7v/VLznz5UcQt5qM6X5qwkYIcK0GdhDz3e99m1e/OjMJbUxPkkYRLW2QY2OoJGLlz25i10WLmC6VmAkaCAyVlofoH6JcLvPAQw9z8MEH881vfpN3vvOd//0/gp34o7HDEmVUSvjtwFV/vh12ErS3QAh7tknpZEvhgJDZJM7suNdkSkMmxZgErSOUavNEW/NUZHNokFK82WNt0E3/rjZWl8QabbKr62NGUjYfViCcno9rJRS6JMaTxMkESrWxsJBWP0ka0IoS2knWK7SkwacBdkhqR5lPTgLTCdjSxrM8PMvDtVxcy8XxHUo5D5N4hI2UViuhGRoQAsfN8jTr7jJyQ0t4/dmH0FYGx4pwREivs4Z1q27lkx95D1I6pBpeccZZzF16EHfdfj1ffNeHieI2IDhwn4N5+6vOJj85ySfe/R5e+c53MXf+MLsvWkir2iDyexFSs/suS3jpsaczNjHBhR//GN3dPeTmLeCbn/scf/ehD7F5ehoci/0POYC9TjucVtrCFg7l9gDn/+PHOPd15zBnYIgXHXYI6zY9DcAll1zCrbfeimVZ7L777px44olcffXVXHTRZ3AsSSFwueLzH2es4TJBF0pAwRbM7S3QSDWbW02uueYaLrv6Mp6eXIszkyACl89/4lPc+LObcaWkXC5z/mVfpZpPyLUtVhx/Cjf/+EdUhuZw2Ve/ytlnn0273ebEE0/kxBNP/PPdmzvxJ2OH1aNM0yaNxqosuRyRCTN02E4bjTEKTPoMEsua1lnvz+gEY2K0jlEqROsIrdukqo1SLZRqkqZ1VFonSWukaQ2lthfSABDCwnV68bwBXK+f1O7hbff/lEMG9uSDv92Fn/3kNtq9RV6xxwj97cex0OB3wdLjWbXLW1mwaA+aM1m5Xb7soV3BTKtFPUxJdBbrChwoBT6TRoMl2EVo1Nq1mLJNWIgxVhEt88QqJlIRkYrQRj/rPMXszL0wAqMyCccMW5PNjdAgUkq2oSCz+vhqKlDKEGiDo21CXaJmcigkQijyWtPXqmInIZQC6HFIdR3qBnsKEqeAlAYrbmWGZoUCNaOwbJtCqtHNJu2cTbPLpU6IMQbXcqn4FbrcMtXRECmg3J8jHWsicw525Xeo7OgUwiq0pyGqYwzURD9jqkAoDDaagWJMT9fg1k2MphE3qMc1xGR71tBNGshFCc1ijrGufvocgTU6gpACozTlTi33Xyt26lH+H0e7vY4HH3ojxiQYozBme93H/yqkDLCsHLZdwLYKWHYBP5hH0e7Cdko4dhnHKeM4FRynguv24Lo9OE73Vqm1NOLyuz5FWyW87ekZnr7q+0wunMsRXY8j2jEjC05h7lFvhHkHg2XDqlU4eRcpBNP1iLFaCw0d8QefXjfFFRMI00JKh7zdw5o4YIPtMH/ePJL1T+PZLiqo4zkBXm4YyIbcyihiFZPohEQnpDolSVOSNCFVqhOqYDb5WpisOiYnDIFtEBjqSUA1KlE2Pr4QOCLCEW0qVgtfTDNGHifN0RA+zXwPRatN0a3hqDZOkoepFgBO0kBYFlZ3N1Z3N9XJzZh2Au0G4zlBc9DKyguJKHtlyl6ZoOMo2KxG6FRTGsih61mZolV8hl+MMZBGENUgrEHcAAxYLi13DqOhT9MYhICK26Sca1LIL9n22gtJyStBPaKl2lkamTEUhYVcOI9xZShakkoaU9Uao6Grf+CvmiR3JOywROm43cyZc9rW4fE2w2QHKV2EdJDCRUoXafnZa8vHkh5S+liWj7RyWNLHsoLOkPq/gLgFmx+D0etg9GHY9ADJ6K/59pxeDokTen65hl9292JbgpWVEzngyJdz5JFHUm0n3PfEJHeunuQlfRHJSA0A25IUXBsr0nhG0JVz8XJ5jCmRpnXieDMqHmWBkFTTIlNuD91zh0k2bIBBh4hxhHBw3e7Z3qMtbdJYEUYpUTNBpjqr3Qls/IKD69sIKdA6IUmmiONJjFGk5BlrlHBsn6X9ORx7SyQ1i7A24gbjtXUUVIshq06CzYwqMqMK1No5/GZMKWxSFCAsC7e3G6vSTapSJsc3oiJFy09pe9m8T9Et0OV2UXAL2+QdaqVpVWPcwMaxJUkrQeYshG5DowVJE6IG6I4AhuVBvo/I7mK0Zqgl2QOh7DmUgwmOPvLlpCk8c3rsqquuYq+99iIJQ1rVmSyoYgxdxRJeXz+r2zESw1zfpbphFIBCdw9BsfRfvm934n8HOyxRys0p/mfWIwt5ZD6PVSgiiw5WKY/VVcIql7PW3Y3d1Y3MPc/62rgFtY0wsw6m1mRtcjVM/Bam1zGbh+kWYGhfrt/rRDZXH+ATL/pn1l79ecYH84x3L6Wy20u5aaaHC79wB4+N1jAGXFty5ElDDHb5FD0b38kS3dNEUZsIqW5uExQVhYqH45RwnBJp2iROJulKqhBXadkB7nAJMVYyyonYAAAgAElEQVRD9tuE4UaEcMDkiFopUStBJdkQ3PEtciUfL2cjLYkxmjStk0YzJEkNMFhWkcl2kZnQoifvMVT2t6mP11rRbozydDyDIwzlwJDgYcc5+iJNpTlK3c5R9fKM5ypMUCYnmljpNPHEOKmQlGMHZWs836bXCSjYAVLa2VcZN7eoZYBRNGuZ/mXBmsZMNHGIoZ1sNb6UDrh58ArglQi1zdh0m1qSCW8UbIuh7gCLGcKwxp2/uAXP7d3uMuskYWbThtn1yuAQbqHIeJTQUpp5gUtar6OSBNv1yJcrz+9+2on/FeywRGnCEDU9TbJhA7rRQDWbmFbrd75f5nJYvb04fb3YfRWcni6cngJOxcet2Dg5hYimoDkBjTGoj0JtE7Sntt2RHUDPEhjaF/Z5LfTvCoN7YboWMNlK+NK176McreB7P0q5b8/XsjE/QCJdeCgk725k3/ll3nPMMg5cVOGF8yusWf04/cVtY222Y1EZyNGYiWjXY5JI0dUXYNkS285j23lSN2a0NUmga2BVYQ6QKHTqUZ+aQadtjLaxXYeg4OIGEiE1WockaYiKsjisMTqLsbrdKMqsn05JtWFuJaA7nw1vtU5I0xpJPEOqWoynmW7dUOrhNh1Mo4XRNYxlYYo5NBpXjKEcH6VzNFQe0gISRSWZQaAZdFPsKM56g78DqXFop/MIZA0rnsJoG2P5yHxPdh2cHFgOykA9TJiYiGil7azKxrIYrATkfBul2jRbo9h2Edfp2e44qlqlPjaKsrLKrO7huTieT6Q0o3FCybYoqITpyawyqau/f6fA7l8Zdlii9HpdFp01B1QKKoY0xCQRqtFCNUJ0o0XaCFGNiLQeo5pTpK1JknVP0F5lUWtb8AylcYTBLSrcio03kMMb7sVbfAzekqWInoWo0lwmvXmMqyKj9ZiRapsNM202Ptjm6alNrJ1cTbWdAC8DYHMaUXFsDghqnLLiaPae18XS/iKW/ON+YEIKit0+jmdRnwyZHmlS6gtw/eyS25ZLj9/PhmqJvIrxRRNptRFWjG1H2+wrBdJnWY9L6WHbZWynhG3lmWolbJoJcaRgSV8ez0qJovHOJFY7Ew6OodUWlCPIRwJhIpSMaQWSmidou5otqp6OEhQcG6/VwG1Ok7olItvC0pqaU2LK+OQ9m4IrydngWxoxK5mXqRY1ZwxCaXJDQ+h2P6oaYXfnMI4kSjWNMKURtmhEKdpk9hwlYdFf8cnlsrJNYzTt9tMILHx/eNvUqTQlGRkhrtUIfRcEsyRpjOHpKEYAQ45kZtOm7Ht3XBxvp1XDXxt2WKIkqsNvrwfLBWmD7SEsF9v2sXt9GCyj7IDYKoKVQ9kFlFUktgqEVp4mOZotQa2aUJuJqc20qc00mak1qbUSGht9aptz1B4sUMtZ1KxpNDPbnIJjCeaUA+Z351ix9xAPztzISPggZ95fYuHjD/DkYDfnfuirVIaGn/fH9PMOtiOpTrSZGWsRFF2kFMRhmvU0AS0sQq9Ctz+AqE6gWpOk/SCExHF62OI1aERWaGmEjUGQGE2YKqaq47SSFNfVBG7MeDNGGY02gBa4kSAXQi7KPKm1gIZvaAUSFbg4lotnHJwmeGFEod0AP4doNxC2jT0wB+W5TI9swi8U8YsV6mFKI0o7D5ds8sqzJZ4tsWWmHB6GKa5vM9FMSBoxqYR0pkWYaHRneG4Lga8hQFIp+wQFZxsyDMMRtI7I5RYh5VYhAFWrkWzahFaKZuBlMcn+wVkSnExSmqlmru/QHB/FdERW/OIzs2B34q8FOyxRPs583sLXSBONUoZEG1KlSZUhVppEdX7ofxAukAcq4EIwZNEV2BQtQ5eK6G3XKc1sojC2gUp9kkpYo9/WzFu2gLkv3IvCQQfg774766pPc9KPr2RZtJQX/XY9vxqosOSAg/8oktRGb9OUUbOv01SRJCmpo7CUS7sOiYyIvCYUAGlI0CRaU40M0jdoV2LiTgJ+NPFHfZ/SznqezRQsI8iHklxL48UddXIpaPgGVfCpdM+h23Lp60x+NasxrWqEIKbQ6tSqhyF2Xz92bw/aGKob1mO7LqW+fqSUlHOZ5W2sNO1Y0U4UUaKJEk1DpyjdKbWKE4iTTO9EShwh6M67WMpgWplXe1B0yZc95LN660lSJUmmcN1ebLsAgFGKZGQENTOD8H1aOR+TJPj5AkEhI8FEa0aihIItcarTtMKQoFCk3ajjFwp/1Pe5E39Z2GGJMnAs9hruwpYCKQWOJbClxLEkjiU6S4lrZ813JJ5t4TsS37YI3KwVPJuca5F3bQq+jWNJlFazKTWJTohVTByHRGueIn34UdTDjyIeup+Jm69nAkhzHo8NSU5YpCjNEVyzd5VN3SkL95zm3p+fRztt007btNJWtkxa2eukzceXfhw9+Qc8LSDLbw8gn5TJxUVkLGnnaggJHhYIi1hntrG+tDDNJiYJMXmwpIvn9SKRYBK0DgnjhOl2HoNkr+F+Xv3qU7jiXy7FTDdJmk0WH300B+y1F9dedRUbwjrnvuNvGds0jtCChQsX8pOf/IQkTnnH376T2+64DSkMgW3x7xdfzJLd98AeGkQ6DsZoZjaNYIyhPDDEueeey4oVKzj11FM7vUgLz7YoA/fccw8f+MAHGB0dwyjDIYceyqVfvpTv/tu/c+/99/Plb1xGEirq0yEqySaDCt0etrN9toLWMWG4AcsK8LwBAFS9QbJpIyZJsPt6aWhF2mohpOS6m2/h05/+NEIIKgODfOzrl7Ogp8LG9et4+/s/yNp165g/by4/uPZHVCrudse78sor+cQnPgHAeeedx1lnnfUn3N078efGDkuUxp4g7fs6MRoMKKNmcwe10Wil0alGhQplOk1nf0t1OruempREJ9lrnZKadLtE7W0ggH2yVqlb7L7esMf6mD3XGs55EuBRJovw6wUxT6R38NTSAqpcILADck6Obr+becV55OwcvgzI23kqVg8m7ViAKzEr+ea6Nq7n4HkOjmvPysKFrYT6hKDU6qWrL4fjWWhjeKodUVOaXt+jkJckm0ZIZiZRPREiHYdOIn4tKjETdVMgZshOyOdy/ObBVTTXbSLwfW655x7mzBnGKhZJh3r5+zd9gJccfTjn/935WNLioYceImwmXPlvVzGycQP3/PAHOCphw+goXfPn486fN/t11ScmSMI25YFBbHd7gtmCsbExTjvtNK6++mp2XbwPaZxy61030JypYRIDtqC2uU3UTrFsSVdfgBvYzzmpsiUuCRAE80Eb4tFNqOkphOfhLl5Mo1En6kz+BV1l3vve9/Kb3/wGp6vMuz/wQX709a+x65vO4SuXf4Njly/n3FefzlevvIpPf/rTXHjhhdscb2pqigsuuIB7770XIQT7778/J510EpXKzpnxvxTssESZmpSpcGobTcktzZIWjnCwhLW1SQspZEdLUs7mGNrSnv27Ix1saW+zdKQzWwroShfHcnCFy9qn1vLw/Q+TlBPWHWjztdYNXFp+G+75XyaSPkc8pTnykQbQwNttN4KDDiBZvDfV8hI2b0oZX19nalMT+VoPu5bDsiUjkxfRjn6baWVKeKa6+bORKZFrWG+wHJml+2BoK80DgCc0wqTk5QIWyjeiKgnCONRrFdx2xBI1ilSdJH1jOO6ww/jpL37BGWefzfc+8xle+4bXs/L2layrrWNyfJI9V+yJJS1Uqlk4Zyn18TpTG59kuFLMjMaEYP6SXXAXLgCgUCgwvmkjrVqVn91+BzffdjtXXHEFADfddBNf+MIXGBsb43Of+xwrVqzgS1/6EmeddRb773cgM2MtChWP008/jWQmJDEQx5o4VNx2101c/PkLieOYnp4evvWtbzEwMMBtt93Gu9/97i13B9dddzlKVXjpCUdRm5oiTVMuvegijjzpJJoz07TrNaRlIS0Lv1DEGEOt0aDt5QgbdeYMZTPbN9xyMz/58Y8BOOfcc1l+/PHbEeUNN9zA8uXL6e7uBmD58uX89Kc/5TWvec2f52bfiT8ZOyxRLu5azNUrrv4fPabWmkcffZSVK1cyOTnJbvN344QzTuD997+fJc4Slj08xW39fYz1d9N/zqdp/foJ0gfuwV79IIVvfjMT3BUW+fJC5i3ak6X77Iub76FnbgHLkkw2baL0jxN5FTKr004TjUo0RmuEpXDRRMYhMhIXEI6LlfqIjRFCpXQzmSkG+R4mFZgkASF4zVln8c+XXMIr/+ZvePjhh3ndma/j+luuxxIW73vn+3jda1/Hl7/4ZQ49+HDOPOkkFnTnOPXYozn67LP5+atexdEHHcSZb3sb+y9aOHuOtYlxvFwOv7DtBMjatWu57bbbePLJJznqqKNYvXo1jzzyCGeddRbNmQhpCfyCS9iIaVRjEmOwbEn3nDzHHn8Up55xCkIILr/8cj7zmc/w2c9+losvvpgvfelLHHTQXmzevIpicYAvX/QNjtl/f/7+He9ADg4RCQgbDRrTUzi+TxKGlPr6cV2Xr3zlK+yz9974uRy7LFrEZz/0Abr6BxgbG6e7WCBNEubNW8D4+Ph212Ljxo3Mm7e1Fz137lw2btz4fG6xnfhvwg5LlP+TiOOYBx98kLt//iuqk026S/0cvtchlHI93HzrgzySPsKJY6ey6T9/wKYXDCHFbtz+3aeRdkB58Yn0HHYqVo9NufEU/rpfk/v1A4QPXwf3X0tywKUkT65G+QGLym9BDngIz0M4DsL63ZVCWsdZsnjaIKza6LaPLZu4TgiJRIUKO81mlE07JrJcTN7g5GOkAibbCNfFmTsXhOCFhx3G2ve8h+985zssP345o81RBIKFpYXMP2IR9/78YW658cfcuvIGjnjFl7nzxp8Rv/BAfv7LX/HAtT/gjkceYfmJJ3LNNddw+GGHZRa0rkfXwOB2w+PTTz8dKSVLly5l8eLFPPbYYwAksSKJFLmSS3W8RRIpLCDI27iBjWVLNmzYwBlnnMHIyAhxHLNo0SIADj30UN773vdy6qnHcvLLj6e7HvHCxYt52wUXYCoVTjnlFHZbtoyZ0U24QY40iXD9AC+XJ0kSvvTlr/CdO37BHosW8vG3v5XLrriSj//zp7Lr326T6yr/ztzJ59Jb2Jln+ZeFHZYop0aa3PD1Rzpuix2nVyEAk0lvIbaOXIWYVfJ+rvvXGEAbtDEYDVobVKKJ44iwHZHGBqFtLHajG2ASVq2pAlV+vuxHOBWXE9a4jJYcjIBjzj2d+XvsQrEneNZM7K7ASwHQrRbtRx5hDSB9H91uY2rVbc5LSAm2nRGmEJnU25a6dm0gc4YgeNbvVDgOMhcwLYs0jEOETd4W9NfGMS6ookL4efyuhbPulQAnnXQS7//A+/nGD79Bc6aJJ3zq4zEmDBmyI95w3BGcedIJrHj733Ltbx7jtbvvQWXzKCcccwwnv+UtDC1axPe//z322mVRNikyOISUFmG4rVXvs0lECMEee+zBL++6h8MPXk6rFiOkIOdZuMZg+1tv83e+8528733v46STTmLlypWcf/75AHzoQ3/HUUftwU9/egtHH3U6P7niSo4+/XRuP+44rrvuOl7/+tfzlnPO5jWnn47rZ543hf4ehBA88MADhFqzcPFigqkxTnnFK/jKv34DgP6+PkbHxthtaJiRkRH6+/u3u3/mzp3LypUrZ9c3bNjAkUceuf2NthP/a9hhibI20aY63sKYLdbe5k9zduggU9BJ0TJGW0nmh21bWGmA7PhAb0FktXms/CuWTb6Q0n0389u5PfiF+Wx6QjI18jRB0SVX2tryZY9cycWyJTKXI3/QQVirVuHOn58dWylMHGPiGB1H6LiNTmK0ikDpZ+RiWwjbRtgu0nEzYVvXRWFRq2mMEXglh2o7JlYa41vIsIFlSSxngKi5mTTfJGw8jV/sHNsYTn7NybSsFnvuvhe/vmUVOtG4zUl+fvtNHPzCF+LPn8+jseKptevYe8kinr7zDlpSsvDAgzDAQw8+yJIF88HA4MAAjz+xmhe84AX84Ac/oPiM/MNrrrmGs846izVr1vDUU0+xbOkyzn7tmzhy+Us4+vDjOOzwQ8gVHa76yjc49oTjtiHWarXK8HCWcnXllVfOnvujD61kt90WslfvG7nv7sd4qtWivHkzw8PDnHvOOYxteJpfP/oobx0YYHrTBrxcDjfIDL9y/YOsfmwV6dPrEIHL3ffdP6uoc/zy5Vzzw2v5+IsO4corr+Tkk0/e7p45/vjj+fCHP8z09DQAN954I5/61Kf+tBtxJ/6s2GGJsme4wK6HzMlUbyRISyKlQNoCyxLZuiWwbInlyGxpS2xXYjsS27WwXYsoaTM6von1G9eyZs2T1OqZQMX8efPZfbc9WLJoGY70SCI1OzRMQkUcpvxk4lrSmZjXqH2I1L20nGF6evZjYkODdj0maj23olFQdChUfAoVj6EDoVmNkJYCEWNoo60G2gs7Xq8Cywqw7AK2lceycluVip4FC6jkNZNjTcJqTA5NX3uCptPFdKmLqFJhru+Q0xXaU6tJghpmajUAa6bXYVds3vTGt1BsdOOpCEtF2HGDB9eu5d0XXwyWhdGGN7/pjRxzwP78+N/+jbdeemnHVhf23mN3znnD66kMzuHTF17IihUrmDdvHnvuuSeNxtZSxRe84AUcccQRjI2N8S+XXEpzWlHMVbjsX77BJy/6KJs/tBmJ4NADD+HUM1+9zWc8//zzOe200xgeHuZFL3oRa558kvbo43zxK5dzx2334rg+u++xBy992cs6epQXIYFcEPDNq64iajTQSlPozkoZE22gp5d3fOgfOO2kFXi+z6JFi7niiiswxvD2N7+Rt7zrPSxbtoz58+dzzTXXAHDvvfdy2WWXcfnll9Pd3c1HPvIRDjzwQAA++tGPzk7s7MRfBnZYPcrng1arxejoKKOjo2zatIkNGzYwM5NV23iex+LFi1m6dCnLli2j8AcSi40xnP7/Tkcg+PztC7nj0fvY3N/NWy+7CsfPqjuU0oT1hFYtplmNaFVj6lMNqpMT1CZrNGcSdjtxmIVzd3n23pEWSFtg2zaWbSHtjPyzh4DoKHtvO4SNWyEbppo0sOhOFa5wsCwo9eVpSsOGMMZoQ0VISlpgkhEip8FUaqEwlFQ33fhY1QlMHCELBRq9fYwhSbSh27EY8lxsKYjXr0fVG3hLd8EIwdTIRrRSVAbnzPbUfh/iMKUxHZHGCsuWqFSTL3vkuzLvmmQ80610Bp5bxsykKcn4OGl7irTPYJmAXGnJduru1fExwkad8sAQbhCwef1avCBHeXAIgPXtiOlE0Ts9Tt7bNqYah22mNm6ga2BwNhn9/xp26lHugNBa02g0qFarVKtVpqenmZqaYmpqiomJCZrNrcK7pVKJ4eFhDjroIBYsWMDg4CDW75k8eTZ+M/kbHpt6jH/a7X1M/ewLjO46jz0POxLLtQnDEaJolDDcRBhuoN1+mna6nqZ4iqgwglWAygLokT5u/isU+xpAAKZjl6AMKjWoNEuJ0eo5eqYCpJQICcJoVJoSklnpDqLxAg9tBEmsmB5tIqSg35hOaELREoqGJ4hSiS00g4kg12hCOIl2HKqDc5jwfLSGvCWYn3Mp2Nn3oxoNVK2G3d+PBqa3kOTQHFz/95NkEimaMxFxmCItSbHHJ26laJ1V2ADoRGFihdUhzWfCaI2anCSdmEBbinQApPTJ5Rdv9+BozkwRNuoUunvwCwUaU5MYrclXst5eI1VMJ4pC1MIxmmJf3zb7iDt5ll7wPJWnduIvBjssUU5NTbFy5UqazSbNZpNGo0Gz2dxuBjKfz9Pd3c2yZcvo6+tjYGCAgYEBCoUCxpiO8rnCmBZJkqJ1gu6onysdoVWI0m20apOqJiptkqo631j1U1xpseSmH7Ax72XeNpVvc+vKS9jGUwJwnApBMJ9K+WByuUXkC0sp5JcRBPN57LHHCXK/34jKaINSJkui7yxVotDtCBUmxNJBCTuzy5UCgdXRXDTYtpVtp7NJLq9gUbdqVNOp7PPLLirNiFytDYRMdpWZKlWwpKTLlvQ4Nnnbesa5aJKREYTrYooFpjvSZN1zhn+vWEQSKZrViLidIqSgUPYIii5KaeqTIbmSOzvxZdrZg0EGW29vozVqZoZ082ZMkiC68qhyiBCCXG7BduGIsFGnMTVFUCySL1eymu7qDCtOO4NUZ9cn0hoDfPnCT3HI4UdgWdv+nKJWC8f3kf+FB+hO/GVihyXKev0JHn/8LhwnwnFjisWQnp4Qz2tnzW/h+y1sO+mQpyGKNevWG9at71hFPE9EGm4bC9gvJ3FuWsOGoSEK/TYLdj8cz+vD8wbxvUE8fw6BP4xt/2nDNiEFthQYC3S9ntUp1+u0bY/xfA+xEPQVHAa6ttWOfCYajRbN6YSwblC2oVTooo8cZmxzNnlU8FGlkKKoUtzcIPDy2P39SHtbklBTU5goQg/0Ux3ZhLRtKkNzsJ3tq26MMSSholWLicOMIPNdHsEzSLE1HYMQBKWt2+t2inAthC0zgpyeJp2YwCQJMgiwh4cIGQWjyQVLkHLbYydRSHV8DMf3KfX2d1TSZzBac9ddd+F4HmNRwmiUUK5NUnYd/Py2oRaVpiRROBvL3Im/buywRDkwMJ9Xvqrr97xDdP7dkhckOylDVvY/wspmj4WFwEJICym2qqNnzUdKFyNcWtrQUoaWUtyy8S6ijd/mqPQk2pM/otrnUth3T+5Ru6BbGlohkvVYciO2fGDW6Ctn58g7eQpugZJTouSVnjMH75kwxqCbTVS1iq7Vsplxx2Gqe4gpbeHaksWVHAXP3m67MA1pJA1qcY0wDRGBpKJ68cM8zEA7ruMJC3fhQqxCgTRt0m6vg76UdKKGWl3D6ipj9/Ygfb8TF9xMlAsIa1XcIKBrYHC7npjWhqiZZM6PiUZagnynB/nMdCmVasJGQlBwsaysR6gThUk0smCRjI1lxKwUMghw5sxB5APa7TVoHZMLFmJZ2/ZiVZoyMzqCtCzKA0MIKdFK0arO4OcLOJ5HpDVjcYKfRORUSrFn+x593O4Mu5+v4PNO/EVhhyVK3x9i2dLznte2iU6Yak8xFWZtMpxkqrH19XQ4nbVommpUpZE8t7hs9fvfZ21vBS0Ml6sfE973R4hbPAuX7H4JckpiCWtrOaWwsFKNiBJEGCETlZVflnLEQZHNMaTKUM4LevI2hpBqpGYFPCIVEabhbM16YAcM5gcpuUWYqpK0RoicErFbJKaI17YIrBTHz5HPL6HVWkfaF+HEOdTYDGpmGlkooI2h6UhSo8mVuij2bo3paW1IwpSwmRK3007CuUWxx8fPOYjn0OFs1zL/m1ynN2mUQk1OoxtV1EwmoGkVi1i9vR2Fek2rtRalIoJg/qwi0BYYrZkZG0FrRfeceVh29vNo1aporclXKhhj2BjGmR9OfZpS38BzDq2jVgtpWdju9nHSnfjrww5LlNDxrFZR1muKatTiGtWoSjWuMhPOMBPNMB1NzxLfFmKsxbXn3J8jHbr9brr9bip+hQVdCyh7Zbq8LrrcLkpeiUbc4JO//CRnLjqdI5/4Prfu1sf8PffgprO+jS3t2R6swWQiGzolVjGhCmmnbZpJM3P7S+rUohrFpEjJLaFUQprERDpEoVGS7OoWIOsdG6AJugl2JotWV1B/1kexpIVruZT9zJyr4BSwpY1qNEmeWoeJIuxSCX+wByNsWvWYsJEQtRKkJXB9G8dfAGIziTuNvbCI3fQJZ6q0pQApKdgunuMRt2LSFJIwS5syJouD+nkHv2Bju9bvrFDRStNuJHi+hanPEDcaqHoDjEZIG7uvD6tSQbpbVNZT2u21KBUSBPNwnG39aowx1CbGScKQ8sAQjud1ttO0qjN4uRyO5zOdpNRTTbFZI5/LPac5mDGGuN3CDXI7K2z+j2CHJcpHJx/lDT95A8kWU6nngCUsurwuKl6F7qCbZZVls0TYE/TQ4/dQ8Sv0BD10+90UnMIf/GF87r7PYQmL12yaz1O2IFQp+x3zUrq83xcG2BZGKaLVT9J++AE2zE/p3ljPaq7JarNlsQurWIRcQGo0U62QqWaIQVMKbEq+M1thtEUIZIvIhyW37R2ZNCXetCGLazou7oIF2b47KHb7FMoeUTvNPHbaKWHTAKWsiRijmxhLIrCxrBJtY9GqCiBTUpdoXBs8X+IENsKxsh6kMVloQessZJCmmCTBRBHtEAwB9uRGEh0jHAerXMYkDnZP1zYui1rHtFpr0SYmCOZvR5IAreo07frWGe4taNdraKXIl7tJtWFTmOColELcpjh3wXNenzT+/+ydd3hUVfqA33unl/ReCF2QJihVmlJEpCkiYMGC4OqKsioia1vc1RXr2svPsoouHZEqK1ItoFIjvYaW3qff+vtjkoGQhF6XeZ8nT9rcO2dmMl/OPef73k9CU9Xwbvf/EJdtoEyyJ3FXs7uINEfiNDmJNAfX/KIt0USaI4m2RuM0Ve3od6bImsy83fPomt4V5bMl5KQnY3E4aXB1+1qP0QIBpD178O/cSWDbNvxbt+HbsiXU30f/8ANEmx0x3o7odCKYzQiCgK7ruPwKOWUBAopGhNVBapQVSw3uxZrQdT24CZKXh65pGBMSMCYkVClZrEQ0iNx111189dVXwbVNn0RGRjpXt2nD5I8/IL+ggCf++izZuXkoqkpGRgZzps5GD3gZ/+xTrPzpJwSCuahfv/469dLTax3XA888Q9/u19F78H0YRQVLUlzoca9Z8QvjJ4wnv7QQQRDo0qULb775MlOm/B/r12/hgw8+wWisPgP0e9y4ioqwOiOqNP3SdQ1vaQlmmw2zzcZBXwBF14krLyYyLqHKJffUqVP55z//iSAIJCUm8tbLL5GQUY+JEyfyySefkJCQAMA///lPbrrppmpjWLx4MWPHjkVVVUaNGsWECRNO6nUKc364bANlvMkVe5EAACAASURBVC2ex695/Lze58+Hf6bIX8Rt1s64Nvyd3Ksa06xTFwS/H/++fcjZ2ciHDiMd2I+0fz/SvqxgK9mKdBTBYsHSpAnRt9yCtWUL7G3asMfrreJvBPAEFHLL/XgCChajgXpxDiKsNbsXa0Lz+4NtDrxeRLsdc2oqorX21B2Hw8HmzZvx+XxYrVa+/+9CkpIS0FQZs9XGW2++xQ3XduCB8bcjimZ27CjCEe9k6tT55Ltc/LFjB4KmcXDfPuxmM6aIiOAssmKjShBFEEUEowkxMhISktEFEUeCE2NFClBeXh7D776drz/6gq79eqBpGtOnf0F+/hZAxGSKqjFIyoFAcIfbYiUyoWrTL7/LjaooRCYk4lJUimUVh8+N02KpYjRSFIWxY8eydetW4uPjefTPf+bLKVN5tV0HAB577DHGjRtX6/OnqioPP/wwS5YsIT09nXbt2jFw4ECaNWt2Uq9XmHPPZRsozxW6JKF6PME0nHIXWnkZank5amkp0z1TiBYspLz0FfsjHSiaiuOL/7DzjfernEOw2zHXqYO1eTOi+vfHckVjLI0bY65bF8F4zEu2bVvoywnbD7Kp3IuqaQgImIwiJlGAnJMdPMFL28rLeLOZFmYzLx4nSFZy44038u03s7mhe3e+njyZwQMHsnbjJpwGI7n5+dw4cCB2e338/mwaN47A683i8OEDpKSkIFYEwozGjUPnczqdobLFWbNmsWDBAr744gsEg4Hvv/+Bd997l6KSwpCP8r133+OuW2+nU9fOFXbyw/Tr1w6jMRKzeQ+CENSWzZ8/nxdffBFJkoiNjeWdVyeRGB/Ppp27eLz/gODjFgRWrlzJgZ07GP3wGHyBAB5J5rk33qTPlY2JSM+o+rRVLBF4PB5iY2MpKy2l4TVXn+STHjSzN2rUiAYNGgAwfPhw5s6dGw6UFxGXbaBUiotxLfkBXVVAqVj/UhWoXAeTlVDQqBRN6LKE5g+g+/1ogYrPfj+a14vu9QY/yzWveZbbYPUjBm7aaEDJ2k92g1TsBiP1+/bHnJKCKSUZU1oaprQ0DHFxp7QJ4PLLFLgClPkkNE3HXNHG4jje3uqoKlpACm6GGI0IZnMVa1Jt6BU74zf2uI7X3vgXXdq1Y8eePfz50bGs25SJUljIQ/ffz10PP8wHn35Kz549ufPOQcTHe+nX72puvPE9fvxxJT179uauu+6iTZs2JximxuEDh/hhyTKy8w4e8VFm/sGdg4ahGEuQPIWAgNWaiskUi3jUumuXLl1Ys2YN6Dpvv/E67374Ee++/z7/GvMo77//Pp07dw4GaFVh1uzZ9LmxD6P/+ix5vgDWnINExMaFdsMrMZlMfPjhh7Rs2RKH3U7djDp88OGHod+/9957TJ48mbZt2/LGG29UM5fX5KP89ddfj//EhzmvXL6BMjeX3L/9rdbfCyYTmEyIJhOYTYgmc3D9z2pFsJgRrTbE+DhMNjui1YpotyE6HIh2O6IzAtHpxBAZgRgRgSE6mmklS1G3f8A9A57Dt2wihQ4L7QcOJmX43ac1fndAYc6Gw9QR/MiFHkwGkb83SiPWYTnplrZQUfOcmxvcrIm0YkpNxXASDbAUWcbnKsdXXoau6zRv2pTsvDyW/PQT/fsPCK6TBgKAwE1Dh7L3pptYvHgx3333HR079iIzcyP168eybt08Vq5czY8/bqBnzx7MmDGdXr1uqPV+VUlj0IDB2CMsNI4M+ii3bv0DTQ+g2AqRlAKMxiis1uRqieRAyEd56OBBpECABg0bYrJY6dy5M48//jh33nknt9xyC3YRrr66DY/99RlyJZW+vXvStekV2CKqbwTJssyHH37Ihg0bSIyOYuxf/sKbb7/Nc88/z0MPPcRzzz2HIAg899xzPPHEE3z++edVX4Owj/Ki57INlJZGjWi0ciWC0RBc/6oQ3QomExhqT0s5XRbMe5rmcc2JWLqOvUmx6LrOlV2uO6Vz6LrOuv0lzFx7iAWZ2XgklS9vSSU9xk603VRrVU1t51LLylBycoKbNfEJGBNr3qypRFNV/B43frcLyRfMU7Q4HAiCQFx6BjffcgtPPjmeFStWkH/oELqiYIyPRzSbiY2N5Y477uCOO+6gf//+/PTTL9x6662YzQn0759Onz49iY+PZObML7j22isQBFAUD6JowVdxX4qkoqk6FpsRVfWhqh40zY/Pt58mV2awceNOhgwdWeNaZCWPPPIID40eRfeOHdiwdRuvvPEmABMmTKBfv34sWrSITh07MvXfn9Hjhj583rotK75bxISHHmL8U09x38iR1c65ceNGABo2bEhx9iFuGTSIDz//NwBJSUmh240ePZr+/ftXOz49PZ2DBw+Gvj906BCpqccvSw1zfrlsA6VgNmNKqi5RPRdsK9rGjpIdPHvVOMq/f4e8Fg1JSEkm7pi1rprQdZ3MQ2Us3pLL/E3ZHCrxYTcbuKllCnd0yMDmySXWUXvTrZrQAgHknBw0txvRZsOcllbrZo2qKAS8HgIeN5LPF0wEN5lwxsZhc0ZgMAV7XQuCwMiRI4mKiqJFixYsycxEEASMCfEsW7aMjh07Yrfbcblc7Nmzh4yMDNavX09ycjKpqakYjbFs355D8+aN0LQACQmxbNjwA40b12PmrMlEOJ2UFRcAOt/M/Zrhd3YgK+sw+/YdoHnTa0h3tqDbgD7ccutddOgQ3ET5+uuv6dWrV5XHU1JSTLTTgS0ikhnfzAn9fM+ePbRs2ZKWLVuyavly9mRlYcqojzMxhftuGYTVXc6mzMwan6O0tDS2bt1Kfl4eut/Pj6vXhIw6OTk5pKQETUNz5syhRYsW1Y5v164du3btYt++faSlpTFt2jSmTJlySq9pmHPLZRsozydz98zFJJrottvCYUWiKOCl67Xdar19mU9m9Z4iVu7MZ/n2AnLL/RhEgS6N4nms1xXc2CIZR0XJ4bZtuSc9Dl3TUAoLUQoKEAQBU0oKhtjYqmoxTUPy+5F8XgI+L0ogmOtoMJmwR0VjdToxmi01zrjT09MZO3YsSlERuiQFlylEkXXr1jFmzBiMRiOapjFq1CjatWvH4sWLGT16NIGK+2jfvj2PPfY0VquVSZNeZfjwv5CWlkqzZlfgdntQA3ZEo07Tps3o3/8h8vML+PjjT3EaUrDHK0ydOpVx48aRn5+PKIp069aNwYMHh8YX8Hl5/M9/5k+P/oU6GRlBH+W+fQC89dZbLF++HFEUaVg3gxv6DeCz+Qv56u1/YTGIRMfEMnny5Bqf19TUVP72t7/RrXt3RKB+g/pM/uprAMaPH8/GjRsRhGCb3o8//hiA7OxsRo0axaJFizAajbz33nv06dMHVVUZOXIkzZs3P+nXNcy5J+yjPMfIqkyPmT1on9yesZNL+SP/ENsdJka//zmR8Ynouk52mZ9NB0tZm1XC71nFbMkuQ9PBaTHSpVE8vZsl0fPKRKLt1WeONfkAa0J1u5Gzc9ClAIbISIwpKYgmE5qqIgf8SH4/st+H7PcHK2QEAZPFitlux2J3YKzIzzwRuqIQ2LULwWrFXK/eWVvC8JQG8JQFiE11VOvDLed6wChiiq9d0SYHAhRnH8JgNBKbml6r0aesIA+/y0VxfDK6DjFFucSlpJ2UI9NdXIS7pJjEeg0uG2NQ2EcZ5qyw6tAqSgOl3BLdHc+av3KobXMsCam8/3sRW7P3sS2nnEJ3sGbZYhRpXSeaR3s2plODOK6uGxPcvT4DNFlGyc1FLSsDsxkhJQXZIOItLkIJ+FEqd+kFMJmt2KOiMFntmG3WKrvFJ4ucn4+uaphTUs5akNQ1HZ9LwmwzVguSuqKhKxoGh6nW41VFpjQ3G1EUiUlOrTWIqYqC3+VCtjmQBZHYknzsEREnFSQhKOo1WiyXTZC8nAgHyrOEX1Yp8kgUuALkl/vJK/eTXeZnccGXGLRIFr/6CwPMRtwBLyt8Kez4KYtGiU6ua5JIq/QoWqVH0ywlErPxzAKjrutomooqSUglJchuN6oAmt2KputQWgyAwWjEaLFgjYjEZLVisliD+YxngObzoRYXY4yLO26C+qni98pomo49ovqMWvMH3ZOCtebgpKkqJTnZaJpGbGp6aE21Jip38EutDqJ8Hsy6RkRsfJXbdOjQIbRUUMlXX31FixbNkf1+bJEnX4oa5tLhsg2UsqpR4pEIKBqyqhFQgh9+WSWgaPgkFZ+s4JVUfJKKy6/gCSi4Awouv0KZT6bMJ1PqkyjxyLgD1S3iJrMba4NNxGu9uTFnE1l164Ag8sq4ETSum3xas0Vd1/F73LiLCnEVFSJhwFVUiKrIqHLwQ9OOWIgEg4jBbMZisWA0mzGaLRjNlmq5gGeKrutBIa/RiLGGToNncl6fS8ZgEjHVEAw1vwoGEaGGfzCaplGSm40qy8SkpIZEFzWhaRqe8jICFitWg4iluJSIpORqs8Pa8hsrN7pOZGgPc2ly2QbK7TkuBrz30ykdYzMZcFqNRFiNRNlMxDnNNExwEOMwE2s3Ex9hId5pITnSSlKkhe8OzOD1dRr/17ov8msL2VqvFXUbXUGzBkdSPzRNRfL68HvcBDxufG4Xflc5PrcLX3kZ3vJyvKUleMpK8ZQW4ykpQZGOzGi6PPwk3rJSDEYjomjApIMoKxgMRswJCZiios5LTp5aWorm9WJKSztuP/FTRQ6oKJJKRKy12uPQdR09oCLaq5dn6ppGaW520AaUnIL5BIIKryvo6gxExhJZnI/Fbq8m4z0ekj+YwmSynb2ZdJiLh8s2UKZEmvlHn7oYRQGTAGYRjCJYDQImQ/B7s6hjMQjBzyKgqaiqiqYoqKqCpgRQZTeKLAdndPky6mGZclmiKBDgjx1z6ac1ZNOa/1DeKA2X14OQc5jPH3sQ2ecl4PMhV7zBasPqjMAeFY0jKpqURk1wRMcQERePMzaOiLgESmSFhLQ6KPn5qKVlCAYDxsREDDExx82JPJvoioKSm4dot2OIjj6r5/a5gj26LTWsQeqSCrqOeMxMU9c1SvNykXw+ohKTThjwdF3HVVqKYjITpQRA04iITzylfzCy34fRbK4mIQ7zv8Fl+6qqhYfJ/+gcGVoEIdjiABmHLYKi4lxkpx10jejkVKzOCMxWa0VvaAdWhwOz3YHVGYHVEczxszojsEVEHndjQDp0iOKDBwns2g2CEEwaT4g/qzO6kyG4gaNgTql7VmevqqIR8CpV+uEcjeYPtuMQjrKz67pGWV4eAa+HyITEGitpjiW3rBxBkTFFxaCXlRARF4fxOGuZx6LrOpLf/z/baTHMZRwoI+IT6HHfnxAEEUGs/BAQRUOwO6HBgGgwIBqMiKIY+tpgNCIaDRiMJkSDAYPJhMFoCm6OmM0YTGYMRiOv/P4Ks3fMYHHDpygc+RCrOrUirWkzbnmq9rLJkyWwZw9Fn35G2bx5aO+8jbFuvWCAPIU399lC83qPbOCc5O7wyeJzBbMBbDVs4gDoATXYG6eyqVhFkPR73ETEJ2A/iY2VYllBLi/DZDBg8LgQzRbsUTEnPO5oFElC17RQm+Ew/3tctoHSHhlFmxsHnJNzy6rMwr0L6ZHRA3XxckpjovB4PTQ5TpL5idB1He+aNRR/ORn3ihUIVisxd95BcVISptSUszj6UxuTnJ2NvWVL7rrzTr76OphkrSgKKSkpdOjQgQULFpCXl8f999/PwYMHkWWZevXqsWjRIjRN4y9/+QvLli1DEASsViszZsygfv36aJoeNJjbjRiO2qi599576d+/P7cOvhVdUhErW9RqGssWf8ezEydSXFKKaDDQpUsX3nnnHWbMmMHatWt57733qozfrajkutzEygFMVhuy30d02qmnNcl+H3MXLuS9Tz5D0zT69evHq6++CkAgEODuu+9m3bp1xMXFMX36dOrVq1ftHGEf5cXNZRsozyWVuZOD6txE+X+fprDFFRgCLhpe0+GUz6WWlVE2dx6lM2cQ2LUbQ0wM8WPGEHPnHRhjYig5SrN2vlGLitD8/qCPcssWfD4fNpuNJUuWkJaWFrrd888/T+/evRk7diwAmRWlgNOnTyc7O5vMzExEUeTQoUM4KlorBDwy+lG9uo9FDwQvu0WrAU1V2bk5k3tHP8BXX37B9b16o+s6s2fPxuVy1Xi8X9XI8gWI9AV7lst+H/aoKMynMSvMzc7mH6+8yvoNG0lMTOSee+5h6dKl9OzZk88++4yYmBh2797NtGnTeOqpp5g+fXrV5zHso7zoCQfKc8C3u78lwZZAy90y2WVlHNYC1G/d9qQ78umyjOeXXyibOxfXD0vRJQlrixak/POfRPa7CbGWNJcX5m9ha3bN/XxOl2apkfxtQPVyOk2WUfLzESvaQvTt25eFCxcyZMgQpk6dyu23386PP/4IBOudb7jhiBGoVatWoZ+HfJQESyAhOFNNSI7lwK48TBZDFR8lwA8//MBbb/yLvLw8Xn/zdTq3vZr/+/QzRoy4i+t79QaCtedDhgypNu758+fzjxdfxOX3Ex0Ty/+9+k+SEpNYs/Y3/vbypNCxq1atwu12M2zYMMrLy1EUhQ8//JCuXbtWO+fuXTtp1LAhiRVpUb169WL27Nn07NmTuXPnMnHiRACGDBnCmDFjQpVPlYR9lBc/52db9DKi0FfIj4d/ZEDDAbgXfkdpSiJer4cm11Z/gx2N5vfjWr6c7GeeYWeXrhz804N4fllN9JAh1P9mNvVnzSR68C21Bsnzia7rKNnZ6ICpQvgwfPhwpk2bht/vJzMzMySmAHj44Ye5//77uf7663nppZfIzs4GYOjQocyfP5/WrVvzxBNPsGHDBiCYEqQDtkhTjZfBWVlZLJ29mDn/mcFDDz2Ex+1m74EDdOx07QnH3vHazkxeupzpP61m+KABvP/xJ2iqyv99+SXvv/8+Gzdu5Mcff8RmszFlyhT69OnDxo0b2bRpE61bt652PlWRyUhPZ9eevWRlZaEoCt9++23IBnS0a9JoNBIVFUVRUVGVc9Tkozx8+PAJH0uY80d4RnmWWbBnAaquMii5N+7ld1LYuS1GdykNj+mLo2sagd278a75FffPP+H97Xd0nw/R4cDZsweRN/bF2aVzUKB7ktQ08zsXaOXlqC4XpuTkUJfDVq1akZWVxdSpU6v1hOnTpw979+4N+SjbtGnD5s2bSU9PZ8eOHSxbtoxly5bRs2dPZs6cSdtWnQCw2mvenLptyBBkxU9CUiR1M+qQX+5GPIm0HFXX+XX3Xl7661OU5+cR8Hqpk56O1emka7fuIR/l4MGDQ5fAI0eORJZlbr755hoDpeT3Ex0VxXvvvsOwYcMQRZFrr72WvXv3Aifnmgz7KC9+woHyLKLrOt/u/parEq4ids1ODksSh/xuGlzdDlGW8WzchO+PTHwbN+Fbvx61pAQAc926RA8ejPP667G3bxcKPhcjuqIg5+Qg2mwY4uKq/G7gwIGMGzeOFStWVJs1HeujXLVqFbfeeisWi4W+ffvSt29fkpKSmPPNHFo2aocoCqHdbL/fX+VcAbcHt1yKyWLFaLZgtlho3rw569atY9CgQTWPW9fZ5w0wcdzjPP7YYwzqcR3/XbiQ1999l4i4hCo+yo4dO/LDDz/QrVs3Vq1axcKFCxkxYgRPPvkkd99dVbQs+30IosjNtwzmlsG3AvB///d/GCpStCpdk+np6SiKQllZGbGxsVXOEfZRXvyEA+VZZHPhZvaU7eHpjFEUffBvShPj8Hk9RC1Zzs6Pvwzdzly3Ls7u3bG3b4+9fXvM6WnHOevFhZybi66qwf49x8x6Kn2ULVu2ZMWKFaGfn4yPUtM0MjMzadIoaKJJSkpi27ZtNGnShDlz5hAREYEqywS8HmZ/+w3DBg2m2Odn3759NGnShDFjxtC+fXv69etXzUep6TrlqopH1ZBc5VyRUQdPaTEz5szBWJHOdbSPcvXq1Wzfvh2bzUZaWhqjR4/G4/Gwfv36GgKlH5PFQkFBAYmJiZSUlPDBBx8wY8YMIPjP48svv6RTp07MmjWLHj16VHvewj7Ki59woDwJdF1H9/lQS0tRiktQS4pR8gtQCgpQ8vORc3ORc3L4d6N9mJvoNHn0IyQZDqcnYNB1Mho3xTnwFqzNW2Bt0RxjzKnl6V0sVDZJMyYk1JgzWemjPJaT9VG2a9eOu28fhcVmZNKkSfTv3586derQvHlzykpLKDx0AE3TuLLRlQy6fTj5xQV89NFHWK1WrFYr06ZNq+ajHHTLLRRICrIGGTYz/3jhBW4bMoTEhHjaXXMNuYXBmW+lj9JgMNCsWTP69u3LtGnTeO211zCZTDidzmo+Sk3TUCQJe3Q0Dz/8CJs2bQKCu/xXXHEFAPfffz8jRoygUaNGxMbGMm3aNCDso7zUuGx9lEpBAWVz51Y0C/OheX1oFQ3CNLcb1eNGc7lRXeVopWW1Ng0TIyMxJSejpCUwov16utCIJ3Y1ouSbOazo1Ip6bdrS79Enz8XDBE7eR3mm6IpCYPduBIMRc8MG56Q80ueWcBX5iU60Y65oQ6tIEuVFBUheL2abjcjYBLRCCUOUBUMtqUOVKJrOXl8An6ZR12om2mRE13Xys/aiaxrxGfVOqQLnWCSfj+LsQ8FqK0ft7Sf+lwn7KP/HKdi2lYVzp6EDVFTnUPlhqPgcY4G4JBBTgmWBhmDFDqIh2FfHaAAhGDDckosbfoon1WFnQW4mSqtG+L0eDu/YxuTxj1RUAAmhKiBRNGAwGhBEAwajMVjdYwp+BA0/ZkwWKyaLBbPVhtlmw2yzY7HbsTicWJ3OkyrPO1vIObnoSsUl9zkIksdagjRVxVNagresFEEQiIxPwBYZhear0KpZjl+mKWkae70BJF2nntVMlCn4p+4qKkTXNGwREWcUJAHkQHDt1GS98JkIYc4tl22gtDRsSGzHTiAeKYEDKlq0CkBlq9aK7ytuIoS+qLrOtDf3NySrmZTINDw7snCnJCIE/CTWrQeCiK6pwUt4TUPTNHRVRVUqxBqqElSkKXJQsCFJyFIARZLgBDP+LmPGU3DAVlFiacBgMCIaK0stjaEgfCa7qGpZGWpZKcbExLNepliJIgUtQY5oE56SYjxlpRUBLRLnUS1i9YAKooBgqj1Y+1WNvb4Aqq7TwGbBaQwGVUWW8JYHA29E/Omp4I72UaqKjK7rTJ02nZYtW57W+cJcGly2gTI2LZ3Bz/zjrJwrqyyLp7/9D2OvHsu1S1wUHC5keWoSTTt356YxT5z2eXVdR5ECyH4/AZ8XyeutaPTlCerY3C5MVhtmqxVVVVFlBdnvR1PVaucK1qQbg5sXlTPXiq+PF0Q1WUbOzka02TDGx9d6uzPFU+pB19y4i4JeR4vDgTMmrppDMlTfXcuY3YrKPl8AURBoZLdgq9h91nWd0rxc0MEeFXXakuJKH6Wu6xQc2IfFZicqMfm0zhXm0uGyDZRnk292f4NBMDCw/gDKFtyOu93VBLzFNOl0/CTzE1HZt8ZksWKPqllftm3btmpvVF3TgrNURUGTlSMzVUXG73FXCaSCIFQETRMGkzkk9jCaTAiiiHz4MLqmYU5PP6uX3MF/AhIBjxu/xx2cPQsCNmcE9uhoTObql7O6evy2DyWywkG/hFkQaGC3YD5qvN6yEpRAAEEQcMTE1nj8qaApCpqiYrKERRiXA+FAeYbIqszc3XPpmt4V5/aDFGXnkNehNZbcAHVbtbkgYxJEEaNoxmgyQw1XypqqosgSqiyjSBWfZYmA11sl+VkURURFwRgVger1YJCkikv5oEFJEE4ucOq6jqooqLJUMUMOIPl9oYBtMFkQxAhiU+MwWWpfN9QClVq1Y/2TOnmSTF5AwWEUqWe1YDxqOUUOBHAVB1tg2CKPr647WaTQ+mQ4UF4OhAPlGbL84HKK/cXcdsVtlH08D81hZ3/OQRq373zGmwXnCtFgwGywwTFtC3RdDwVNxetFKilBMxqQVAV/cVEN5xERKrV0ghhct62IT7pWuR6roqkKHLXUajCZKlycdsxWGyV5ASwW8bhBEirWJ4Wq65OarnPQL1Eqq8SYDKRbzYhHXZZrmkZZfi6CIKCjn7JCrTZkvz/Yt7yGmW+Y/z3CgfIMmbVzFsmOZDrFtWXv4nG4O7VHKjhI0xPUdl+MBN/45mBVyeFsjBpYMuohVOQ/aoocnBkqCpoaDICaqqFrGroe/KgMiIIgIpqMmERLyN9prNjRP3pG5/fIaKqGLfbEAUeXVATLkfVJWdPY55PwqRrJFhOJ5uotIVxFBSiShCAI2BxnvtNdiRzwY7JUb08R5n+TsBTjDDjoOsjqnNUMbjwY38pVaC4XuTERWCMiqdPiqgs9vNNC13Xkw9nokoypTjpCxW6zKIoYzRYsdgf2yCicMbFExicSnZRMbGoaY5/6K3FpdYhLq0NUUgpNrmrNiFEPEJWYhFdWGXrHnbTr2IkWLVuGasE1TWPs2Efp3qcjbTtcTbt27di3b1/N41I1dFlj1NgHmDVrFl5VZZc3gF/TqGczk2Qx8fvvv9OtWzeaNGlC06ZNue+eeyjKy2PWvPn89W8TsUefndmkrmsogUCVy+5nnnmGOnXq4HRWbTvx5ptv0qxZM1q1akXPnj3Zv39/6HdffvkljRs3pnHjxnz55ZfURHFxMb1796Zx48b07t2bkoqy1zDnl3CgPANm75yNKIgMbjSYsrnzIDGR/fv3ckX7a896l8PzhVpcjFpehjEpEcNJJlE7HA42b96Mzxfs/1Obj3LTpk1s3bqVSZOCOrMp/5lKdnY2v61exx9//MGcOXOIrqXnji5VdJYUBTyKym5vMEWnsd1ClMlIXl4et912G6+88go7duzgj02b6Ny+HQFFQVMVDEbTcbswngqKJKHrepXzDRgwgN9++63abdu0acPatWvJzMxkyJAhjB8/HggGwBdeeIFff/2V3377jRdeeKHGIDhp0iR69uzJDFiCsQAAIABJREFUrl276NmzZ+i5C3N+uTTfzRcBkioxZ/ccuqV3Iy5gYtePP+Ie2Bd5z9YzMpmfEd9NgNw/TvtwXVMR/H4sBgOCxQIIkNwS+p74zXk6PsqDWYdITEzGERkMOJU+SgCn04nb7QZg1qxZzPtmLp+89gF+YMH3S3j3nXcoKyzgX2++Sf/+/Xn//fe555576NSpU8W6ZB4D+92EPTIKXdMxVgS1+fPn8+KLLyJJEnFxcfznP/8hKSmJlStXhsovT+SjlP3BIH30jnfHjh1rfF6uv/76Krf5usIC/9///pfevXuHBBm9e/dm8eLF3H777VWOnzt3bqhu/p577uG6667jlVdeOeHrEebsEp5RniZL9i+h2F/M7U1up3zhIlAUDluM2KOiSW926dXp6rqG7g+mzwgWy5HE+pPkVH2Umqpx0w2D+GHZYq6+5uoqPsoax6fpHHYY8GkaBQcPsObHVSxauJAHH3wQv9/P5s2bueaaa4IdFQvzUaQAkQmJ+FzloRxSgC5durBmzRo2bNjA8OHDQy0bXn/99ZP2UcoBfzDB/xSvGj777DP69u0LnLyDMi8vj5QK52dKSgr5+fmndJ9hzg7hGeVpMn3HdDIiMuiY2pH9376JeGVTsnZto1XPGxHF89sFMcRJzPxqQtc0pH370AIBLA0aIJxGysup+ih/W72elORUtmzexo8/r6zio+zZs2eV42VNwy2CSwS7QWTQ8GEYDQYaN25MgwYN2L59e+i23vIyfC4XzphYtIrqJ4v9yBLCoUOHGDZsGDk5OUiSRP369QHo3LnzSfsoT2cj5+uvv2bt2rWsXLkSCDsoLzXCM8rTYEfxDjbkb2BYk2FIO3fj37KFsnZtUGWZpp0v0GX3aRLcvDmM5vNhTk9HPIO8wEof5bGXj3DER/nVV1/Rrl07lv+wApPFgDPSTt++fXnttdd4+umn+fbbb4EjQcOtqGSVudCAekYjVlGsVlUjCALNmzfn1zVrcBUVYHE4cETH4CktqXBWHpFnPPLII4wZM4Y//viDjz/+OOS6nDBhAp9++ik+n4+OHTuyffv2kI8yLS2NESNGMHny5GAOqiSdUn33Dz/8wEsvvcS8efOwWI4sM5yMgzIpKYmcnBwguIRR2W4izPklHChPg6nbp2I1WBnUaBBlc+aAycQB2UdkQhIpjZte6OGdEkp+AWpZGcakJAyRZybZGDlyJM8//3y1uudly5bh9XoBcLlc7N69h5TkVLbv2RJqC1Hpo6xbty4QDBCrN/3Bbo+P5fPn41R0Iiv6d8+cORNN09izZw979+6lSZMmPPjAA3w1eTKbNm8hKiEJv8fN9JmzcElylZlaWVlZaKPp6J3mSh/lU089Rdu2bdm+fTv79+8nMTGR0aNHc//997N+/Xpkqfr65PHYsGEDf/rTn5g3b16VINenTx++//57SkpKKCkp4fvvv6dPnz7Vjq/0WVaOtzYxcZhzSzhQniJlgTIW7l3ITQ1uIlK0UzZ/PqZuXTiwfQtNr+16SV0+KSUlKAX5GKKjz0od9/F8lG3btqVVq1Z06tSJu4bfzTVXt6W0vIgBAwbQokULWrVqhdFoDDXfevLvLzL05kH8acBNNEhIxiAICIbgn2uTJk3o3r07ffv25aOPPsJkMmLWVT56923+8errXNmsGVe1uZrfN2wgISmpylgmTpzIbbfdRteuXYk/6jG/9dZbtGjRgquuugqbzUbfvn1ZsWIFrVu3pk2bNsyePZuxY8eiVFbkHBMox48fT3p6Ol6vl/T09FBDsSeffBK3281tt91G69atGThwIBCcYT/33HO0a9eOdu3a8fzzz4c2dkaNGkWlJnDChAksWbKExo0bs2TJknAb2wvEZeujPF0+3/w5/1r3L2YPnE3KhsMc+vOfKXt4ND//tIy7X32XhLr1z8n91sbp+ihVlwtp/wFEpwNzRsY5UafVhCKrFGd7cERZcERXv3xVdZ39vgAuRSPebCTFYkLJ8SDajBhjqs/iNE2lJDsbRQoQk5qG2WrD5yqnLD+P6OQUrA5ntWPOhNLcHGQpQEJGvbN63kuVy8VHGZ5RngKyJjNl2xQ6JHfgipgrKJvzDYa4OLLyc4hLzyD+EnnzqB4P0oGDiFYL5jp1zluQBPC5ggJkq7N6hUxA09jl8eNSNdKsJtKsZlA00HQEc/UNMl3TKM3NRZb8RCUlY7ba0HUdd0kxRoulyibO2aJyIyfM5UV41/sUWHpgKXnePJ7t+CxKYSGu5Ssw3zaEw1t/p8vwuy+Jy27N50Pevx/BZMJcr15QRHy+7lvT8btlrA4TBmPV4Oyp0KMBNLBZiKhwSOoVIgyxmghDozQ/F8nnJSoxKTRz9LnKUWWZ6OSUs/56qIpCn0E3o6halTLMr776Kuyj/B8nHChPga+3fk2diDp0S+9Gyef/BkUhNz0RtkLTzt0v9PBOiObzIWVlgcGAuV7dUHni+cLvCYpubRFVZ5OVejSTIFDfZsFqOBJEdUkDUQDDkaCn6zpleXkEPJ6g+bzC9K5rGp6SYkxW6zmZTSqBAItmzyI2NR3zORIYh7k4CV96nyQb8jewqWATd115FwICpTNnYW3Thp1bMklr2oyoxKQTn+QCEgqSooi5fv3z3hI32OpBwmg2YKrYvdZ1ndyAxAGfhN0g0thurRIkATRJRTxKhKFpGqV5Ofg9biLiE6p4On2uclRFwRkTe05m95WtH4xnqRQyzKVDOFCeJJ9v/pxoSzQ3N7oZ37p1SFlZqL2up/jwQa7scv2JT3ABUT0epH1ZFyxIAkh+FVXWsFc0BNN0nQN+ibyAQozJQANbVYckBEUYKFpofVJTVUpzs0MzScdRQVLTVNwlxaHeQucCORDAaDafth09zKVL+BU/CfaW7mXFwRXc3vR27CY7pTNnITocHBRURIORKzp1udBDrBXV5ULK2g9GwwULkgC+cgnRIGBxGIPdEb0BSmWVZIuJOsc4JCvRpQpRr9mAKssUZx9G9gc3bo41vntLS9FUFWds/DmZTeq6Ht7IuYwJr1GeBP/e8m+sBivDmw5HLS2lfPFiIgYNYsevP1O/zTXYnBEXeog1ohQXI2fnBHe369U772uSoXHIKpJfwRFlQapoISvrOnVtwRaytaEFNBAEFE2iNCcHXdeJTk7FYq86Y1QVBU9ZKVaHE/M5Mo5rFQ7Os2UgCnNpEZ5RnoAcdw4L9i7g5kY3E2uNpfTbb9EDAdztWuMpLaF5954nPsl5Rtd15NzcYFMwpwNz/frnNEgKgsCIESNC3yuKQkJCAv379wdg/+5D3DlyKB27taNZ8+Y8MPhmGtosRBpEHn30UVq0aEHLli2r+Sh1SSWAj5KcwwiiSFxaOn/685+ZNWtWlfv3lBSj6xpb9+yt4qMcNWoUXq+XL774gjFjxpzRY6ysyDHWMqOszUf5xRdfkJCQQOvWrWndujWffvpp6HdhH+WlQ3hGeQI+2/wZACNbjAx28ps2HVvr1vyxZyfWiEgaXN3uAo+wKrqiIB06hOZ2Y4iNxZRy9tNkjuVoH6XNZqvio9RUjRdefIHrru/JgLEPYxIEPLt24DAamDo16KPMzMxEFEUOHTqEo8KBqSkqLm8RkurHYncQlZhUY68bRQrgdZXh9ge4/Y47mDZtGp06dULXdWbPno3L5Torj7Gy9YOplqWLAQMGMGbMGBo3blztd8OGDeO9996r8rNKH+XatWsRBIFrrrmGgQMHEhNTVS5c6aOcMGECkyZNYtKkSWHN2gUgHCiPQ54nj292fcPNjW4mxZmCZ80apKwsYv8+kT3zptGyRx8MxounL86kX15iW94foGlBVdohI2Se2TmbxjblqfZPnfB2tfkofW6ZvPxcrr6hJzZRpL7NgrFN0MKTk5NDSkpKaHOk0kcZ8HqITUhkT+ZGHJExLF6xnIULF/LFF18AQcnE22+/TV5eHn9/9hl6dO3CF1OmhXyUEJzlDhkypNo4T9dHGfD7ee2lF+nfoFGNj782H2VthH2UlxbhS+/j8O8t/0bXdUa1HAVAydRpGKKiyImwo8ryRXPZres6SmEhalkZ6DqC1Xbe1yNr8lHqgLtcYsS9o3l+7MM80L8vr7z8z5AIY+jQocyfP5/WrVvzxBNPsHbt75Tl51KSE/x9pDkOZ2z1VJ+srCxWrlzJnFkzeXz8UxhsdrZu3co111xzwnGejo9yw4YNLFs4j9ZXn15XzdmzZ9OqVSuGDBkSMgaFfZSXFuEZZS3kefKYtXMW/Rv2J82Zhpybi+uHH4i9+27W/rKSuPQMkmqZXZxPdEUJuiS9Xp5sNgZTWuoF2bQ51kep6Tp+WUXQdHr078u+G/fw3//+N+Sj3Lx5M+np6ezYsYOlS5fy/eLv6N2rN5+8+w439uuHgIDRZAmJMI5m6NChCEBSdBR162ZwoEJDdjKcjo8y4PfTrX1bru1+3Sk/LwMGDOD222/HYrHw0Ucfcc8997Bs2bKwj/ISIzyjrIWPMj9C1VUevOpBAEqmTAVdR+95HTk7t9P8ul4X9A9bl2WKPvscpaAA3e/HlJaGKaPOBdvZhiM+ytuGDSfHLyOoOhgEUiItxMXFVfFRrlq1Cl3X0SSJdi2aMWHsozz26CMsX70GR0XCuFiRP1npjKxEEAQ8ZaUosozBZEIUDTRv3px169adcIyn46NMTkzkkXHjmTZz1gnOXp24uLiQg3L06NGhMYZ9lJcW4UBZA1llWczZNYehVwwlzZmG5vdTOmMGzh7Xs31rJqLBSPNuPS7Y+DyrV7Pv1iHkv/YagtmMuVEjjDExF3xGMnLkSJ5+7jlsjRqjyCqCDs4oC8uXL6/io9yzZw+JcbEs+24RO/7YhCCIRCUmsztrP/UbNABVJzE+ge17d6BpGnPmzKlyPzNmzMBVVEhOQSFZWftp0qQJY8aM4csvv+TXX38N3e7rr78mNze3yrGn46Mcccft3DF0CJsyT33BN+eo2e68efNCpp2wj/LSInzpXQPvbXwPs8HM6FajAShfsAC1tJSoO+5k6+fv0rBt+2oJz+cD//btFLz1Nu4VKzClpZH+3rsciou7YEnkxxKfkkrfUX9C1nSiFRAAq8PEunXrGDNmDEaDAUVRGH7rYBrXSWflL6t58plnkeSgUah9+/aMGTMGTVJ56a8vMGjoYOpk1KFFixahRmMA9eqkc8vtd1JcWspHH32E1WrFarUybdo0xo0bR35+PqIo0q1bNwYPHlxljJU+yrS0NDp27BhKR3rrrbdYvnw5BoOBZs2a0bdvX6ZNm8Zrr72GCDjsdqZMn17rYx8/fjxTpkwJ+ShHjRrFxIkTeeedd5g3bx5Go5HY2NjQhtTRPkqgmo/ywQcfpG3btkyYMIGhQ4fy2WefkZGRwcyZM8/SqxXmVAj7KI8hsyCTOxfdyQOtHuCRNo+g6zr7Bt0MgoA8YRwL3prE4L++QP3WJ944OFv4tmyh6JNPcS1ejBgRQdwDo4m9+25Ei+W0fZRnm0r7jyAI1DOZ8OR6sUdZcESZkXxefOXlBLwedF3HbLfjiIrGbLPXOAtWSv1oHgVTqqPa7/1uF6V5uUTExeM4S326j4eu6xTs34vF4SQq4eKu578QXC4+yvCM8ih0XeeV318h3hbPyBYjAfD8/AuBnTtJeeklli7/noi4BOq2an3ux6KquFeuouTrr/D8shrR4SDuoQeJu/deDFFR5/z+T4VyWSGrwv7TwG4hUBJMztZkFwX7y9FUFdFgwBYZhT0yqkoPm5rQAyqCWawWJDVVpbyoAJPFct5m9KqioKlauHTxMiccKI9i0b5FZBZk8vdr/47DFEx8LvrsU4yJiWgd2pI18990unX4Oe2yKB06TNncbyn7Zg7y4cMYExNJeOJxYoYPxxBx8ZVKFlco0myiQLqg4y8qIuA1o2t+fC4XFrsDqzMCi91+UoJgXdPRZQ0xonowdRUVoqkqMcmp5209NtT6wRzckOnQoQOBQKDKbcI+yv99woGyAq/s5V/r/sWVsVcyqFFwwdy3eQve1WtIfHIcm1cuRRAEWvasvuB+psjZ2bh+WEr54sX41q8HwN6xI4lPPklEzx4Iposnqf1o8vwBciUVq6YQWVRIuaoiGCIQBAFnjA1bRHyN1TTHo1KEIR5jNPd7PPhc5TiiY87r7E4OBJcTKmfBR28Whbl8CAfKCj7a9BF53jxe6/4aohCc+RR//hmi04nzlpv5Y/wjNGrbkYjYM2/CpQUC+DZswPPzL7h/+onAtm0AWJo0IeEvY4kaMABTxc7sxYSuaUh+P5LPS4EGLosdS8BHjLcci8OB2ebAXaxjthlwRJ+e6kyXNAAE85HZp6aqlBfmYTRbcMbEnpXHcrJUqtXOZ7uMMBcf4UBJsE/35K2TubXxrbRJDFZfBPbto3zxf4kbeR97tmTid5VzVe+bTvncuq6j5Obi++MP/JmZeDduxL8pE12WwWjE3ro1iU8+ifP667E0OL+NyU5EpVpM8vmQfD5kvw9d1ymPiMZndRCpq6RFOjDFx1bkNgbQ9QD2yNM37GiSimAUQ4nmuq5TXpiPrmpEJSed14Cl6zqK5MdylhuUhbn0uOwDpaZr/H3N34myRPHYNY+Ffl700ccIZjOx997LD29PIiYljYwWrWo9j65pKAWFSFlZSPv2Edi7h8DOXQR27ECtNL6YTFivvJKYu0dgb9sWe7v2GJxnv2XB6RIMjAFkvxfJ50fy+9C14AzPaLZgi4yi0GLHpwskmo0kW2yhtUJdCxrMzVYjJsvpreHquo4uqYjWI3+WPlc5frcbZ2zceVechTdywlRy2QfKr7Z+RWZBJi93fZkoS3A3Wdq/n7IFC4i96y4KS4vJ2bmdrjcPxb9lK0pRIUpBAUp+PkpuXkhnJh8+jH5UBYlgt2Np1Ahnzx5Yr7wSW/PmWK68EvEi8hlqmoYc8CP7fUh+P7Lff1RgNGNzRmC22TBZbWAwhNrIplhMJFqOrJsKgsDwYXfw9isfYo8zoygKKSkpdOjQgQULFpCXl8f999/PwYMHkWWZevXqsWjRIjRN4y9/+QvLli1DEASsFitfv/M5ja5qAoAiSbgKCzDb7KFUoHvvvZf+/fvXKLz47bffGDduHHl5eQiCQJcuXXjnnXeYMWMGa9eurWbwORFKxaZN5UbO8XjmmWeYPHkyJSUlVXI+IZggP3HiRARB4KqrrmLKlClAMIH8xRdfBODZZ5/lnnvuqXbe4uJihg0bRlZWFvXq1WPGjBnVDENhzj2XbaBUCgrYNP9L3ha/5lqpDldP/p3DnlVoLhe+P4IGnrJFi1i36nuMDivWf0wiS6uac2qIi8OUnIylQQOcXbtiyqiDOaMulgb1MSYnX1TrWrquoyoysj+AHPAh+/0okhSqOTaaLdgiIjBbg4HRcFQppKLp7PMG8Koa6VYzceaqfzaVmjVFkzBZI1i8eHGo+gWCydS9e/cO2XkyKypcpk+fXkWztn/nPqySAcFsCPXGEUSRqMSkE+5y5+Xlcdttt51VzZoc8FfZyDketWnWdu3axcsvv8zPP/9MTExMSGoR1qxdWly2gdKfn8s/8r/AGgX3fXkIt1iG6HAgWMyoRUWY6qRDmzbk7N9G0zr1qTPsPoyxsRjj44MfCQkIF0lFzLFUBkUlICFLfpRAADkQQFMrWiuIAiaLFXt0dDAwWqy17k7LmsZeX4CAdnwjeY/uvVi5eim31x9WRbMGwTK+G264IXTbVq1ahX5+tGYtLSEFzasgGEUinE52Z24kJiWNOd9+y4IFC2rUrL355pv079+f999//6xr1g4ePMD9f3oIXyCAoih8+OGHdO3atcbHX5tm7ZNPPuHhhx8OBcDKWu2wZu3S4rINlNtjfOxPNTKpy8t0fKhv6OeHH38c+XA29aZMYc3S7+DAdjqPf5qoxOQLONqa0TUNTVUJeD0oskzR668j7dwVunyuRBBEBFFAEMXghyDir+WcR6MB/kaNUB4fR/2jem1XGYOugw5DBg/l7Q9f45bBg8jMzGTkyJGhQPnwww+H5LW9evXivvvuIzU1laFDh9KlSxd+/PFHevbsybC+t9LmqtZ4y8vQAWdsXLW2D3BEs7Znzx6uv/56du/ezebNm2u8dD2WSs2aIAh8+umnvPrqq7zxxhshzVrnzp1xu91YLBZmzpxFrx49+MfLL6Oqaqhe/VTYuXMnELQTqarKxIkTufHGG8OatUuMyzZQtk1tz4LBC0lzHrlE9G3eQvmi74h78E/oERFsWvIdDdt2uGBBMtjitRxXYQGuokLKC/MpL8inNC+XsrwcSnNz6PjAWEpswTU0VVYAEA3GYGAUKgLjady3CvhVDV2HBjYLjhqCJFT06gbad7qarKeOaNaOpk+fPuzdu5fFixfXqFlbtmwZS5cu5cYh/fj68y+5uvWVCFBrieLQoUMRRZHGjRvToEEDtm/fftKP62Q1aylJSbRq0YJxzzyLwWLh5ptvpnXrU6/IUhSFXbt2sWLFCg4dOkTXrl3ZvHlzWLN2iXHZBkqgSpAEKPjXvzBERxN3//1sWvZf/G4XbQcMruXokyd4Kawg+bxIPh8BrwfJ68HvceN3u/G7XfjcLnzlZXjLSvGWleIpK8VTUoKmKlXOZTRbiEpMIiopmbqt2mB1RhCbmobBZEZ8+eWz8mY7um67gc2CrQYnZOXj8pRJCIDZZgxp1lasWEFRUVGV28bGxnLHHXdwxx130L9/f1atWsWtt96KxWKhb9++9Lm+N3HWSGbPnUX7di9WzHyDj6Umzdqx31dq1k5k13nkkUd4/PHHGThwICtWrGDixIlAULPWr18/Fi1aRMeOHVk4by6d2rdj6Q9LWLJ0GSNGjODJJ5/k7rvvPoVnMjhT7NixIyaTifr169OkSRN27dpFenp66JIaggH8uuuuq3Z8pWYtJSUlrFm7gFy2gdJbXsaedb+ia1rwUnXLVsw//4w0sB/rli7m17kziUxI5MAfG8nauBZNVVFVFU1V0BQFVVHRFBlFUVBlGVWWUGUZRZJQpACKLAVTbQLB3eTK9cHaEA1G7JGRwXroqGhiU9NxxsbhiIkjIi6OiLgEIhMSsUVEVgkU27ZtO6t9rMsVlSxfIFS3bTnOhpTfLaMpGgjBYDVy5EiioqJo2bJllSCwbNkyOnbsiN1uD2nWMjIyWL9+PcnJyaSmpiJ7A2zYsoFmVzYlOjmFpKQktm3bRpMmTZgzZw4RR5Vvzpw5k3vuuYd9+/axd+/ekGatffv29OvXjw4dOgBBzVqvXr2qjPlEmrWWLVuyevVqtm7ZilSvDld1uJbRo0fj8XhYv379KQfKm2++malTp3LvvfdSWFjIzp07adCgAQ0bNuTpp58ONQv7/vvvefnll6sdX6lZmzBhQlizdgG5bAOlq6iQ7z96BwBB0+m68yCy2cSqfdvQ9wcv5SSvl19m/gcIBjLRaMBgNCIajEc+m0wYjRWfzRbMdjv26BiMZjMmixWTxRJMsbFYMdtsmG12zHY7FpsDq9OJ1eHEGhGByWK94JdeR+q2RerbzJiOEyR1LTibPDpnMj09PbQhcjQhzZrRiKZpjBo1inbt2rF48WJGjx5NIBBAlWVat2rJ4+PHYzSZmDRpEv3796dOneqatSZNmtC9e3fy8vLOmWbt+q5dmPXNN9z30BhMJhNOp5PJkyfX+nzUplmr9E42a9YMg8HAa6+9RlxcHEBYs3YJcdlq1lRFxlNagiCIuKfPoPSdd0l48w3sXbow9blxGC0W7nzpDQwG40WV5nMsZ0Ozpus6BZJCTkDGaRSpZ7NgOEHQ9pZLuEv8RCfaMdtO//+trmmU5OUgeb1ERsRjT7zwOYJBtdo+LHb7RbmJdzFxuWjWLt4I8P/snXd4VFX+uN87NZNegQChCYSSCoQiRTAUUURFFBQLorgiuNVV1rbqT1ZUdF1cXb+47kbFBQS7NOkgFkTAIKGJhBrS22TqnXt/f0wyJpCQnkwy532ePMnMPffOmZvJJ5/T3tPMaHV6giM7YFJUSv7zXwLGjCby2ms5dXA/RRfOM/ymW9Hp2/8aX1VVOW93kmV3EqLX0rMOQVJRVMqK7Rj8dI0LkqpKcU42DouFAF2I1ywVVFwuFJerxj28Bb6Hzza9K7jwt7+h2u10XPgXFJeLb1b/j8iY7vQdNrK1q9bsKKrKaZuDYqeLSIOOzkZ9nZr/lhIHqqISENrwVUYVQdJWZiYwKByDw4BkaD59XX1wXmZFjtCs+SY+HShLt22jdP0Gon73W4y9epKxcyuFWeeY+sfH2n0m6VQUMq0OLC6Fzn56ogx1U7m5ZAVriQOjv75Ra7qLcy5gM5sJiojAqPijyDKSzjumx1Q4KHXVLDcVmjXfpH1Hg8vgMpdx4ZlnMfbpQ8S99+KSZb5Zs4KoHr3onVK/zezbGlaXwnGLHaui0N1kqHOQBMoNQSoBoQ1blaQqCsXZFUEykoDQcFSHC8mgbfXBrAqcDrdaTdPO/1kK6o7PfhIcJ0+iumSi/9+zSAYDB7dspCg7i5G3zmrX2WSJ7OJniw0VuMLfWOOSxOpwOlzYzE5MQQZ0+vpnk4rLReGF89jKzARFRhEQGobqUlBlBU0Ds9PmQLbb6yTCEPgOPtv0NsXH0XvzZjRGI3ZLGV+vfp+YAfH0GjS0tavWLKiqSo5D5oLdiUmroYfJgKEe/xBUVcVcaEPSSASE1D+bdMkyRRfOIzschHToiCko2H3dakS9rYlLlnHJcrXNboHv4rOBEvAoz/Z8ugZraQlX3Xmv1zT/mhKXqnKmfNAmVK+lq5+h1pHti3FYZZw2F4HhfmhqWKlTE067naIL51EUhdBO0Rj9f3VwKg6Xe8J6AzLU5kB2lA/kiEApqIR3/BtvRYpzstm39lP6jxpLx169W7s6TY7VpXCszEax7CLaqKdbA4KkoqiUFtjR6TWYAi/tz5QBSipsAAAgAElEQVQkiTvvvNPzWJZloqKimDJlCjazmaM/HmDWnHuZcMNNJA9J8awFVxSF3//pDySnDiMhMYGUlBTPBPCamD17NmvWrKn22J49exgzZgyxsbH069eP++67D4vFQlpaGgsWLKjTe60Y8dbVs+n9+OOPExMTQ2Bg1SlOp0+fZty4cSQnJ5OQkMC6des8x55//nl69+5NbGwsGzdurPa6J0+eZNiwYfTp04cZM2bgcDjqVS9B0+DzgXLbO8tAIzHqtvotTfN2VFUlz+HkuMWGAlxhMtKhjtN/LsZSYkdxKQSGV796qMJHabVaAdi0aRNdunRBdjooys7ipaVLmXzdFNLT08nIyGDx4sUArFyxkvNZ59n/zQ8cPHiQjz/+mNDQhm1DW+GjfOGFFzh69CiHDx/mmmuuqbePUrbb0er19d4U7frrr2fPnj2XPP/cc89x6623sn//flauXMmDDz4IQEZGBitXruTQoUNs2LCBBx98EFc1y1wfffRR/vCHP3D8+HHCwsJ4++2361UvQdPg04HyxA97OLH3O0bcfBvBke1HNlAx9eeczUmgVkNffyOBNdh/akN2urCUOPAL0GPwq7mnZvLkyaxduxaA/73/Pjdcdy0upxNTUDCFxSV0697dU9bjozx3jk4dOqLzc2epXbt29XgbK2dma9asYfbs2Z7HmzdvZvTo0fTt25cvvvgCoEYfZceOHavU8/PPP2fYsGEkJyczfvx4srOzAdixYwdJSUmMSk0l9boplJaWkpWVxZgxY0hKSiIuLs6jjauO4cOHe3RolZEkiZKSEsC9zrxz586A2zM5c+ZMjEYjPXv2pHfv3pcEWlVV2bp1q8ereffdd/PJJ5/UWAdB8+GzfZROh51taf9HeJcYBl/XPkQDu1Yd48KZUuzlPkqDRoNekvixEdcMivAj8eqYWieXz5w5k2effZYJV49j/7593HTtZPb8sI/gqA7MX7CgWh/l9Kk3c9Vr4xg0YgipqanccccdJCcn11qn5vJRvrZ0KX26RCMZjZhMJpYtW8akSZN4/PHHG+yjfPrpp5k4cSKvvfYaZWVlbN68GYBz585Vkf1W56PMz88nNDQUXbltviZnpaD58dmMMjfzJNbSUsbfOw+tzjv3za4PDkWhRHZhUxQ0koRJ6w6SjUFxKSiyQmCYEa3u8h+VuLg4Tvz8M/996y3GXz2O4MgotDodkiR5fJRz587lyJEjJCcnk5ubS+fIaH76aj/PP/88Go2G1NRUtmzZUmu9GuujnDRpEvHx8bz00kscOnQIcPso//SnP/Hvd97FbLWj0+lISUnhv//9L08//TQHDx6sYjCqKxXmoLNnz7Ju3TruvPNOFEWpk49SOCu9B5/NKDv37cf9b/y3yghsW0RVVXLsTrIdTmKmdifFoCfKoGv0H5TLpVBwvgydXoNfNQM4F1Nw9jQTxl7Fsy+8yLZt2zz6sAou9lHu2LGDqVdeg1+QP5MnT2by5Ml07NiRTz75hNTU1Cr1bykf5bjRo/j800+5atw4Nm/ezJgxY9i5cydr165tsI/y7bffZsOGDQCMGDECm81GXl4eXbt25cyZM55yZ8+e9TTLK4iMjKSoqAhZltHpdNWWEbQMPptRAm06SKqqyuc5RWRXWH+0WmID/Bo8YHPxtc0FNlRVJSiiZv2b4nJRkpvj3mYW+M38Bfz1r38lMTGxSrmtW7d6mq0VPsqYLl3Z/+N+sgrcfYSKopCenk738r7MCh+loih8/PHHVa63evVqFEXhxIkTVXyU77zzTpUlhsuXL+fChQtVzr2cjzK29xX8bsF8hgwZwpEjRzh16hQdOnRg7ty53Hvvvezbt6/e97Jbt26eLPnw4cPYbDaioqKYOnUqK1euxG63c/LkSY4fP87QoVXn8EqSxLhx4zyj/MJH2Xr4dKBsi6iqyrb8Eq7bd5y5hzIB6OlvpGctkt36YLfI2C0yASHGalfgqKqKtaSEvDOnsJQUIwERXbtxRZ8+NfoohwwZQkJCAiNGjOC+++5jSPwgcvJyufHWacTFxZGQkIBOp/NM46nwUV599dWXDJJU+CgnT57s8VF27NjR46OMjY2lf//+7Nq1i+Dg4CrnVvgoR48eTWRkpOf5V199lRFjxjJu8nWYTCYmT57M9u3bSUpKIjk5mQ8//LDa91bBI488QteuXT0+yopM9eWXX+att94iMTGR2267jbS0NE8GfOuttzJgwACuueYaXn/9dbTlI+3XXnst58+fB+CFF17glVdeoXfv3uTn53PvvffW/gsUNDk+66Nsa7hUlY15xfzzdA77Six0Mer5U89OJBXlMKCRPsoqryMrFGSVodVpCOvkf0k2abdaMOfn4bTb0fv5ERwZhb4BOjK5wIpid6HvFOAV/W6Ky0VO5i8EhkcQGBbe2tVpM/iKj9Jn+yjbCkVOmVUXCkg7l8dJq4NufgaWxMZwa6cwDBoNh4uablc+VVUpLbChqhAcWbXJ7bDZKCvMx26xoNXpCOnQCb/AwAYFOVVVUewuNF4mwgCxIkdQPSJQeiEuVeWrQjOrLhSwLrcIm6IyJNifv/TqzHVRIfVeWVNXrGYnDqtMYJgfOr0WVVVx2myUFRVgt1jQaLUERUTiHxzSOHGISwWXiuRlIgyoXq1WGeGj9E1EoPQSnIrKd8Vm1uYW80VuEbkOmRCdlhmdwrmzcwRxQU23gVh1yA4X5kK3tdwvUIe1tARLcTFOuw2NVktgeAT+ISFoNI0PbordvQLFW0S94F66qNXp0Gov/ychfJS+iQiUrcgZm4NdhaVsyy9lZ2EpxbILP43E+IhgbugQxoSIYPzqKaBoCKqiUpJnRUJGo7GRd/qCeysEvZ7gqA74BQY1qZtRdbhAIyHpvWcsUbbbRbNbUCMiULYQsqJypMzKvhIL35eU8V1RGadtbsFBJ4OeyZEhTIoMZkx4EAH1XGfcUFRVxeV0UpxXhNNWBqqMVZYw+gdgCg7BYDI1Sx+iavcuUa/iciE7HfgFeceePQLvQwTKJkZVVfKcMsfL7By12DhstvKT2cphsxWr4p5hEKnXMTQkgPtjohgZGki/gJbbqlZxuXDYrDisVuyWMlxOJwBanRH/0DD8AgNrbX42hgpRrzbAe1ZD/apWE5uJCapHBMoGYHEpZNkdnLc5OWN3cMbq4JTNwUmLnV+sdorlXy0wITotAwNN3Nk5kuRgf5KD/enuZ2iRwKgqCrLTiWy347TbcNhtnkELSZLQ+5lQFRM6g4mw6KAWqZOnf9KLBnIaqlYT+A4+HShVVcWiKJhl9zrpUtlFkeyiRHZR4JQpdLrId8rkO2VyHTK5DvdSwRJZqXIdDdDZT08vk5EbOoTSx9+P3v5GYgP8iG6ClTK1vQfZ4Sg3cztxOd1fstOBy+n0rBeWNBr0RiOBYeHoTSb0Bj+Ksi1IWpWQDo2byyhJEnfccQfvvfce4PZRRkdHM2zYML744guys7O59957OXPmDE6bg25durF+ywa3j/L3v2fr1q1IkoSfnx8ffPABPXv2rPG1Zs+ezZQpUzxGncrs2bOHhx9+mOzsbCRJYtSoUSxdupQPPviAvXv38s9//rPaa3oGcnQN/3N4/PHHeffddyksLMRsNnueP3XqFHPmzCE3N5fw8HCWL19O165dAfdKm+eeew6AJ554olqpR0FBATNmzCAzM5MePXrwwQcfeAxLgpbDZwPlj6UWJu89hlJLuRCdlgi9jiiDjr4BfowOC6KTUU+0UU9no56ufgY6Gw3oNXUPNKqiIFcKai6nE6fD7s78HHZkmw2n3Y7dasFhteKwlGG3WrCZS7GZS7GUlGAtKaasqIChcxaQZ/y1GStJElq9Hq1ej9E/AJ3RiN5gRKv/NWCrqkppvg3ZqRDSwb9W4UVtVPZRmkwmj4+ygqeeeooJEybwu9/9DueFMtKPHkKSJFauXMn58+dJT09Ho9Fw9uxZAgIatqy0wke5cuVKRowYgaqqfPjhh3XyUcp2e6O3frj++utZsGABffr0qfL8ww8/zF133cXdd9/N1q1b+ctf/sJ7771HQUEBzzzzDHv37kWSJAYPHszUqVMvCYKLFy8mNTWVhQsXsnjxYhYvXswLL7zQqLoK6o/PBsqgkkL+8P16dKjoJQkdoEdFr5Hc33HfHEl1m15URUFVFLf5pfzrjKJw2vO8y33M5UIp/3K5ZPfPsvzr4/Kf64tOb8AvKAi/gEBMwSFE9ehFj9BBGAMCCOnQsTwj0qPR1S7EsJmd2Mqc+IcYMZqa5iNQ4aOcPn06K1as4LbbbvP4G7Oyspg4cSKq7O6fTBqU5Hk+OjraM6JekWmB20dZkZmtWbOGL774grS0NMDto/zHP/5BdnY2r7zyClOmTKnRR3kxn3/+Oc899xwOh4OIiAjee/ddJKeDvek/svCJpzzn7ty5E7PZzIwZMygpKUGWZf71r38xevToat9/ZWVaZTIyMvj73/8OwLhx47jxxhsB2LhxIxMmTCA83L0KaMKECWzYsIHbbrutyvmffvop27dvB9w+yrFjx4pA2Qr4bKAMdjkJyTzm3q9FksonUEvIGgkZCZtG43n+1+8aNBqN+2dN+c86nednd5Byf9doNGh1OjRaLRqt+3uFOVur06PV6dAZDGh1evd3gwG9wYjOYEDvZ0JvNGIw+WMwmTD6+9eogjt8+LBno65tacvIOfXLZd+3qrjnTEoaCZ1eA7Ukwh2692Lc7PtrvZ8VPsopU9wm8zlz5ngC5fz585kxYwavLX2Nq4eNYc6Dc+kaFMOtt97KqFGj2LVrV6v5KF94YTELf/sQr73xJq+//jojR47EbDbj5+fXJD7KxMREzzrxjz/+mNLSUvLz8zl37hwxMTGecjW5JrOzsz1r3aOjo8nJabqVWIK647OBMrJbD+5/47+tXY2WRXUby5GoU5CsDwkJCWRmZrJixQrPnjgVVPgo1374GRs2bGTwsCH89NNPdO3alaNHj7J161a2bt1Kamoqq1evJjU19bKv1Vgf5YwZM8jKysLhcNAtxp3Fjhw1ij/+8Y/MmjWLadOm0bVrV1JSUpgzZw5Op5Mbb7yRpKSket+XJUuWsGDBAtLS0hgzZgxdunRBp9MJ12Qbw2cDZXvkcpmfqqoU51hx2F2EdfRH3wyjzlOnTuXhhx9m+/bt5OfnVzkWHh7OjOtvYeZNM7hp9i3s3LmTm2++GaPR2Ko+yiceewytTsdjjz3G9ddfz7p16xg+fHiT+Sg7d+7MRx99BIDZbObDDz8kJCSErl27eprU4A7gY8eOveT8jh07eroosrKy6NCh/WxZ0pbwnqURgmalrMiOwyYTFG5sliAJMGfOHJ566qlL1j1v3bqVshIzyAplDgsnTpygW7du7Nu3z6MTay0fpaoq6IxGTpw4QXx8PI8++miT+ijz8vJQyrfmeP7555kzZw7gzrK//PJLCgsLKSws5Msvv2TSpEmXnD916lSPN1P4KFsPESh9ALvF6d4gLFCPKdDQbK/TtWvXGn2UKUNTGDxhBKMmXMV9991HSkoKOTk5XH/99a3mo4wID0dVVPRGI6+++ipxcXEkJiY2qY9y+/btxMbG0rdvX7Kzs3n88ccBd4b95JNPkpKSQkpKCk899ZRnYOe+++6jQhO4cOFCNm3aRJ8+fdi0aRMLFy5s2C9H0CiEj7KNU50PsDKy00VhlgWtXkNYR3+kekxjakrkfCuKw3v8kwAOq4WC8+cIi+7cpm33rYmv+ChFRtmOURWVklwbSBASaWq1IOnxTxobv5dPU+Kso1pNIBCDOe2Y0kIbstPlnlTeiqYe1amAoiL5ec+yRQCn3eaenF+Pte3CR+mbiEDZTrGZHdjMTvyDDU02qbyhqDYZAI0Xre8Gd0apr+f6buGj9E1E07sdIjtdlBbY0Ru1BIS2frNSsbuQ9BqkFnBr1hXF5cLldKL3a/37I/B+vOeTK2gS3BJed79kcGTz+CTrWx/V4ULy867Gi9PunpupMwi1mqB2RKBsZ5iL7MgOF8ERfo2WXTQFql0G1Tub3SA2ExPUjdb/SxI0GQ6rjLXUgSnQgNHfO8S4irV82wcvC5Sy3YaufO29QFAbIlC2ExSXQkm+Da1eQ2BYy2ZJkiRx5513eh7LskxUVBRTpkxBscvkFudx/fXXk5iYyIABAzxrwRVF4be//S1xcXHEx8eTkpLCyZMnL/tas2fPZs2aNdUe27NnD2PGjCE2NpZ+/fpx3333YbFYSEtL80xkr8Bpt6MTRnNBHfGujiNBgyktsKO4FMKiAlp8vmRNPkpVcW9L+8xLizw+SoD09HQAVq1a1So+SrfkWMZfNLsFdURklO0Au8WJ3eIkIKT51nHXRoWPEvD4KCnfI+hC7oUqrsmEhASgeh9lhbg2MPDXjb7WrFnD7NmzPY83b97M6NGj6du3L1988QVAjT7Kjh07Vqnn559/zvARI5hw/Q1cP+1msrOzAdixYwdJSUmeJYulpaVkZWUxZswYkpKSiIuL82jjqiMwMJDHH3+cxMREhg8f7rnu559/zrBhw0hOTmb8+PGe559++mnmzJnD2LFj6dWrF0uXLq3P7Ra0MCKjbOOoikppgQ2dXotj51nKssqa9PqGzgGEXn9FreWq81Hu3LwdyaBl/oIFzJgxg3/+85+MHz+ee+65h86dO7eaj3LrhvWUFRfy2eZtvPjii7z88sssWbKkUT7KsrIyhg8fzqJFi3jkkUd46623eOKJJy7xX1a8HsCRI0fYtm0bpaWlxMbGMm/ePPR67+hbFlRFBMo2jt0iowSqhET5UdaKM4Eu9lGqLrcZXuOn9fgoN2zYwPr160lOTm5VH+VvFywgOzsbl6p69ucZOXJko3yUBoOBKVOmADB48GA2bdrkeb3K/svK+wFdd911GI1GjEYjHTp0IDs7u0rmLfAeRKBsw5w7WojT7sI/2IDeqK1T5tecVPZR5px1682k8lVB4eHh3H777dx+++1MmTKlVX2Uc+64nRtuvJH9hw57LD8LFy7kuuuua7CPUl9pTyKtVossy57Xq+y/rHg9AGOlPtLK5wi8D9FH2UZxyQo7VhxF0koEhHjHoERlH6VqdyFJEhq9lq1bt3qaraWlpa3qoywqKqJjhw7ojX4ezyPQbD7Ki/2XgraJyCjbKAc2n6bwggU//8BWswJdTIWPUnUpbhGG1l2vH374gQULFqDT6VAUxeOj3LBhA3PnzvVIJoYOHXqJjzImJoa4uLgqW8BW+Cizs7M9Pko/Pz+PjzInJweNRsOYMWOYNm1alTo+tnAh9z/0W2JiujHiyis905FeffVVtm3bhlarZcCAAUyePJmVK1fy0ksvodfrCQwM5N133633PanwX3bp0oXhw4fXOv1J4J0IH2UbpLTAxv/++i3d4iLoMUZ3WR9la+AyO3AV2dF19Eej964J3SV5uVhLi+nQ44pWX97ZHhA+SoHX8vVHP6MCo27pU2vZ1kCxykg6DZIXLKG8GKfdht7gJ4KkoF6Ipncb4/zPRfy8N4ch1/UgKNwPslu7RlVRXQqq3YUmyOB1wUhVVWS7Hf/gkAZfQ/gofRMRKNsQqqLy1QfHCQwzMmhi99auTrUolnL3pL/3fbRkhx1VVdH5NXzpovBR+ibe1zYS1Mix77PJPV3K8BuvaLUVOLWhWJxIeq3X9U0COMunGRnEGm9BPRGBso3gcip899kvRMYE0jelY+0ntAKKw4XqVNAEeF82Ce7+SY1Wi0bnnfUTeC8iULYRftp5jtJ8G1fe1NtrpgNdjGKRQQJNK289URNOmw29nxjIEdQfESjbAA6rzN71mXTtF0bMgPDWrk61qKqKYnGi8dN51ZYPFSguF7LTiV40uwUNwPs+0YJLSN92BpvZyfAbW3eJYk1IksSdt98BiorGX1/FRwluBdqUKVMu8VG+/vrrHmNPhaFHkiQOHz7coHpce+21FBUVVXusYusHQz0GcrZv305ISIinfuPHj2f79u0eQ1EFsizTsWNHsrKyGlRvgffjnW0kgQe7VebA5jP0SIikY4/g1q5OtXh8lE47wX6BbNqwwbNsD+Cpp56q1kc5f/585s+f7yn32GOPkZSU1OAJ9OvWravxWMVATn1lvaNHj/ao3MC91PLs2bNkZmbSo0cPwK19i4uLIzo6uv6VFrQJREbp5fy45Qx2i8zQKT1rL9yKTLpqAhu/2owkSb/6KMvJysqq1kdZmZ07d/LBBx/wxhtvAG4Rxj333EN8fDzJycls27YNgLS0NKZNm8Y111xDnz59eOSRRzzX6NGjB3l5eWRmZtK/f3/mzp3LwIEDmThxIsVFhegMRn744QcSEhIYMWIEf/7zn4mLi6vX+9RoNNxyyy2sWrXK89zKlSurvF9B+0NklF6M3eLkxy1n6JkYSVS3oFrLr1+//hIJRGPp1KkTkydPrrXcrTdM5/k3lzD1lhs9PsoK0e38+fOr9VFWUFRUxD333MO7775LcLA7a3799dcBOHjwIEeOHGHixIkcO3YMgAMHDrB//36MRiOxsbE89NBDxMTEVKnP8ePHWbFiBW+99Ra33norn376KXfccSf33HMPy5Yt48orr2ThwoW1vq9du3Z59Gq33HILjz/+OLfddhv3338/jz76KHa7nXXr1vH3v/+9DndT0FYRGaUXc3D7ORxWmRQvziZVlwIqJKYkkXnqVx9lZSp8lHPnzuXIkSMkJyeTm5vrOT5v3jzuuOMORo4c6Xnuq6++8uzD069fP7p37+4JlKmpqYSEhODn58eAAQM4derUJfXq2bOnJ8AlJyVx6sxZyqw2SktLufLKKwG4/fbba31/o0eP5sCBAxw4cIDHH38cgJSUFMxmM0ePHmX9+vUMHz7cY2YXtE9ERumlOB0uftx6hu7xEUTF1J5NAnXK/JoaV6kDAG2AoYqPMj8/v0q5mnyU77zzDpmZmbz33ntVyl9O1lIXj2PlMqqi4JJldE24R87MmTNZuXIlhw8fFs1uH0BklF5KxlfnsZmdDJ7knUsVAVRZQSlzggSSXlPFR1mZmnyUv/zyC48//jjvv/8+uosmgY8ZM4b3338fgGPHjnH69GliY2MbVE9FdiJpNER26EBQUBDffvst4O5bbCi33XYby5cvZ+vWrUydOrXB1xG0DURG6YW4ZIUDm07TuU8o0b1DW7s6NVKRTVZQ4aO8mJp8lL/5zW8oKyu7xBn52muv8eCDD/LAAw8QHx+PTqcjLS2tSpZYr3rKMhqtDkmSePvtt5k7dy4BAQGMHTuWkJCGCTIGDBiAv78/gwcPbvDOkYK2g/BReiFHvsliyzuHmfJQIt0HRly2bHU+wJZAcbqQsy1oAvXoQr13ErficpGT+QuB4REEhoVjNps9OzwuXryYrKws/vGPf7RyLdsuvuKjFBmll6GqKgc2nyaiSwDdvHgVjqvIDhoJbZChtatzWS6eaL527Vqef/55ZFmme/fupKWltWLtBG0FESi9jLOHC8k/V8bVd/X32jXJSpkT1e5CG+bnlcsVK+Ow2UD6daL5jBkzmDFjRpUyGzdu5NFHH63yXM+ePS/Zq0fgu4hA6WXs33wa/2DDZQ1BqqqiOpVWMYirsoKr2IFk1Hqlc/JinDYreoMfGk3N92rSpElMmjSpBWslaGt4/yfdh8g/Z+ZMRgHDbuiFVu/+w1ZVFceZUmyHC3CcLcV5ocw90qwAEshTg3DmWNxbLxi1aIzaZgugqqIi55cvBQz1fguPqqo47TZMQQ03mgsEIAKlV5G+7Sw6vYa40V1Q7C7K9lzA/PU5XIXu/kB9J3/8+oajDTIgGbWoskKxoRgkUGwyWJy4cE/V0fjr0Zh0TRY0VVXFVWhDdbrQRZiQ9N7d5IZyo7miom+E0VwgABEovQZbmZNj312gz9COyD/lUvDlKRSLjKFnCMHju2PqH47GX3/JeecPH0Yf5e9ujssKqs2FYpVxFdtxFduR/HRoA/RIftoGZ4DuIGlHscpoQ4xe65u8GIe1/sYggaA62sYn3gfI2H0e2anQJdtM0aE8jL1CCL6mB8ZudTMGSZKEpNeCXos2yIDiVFAsThSLEzlfBq0GbYAOTYC+XgMwqktBLrC5B2+CDWgCLw3W3orTZkWr16PVtZ06C7wT728/+QAul8LBL08ToZcIsrkIvy2WyLnxdQ6S1aHRa9CFGNF3CkAb7oekk3CVOHBmleHMs+Iqc6IqNc+hVRUVV6kDZ7bFM8KtDTZWm5VKkuRZlw14hY9SVVUcNmujsskKH2VycjL9+vXj4Ycf9hxLS0tjwYIFVcqPHTuWtjx/V1AzIqNsZVSXQsaynzCbnSRcEUzHBxKadG6iJElo/fVo/fWVskwZl82GqxAkvRbJoEHSSiBJqIp7RF21u0BVkYxatKHGy24W5vFRWq2YTCY2bdrU6j5Kl9OJ4nKh9zM16FoVVPgorVYrycnJ3HTTTVXkHQLfQATKVkSxyeQvP8yRQ/n4+2lJ+H0y2kbsXnjs2P+j1FyXbEwFxZ01orp/hkrZpSSBRkLSSAQGDyA26slarzh58mTWrl3L9OnTPT7KCs1aVlYWEydO9JS9nI9y3759gNtHOW/ePPbu3YtOp+OVV15h3LhxpKWl8dlnn2GxWDhx4gQ33XQTL774IuD2Ue7duxez2cw1kyYxOCmRAwd/okvXrnz66aeYTCa+//577r33XgICAhg1ahTr16/np59+qvX9mUwmkpKSOHfuXK1lBe0P0fRuJVxlTnLfOkjB8SJyZJWB47s1KkjWj/JAqNO4M0pj+Zeh0nedxl2mjlessOnYbDbS09MZNmyY59j8+fO59957GTduHIsWLeL8+fNVzq3wUb7zzjvV+ihXrFjB3Xffja3cUn7gwAFWrVrFwYMHWbVqFWfOnLmkPj+fOMG9d9/FT4cOERoayocffgjAPffcw5tvvsk333yDVlv3+8R+Q3oAACAASURBVF1YWMjx48cZM2aM57lVq1ZV6ToQze72i8goWwGX2UHevw/izLOS3TsMaX8uA0Z2rv3EWujbt/bMr7lISEggM/PyPsoNGzawfv16kpOT+emnn4iKigJq9lE+9NBDQM0+SsDjo7xY3NstJoZBgwYhSRKDBw8mMzOToqKiS3yUlbd5qI5du3aRkJDA0aNHWbhwIZ06dfIcq5ARVzB27Nj63DJBG0JklC2MYnGS9++DyPk2Qu/oz/EjhfRMjCQwrOlcia1FhY+yOj9jhY/yvffeIyUlhZ07dwJ4fJRPPlk1yDfGR+mSZQx6vad/sqJMQwQwo0ePJj09nYMHD/Kvf/2LAwcO1PsagraPCJQtiGKXyf3vIZy5ViLuGsC5Yie2MidxY7rUfnIbwFt8lL+KMKoO5ISFhTXYR9m3b1/+8pe/8MILL9T5HEH7QTS9WwhVVsh/7zDOc6VE3DEAvz5hZLy8j5AoE11j28c2At7io3R6RBiXlm+Mj/KBBx5gyZIlnDx5ss7nCNoHwkfZAqiqSuEHx7DszyHslr4EDO5I4YUy/vf0d4y46QoGNcJi3lo+Sm8m78wptDo9YdGX9vsKH2XTInyUgiaj5MtTWPbnEDyxOwGD3VagjK/Oo9FI9Bsh9oJuSlyyjOxwYAqqfrK+8FEKGoIIlM1M2Q/ZlG47Q0BKJ4LGuUdmXU6FI99eoGdiJP7B3i2+bWs4bFbg0v7JCoSPUtAQRKBsRuyZxRR+dBzjFSGE3niFZ/nfLz/mYjM7GTCq8VOCBFVxWq1IGk29dlwUPkpBbYhR72ZCLrSR/95hdGF+RMzqX0VEcXj3eQLDjXTt751bPbRlHFYrBj+T17syBW0LESibAcXhIv/dDFSXQsTdA6ro0UryrZw5Ukj/EdFoNOKPuSlxyTKy04HB1Lj13QLBxYhA2cSoqkrh6mM4L5QRfls/9FH+VY4f+eYCAP2uFIM4TY3Devn+SYGgoYhA2cSU7jiL9WAeIdf0wBRbtWmtKipHvs4ipl8YwRHij7mpcVgtaLT1658UCOqCCJRNiPVoASUbMzElRhE4puslx88eKaS0wEb/K9vXII43+ChVVeXmGTOw2p1N1j9ZXx+loP0iRr2bCGeelYIVR9B3CiDs5j7V/rEe/vo8Rn8dPZMiW6GGzYc3+ChdspPl/36L4MgOjXw3VRE+SgGIQNkkKHaZ/HcPIWkkIu4cgMZwqb7LVubklwN5DBgZja6ZdGpPHj/LT2Zrk14zLtDE/+tzaXZ8Ma3to3RYraRcNY7v9+whp7CQyZMnM2rUKL7++mu6dOkifJSCRiGa3o1EVVQKVh1DzrMSfnt/dOHVbz3w895sXLLSbgdxWttH6bBYkACt3j3D4Pjx48yfP59DzeijFPgOIqNsJCVbTmPLyCdkSi/8eofWWO7w11lEdAkgqltQs9WlLplfc9GaPsrMzExiYzq7RcPlXR49e/YkKSkJoNl8lALfQWSUjcByMI/SLafxH9yRwMuId/PPm8k5VUq/EdHteiJ0a/ko7VYriktBkjQ1lhE+SkFjEIGygTjOmSn84CiGmCDCbup92QB45OssNBqJ2GHtOxtpLR9lhX+ytn9CwkcpaCii6d0AXKUO8t89hMZfR8RdA9z7y9RU1qVwdE823eMjMDXh7oreSGv5KJ12e53nTjaljzItLY1PPvnEc/zbb7+la9fW6/4QNB/CR1lPVKeL3GUHcV4oI+qBRAxdAi9bPjM9j7VvpHPtvHh6JkY1eX183UepuFzknjqJf0goQRG1T7sSPsqmRfgoBZfgHuE+iuNsKRGz+tcaJAGOfJOFKUhPt7iIFqih7+GwWVFVFaO/f+2FET5KQcMQgbIeFG/IxPpTPiHX9cQUV3v2YjU7OJmeR/zYrmi1oju4OXBYLEgaDXq/6qdlXYzwUQoaggiUdaT0q3OYd54lYHg0gaPqthnY8e+zUVyqsJg3E6qqYrdaMJhMVUa864vwUQpqQ6Q5dcDyYw7FX/yCaWAEoVOvqPMUnyPfXCAyJpDIrrU30QX1x+V04nI6MfoHtHZVBO0cEShrwXqkgIJVxzD0DCZ8Zj+kOjok886ayT1dSv92uhLHG7Bb3VOODKa69U8KBA1FBMrLYPu5iPzlGeijA4i8eyCSvu6368g3WWi0En1SOjZjDX0bu6UMncGATq+vvbBA0AhEoKwB24ki8t85hC7CROScODR+de/OdbkUju25QI+ESEyB7XvuZGuhKC6cVqtodgtaBBEoq8H2cyH5aYfQhvkRdV882oD6ZSynf8rHWuqkv48M4rSGj9JhqZgW9GugvPbaaykqKmqy91Wbj1KSJLZs2eJ57uOPP0aSJNasWdNkdRB4ByJQXoQ1I5+8tAx0EX5E3R+PtgGraQ5/nYUp2EDMQN/YPKyyjxKo0Uf5448/kpGRweLFiwG3VejAgQOer6lTpzJr1qw6TaC3W8rQaKtOC1q3bh2hoTWLSRrC6NGj2b9/P/v37+eLL75g9+7dnmPx8fGsWLHC83jlypUkJiY26esLvAMxPagSZXsvUPjhcfRdAom8J67emSSApcTBqYP5JKTGtPjcyWc+P0TG+ZImveaAzsH89fqBtZZraR9lUX4ep86c5ebp03nxxRcB6NGjB3v37sVsNreIj3L06NHs2rULp9OJ3W7n559/9hiLBO0LkVHino9X/GUmhWuOY+wdStTchAYFSYBjey6gKKrPNLsraEkf5f79+3nzH6+y97vvPD7Ki2kJH6UkSYwfP56NGzfy6aefMnXq1DpfT9C28PmMUnG4KFxzDGt6Hv5DOhJ2Y+/LSi4uh6qqHP46i449gwnv3PKDDHXJ/JqLlvRRXjV6FMFBQYSEhzNgwABOnTpFTExMlddsKR/lzJkzWbp0KcXFxbz88sv87W9/q++tE7QBfDqjdOZZyX3jgHvXxMk93HvdNDBIAuScKqXgfJnPzp1sKR+lVqPBYDKh0Wo9rsmLaSkf5dChQ/npp5/Iy8ujb9++9b6+oG3gs4HSfrqEnNf24ypxEHlPHEFXxTRaqnvk6yx0eg29h/jm3MmW8FG6ZBnV5cIYUP/VTs3lo3z++edFJtnO8dmmt75TAKYBEQRP7I4urG5ChcvhdLg49n02vQZFYTT55m1tCR+l7HD3U/o1cP5kU/ooK5g8eXKD6iJoO7Saj1KSpHBgFdADyARuVVW1sJpyLuBg+cPTqqrW2mPenD7Kmjj6bRab0w5z4x+T6dI3rMVe19d8lPnnzoCqEtG1W4POFz7KpsVXfJSt2fReCGxRVbUPsKX8cXVYVVVNKv/y2mHFjN1ZhESZ6NynaefxCX7FJcs4bbYGNbsrWLt2rWdy+65du3jiiSeasIaC9kprthFvAMaW//wOsB14tKbC3kxRtoXzx4sYfmOvdr15WGtjt5QBYAxo+IwC4aMUNITWDJQdVVXNAlBVNUuSpA41lPOTJGkvIAOLVVX9pIZyrcbhr88jaST6DffN0e6WwmY2o9Pr0embdv288FEKaqNZA6UkSZuB6rYefLwel+mmqup5SZJ6AVslSTqoquqJal7rfuB+gG7dGtZ/1RBcssLhr7PoHhdBQGjdNrgS1B+XS8ZhtRAQFi6ydkGL06yBUlXV8TUdkyQpW5Kk6PJsMhrIqeEa58u//yJJ0nYgGbgkUKqqugxYBu7BnCaofp3ITM/DWupk4Oia9/UWNB57mbvZ7deI/kmBoKG05mDOZ8Dd5T/fDXx6cQFJksIkSTKW/xwJjAQyWqyGdeDQrnMEhhnpNlBsHtac2MylbvekQWjrBC1PawbKxcAESZKOAxPKHyNJ0hBJkv5dXqY/sFeSpB+Bbbj7KL0mUBbnWjlzuJABozqjqaP5XFB/XLKMw2rFLyBQNLsFrUKrBUpVVfNVVU1VVbVP+feC8uf3qqp6X/nPX6uqGq+qamL597dbq77VkbH7PJKEzy5ZrKC5fZQVze7apgU1tY9SIKjAN5eQNAEup8Lh3efpHh9JYBOs7GnLVPZRmkymGn2UFat20tPTAbdVaP78+Z5yjz32GElJSZdMYLaV1a3ZvW7duqZ6SwJBFUSgbCAnDuRgLXUSf1Xdtq5tEdYvhAsHay9XHzrFw+TFtRZrNh/l99+DovDC4ue5Nqa7x0dpsVg4ceIEN910U7P5KC/3WvPmzeP777/HarUyffp0nnnmGU8d7r77bj7//HOcTierV6+mX79+9bjhAm/EZ6UYjeWn7ecIiTIR0983LOa10Vw+ym+/2skbr77CAwse8vgoDxw4wKpVqzh48GCz+yhreq1Fixaxd+9e0tPT2bFjhydLBoiMjGTfvn3MmzePJUuW1Oc2CrwUkVE2gLyzpWSdKGbk9N513r62RahD5tdcNJeP0lpqZsDAgVV8lKmpqR6ZRXP7KGt6rQ8++IBly5YhyzJZWVlkZGR4MuUKycfgwYP56KOP6n8zBV6HyCgbwMEd59DpNfTzMYt5bTS1j1J2OJAddkyBQVWOVeeavJim8lFWd52TJ0+yZMkStmzZQnp6Otddd50n2618Tk11E7Q9RKCsJ7YyJ8e+u0CfoR3xa+B2Ee2VpvZRLl/+Hkhw6tx5j4+yMTTGR1mZkpISAgICCAkJITs7m/Xr1zeqXgLvRzS960nGV+eRHQqJV8fUXtjHaEof5bx587jnzju5+rqpGP38PD7KxtIYH2UFiYmJJCcnM3DgQHr16lWly0DQPmk1H2Vz0lw+SpdLYfkT3xDSwZ8b/5Dc5NdvCO3VR2m3lFGYdZ7Qjp3wu6jp3RiEj7Jp8RUfpcgo68Ev+3MxF9oZM1PsjdLcWEtL0Wi1GBtoMq+JtWvX8vzzzyPLMt27u6cbCQS1IQJlPUjfeobgKBPd4yNbuyrtGsXlwl5mxhQcjKRp2m504aMUNAQRKOtI1s9FXPilhNEz+oh13c2MrcyMqqqYgoJb5PWEj1JQG2LUu47s+/I0fgF6+l8pdGrNjbWkBJ3BiM4g/J4C70AEyjpQkFVGZnoe8WO7oDfWvppD0HCcdhtOu83d7BamIIGXIAJlHTiw6TQ6vYb4sV1buyrtHmtJCZIkXTLJXCBoTUSgrIXSAhtHv7tA/yujMQUJaWxzoigKVnMpfoGBaOqwDlsgaClEoKyF/RtPAZA8qXsr18R7qc1H+dlnn7F4ce3r0G3mUlRFwRTkngQ+duxYYmNjPb7KNWvWNKh+r776qmdlUHPy1VdfMXToUPr160e/fv1YtmyZ59gnn3xCRsavzumxY8fS0nvPCxqOGPW+DOZCO4d2n6ffiGiCwn3bOXk5avNRTp06lalTa9+S3T2IY0Dv9+u9fv/99xkypHFzl1999VXuuOMO/P3963yOLMuXLKu8HBcuXOD222/nk08+YdCgQeTl5TFp0iS6dOnCddddxyeffMKUKVMYMGBAs9VB0HyI38Jl2L/pFKoCg69pG9nkC3te4EjBkSa9Zr/wfjw6tPbt1i/no0xLS2Pv3r3885//ZPXq1TzzzDNotVpCQkLYuXMnLpeLh//0JzasX49Wp+M3DzzAQw89VONrLV++nKVLl+JwOBg2bBhvvPEGWq22Wkfk0qVLOX/+POPGjSMyMpJt27YRGBiI2WwGYM2aNXzxxRekpaUxe/ZswsPD2b9/P4MGDeLZZ5/loYce4uDBg8iyzNNPP80NN9xQbZ1ef/11Zs+ezaBBgwC3au3FF1/k6aefJiwsjM8++4wdO3bw3HPPeZRvq1ev5sEHH6SoqIi3336b0aNHk5aWxtq1a7HZbJSVlbF169Z6/b4EzYMIlDVgLrRzaNd5Yod1JDjS1NrV8XpmzpzJs88+y5QpU0hPT2fOnDmeQFmZZ599lo0bN9KlSxfPtg3Lli3jxM/H2fzFZ0Rf0ZuiomJP+VmzZmEyue//li1byMnJYdWqVezevRu9Xs+DDz7I+++/z1133cWiRYsIDw/H5XKRmppKeno6v/3tb3nllVfYtm0bkZG1LxQ4duwYmzdvRqvV8thjj3H11Vfzn//8h6KiIoYOHcr48eMJCLh0tdChQ4e4++67qzw3ZMgQDh06xJVXXsnUqVOZMmUK06dP9xyXZZk9e/awbt06nnnmGTZv3gzAN998Q3p6OuHhwnXqLYhAWQN7151EVVRSruvZ2lWpM3XJ/JqLy/koKzNy5Ehmz57Nrbfe6pFhbNq0iTtuvYWgsHA0Gm2VAHFx03vFihX88MMPpKSkAGC1WunQoQPAZR2RdeWWW27xCH2//PJLPvvsM49812azcfr06WrX1quqWu10pstNcarsrczMzPQ8P2HCBBEkvQwRKKuhKNtCxu4s4sZ0EdlkPajwUW7fvp38/Pxqy7z55pt89913rF27lqSkJA4cOIDsdADgH1y7yUdVVe6++26ef/75Ks9XOCK///57wsLCmD17dhVHZGUqB6+Ly1TOFlVV5cMPP6yT3m3gwIHs3bu3Sl/sDz/8cNk+yZq8ldVlrILWRYx6V8Oez39Bq5MYcm2P1q5Km6ImH2VlTpw4wbBhw3j22WeJjIzk9OnTjB4+nOWrVkP5uu6CgoIaz09NTWXNmjXk5OR4yp46deqyjsigoCBKS0s9jzt27Mjhw4dRFOWy67knTZrEa6+95hH+7t+/v8ay8+fPJy0tjQMHDgCQn5/Po48+yiOPPFJtHQRtCxEoLyI7s4Tje3NIvDoG/2Axb7I+1OSjrMyf//xn4uPjiYuLY8yYMcRecQW3Tb+Znr16kZCQQGJiIv/73/9qPH/AgAE899xzTJw4kYSEBCZMmEBWVlYVR+ScOXOqOCLvv/9+Jk+ezLhx4wC3Xm3KlClcffXVREfXbKl/8skncTqdJCQkEBcXd4mFvTLR0dEsX76cuXPn0q9fP6688krmzJnD9ddfD7j7cF966SWSk5M5ceLEZe+RwPsQPspKqKrKRy/tozjPyh3PDMdg8v6eibbso1RVlYJzZ1BVlYiu3cSSxTaIr/goRUZZiZ/35nDhl2KG39CrTQTJto7TZsNpt+MfEiqCpMCrEdGgHKfDxdcf/0xkTKDYNKyFKCsuRKPVNqnBvLkR7krfRATKcvauPYm5wM6EewYK32QLIDvs2MvKCAwLR9PEct7mRLgrfZO28wltRvLPmTmw6Qz9roymc5/Q1q6OT1BWVISkkTA1YHMvgaCl8flAqSoqO/53FINJx5XTrmjt6vgELqcTm7kUU1AIWq1o1Ai8H58PlAd3nCXrRDEjp/fGFCimA7UEZcXupYsBISJ7F7QNfDpQFmVb+OajE3SPjyB2eKfWro5P4JJlrCXF+AUGodXrW7s6AkGd8NlAqbgUNqdloNVrGHdHPzE9pRHUx0dpKS5EVVUCQsNqvW5b8lFaLBZmzZrlmUw/atQozGYzmZmZxMXFNeraFfuQC1oPn+0guvBLMTmnSplwzwACQsQmVo2hrj5KlyxjKS7GFBSEzlC3bo624qP8xz/+QceOHTl48CAAR48eRS8y5naDzwbKzn3CuP2vwwjp0H6kFxf+9jfsh5vWR2ns349Ojz1Wa7m6+Ciff+ZpPlu7jn+8+X9odboqPspHH32UjRs3IkkSc+fObXM+yqysLLp3/9VbWlmk4XK5mDt3Ll9//TVdunTh008/xWQyceLECebPn09ubi7+/v689dZb9OvXj5MnT3L77bcjyzLXXHNNnX5PgubFZ5veAKEd/UWTu4mYOXMmK1euxGazkZ6ezrBhw6ocVxUFS0kRr77xLzZ++SU//vgjn332GeD2UZ48eZL9+/eTnp7OrFmzPOfNmjXL0/TOz8/n8OHDHh/lgQMH0Gq1vP/++wAsWrSIvXv3kp6ezo4dOzw+ys6dO7Nt2za2bdtW6/uo8FG+/PLLLFq0iKuvvprvv/+ebdu28ec//5mysrJqz5szZw4vvPACI0aM4IknnuD48eOeY8ePH2f+/PkcOnSI0NBQj7j3/vvv57XXXuOHH35gyZIlPPjggwD87ne/8wT9Tp1E37k34LMZZXukLplfc1Gbj9Jpt6OqKiNHjbrER7l582YeeOABT1O3Lfook5KS+OWXX/jyyy/ZvHkzKSkpfPPNN5hMJnr27ElSUhLwq3vSbDbz9ddfc8stt3iuYbfbAdi9e7cnmN55552XrAQStDwiUAqajJp8lC6XC9npwD8ohGVvvXWJj7Im6W11eKuPEtyDLtOmTWPatGloNBrWrVvHzTff7PFOgts9abVaURSF0NBQj5btcnUUtD4+3fQWNC01+SjtZe7+wICwsEt8lGfOnGHixIm8+eabHnltW/RR7t69m8LCQgAcDgcZGRlV+iwvJjg4mJ49e7J69WrAHZR//PFHwG2BX7lyJYCnW0HQuohAKWgyqvNROu12nDYbOoMBrU5/iY8yMTGR++67j27durVpH+WJEye46qqriI+PJzk5mSFDhnDzzTdf9n69//77vP322yQmJjJw4EA+/fRTwD2C/vrrr5OSkkJxcfFlryFoGYSPso3j7T7KwqxzOO02ImN6oCnv+xO0H4SPUiBoJHZLGXaLhYDQcBEkBW0aMZgjaBZUVaU0Px+tXl+nTcPaCsJH6ZuIQCloFqwlJcgOO6EdOyG1Id9kbQgfpW/Sfj7BAq9BcbkwF+ZjMJkwBoh1yoK2jwiUgibHXJCPorgIiogS8wEF7QIRKAVNitNuw1JSjH9wKHqjkI0I2gciUAoaTYVmTVVVSnJzUVC5ov+AajVr9aGtaNY2btzoqWNgYKCnznfddVeDrud0OnnkkUfo3bs3cXFxDBs2jI0bNzZxrQX1QQzmCBpNhWYtP/sCst3Gnh8PVqtZawhtQbNWeYBn7NixLFmypFF1/stf/kJBQQEZGRkYDAaysrLYvXt3g68naDwioxQ0CZMmTeSTDz/E6O/PR598ym233eY5lpaWxoIFCwBYvXo1cXFxJCYmMmbMGMC9Fvzhhx8mPj6ehIQEXnvttcu+1vLlyxk6dChJSUn85je/weVyATBv3jyGDBnCwIED+etf/wpQRbNWsTKnsgh3zZo1zJ49G4DZs2fzxz/+kXHjxvHoo49SVlbGnDlzSElJITk52bNypj7Isswf//hHhg4dSkJCAv/+978BtwgkNTWVadOmERsb68k+S0tLSUtLY+nSpRjKnZ3R0dFMnz693q8taDpERtmO2PXBMfLOmJv0mpExgYy+tW+t5aZMGM/iJS8z8667SU9PZ86cOR4fZWWeffZZNm7cSJcuXSgqcu+dU1mzptPpqqz1njVrFiaT2xm6ZcsWcnJyPJo1vV7Pgw8+yPvvv89dd93FokWLCA8Px+VykZqa6tGsvfLKK2zbto3IyMha30eFZk2r1fLYY49x9dVX85///IeioiKGDh3K+PHjq4gzamPZsmV06NCBPXv2YLfbGT58OBMnTgRg3759ZGRk0KFDB4YPH863336LwWCgZ8+ewmruZYhAKWgSevfowfkL2axes6ZazVoFI0eObJeatZr48ssvOXz4sEdyUVxc7HFVDh8+3LPWPCkpiczMTPr2rf2fkqDlEYGyHVGXzK+pcckyqqpi8DNxw403VqtZq8ybb77ZbjVrNdX3jTfeIDU1tcrzmzdvvkS/Jssyffr04eTJk5SVldUrcxU0L6KPUtBgVFWlODcbgOCoDtx7773VatYq0141a5e7xhtvvOF5b0ePHsVqtdZYPigoiLvuuovf//73OJ1OAM6fPy90a62MCJSCBmMpKcZhsSBJEjqDoVrN2sW0V81aTfzmN7+hT58+JCUlERcXx7x58zxBsyYWL15MSEgI/fv3Jz4+nmnTpnm6FwStg9CstXFaS7PmdNgpOHsGg8mf0E7RYgWOjyI0awJBDSiKQnH2BSSNhuCoDiJICto9YjBHUG9K83KRHQ7Corugrcfe1+0BoVnzTXzrUy5oNNbSEqylJQSEhWGsx0qX9oLQrPkmouktqDNOu42S3BwMJhOBYRGtXR2BoMUQgVJQJxSXi6LsC2i0WkI6dBL9kgKfQgRKQa2oqkpRdhaKLBPSsZPP9UsKBCJQCmqlNC8Xh9VKcFQHDH6m1q6OQNDiiEApuCxlxUVYSooJCA3DFBRcbZkKH2UFsiwTFRXlMz5KQftHtKEENWIzmynNy8UvIIDA8JoHbyp8lFarFZPJxKZNm3zKRylo/4iMUlAtDquV4pwL6P38CK7D4M3kyZNZu3Yt4Db8+JqPMi0tjWnTpnHNNdfQp08fHnnkEc+x6uoF0KNHD/76178yaNAg4uPjOXLkyGXft6D1EP8y2xHb0paRc+qXRl9HVRScdgeSJBHdN5bUex6o9ZyZM2fy7LPPMmXKFJ/1UR44cID9+/djNBqJjY3loYceIiYmptp6VejfIiMj2bdvH2+88QZLlizxiH0F3oXIKAVVUBUV2eFAkkBnMKCR6vYRSUhIIDMzkxUrVtTJR/nWW295MsHafJQHDhzgwIEDREREsGXLFo+PMikpiS1btvDLL+5/Dh988AGDBg0iOTmZQ4cOkZGRUe/3f7GPcvHixSQlJTF27FiPj7ImUlNTCQkJwc/PjwEDBnDq1Kla61Xh5Bw8eDCZmZn1rq+gZRAZZTti3Oz7G3W+7HBQkHUOVJXwzl3RlW9FUFemTp3q0z7K6vyStdWr4pyK8gLvRGSUAgBkp4PC8iAZFt2l3kESYM6cOcJHeRGXq5eg7SAySgGyw05B1nlPkGzoftx19VEeP34cVVVJTU0lMTGRuLg4jh07RkJCAnq9nrlz53oGfy6mso9SURT0ej2vv/46w4cP9/goe/XqVa2PMjo6mm3btnl8lDExMcTFxWE2V7/P0JNPPsnvf/97EhISUFWVHj168MUXX9TrnlT2ZF5cL0HbQfgo2ziN9VE67TYKs86DhDtIGhoWJAW+ia/4KEVG6cPYLRaKsrPQaDQNbm4LBL6ACJQ+itVcSklONlq9nrDozmh1+tauUptAGZqcrQAAIABJREFU+Ch9ExEofQxVVSkrLMBcWIDBZCK0YzSa8ukwgtoRPkrfRARKH0JRFEpyc7CZSzEFBRMcGYWkERMfBILaEIHSR5CdToouZCE77ASGRxAQGiackgJBHRGB0gewmc2U5GaXj2x3xuhf/RI8gUBQPSJQtmMURcGcn4elpBi9nx8hHTqh04tBG4GgvogOqnaKw2aj4OyZcpdkKOGduzRbkJQkiT/96U+ex0uWLOHpp5++7Dl1cVRu377d47S8mB49epCXl1fvulbw9NNPs2TJkgaf39Drzp49m549e5KUlES/fv145plnPMfGjh1LU87/zczM5H//+1+TXc+XEYGynaEoCqX5uRScO4OqKoR17kJQRBRSHeUWDcFoNPLRRx/VK3BNnTqVhQsXNludLkdrr6l+6aWXPKKPd955h5MnTzbL64hA2XSIQNlOUFUVW5mZ/LOnKSsqwj84hIiu3TCamn9LWZ1Ox/3338/f//73S47l5uZy8803k5KSQkpKCrt37waqOipPnDjB8OHDSUlJ4amnnqriizSbzUyfPp1+/foxa9YsKq8ke+mllxg6dChDhw7l559/BuDUqVOkpqaSkJBAamqqx/ZzsWsSICMjg7Fjx9KrVy+WLl3que4rr7xCXFwccXFxvPrqq7U+v2jRImJjYxk/fjxHjx6t832rkGNUp22ryZlZ0/3csWOHxwSfnJxMaWkpCxcuZNeuXSQlJfH3v/8dl8vFn//8Z1JSUkhISOD//u//6lxXX+f/s3ffcU2d+x/APyeBJEDYQxFBUEFG2IgL99bqrbMW62rd1Tpab1vb69a62uuParXaOrDOuq7aal3UgVZFUfZUUFCUTSAQSHJ+f9CkAYEECaL4fb9evjDnnDznyUn48pz1CR2jbAYqpFIU5+VAcjELyJNDT18fZZxSlCGrwW3zWhnBbFg7jct9/PHH8PLyqhJYCwDz5s3DggULEBQUhEePHmHgwIGIj49/YZl58+bh/fffx7Zt26rMi4yMRGxsLFq1aoVu3bohPDwcQUFBAAATExPcunULoaGhmD9/Pk6fPo05c+Zg4sSJmDRpEnbu3IlPPvkEJ06cAFA1a3LZsmVISEhAWFgYxGIxOnTogFmzZiEqKgq7du3CzZs3wbIsOnXqhJ49e0KhUNQ6/eDBg4iMjIRMJoOfnx/8/f3r3FaLFi3CqlWrkJKSgk8++QQ2NjYat6+m7blx40Zs2bIF3bp1Q3FxMQQCAdauXYuNGzeq7k/fvn07TE1Ncfv2bUilUnTr1g0DBgyAk5OT1ut/W1GhfIMVZD1FqbgIuRmPwOFywBMYgOVXAHj1l/2YmJhg4sSJCAkJUQXtApVZk+r5i0VFRVWSfADgxo0bqmIWHByMzz77TDUvMDAQrVu3BgD4+PggLS1NVSiVKervv/8+FixYoGrr2LFjAIAJEyZUKdzqWZMAMHToUPD5fPD5fNjY2ODZs2e4du0aRowYoRrljRw5ElevXgXLsjVOVygUGDFihOprJrT5yosNGzZg9OjRKC4uRt++fXH9+nV07dpV4/OA2rdnt27dsHDhQowfPx4jR45UbTN1586dQ1RUlOq7hwoLC5GcnEyFUgtUKN9AOY/SEHH6OOKuhqHb7M9gZGYOIzNzcByb9g6b+fPnw8/PD1OmTFFNUygUuHHjRpXiWR81ZTwqqV8HWts1oerTq+/i1tR2bSExdYXHvOz1qEKhEL169cK1a9deKJS1ZWbWtj2/+OILDB06FL///js6d+6MCxcu1Pgavv/+e7qz6CXQMco3hEIhR+qdmzi6Zgn2LJqDxBvX4DtoGIQWljC2tHotbkO0sLDA2LFj8fPPP6umDRgwAJs3b1Y9vnfv3gvP69y5M44ePQoAOHjwoNbrO3TokOpnly5dAABdu3ZVtbFv3z7V6FNbPXr0wIkTJyCRSFBSUoLjx4+je/fudU4/fvw4SktLIRaLcerUKa3XJZPJcPPmTbRr9+KhjdoyM2vbnqmpqfD09MTnn3+OgIAAJCQkvJDDOXDgQGzduhUVFRUAKg9FlJSU1Gv7vK1oRPmay814jLirlxB3NQzFuTkwMrdAt/cmwLv/YBgYm7xwvK+pffrpp1V+kUNCQlTHL2UyGXr06PHCcUjltyR+++23GDp0KExNTbVal1QqRadOnaBQKHDgwAHV+j788ENs2LAB1tbW2LVrV7367+fnh8mTJyMwMBAAMHXqVPj6+gJArdPfe+89+Pj4oE2bNujevbvGdSiPUZaXl6Nv376qr4NQV1tmZm3bc9OmTQgLCwOXy4W7uzsGDx4MDocDPT09eHt7Y/LkyZg3bx7S0tLg5+cHlmVhbW2tOuRB6kZ5lK8ZVqFA1oNkPLgbgeSb4cjNeASGw4Gjly88+wxEW/9AcNW+RrWheZSvA4lEAgMDAzAMg4MHD+LAgQN1fuMheX1QHiV5JRQKOXIepeNJUgIex0XjcWwUSosKAYZBa1cP9J48HR26dIeRmXlTd7XR3LlzB3PmzAHLsjAzM8POnTubukuEVEGF8hVhWRaSwgLkP81EXmYGsh+l4XnaA2SnPUCFtPJgvdDCEo7efnDy9kMbbz8Ymmi3C/qm6969O+7fv9/U3dCpjz/+WHWNo9K8efOqnOgibw4qlA2gUMhRXlqKcokEZSXFkJYUo1RcBElREUoK8lFSkIfivFyIc7JRmP0cFWWlqufqCwxg3cYJot79YdveBbbOrjBt0ZISfZqJLVu2NHUXiA69tYWy8PkzXD/8C1iWhUKhAPv3P4VCDoX8738yGeRyORSyCsgrKiCrqICsvBwV5VLIysogqyivfQUMA0MTUwjNLWHW0hb2Ii+YtWgFc9tWsGxtD2MLK8qCJOQN8dYWygppGTIT48AwHDAc5u+fHHD09MDhcMDhcsHh6kFPXx9cAwNw9Xng6utDj8eDHo8PfT4fPIEBeAaG4BkagG9oBIGRMQRCIQxNzWBgbFLlpAsh5M311v4mW9m3wdTvf9a8ICHkrUf7foQQogEVStJglEepfbt15VHWtqy3tzdcXFwwceJEZGZmauyDrnMtCRVKogOUR1k/2uRRyuVy1bL3799HYmIifH190bt3b5SX13ESkTQKKpSkwSiPUjd5lI6OjlixYgWCgoLw66+/VlmWYRgsWLAALVu2xJkzZwAAs2bNQkBAADw8PLB06dIa13Hu3Dl06dIFfn5+GDNmjOpWSFI/b+3JnObozJkzyMpqeAalupYtW2Lw4MEal6M8St3kUQoEAly7dg0AcPbs2Ree6+fnh4SEBPzrX//C6tWrYWFhAblcjr59+yIqKgpeXl6qZXNycrBq1SpcuHABRkZGWLduHb777jssWbJE09tJqqFCSXSC8ih1k0f53nvv1flc9RH14cOHsX37dshkMjx9+hRxcXFVCuVff/2FuLg4dOvWDQBQXl6uSlki9UOFshnRZuTXmCiPsn5qyqOs6Wsh1EVGRqJv3754+PAhNm7ciNu3b8Pc3ByTJ0+uklup7HP//v1VyUrk5dExSqIzlEepuzzK6liWRUhICJ4+fYpBgwahqKgIRkZGMDU1xbNnz1THLdV17twZ4eHhquO3EokESUlJ2m8MokIjSqJTlEepmzxK9WVXrlwJiUSCzp07IywsDDweD97e3vD19YWHhwfatm2r2r1WZ21tjd27d+P999+HVCoFAKxatQouLi712iaE8ijfeJRHSZoS5VES8opQHiV53VGhJE2O8ijJ644KJSGNgPIomxc6600IIRpQoSSEEA2oUBJCiAZUKAkhRAMqlKTBKI9S+3brk0dZW67kzp074enpCS8vL4hEItU1p0uWLMGFCxdeWL6u7Ui0Q2e9SYMp8yi//PJLWFlZafWc4cOHaxUg0RhehzzK0aNHo6ysDO7u7pg4cSKcnJyqLKPMo6wuIyMDq1evxt27d2Fqaori4mJkZ2cDAFasWNHofX9b0YiSNBjlUTZuHqVCocCkSZPw9ddf4/nz5zA2NlZtI6FQqCqykydPxpEjRwBURrS5uroiKChIlaYEACUlJfjwww/RsWNH+Pr60h1QWqIRZTOSlLQS4uJ4zQvWg7HQDS4u/9G4HOVR6j6Pctu2bZDJZBg/fjxEIhG++uoryOVytGjRAk5OTqr7xIcNG1al/bKyMkybNg2XLl1C+/btq0S3rV69Gn369MHOnTtRUFCAwMBA9OvXT2Nq0duORpREJ9TzKNVduHABc+bMgY+PD4YPH15rHuWYMWMAVOZRqlPmUXI4HFUepZJ6HuWNGzdUbSnbmDBhgqroALXnUVpZWdWYRykUClW5k7VNv3r1qiqP0sTEROs8ynv37iErKwsXL17E9evXVfOq51HOmDFDVSSByji4s2fP4siRI3BxccGCBQteOB6ckJAAJycnODs7g2EYfPDBB6p5586dw9q1a+Hj44NevXqhrKxMNeomtaMRZTOizcivMVEeZf1ok0fZtWtXhIWF4dNPP4VAIFCtT3nIoX///pgyZcoLxbK2PrEsi6NHj6JDhw4v1ee3FY0oic5QHqXu8yg/+ugjDBkyBGPGjIFMJsOTJ09w9+5d1fx79+6hTZs2VZ7j6uqKhw8fIjU1FQCqBPcOHDgQ33//varwR0ZGat3ftxmNKIlOUR6lbvMoAWDhwoUoLCzEhAkTsHbtWnz22Wd48uQJBAIBrK2tX9ieAoEA27dvx9ChQ2FlZYWgoCDExMQAAP7zn/9g/vz58PLyAsuycHR0xOnTp+u1jd5GlEf5hqM8StKUKI+SkFeE8ijJ644KJWlylEdJXndUKAlpBJRH2bzQWW9CCNGACiUhhGhAhZIQQjSgQkkIIRpQoSQNRnmU9Wt348aNcHV1hUgkgre3N0JDQwHUnj/5skJDQyESieDh4QF3d/dGeb1vCyqUpMGUeZT1KVzDhw/HF1980Yi9ql1T5lFu27YN58+fx61btxATE4MrV67UeR/5y5DJZDhz5gw2bdqEc+fOITY2VpVfWdOyRDO6PKgZ+U9yBmKKS3XapkhogJXOretcRj2PcvXq1VXmZWdnY+bMmaqEmk2bNqFbt27YvXs3IiIisHnzZqSmpmL8+PGQy+UYPHgwvvvuOxQXFwP4J48yJiYG/v7++OWXX1SBDxs2bEBYWBgAYP/+/Wjfvj3S09Px4YcfIjs7W3ULo4ODAyZPngwLCwtERkbCz88PxsbGqjzKR48eYf78+fjkk08AVOZOKi96nzp1KubPn1/n9NWrVyM0NBT29vawtrauM2ZtzZo1CAsLg4mJCQDA1NQUkyZNemG5c+fOYenSpZBKpWjXrh127doFoVCIFStW4NSpUygtLUXXrl3x448/gmEY9OrVC127dkV4eDiGDx+O//3vf9i4cSNatWoFoPK2xmnTpgHAC8uq7w2QmtGIkujExx9/jH379qGwsLDKdGUe5e3bt3H06FFMnTr1hecq8yhv376t+sVWioyMxKZNmxAXF4cHDx5UuYhbmUc5Z84cVdFS5lFGRUVh/PjxquIH/JNH+e233wKojCP7448/cOvWLSxfvhwVFRW4c+eOKnfyr7/+wo4dOxAZGVnndGUe5bFjx3D79u1at5FYLIZYLK4zBAMAcnJysGrVKly4cAF3795FQEAAvvvuO9Xru337NmJiYlBaWlrlPu2CggJcvnwZn376qeoPS23UlyWa0YiyGdE08mtM6nmU6pFqFy5cQFxcnOpxbXmUynDd4OBgfPbZZ6p5yjxKAKo8SmUikHoe5YIFC1RtKRO9J0yYUCVIuLY8Sj6fX2MeJQBV7iTLsjVOVygUqjxKAHXmUbIsq1Uk219//YW4uDh069YNAFBeXq5KRwoLC8P69eshkUiQl5cHDw8PVXBv9SzLutRnWUKFkugQ5VHWzcTEBEZGRnjw4AHatm1b63Isy6J///5V4tGAyuTy2bNnIyIiAvb29li2bJnq6ySAqq/Pw8MDd+7cQZ8+fWpcByWa1w/tehOdoTxKzXmUX375JT7++GMUFRUBqBxhb9++vcoynTt3Rnh4uOp7gCQSCZKSklRF0crKCsXFxarvx6ltPf/+97+RlZUFoDKSrnr6PNEejSiJTlEeZd15lLNmzUJxcTE6duwIfX196Ovrv3Cc0NraGrt378b7778PqVQKAFi1ahVcXFwwbdo0eHp6wtHRER07dqx1PUOGDMGzZ8/Qr18/1S7/hx9+WK9tQf5BeZRvOMqjJE2J8igJeUUoj5K87qhQkiZHeZTkdUeFkpBGQHmUzQud9SaEEA2oUBJCiAZUKAkhRAMqlIQQogEVStJglEdZv3Zry6OsjXpOpbavu77rIHWjQkkajPIotfcq8ii1XYdcLtfpepszujyoGVl+KhZxT4p02qZ7KxMsHeZR5zKUR6mbPMqLFy/is88+g0wmQ8eOHbF169YqwR3VrVy5Evv27YO9vT2srKzg7++Pzz77rM51ODo64sMPP8S5c+cwZ84cjBs3rs73llSiESXRCcqjbFgeZVlZGSZPnoxDhw4hOjoaMpkMW7durbWtiIgIHD16VLVe5a65NpmXAoEA165doyJZDzSibEY0jfwaE+VRNiyPMjExEU5OTnBxcQEATJo0CVu2bFH9Aaju2rVr+Ne//qXa1spMSm0yLymLsv6oUBKdoTzKutWVR1nf45S1La9N5iVlUdYf7XoTnaE8ypfPo3R1dUVaWpoqg3Lv3r3o2bNnre0EBQXh1KlTKCsrQ3FxMX777TeN6yAvj0aURKcoj/Ll8igFAgF27dqFMWPGqE7mzJw5s9Z2OnbsiOHDh8Pb2xtt2rRBQECAartpk3lJ6ofyKN9wlEf59iouLoZQKIREIkGPHj2wfft2+Pn5vdI+UB4lIa8I5VG+nOnTpyMuLg5lZWWYNGnSKy+SbxMqlKTJUR7ly9m/f7/O2iJ1o0JJSCOgPMrmhc56E0KIBlQoCSFEAyqUhBCiARVKQgjRgAolaTDKo6xfu42dFSmVStGvXz/4+Pio7l4iDUOFkjQY5VFq71VkRUZGRqKiogL37t3TOgCDsinrRpcHNSdnvgCyonXbZktPYHDdIz/Ko9RNHmX1rEixWIzt27ejvLwc7du3x969e8Hn8+Hs7IzU1FQUFhbCwsICf/75J3r06IHu3bvj22+/xQcffIDs7Gz4+Pjg6NGjSEtLqzHnkrIptUcjSqITlEfZsDxKJfWsyJEjR+L27du4f/8+3Nzc8PPPP4PL5cLFxQVxcXG4du0a/P39cfXqVUilUmRkZCAwMBA//fQTunfvjnv37sHOzq7OnEvKptQOjSibEw0jv8ZEeZQNy6NUUt9VjomJwddff42CggIUFxdj4MCBACrvZLpy5QoePnyIL7/8Ejt27EDPnj3RsWPHF9rTlHNJ2ZTaoREl0Zn58+fj559/RklJiWqaMo/y3r17uHfvHjIzM2FsbKx1m801j7I26n2cPHkyNm/ejOjoaCxduhRlZWUAKgvl1atXcevWLQwZMgQFBQWq3e/69Lv6+kjtqFASnaE8ypfPo6yJWCyGra0tKioqsG/fPtX0Tp064fr16+BwOBAIBPDx8cGPP/5YY8RbfXMuSc1o15voFOVRvlweZU1WrlyJTp06oU2bNvD09FQdsuDz+bC3t0fnzp0BVI4wDxw4AE9PzxfaqG/OJakZ5VG+4SiPkjQlyqMk5BWhPEryuqNCSZoc5VGS1x0VSkIaAeVRNi901psQQjSgQkkIIRpQoSSEEA2oUBJCiAZUKEmDUR6l9u1OnjwZTk5O8PHxgY+PD7p27Vrjcg19fZqkpaVBJBI1WvvNDRVK0mCUR1k/GzZsUN37fv369VeyTsqbbBi6PKgZWXdrHRLyEnTapquFKz4P/LzOZSiPUvs8ytrk5ubi/fffR3Z2NgIDA1VhFuvXr4dAIMAnn3yCBQsW4P79+7h06RIuXryIXbt24ZdffsGsWbNw+/ZtlJaWYvTo0Vi+fDmAF/MtnZ2d8eGHH8LQ0LDKPfCxsbGYMmUKysvLoVAocPToUTg7O9f7NTRnNKIkOkF5lJrzKJUWLVqk2vUeP348AGD58uUICgpCZGQkhg8frvrD0qNHD1y9ehUAEBERgeLiYlRUVODatWuq+8pXr16NiIgIREVF4fLly4iKilKtSz1vcsqUKQgJCcGNGzeq9Gfbtm2YN28e7t27h4iICFWsHfkHjSibEU0jv8ZEeZSa8yiVNmzYgNGjR1eZduXKFVW/hw4dCnNzcwCAv78/7ty5A7FYDD6fDz8/P0RERODq1asICQkBABw+fBjbt2+HTCbD06dPERcXBy8vLwD/5E0WFhaioKBAlRw0YcIEnDlzBgDQpUsXrF69GhkZGRg5ciSNJmtAI0qiM5RH2TA1taOvrw9HR0fs2rULXbt2Rffu3REWFobU1FS4ubnh4cOH2LhxIy5evIioqCgMHTpUlVsJ/POa6woNDg4OxsmTJ2FgYICBAwfi0qVLOnk9zUmzHFHeuXOniGGY5L8fmgIorOOnFQBtzkIol9c0Xf1xTf+v6ae+ln14YX3nz5/3lMvlMoVCweVwOKoj9uqPa/p/TT9ZlmW4XK7GMx01rMswJiZGolAouD179uRs3bpV791335XFxMRUBAQECBYvXiz/6KOPKhQKBTcxMZF1c3NTPHr0iJednY2oqCi5m5ub3rfffqsYPHiw4tdff2UUCgUvJiZGkpqayiksLNSPiYmRAkB2djbv0aNHipiYGFl5ebnBf//7X/n06dPLT548yXV3d9eLiYmRenh48NetW6d49913K44dO6YnEom4UVFRstzcXG5aWpo8JiZGrlAouFlZWRwDAwMmJiamHABKS0sNEhISylq2bMndsmWL/rBhw0plMpne/v379b/55hspy7LYsmULf9iwYaUsy2L//v0G1afLZDIcOXLEYPTo0RUxMTE1vifKfkRFRUH9PXFzc+Nu3LgRs2bNKr98+bJ+fn6+fmxsrNTS0lLu7OzMX7NmDWfFihVSKysrxZw5cwzc3NwUsbGx0vj4eA6Hw+E9fvy4LDIyknvq1Cl+27Zty5XbKC4urtTMzIzL4XDkPB7PYPfu3VI/Pz/mu+++45SWlupFRUWVZ2RkKOzt7dlevXpxb968yf39999hY2NTrs1nIysrS8/d3b36FzU1uyFpsyyUAA6xLDsdABiG2c6y7PQ6fkZoEwmlXF7TdPXHNf2/pp8A/LSNpaq+vvv376eJRKKcBw8etGnbtm26crr645r+X9NPiURiKBKJ4jX1ofq6APiKRKL4Bw8etFm1alXm4cOHPQ0NDXNEItGTlStXOi5btowzcuRIgUwm0+vcuXPemDFjHp08edKFz+eXCYVCZuvWrVnBwcGue/fu5Q4cOPC5UCi0FYlE8WlpacZ6enotRCJRCgDw+XwHMzOzEpFIlMswjKdcLpcFBwezCoVCfvDgwWSRSCTdvn07b8KECa6hoaGMpaWlNDQ0NI3L5dryeDzG0tKyUCQS5T948KCNoaFhBZfLNRGJRAl/b1cfJyenFH19fdsRI0YYBAcHcxUKhf6kSZMyxo4d+xwA4uPjWwQHB1sBgPr069evu40ePZprZ2cn7dSpU55QKCwViUTPanpPeDwe89///td4x44dXIZhFABw4sQJ8X//+9+MESNGeIwdOxb+/v4yW1tb1tDQsFgkEqX5+/t7/Pzzz/xx48bFm5iYKAQCgahnz57ZIpHomUgkwsGDBx3fffddIzs7O8bf37/AzMysQLmNnJ2dk0tLS+3atm2bvnPnTsOpU6c6CgQCvb59+2ZzOBxzoVBYfPXqVemvv/5qyeVy9WxsbEoWLlxYIRKJ0rT5bMjlcqvqn92/P9PNSrPMo2QYZhjLsqfU/1/HT20LparNuqbXtG5N/QCwtB6Fssr67t+/n+bt7Z2Tm5tramlpqRppqj+u6f81/Xz69GkrbQpl9XXVNV2bfvB4PHFZWZkxh8PB0aNHOfv372/z559/vhiF/pL9qO1xbf0BAEtLy8KYmBg3XW6Pl3lPXpd+1PbZuH//vpW3t7ej+rTaflfeZM2yUNaHtoXyde2DslDqoh/a/kLq2tmzZ4Xz5s1zYFkWJiYm8uXLl3OHDh0ap/mZjauptsfr2I/a+lBToWyOmuuud328DrsJr0MfYGVlld0U6x00aFBxYmKiqjBmZWVZNUU/qmvI9pgwYYLD7du3herTZs2a9WzevHm5r7IfuvI69KEpvfUjyjedLkeUhNTX2zKipMuDCCFEAyqUhBCiARVKQgjRgE7m1IFhGCMAV1B5+c7pJuqDG4B5qLww/iLLsluboh+5ublmBQUFpjKZTM/Gxibb3Ny8qCn6UVpaynvy5ImtXC7nuri4PHhV65XL5ZyHDx86MAzDGhsbi21sbPJe1brVNdXrr075ecjLyzNiGGYAy7Lnmqovr0KzHFEyDLOTYZjnDMPEVJs+iGGYRIZhUhiG0Sbj63MAh5uyHyzLxrMsOxPAWAAvdQlRamqqY2RkpHd0dLSH+vS8vDyTqKgoUVRUlCgjI6NlXW1YWloWtGvXLr1t27ZpeXl55tVej/+0adNUSQpLlixpsXDhwlbV21Dvx759+0wXL17csq5+nD592rh3797t1dswMDAob9euXXqvXr1Mnz59+tJ/6BcuXNjqs88+c9N2u+Tm5pqZm5vnt2vXLr2wsNCsrnaXLFnSoq51L1u2rIWTk5OHi4uLe4cOHdwnTpzocevWLe/o6GiPnj17ts/JyeECgKGhoV9UVJTop59+8urZs6eH+uuv7+sNCQmxTEtL069rmfp8TpSfBzMzsxwA79W3P2+a5jqi3A1gM4BQ5QSGYbgAtgDoDyADwG2GYU4C4AL4ptrzPwTgBSAOgKAp+8Gy7HOGYYYD+OLvturNysoqx8bG5nlaWpqTchrLssjIyHBwdnZO4vP5FXFxcW7m5uYFAJhjFfEyAAAgAElEQVSMjAw79ec7OTml8Xg8GQBkZmba2tjYVLlUhMfjsb///rv506dPs2xtbWu9BVK9H+PHjy8EUFhXP3Jycuzkcjk/MTGxffV+NERFRYWy3yXt27dP12a7lJeX88zMzJQXbb/0pSLr16+3vnjxosnt27cTrKys5GVlZcySJUvsbW1t8woKCtpcvnw5RdkPAIyzs3NSeno6X6FQtC8pKREYGRmV1b2GF8lkMvzyyy9WPj4+pY6OjhW1LVefz4myH2Kx2BSVn+dmrVkWSpZlrzAM41htciCAFJZlHwAAwzAHAfyLZdlvALwQo80wTG8ARgDcAZQyDPM7y7KKV92Pv9s5CeAkwzC/Adhf2/qeLP7KXpqcbFhLGwxXLuc91NPrAAByVsHRVyj0srh6TgCgr5BzMwBnHodbzlN7Ht/ZWcJbs1rGsiwePXpkZ2pqWmhsbCxRb5vL5bITJ07MXrNmTYvvv/8+s0qfnjzRmzJlSpvMzEweAKxbt+6pg4MDQkJCLCMiIow2b96c+/Tp04oJEya0lcvlTI8ePWS7d+92l0gkd62srDLLyspazZs3T5GYmGjg6elpf+LEiYccTuWO0IoVK1pcu3bNBAAOHDjwQCQSSZOSkniTJk1yzM3N1bO0tJSFhoamOTs7l48aNcrR3NxcFh0dbejl5SUxNjZWJCcnc/r27evw5MkT/syZM22+/vrr52Kx2Gjv3r3siRMnnAFgzJgx0tmzZ5vxeLzyb775xubw4cNClmX1J06caLNkyZLnAPD555+3PHTokFWrVq3KLS0tK3x9fatsH3Xfffed7Z9//plgZWUlBwCBQMCuX7/+UVlZGa+goAB2dnaeERER8UZGRnygcgTJMAxfIpHIhg8f3i4tLQ2dOnUSL1myBABw7NgxkxUrVrQqLy9n2rRpIz148GCaqampws7OzvP999/PCQsLM/noo4+yY2JiDCdOnNhWIBAoIiIi4oVC4QvF3tTUtLisrEz97YdYLDbi8XhSAwODcgAwMzPLy8/PNzM0NMx69OiRnUAgKGVZ9m5tr7e5aJa73rWwA/BY7XHG39NqxLLsVyzLzkdlYdpR3yKpq34wDNOLYZgQhmF+BPC7jvoAlgWHAaN6TRwwCpZFrTE4T58+tRGLxSb5+fnmWVlZ1tXnL1q06PmxY8cscnNzuerTZ8yYYb9w4cJnMTEx8cePH0+dO3dulbDD8vJy3urVq3mzZ89+HhMTE29raytVnx8fH2+wZcuWxykpKbGPHj3inz171uTBgwcOADh8Pt8wOjo6fsaMGc/nzp1rDwAzZ850CA4Ozk1KSop77733cmfNmmWvbCs1NVUQHh6etGPHjgwASElJEfzxxx8P9+/fL924cWMrqVTKhIeHC0+cOKF/586d+IiIiPhDhw4Z3rlzxzA+Pl56+PBh82PHjon/+OOPx6Ghodbh4eEGV69eNTx+/LhFdHR03OnTp1Pu379fNaJITX5+PkcikXBcXV3La31j1LaL+uOYmBj9r7/+uiQmJibhwYMHZqdOnRLeu3ev1Zo1a2yvXLmSFBcXF+/n5ydZuXKlardfIBAo7ty5kzh79uw8kUgkCQ0NfZCQkBBXU5Gsqx/6+vqq/vJ4vPKKigqe8vNQWlpqxDDMTG3be1M1yxFlLWoqAho/MCzL7m7KfrAs+yeAP7VpuNWa1Y9rm1dWVsZLTk52dvL0TASAnJwc88LCQhOnv493PX/+3KKkpMTIycmpxjZatWr1vFWrVs9ra9/CwkIxZsyY3LVr19oYGBioCnB4eLhJcnKyKqCyuLiYW1xcXGX3OSoqSv/ixYt5ADBu3Ljib775xlI5z9PTs6Rdu3YVAODh4SFJS0vTGzJkyCOGYUynT5+eDgDTpk3L+/rrr+0BIDIy0ujMmTOpADBr1qy85cuXqwrzyJEj8/X0/vnIDxgwoMDAwIA1NzeHhYVFRUZGht7169cN+vbtKzUxMVEAwMCBA0tu3rypZ2BgYDR06NDnnp6eTwBg6NCh/LCwMGOFQoEhQ4YUGBsbK5Rt1raNqkedHT161OSrr75qLRaLuT/99NPjugJzRSKR1NHRUW5gYCAfN25cZlRUlGHr1q1LUlNTrQMDA10BoKKigvH39y9WPmfixIn5tTbYMKzy83D//n0rlmW3NdJ6XhtvU6HMAGCv9rg1gCdvaz+UIwPl479HDrUev9LGl19++czPz8993LhxqjuFWJaF+q6esmCr90O9DfU+AQCfz1f9Efk7M1JVaZS74ADAMIzGP3pCobDKXkFNbTMMI2NZVjUqZlmWyzCMQhd5lBYWFgoDAwNFQkICz9XVtXzUqFFFo0aNiuvdu3d7qVRapZHq24VhGI76+8MwDFiWRVBQUNGpU6ce1rQ+ZfFuiMb4nLyJ3qZd79sAnBmGcWIYhgdgHICTb2s/hEJhiVQqFZSWlvIUCgVTUFBg8fdJlJfWokUL+bBhw/L379+vulc7KCioaN26dTbKxzdv3qxyckwoFJaIRCL2559/tlYoFMyBAwesoOXJktDQUAsA+Pnnn819fX1LAMDX17fkp59+MgeAH3/80SIgIKC4rjaq69evX96lS5d42dnZ/IKCAu758+eFffv2zenTp0/x77//biYWizlFRUWc33//3bx3797iPn36FP/2229mxcXFTH5+Puf8+fO1nhEHgPnz5z+dNm1aG+WZbYVCAalU+sLvoVAoLAEqLwdiWRYxMTG8nJycErlcjiNHjlh0795d3KtXr5KIiAhhTEwMHwDEYjEnKiqKX72tv9uTFxYWcmuaV5fG+Jy8iZrliJJhmAMAegGwYhgmA5XXQf7MMMwcAH+g8gzzTpZlY9+GfiQnJzuVlJQYy+VyvXv37nnZ2to+adGiRY69vf2j5ORkFwCwsLDIeZkzqtV99dVXWXv27FEdw9y+ffvjqVOnOri4uLjLZDKer68vs3jxYiY/P99eLpdLOBwO1q9f/3j69OkO33//vX3v3r3FQqHQoK51KEmlUsbLy8tVoVAwBw8efAAAW7dufTRp0iTH//u//2upPJlT2/MlEolFQkJCC7lcrqdQKJCfn2/Ro0ePrLFjx+Z27tzZAwDee++9oj59+hQAQHBwcK6fn58bAEyYMCG7W7dupQAwYsSIPJFI5GFnZycNDAysszD/+9//zpZIJJyAgAA3Ho+nMDIyUnh7e3MtLS3ty8vL9ViWZXJzcy1sbW2fA2CTk5Ndnj17xvHy8pJ+9dVX1gkJCQ6dOnUST5gwoYDL5eLHH39MGzduXNvy8nIGAJYuXZrp5eUlrb7eiRMn5sydO7fNokWLaj2Z8yo/J28aCsV4wzWHUAyxWMwxMjJScDgcbN++3fzQoUMWFy9eTG3qfhHN3pZQjGY5oiRvlvDwcEP1PMrdu3enNXWfCFFHhZI0uep5lM2BLvMoSdOjQklII9i7d++jpu4D0Z236aw3IYS8FCqUhBCiARVKQgjRgAolaTBtY9bUbdq0yfuLL76oc5kdO3Z49erVy7mmeXZ2dp4NjVnTFIfWGO2OGjXKcdeuXeYAEBgY2MHR0VHk4uLi7uTk5DFx4kQH5YXoAJCamqrft2/fdm3atBHZ29uLpkyZYl9WVsYcPXrUxNXV1d3V1dXd0NDQ19HRUeTq6uo+YsQIx9OnTxsbGxv7uLm5uTs5OXlMnz5d9b6EhIRYmpube7u6uro7OTl5LF++3KamPpIXUaEkDaYWs6Z14erdu7di5cqVtd473piUMWuvg9DQ0AdJSUlx8fHxcXw+XzF48OD2QOUdO++++2774cOHF6Snp8c8fPgwpqSkhDNv3jy7UaNGFSUkJMQlJCTEqYddHD9+PA0AAgICiuPj4+Oio6Pjzp8/b3ru3DlVUMewYcPyExIS4m7cuJGwadMm25SUlDozKkklKpSkwdRj1qrPe/LkiV7fvn29PDw8fD08PPyOHj1qBwAnTpzgfPTRR3ZlZWW806dPizw9Pb09PDx8Z8yY4WFoaOirfH5xcbGgX79+Po6Ojn7Dhg1rr1AoUFRUZMiyrP6XX37pIRKJfD09Pd2Vt/ElJSXxunTp4uLi4uLepUsXl+TkZB5QOZKbOnVq606dOrnMnj27NVCZTBQYGNihdevWnqtWrVKNrpYtW9bC2dnZw9nZ2WPFihUap3/++ectHR0dRV27dnVJTk6u8RZCTQQCAbt169aMJ0+e8G7cuGFw6tQpYz6fr1BeTqSnp4dt27Y9PnTokJVYLNbq91YoFLIeHh6ljx494lWf17JlS7mDg4P08ePHVCi1QJcHNSMXQ+Pt8zKLa8yjfFkWdkJJ34lutaYSKS1atOi5p6enx7Jly7LUp8+YMcP+008/TRsyZEhRYmIib9CgQaLhw4dXWWbdunX86dOnP5k7d+7TpUuXukItYSkhIUH//v370Xw+32zIkCGt/r73WgKgokWLFtlXr14t2bp1a+u5c+fah4WFpShj1ubOnZu7adMmy1mzZtlfuHAhFfgnZk1PTw8LFy5slZKSIrh+/XpiQUEB183NTbRo0aLsW7duGezfv9/yzp078SzLwt/f361v375ihULB1DZdGbNWUVEBHx8f97ryKOuip6cHNzc3SUxMjODZs2f63t7eVdqxsLBQ2NralsfFxfE7depUqqm97Oxs7sOHD/kDBgwQV5+XnJzMk0qlHG3aIVQoiY7UFbOWlJRksmDBAi4AlJSUMFlZWVWKeXR0NC5duvQUAKZMmfJ0w4YNqq9/8PLyKmnXrl1FUVGRpEOHDmxqaiqvZ8+eUgB6PXv2tMrIyLAYOHAgs379eiHwcjFrBgYGMmXM2p9//ikcMmRIgTJmbejQoflhYWHGLMuipun1iVnThvKW4r8j2V64v7h6VFtNIiIihC4uLu5paWmCjz/+OMvBwUEVa3fq1Cnz9u3bG6elpQm+/fbbNENDQ7qHWQtUKJsRbUZ+jamWmDXmwIEDpd7e3slcLlcRHx/fwdjYWOs2eTyeAqiMUVNGof39VRVs27ZtkxwdHRETE9NBVzFrtWUf6CJmTROZTIbExERDLy+vJ9bW1rL//e9/Vb6bKC8vj5OVlcVzc3N7IfRCXUBAQHFYWFhKVFQUv1evXq5jxozJ79q1aylQeYwyNDT00YULF4xGjRrlPGLEiEL1QkpqRscoic7UFLPWrVu30l9++UWPy+UqJBKJIDIy8oUEcJFIhB07drQEgN27d7eEhpg1hULBBcCGhoZaZGdnW509e5ajq5i1uuLUdBGzVhupVMrMmTOnta2tbXmnTp1Khw8fLi4rK+Ns3rzZEqgsorNnz7YfM2ZMjrY5k15eXtJ58+Y9/eabb1744rh+/fqVjBw5MnfdunU6P/PfHNGIkuhU9Zi1H3/8MfXDDz90d3Z29pPL5ay/v79syJAhVZ7z+eefSxcvXtxiy5Yttj179iwXCoV1jnBatmyZBcA1Pz/fesiQIYxCoeAeOnToMVC/mLWaBAUFSWqLU9NFzFp1EydObMvj8RTl5eWc7t27F505cyYFqAwlPnHiRMr06dPbbNiwwVahUKBPnz6FISEhmZraVPfpp59mt23btmVCQsILJ3SWLl2aFRAQ4L5q1aqn5ubmuvqqk2aJYtbecG96zFpZWRkvKirKOSAgIJZi1t48FLNGyCsSGxvLmTBhgjvFrJHXFRVK0qQEAkH5lClToqdMmdLUXdEpillrXqhQEtIIKGateaGz3oQQogEVSkII0YAKJSGEaECFkhBCNKBCSRrsZfIo9+3bZ7p48eIX7hhRd/r0aePevXu3r2nem5xHaWBg4Jufn6/63ZsyZYo9wzD+DXk9SnVtM20UFRVxzMzMfHJzc7nq0/v169dOecfT24gKJWmwl8mjHD9+fOGaNWuyNC+pe02dR2lvby89cOCAGQDI5XKEh4cb29jYvBYhmSYmJoru3bsX7t+/X3UrZm5uLvfOnTvC9957r7Ap+9aUqFCSBtOURzlw4MB2IpHITSQSuSlDZENCQiwnTpzoAACxsbF8b29vV5FI5DZ//vxW6nmUJSUl3EGDBrV1cnLyGD58uJNC8c+dditWrGjh6enp5unp6fYm5VGOGjUq78iRIxYA8Ntvvxl37NixWE9PT3WL3A8//GDh6enp5urq6h4cHNxGJqu8o3P8+PEOIpHIrX379h4LFixQjdiPHDli4uTk5OHv79/hyJEjqgL37Nkzbr9+/dq5uLi4e3t7u968edMAAFxcXNxzcnK4CoUCZmZmPsr7yd99912nEydOGI8bNy7v119/tVC2s2/fPrMePXoUaXuPeXNE11E2I39s3WSf8zhdp3mUVvZtJANnzW9QHuXChQufDRw4sDg5OZk3cOBA5wcPHsSqLzNnzhz72bNnP58xY0be+vXrrdXnxcfHG9y7d++Bo6Njhb+/v+v58+eFAwcOLAYAExMTeXR0dPzmzZst36Q8ShcXF+nvv/9ulp2dzd2/f7/FhAkTcv/8809TALh7967gyJEjFhEREQl8Pp/94IMPHLZt22Y5Z86c3O+++y6zRYsWcplMhq5du3a4efOmgaenZ9mcOXMcz58/n+jh4SF955132irX8+9//7uVt7e35MKFC6knT540njRpklNCQkJcQEBA8YULF4Tt2rWTtm7dWnrt2jXhnDlzciMjI4327NmTzufz2blz5zpmZWVxW7ZsKf/1118tPv744yZJo39dUKEkOlFXHmVycrKB8nFxcTFX/fgcAERGRgrPnTuXAgBTp07NXbZsmep4p6enZ0m7du0qAMDDw0OSmpqqCneYNGlSHgBMmzYt7+uvv7b/u603Io9y2LBh+Tt37rS4e/eu0b59+9KV08+ePWscExNj6O3t7QYAZWVlHBsbGxkA7Nmzx2L37t1WMpmMyc7O1r9//75ALpejdevWUk9PTykAjB8/Pvenn36yBoBbt24ZHz16NAUAhg8fLp4+fbpebm4ut3v37sWXL18WpqWl8aZOnfp8165d1g8fPtQ3NTWVmZqaKgCgf//+BXv37jX/4IMPCuLj4w1HjBhRpM3raq6oUDYj2oz8GlMteZSIiIiIFwqFL5W+UlNmpPIxh/NPvX3T8ignTZqUHxgY6DZ69OhcLvef8yYsyzJjxozJ3bJlS5WUoISEBN7mzZtb3LlzJ97a2lo+atQox7KyMk5d66+pzwzDsP379xdv377dJiMjQ7pu3brMkydPmv/yyy/mnTt3ViUfBQcH561Zs8aWZVlmwIABBerb6m1ExyiJztSURxkUFFS0bt061fG869evG1R/no+PT/Hu3bvNAWDnzp0W1efXJjQ01AIAfv75Z/M3LY/S2dm5fPHixZnz58/PVp8+aNCgotOnT5tnZmbqAZXHGZOSknj5+flcAwMDhYWFhfzx48d6yl11Hx+fsoyMDF5sbCwfAA4ePKjafp07dxbv2rXLEqg8G25ubi6zsLBQtG/fviI/P1/v4cOHAnd39/IuXboUb9mypWWPHj1U2+qdd94Rp6WlCX766Sfr4ODgvPpsw+aIRpREp6rnUW7fvv3x1KlTHVxcXNzlcjnTqVMncdeuXavcB/39998/Hj9+vFNISEjLAQMGFAiFQrk265JKpYyXl5erQqFgDh48+AB4s/IoFy1a9EI8nr+/f9nXX3+d2bdvXxeFQgF9fX02JCTkUd++fUtEIpHE2dnZw8HBQerv718MAIaGhuz333+f/s4777S3sLCQderUqTg+Pt4AANatW/ckODjY0cXFxd3AwECxe/fuh8r1+Pj4lMjllZu5V69e4m+++cauX79+qu/W4XK5GDJkSP5vv/1mPnjw4Be+c+dtQ3mUb7g3PY8SAMRiMcfIyEhBeZRvHsqjJOQVCQ8PN5w3b54D5VGS1xUVStLkBg0aVJyYmBjX1P3QJcqjbF6oUBLSCCiPsnmhs96EEKIBFUpCCNGACiUhhGhAhZIQQjSgQkkajPIotW9XmzxKX19fVwBITEzkbdu2TeOdSvXdFg3NrHwbUaEkDUZ5lPWjKY8yMjIyAQCSk5P5hw4d0vqWTtJ4qFCSBqM8St3mUSpf/1dffWUXEREhdHV1dV++fLmNTCbD9OnTW7u4uLi7uLi4r169WtWH9evX27i7u7u5uLi4R0ZGCoDKtPIxY8Y4ikQiNzc3N/dffvlFq/vQyYvoOspmJO9Ikn1FVolO8yj1WxpJLEa7UB7lK8qjVLd69erMb7/9tkVYWFgKAKxbt846PT2dHxsbG6evr49nz56pYoesrKxkcXFx8WvXrrVeu3Zti0OHDqUvXrzYtnfv3kW//vprWk5ODjcgIMBt+PDhb3Vc2suiQkl0gvIodZNHWZdLly6ZzJw5M1tfXx9AZVqTcl5wcHA+AAQGBkpOnjxpDgB//vmnyR9//GEWEhLSEqgMEUlJSeHV0DTRgAplM6LNyK8xUR6l9mrLo6wLy7K1vk6BQMACgJ6eHqvcRizL4siRIyne3t5S9WWfPHmiX+8Ov+XoGCXRGcqjbHgepTpTU1N5cXGxqor269evaNu2bdbKk1Hqu9416d27d9G3337bQnlcNzw8/IVtT7RDI0qiU5RH2bA8SnWBgYGlenp6bIcOHdyDg4NzFi9e/DwpKYnv6urqoaenx06aNCl78eLFtRbatWvXPpk+fbqDq6urO8uyTOvWraXK452kfiiP8g1HeZSkKVEeJSGvCOVRktcdFUrS5CiPkrzuqFAS0ggoj7J5obPehBCiARVKQgjRgAolIYRoQIWSEEI0oEJJGozyKLVvd9SoUY42NjZepaWlDAA8ffpUz87OzlN9meXLl9vw+Xy/3Nxc7e5tJI2OCiVpMMqjrB8ul8uGhIRY1Tb/yJEjliKRqGTfvn0Ui/aaoEJJGozyKOuXRzljxoznW7dubVFTwY6NjeVLJBLOihUrMg8fPqy67z0kJMSyX79+7fr06dPezs7Oc82aNdbLli1r4ebm5u7t7e2qvO87NjaW3717d2cPDw83f3//Dspsyp07d5o7Ozt7dOjQwT0gIKCDpj6Squg6ymbkxIkT9s+fP9dpHqWNjY3k3XffpTxKHeZRtmnTprxjx47FP/zwg+XYsWML1eft2bPHYuTIkXmDBg0qnj59uiAzM1PPzs5OBgBJSUkG9+/fjystLeV06NBB9J///CczPj4+7qOPPrL/8ccfLZcsWfJ86tSpbbZv357u6ekpvXTpktGsWbMc/vrrr6S1a9fanjt3LsnJyakiJyeHdunriQol0QnKo6xfHuXSpUuf/utf/2o/evToKoXy+PHjFseOHUvhcrkYPHhwfmhoqPmXX36ZDQBdu3YVm5ubK8zNzRVCoVA+ZsyYgr+3kSQqKsqwsLCQExkZKRwzZkw7ZXvl5eUMAAQEBBSPHz/ecdSoUfnjx4/P16aP5B9UKJsRbUZ+jYnyKLUnEomk7u7ukj179pgrp928edMgPT2dP2jQIBcAqKioYOzt7aXKQsnj8VSd4HA4qgxKDocDmUzGyOVyGBsbyxISEl64HXT//v2PLl26ZHTy5ElTHx8fj3v37sW2bNlSq5QmQscoiQ5RHqX2eZRA5ahyy5YtqjP/oaGhFp9++umTzMzM6MzMzOjnz59HZWVl8ZKSkrRKJbewsFC0bt26fOfOneYAoFAocOPGDQOg8thlnz59SjZt2vTE3Nxc9uDBA0o6rwcaURKdojxK7fMoAwICyjw8PCSxsbGGAHDixAmL06dPJ6svM3jw4Pw9e/ZYtGjRQqtT9QcOHHgwbdq0NuvWrbOVyWTMiBEj8rp06VK6YMGC1mlpaXyWZZmgoKCizp07l2rbT0J5lG88yqMkTYnyKAl5RSiPkrzuqFCSJkd5lOR1R4WSkEZAeZTNC531JoQQDahQEkKIBlQoCSFEAyqUhBCiARVK0mCUR6l9u3XlUSYmJvIEAoGfq6ure7t27TyCg4Md5HJ5jdshMTGR16JFCy+5vOq1+a6uru5hYWGG69evt968ebOlcp27du0yB4DAwMAOV65c0WlwytuAznqTBlPLo8yytbWVafOc8ePHFwIo1LhgI3hd8ig///zz7Orz7O3tpQkJCXEVFRXo0qVLh19++cXM0tLyhTuVOnToUG5ra1t+9uxZ4dChQ4sBIDIyUlBSUsLp3bu3pHfv3nUmGJH6oRElaTDKo9RdHqWSvr4+AgMDi6u3d/nyZUM3Nzf3uLg43ujRo/P279+vujd+7969FiNGjMgDGm/E/LaiEWUzEhf/uX1JcZJOd6uMhC4Sd7d1lEf5ivIolcRiMefKlSsmS5YsyVROO3/+vNH8+fMdTp48meLs7FwuFArz/P393SsqKh7p6+vjxIkT5ocOHaJbPxsBFUqiE5RHqZs8ysePH/NdXV3dGYbB4MGDC8aOHVt0+vRp45SUFMHs2bMdz58/n+To6FgBAA4ODjJnZ+eykydPmtja2lbo6emxHTt2LNNm/aR+qFA2I9qM/BoT5VFqr6Y8SuCfY5TVl7exsamQSqWcv/76y9DR0VFVXMeMGZN34MABCxsbm4pRo0bl1bsjRCt0jJLoDOVRNiyPsi4mJibyM2fOJC9dutTu9OnTxsrpEyZMyA8LCzP93//+Z6EcYRPdoxEl0SnKo3z5PEpN7O3tZadPn04ZPHiws6GhYVqfPn1KrKys5D4+PsU5OTn6rq6u5fV5rUR7lEf5hqM8StKUKI+SkFeE8ijJ644KJWlylEdJXndUKAlpBJRH2bzQWW9CCNGACiUhhGhAhZIQQjSgQkkIIRpQoSQNRnmU2rdbVx5l//792+3du1d1Z4+jo6Po3//+t63y8cCBA9vt2bNHNX/KlCn2NjY2L2RSEt2jQkkaTC2PUuvCNX78+MI1a9ZkaV5S916XPMrq0zt37lwcHh4uBICsrCyukZGR/NatW0bK+ZGRkUa9e/cuBgC5XI6zZ8+a2dralp85c8a4eltEt6hQkgajPMgbyv4AACAASURBVErd5FH26NGj+Pbt20YAcOnSJeGAAQMKc3Nz9RUKBRISEnh8Pl/h4OAgAypH2y4uLqVTp07NVs+kXLhwYauRI0c6duvWzdnOzs5zz549ZjNnzmzt4uLi3r17d2epVMoAwNWrVw07duzYwcPDwy0oKMg5PT1dHwBWrVpl065dOw8XFxf3d955p62m1/K2oOsom5H58Y/sE0rKdJpH6WokkGxyc6A8yleQRxkUFCRJSkoyKCsrY8LDw4W9e/cWP3z4kB8ZGSm4deuWoXrAx/79+y3Gjh2b9/777xesXLnSTiqVMso0pPT0dP7169eT7t69K+jTp4/rnj17Urdt25bRv3//docPHzYdO3Zs4SeffOLw22+/pbRq1Uq2Y8cO888++8zu119/TQsJCWmZnp4ebWBgwObk5HA1ve9vCyqURCcoj7LheZQGBgass7NzWXh4uGFERITRsmXLslJSUviXL18WRkZGGnbp0qUEAMrKypiwsDDTbdu2PTY3N1f4+PiUHD9+3GTcuHGFANCvX79CPp/PBgYGlsrlcmb06NFFf2+/0ocPH/KioqL4ycnJBn369HEBAIVCAWtr6woA6NChQ+mIESOchg8fXjB+/HitXsvbgAplM6LNyK8xUR6l9mrLo+zYsWNxWFiYsKSkhGttbS0PCgoq+b//+z+bmJgYw7lz52YDwNGjR03EYjFXJBJ5AEBpaSnHwMBAoSyUytfF5XKhp6fHKrcTh8NRvkamffv2pffu3Uuo3q+wsLDkM2fOGJ84ccJs/fr1rZKTk2P09fXr/fqaGzpGSXSG8igbnkcZFBRUvGfPHmt3d3cJAHTq1Ely9+5do6dPn/L8/f1LAeDgwYMWmzZtSs/MzIzOzMyMTktLi7569aqJWCzW6vfZy8urLC8vT+/ChQtGQGVcXUREhEAulyM1NZU3bNgw8Q8//JAhFou5hYWFtPsNGlESHaM8yoblUfbp06c4IyOD37lz5yyg8kvGLC0tZba2tuVcLlf5XTqme/bsSVc+x8TERBEQEFB88OBBU23WKxAI2IMHD6Z+8sknDmKxmCuXy5lZs2Y98/T0lAYHBzuJxWIuy7LMjBkznllZWdG1R6A8yjce5VGSpkR5lIS8IpRHSV53VChJk6M8SvK6o0JJSCOgPMrmhc56E0KIBlQoCSFEAyqUhBCiARVKQgjRgAolaTDKo9S+3bryKGuTmJjIEwgEfq6uru7t2rXzGDFihKMyBejKlSuGkydPtq/peQ3dRuQfVChJg1EeZf3UlkdZF3t7e2lCQkJcYmJi7NOnT3k7d+40B4AePXpIdu/e3aT3+L8N6K9NM7LoyH37pCyxTmPWXFoaSzaM9q7zF1E9j/L777/PVJ/35MkTvSlTprTJzMzkAcB33333aMCAASUhISGWERERRqGhoY9iY2P5wcHBTnK5nOnXr1/h9u3bW0gkkkjgnzzKxMREA09PT8mJEyceKkMeVqxY0eLatWsmAHDgwIEHIpFImpSUxJs0aZJjbm6unvIWRmdn5/JRo0Y5mpuby6Kjow29vLwkxsbGCmUe5ZMnT3gzZ8589vXXXz8HKnMn9+3bZwVU3qq4ZMmSOqd//vnnLQ8dOmTVqlWrcktLywpNMWvKPMqFCxdmq09XKBSYNWtW60uXLpkyDMMuWrTo6bRp0/LVl9HT04Ofn19JZmamPlA56v72229bhIWFpWRlZXFHjRrVNi8vT9/X17dE/a67RYsW2R45csTC1ta23NLSUubr6ytZsWLFs9jYWP7MmTMd8vLy9AQCgeKnn35K9/X1Laur/28jGlESnVi0aNHzY8eOWeTm5lYJUVDmUcbExMQfP348debMmY7Vn6vMo4yJiYlv1apVleFefHy8wZYtWx6npKTEPnr0iH/+/HnVRdzKPMoZM2Y8nzt3rj0AKPMok5KS4t57773cWbNmqXZLlXmUO3bsyACAlJQUweXLl5Nu374dv3HjxlZSqZS5evWqoTJ3MiIiIj40NNQ6PDzcoK7pyjzK06dPp9y/f98IGqjnUapPDw0NNYuOjjaIj4+PvXjxYtKSJUtaKwN1lSQSCXPnzh2jYcOGFVVv94svvmjVpUuX4vj4+Ljhw4cXPH36lAdU7p6fOnXKPDo6Ou63335LjYqKUvVx6tSpbX744YdHsbGx8Rs2bMiYNWuWg6b+v41oRNmMaBr5NSbKo2x4HuXVq1eNx44dm6enpwd7e3tZp06diq9du2YYEBBQ+vjxY76rq6t7eno6f/DgwfmdOnUqrd7mX3/9ZXzs2LEUABg3blzhjBkz5ADw559/CgcPHlzwd9Qd279//wIAKCws5ERGRgrHjBnTTtlGeXl5/TPj3gJUKInOUB6l9mrKo6xrHcpjlOnp6fo9e/bssG/fPtPx48cXVl9OfZtoalcul8PY2FiWkJDQrG4fbQy06010hvIoG5ZH2bNnT/GRI0csZDIZnjx5onfr1i1h9+7dS9Sf06ZNm4oVK1ZkbNiwwbZ6e507dxbv3LnTEgAOHz5sUlRUxAWAXr16Ff/xxx+mEomEKSws5Fy4cMEMqNwLaN26dbnyxJBCocCNGzdeeH8IjSiJjlEe5cvnUU6YMKHg+vXrQjc3Nw+GYdjly5dnODg4yBITE3nqz/vggw8KVq9e3ers2bNVQjfWrl37ZNSoUW3d3d3dunTpUmxra1sOAD179pQMGjSo0N3d3cPOzk7q5eVVYmpqKgcqT4JNmzatzbp162xlMhkzYsSIvC5durywW/+2ozzKNxzlURJtFBYWckxNTRVisZjTpUuXDtu2bUsPCgqq8+y8NiiPkpBXhPIoG98HH3zQJjk52UAqlTLjxo3L1UWRfJtQoSRNjvIoG9+pU6ceNsV6mwsqlIQ0AsqjbF7orDchhGhAhZIQQjSgQkkIIRpQoSSEEA2oUJIGozxK7dutbx6lr6+va23t7Nq1y7ymeUohISGWEydOrBJyERgY2OHKlSuGAPDFF1/Uuf3JP6hQkgajPMr60SaPUiaTAQAiIyMTGqsfISEhL9wGSWpGlwc1Jyc+tsfzOJ3mUcLGXYJ3t1AeZR3TdZVHefr0aeOVK1fa2tjYVMTFxRmmpqbGGhoa+kokkkiFQoHJkyc7hIeHG9vb20vV76g7dOiQ6RdffNHawsJC5unpKUlPT+eHhYWl1NWH2bNn20mlUo6rq6u7i4tL6cmTJ+k6yzrQiJLoBOVRNjyPEgCioqKMNmzYkJmamhqrPn3v3r1mKSkp/MTExNjdu3en3717VwhU5lPOmzevzZkzZ5Lv3LmTmJubW2Xwc+rUKXNXV1d35b+YmBhDAPjhhx8y+Xy+IiEhIY6KpGY0omxONIz8GhPlUTY8jxIAvLy8SlxdXcurL3/58mVVVqWjo2NFly5dxABw7949gb29vVT5nHHjxuX99NNPqlCSYcOG5YeGhqoufg8MDOygTf9IVVQoic5QHqX2asqjBABDQ0NFbc+paT0UavNq0K430RnKo2xYHmVdevbsKf71118tZDIZ0tPT9f/66y9jAPD29i57/PgxXxnFdujQIa23n56eHqv8NkdSNxpREp2iPMqXz6Osy4QJEwouXrxo0qFDBw8nJ6eywMBAMQAIhUL2u+++Sx80aJCzhYXF/7N351FN3GsDx58sQAgJGFZlR0jIAiKioIhVUVTaSlVUFJXWKiqtr/vSelu12kXrUktr1dpbFWvRipYqWikKotW6YFlkSVgUUEQRwp4QsvD+QYeLFkiAKBKezzn3nEsIk0m0j7/JZL4oiH8wNDFnzpynPB6P7+bmJsH3KTuGPcpeDnuUiGhNqlQqCAsLs2ez2Q2bNm0qexmPjT1KhF4S7FF2z549e8yjo6PN5XI5SSAQSFatWtWr/+F8FeGKspfThRWlLnrVepQvCq4oEUJdhj1K3YJnvRFCSA0clAghpAYOSoQQUgMHJUIIqYGDEnUb9ig1365KpYJ169YNcHBwcHN0dHTz8fHhpKSk0Ijvt/e8iO3OmzfPnsvl8p2dnQU0Gm0IEbtQ16ZE3YNnvVG3tepRPh4wYIBCk5+ZM2dONQBUq73jC9CTPcpt27ZZ3Lx50ygzMzObyWSqTp8+bTx16lQXkUiURafT1X5WjzibLhKJ9N988022UCjUqV/z+6rCQalDPr72sV1+Zb5We5QuLBfJ1pFbsUfZwe2d6VFGRkYOuHTpkoioDU2bNq0mKiqq/sCBA2YrV6585vOwne1c/vnnn/T33nvPvqGhgezk5CT7+eefC2tra8lBQUHOGRkZwqtXr9Jfe+013v379zMcHR3lNjY27nl5eZmzZ892NDU1VaSlpRk9ffpU7/PPP38QFhamUQWpr8BDb6QV2KNU36MUi8VkqVRKFggEsta3e3l51WdlZdFa39aVzuXbb7/ttGPHjoe5ubnZbDa74cMPPxzg6Ogor62tpdTU1JCTkpIYAoFAkpCQwMjKyjLo379/I7GKLS8vp965c0d46tSp/E2bNtmoe6y+BleUOkTdyu9Fwh5l53qUrTU1Nf0roZaUlMTozHYfP35Mkclk5IkTJ9b985qUz507dyBA8yC+dOkS49q1a8x169aVJiQkGEulUvKIESNaAh5BQUFVZDIZfHx8pGVlZfrtPU5fhStKpDUffvjhk59//tm8vr6+5e8V0aMUCoXZQqEwu6ysLIPFYrXbXHyeLvUoTU1NVYaGhqrs7OxnBlFqaiqdz+c3dHW7/+xfu3f28/OrvXz5MuPx48d6s2fPrsrMzKRfu3aNMWbMmFriPjQareUJ4mXN/4aDEmkN9ijV9yiXLl36+P3337evq6sjAQDExsYyb9++zQwPD3/mGvDObnfAgAEKGo2mSkhIMPrnNTHz9fWtBQAYP3583S+//GLm4uLSoKenB0ZGRsorV64Y+/v7d+q16cvw0BtpFfYoO+5RbtiwoayyspLC5/MFZDIZLCws5KdPn85/vgDv5+cn6Wzn8vDhw/eJkzmOjo6y6OjoQoDmmrpSqSSNGjWqFgBg+PDhdWKxmGpqaqrxyr6vw3pQL6cL9SDsUfZeWA9C6CXBHiV61eGgRD1u0qRJdSKRSKc+ON1XepR9BQ5KhF4A7FHqFjzrjRBCauCgRAghNXBQIoSQGjgoEUJIDRyUqNuwR6n5di9dumQ0aNAgLpfL5Q8cOFCwatUqa5VKBSwWy+Pp06cUAICioiI9EonkFR8f33LWnMVieTx+/JjSevttbau9feju69XX4QuHug17lJpbsGCBU3R0dMGIESOkCoUC0tPTaWQyGTw8POoTExMZISEh1UlJSUY8Hk9y9epVo4kTJ9alp6cbsFgsRf/+/ZXqttVTz0vX4aDUIY82/MdOlpen1R6lAZstsf78M+xRdnB7Z7qRYrGYam9vLwcAoFKp4OXl1QDQfFnhtWvXjEJCQqqvXbvGWLp06ZNff/2VBQBPLl++zGjrmvX2toW0Dw+9kVZgj1KzbuSiRYue8Hg8t4CAAOcdO3aYSyQSEgCAn59f/a1btxgAAH///bfRnDlzqkpLS/UBAP766y+Gr6/vvwZle9tC2ocrSh2ibuX3ImGPUrNu5M6dO0vnz58vjouLM/7ll1/MTp48aXbr1i3R6NGj60NDQ+k1NTVkhUJBMjExUdnb28syMzMNUlJSGOvXr3+i6bbaS85pkqJDbcNBibTmww8/fDJkyBD+rFmzWiIdRI/y+TqOpnSpR0kQCAQygUDwdNWqVU/NzMwGP378mNK/f3+lvb297JtvvjF3d3eXAAB4e3vXx8bGmlRUVFA9PDzaPKxua1tmZmYKYjVKqK+vp5ibm2tUZUL/hofeSGuwR6m+G3n8+HETlap5Xt+9e5dGoVCaiAHm7e1dt3//fkuiPO7n51d34MABS09Pz/rW/yio29a4cePq4uPjTYiV+5EjR/pxuVxJ69U06hx85ZBWYY+y427kTz/9ZPbBBx/Y0Wg0FZVKbfrhhx/uEwPMz8+v7tChQ5ajR4+uBwAYOXKk5MmTJ/pz585tM6PX3rZ8fHyk4eHhZcOHD+eSSCQwMzOT//jjj516HdCzsEfZy2GPEvUk7FEi9JJgjxK96nBQoh6HPUr0qsNBidALgD1K3YJnvRFCSA0clAghpAYOSoQQUgMHJUIIqYGDEnUb9ig13257DUn0asOz3qjbsEepuVepIalQKAAva9QMvko65FJUjp24pE6rPUpTG4ZkXBgPe5Qd3K6NHmVSUhJ91apV9g0NDWQajaY6fPjwfQ8PD9no0aNdvvzyyxIfHx8pj8fjv/HGG5U7d+4sXb58ubWDg0PjlStXmNOnT6+cO3duFQBAUFCQU0hIiDgkJKT6/ffft7127RqzsbGRFB4eXrZ27dryuLg45tatWwdYWlrKs7Oz6QUFBVmd/CvRJ+GhN9IK7FF2r0fp4eHRcOvWLWFOTk72pk2bStatW2cLADBy5Mi6xMREhlgsJlMolKYbN24wAABu3LjBGDduXG14ePjTw4cPmwEAVFRUUO7cucOYOXNm9Z49e8xNTEyUmZmZOenp6TlHjhyxEAqF+gAAGRkZRjt27CjBIak5XFHqEHUrvxcJe5Td61GKxWJKSEiIU2FhIY1EIjXJ5XISAMCYMWNqv/76a6uBAwc2Tpgwofry5cvGtbW15IcPHxp4eHjIPDw8ZCtWrHAoKSmhHjt2jPXGG29U6unpwcWLF42FQiH9zJkzLACA2tpaSnZ2Nk1fX79p0KBB9Vwut1GjP1gEADgokRZhj1IzbTUk169fbzN69OjahISEApFIpO/v7+8KAPDaa69JFixYQL9y5Yps4sSJNeXl5dQ9e/aYu7u71xPbmzlzZsUPP/xgeurUKVOiEtTU1ETatWtXcXBwcE3rx46Li2PS6fRnXgekHh56I63BHmXXe5Q1NTUUW1vbxn/2u+X1o9FoTQMGDJCfOXOGNXbs2PpRo0bV7t27t//IkSNbnteSJUvKDxw4YAUAMHTo0AYAgICAgOp9+/ZZyGQyEgBARkaGQU1NDf733kW4okRahT3KrvUo169f/3jhwoVOkZGR/UeNGvXMKnDEiBG1V65cMWYymaqAgIC6RYsW6Y0dO7blcezs7BTOzs4NkydPbjnsX7lyZXlhYaGBu7s7r6mpiWRqaio/f/48puu6CHuUvRz2KFFtbS2Zz+fz09LScszMzF7qr3vAHiVCLwn2KLsuNjaWGRER4RgREfHkZQ/JvgRXlL2cLqwodVFf6VHiihIh1GXYo9QteBYMIYTUwEGJEEJq4KBECCE1cFCibsPMmubbfdUyawsWLLDbsmVLywUBfn5+7JCQEAfi6/DwcNvNmzdr/XXqbfBkDuo2zKxp7lXLrI0cObIuJiaGBQBlSqUSKisrqXV1dS1hk9u3bzNmz57dYw2BVwWuKFG3tc6sPf+9R48eUSdOnOjs5ubGc3Nz4/3xxx9GAACRkZFmYWFh9gAAWVlZBh4eHlw3NzfeihUrrOl0uifx80RmzcnJSRAUFOREXP4H0JxZc3d357m7u/MyMzMNAAByc3P1R4wYweFwOPwRI0Zw8vLy9AEAgoODHRcuXGjr4+PDee+992wBmstE3t7erra2tu6ffvppy6pq8+bNVmw2W8BmswWtV1vt3b5+/fr+jo6Obr6+vpy8vDyDjl6rjjJrnp6eXB6Px/f09OSmp6cbAACMHj3a5ebNm4YAADwej79mzZoBAADLly+33r17t/mUKVOcfvrpp5bLJoOCgpyOHTtmolAoYPHixbZubm48DofD37FjhzlA8yrdx8eHM3nyZCdXV1eBv79/3Z07dxgAAHfu3DF0dXWVGhkZKZ8+fUqRSqWkgoICmq+vb7vZuL4CV5Q6JH7fHrvyB0Va7VGa2zlIJkasULuiWLt2bZm7u7tg8+bNj1vfTmTWJk6cWJeXl6c/ceJE9r17957JexGZtcWLF4u//PJLi9bfy8nJMUxLS7vn6Ogo9/Ly4iYkJDAmTpxYB/C/zNq3335r9n//9392SUlJ+URm7f/+7/8q9uzZYxYREWF38eLFAoD/ZdaoVCqsWrXKOj8/n3b9+nVRVVUVhcfjua1du/bprVu3DImcWlNTE3h5efHGjRtXq1KpSO3dTmTW5HI5DB48mN9Rj5LIrPn4+NROmDCh+v3336+g0+lNRGZNT08PYmNjmevWrbONj48vIDJrbDZb9nxm7d133y1ydXVt+Oqrr6zmzp1bRWTWTp06db91Zk0qlZKGDRvGnTx5cg1Ac2YtNTU1iygIUanUpry8PP3k5GSj4cOH15eUlOglJiYyWCyWwtXVVUqj0fr8h61xUCKtwMxa782seXl51SUlJRn99ddfjLVr1z4pLi7Wv3btmpGJiYlS3bXrfQUOSh2iycrvRcLMmmZetczaiBEj6q5fv84QCoWGw4YNkw4cOLBxz549VgwGQzl//ny86gvwPUqkRZhZ652ZtdGjR9ddvHixX79+/ZRUKhWsrKyUNTU1lNTUVMbYsWPr2/qZvgZXlEirMLPW+zJr3t7e0qqqKuq0adNarkPncrnS+vp6iqafYtB1GMXo5XQhioGZte7BzNqLhytK1OMws9Z1mFl7OXBF2cvpwopSF2FmTbfgihKhFwAza7oFz3ojhJAaOCgRQkgNHJQIIaQGDkqEEFIDByXqNuxRar7dV61HCdB8tdSJEydMeno/XmV41ht1G/YoNdeTPUq5XA56enr/uj0lJYWekpJiFBIS0iN/Hr0BrihRt2GPsvs9yidPnlDGjx/vzOFw+B4eHlyiQblq1SrradOmOY4cOZJtY2PjfuTIkX5Lliyx5XA4/FGjRrGJa7mvXr1KHzZsmKtAIOD5+fmxi4qK9AAAvL29XZcuXWozbNgw108//dTqxx9/ZLHZbIGrqyt/6NChrg0NDaQvvvjC+uzZsywul8s/ePAgS8M/9j4FV5Q6RByTayd/XK/VHqVefyOJ6XQO9ihfcI9y3bp11h4eHpKLFy8WnDlzhvn22287CYXCbACAoqIig+vXr+f+/fffNH9/f+6RI0cK9u/f/zAgIMD5l19+MZk5c2b1smXL7M+dO5dvbW2tOHjwIGvNmjU2J0+eLAQAqKqqoty+fVsEAMDhcPh//PFHrpOTk7y8vJxCo9GaPvzww0cpKSlGUVFR+NnPduCgRFqBPcru9Shv3brFPHXqVD4AQFBQUO2iRYuoFRUVFACA8ePHVxsYGDR5e3tLlUolafr06TX/vB7S+/fv62dkZBjk5eUZ+vv7cwAAVCoVWFhYtLy/MHv2bDHx/4cOHVo3Z84cx+Dg4Mo5c+ZUdrSv6H9wUOoQTVZ+LxL2KDXTVo+yre0Tz4nYTwqFAlQqtYl43mQymdhnkouLizQtLU3Y1uMRQxwA4Oeffy5OTEw0OnPmjMngwYMFaWlpWW39DHoWvkeJtAZ7lF3vUQ4fPrz20KFDZgDNZ/tZLJbC1NRU1dG2CIMGDWoQi8XUixcvGgE05+dSUlLaPEmUlZVl4O/vX79nz55HLBZLce/ePX1jY2NlXV0dzoIO4IoSaRX2KLvWo9y+ffuj0NBQRw6Hwzc0NFQdPnz4vqb7TKPRmo4fP16wbNky+9raWopSqSRFREQ8ISK+ra1cudK2sLDQoKmpieTn51czfPhwqbOzc+POnTsHcLlc/urVq0vDw8PxkPw5WA/q5XShHoQ9yt4L60EIvSTYo0SvOhyUqMdNmjSpTiQSZff0fmhTX+lR9hU4KBF6AbBHqVvwTBdCCKmBgxIhhNTAQYkQQmrgoEQIITVwUKJuwx6l5tttr0cZFxfHTEhIMNL2/gAAUCgULy6Xy3d1deXz+Xzei3ocXYZnvVG3YY9Sc+31KBMTE5kMBkMZEBBQr+3HNDAwUBElolOnThlv2LDBNiAgQNT6PgqFAloHQ9CzcEWJug17lN3rUYpEIv2oqCiL/fv3W3G5XP6FCxcYHT2Pd955x87T05Nra2vrfujQoZZ+5Mcff2zl5ubG43A4/JUrV7a5oq+urqaYmJgoAJpXsT4+PpzJkyc7ubq6Cjra774O/wnRIbGxsXZlZWVa7VFaWlpKpkyZgj3KF9ijdHV1bQwLC3vKYDCUW7ZseQIA4O/v79Le83jy5IleSkqKMC0tjTZ16lSX+fPnV54+fdo4Pz+flpGRkdPU1ATjx493+f333xmBgYF1MpmMzOVy+TKZjFReXq53/vz5XGJ/MjIyjFJTU7O4XG6j+r8NfRcOSqQV2KPsXo/y+ft19DyCgoKqKBQKeHl5NVRUVOgBAFy4cMH4ypUrxnw+nw8AIJFIyEKhkBYYGFjX+tD74sWLRvPnz3fKzc3NAgAYNGhQPQ5J9XBQ6hBNVn4vEvYoNdNWj7IzP0+j0Vp2htivpqYmWLFiRenatWs7DKSMHz++vrKykkqcCKPT6Rql3Po6fI8SaQ32KLveo2Qymcra2tqWgdnZ5xEYGFhz9OhR8+rqajIAwP379/VKSkr+tRBKTU2lqVQqsLKy0uikG2qGK0qkVdij7FqPMjg4uGr69OnOv//+e789e/YUd/Z5TJs2rSYrK4s2bNgwLkDzSvHYsWP3bWxsFMR7lADNK899+/YV4hnuzsEeZS+HPUrUk7BHidBLgj1K9KrDQYl6HPYo0asOByVCLwD2KHULnvVGCCE1cFAihJAaOCgRQkgNHJQIIaQGDkrUbdij1Gy78+bNs+dyuXxnZ2cBjUYbwuVy+Vwul3/o0CHWihUrrGNjY5maPk5Hrw3SPjzrjboNe5SaIc6Ei0Qi/TfffJNNhCr+Ufky9wX7k52DK0rUbdij1LxH2Z7g4GBHoi1pY2PjvnTpUpvBgwdz3dzceH/++Sfdz8+PbWdn59Y6Q1dbW0sJCAhwdnZ2FoSGhtorlc1Xfp4+fdp48ODBXD6fzwsMDBxIXP9tY2PjvmbNmgFeXl6uP/74I6vNHUFtwn9SdEh2n2IYiwAAIABJREFUznq7+rpcrfYojRgcCZ+3HXuUWupRasrOzq4xLS1NuGDBArt3333X8ebNm0KpVEp2c3MTrFu37ikAwN27d41SU1MzORxO42uvvcaOiopiTZo0qfbzzz8fcOXKlVxjY2PVf/7zn/5bt2612rlzZykAAI1GU925c+dfWTfUMRyUSCuwR6lZj1JTM2fOrPrn+Uvq6+vJLBZLxWKxVAYGBqry8nIK8drw+fzGf+4vvnr1KoNGo6kKCgpo3t7eXAAAuVxO8vLyagl1hIWFvdRDfF2Bg1KHaLLye5GwR6k9RHOSTCaDvr5+y4OTyWSQy+Wkth6XRCJBU1MT+Pn51Zw9e/Z+W9slBjrqHHyPEmkN9ijV9yi16e7du0ZCoVBfqVRCTEyM6ahRo2rHjBlTn5KSwiDes62trSVnZGR06X1T9D+4okRahT3KjnuU2jR48OC61atX2wqFQkMfH5/aefPmVVEoFDhw4EDhrFmzBjY2NpIAADZt2lQyaNAg2cvaL12EPcpeDnuUqCdhjxKhlwR7lOhVh4MS9TjsUaJXHQ5KhF4A7FHqFjzrjRBCauCgRAghNXBQIoSQGjgoEUJIDRyUqNuwR6nZdiMjI80mT57s1Pq20tJSKovF8pBKpVq7DhJbldqHZ71Rt2GPUjNz586t3LRpk21tbS2ZuOb66NGjrICAgCpDQ8NX4soPuVwOenp6Pb0brxxcUaJuwx6lZj1KU1NT1bBhw+qOHz9uQtwWExNjGhoaKgYAuHr1Kn3YsGGuAoGA5+fnxy4qKtIjXp9Ro0axBQIBz8vLyzU1NZVGPKd33nnHztPTk2tra+tO9CxbS05OpvN4PH52drZ+TU0NecaMGY5ubm48Ho/H/+mnn/oRfxaBgYED/f39XUaNGsVp9w+6D8MVpQ5ZkVNsJ6xv0GqPkmtEk+zh2WOPUks9ylmzZomjo6NNw8PDKwsLC/UKCwsN3nzzzVqZTEZatmyZ/blz5/Ktra0VBw8eZK1Zs8bm5MmThQsXLnT4/vvvi9zd3WWJiYlGERER9jdu3MgFAHjy5IleSkqKMC0tjTZ16lSX+fPnt2TUEhISjFasWGF/5syZfDab3bh06VKbsWPH1pw8ebKwvLycMnToUF5QUFANAMDff//NyMjIyLKystLoOvu+Bgcl0grsUWrWo5w5c2bV6tWr7cViMTkqKor1+uuvV1KpVEhNTTXIy8sz9Pf35wAAqFQqsLCwkFdXV5NTU1MZM2bMcCa2QcQuAACCgoKqKBQKeHl5NVRUVLQcM+fn59Pee+89x4SEhFxHR0c5AMDly5eN4+Pj+0VGRvYHaI6K5Ofn6wMAjBo1qgaHZPtwUOoQTVZ+LxL2KNVjMBhNo0ePrjl27Bjr1KlTprt27Xrwz/ZJLi4u0rS0NGHr+4vFYjKTyVQ89/t1WhDdyuf30dLSUi6Tycg3btygOzo6VhPfj4mJyffw8HimJPTnn38a0el07FR2AN+jRFqDPUrNepSzZ88Wf/vtt1bl5eV6/v7+9QAAgwYNahCLxdSLFy8aATSv9lJSUmimpqYqW1vbRuJ33KhUKvjrr7/+9Ro+z9jYWPn777/nbdq0ySYuLo4JADB27NiaXbt2WRHv8167dk3tdlAzXFEircIepfoe5bRp06qXLFniOHv27HJiVUyj0ZqOHz9esGzZMvva2lqKUqkkRUREPBk6dGhDdHT0vfDwcIft27cPUCgUpKlTp4pHjBghVfc4dnZ2iri4uPzAwEA2nU4v3LZt26NFixbZc7lcflNTE8nW1laWlJSU35nXp6/CHmUvhz1K1JOwR4nQS4I9SvSqw0GJehz2KNGrDgclQi8A9ih1C571RgghNXBQIoSQGjgoEUJIDRyUCCGkBg5K1G3Yo3zx20U9C896o27DHiXSdTgodcjamHS73Me1Ws2scfozJTume3QY22jdo/zmm29KWn/v0aNH1Pnz5zuUlJToAwDs3r27eMKECfWRkZFmKSkpRlFRUcVZWVkGoaGhTkqlkjR+/Pjq77//3koikaQC/K9HKRKJDN3d3SWxsbH3icv+tmzZYvXnn38aAwBER0ffc3Nzk+Xm5uq//fbbjhUVFVTiEkY2m90YHBzsyGKxFHfv3qUPGjRIwmQyVUSP8tGjR/pLlix58tFHH5UBNHcnjx07Zg7QfKnixo0bO7x9/fr1/U+cOGFubW3daGZmJu8os+bt7e3q5eVV9+effxrX1tZS9u/fX/jP50j1Q0NDnaRSKRkA4Ouvvy4OCAioj4uLY27ZssXa1NRU3tZrgF4OfLWRVqxdu7bs9OnTphUVFZTWtxM9yszMzJxff/21YMmSJY7P/yzRo8zMzMyxtrZ+ZrmXk5NjuHfv3gf5+flZxcXFBgkJCS0f4iZ6lIsXLy77v//7PzsAAKJHmZubmx0SElIRERFhR9yf6FEePHjwIUBziiw5OTn39u3bOTt37rSWyWSkq1ev0onuZEpKSk5UVJTFtWvXDDu6nehRxsXF5aenpxupe60UCgXp7t27Odu3b3+wZcsWawAAa2trxdWrV3Ozs7NzTpw4cW/lypX2mrwG6OXAFaUOUbfye5GwR6lZjxIAYMaMGZUAAL6+vvVr167VB2huTC5YsMAhOzvbkEwmQ1FRUUspvaPXAL0cOCiR1mCPUjNEQ5JKpYJSqSQBAHz22WdWlpaW8lOnTt1XqVRgaGjo1dF+duoBUbfhoTfSGuxRatajbEt1dTVlwIABcgqFAt99952ZUomx8VcJriiRVmGPUn2Psi0rVqwoCw4Odo6NjWX5+fnVtn77AvU87FH2ctijRD0Je5QIvSTYo0SvOhyUqMdhjxK96nBQIvQCYI9St+BZb4QQUgMHJUIIqYGDEiGE1MBBiRBCauCgRN1GoVC8uFwun81mCwIDAwfW1ta+sL9XkZGRZmFhYfYAze1HS0vLQVwul+/g4OA2YcIE5zt37tDa+9kVK1ZYx8bGMrW1L97e3q6Ojo5urq6u/CFDhnDT09MN1P8U6o1wUKJuMzAwUAmFwuy8vLwsPT29pl27dlmo/yntWLJkyROhUJhdVFSUOWPGDPHEiRNdHz169K9PcygUCtizZ8+jKVOm1Grz8aOiou6JRKLs0NDQ8pUrV9qp/wnUG+HHg3RJ7Pt2UJat1R4lWPIlMGWvxlUiPz+/uoyMDEMAgPHjxzuXlpbqy2Qy8pIlS56sWbOmfPv27Rb379832L9//0OA5hXinTt36EeOHHnw3Xffme7bt89KLpeThgwZUh8VFVVEpVLh66+/Nvvqq68GWFhYyJ2dnRv09fXbvJwsPDy88vz58yb//e9/TT/++OMyGxsb99mzZ5cnJSUZL168uCw+Pt7kzTffrDYyMlIePnzY/Pz58/cAmkvqu3fvtkpMTMw/ffq08ZYtW6wbGxtJDg4OsuPHjxeamJiovZxw3Lhxdfv27cOyuY7CFSXSGrlcDvHx8cbu7u5SAIBjx44VZmVl5aSlpWUfOHDA6vHjx5R58+ZVnj9/viUcERMTYxoaGlr5999/02JiYkxTUlKEQqEwm0wmN+3fv9+sqKhIb9u2bdbXr18XXr16NTc3N/dfUY3WPD09JUKhsOXwm0ajqe7cuSNatGhRJXHb1KlTa1JTU41qamrIAADR0dGs6dOni0tLS6mff/75gCtXruRmZ2fnDBkyRLJ161aNht/p06dNuFyutLOvGeodcEWpSzqx8tMmmUxG5nK5fAAAHx+f2uXLl5cDAGzfvt3q3Llz/QAAHj9+rJeVlUUbN25cvZ2dnezSpUtGAoGg4d69e7SAgIC6bdu2WWRmZtI9PDx4AAANDQ1kS0tLxZUrV4yGDx9ea21trQAAmDZtmjg3N7fd9yGfbxeEhYVVPn8fPT09GDNmTM3x48dN5s+fX5mYmGjy7bffPrxw4QKzoKCA5u3tzQUAkMvlJC8vrw4jF2FhYQNpNJrK1tZWtn//fvyQuY7CQYm6jXiPsvVtcXFxzOTkZGZKSoqQyWSqvL29XYlfczB9+vTK6OhoFpfLbQgMDKwkk8nQ1NREmjFjRsXevXuf+VUSR48e7deZ3mNaWhrdy8ur5VcxEEHd582aNUu8d+9eS3Nzc+WgQYMkLBZL1dTUBH5+fjVnz569r+njRUVF3Xvttdfa/dUPSDfgoTd6IaqqqigmJiZKJpOpSk1NpbX+FQlz586tvHDhAuvkyZOmoaGhYgCASZMm1cTFxbFKSkqoAABPnjyh5Obm6r/22mv1N27cYD5+/Jgik8lIv/76K6u9xzx8+HC/q1evmrz77rtidfv3xhtv1GZlZdEPHjxoPmPGDDEAwJgxY+pTUlIYmZmZBgDNVaOMjAw8k41wRYlejODg4Orvv//egsPh8J2dnRs8PDzqie9ZWFgo2Wy2NC8vz3Ds2LESAAAvL6+Gjz76qGTcuHEclUoFenp6TZGRkcXjxo2rX79+/aPhw4fzLCws5IMGDZIQVXAAgP3791v98ssvZlKplMzhcKTx8fEi4jC9I1QqFcaNG1cdExNj9ssvvxQCNP/emgMHDhTOmjVrYGNjIwkAYNOmTSWDBg2Saf0FQr0K9ih7OV3oUaLeq6/0KPHQGyGE1MBDb4TUCAgIcH7w4MEz71V+9tlnD4ODg2t6ap/Qy4WDEiE1EhIS8NdS9HF46I0QQmrgoEQIITVwUCKEkBo4KBFCSA0clKjb+mKPcunSpTYRERE2xNe5ubn6tra27uXl5RRtbB+9WnBQom7riz3Kbdu2Pbpw4UK/v//+mwYA8N5779n95z//KTE3N1dqY/vo1YIfD9IhH1/72C6/Ml+rPUoXlotk68it2KN8DoPBaNq2bdvDiIgI+9WrVz+ur6+nREREqL3GHPVOuKJEWtPXepQhISHV/fr1Uy5evNhp//79Rd157dCrDVeUOqQzKz9t6ss9yvfff7+soaGB5OHhgeEMHYaDEnVbX+5RkslkIJPxwEzX4Z8weiGwR4l0Ca4o0QuBPUqkS7BH2cthjxL1JOxRIoQQAgA89EZILexRIhyUCKmBPUqEh94IIaQGDkqEEFIDByVCCKmBgxIhhNTAQYm6rS/2KFHfgoMSdVtf7FGivgU/HqRDHm34j50sL0+rPUoDNlti/fln2KNsg42NjfvMmTMr4uPjTRQKBenEiRP3PD09G5KSkuirVq2yb2hoINNoNNXhw4fve3h4yCIjI83i4uL6SaVScnFxsUFgYGAV8TqgVxuuKJHW9LUeJQCAubm5Ijs7O+fdd999um3bNisAAA8Pj4Zbt24Jc3Jysjdt2lSybt06W+L+2dnZ9NjY2Hs5OTlZZ86cYeXn5+t19nVGLx+uKHVIZ1Z+2tSXe5ShoaGVAADe3t6SM2fOsAAAxGIxJSQkxKmwsJBGIpGa5HJ5S8TDz8+vxszMTAkA4OLi0lBQUGDg4uIiV/siox6FgxJ1W1/uUdJotCYAACqV2qRQKEgAAOvXr7cZPXp0bUJCQoFIJNL39/d3Je7f+m0DCoXyzBBFry489EYvRF/uUdbU1FBsbW0bAQAOHDhg3tmfR68eHJTohQgODq5WKBQkDofD37Bhg3VbPcqSkhKDtnqUHA6H7+/vz3nw4IGeg4ODnOhR+vn5cQYNGiRp/Tj79++3Ij4edOzYMbPO9iiTk5NNQkJCqgGe7VFyOBy+l5cX9+7du+0e5rdn/fr1jzdv3mw7ZMgQrlKJv5RRF2CPspfDHiXqSdijRAghBAB4MgchtbBHiXBQIqQG9igRHnojhJAaOCgRQkgNHJQIIaQGDkqEEFIDByXqttY9Sn9/f5fy8nJKV7e1ePFiWxcXF8HixYttV61aZU0ikbyIK2UAAD755BNLEonkdeXKlQ4rSVu2bLFs3cW0sbFxLy0tbffkZXFxMfXNN98caGdn5+bs7CwYPXq0S1euytFUbW0tOSgoyInD4fDZbLbAy8vLtbq6miwSifTZbLbgRT0u6hoclKjbWvco+/Xrp9ixY0eXe5THjh2zuHv3bvaBAwceAgCw2WxpVFSUKfH93377zdTZ2blB3XYOHDhgVVdXp9Hfb5VKBUFBQS6vvfZa7YMHDzILCgqyvvjii5JHjx69sLLP559/bmlpaSnPzc3NzsvLy/rxxx8L28vHoZ6HHw/SIZeicuzEJXVa7VGa2jAk48J4GleJhg8fXk/0KFUqFURERNgmJiaakEikprVr15aGh4dXtne7v7+/i1QqJXt6evJWr15dCgDw+uuvV50/f77fl19+WZqdna3PZDIVVCq1ZaDMmTPHPj093aihoYE8efLkyq+++urRp59+allWVqY3evRoDovFUty8eTO3o32Oi4tjUqnUpnXr1j0lbvP19ZV29Bzi4uKYn3zyibWFhYU8Ozub/vrrr1e6u7tLv/vuO6t/rkkvEAgEsuDgYEcDAwOVSCQyrKio0Pviiy8ezJ49u7q0tFTPwcGhkXg8Dw8PGfH/lUolzJo1yyElJYVhZWXVGB8fn89gMJqSk5Pp4eHhjnQ6XeXj41OXmJhokpeXl6Xpnw3qOlxRIq1RKBSQlJTEnDJlShUAQFRUVL+7d+8a5uTkZF26dCl348aNtkVFRXrt3Z6YmJhPrE7Dw8MrAQCMjY2V1tbWjbdv36YdOXLEdPr06c9k03bv3l2SmZmZIxQKs65du8a8efOm4UcffVRmaWkpT05OzlU3JAEAMjIyDD08PCRtfa+9fQUAEAqFhvv27XuQk5OTFRMTY5abm0u7e/duzrx588p37dplSWzjwYMHBrdu3RKdPXs2b8WKFQ4SiYS0aNGi8m+++ab/4MGDucuWLbO+e/duy2F+cXExbdmyZWX5+flZJiYmyqioKBYAwMKFC5327t1blJaWJqRQKLj6fIlwRalDOrPy0yaiR1lSUqLv5uYmmTJlSg0AwNWrV5kzZ84UU6lUsLOzU/j4+NT9+eef9PZud3BwqG5r+zNnzhQfPXrUNDEx0eTKlSuio0ePthR5jhw5Ynr48GFzhUJBevr0qV56ejrNx8dHqq3n1t6+mpiYqNzd3esdHBzkAAD29vaywMDAagAADw8PaXJycsvv5gkODhZTKBRwd3eX2dnZydLS0mi+vr7S+/fv342NjTVOSEgw9vX15SUnJwuNjIxUNjY2MmJF6+npKSksLDQoLy+n1NfXkwMCAuoBAN5++21xQkJCv7b2GWkfrihRtxGrwMLCwruNjY2kbdu2WQL8O6JL6GyIZdasWVUxMTFmNjY2jaampi19SaFQqP/tt99aJScn5+bm5mb7+/tXNzQ0dPrvtLu7uzQ9Pb3Ntyw62lcDA4OWb5LJ5JY2JZlMBqVS2dKZfL6nSXxtYmKievvtt6t++umn4qlTp4p/++03E4B/NysVCgXGa3oYDkqkNWZmZsrIyMjivXv3WslkMtLo0aNrY2JiTBUKBTx69Ih669YtxqhRo+rbu7297TIYjKbNmzc//Pjjj0tb315ZWUkxNDRUmZqaKh88eEC9fPmyCfE9IyMjZXV1tUZ/vydPnlzb2NhI2rVrV8tKNTk5mX7u3DlGZ/e1LadPn2YplUrIysoyePDggYGHh0fDH3/8YfT06VMKAEBDQwMpNzeX5ujo2NjeNiwsLJRGRkaqS5cuGQEAHD161LS9+yLtw0NvpFUjR46U8ng86Q8//MCKiIgQX79+ncHj8QQkEqnpk08+eWhvb6+YN29eVVu3d7Td1r/zhjBixAipm5ubhM1mC+zt7WWtf23D22+/XR4YGMi2tLSUq3ufkkwmw5kzZwree+89uz179vQ3MDBosrW1lX3zzTcPAgMD69ra14yMDI1fExcXF5m3t7drRUWF3p49e4rodHpTbm4ubenSpQ4AACqVijR+/Pjqt99+uzIvL0+/ve0cOHCgcMmSJQ50Ol01cuTIWiaTibHLlwSX9L0c9ihfbcHBwY5vvvlm9fz58/816DururqaTPxGyA0bNvQvLS3VO3ToUI+8L03oKz1KXFEi1Ev88ssvJrt27RqgVCpJNjY2sp9//rmwp/epr8AVZS+HK0rNPX78mDJmzBjX52+/fPmyqH///ngY2wW4okRIx/Tv31/5/G+LREgTeNYbIYTUwEGJEEJq4KBECCE1cFAihJAaOChRt+lCj5JEInlNmTLFifhaLpcDi8XyGDt2rEtXn0tr3t7erur2Gb26cFCibuvtPUoAAENDQ5VIJDKsq6sjAQD8+uuvxlZWVvKuPIeukstf6sOhTsCPB+mQ+H177MofFGl11WJu5yCZGLFCp3uUhHHjxlWfPHmy3/z58yujo6NNg4ODxdevX2cAANTU1JAXLFhgn5OTY6hUKkn/+c9/Hs2dO7cqMjLS7MyZM/1UKhVJJBIZvv/++48bGxvJJ06cMNPX11f98ccfeVZWVkoAgMOHD5stX77cvq6ujvL999/fHzt2rGTVqlXWpaWlesXFxfqmpqaKnTt3loSGhjpJpVIyAMDXX39dHBAQUB8XF8fcsmWLtampqVwkEhm6u7tLYmNj75PJuNZ5GfBVRlrTW3uUhHnz5olPnDjBkkgkpJycHPqIESNa4hcbNmwYMHbs2JrMzMycq1evij766CPbmpoaMgBAbm6u4alTp+7dvn0754svvrCh0+mqnJyc7KFDh9YfOHDAjNiGRCIhp6amCiMjI4sWLVrUcpifkZFBj4+Pzz979ux9a2trxdWrV3Ozs7NzTpw4cW/lypX2xP1ycnIM9+7d+yA/Pz+ruLjYICEhgdGVPyfUebii1CGdWflpk670KH18fKQPHz40OHjwoOn48eOf2ZfLly8bx8fH94uMjOz/z3Mm5efn6wMA+Pr61rJYLBWLxVIxGAzljBkzqgAA3N3dJRkZGS0r/NDQUDEAQGBgYF1dXR2ZeC930qRJVQwGowkAoLGxkbRgwQKH7OxsQzKZDEVFRS3vz7q7u9c7OzvLAQAEAoGkoKCg3YAG0i5cUaJu6+09ytYmTZpUtWnTJruwsDDx8/scExOTLxQKs4VCYXZpaendIUOGNAA82498vkupUCjUdimNjIxantNnn31mZWlpKc/Jycm+e/dutlwub3k+rfuXFArlmW2jFwsHJdKa3tqjbC0iIqJ89erVj7y9vZ9ZlY4dO7Zm165dVipV80y7du2aYWe3HR0dzQIAiI+PZzCZTKWZmdm/ri+vrq6mDBgwQE6hUOC7774zUyrxEvRXAR56I63qjT3K1pydneUff/xx2fO3b9u27dGiRYvsuVwuv6mpiWRraytLSkrK13S7AAAsFkvp6enJJU7mtHWfFStWlAUHBzvHxsay/Pz8ag0NDVVt3Q+9XFgP6uWwHoR6Ul+pB+GhN0IIqYGH3qjPwB4l6ioclKjPwB4l6io89EYIITVwUCKEkBo4KBFCSA0clKjb+nJm7fr164YnTpww6eg+AABxcXFMbSXb0MuHgxJ1W1/OrKWkpNDPnTundlCi3g0HJdKq4cOH15eUlOgDNGfWFi9ebMtmswUcDod/8OBBVke3t86sEbcRmTUAACKzZmpq2nIVz5w5c+zd3Nx4Li4ugpUrV1oDALTOrPn4+HA03XciswYAQGTWiO/V1NSQZ8yY4ejm5sbj8Xj8n376qV9DQwPpiy++sD579iyLy+XyDx48yEpKSqJ7enpyeTwe39PTk5uenm7Q/iOi3gI/HqRDxDG5dvLH9VrtUer1N5KYTudoVCUiMmsLFiwoB3g2s1ZaWkr19vbmTZgwoS4pKcmordsTExPz6XS6J/ERnlWrVhm2zqzFxMT0mz59emXretDu3btLrKyslAqFAnx9fV2JzNq+ffuskpOTcwcMGNDhpZGtzZs3T7xp06YBISEhVTk5OfQFCxZUED1KIrN28uTJwvLycsrQoUN5QUFBNR9++OGjlJQUo6ioqGIAALFYTL5165ZQT08PYmNjmevWrbONj48v6Mxrjl49OChRt/X1zFprYrGYEhIS4lRYWEgjkUhNcrkcCz86AAelDtF05adtxHuUFRUVlAkTJrhs27bN8qOPPirTZmZt48aNtu7u7pK2Mmt37tzJsbCwUAYHBztqK7P2xx9/iMrKylr++yAyax4eHrLW9//zzz+NWn+9fv16m9GjR9cmJCQUiEQifX9//39dCYR6H3yPEmlNX8ysGRsbK1ufNKqpqaHY2to2AgAcOHDAHJBOwEGJtKp1Zm3evHlVAoFAyuPxBGPGjOG0zqy1dXtH2120aFGln5+fpPVtrTNr8+bNc2wrs9aZkzkAHWfWFAoFifgY1EcffWQDABAYGFibm5trSJzMWb9+/ePNmzfbDhkyhIstSd2BmbVeDjNrqCdhZg0hhBAA4Mkc1IdgZg11FQ5K1GdgZg11FR56I4SQGjgoEUJIDRyUCCGkBg5KhBBSAwcl6jZd6FF29jmIRCJ9NpstAMDWZF+AgxJ1my70KLX5HJDuwUGJtKo39yg78xxQ34Kfo9QhsbGxdmVlZVrtUVpaWkqmTJnSJ3qUnXkOndkm6v1wRYm6jehRsliswVVVVdSu9ijb2z7Rozx37hxrzpw5la2/d+TIEVM+n8/j8/n8vLw8Wnp6Ou1lPIeuPAbqvXBFqUM0Xflpmy70KDv7HFDfgitKpDW60KPU9Dl0druod8MVJdKq1j3KiIgI8fXr1xk8Hk9AIpGaWvco27q9o+0uWrSo8vnbWvco7e3tZW31KC0tLeU3b97M1fZzEIlE//o1EEh3YY+yl8MeJepJ2KNECCEEAHjojfoQ7FGirsJBifoM7FGirsJDb4QQUgMHJUIIqYGDEiGE1MBBiRBCauCgRN3Wm3uUjx8/pnC5XD6Xy+Wbm5t7WFpaDiK+bmhoIGm63yqVCmbOnOng7Ows4HA4/KSkpGf2r7KykhwaGupgZ2fnxufzeW5ubrw9e/aYabp91LNwUKJu6809SuJMuFAozA4LC3u6ZMmSJ8TXNBpNo6sxFArXFEnOAAAgAElEQVQFnDt3jvnw4UODgoKCrDt37gg5HE5j6/vMnj3b0dLSUl5YWJiZnZ2dEx8fn1deXv6vwS2XyzV5SPSS4aBEWqULPcrW/P39XQQCAc/FxUWwe/duc4DmYcZkMgcvW7bM2t3dnXf58mUjAwMDVXl5OVUmk5GYTKbKxsamZR/T09MNcnJy6Lt27XpEoTQvtm1sbBSffvrpEwCA2NhYpq+vL+fNN98cKBAI+OHh4bY7d+5sScktW7bMeuvWrZbdeR6oe/BzlDokO2e9XX1drlYTYEYMjoTP295nepTPi46Ovm9lZaWsra0lDx48mDdv3rzKfv36Kevq6iheXl6SyMjIRwDNQ7yqqooaEhLiePr06ftk8v/WIGlpaYZ8Pl9CDMm2pKWlGaWnp2ex2ezG5ORk+gcffGC7Zs2acgCAM2fOmF6+fFnUneeBugdXlKjbdKFH2Z7PP//cytXVlT906FDukydP9HNycgwAAPT09JrmzZtXBdC8Qp4+fbpzYmKiiEKhNC1ZssQWAGD27NkOp06dMn5+m2vWrBnA5XL5/fv3H0TcNnjw4Do2m90IADB69GhJaWmp/oMHD6hXr16lW1hYyB0dHfGYvAfhilKHaLry0zZd6FG2JTY2lnn9+nXmnTt3chgMRpOXl5erVColAzQ/Z2LVWFxcrFdXV0dxc3OTHT9+vHDixIku69atG5CZmUk/ePBgcUFBgf7GjRvpSqUSKBQK7Ny5s3Tnzp2ldDrdk3gsOp2uav3Yb7zxRuWxY8dYhYWFBtOnTxdr6zmhrsEVJdIaXehRtlZVVUXp16+fgsFgNKWkpNDu3r1r1Nb97Ozs5I2NjeTff/+doaenB4cPHy7ct2+flaenZz2DwWjy8PCQubq6SletWmWtVDZfUi6RSDosd82dO1ccExNjev78+X5z5879V2IOvVy4okRapQs9SsLMmTOrf/jhBwtXV1e+i4tLw6BBg9oc5hQKBaKjo/NXrlxpv2LFCjKdTlft2LGjePfu3f2joqL6hYWFVUVHRxe+//77tvb29u4sFkthYGCg+uSTTx6299gjRoyQisViqq2traz1iSHUM7BH2cthjxL1JOxRIoQQAgA89EZ9CPYoUVfhoER9BvYoUVfhoTdCCKmBgxIhhNTAQYkQQmrgoEQIITVwUKJu6809SgCABQsW2G3ZsqWlzuPn58cOCQlxIL4ODw+33bx5s9XzPxccHOx46NAhFgBAdHS0CY/H47u6uvKdnZ0FO3bsMH/+PpoQiUT6bDZboOn90cuBgxJ1W2/uUQIAjBw5su7GjRsMAAClUgmVlZVUkUhkSHz/9u3bjNdee62uvZ+XyWSk5cuXO8TFxeWJRKLszMzM7AkTJtRq8tiod8BBibSqN/Yo/f396+7cucMAALhz546hq6ur1MjISPn06VOKVColFRQU0Hx9fSUqlQrCwsLsnZ2dBWPGjHEhwrtVVVVkhUJBsrKyUgAAGBoaNnl4eMiI7ScnJzM8PT25tra27sTqsrq6mjxixAgOn8/ncTgc/k8//dTv+f3Kzs7W5/F4/OTkZLpCoYDFixfburm58TgcDp9YsaKXAz9HqUNW5BTbCesbtNqj5BrRJHt49jrdo3R0dJRTqdSmvLw8/eTkZKN/hr1eYmIig8ViKVxdXaU0Gq3pyJEj/fLz8w1EIlHWw4cP9dzd3QXvvPNOhZWVlTIgIKDK3t5+0MiRI2tef/316kWLFomJ/uSTJ0/0UlJShGlpabSpU6e6zJ8/v5JOp6vOnTuXb2pqqiotLaX6+PhwQ0NDq4h9Sk9PN5g1a5bzf//73/u+vr7SnTt3mpuYmCgzMzNzpFIpadiwYdzJkyfXcLncxnafGNIaXFGibtOFHqWXl1ddUlKS0V9//cUYNWpUna+vb/21a9eMrl69yvD29q4DAEhOTm7Zb0dHR/mIESNaDq9PnDhRdOHChdyhQ4fWR0ZG9p85c6Yj8b2goKAqCoUCXl5eDRUVFXoAACqVirRixQpbDofDHzt2LKesrEz/4cOHVAAAsVhMnTJlisvRo0fv+fr6SgEALl68aPzLL7+YcblcvqenJ6+yspKanZ2t1fYmah+uKHWIpis/bdOFHuWIESPqrl+/zhAKhYbDhg2TDhw4sHHPnj1WDAZDOX/+/JboCInU/u8b8/b2lnp7e0sXLVokdnFxcQeAQgCA1r97h3juBw4cMK2oqKDevXs3x8DAoMnGxsadaF0ymUzlgAEDGi9fvswYOnRowz8/R9q1a1dxcHBwTVeeH+oeXFEirenNPcrRo0fXXbx4sV+/fv2UVCoVrKyslDU1NZTU1FTG2LFj6/+5T+3JkydNFQoFFBUV6d24cYMJ0Px+Y1xcHJPY1s2bNw2tra07PCSurq6mmJubyw0MDJrOnj3LfPTokT7xPT09vaYLFy4UREdHm+3fv98UACAgIKB63759FjKZjAQAkJGRYVBTU4P//b4kuKJEWtVbe5Te3t7Sqqoq6rRp0yqI27hcrrS+vp5CvM85b968qkuXLhm7uroKnJycGry9vWsBmk9O7dixw2rp0qUONBpNRafTVf/973/vd/R4CxcuFAcGBrq4ubnxBAKBxMnJ6Zkz+cbGxqr4+Pj8MWPGcBgMhmrlypXlhYWFBu7u7rympiaSqamp/Pz58wXqnhfSDuxR9nLYo0Q9CXuUCCGEAAAPvVEfgj1K1FU4KFGfgT1K1FV46I0QQmrgoEQIITVwUCKEkBo4KBFCSA0clKjbsEepvR5lRyIjI83CwsLstbEt1Dk4KFG3YY8Se5S6Dgcl0irsUWrWo4yLi2N6e3u7Tpo0aaCTk5MgKCjISaVq7n2cOHHCxMnJSeDl5eX6zjvv2I0dO9al238wqFvwc5Q6ZG1Mul3u41qt9ig5/ZmSHdM9sEep5R4lAEBOTo5hWlraPUdHR7mXlxc3ISGBMWrUqPrly5c7XL58WcjlchsnT57s1IU/NqRluKJE3YY9ys73KAEA3N3d652dneUUCgUEAoGkoKBAPy0tjWZnZycjgryzZs0Sd+X5IO3CFaUO0XTlp23Yo2zWmR4lAICBgUHLFxQKBRQKBUZqXlG4okRagz3KZpr0KNvj4eHR8ODBAwORSKQPAHDixAlTdT+DXjxcUSKtwh6lZj3K9jAYjKbdu3cXTZo0iW1qaqrw9PRs9x8Q9PLgUr+Xwx6l7qmuriabmJioiLPsbDa7YdOmTWU9vV9twR4lQqhH7Nmzx5z4AH9NTQ1l1apV+A9hD8MVZS+HK0rNYY9S+/rKihLfo0R9BvYoUVfhoTdCCKmBgxIhhNTAQYkQQmrgoEQIITVwUKJu6+09SoTUwUGJuq239ygRUgf/hdUlse/bQVm2VjNrYMmXwJS9Gsc2hg8fXp+RkWEI0HxpX0REhG1iYqIJiURqWrt2bWl4eHhle7e37lGuXr26FOB/Pcovv/yylOhRUqnUlg//zpkzxz49Pd2ooaGBPHny5MqvvvrqUeseJYvFUqi7hFEkEukHBgayvb2961JSUhhWVlaN8fHx+QwGo2nXrl3mhw4dspDL5SRHR0dZTEzMfSaTqQoODnZkMpnK9PR0o6dPn+pt3br1IZFPQ7oH/8VFWkP0KKdMmVIF8GyP8tKlS7kbN260LSoq0mvv9sTExHxidRoeHl4JANC6R3nkyBHT6dOnPzOMdu/eXZKZmZkjFAqzrl27xiR6lJaWlvLk5ORcTa7zBgAoLi6mLVu2rCw/Pz/LxMREGRUVxQIAmDNnTmVmZmaOSCTKdnV1lUZGRra0MInO5G+//Za3adMmG+29kuhVgytKXdKJlZ82ET3KkpISfTc3N0lXe5QODg7VbW2f6FEmJiaaXLlyRdQ63HvkyBHTw4cPmysUCtLTp0/10tPTaT4+PtLOPgcbGxuZr6+vFADA09NTUlhYaADQXDzfuHGjTW1tLaW+vp4yevToln1srzOJdA+uKFG3EavAwsLCu42NjaRt27ZZArTfnexKjzImJsbMxsamsa0eZXJycm5ubm62v79/dVd7lPr6+q3bkE0KhYIEALBo0SKnb7/9tjg3Nzd7/fr1j2QyWcv22+tMIt2DgxJpTW/uUbZHIpGQ7e3t5TKZjHT8+HFsQ/ZReOiNtKq39ijb88EHHzzy9vbm2djYNPJ4PEldXV2XP/qEei+sB/VyWA9CPamv1IPw0BshhNTAQ2/UZ2CPEnUVDkrUZ2CPEnUVHnojhJAaOCgRQkgNHJQIIaQGDkqEEFIDByXqthfVo0xPTzfw9vZ25XK5/IEDBwpmz57tAABw/fp1wxMnTpio29aqVausN27caNWV/fjuu+9MORwO38XFReDq6soPCQlxUPe8vL29XdvqZEZGRpqFhYXZd2U/0KsBz3qjbiOu9QYAmDZtmuOOHTsstm/f/rgr2zp27JjF06dP0wwNDZv8/PzYy5YtezJ37twqAIBbt24ZAgCkpKTQU1JSjEJCQtqMaHRXTEyM8d69e63i4+PznJyc5AqFAr799luzkpISqrm5OX6MqA/CQalDPr72sV1+Zb5We5QuLBfJ1pFbe6RHWVZWpufg4NBIbNvb21va0NBA+uKLL6wbGhrIXC6XsXr16tJPP/3U5q+//hJaW1srlEolODk5ud28eVPYer+ysrIMlixZYi8Wi6k0Gk31ww8/FHl6erYZAP7iiy8GbNu27aGTk5McAIBKpcKKFSsqiO+vWbNmwIULF/rJZDLy0KFD644dO1ZEJjcfnB0+fNhs+fLl9nV1dZTvv//+/tixYyWtt/3o0SPq/PnzHUpKSvQBAHbv3l08YcKEdq9zR68GHJRIa4ge5YIFC8oBnu1RlpaWUr29vXkTJkyoS0pKMmrr9sTExHw6ne5JrE4lEgn59ddf53h6etaPGzeu+v33368wNzdXfvjhh49SUlKMoqKiigEAhEIh7YcffjDduHFj2W+//WbM4/GkAwYMeOba8YULFzp8//33Re7u7rLExESjiIgI+xs3brR5DXh+fr6hr6+vpK3vAQCsXbu2bOfOnaUAAFOmTHE6fvy4SWhoaDWxz6mpqcLff/+dsWjRIqe8vLys1j+7ePFiu1WrVj2ZOHFiXV5env7EiRPZ9+7dy2rrcdCrAwelDunMyk+bXlSPcvny5RVvvfVWTWxsrPHZs2f7HT582CI7O/tfHxiPiIgoDwoKctm4cWPZjz/+aP7OO+88c+17dXU1OTU1lTFjxgxn4rbGxkaSJs/t1q1bhmFhYU719fXkjRs3loSHh1f+/vvvzN27d/dvaGggV1VVUfl8vhQAqgEAQkNDxQAAgYGBdXV1deTn39e8du2acV5eniHxdV1dHaWyspLMYrFUgF5ZeDIHdduL7FE6OjrKV6xYUXHp0qUCKpUKKSkphs/fx8XFRW5ubq44c+YMMzU11WjGjBnPDFylUglMJlMhFAqzif91tIpzcXGRXr9+nQ7QfLgvFAqzx44dWyOVSskSiYS0evVqh9OnTxfk5uZmz507t7x1A5NEenb+Pv91U1MTpKSk5BD7UVZWloFD8tWHgxJpjbZ7lDExMcYymYwEAFBcXEytqqqiODg4NBobGyuf/8Vh77777tOFCxc6BQUFianUZw+UTE1NVba2to0//vgjC6D5vdO//vrrXwOXsG7duscffPCBbUFBQUu1vKGhgQTQfGgNANC/f39FdXU1+ezZs6zWPxsdHc0CAIiPj2cwmUylmZnZMyd//Pz8arZv325JfH39+vV29wO9OvDQG2mVNnuUFy5cMF6zZo29gYGBCgCAuF9gYGDtzp07B3C5XP7q1atLw8PDK2fPnl29dOlSyqJFiyr+vVcA0dHR98LDwx22b98+QKFQkKZOnSoeMWJEm78yIiQkpLqsrIwaGBjIViqVJGNjYyWXy5W+9dZbNebm5so5c+Y85fP5Altb20YPD49nBjyLxVJ6enpyiZM5z2/7+++/f7Bw4UJ7DofDVyqVJB8fn1pfX9/irr3a6GXBHmUvhz3KZleuXKGvXLnS7s6dO6Ke3pe+pK/0KHFFiXq9DRs29D98+LDFoUOH/rWCQ0gbcEXZy+GKsuvWr1/f/7fffnvm9+C89dZb4q5+WL4v6isrShyUvRwOStST+sqgxLPeCCGkBg5KhBBSAwclQgipgYMSIYTUwEGJug17lJoTiUT6bDZb0J1tdOd5oa7Bz1GibsMepfYpFAp4/lJM1HPwT0KHPNrwHztZXp5We5QGbLbE+vPPsEepQY/S29vb1d3dXZKenk4Xi8XUQ4cO3f/ss88GiEQiw7feekscGRn5CKB5CE6bNs0xMzOTPnDgwIaTJ08WMplMlY2Njfvs2bPLk5KSjBcvXlw2cuRIiab7jF4sHJRIa7BHCaCvr69KSUkRbd261XLGjBkut2/fzrG0tFQ4Ojq6b9iw4QkAQGFhIe3AgQOFEyZMqJ8xY4bjjh07LLZs2fIEAIBGo6mIyzBHjBjB0XSf0YuFg1KHdGblp03Yo/xfj3Lq1KlVAAAeHh5SFxcXqYODgxwAwM7OTnbv3j19MzMzZf/+/RuJqvm8efMqIiMjLQHgCQBAWFhYZXf3GWkfnsxB3YY9yv/1KGk0WhMAAJlMBgMDg5YnSiaTQaFQkAA6blYymUxVV/YZvVg4KJHWYI9SM6WlpfoXL140AgD4+eefTX19feuev09n9xm9WDgokVa17lHOmzevSiAQSHk8nmDMmDGc1j3Ktm5/flsXLlwwdnV1Fbi6uvIDAgI4rXuUubm5hlwul3/w4EEWAMDs2bOrJRJJhz3KQ4cOmbu6uvLZbLbg1KlT/dp7DiEhIdVLliwpCwwMZDs7Ows8PT25FAoFnu9RBgYGujzfo9TEwIEDG3788UczDofDr6yspK5Zs+Zpd/cZvVgYxejlMIrRDHuUPaOvRDHwZA7q9bBHiV40XFH2crii7DrsUXZfX1lR4qDs5XBQop7UVwYlnsxBCCE1cFAihJAaOCgRQkgNHJQIIaQGDkrUbbrWo2zr52xsbNxLS0upAACenp7czm4T9W74OUrUbbrWo1QnNTVVqP5eSJfgoNQhl6Jy7MQldVrtUZraMCTjwnh9qkepDp1O95RIJKlxcXHMzZs3W7NYLMW9e/doPj4+tUePHi2mULodQkf/z959xzV1r38Af5IASSCsMMIeQgYJoBRliYi2WrXaWq3WDWpV0F5XXffaH62zWMVaqlVrb1WoFr1ona3WgbiqLRYRCBCgBVT2JiSEDH5/cMNFBAIkFhKe9+vV16uc5HzPyQEen+8ZHwYYLJRIY3QljxIA4NChQ4zTp09bKL8uLy/X7+x96enpRqmpqRksFqs5JCSEGRcXZ75w4cIaTRxPNHBgodQhven8NEkX8ygjIiLKlGG6AK3nKDt7n5eXVyOXy20GAJg5c2b1nTt3aFgodQ9ezEFq07U8yt7oLlsS6Q4slEhjdCWPsjfS09ONsrOzDeRyOSQmJtJHjRrVoIlx0cCChRJplC7kUfbGsGHDhB999JEDi8XiOTk5SebPn1+riXHRwIKhGFoOQzFa9Uce5aVLl4xjYmIYSUlJeX/XNgeawRKKgRdzkNbDPEr0qmFHqeWwo+w7zKNU32DpKLFQajkslKg/DZZCiRdzEEJIBSyUCCGkAhZKhBBSAQslQgipgIUSqU0X8ygJBIJvRkYGWblsy5Yt1gQCwff27dsaTWfqi9jYWIsFCxY49fd+DCZYKJHalM965+bmZpqZmcl2795t1dexTpw4YZWens4/fPjwsxUrVjitXLmyTPls9po1a8oBWvMoL1++rLJQqoPJZIrj4uLabh06f/483c3NrU+xbOpQKBQgl8v/7s2iDvCGcx1y9eA+x8qnhRrteCwdnUVvRq4edHmUkyZNqv3pp5/MPv/88xI+n29gbGws09PTa7uXbu7cuU5paWlGTU1NxClTptR88cUXxQCtKUMzZ86sunr1qqlMJiOcOnXqTx8fn6bLly/TPvroIyeA1uCM+/fvZxOJRJgwYYJ7XV0dSSaTEaKioornzZtXm5OTYzBx4kRmUFBQw6NHj2jnz5/P+/nnn42/+OILWysrK6mbm1uTgYEB3tf3N8JCiTRGl/IoTUxM5HZ2ds2///47JTEx0ey9996riY+Pt1S+vnfv3ucMBkMuk8kgKCiI/fDhQ6q/v78YAMDS0lLG5/OzoqOjraKjoxmnTp0qjImJsYmNjS0cP358Y11dHdHQ0FABAHD58uU8Op2uKCkp0fP39+fMmTOnFgCgoKCAcuTIkYLvv/++qLCwUD86Otru0aNHWXQ6XR4UFMT29PQUafr7h7qGhVKH9Kbz0yRdzKMEaM2XjI+Pp9+8edP09u3bOe0L5fHjx+nHjh2zlMlkhIqKCv20tDSKslDOmTOnBgDAz89PdOHCBXMAgICAAOG6descZ86cWT179uwaNzc3hUQiIaxevdrhwYMHNCKRCOXl5QbPnj3TAwCwtbVtfv311xsBAG7fvm0UEBDQYGdnJwMAmDZtWrVAIKD09PuD1IfnKJHadDWPctasWbWJiYkW9vb2zXQ6XaFcnp2dbbB//35GcnKyQCAQ8MeOHVvX1NTU9rtEoVBaAAD09PRaZDIZAQBg586dpd9++22hWCwmBgUFeaSmplIOHz5Mr6qq0ktPT8/Kzs7mW1hYSMViMREAQNlxKmHOZf/CQok0RtfyKGk0Wsunn3767P/+7/9K2i+vqakhUalUBZ1Olz99+lTv1q1bKi8sZWZmkv38/MQ7duwo9fLyaszIyKDU1dWRLC0tpWQyueXixYvGxcXFBp2tGxIS0vjgwQPj0tJSkkQiIfz444/mqraHNAun3kij2udRRkZGVt+/f5/m4eHBIxAILe3zKDtb3nGsK1eumKxbt86JTCYrAADa51Hu2bPHlsPhcD/66KOSJUuW1MyePbvuww8/7DaPcsmSJc67du2ylclkhHfffbc6MDBQrOrzLF269KU/6xAYGCj29PQUMZlMnpOTk8TX11eoapzPP//c+v79+yZEIrGFxWKJ33vvvbra2lrSxIkT3T09PT14PJ7I1dW104tLzs7O0o0bNxYHBAR4WFlZSb29vUVyuRxbzL8RhmJoOQzFaNUfeZRo8IRiYEeJtB7mUaJXDTtKLYcdZd9hHqX6BktHiYVSy2GhRP1psBRKvOqNEEIqYKFECCEVsFAihJAKWCiR2nQtZq2r7aobb3bp0iXjMWPGuPd1fdR/8PYgpDblI4wAANOmTXPZvXu3VV+vHJ84ccKqoqLiMZVKbQkODmauXLmybN68ebUAAL/99hsVoDVmLSUlxej999+v6360vlHGu3XcLhq8sKNEGhUQEND4/PlzA4DWRwWXLVvmwGQyeSwWi3vkyBHz7pa3j1k7cuSIeXcxaxcvXjTncDjcI0eOmDs7O3sWFxfrAbQ+1+3k5ORZUlLyQhOQmZlJHjVqFJPH43n4+vqyU1NTuwyV6Gy7yv8vLS3VHzVqFNPZ2dkzIiLCQbn87NmzJsOGDeNwuVyPiRMnDqmrqyMCtD6G6erqyvP19WUnJiaaqXt8Uf/AjlKHVCcKHKWljRrNo9S3MRLR32P1KJVIV2LWVqxYUdbZdgEA+Hy+YVpaGp9KpSrc3d09161bV2ZkZNSyc+dO29u3bwtMTEwUmzdvttm2bRtj69atpR9++KHLtWvXcng8nmTy5MlD1PleoP6DHSVSmzJmzdzcfFhtba1eX2PWOo67atWqqvT09Mxp06ZV375923jEiBEcsVj80jPOkZGRlQkJCRYAAKpi1jgcDnf58uXO5eXl+l19nu62GxwcXG9hYSE3NDRscXd3b8rPzyffunXLKD8/n+Ln58fhcDjchIQEi6KiIoPHjx9THBwcJF5eXhIikQhz587t9Dl0NPBhR6lDetr5aZryHGVVVRVp/Pjx7tHR0dYff/xxuSZj1lavXl3FZDJ5PYlZO3fu3J/tX28fs6budtsni5NIpBapVEpoaWmB4ODg+osXL77wCOX9+/epGI+mG7CjRBqjKzFrXW23q/eHhoY2pqSk0JR/jKyhoYH45MkT8rBhw5qePXtmkJmZSQYASEhIoHc1BhrYsFAijWofszZ//vxaHo8n9vDw4IWGhrLax6x1trzjWFeuXDFhs9k8NpvNHTduHKt9zJpAIKAqL+YAAMyePbtOJBJ1G7N29OhRSzabzWUymbwzZ850eWGlq+129X47OzvZ4cOHC2bNmjWExWJxfX19Oenp6RRDQ8OWr776qnDy5Mnuvr6+bEdHxy6LLRrY8FlvLYfPerfCmLX+MVie9cZzlEjrYcwaetWwo9Ry2FH2HcasqW+wdJRYKLUcFkrUnwZLocSLOQghpAIWSoQQUgELJUIIqYCFEiGEVMBCidSma3mUCHWE91EitelaHiVCHWFHiTRKF/Iop0+f7hIeHu7o4+PDcXBw8Dp69Kg5QGsKUWBgIIvL5XqwWCzu999/bwYAkJOTYzBkyBDerFmznN3d3XkjR45kCoVCTMPQIdhR6pBz5845lpeXazSP0traWjR16tRBlUcJAFBWVqafkpKS/fjxY8q7777rvnDhwhpDQ0PF5cuX8+h0uqKkpETP39+fM2fOnFoAgKKiIsr333//Z1BQUOGkSZOGxMXFmS9fvry6r8cdDSzYUSK16VoeJQDA22+/XUsikcDX17epqqpKHwBAoVAQVq9e7cBisbhjxoxhlZeXGzx79kwPAMDe3l4SFBQkBgDw8fERFRQUkPt6PNHAgx2lDulp56dpuphHSaFQ2nZSub+HDx+mV1VV6aWnp2eRyeQWe3t7L7FYTAR4OadSuRzpBvxmIo3RlTzKrtTV1ZEsLS2lZDK55eLFi8bFxcUGvR0DaScslEijdCGPsisffPBBdVpampGnp6fH999/T3d1dW3q/RFC2ghDMbQchmK0wjzK/jFYQjHwHCXSeur/epEAACAASURBVJhHiV417Ci1HHaUfYd5lOobLB0lFkoth4US9afBUijxYg5CCKmAhRIhhFTAQokQQipgoUQIIRWwUCKN2Lhxo427uzuPxWJxORwO9+bNm0ZdvXf69OkuykSe7kRFRTFcXV15TCaTx2azufv377fQxL7a29t7KdOFfHx8OACtCUCHDh1quwJ++/Ztw/DwcEdNbK+9uLg4MwKB4KsqvUh5fN5//33nR48eUTrut6bExsZaLFiwwEmTY+oivI8Sqe369etGV69eNUtPT+dTqdSWkpISPeWjh331+eefW928edPk0aNHWXQ6XVFVVUU6efJkr5+mUSU1NTUbACA3N5d86tQpekRERDUAQEhIiCgkJESk6e0lJCTQX3vtNWF8fDzdx8enWNX7T506VajpfUC9hx0lUtvz58/16XS6jEqltgAA2NraylxcXKTr1q2z9fT09GAymbzZs2c7KxSKl9a9c+eO4YgRI9g8Hs8jODiYWVhYqA8A8MUXX9gcPny4iE6nKwBanyP/xz/+UQUAcP78eWMPDw8ui8Xizpgxw0WZKGRvb++1Zs0aO2VepLJrKy0tJY0cOZLp4eHBnTNnjnP7W+IMDQ19AAA2b95sn5KSQuNwONwtW7ZYX7p0yXjMmDHuAABlZWWkN954w43FYnGHDh3KefjwIRWgNUF9xowZLn5+fmwHBwev7du3W3d3nOrq6ogpKSm0o0ePFvz4449tHbVCoYAFCxY4ubm58UJDQ90rKyvbGhg/Pz/27du3u4zOS0pKMvTx8eF4eHhwfXx8OGlpaWSA1k5x/PjxbqNGjWI6Ozt7RkREOCjX+fLLLy1cXFw8R4wYwb5//z6tu31GrbCj1CH8rI2OjUKBRvMojWgsEddjV7epRFOnTq3/7LPP7FxcXDyDg4PrZ8+eXf3WW28J169fX75nz56S/77HNSEhwXTOnDltqeQSiYSwcuVKp8uXL+fZ2dnJjhw5Yr5u3Tr7b775pqixsZHE4/EkHbclEokIy5Ytc/3ll19yvL29Je+++67L7t27raKiosoBACwtLWV8Pj8rOjraKjo6mnHq1KnCTZs22QUGBgr37NlTkpCQYPrDDz9Ydhx3x44dz2NiYhhJSUl5AACXLl0yVr62YcMGu6FDh4quX7+ef+HCBeOwsDBXZRJRXl4e5f79+zm1tbUkDw8Pz/Xr11eQyeROb04+ceKEWWhoaJ23t7fEzMxMfvfuXcPg4GBRfHy8WV5eHjknJyfz2bNn+l5eXrzw8PBOn1nvaOjQoU2//fZbtr6+Ppw7d854w4YNDlevXs0HAODz+YZpaWl8KpWqcHd391y3bl2Zvr4+REdH2/23U5cHBQWxPT09Nd456xoslEhtpqamioyMDP6VK1eMb9y4YRwWFuYWFRX1zMTERL53716bpqYmYm1trR6XyxUDQFuhfPLkCTk3N5c6duxYFkBrZ2VlZSVtaWkBAqHzmXtaWhrFwcFB4u3tLQEACA8Przpw4IA1AJQDAMyZM6cGAMDPz0904cIFcwCABw8eGJ89ezYPAGDWrFl1y5Ytk/fm8/3222/GZ86cyQMAePvttxuWLl2qV1VVRQIAGD9+fC2VSm2hUqkyOp0uffbsmZ6bm5u0s3FOnz5NX7VqVTkAwPTp06vj4+PpwcHBouTk5LZ8ThcXF2lgYGBDT/eturqa9P7777sWFBRQCARCi1QqbTtwwcHB9RYWFnIAAHd396b8/HxyeXm5XkBAQIOdnZ0MAGDatGnVAoGgy/OlqBUWSh2iqvN7lfT09GDy5MkNkydPbvD29hYfOXLEMicnx/Dhw4d8d3d36dq1a+2amppeONXT0tJCcHd3Fz9+/Di743hUKlXB5/MNuFxuc4d1ut0PZY6knp5ei0wmaysaRGLfzzJ1tk0CgdACANC+eySRSNB+m+2VlpaSHjx4YCIQCKgffvghyOVyAoFAaDl48OCz/47Xp33buHGj/ejRoxuuXbuWn5OTYzB27Fi28rWOGZnKItrXbQ1meI4SqS0tLY2cnp7eluidmppKdXd3lwAA2NjYyOrq6ogXL1586Sq3t7d3U3V1td7169eNAFqn4ikpKRQAgNWrV5dEREQ4V1dXEwEAqquriXv27LEcNmxY0/Pnzw0yMjLIAABxcXEWo0aN6rYDCwgIaPjuu+8sAABOnz5tUl9f/9JfiTQ1NZULhcJO/3pkQEBAw9GjRy0AWqfk5ubmMuW5056Kj483nzZtWlVxcXH68+fP00tLS584ODg0//LLL7TRo0c3/Oc//6HLZDIoLCzUf/DggbHqEVvV19eTHBwcmgEADh8+/NIphY5CQkIaHzx4YFxaWkqSSCSE9udKUdewo0Rqq6+vJ61cudKpvr6eRCKRWlxcXCTHjx8vNDMzk3G5XJ6Dg0Pz0KFDXwrmpVAoLQkJCfkrV650amhoIMnlckJkZGTZ8OHDmzZs2FAhFAqJr732GldfX79FT0+v5R//+EepoaFhy6FDhwpmzJjhJpfLYejQoaJ169ZVdLd/0dHRxdOnTx/C5XI9AgMDhba2ts0d3+Pn5yfW09NrYbPZ3Dlz5lT6+vqKla/t2rWreM6cOS4sFotLpVIVx44d63VK0X/+8x+LDRs2lLRf9s4779TEx8fT4+Pji27cuGHCZrN5rq6uTX5+fi8U/vYd4NChQ7nKr6dMmVK9cePG0g8++MA1NjbWZtSoUfWq9sPZ2Vm6cePG4oCAAA8rKyupt7e3SC6XY4upAoZiaDkMxdBtLBaLe+HChTwOh/NScR8IMBQDIdSvgoKCmGw2WzxQi+RgglNvhDSotLSUFBoayu64/NatWzk2Nja9utp+//79XM3tGVIHFkqENMjGxkbem7/2iLQDTr0RQkgFLJQIIaQCFkqEEFIBCyVCCKmAhRJpBOZR9kxneZQ5OTkGTCaTB9D65I8ytQgNHFgokdra51EKBAJ+UlKSYMiQIWrd+9c+jzI3Nzfz/v37Oa/i4YiOeZTK5SEhIaJjx45p/Nn59nmUmh4bvTpYKJHaMI9SvTzKrnS13bq6OuJ7773nwmKxuCwWi3vs2DEzAIC5c+c6eXp6eri7u/PWrFljp2p81HN4H6UOWZ1V5Jjd2KTRPEqOEUW0z8MJ8yhfYR5lV8e1q+1u2rTJ1sTERC4QCPgAABUVFSQAgL179z5nMBhymUwGQUFB7IcPH1L9/f3FXY2Peg47SqQ2ZR7l/v37C62srGRhYWFusbGxFj///LOxt7c3h8Vice/fv2+ckZFBbb9e+zxKDofD3b17t21xcbF+b/Mo796921bU2udRPn36lAzQmke5aNGiKoDWPEoTE5Ne51EuXry4CqA1j7K2tvalPEpbW9u2PMquxjl9+jR99uzZNQD/y6Psy3Zv375tsmbNmnLl+6ysrOQAAMePH6dzuVwPLpfLzc3NpaSlpWHOpIZgR6lDVHV+rxLmUaqXR9mb7Xb2D0l2drbB/v37GY8ePcqysrKST58+3aXj8UZ9hwcSqQ3zKFXrLo+yu/3ubLuhoaH1e/fubTsfWlFRQaqpqSFRqVQFnU6XP336VO/WrVumvdk/1D3sKJHaMI9Ste7yKKOioko7W6er7X722WclCxcudGIymTwikdjyr3/9qzgsLKzW09NTxGQyeU5OThJfX19hb/cRdQ3zKLUc5lGi/oR5lAghhAAAp94IaZQm8yjRwIGFEiENwjxK3YRTb4QQUgELJUIIqYCFEiGEVMBCiRBCKmChRBqBeZSqkUgkXw6Hw1X+l5OTY6DumJ9//rmV8rj09Lii3sOr3kht7fMoqVRqS0lJiZ5EIuk81aKH2udR0ul0RVVVFenkyZNmmtpnpY55lBEREdUArXmUISEhXSb79AWZTFZo+or4hg0bun0qCWkGdpRIbZhH2bM8ys7k5OQY+Pr6sv+b+uNx7do1I4DWZ7tHjBjBnjRp0hAXFxfP5cuX2x88eJDu5eXlwWKxuJmZmWTlPkRFRTHaj3n+/HnjcePGuSm//vHHH03Gjx/vBqjPsKPUIesT0xwFpQ0azaNk2RiLdr83FPMoNZBHKZFIiBwOhwsA4OjoKLl27Vq+nZ2d7M6dOwJDQ8OW9PR08uzZs4dkZGRkAQBkZ2dTExMT/7S2tpY5Ozt7kcnkyvT09Kxt27ZZx8TEWH/33Xedfl+mTJnSsHr1aqfi4mI9Ozs72XfffWcRHh6Oj7mqAQslUpsyj/LKlSvGN27cMA4LC3OLiop6ZmJiIt+7d69NU1MTsba2Vo/L5YoBoK1Qts+jBABQKBRgZWUl7W0e5YEDB6wBoBzgxTzKCxcumAO05lGePXs2D6A1j3LZsmW9zqM8c+ZMHkBrLuTSpUtfyqOkUqlteZRubm7SzsbpbOrd3NxMWLx4sTOfz6cSiUQoLCxsS2Hy8vJqdHZ2lgIAODk5SSZOnFgHADB06FBxcnKyMXSBSCTCzJkzq44cOUJfsWJF1R9//EE7e/Zsr4M80P9godQhqjq/VwnzKLvPo+zKjh07GNbW1tIzZ878pVAogEql+ipfaz82kUhs+2xEIhHkcnm324mMjKx666233CkUSsuUKVNq9PX1e7NbqAM8R4nUhnmUfVdXV0eytbWVkkgk+Prrry3kcs08Du7i4iJlMBjSmJgY2yVLluC0W03YUSK1YR5l361evbp8+vTpbufOnTMPDg5uoFKpGinAAACzZs2qOnDggJ6vr2+TpsYcrDCPUsthHiXqyoIFC5x8fHxEa9aseWU/H4MljxI7SoR0EI/H86BSqYrDhw/323lrXYKFEiENGih5lJmZmVl/17YGAyyUCGkQ5lHqJrzqjRBCKmChRAghFbBQIoSQClgoEUJIBSyUSCMwj1I1AoHgu2TJEgfl11FRUYy1a9faaXIbqmBmZd/gVW+kNsyj7BkDA4OWn376ybykpKTU1tZW1tv1pVIp4DPb/QM7SqQ2zKPsWR4liURqWbBgQcXOnTsZHV8TCAQGgYGBLBaLxQ0MDGTl5uYaALR2gB988IGDv78/a/ny5Q5r1661mzZtmsvIkSOZ9vb2XsePHzeLiIhwYLFY3FGjRjGV/0D15NijnsNCqUvOrXCEb8awNfrfuRUqp59Tp06tLy4uNnBxcfGcN2+e0+XLl2kAAOvXry/PyMjIys3NzRSLxcSEhATT9usp8yjPnz+fn5mZmRUWFla5bt06+5qaGqKqPMpTp07lCwQCvkwmg927d1spX1fmUS5atKgiOjqaAQCgzKPMysriv/3227UlJSUv/QmGHTt2PB8+fLgwOzub/8knn5S3f02ZRykQCPjbtm17HhYW5qp8LS8vj5KcnCz4/fffs/bs2WOnqpNev359+dmzZ+nKmDaliIgIpzlz5lQJBAL++++/XxUZGdl23PPz8yn37t0THDly5BkAQGFhIfnmzZt5iYmJeREREa5jx46tFwgEfAqFojh9+rRpT4496h0slEhtyjzK/fv3F1pZWcnCwsLcYmNjLX7++Wdjb29vDovF4t6/f984IyOD2n699nmUHA6Hu3v3btvi4mL93uZR3r17ty2bsX0e5dOnT8kArXmUixYtqgJozaM0MTHpdR7l4sWLqwBa8yhra2tfyqO0tbVty6Psbiw6na6YMWNGVXR09AvdZ2pqqtHSpUurAQAiIyOrHz16RFO+Nm3atBo9vf8N+8Ybb9SRyeQWPz8/sVwuJ7z33nv1AAA8Hk/8119/GQAAqDr2qHfwHKUumXoA8yi1II/yn//8Z9lrr73GnTVrVo/CKmg02gvzZuU2SSQS6OnptSg/G5FIBJlMRhCJRISPPvrIubtjj3oHDx5SG+ZR9g6DwZBPmTKl5uTJk21/ksLHx6fx22+/NQcAOHz4MH348OHCvo4vEomIAN0fe9Q72FEitWEeZe9t3ry59Pjx423nVg8ePFgUFhbm8uWXX9pYWFjI4uLiCvo6tqWlpXzu3LkV3R171DuYR6nlMI8S9afBkkeJU2+EEFIBp94IadBAyaNEmoWFEiENwjxK3YRTb4QQUgELJUIIqYCFEiGEVMBCiRBCKmChRBqBeZSqkUgkXw6Hw2UymbyJEycOaWho6NXv39atW63brzN69Gj3ysrKTp8mUmr/WVHfYaFEamufRykQCPhJSUmCIUOGvPT0S2+0z6PMzc3NvH//fs6reDiiYx6lcnlISIjo2LFjGn12nkwmK7Kzs/m5ubmZ+vr6LTExMVaq12olk8ng8OHDDKFQ2PY7m5ycnGdpaYm3HP0N8F8aHfJ/9/7PMa8mz1CTY7qbu4u2jdzWbcHoLI8SoDUT8cqVK2YSiYQ4fPhw4YkTJwo7hlPcuXPHcO3atY4ikYhobm4uO3HiRIGzs7P0iy++sLl+/bqgqzzKTZs2OSofYYyLiyukUqkt9vb2XjNnzqy6evWqqUwmI5w6depPHx+fptLSUtL06dOHVFdX6/v4+DR2zKMUiUSpmzdvtv/zzz8pHA6HO3v27EpfX19xTEwMIykpKa+srIw0d+5cl6KiIjKVSlV88803hf7+/uK1a9faPX361KCwsJBcXFxsEBERUfbxxx+/ENHWleDgYOGTJ0+oAABvvPGGW0lJiYFEIiFGRESUrVu3rlK5b0uXLi27efOmyfjx4+vKy8v1R48ezTI3N5c9fPhQYG9v75WSkpJla2sr62oMpBnYUSK1YR5lz/MoAVqTyq9evWri5eUlBgA4ceJEQWZmZtbjx4/5hw8fZpSWlpIAAMRiMdHT01P85MmT7D179pRYW1tLk5OTBQ8fPhR0HLOrMZBmYEepQ1R1fq+KMo/yypUrxjdu3DAOCwtzi4qKemZiYiLfu3evTVNTE7G2tlaPy+WKAaBOuV77PEoAAIVCAVZWVtLe5lEeOHDAGgDKAV7Mo7xw4YI5QGse5dmzZ/MAWvMoly1b1us8yjNnzuQBtOZRLl269KU8SiqV2pZH6ebmJu1sHIlEQuRwOFwAAH9//4ZVq1ZVAgDs2rWLcfnyZTMAgNLSUv3MzEyKjY1NI4lEgvDw8Jqe7GNXY/Tmc6KuYaFEGoF5lKrzKJXnKNsvu3TpknFycrJxSkpKtrGxscLPz48tFouJAAAGBgaK9oG9XeluDKQZeDCR2jCPsu9qa2tJpqamcmNjY0VqaiolLS2ty7sFjIyM5HV1dS/9zvZmDNQ32FEitWEeZd9Nnz697ptvvrFisVhcNze3pu6yI8PCwionTpzItLa2lrY/T9mbMVDfYB6llsM8StSfMI8SIYQQAODUGyGNwjxK3YSFEiENwjxK3YRTb4QQUgELJUIIqYCFEiGEVMBCiRBCKmChRBqBeZRIl+FVb6S29nmUVCq1paSkRK8nKTrdaZ9HSafTFVVVVaSTJ0+aaWqflTrmUUZERFQDtOZRhoSEiDS9PaSdsFDqkOJ/bXaU5OZqNI+SzGSK7HbuwDxKNfMoc3JyDCZOnMj08/MTpqSk0BgMRvPVq1fzaDRaS0xMjOXRo0etpFIpwcXFRZKYmPiXsbGxYvr06S7GxsbytLQ0o4qKCv1t27Y9W7hwYY/ShJBm4dQbqQ3zKHuWR1lUVERZuXJleV5eXqapqak8Li7OHABg7ty5NRkZGVk5OTl8Npstjo2NtVSuU1ZWpp+SkpJ9/vz53E8++cS+J98PpHnYUeoQVZ3fq4J5lD3Lo7S3t5cEBQWJAQB8fHxEBQUFZACAR48eUaOiouwbGhpIjY2NpNGjR7cdo7fffruWRCKBr69vU1VVlX5v9htpDhZKpBGYR6k6j9LAwKD9e1uUmZFLly51TUxMzAsMDBTHxsZaJCcnG3f8PF3tB/p74NQbqQ3zKNUjEomITk5OUolEQkhISKCrXgP93bCjRGrDPEr1bNq0qdjPz8/D3t6+2cPDQ9RVwUb9B/MotRzmUaL+hHmUCCGEAACn3ghpFOZR6iYslAhpEOZR6iaceiOEkApYKBFCSAUslAghpAIWSoQQUgELJdIITeZRzp8/34nD4XDd3Nx4FArlNQ6Hw+VwONyeZFiqKyEhwZTH43m4ubnxXF1deZGRkfYlJSV6dDp9qPI9V65coREIBN+ioiI9AICysjKSubn5UIVCAe+8846rvb29F5vN5rq4uHhOmzbNpaCgAJ/R1nJ41RupTdN5lPHx8UUArdFkkydPZv5dV5F//fVX6saNGx0vXryY6+3tLZFKpbB3714rW1tbmYmJifzJkydkb29vyZ07d2geHh6ipKQkWlhYWG1SUhLNx8enUfk8eXR09NP58+fXyuVy2LJlC2PMmDGs7OxsfvvnwpF2wUKpQ27EZTlWPxdqNI+Sbk8Tvb7A42/Po+xsO2lpaeR58+YNSU9PzwIA+OOPPyhhYWGu6enpWQwGw3v69OlVd+7cMSEQCC0JCQl/crnc5qdPn+otXrzYubi42IBAIMC+ffuKXn/99ZcepwQA2Llzp8369etLlMlE+vr6sHHjxgoAgBEjRghv3bpF8/b2ljx48MBoxYoVZffu3aOFhYXV3rt3j+bv7y/sOB6JRIKtW7eWXbx40fzHH380mTVrVl3H9yDtgFNvpDZN51F2tZ2hQ4dKyGSy4vfff6cAAHzzzTeW8+bNa3t809zcXJ6enp61aNGiipUrVzoCAERERDht3LixNCMjIysxMTE/IiLCpavxc3JyqAEBAZ0W0cDAQOGvv/5KAwAoLi4mL1iwoDY1NdUIAOC3334zCgkJealQKnl5eYmysrIoXb2OBj7sKHWIqs7vVdF0HmV32woLC6v85ptvLIcNG/bs4sWL5mlpaW3T8vDw8GoAgGXLllVv3brVAQDg3r17Jvn5+W1Fqq6ujiQUCgk0Gq1X0+DQ0FDhwYMHGenp6WQXF5cmY2NjhVQqJdTX1xOzsrIMR40a1WmBBcB4NF2AhRJphKbzKLsSHh5e4+XlZXvy5Enha6+9JrS0tGx7LFCZEdlhG/D48eOs9rmOXWGz2eIHDx4YDR8+vKnja8OGDWuqrKzUO3funKm/v38jAICnp6foq6++snRxcWnqrvBmZmYaTp48GafdWgyn3khtryKPsivGxsaKkSNH1q9fv95p0aJFVe1fi4uLowMAHDlyhO7r6ysEABg5cmT9rl272v5UxP3796ldjb1p06bSPXv22CqzLmUyGXz66acMgNbg32HDhjV+88031sHBwUKA1un4oUOHrEeMGNFpN6lQKGDLli3WNTU1pKlTp9Z397nQwIYdJVLbq8ij7G57CxYsqE5KSjJ9++23Xyg+IpGI6OXl5aG8mAMA8O233xYtWrTIicViWcrlckJQUFBDUFBQUWfjjhw5Urxjx46nM2fOHNLU1EQkEAgwYcKEWuXrAQEBwnv37pmMHDlSBAAwevToxg8//JA8cuTIF85Pbtq0yXH79u12EomE+NprrwmTkpIEeMVbu2EepZYbjHmU//rXv2wkEgkhJiamRLmMwWB4Z2ZmZrafiqNXb7DkUWJHibTK2LFj3YuLiw2Sk5Nz+ntf0OCBhRJplZs3b+Z1trysrOxJT8fYu3ev5TfffGPdfllAQEDDsWPH+uWuATTw4dRbyw3GqTcaOAbL1BuveiOEkApYKBFCSAUslAghpAIWSoQQUgELJdIIXc6jfNXbRAMf3h6E1KbreZR/x7bRwIaFUodcPbjPsfJpoUbzKC0dnUVvRq4e9HmU2dnZBmFhYS41NTV6lpaW0vj4+AI3NzfpO++840qn02WPHz82qqio0N+5c+fTBQsW1HY2PtJeOPVGahsMeZRLly51Dg8PrxQIBPxp06bVrFixwlH5WmVlpd6jR4+yz5w5k/fJJ5/gVF0HYUepQ1R1fq/KYMijTEtLM7p582YuAMDy5curPvvss7aC+Pbbb9cSiUTw9/cXl5eXG/RmXKQdsFAijdD1PMrutB8bn3TTTTj1RmobDHmUw4YNE/773/+mAwAcOnTIws/Pr0HVcUG6AztKpLbBkEd58ODBooULF7rExMTYKC/m9PmAIa2DoRhabjCGYmAe5cAxWEIxsKNEWgXzKFF/wEKJtArmUaL+gFNvLTcYp95o4BgsU2+86o0QQipgoUQIIRWwUCKEkApYKBFCSAUslEgjMI/yRRkZGWQOh8PtuPydd95xtbe392Kz2VwXFxfPadOmuRQUFOirv+foVcLbg5DaMI+yd6Kjo5/Onz+/Vi6Xw5YtWxhjxoxhZWdn88lkMt6CMkBhodQh1YkCR2lpo0bzKPVtjET091iYR/kK8ihJJBJs3bq17OLFi+Y//vijyaxZs+pUr4X6A069kdowj1K9PEovLy9RVlZWt2EgqH9hR6lDVHV+rwrmUaqXR4kPfQx8WCiRRmAe5f+211uZmZmGkydPxmn3AIZTb6Q2zKPsG4VCAVu2bLGuqakhTZ06tV71Gqi/YEeJ1IZ5lJ3Lz8+nMBgMb+XXu3fvLgIA2LRpk+P27dvtJBIJ8bXXXhMmJSUJ8Ir3wIahGFpuMIZiYB7lwDFYQjGwo0RaBfMoUX/AQom0CuZRov6AU28tNxin3mjgGCxTb7zqjRBCKmChRAghFbBQIoSQClgokUZgzNqLuotZi4+PN1N/T9HfCa96I7VhzBrSddhRIrV1FrPm4uIiXbduna2np6cHk8nkzZ4921mhULy07p07dwxHjBjB5vF4HsHBwczCwsIuQ2zT0tLIXl5eHsqv//jjD4ryawaD4b18+XJ7Ly8vD29vbw6fzzcAAHj69Kne+PHj3Tw9PT28vLw8bty40WWnqypmzd/fn8VisbhBQUHM/Px8fYDWDnHhwoWOPj4+HAcHB6+4uDjsFnUQdpQ65Ny5c47l5eUazaO0trYWTZ06tdv7C6dOnVr/2Wef2bm4uHgGBwfXz549u/qtt94Srl+/vnzPnj0l/32Pa0JCgumcOXPawh+UMWuXL1/Os7Ozkx05csR83bp19v/5z38KOttO+5i1ESNGNHUVp7yOaQAAIABJREFUs7Zv3z6LlStXOl6/fj1fGbP2+uuvNyo71Nzc3MzOxs/JyaF+8sknJZ29poxZi4yMrN6zZ4/lihUrHK9cufInwP9i1n7//XfqrFmzhvQ0jxJpDyyUSG0Ys6ZezBoa+LBQ6hBVnd+rhDFr/9se0j14jhKpDWPWkK7DjhKpDWPWOtdVzBrSPvist5YbjM96Y8zawDFYnvXGjhJpFYxZQ/0BCyXSKhizhvoDTr213GCceqOBY7BMvfGqN0IIqYCFEiGEVMBCiRBCKmChRAghFbBQIo3APMoXdZVHibQT3h6E1IZ5lEjXYUeJ1IZ5lD3LoywoKND39vbmKD83gUDwLSgo0AcAsLe39xKJRH3+xwW9WthR6hB+1kbHRqFAo3mURjSWiOuxC/MoNZBH6eLiIm1oaCDV19cTk5KSaDweT3Tt2jVaUFCQyMbGptnQ0BBvah6gsFAitWEeZc/zKH19fRtv3LhBu3fvnvGGDRtKrl27ZiIWi4mBgYHC3uwP+nthodQhqjq/VwnzKP+3ve4EBwc33Lp1i1ZaWqo/e/bs2i+++MKmubmZMGPGjJrebBP9vfAcJVIb5lH23BtvvCE8ffq0hbu7e5O+vj4YGRnJb9++bTJ27FjsKAcw7CiR2jCPsnOd5VEuWLCgVi6XE0aNGtUAABAQECCsrq7Wo9PpL1/pQgMGhmJoucEYioF5lAPHYAnFwI4SaRXMo0T9AQsl0iqYR4n6A069tdxgnHqjgWOwTL3xqjdCCKmAhRIhhFTAQokQQipgoUQIIRWwUCKN0IU8yr1791oSiUTf9k8Hubq68pRJQWjwwtuDkNp0JY8SAIDBYDRv3brV9sKFC3/9XdtEAx92lEhtupJHCQDw5ptv1mZkZBgqn/du7/Tp0ybDhg3jcLlcj7feemtIfX098dq1a0aTJk0aAgBw7NgxMyqV6iORSAj19fVEZ2dnzx4eQjTAYUepQ1ZnFTlmNzZpNI+SY0QR7fNwGhR5lAAARCIRVq5cWbp161ab06dPFyqXP3/+XG/37t22d+7cERgbGys2btxos3PnTustW7aULVq0yBAA4Pbt28Zubm5N9+7dM2xoaCD6+Pi89Hw70k5YKJHadC2PMjIysvqLL76wzc3NbcuWvHnzJi0vL48yYsQIDgCAVCol+Pn5CclkcoudnV1zeno6OS0tzXDFihVlSUlJtMbGRlJwcHCfEobQwIOFUoeo6vxeJV3Io1Qik8ktkZGRZdu2bbNpP87o0aPrz50799K5y8DAQOGPP/5oSqFQFG+99VZ9eHi4S1NTE/HAgQOdphQh7YPnKJHadCWPsr1Vq1ZVJiUlmdTV1ekBAIwZM0b48OFDmvLcZ319PVH5mUNDQxsOHjzI8Pf3b3RycpJVVFToFxYWkn18fHoVAIwGLuwokdp0JY+yPSqV2rJ48eKKLVu2OAAAODo6yr7++uvCmTNnukmlUgIAwJYtW557eXlJxo4d21hRUaEfGhraAADA4XDEdXV1pJ4fQTTQYSiGlhuMoRiYRzlwDJZQDOwokVbBPErUH7BQIq2CeZSoP+DUW8sNxqk3GjgGy9Qbr3ojhJAKWCgRQkgFLJQIIaQCFkqEEFIBCyXSCF3IowQAOH78uBmLxeIOGTKEx2KxuCdPnjRVvrZv3z6LoqKitjtFGAyGd2VlJd5YPgjg7UFIbbqSR3n37l3DqKgoh2vXrglYLFZzZmYm+c0332SxWKzc4cOHN8XHx1v6+fmJnJycZH/H/qCBAztKpDZdyaPctWsXY/369SUsFqsZAIDH40lWrVpVEh0dbXPkyBHzrKwswzlz5rhxOBxuU1MTAQBg586dDA8PDy6LxeI+efLkpQxLpBuwo9Qh6xPTHAWlDRrNo2TZGIt2vzd0UORR5uTkUD/55JOS9ssCAgJEcXFxVomJiQWHDh2y/uqrr4qCgoLEytcZDIY0KyuLv337duvo6GjGyZMnMTFIB2GhRGrTpTxKAuHFMwYtLS2dxrcpzZkzpwYAwM/Pr/Hq1aumXb0PaTcslDpEVef3KulCHiWLxWr69ddfjXx9fdvSi3777TdDFovVZZqR8nQDiUQCuVze5/OyaGDDc5RIbbqSR7lhw4bSPXv2tCWb8/l8gy+//NJm48aNpQAARkZGivr6erzKPQhhR4nUpit5lCEhIaKoqKjnkyZNYspkMtDX12/ZuXPnsxEjRjT9d7uVERERLhQKRfH48eOsvh8xpG0wFEPLDcZQDMyjHDgGSygGdpRIq2AeJeoPWCiRVsE8StQfcOqt5Qbj1BsNHINl6o1XvRFCSAUslAghpAIWSoQQUgELJUIIqYCFEmmEtudRyuVyMDU1HVZdXU0EAMjPz9cnEAi+yrQhhUIBZmZmwyorK0krV660s7a29uZwOFxnZ2fPN9980y01NbXbJ4qQdsPbg5DadCGPkkQigZeXV+OtW7do06ZNq7958ybNw8NDdOfOHdrrr7/e+Mcff1Csra2blTe0f/jhh6VRUVHlAACHDx+mjx8/npWenp5pY2ODN7zrIOwokdp0JY8yICBAePfuXRoAwP3792krVqwoe/DggREAQHJyMm348OEvPYYJ0JpWFBAQ0HD06FF6jw4Y0jrYUeqScyscoZyv0TxKsOaKYOqBQZFHGRwcLNy9e7cNAMDjx4+Nvvrqq2cHDx5kAAD8+uuvtNDQ0IaujoGPj48oOzsbp986CgslUpuu5FGOGTOmMSwszKiuro4IAECj0Vrs7Oyac3JyDFJSUmiffvppScd1lPDBDd2GhVKXqOj8XiVdyKM0NTVV2NnZNe/fv99SmXbk5+cnTExMNBMKhSRPT09JV+s+fvzYcOTIkcKefg6kXfAcJVKbruRRAgCMGDFCeOjQIevAwMBGAIDg4ODGQ4cOWfv4+HRZBL/99lvzBw8eGCs7WqR7sKNEatOVPEoAgJEjRwrj4+OtQkNDhQCthbK0tNQgPDy8ov379u/fb3Py5ElLsVhMZLPZ4l9++UWAV7x1F4ZiaLnBGIqBeZQDx2AJxcCOEmkVzKNE/QELJdIqmEeJ+gNOvbXcYJx6o4FjsEy98ao3QgipgIUSIYRUwEKJEEIqYKFECCEVsFAijdD2PEqEuoO3ByG16UIeJULdwUKpQ/7v3v855tXkaTRmzd3cXbRt5LZu7y/sLI8SAGDdunW2V65cMZNIJMThw4cLT5w4UUgkvjiJuXPnjuHatWsdRSIR0dzcXHbixIkCZ2fnThOE0tLSyPPmzRuSnp6eBdCaRxkWFuaanp6exWAwvKdPn151584dE+UjjFwut/np06d6ixcvdi4uLjYgEAiwb9++otdff73TXMmVK1falZaW6v/111+UkpISgxUrVpT+85//rABovdG9rKxMXyKREJcvX162du3aSqlUCnQ6fdj8+fMrbty4YUqlUhWXL1/Os7e3l/X6QKMBDafeSG1Tp06tLy4uNnBxcfGcN2+e0+XLl2kAAOvXry/PyMjIys3NzRSLxcSEhATT9usp8yjPnz+fn5mZmRUWFla5bt06+6620z6PEgCgqzzKRYsWVaxcudIRAECZR5mRkZGVmJiYHxER4dLdZ8nPz6fcuXNH8PDhw6xdu3bZy2StNe+HH374KzMzMys1NTXrwIEDjIqKChIAgFAoJIWGhjbk5OTwhw8fLjxw4IBl344iGsiwo9Qhqjq/V0VX8igBACZMmFBHoVBa7O3tZaamprLi4mI9Jycn2c6dOxlXrlwxAwAoKyszyMrKIgcGBoooFIpi5syZ9QAAvr6+ojt37tD6fCDRgIWFEmmELuRRAgCQyeS2v1dBJBJbpFIp4dy5c8b37983fvToURaNRmvx9fVli8Vi4n8/d9u4JBKpRS6X9/ncLBq4cOqN1KZLeZSdqa2tJZmZmcloNFpLSkoKJT09vcsr+kg3YUeJ1KZLeZSdmTlzZt23335rxWazue7u7k3e3t6dXgxCugtDMbTcYAzFwDzKgWOwhGJgR4m0CuZRov6AhRJpFcyjRP0Bp95abjBOvdHAMVim3njVGyGEVMBCiRBCKmChRAghFbBQIoSQClgokUZ0lke5detW64aGBo39jMXHx5s9evSo2yd3pk+f7mJvb+/F4XC4XC7XQ/nUD0LqwEKJ1NY+j1IgEPCTkpIEQ4YMaT58+DBDKBR2+jOmTOXpjXPnzpk9efJE5SOI27dvf5adnc3fvn378+XLlzv3ekMIdYD3UeqQ4n9tdpTk5mo0j5LMZIrsdu7odR7l9u3brcvLy/VHjx7NMjc3lz18+FBgaGjos3Tp0rKbN2+a7N69+5mhoaGisyzKzMxMckREhFN1dbUehUJRfPvtt4WVlZWk69evmz148MB4165dtmfOnMnn8XiS7vZrwoQJDXPnziUDAMTExFgePXrUSiqVElxcXCSJiYl/NTc3E7y9vblFRUXpJBIJGhoaiEwm07OwsDA9Ly/PoOM++Pj4dPtoJdJd2FEitXWWR/nxxx+XW1tbS5OTkwUPHz4UAACIxWKip6en+MmTJ9mhoaGNXWVRfvDBB85ff/11UWZmZtbu3bufRUZGOo0bN67xjTfeqFV2i6qKJABAQkKCGZPJFAMAzJ07tyYjIyMrJyeHz2azxbGxsZYWFhZyDocj+umnn4z/+37T0aNH15HJ5JbO9uFVHkM0sGFHqUNUdX6vSld5lB3fRyKRIDw8vAag6yzKuro6YmpqKm3GjBluyvWam5t7FV328ccfO+zatcuWTqdL//3vfxcAADx69IgaFRVl39DQQGpsbCSNHj26DgBgxowZNT/88IP5lClTGk6fPk1fvnx5hSb2AekWLJRIIzrmUcbHx1t0fI+BgYFCT6/1R66rLMrq6mqisbGxTJ2/k7N9+/ZnCxcurGm/bOnSpa6JiYl5gYGB4tjYWIvk5GRjAIDZs2fXbt261b6srIyUkZFhOGXKlPr6+nq19wHpFpx6I7V1lkfp4ODQbGRkJK+rq+v0Z6yrLEo6na5wcHBo/u6778wBWjvNX3/9lQoAQKPR5PX19X36mRWJREQnJyepRCIhJCQk0JXLTU1NFUOHDm1ctmyZ0+uvv16np6cH3e0DGpywUCK11dfXkxYsWODq5ubGY7FY3OzsbOquXbuKw8LCKidOnMj09/dndVxHmUW5adMmBzabzeXxeNzk5GQaAMAPP/zw59GjRy3ZbDaXyWTyzpw5YwYAMHfu3OrY2FgbDw8PbmZmJrnjmN3ZtGlTsZ+fn8eoUaNYTCbzhYsyM2fOrDl//jx99uzZ1cplXe0DGpwwFEPLYSgG6k8YioEQQggA8GIO0lLz5893+v3331/4i4eRkZFlq1atqupqHYT6Cgsl0krx8fG9+rs3CKkDp94IIaQCFkqEEFIBCyVCCKmAhRIhhFTAQok0YqDkUSL0KuBVb6S29nmUVCq1paSkRE8ikRDmz58/ZMmSJdXGxsaKjuvIZDJQPvfdU+fOnTOTyWR1vr6+GHeG/lZYKHXIjbgsx+rnQo3mUdLtaaLXF3hoTR6ln58f29fXV3j37l2ThoYG0qFDhwomTJggzMnJMZgzZ46rWCwmAgB8+eWXRePGjWu8dOmS8datW+3odLo0JyeH6uXlJTp37txfRCJOttD/4E8DUttAy6OUyWSE9PT0rF27dj3dunWrHQCAnZ2d7M6dOwI+n5916tSpP9esWdOWL5mVlUU9cODA07y8vMyioiLytWvXaF2NjQYn7Ch1iKrO71UZaHmUM2bMqAEACAoKaly/fr2BcozFixc78/l8KpFIhMLCwrZQDS8vr0Y3NzcpAACPxxPl5+cb9OU4IN2FhRJpxEDKo6RQKC3KfZLL5QQAgB07djCsra2lZ86c+UuhUACVSvVVvp9MJrclw5BIJJDJZBjSi16AU2+kNm3Io6yrqyPZ2tpKSSQSfP311xZyubwvw6BBCgslUps25FGuXr26/IcffrAYOnQoRyAQUKhU6ktX4hHqCuZRajnMo0T9CfMoEUIIAQBezEFaCvMo0d8JCyXSSphHif5OOPVGCCEVsFAihJAKWCgRQkgFLJQIIaQCFkqktpycHAMmk8lrv2zt2rV2UVFRjNjYWIuCggJ95fL333/fWZkpaW9v71VSUqIHAODj48NRjnXo0CG68v23b982DA8Pd9TUvhIIBN8lS5Y4KL+OiopirF271q67ddLS0sh+fn5sDofDHTJkCG/27NnO3b1/3LhxbvHx8WbKr11cXDw3bNhgq/z6zTffdDt+/LhZ52ujgQgLJXqlvv/+e8uioqK2Qnnq1KnCzvIkU1NTswEAcnNzyadOnWorlCEhIaJjx45pLOzDwMCg5aeffjJXFuieWLFihdPKlSvLsrOz+X/++WfmmjVryrt7f0BAgPDevXs0AIDS0lKSkZGR/LfffjNSvp6ammo0ZswYYd8/Bfq74e1BOuTqwX2OlU8LNZpHaenoLHozcnWfC1VGRobhggULhlAoFEVKSkrW2LFjWXv27HkaEhIiav8+Q0NDH5FIlLp582b7P//8k8LhcLizZ8+u9PX1FcfExDCSkpLy6uvriYsXL3bKysqiyuVywubNm4vnzZtXm5KSQlm4cKGrVColKBQKOHPmTL6Xl1enMWwkEqllwYIFFTt37mR89dVXz9u/JhAIDMLCwlyqqqr0LCwsZHFxcQVMJrO5vLxc39nZuVn5Pj8/PzFAa/jwihUrHO7du2fc3NxMWLJkSfn69esrQ0JChJs2bXIAALh58yZt/PjxddevXzdVKBQgEAgMyGSywsnJSdZVRmZfjzV6dbCjRK+Up6enKC4u7s/s7Gw+jUZT+bzsjh07ng8fPlyYnZ3N/+STT17o3P71r3/Zjhkzpj4jIyPrzp07OR9//LFDfX098auvvrJavnx5WXZ2Nv/JkydZrq6uzV2NDwCwfv368rNnz9KrqqpI7ZdHREQ4zZkzp0ogEPDff//9qsjISEcAgBUrVpRNmjSJFRISwtyyZYt1ZWUlCQBg3759lqampvKMjIystLS0rOPHj1tlZ2cbBAcHiwQCAbWpqYlw79492siRI4Vubm5NqamplKSkJNrw4cOFAN1nZKKBBTtKHaJO56cOAqHzVLKulvfVrVu3TK5evWoWGxtrA9CaOJSXl2cQGBjYuGfPHttnz54ZzJo1q6arblKJTqcrZsyYURUdHW3dPhwjNTXV6Oeff84HAIiMjKzesmWLAwDAqlWrqt555536c+fOmVy8eNHs2LFjVnw+n3/9+nWT7OxswwsXLpgDADQ0NJD4fD6Fw+E0M5nMpnv37hmmpKQYffrpp6V5eXnk5ORkWmpqqmFgYGAjQPcZmWhgwY4SqY3BYMjq6upe6M6qq6tJlpaWMk1up6WlBRITE/Oys7P52dnZ/JKSkvTXXnutKSIiovr8+fN5VCpVMXHiRNaFCxeMVY31z3/+s+zkyZOWjY2NPfodcHFxka5evbrqxo0b+Xp6epCSkkJtaWkhxMTEFCn35/nz5+nTpk2rBwAYMWKEMCkpidbY2EiysrKSBwcHN/7666+0lJQUWmhoqBDgfxmZWVlZ/PT0dL5UKsXfxwEKvzFIbaampgpra2vp+fPnjQEAysrKSLdu3TIdO3askEajyTsWURVjyYVCYafvHzNmTH1MTAxDoWhtAu/du0cFAODz+QYeHh6Sjz/+uHz8+PG1jx8/pqraDoPBkE+ZMqXm5MmTlsplPj4+jd9++605AMDhw4fpyilyYmKiiUQiIQAAFBUV6dXW1pKcnZ2bx40bV3fw4EEr5WtPnjwhK/Myg4ODhcePH7ficrkiAAB/f3/RH3/8YVRSUmLg6+srBsCMTG2ChRJpxPHjx//auXOnLYfD4Y4ePZq9cePGYh6PJ1mwYEHlP/7xD2cOh8MVCoUq5+J+fn5iPT29Fjabzd2yZYt1+9eio6OLZTIZgcPhcJlMJu/jjz+2BwCIj4+ns1gsHofD4ebm5lKWLVvWo2CMzZs3l9bW1radfjp48GBRfHy8JYvF4v7www8WX3/99VMAgCtXrpiw2Wwem83mjhs3jrVly5ZnTk5OsjVr1lRyOJwmLy8vDyaTyVuyZImzVColAACMHTtW+OzZM3JAQEAjAIC+vj5YWFjIPD09G0mk1n8HMCNTe2AepZbDPErUnzCPEiGEEADgVW+kg0pLS0mhoaHsjstv3bqVY2NjgycCUa9hoUQ6x8bGRq7OX3FEqCOceiOEkApYKBFCSAUslAghpAIWSoQQUgELJVKbNuVRItQXWCjRKzXQ8igR6gu8PUiHVCcKHKWljRrNo9S3MRLR32PpRB5lTk6OwcSJE5l+fn7ClJQUGoPBaL569WoejUZriYmJsTx69KiVVColuLi4SBITE/8yNjZWTJ8+3cXY2FielpZmVFFRob9t27ZnCxcurOnr8UDaCTtK9EoNtDzKoqIiysqVK8vz8vIyTU1N5XFxceYAAHPnzq3JyMjIysnJ4bPZbHFsbGxbWEZZWZl+SkpK9vnz53M/+eQTe3WOB9JO2FHqEHU6P3VoUx6lvb29JCgoSAwA4OPjIyooKCADADx69IgaFRVl39DQQGpsbCSNHj26TrnO22+/XUsikcDX17epqqpKv6uxke7CjhKpTZvyKA0MDNq6WhKJ1CKTyQgAAEuXLnXdv39/kUAg4G/cuLFYIpG0/W5QKJS2dTBEZnDCQonUpo15lB2JRCKik5OTVCKREBISEuiq10CDCRZKpBHamEfZ3qZNm4r9/Pw8Ro0axWIymS9dlUeDG+ZRajnMo0T9CfMoEUIIAQBe9UY6CPMokaZhoUQ6B/Mokabh1BshhFTAQokQQipgoUQIIRWwUCK1zZgxw4VOpw/tGLXW0aVLl4yvXbtmpPx67dq1dtbW1t4cDofL4XC4y5cvtwcA8PPzY9++fbvTcI8ffvjB1MPDg8tms7lubm683bt3W3Y3FkKagBdzkNoWLVpUuWrVqvKFCxe6dve+mzdvGtNoNPm4ceMalcsiIiLKtm7dWtaT7YjFYsKqVaucf/311yw3NzepWCwmCAQCg76MhVBvYEeJ1DZx4kShlZXVC891b9++3drNzY3HYrG4kydPHpKTk2MQFxdndejQIQaHw+FeuXKF1pOxDQ0NfVavXm3n7e3NuXXrlpFMJiMwGAwZAACVSm0ZOnRolwEYCGkKdpQ65Ny5c47l5eUazaO0trYWTZ06tdepRLGxsTaFhYXpVCq1pbKykmRpaSlfsGBBBY1Gkyu7vl9++cXk0KFDjNOnT1sAAOzYsePZ9OnT69uPIxaLiZ6enuJ9+/YVAwCMGzeu1snJyXvkyJH1kyZNqlu6dGk1idT6aLiqsRDqK+wo0SvBZrPF7777ruvXX39N19fX7/I52YiIiDJlGlBnhY1EIkF4eHhbUO6pU6cKr1y5Ihg+fHhjbGyszcyZM116OhZCfYUdpQ7pS+f3qiQlJeX+/PPPxufOnTP7/PPP7XJzczP6Mo6BgYFCT+/FH1M/Pz+xn5+feOnSpdXu7u5eAFCggV1GqEvYUSKNk8vlkJ+fbzBlypSGr7/++llDQwOprq6OZGxsLG9oaOhx5FpHdXV1xEuXLrVlTT58+JBqZ2fXZZo5QpqCHSVS25QpU1wfPHhgXFNTo8dgMLzXrVtXnJCQYNHQ0EBqaWkhLFu2rMzS0lI+ffr02vfee8/t559/Ntu3b19Rb7ejUChg9+7djA8//NCZQqEoDA0NFf/+97//ehWfCaH2MGZNy2HMGupPGLOGEEIIALBQIoSQSlgoEUJIBSyUCCGkAhZKhBBSAQslQgipgIUSqS0vL0/f39+fNWTIEJ67uztv27Zt1qrX+p/2sWr29vZeLBaLq4xLu3btmlFOTo5BVxFucrkcwsPDHZlMJo/FYnE9PT09srOzDboaS/1PiwYjvOEcqU1fXx9iYmKeBQcHi2pqaog+Pj7cSZMm1fv6+vbp72MnJycLbG1t29KIcnJyDDp7n1Qqhe+++45eWlqqn52dnUkikSA/P1/fxMRE0dVYCPUFFkqkNmdnZ6mzs7MUAMDc3Fzh5uYmLioqMoiMjHT29fUV3r1716ShoYF06NChggkTJgiFQiFh1qxZrgKBgMJkMpuampoIPd1WbGysxc8//2wqkUiIIpGIOGHChDoGgyFVJgi5ublJX9HHRIMYFkodws/a6NgoFGg0Zs2IxhJxPXb1OGwjJyfHgM/nG44ePVr42WefgUwmI6Snp2edOnXKdOvWrXYTJkwQ7Nmzx5pKpSoEAgH/4cOH1JEjR3LbjzF69GgWkUgEAwMDxZMnT7I7buOPP/6gPXnyJJPBYMjz8/P1Q0JCOBwOx3jUqFH14eHhVSNHjhT3dCyEegLPUSKNqaurI06bNs0tOjr6KZ1OVwAAzJgxowYAICgoqPHZs2cGAAB3796lzZ8/vwoAwN/fX8xisUTtx0lOThZkZ2fzuypso0aNqmcwGHKA1g4yLy8vY+vWrc+IRCJMmjSJff78eeOejoVQT2BHqUN60/lpmkQiIbz11ltuM2bMqA4LC6tVLqdQKC0AAHp6eiCXy9um2ARCj2fbLzE0NFS0/5pKpbbMnDmzfubMmfUMBkN69uxZs3feeaehzxtAqAPsKJHaFAoFzJo1y5nFYjX9P3v3HdfUvf8P/J0BhEDYssMMmYQhiIJQFMFZuVq0gjhbvypcqxZc1dZr7XXQ6yptadVea0WoirZWsWqxKlq1aBAFQYYoQ4bKJoSErN8f3PDDRdCAMt7Px8PHQw7nfM7nnMZ33+ck55UNGzao/c4af39/4cGDB00AAG7cuEEpLHz92wV//fUXtaSkRAug/R3wnJwcXXt7e4xeQz0KO0qksbS0NP3jx4+buri4tLLZbC4AwOeff17xsvVXrFjxODw83JHJZHJ5PJ6Iz+e3vGxddaqrq8mLFi2yb2trIwIAeHh4tKxZs+YX39dWAAAgAElEQVTx646H0ItgzFo/hzFr6G3CmDWEEEIAgIUSIYTUwkKJEEJqYKFECCE1sFAihJAaWCgRQkgNLJRIYyKRiMDn8zksFovLYDB4H3/8sTUAwM8//2zI4XC4LBaL6+zszPvPf/5j9jrjq+LSWCwWd+TIkS5lZWUaf/43Pj7edM6cOXaajoMGB/zAOdIYhUJR/vXXXwWGhoYKiURCGDZsGOvMmTNNy5Yts7927dpdZ2dnaWtrK6GwsPCFcWndoYpLW7Jkic369eut9u/f363HNWUyGZDJ+DJHmsGOEmmMSCSCoaGhAgCgra2NIJPJCNra2kqZTEawsLCQAbQ/j+3u7i4BANi3b5+xi4sLj8Vicb29vVkA7R3e2LFjnQMCAlzs7e1dFy9ebPuifY0aNar5wYMHOgAAu3fvNmEymVwXFxdeVFSUjWodKpXquXz5cms3Nzf2n3/+qZ+enk719PRks1gsLp/P59TX1xMBAKqrq7XU7Q8hAOwoB5Tld8vo+S3iHo1ZY+tRRLs4dmq7N5lMBq6urtyysjKduXPnPg4KCmoJCQlpsLOzcxs5cmTTxIkTGxcuXFhHIpFg69atVn/88Ueho6OjtKamhqQaIy8vj3r79u08XV1dBYPBcF2xYsUjBoPxVL7kiRMnjLhcbmtJSYnWhg0bbDIzM+8OGTJEFhAQwExMTDSaPXt2Q2trK9HV1bV1165dlWKxmMBgMFyTkpKKAwMDRXV1dUR9fX1Fd/eHEAB2lKiHkMlkyM/PzysrK8u+efOm3o0bNyiHDx8uPXPmTKG3t3dLfHy85fvvv+8AAODt7S2MjIx02L59u5lM9v/Dx/39/ZtMTU3lVCpVyWAwxMXFxTqq3wUGBjLZbDa3ubmZ+MUXX1T/9ddfeiNGjGi2traWaWlpwYwZM+rS09P1AQBIJBLMmzevHgAgOzubYm5uLg0MDBQBAJiYmCi0tLTU7g+hzrCjHEC60/n1NjMzM7m/v3/zyZMnDYcNGyb28fFp9fHxaV24cGEdg8HgA0BJcnJy2fnz5/VOnDhh6OHhwbt161YuAIC2tnZH8ACJRFJKpdKOLLZnv9Khq4wCbW1theq+pFKpBAKB8MKVu9ofQp1hR4k0VllZSVZdQguFQsLFixcNOByOODU1tSNANyMjQ9fa2roNACA3N1cnKCioZdeuXZXGxsay+/fvv/KbPO+8805LRkYGraqqiiyTySAlJcVk1KhRwmfXc3d3Fz969Eg7PT2dCgBQX19PlErx6hq9GuwokcbKy8u15s2b5yiXy0GpVBL+8Y9/1I0fP755ypQpTkuWLLGnUCgKKpWq+O9///sAAODjjz+2LSkp0VEqlQR/f/+mESNGtAoEgle6t2pvby9dv359RWBgIFOpVBLGjBnTOGvWrIZn16NQKMqkpKTipUuX2onFYiKFQlFcunSpsKeOHQ0OGLPWz2HMGnqbMGYNIYQQAGChRAghtbBQIoSQGlgoEUJIDSyUCCGkBhZKhBBSAwsl0hiJRPJis9lcFovF5XK5nLS0NL1X2T4mJsZ6/fr1Fr01v5e5cuWKLoFA8Dp27JiBallBQYG2i4sL71XGaWxsJEZGRtrR6XRXDofD5fF4nO3bt79WpBzqm7BQIo3p6Ogo8vPz8woKCvK++OKLirVr1/ZIEk9vP0GTmJhoOnToUGFycrKJJuNERkY6GBsby0tKSu7cvXs3Ly0traiuru65hzk6P9eO+hcslKhHNTY2kgwNDTsqwmeffWbh6urKYTKZXFWgLwDA6tWrLR0cHFz9/PyYRUVFHWEUPj4+rCVLltgMGzaM9e9//9uisLBQ29fXl8lkMrm+vr7MoqIibQCAly0PCwtziIyMtBs+fDjT1taWf+rUKf3p06c7ODk58cLCwhxU+1EoFJCammp84MCBksuXLxuIRKKO57xlMhm89957Dkwmkzt+/Hin5uZm4pEjRwwmTpzopFonNTWVFhQUxMjNzdW5deuW3ldffVVBIrUHIVlbW8s2bdpUrVpv+PDhzMmTJzuyWKxX6lRR34GPMA4gK4/ephdWN/dozBrTkib6zzT3LsM2JBIJkc1mcyUSCaGmpkbr999/LwQA+OWXXwzu3btHyc7OvqtUKiE4OJhx+vRpfX19fcWvv/5qkpOTkyeVSsHDw4Pr6ekpUo3X0NBAunHjRgEAQFBQEGPmzJm1H330Ue2uXbtMo6Ki6OfOnStevHix3YuWAwA0NjaSr127VpicnGw0Y8YMl/Pnz+d7eXm1urm5ca5evarr5+fXmpaWpk+n0yU8Hk8yfPjw5pSUFMO5c+c2AACUlJRQdu/eXTJ27NiW6dOnO/znP/8Z8tlnnz1atmyZfVNTE9HAwEDx888/G0+bNq3u1q1bFA6HI1IVyRfJzs7Wy8rKymWz2W098J8EvQXYUSKNqS69Hzx4kPvrr78WzZ8/31GhUMCZM2cMLl26ZMDlcrk8Ho9bXFxMyc/Pp1y4cEF/4sSJDTQaTWFiYqIYO3bsU89oR0RE1Kn+npWVpbdw4cI6AICoqKi6zMxM/a6WAwBMmjSpgUgkwtChQ0WmpqZSHx+fVhKJBEwms1UVpXbw4EGTadOm1QEAhIeH1x06dKjj8tvS0rJt7NixLQAAs2fPrr169aq+lpYWjBo1qunQoUOGUqkUzp8/bxgREfHcs+WrV6+2ZLPZXHNzczfVMjc3txYskv0bdpQDiLrO700IDg5uqa+vJ1dVVZGVSiUsX768auXKlU89i75x40ZzAuHliWY0Gk2hyRwoFIoSoD2XsnOUGpFIBJlMRpDJZHD69GnjtLQ0ox07dlgplUpoaGggq5LPn52b6ufw8PC6b7/91tzMzEzu5uYmMjY2Vri7u4vv3r1LlcvlQCKRIC4urjouLq6aSqV6qranUqkaHQ96+7CjRD0qKyuLolAowMLCQjZhwoSmxMREs8bGRiIAwIMHD7QqKirIQUFBwlOnThkJhUJCfX09MS0tzehl43l6erb88MMPxgDtX/3g7e0t7Gp5d/z2228GbDZbVF1dnV1RUZFTWVmZM378+Prk5GQjAICqqirtc+fO6QEAJCcnm/j5+QkBACZNmtScm5tL3bt3r9n06dPrAABcXV0lbm5uLcuWLbNRvVkjEokwbGaAwY4SaUx1jxKgPSj3u+++KyGTyfDee+815ebmUoYNG8YGaO+skpKSHvj7+4umTp1a5+rqyrOxsZH4+Pi8tMh99913ZXPnznX46quvLE1NTWUHDhwo6Wp5dyQnJ5uEhoY+ddkcFhZWv3v3bvPg4GChk5OTeN++fabR0dH2jo6OkhUrVjwBaE9xHzNmTOPRo0dNjxw50rG/gwcPlixZsoRub2/PNzIyklEoFMVnn3328BVOIerj8P98/RzGrKG3CWPWEEIIAQAWSoQQUgsLJUIIqYGFEiGE1MBCiRBCamChRAghNbBQIo1pGrP2IlevXtU9fPiwYedlR44cMXB1deU4OTnxHB0deQsXLuyRlKKwsDCHH3/80bgnxkIDExZKpLHeiFkTCATUU6dOdRTKGzduUGJjY+0SExMf3L9/P7ewsDDXyclJoul+EOoOLJSoR3WOWSstLdXy9vZmsdlsrouLC+/MmTP6AABUKtUzKirKhsfjcfz8/JgXLlyg+vj4sGxtbflJSUmGYrGYsGXLFuuTJ08as9ls7t69e403b95sGRsbW+Xp6SkGANDS0oI1a9Y8Aeg6cm3evHl0T09Ptq2tLV/VNSoUCpgzZ46ds7Mzb9SoUYyamhp8Qg11CV8gA8nxf9LhcV6PxqyBOVcEU759rZi1ffv2mYwZM6YxLi6uWiaTQXNzMxEAoLW1lTh69Ojm7777riIkJMT5008/tbl8+XLhzZs3KfPnz3eMjIxs/OSTTyoFAoHegQMHygAAdu7cablq1apHL9p/V5Frjx490hIIBPm3bt2iTJ06lTF//vz6xMREo3v37ukUFBTkPnz4UIvP5/PmzZtX26PnDQ0oWCiRxlSX3gAA586d05s/f75jYWFh7ogRI1oWLVrkIJVKidOmTav38/NrBQDQ0tJSTps2rQkAgMfjtero6Ch0dHSUPj4+rRUVFdqvuv+srCy906dPFwO0R659/vnnHZf+oaGhDSQSCby8vMS1tbVaAADp6em0999/v45MJoODg4PU19e3uSfOAxq4sFAOJGo6vzehc8zahAkThJcuXSo4duyY4bx58xyXLl36aMmSJbVkMllJJLbf9SESiaCjo9MRiyaXy1+Yv8ZkMsUZGRlUX1/f1leZjypyDaA9sEOlq5g3hJ6F9yhRj+ocs1ZYWKhtY2MjjY2NrZk1a1bNzZs3u31bwMDAQC4UCjten5988kn1jh07rLKzs3UAAORyOWzYsMEC4NUj1wIDA5tTUlJMZDIZlJaWav3999+01ztaNFhgR4k09rKYtbNnz9Li4+MtyWSykkqlypOSkh50d8wJEyY0b9u2zYrNZnNjY2Or/u///q8+Li6uPCIiwqm1tZVIIBAgODi4EeDVI9dmz57d8OeffxqwWCyeo6Oj2MfHBy+9UZcwZq2fw5g19DZhzBpCCCEAwEKJEEJqYaFECCE1sFAihJAaWCgRQkgNLJQIIaQGFkqksTcVs5aYmGjEZDK5jo6OPBcXF54m0WgFBQXaLi4uPE3niQYH/MA50ljnZ72PHTtmsHbtWtuQkJACTcYUCARUgUCgN2PGjEYAgGvXrumuW7fO9o8//ihks9lt+fn52iEhIUwGgyEJCAgQ9cRxIPQy2FGiHtVbMWtxcXGWMTExVWw2uw0AgM1mt8XExFR/+eWXFgAAPj4+rEuXLlEBAKqqqsg2NjZ8gPbO0cvLi8Xlcjk91e2iwQc7ygHksyuf0e/V3+vRmDWGMUP0xcgv+kTM2urVq6s773fEiBEtu3fvNu9qbtbW1rLLly8XUqlUZU5Ojk5ERITTnTt37mp2VtBgg4USaexNxKwplUqCKnGo0zK1c2trayN8+OGH9nl5ebpEIhFKS0t1ND1eNPhgoRxA1HV+b0Ivxqy1Xrt2jTp8+PCOmLXr169T3d3dWwAAyGSyUi6XAwCASCTqGGPTpk0W5ubm0mPHjj1QKBSgq6vr1XtHjwYqvEeJelRvxaytXr26eufOnVYFBQXaAO33HhMSEizWrl1bDQBAp9Ml169f1wMASEpK6ng3vLGxkWRlZSUlkUiQkJBgqiqmCL0K7CiRxt5UzNrGjRsfTp48mdHW1kasqKjQPnXqVIG7u7sEAGDNmjWPZsyY4XTo0CHTgICAJtU4y5cvfxwWFuZ8/PhxY39//2ZdXV1Fz58BNNBhzFo/N1hj1qKjo20yMzP10tPTizqnmKM3a7DErGFHifqlhISEirc9BzR44D1KhBBSAwslQgipgYUSIYTUwEKJEEJqYKFECCE1sFAijali1lR/1q5da/k649jY2PCrqqp65ZMYnWPVUlNTaTQazYPD4XCdnJx4sbGxVj2xj87BHGhgwY8HIY11fta7v/D29hZeuHDhXlNTE5HP53OnTJnS2J24NqlUClpaWm9iiqgPwY4S9RobGxv+xx9/bM3lcjlMJpOblZVFAQBobGwkTps2zYHJZHKZTCZ3//79Rs9uu2HDBgsXFxeei4sLb+PGjeYAAE1NTcRRo0YxWCwW18XFhbd3715jAIDLly9Thw0bxuLxeBx/f3+X0tJSLdVyFovF9fDwYO/YseOFKUMGBgYKPp8vKigo0BGJRATVvDgcDvfkyZM0AID4+HjTCRMmOAUFBTECAgKYAACffvqpBZPJ5LJYLG50dLSNaryff/7ZmM/ncxwcHFxVsXKo/8OOcgCpXLuOLikq6tFLPx0XF5H15k3dillT/ax65BAAwMzMTJaXl3d369atQ7Zu3Wpx+PDh0jVr1lgZGBjICwsL8wAAnjx5Quo83uXLl6nJycmmmZmZd5VKJXh5eXHGjBnTXFRUpGNpaSm9ePHiPQCA2tpakkQiISxdutTu1KlT96ytrWV79+41XrFihU1KSkrJhx9+6LBz586ySZMmCRctWmT7orlXV1eTsrKy9DZs2FAZFxdnDgBQWFiYl5WVRZk4caJLcXHxHQCAmzdv6mdnZ+daWFjIjxw5YnDq1CnjzMzMfBqNpnj06FHH/GUyGSEnJ+fu4cOHDTdu3Gg9fvz4wtc786gvwUKJNNbVpffMmTPrAQB8fHxEJ06cMAYAuHTpksGhQ4fuq9YZMmTIU0kVFy9e1J84cWKDgYGBAgBg0qRJ9RcuXKCFhoY2rlu3jh4VFWXzj3/8o3H8+PHCGzduUIqKinSDgoKYAAAKhQKGDBkira2tJTU3N5MmTZokBAD44IMPas+fP9/x1RICgUCfw+FwiUSictmyZdXe3t7iTz75xOajjz56DADg6ekptra2bsvJyaEAAAQEBDRZWFjIAQDS0tIMZs2aVUOj0RQAAKrlAADTp0+vBwDw8/NrWbly5Qsj41D/g4VyAFHX+b0NquewyWSyUiaTEQDagzMIhBemqYHq9y/i5uYmuXnzZt6xY8cM161bZ3Pu3Lmm999/v4HBYLTeunUrv/O6NTU1pK72obpH2Z39AgBQqVRF5/VeNnan431pZBzqf/AeJXrjRo0a1dT5nuGzl95BQUHC33//3ai5uZnY1NRE/P33341Hjx7dXFJSokWj0RTR0dF1y5cvf3Tr1i2qm5ubuK6ujnzu3Dk9AACJREIQCAQUMzMzub6+vvzs2bP6AAD79+83UTcvf39/4cGDB00AALKzs3Wqqqq03dzcxM+uN378+KbExEQzVWJ750tvNDBhR4k09uw9yqCgoMauQiu2bNlSNX/+fDsXFxcekUhUrl27tnLu3LkNqt/7+/uLZs6cWTt06FAOAMDs2bOfjBw5svXYsWMGn3zyiS2RSAQymaxMSEgopVAoykOHDhUvXbrUrrm5mSSXywlRUVGPvL29xf/9739LFixY4KCrq6sICgpqetl8VFatWvV49uzZ9kwmk0sikWD37t0lurq6z7WZ06ZNa7p58ybVw8ODo6WlpQwODm785ptvMKRjAMOYtX5usMasob5hsMSs4aU3QgipgYUSIYTUwEKJEEJqYKFECCE1sFAihJAaWCgRQkgNLJRIY68as7ZmzZrXimGTSCSE6OhoG3t7e1cXFxcen8/nHDlyxOD1Zv00KpXq2RPjoIEJP3CONPaqMWvx8fFWW7durX6VfchkMvj444+tq6urtfLz83N1dXWV5eXl5LNnz9JefcYIvRrsKFGvqK2tJTk4OLjevn1bBwBg8uTJjtu3bzeLjo62UT3JExoa6ggAkJCQYMLn8zlsNps7c+ZMe5lMBgDtXd7y5cut3dzc2GlpafrJyclDfvjhhzLV0zJ0Ol22YMGCegCA3bt3mzCZTK6LiwsvKiqqI/aMSqV6fvTRRzYsFovr7u7OLi8vJwMA5Ofna3t4eLBdXV05y5Yts37Dpwf1M9hRDiB/HrhLr6sQ9mjMmomNvmjMHM5rxazt3LmzbO7cuY7R0dGPGhoayLGxsTUAAPv37zdXdaA3b96kHD161EQgEOTr6OgoZ82aZff999+bLlmypLa1tZXo6uraumvXrsqMjAxdKyurNhMTE8Wz+y8pKdHasGGDTWZm5t0hQ4bIAgICmImJiUazZ89uaG1tJfr6+gq//vrrisWLF9t+/fXXQ7788suq6OhouwULFjxZsmRJ7ZYtW4b05DlDAw8WSqSxl116T506tenIkSPGq1atss/MzMx90bZnzpyh3blzh+ru7s4BABCLxURzc3MZAACJRIJ58+bVq9v/X3/9pTdixIhma2trGQDAjBkz6tLT0/Vnz57doKWlpQwPD28EAPDy8mo5d+6cAUB7vuTp06eLAQAWLVpU+8UXX7wwrxIhACyUA4q6zu9Nk8vlUFhYSNHR0VHU1NSQnZ2dpc+uo1QqCdOnT6/99ttvnwuV0NbWVpDJ7S9RLpcrqaqq0q6vrycaGxsrnhnjpXMgk8lKIpGo+juoot4AAIhEIgYdoG7Be5So12zcuNGCyWSKf/rpp/sffvihg0QiIQC0Fy/V38ePH9+UmppqXFFRQQZojywrLCx8LvCWRqMpwsPDa/7v//7PTiwWEwAASktLtRISEkzeeeedloyMDFpVVRVZJpNBSkqKyahRo4RdzW3o0KHCvXv3mgAA7N2717Snjx0NLFgokcZU9yhVf6Kjo22ys7N1EhMTzRISEsrHjx8vHDFiRPOaNWusAAAiIyOfcDgcbmhoqKOXl5f4008/rRgzZgyTyWRyg4KCmOXl5S/89q5du3ZVmJmZyZhMJs/FxYU3efJkZwsLC5m9vb10/fr1FYGBgUwOh8Nzc3MTzZo1q+FFY6gkJCSU7dmzx9zV1ZXT2NiIeZKoSxiz1s9hzBp6mzBmDSGEEABgoUQIIbWwUCKEkBpYKBFCSA0slAghpAYWSoQQUgMLJdLYm4pZE4vFhA8++IBOp9Nd7ezsXEePHs0oKip67sPp3RUTE2O9fv16i9fdHg0e+Agj0tibillbunSpjVAoJD548OAOmUyGr776yjQ0NJRx586dPBIJPzOOeg92lKhX9EbM2pEjR8y+//77ctXz38uWLaulUqny3377zaCgoEDbxcWFp9r/+vXrLWJiYqwBALZv327m6urKYbFY3HHjxjk3Nzfj6x69EuwoB5Cz3+2i15SX9mjMmhndXjQuanmfjVnz8PAQ3blzh8Lj8cQvm19kZGS9at9Lly61jo+PN1u3bt1jTc4LGlywUCKNvYmYNYVCAQQC4bnnbbvzCG5mZqbu+vXrbZqbm0ktLS2kwMDAxlc8RDTIYaEcQNR1fm9aT8as8Xg8SWVlpc6zMWvZ2dnUGTNm1JPJZKVC8f+bTbFY3HF5vXDhQsejR4/e8/X1bY2PjzdNT0/Hr49ArwTv1aBe05MxawYGBopp06bVREVF0VX3ML/55htTHR0dRUhIiNDW1lZWV1dHrq6uJrW2thLOnj1rqNpWJBIR7ezspBKJhHDo0CGTN3LwaEDBjhJp7Nl7lEFBQY2LFy+uSUxMNMvMzLxrbGysOHr0aPOaNWusdu7cWamKWXN1dRWdOHHigSpmTaFQgJaWljI+Pr6MyWS2Pbufr7/+uiIqKsrWycnJVSwWE01MTGQCgeAukUgEHR0dZWxsbJWPjw/H1tZWwmAwOu5ZrlmzptLHx4djY2PTxuFwREKhEN8iR68EY9b6ucEas1ZWVkYeO3Ysc8GCBY9XrFgx6I6/rxgsMWvYUaJ+yc7OTvYqn91ESBN4jxIhhNTAQokQQmpgoUQIITWwUCKEkBpYKBFCSA0slEhjz8asFRQUaF+6dIk6b948ek/tw8bGhl9VVdWjn9I4e/asPp/P5zg6OvIcHBxct2zZMqQnx0cDB348CGnsRc96s1istnfeeUf07LpSqRS0tF74td1vVFlZGXnevHmOKSkpxf7+/qKqqipycHCwi42NjXTOnDldfic4Gnywo0S9IjU1lTZ69GgGQHtAbkREhP3IkSNd3nvvPUeZTAaLFi2ydXV15TCZTO5//vMfM9U23t7erJCQEGdnZ2fezJkz7eRy+XNjBwcHO/N4PA6DweBt27bNTLX86NGjBlwul8Nisbi+vr5MAICmpibi9OnTHVxdXTkcDod78OBBIwCA7du3m8+YMaPW399fBABgZWUl27x588OdO3daAgCEhYU5/Pjjj8aqsalUqmcvni7Ux2FHOYDUHS2kS6tbejRmTctST2QyjdntmDU6nS5JS0srfnad7OxsakZGRr6+vr5y27ZtZoaGhvI7d+7cbW1tJQwbNow9efLkJgCAnJwcvaysrDtMJrPtnXfecTlw4IDx/Pnz6zuPlZSUVGJhYSEXCoUET09P7qxZs+oVCgVhyZIlDhcvXsxns9ltjx49IgEArF271mr06NFNKSkpJTU1NSRvb29OaGho0927d3XnzJlT23lcf39/0b179yianjM08GChRBrrTsL5+PHjG/T19ZUAAOfOnTPIz8+nnjhxwhgAoLm5mZSXl0fR1tZW8vn8Fi6X2wYA8P7779ddvnxZ/9lCGRcXZ3Hq1CkjAIDq6mqt3NxcyqNHj8g+Pj7NbDa7DQDAwsJCDgBw8eJFg7NnzxrFx8dbAgBIJBLCvXv3tJVK5Qtj2xB6ESyUA4i6zu9t0tPT68hAUyqVhO3bt5eFhYU1dV4nNTWVRiAQntru2Z9TU1Np6enpNIFAkE+j0RQ+Pj6s1tZW4v8K33P7VSqVcPTo0Xvu7u6Szss5HE7rjRs39CIjIzuyKa9cuULl8/kigPaEI9Vlv0KhAKlU+vzgaNDAe5TojQsJCWn87rvvhqii1rKzs3WampqIAO2X3vn5+dpyuRyOHj1qEhAQ0Nx524aGBpKhoaGcRqMpsrKyKLdv39YDABg9enRLRkYGLT8/XxugPa7tf8ubtm/fbqHKqrxy5YouAEBsbOyTw4cPm169elUXAKC6upq0fv16m3Xr1lUCANjb27dlZmZSAQCSkpKMZDIZFspBDDtK9MZ9/PHHNSUlJTp8Pp+jVCoJJiYm0t9//70YAMDDw0MYGxtrm5+frzt8+PDm2bNnP/UOdFhYWOOePXuGMJlMrrOzs9jd3b0FAMDa2loWHx9fMnXqVIZCoQBTU1Pp1atXi7Zu3Vq5cOFCOzabzVUqlQRbW1vJhQsX7tnb20v37dv3YNGiRQ7Nzc2kyspK7a+//rpk0qRJQgCAjz766Mm7777L4PP5nHfeeadJV1dX8fyRoMECY9b6uYEUs5aamkrbvn27xYULF+696X1v2bJlyI8//jjkypUrBUOGDHn+rXb0QoMlZg0vvRECgE8++eRJYWFhHhZJ9CJYKFGf8e677za/jW4SIXWwUCKEkBpYKBFCSA0slAghpAYWSoQQUgMLJdJYf41ZQ6i78IWHNMWzOS8AACAASURBVNYfY9YQehXYUaJe0ddj1gQCAYXP53PYbDaXyWRyc3JydN7IiUH9EnaUA8jx48fpjx8/7tGYNXNzc9GUKVMGXMza119/PSQ6OvpRVFRUnVgsJshksp47aWjAwUKJNNYfY9Z8fX1btm3bZvXw4UPt8PDwej6f/1S6EEKdYaEcQNR1fm9TX4tZGzp0qDggIKDl119/NZwwYQIzISGhJDQ0tPm5ARACvEeJ3oK+ELOWl5enzeFwJJ9++unjsWPHNty6dUv3jZ0A1O9gR4neuL4Qs5aYmGiSkpJiSiaTlUOGDJFu2bKl8m2cC9Q/YMxaP4cxa+htwpg1hBBCAICFEvUhGLOG+ioslAghpAYWSoQQUgMLJUIIqYGFEiGE1MBCiTT2opi1rtbvHJlGpVI9AQAKCgq0KRTKUDabzWWxWFxPT0/27du3uwyqKCgo0P7+++9NVD/Hx8ebzpkzx64njgmhzrBQIo2pnvVW/WGxWG2vMw6dTpfk5+fnFRQU5M2cObPm888/t+pq/aKiIp3Dhw+bdLUOQj0BCyXqFc92d6NHj2akpqbSurt9U1MTycjISA7Q3jl6eXmxuFwuh8vlctLS0vQAANatW2cjEAj02Ww29/PPPzcHaA/JCAgIcLG3t3ddvHixbU8fFxqc8BHGASTv7mp6i7CwR2PW9PSZIi4nTuOYte4oLy/XYbPZ3JaWFqJYLCZevXo1H6D98cTLly8XUqlUZU5Ojk5ERITTnTt37m7atKmi85M88fHxpnl5edTbt2/n6erqKhgMhuuKFSseMRgM6evMByEVLJRIY92JWesO1aU3AMDevXuNP/jgA/vLly8XtbW1ET788EP7vLw8XSKRCKWlpS+9d+nv799kamoqBwBgMBji4uJiHSyUSFNYKAcQdZ3fm0Qmk5WqxB6A9q7zVbaPiIhoWLp0qQMAwKZNmyzMzc2lx44de6BQKEBXV9frZdtpa2t3hBeQSCSlVCp9PnsNoVeE9yhRr3B2dm7Lzc2lyuVyuHfvnlZ2drbeq2yflpZGo9PpEgCAxsZGkpWVlZREIkFCQoKp6ushDA0N5UKhkNQL00foKdhRol4REhIi/PbbbyUsFovHYrFauVzuc1809izVPUqlUglaWlrK77//vhQAYPny5Y/DwsKcjx8/buzv79+sq6urAADw8fFpJZPJShaLxZ05c2aNsbHx81+wg1APwJi1fm4gxayh/gdj1hBCCAEAFkqEEFILCyVCCKmBhRIhhNTAQokQQmpgoUQIITWwUCKNlZeXkydPnuxoa2vL5/F4HA8PD/aBAweM3uacxowZ4+zh4cF+m3NAAwcWSqQRhUIBkydPZgQEBAgfPnyYk5ube/fIkSP3y8vLu8ykVJHJZD0+p5qaGlJubq5eU1MTKT8//4XzkErx8W/UfVgokUZOnjxJ09LSUq5ateqJahmTyWxbt27d45fFo6WmptKGDx/OnDx5siOLxeIBAAQHBzvzeDwOg8Hgbdu2zUw11s6dO80cHBxcfXx8WOHh4faq6LbKykryuHHjnF1dXTmurq6cP/74o+MRycTEROPg4OCGqVOn1v30008deZVhYWEOCxYssB0+fDgzOjratqmpiTh9+nQHV1dXDofD4R48eNAI4OWxbmjwwkcYB5Dld8vo+S3iHo1ZY+tRRLs4di8N28jJydF1c3N74eOJL4tHAwDIzs7Wy8rKymWz2W0AAElJSSUWFhZyoVBI8PT05M6aNateLBYTt23bZnXz5s08IyMjhZ+fH5PH47UCACxatIgeExPzaNy4ccKioiLtcePGudy/fz8XACAlJcVk/fr1ldbW1tJp06Y5b9mypVo1p+LiYsqVK1cKyWQyLFmyxGb06NFNKSkpJTU1NSRvb29OaGhoU1fzRoMTFkrUo2bPnm13/fp1fS0tLWV6enrhy+LR3NzcWlRFEgAgLi7O4tSpU0YA7eG7ubm5lMrKSq3hw4c3W1hYyAEApk6dWl9YWEgBALhy5YpBUVGRrmp7oVBIqq+vJwqFQmJpaanO2LFjhUQiEchksvLGjRuUYcOGiQEA3nvvvXoyuf1lf/HiRYOzZ88axcfHWwIASCQSwr1797Tt7e2l3Y11Q4MDFsoBpKvOr7fw+fzW3377zVj1c2JiYllVVRXZ29ub01U8GpVK7chgS01NpaWnp9MEAkE+jUZT+Pj4sFpbW4ld5RAolUoQCAR39fX1n1rp22+/NWtqaiLR6XQ+QHsBTUxMNBk2bFglAIC+vr6i8xhHjx695+7uLuk8RkxMjHV3Y93Q4ID3KJFGJk+e3CyRSAhxcXFDVMuEQiER4OXxaM9qaGggGRoaymk0miIrK4ty+/ZtPQCAgICAloyMDNqTJ09IUqkUOhdkf3//pri4OHPVz1evXtUFADh69KjJr7/+WlRRUZFTUVGRk5GRkXf8+PEXfq/O6NGjm7Zv326hys28cuWK7qvMGw0e2FEijRCJRDh58mTxP//5T3p8fLyliYmJjEqlyjds2PBwxIgRohfFoz0rLCyscc+ePUOYTCbX2dlZ7O7u3gIA4OjoKP3444+rhg0bxjE3N5cymcxWQ0NDOQDAnj17yhcsWGDHZDK5crmcMHz48GZTU9PqyspK7aCgoBbV2Gw2u01fX19+/vz5596Q2bp1a+XChQvt/hftRrC1tZVcuHDh3sti3dDghTFr/dxAj1lrbGwkGhoaKqRSKYwbN44xb968mjlz5jS87XmhdhizhlAfsHLlSms2m81lMpk8Ozs7yaxZs7BIojcOL71Rn7Znz56Hb3sOCGFHiRBCamChRAghNbBQIoSQGlgoEUJIDSyUSGN9LWbtyJEjBq6urhwnJyeeo6Mjb+HChbZvay5oYMBCiTTS12LWbty4QYmNjbVLTEx8cP/+/dzCwsJcJycnifot22H8GnoRLJRII30tZm3z5s2WsbGxVZ6enmIAAC0tLVizZs0TAIDk5GRDNzc3NofD4fr5+THLy8vJAO3PdkdERNiPHDnS5b333nMUCAQUPp/P+d/nN7k5OTkYijHI4ecoB5CVR2/TC6ubezRmjWlJE/1nmnu/iVkrKCjQXbVq1aMXzSckJEQYHh6eTyQSYceOHWYbN2603Lt378P/zYeakZGRr6+vr5w7dy49Ojr6UVRUVJ1YLCb0Rrgw6l+wUKIe9bZj1rqa24MHD7SnTJli++TJE622tjYinU7vuCQfP358gyqJyNfXt2Xbtm1WDx8+1A4PD6/n8/ndvnRHAxMWygGkq86vt/S1mDUmkynOyMig+vr6tj67zZIlS+yWLVtWHRkZ2ZiamkrbuHGjtep3enp6HfNZvHhxXUBAQMuvv/5qOGHCBGZCQkJJaGho82udIDQg4D1KpJG+FrP2ySefVO/YscMqOztbBwBALpfDhg0bLAAAmpubSXZ2dlIAgP3795u+7Jjy8vK0ORyO5NNPP308duzYhlu3bum+bF00OGBHiTTSl2LW/Pz8yoYPH94aFxdXHhER4dTa2kokEAgQHBzcCACwbt26yoiICGcLC4s2b2/vlrKyshe+SZOYmGiSkpJiSiaTlUOGDJFu2bKlsrfOH+ofMGatn8OYNfQ2YcwaQn0AxqyhvgAvvVGfhjFrqC/AjhIhhNTAQokQQmpgoUQIITWwUCKEkBpYKJHGBkvMWlhYmMOPP/5orH5NNNBgoUQaGWgxawi9CBZKpJH+FLNWWFio7evry2QymVxfX19mUVGRNkB7pzhv3jy6p6cn29bWlq/qGhUKBcyZM8fO2dmZN2rUKEZNTQ1+nG6Qwv/wA8nxf9LhcV6PxqyBOVcEU74dEDFrixcvtps5c2btRx99VLtr1y7TqKgo+rlz54oBAB49eqQlEAjyb926RZk6dSpj/vz59YmJiUb37t3TKSgoyH348KEWn8/nzZs3r1bTU4r6HyyUqEf15Zi1rKwsvdOnTxcDAERFRdV9/vnnHfcuQ0NDG0gkEnh5eYlra2u1AADS09Np77//fh2ZTAYHBwepr68vJggNUlgoB5IuOr/e0p9i1rpCoVA6xum8XwKB8CrDoAEK71EijfSnmDVPT8+WH374wRgAYPfu3Sbe3t7Cro4tMDCwOSUlxUQmk0FpaanW33//TXvN04T6OewokUb6U8zad999VzZ37lyHr776ytLU1FR24MCBkq6Obfbs2Q1//vmnAYvF4jk6Oop9fHzw0nuQwpi1fg5j1tDbhDFrCPUBGLOG+gK89EZ9Gsasob4AO0qEEFIDCyVCCKmBhRIhhNTAQokQQmpgoUQa62sxa4mJiUZMJpPr6OjIc3Fx4WkSjVZQUKDt4uLC68n5of4H3/VGGlHFrM2cObP25MmTDwDaU3pSUlK6VShlMhmQyT33Mrx27ZruunXrbP/4449CNpvdlp+frx0SEsJkMBiSgICAF4Z3IKQOdpRII30tZi0uLs4yJiamShW4wWaz22JiYqq//PJLCwAAHx8f1qVLl6gAAFVVVWQbGxs+QHvn+KK5IgSAHeWA8tmVz+j36u/1aMwaw5gh+mLkF/0mZq2wsJCyevXq6s7zGDFiRMvu3bvNn59h9+aKEBZK1KPedsyaUqkkEIlPXyh15zHdtrY2wsvmihAWygGkq86vt/TBmLXWa9euUYcPH94Rs3b9+nWqKmiDTCYrVSlGIpGoI0Otq7kihPcokUb6Wsza6tWrq3fu3GlVUFCgDdB+7zEhIcFi7dq11QAAdDpdcv36dT0AgKSkpI7xujtXNDhhR4k00tdi1vz8/Fo3btz4cPLkyYy2tjZiRUWF9qlTpwrc3d0lAABr1qx5NGPGDKdDhw6ZBgQENKnmsHz58sfdmSsanDBmrZ/DmLWuRUdH22RmZuqlp6cXdU4xRz1jsMSsYUeJ+rSVK1daX7p0yUAikRACAwObXjVmLSEhoaK35oYGDyyUqE/DmDXUF+CbOQghpAYWSoQQUgMLJUIIqYGFEiGE1MBCiTTWV2LWBAIBxcHBwVUoFHY8cTNq1CjGnj17notZS01NpdFoNI//fXEZ18/Pj1lRUUEGAIiPjzdVhW8kJiYaZWZmUt7cUaC+CAsl0ogqZi0gIED48OHDnNzc3LtHjhy5X15ert2d7WUyWY/NxdvbWzxx4sT6tWvXWgG0FzmpVEpYuHBhfef1pFKpan1hfn5+XmFhYZ6np2fLtm3bngvOOH78uFF2drbus8vR4IKFEmmkD8asVZ04ccLk6tWruuvXr7f5/vvvywAAYmJirCMiIuxHjhzp8t577zl2PgaFQgHNzc0kY2Pjp6p2Wlqa3rlz54w+/fRTWzabzc3NzcWgjEEKP0c5gFSuXUeXFBX1aMyajouLyHrzpn4Ts0aj0RSbN28uHzt2LHvhwoWP+Hy+RDWf7OxsakZGRr6+vr4yNTWVJhAI9NlsNrehoYGsq6sr37Vr11Of2QwJCWkJDg5uePfddxvnz5//VFeKBhcslKhHve2YNWNjY8XMmTMbY2NjZbGxsY87z238+PENndOGvL29hRcuXLgHALBu3TrLJUuW2CYnJ5f11rlB/RcWygGkq86vt/S1mDUVIpEIz+ZS6unpvTToIiwsrGH69OnO3TtqNNjgPUqkkb4Ws/a6Lly4oG9vby95drm+vr68qakJ/50McthRIo30tZi1V5m76h6lUqkEGo0m37dvX8mz60RGRtZFRUU5fP/99xZHjx4t5vF4zxVTNPBhzFo/hzFr6G0aLDFreEmB+rSVK1da/+9D4Tw7OzvJq8asIdQT8NIb9WkYs4b6AuwoEUJIDSyUCCGkBhZKhBBSAwslQgipgYUSaay7MWsFBQXaLi4uvGeXL1++3Pr48eM0dfu5cuWKLoFA8Dp27JhBT80doe7AQok00t2YNVW02Yvs2rWrcsqUKc3q9pWYmGg6dOhQYXJyssnL5vKyp38Q0gQWSqSRrmLW4uPjTSdMmOAUFBTECAgIYL5sjLCwMIcff/zR+MiRIwYTJ050Ui1PTU2lBQUFMQDai2BqaqrxgQMHSi5fvmwgEokIAO1dqpOTE2/WrFl2PB6PW1xcrP3LL78YeHh4sLlcLmfChAlOjY2NRACAFStWWLm6unJcXFx4ERER9grFSx/9Rugp+DnKAeTPA3fpdRXCHo1ZM7HRF42Zw3mtmDUAgJs3b+pnZ2fnWlhYyAsKCroM8506dWrTsmXL7JuamogGBgaKn3/+2XjatGl1AABpaWn6dDpdwuPxJMOHD29OSUkxnDt3bgMAQElJCWXv3r0lBw8eLKuqqiJv3rzZ6tKlS4UGBgaKdevWWX7xxRcW27Ztq1q5cuXjbdu2VQEATJkyxfHQoUOGM2fObHy9M4MGE+woUY+aPXu2HYvF4rq6unIAAAICAppUMWnqaGlpwahRo5oOHTpkKJVK4fz584YRERENAAAHDx40URXN8PDwukOHDnVcfltZWbWNGTOmBQDg4sWLesXFxRQfHx82m83mHjp0yLSsrEwbAOD06dM0Nzc3NpPJ5F69epV2584dTC5H3YId5QDSVefXW7qKWQN4Ok6tO8LDw+u+/fZbczMzM7mbm5vI2NhYIZPJ4PTp08ZpaWlGO3bssFIqldDQ0ECur68nPrsPpVIJ/v7+TSdPnnzQeVyRSESIjY21z8jIyGMwGNKYmBhrsViMjQLqFnyhII10FbP2OiZNmtScm5tL3bt3r9n06dPrAAB+++03AzabLaqurs6uqKjIqayszBk/fnx9cnLyc++sjxo1qkUgEOjfuXNHBwCgubmZmJ2drSMSiYgAAJaWlrLGxkbiyZMnn/vCMYReBgsl0ogqZu3y5cs0GxsbPp/P58yaNcthw4YNL3xG+8GDBzoWFhZuqj/79u17qmCRyWQYM2ZMY3p6uuGMGTMaAQCSk5NNQkNDnwrDCAsLqz98+LDps+NbW1vLdu/eXRIeHu7EZDK5Xl5e7JycHIqZmZk8MjLyCZfL5U2YMIGhinJDqDswZq2fG+gxa6hvw5g1hBBCAICFEiGE1MJCiRBCamChRAghNbBQIoSQGlgoEUJIDSyUSGNUKtWz88/x8fGmc+bMsetqm87rVFZWkt3c3NgcDod75swZfRsbGz6TyeT+70vFuAcPHnzug+XPWrNmjaXq7y+Lc0PodWGhRG9damoqjcFgiO/evZs3fvx4IQBAenp6YX5+fl5KSkrxqlWr6OrGiI+Pt+r9maLBCgsl6lXJycmGqm7Rz8+PWV5e/lS+wNWrV3X/9a9/2V64cMGQzWZzhUIhofPvGxoaSAYGBh2hGsHBwc48Ho/DYDB427ZtMwMAiI6OtpFIJEQ2m80NDQ11BACQy+UQHh5uz2AweCNHjnR5dlyEXgWGYgwgZ7/bRa8pL+3RmDUzur1oXNTyLsM2VEVK9XNjYyMpJCSkEQAgJCREGB4enk8kEmHHjh1mGzdutNy7d2/H441+fn6tn3zySaVAINA7cOBAmWp5YGAgU6lUEh4+fKi9b9+++6rlSUlJJRYWFnKhUEjw9PTkzpo1qz4hIaFi//795vn5+XkA7ZfeZWVllIMHD9738/MrnThxotOBAweMo6Oj63ry3KDBAwsl0piOjo5CVaQA2u8/CgQCPQCABw8eaE+ZMsX2yZMnWm1tbUQ6nS7pzpjp6emFVlZWstzcXJ2xY8cyJ06cmGtoaKiIi4uzOHXqlBEAQHV1tVZubi7F0tLyuee2bWxsJH5+fq0AAJ6enqKSkhKdnjlaNBhhoRxA1HV+b8OSJUvsli1bVh0ZGdmYmppK27hxo/WrbM/j8SSmpqbSmzdvUlpaWkjp6ek0gUCQT6PRFD4+PqzW1tYX3j7S1tbuCDEgkUjKl62HUHfgiwf1qubmZpKdnZ0UAGD//v3Ppf2oU1FRQX748KEOg8Foa2hoIBkaGsppNJoiKyuLcvv2bT3VemQyWSmRSPA+JOoVWChRr1q3bl1lRESEs5eXF8vU1FTW3e0CAwOZbDabGxgYyFq/fv1DOp0uCwsLa5TJZAQmk8ldu3atdeeotMjIyCccDqfjzRyEehLGrPVzGLOG3iaMWUMIIQQAWCgRQkgtLJQIIaQGFkqEEFIDCyVCCKmBhRIhhNTAQok09joxay9z9epV3cOHDxuqfk5KSjJcu3atZVfbdEUsFhM++OADOp1Od7Wzs3MdPXo0o6ioSFv1+7KyMvK7777rRKfTXZ2dnXmBgYGM7OxsfNwRPQUfYUR9ikAgoAoEAj3Vd3pHRkY2AkDj6463dOlSG6FQSHzw4MEdMpkMX331lWloaCjjzp07eQQCAUJDQxkzZ86sTU1NvQ/QXqgrKyu13NzcuvVMOhocsFCiXlVZWUmeP3++fUVFhTYAwI4dO8rGjh3bcuHCBWpMTIydWCwmUigUxf79+x+wWKy2LVu2WIvFYiKbzdaPjY2tam1tJaqShcLCwhxoNJr89u3bek+ePNH64osvHs6fP79eLpfD3Llz7f7++28anU6XKBQKmDdvXu20adMajxw5Ynb//v1sMrn9pb5s2bLaAwcOmP32228GZDJZSSaTlatWrXqimq8qSAOhzrBQDiB1Rwvp0uqWHo1Z07LUE5lMY752zNqiRYvoMTExj8aNGycsKirSHjdunMv9+/dz3d3dxdevX8/X0tKC48eP01atWmV79uzZ4mcj1+Lj4596PvzRo0daAoEg/9atW5SpU6cy5s+fX3/gwAHj8vJy7YKCgtyKigqyq6ur67x582rz8vJ0rKys2kxMTBSdx/Dw8BDduXOHQiQSwd3dXdRzZwsNVFgokca6ilm7cuWKQVFRka7qd0KhkFRfX0+sq6sjzZgxw7GkpIRCIBCUUqm0W4EWoaGhDSQSCby8vMS1tbVaAACXL1/Wf++99+pJJBLY2dnJRowY0QwAoFAogEAgPPeMLj62i14VFsoBRF3n9zYolUoQCAR39fX1n6pOCxYssAsMDGxOS0srLigo0A4KCmJ1ZzwKhdIxjqrgvazw8Xg8SWVlpU59fT3R2Ni4o6vMzs6mzpgxo14sFhOOHz9u/DrHhQYXfNcb9Sp/f/+muLg4c9XPV69e1QUAaGpqItna2rYBAOzevdtM9XsDAwO5UCh8pddlQECA8Pjx48ZyuRzKy8vJGRkZtP+NpZg2bVpNVFQUXSZrDy765ptvTHV0dBQhISHCyZMnN7e1tRG2b9/esf/09HTqqVOn9DU6aDTgYKFEvWrPnj3lN2/e1GMymVxnZ2feN998MwQAYPXq1dUbNmywHTp0KFsu7/hKHJgwYUJzYWGhLpvN5u7du7db3d7cuXPrrays2phMJm/+/Pn27u7uLUZGRnIAgK+//rqCQqEonJycXM3Nzd2++eYbi7Nnz94jEolAJBLhxIkTxX/++acBnU53ZTAYvH/961/WqvxMhFQwZq2fw5i1do2NjURDQ0NFdXU1adiwYZwrV67k29nZPZV/WVZWRh47dixzwYIFj1esWDHoz1lPGCwxa3iPEg0IISEhLk1NTSSpVEpYuXJl1bNFEgDAzs5O1vlNJ4S6CwslGhCuX79e8LbngAYuvEeJEEJqYKFECCE1sFAihJAaWCgRQkgNLJRIY51j1g4fPmxob2/vWlRUpP3ll18O+eabb0wB2h9rLCkp0epqHE3i2V5mzJgxzh4eHuzOy8LCwhx+/PHHV3oi5+jRowZ8Pp/j6OjIY7PZ3EmTJjl1jmtDAxu+6416zG+//UZbsWIF/cyZM0UuLi5tnVN5Dh48aObh4dHq4ODwxj7MXVNTQ8rNzdWjUqny/Px8bTab3fY649y4cYMSGxtr9+uvv94bOnSoGKA9J/PevXvaLi4uT40plUpBS6vL/x+gfgg7StQjzpw5o//Pf/7T4cSJE/d4PJ4EACAmJsZ6/fr1Fj/++KPxnTt3qHPmzHFis9lcoVBISE9Pp3p6erJZLBaXz+dz6uvriQAA1dXVWgEBAS729vauixcvtlWN/8svvxh4eHiwuVwuZ8KECU6NjY1EAAAbGxv+xx9/bM3lcjlMJpOblZVFUW2TmJhoHBwc3DB16tS6n376yaTzfNPS0mheXl4sBwcH159//tkQAMDNzY0tEAg6tvfx8WFdvnyZumnTJquYmJgqVZEEaM/JnDBhglC13pIlS2yGDRvG+ve//23RO2cYvU3YUQ4gx48fpz9+/LhHY9bMzc1FU6ZM6TJso62tjTBjxgzGH3/8UeDp6Sl+9vfz58+v/+6778y3bdtW/s4774jEYjEhMjLSOSkpqTgwMFBUV1dH1NfXVwAA5OXlUW/fvp2nq6urYDAYritWrHikp6en3Lx5s9WlS5cKDQwMFOvWrbP84osvLLZt21YFAGBmZibLy8u7u3Xr1iFbt261OHz4cCkAQEpKisn69esrra2tpdOmTXPesmVLtWpO5eXlOtevXy/Iy8vTCQ4OZv3jH//ICQsLq0tKSjLx9vauLC0t1Xr8+LFWQECAaNGiRZTVq1dXP3tcnTU0NJBu3LiBn+UcoLCjRBrT0tJSDh06VPj999+bqV8bIDs7m2Jubi4NDAwUAQCYmJgoVJer/v7+TaampnIqlapkMBji4uJinYsXL+oVFxdTfHx82Gw2m3vo0CHTsrKyjvuDM2fOrAcA8PHxEZWXl+sAAJSXl5NLS0t1xo4dK3Rzc5OQyWTljRs3OrrFsLCwOhKJBHw+X0Kn0yW3bt2izJkzp/7EiRPGAAAHDhwwnjx5cv2zc6+uriax2Wyug4OD6/r16zu6x4iIiLrXOnmoX8COcgBR1/n1FgKBACdOnLj/zjvvMNesWWO5devWLrsvpVL5wpxIAABtbe2O5SQSSSmVSglKpRL8/f2bTp48+eBF26ii18hkslImkxEAAH766SeTpqYmEp1O5wO052AmJiaaDBs2rFI152ePwdHRUWpkZCTLyMjQ/eWXX0x2795dCgDAZDLF169fp/r6+rZaWlrK8/Pz89avnL53wQAAIABJREFUX28hFApJqu1pNNpT4cBoYMGOEvUIGo2mOHPmTNHRo0dNd+7c+Vxnqa+vL29sbCQBALi7u4sfPXqknZ6eTgUAqK+vJ0qlL3+PZ9SoUS0CgUD/zp07OgAAzc3NRHVfAHb06FGTX3/9taiioiKnoqIiJyMjI+/48eMd9yl/+eUXY7lcDrm5uTrl5eU67u7uYgCAadOm1W3evNmyubmZ5OPj0woAsHbt2urt27db3bx5s6MjFYlE+G9nEMGOEvUYCwsL+ZkzZwoDAwPZQ4YMeSqUYs6cOTUfffSR/cqVKxUCgeBuUlJS8dKlSzu+M+fSpUuFLxvX2tpatnv37pLw8HCntrY2AgDAv/71r4qXfQFYQUGBdmVlpXZQUFCLahmbzW7T19eXnz9/Xg8AgMFgSHx8fFi1tbVau3btKqVSqUoAgFmzZtV/9tlndsuWLatUbevj49P65Zdfls+ZM8expaWFaGxsLLexsZFs2rSp8vm9o4EIY9b6OYxZQ2/TYIlZw8sHhBBSAwslQgipgYUSIYTUwEKJEEJqYKFECCE1sFAihJAaWCiRxvpqzFpiYqIRk8nkOjo68lxcXHivGq3WWUFBgbaLiwuvp+aG+hf8wDnqMX0pZu3atWu669ats/3jjz8K2Wx2W35+vnZISAiTwWBIAgICRG9iDmjgwI4S9Yi+FrMWFxdnGRMTU6XKoGSz2W0xMTHVX375pQVAezTapUuXqAAAVVVVZBsbGz5Ae+fo5eXF4nK5HC6Xy0lLS9N7k+cR9U3YUQ4geXdX01uEhT0as6anzxRxOXH9LmatsLDwuWi0ESNGtOzevdu8q2OxtraWXb58uZBKpSpzcnJ0IiIinO7cuXP31c8cGkiwUCKNdY5ZGz58uNoEoxfFrKl+p4pZAwBQxazV1dWRVDFrAABSqZTg5eUlVG3TOWZNFZOmVCoJROLTF0zdeVy3ra2N8OGHH9rn5eXpEolEKC0t7TJ8Aw0OWCgHEHWdX2/pizFrTCaz9dq1a9Thw4e3qta7fv061d3dvUW1rlwuBwAAkUjUkbm2adMmC3Nzc+mxY8ceKBQK0NXV9er2iUADFt6jRD2ir8WsrV69unrnzp1WBQUF2gDt9x4TEhIs1q5dWw0AQKfTJdevX9cDAEhKSup4N7yxsZFkZWUlJZFIkJCQYKoqpmhww44S9Zi+ErMGAODn59e6cePGh5MnT2a0tbURKyoqtE+dOlXg7u4uAQBYs2bNoxkzZjgdOnTINCAgoEm13fLlyx+HhYU5Hz9+3Njf379ZV1cXA3kRxqz1dxiz1j3R0dE2mZmZeunp6UWqS3WkucESs4YdJRoUEhISKt72HFD/hfcoEUJIDSyUCCGkBhZKhBBSAwslQgipgYUSIYTUwEKJNNY5Zk2dxMREo8zMTErnZVKpFIyNjd3/+c9/2vT87BDSHBZK9EYdP37cKDs7W7fzsl9++cXQ0dFRcuLECWOF4sWf75bJZC9cjtCbgIUS9YrCwkJtX19fJpPJ5Pr6+jKLioq009LS9M6dO2f06aef2rLZbG5ubq4OAMDPP/9sEh0d/cja2rrt/PnzHbFmNjY2/BUrVlh5eXmx9u3bZ5ybm6sTEBDgwuPxOF5eXixVpFpycrKhm5sbm8PhcP38/Jjl5eX4+WDUo/AFNYAsv1tGz28R92jMGluPItrFsXvlsI3FixfbzZw5s/ajjz6q3bVrl2lUVBT93LlzxcHBwQ3vvvtu4/z58+sBAIRCIeHq1au0xMTE0oaGBtLBgwdNgoODW1TjUCgURWZmZgEAgK+vL3PPnj2lfD5fcv78eb2oqCi7v//+uzAkJEQYHh6eTyQSYceOHWYbN2603Lt378OeOwtosMNCiXpFVlaW3unTp4sBAKKiouo+//xz2xetd+TIEaMRI0Y002g0xaxZs+o9PDysZTJZOZnc/tKcM2dOPQBAY2MjMSsrS3/69OnOqm1Vz30/ePBAe8qUKbZPnjzRamtrI9Lp9Jc+A47Q68BCOYC8Tuf3th06dMgkMzNTX5Uw3tjYSEpNTaVNmTKlGaA9lQgAQC6XA41Gk+Xn5+c9O8aSJUvsli1bVh0ZGdmYmppK27hxo/WbPQo00OE9StQrPD09W3744QdjAIDdu3ebeHt7CwHa49aamv4fe/cdFcXV/gH82cbSlrJ0aUvZ2S7iEhAFkRIjiSWKFRVMTAxEjdiNUWM09miMiYgxiQp2iS3WiL29KsgrHQQBcWnSWRaQLb8/fJcfUWBVICo8n3M8xx1m7txZ14c7M3u/U0MGAKioqCDHx8frP378OEkikSRLJJLktWvXPtq3bx/z+faYTKbSxsbm6R9//GEMAKBUKuHWrVs6AAC1tbUUOzu7JgCAXbt2mfxbx4h6DiyUqMMaGhrIFhYWvdV/li9fbrFt27ZHMTExpgRB8Pfv328SGRlZAAAwceLEii1btljyeDz+7t27mf3796/V0dFpTvMZP3581fnz543q6+tJz+9n//79D3fu3GnK4XD4bDZb8OeffxoBAHzzzTeFEyZMcBKLxRwTExO8PY46HcasveMwZg29ST0lZg1HlAghpAEWSoQQ0gALJUIIaYCFEiGENMBCiRBCGmChRAghDbBQog7rSMxaUFAQy9raWsTlcvlcLpf//fffm3dGn06ePMk4f/68nuY1EdIMpzCif9WxY8eM5HJ5tVgsblAv+/777x+rQzJaI5fLQT33+2VdvHiRoa+vr3j//ffrNK+NUPtwRIm6xKvErLVGV1fXNSIiolfv3r25Fy5c0D9+/DiDx+PxCYLgjxkzhqWeuWNtbS2aPXt2Lz6fzyMIgp+YmKidmZmpFR0dbRYVFWXB5XL5Z8+e1W8riq2wsJDav39/Np/P5wUHB9v36tVLVFRURAUAiIyMZIpEIh6Xy+UHBwfbYyZmz4Ujym5kfux926zi2k6NWSMsGbINo126LGYNAGDJkiU269atswIAiI6OznV3d6+vr68nC4XC+s2bNxfKZDKSo6Oj6O+//87s3bt348iRI1kbNmwwW7ZsWSkAgKmpqTwtLS197dq1ZmvXrrU4ePBgfkhIyBN9fX3FihUrSgAAnjx5Qmktim3RokW9fHx8atesWVMcGxtrsH//flMAgHv37mnHxsYy4+PjM+h0umrSpEl2UVFRJjNmzCjvnHcWvUuwUKIu8bIxawCtn3pTKBSYMmVKJQDA/fv3tW1sbBp79+7dCAAwZcqU8q1bt5oDQCkAQHBwcCUAgLu7u+zEiRPGre2jrSi2O3fu6B87diwbAGD06NE1BgYGCgCAs2fPMlJSUnRdXFx4AM/ms5ubm+OQsofCQtmNvM7I722lpaWlVF+X1JRHoK2trQIAoFKpKrlc/kKYBkDbUWxtta1SqUhjxowp37p1q6QDh4G6CbxGibrEy8Ssvaw+ffo0SCQSrZSUFDoAQHR0tIm3t3dte9swGAxFbW0tRf26rSg2d3d3aUxMDBMA4MiRIwY1NTUUAIAhQ4bUnDx50lgikVABAEpKSihZWVlar9Jv1H1goUQd9roxa+3dzGlJV1dXFRUVlTdmzBgngiD4ZDIZ5s2b96S9bYKCgqpOnTplpL6Z01YU29q1awsvXrxowOfzeadOnTI0MzNrMjIyUojF4oYlS5ZI/P39CYIg+H5+fkRBQQGtY+8UeldhzNo7DmPWOqa+vp5EpVJVNBoN4uLi9GbMmGHfWoo6al1PiVnDa5SoR8vOztYaO3ask1KpBBqNptq+fXvem+4TevtgoUQ9mkgkakxPT8cRJGoXXqNECCENsFAihJAGWCgRQkgDLJQIIaQBFkrUYa8Ss/YyYmJijAiC4Ds4OAjYbLZg586drU5LfBmZmZlabDZbAPAseo3BYPRRR7r179+fAABYv3692S+//NLu88Bra2vJw4cPdyAIgs9mswVisZhTXV1NBgCgUChidZtcLpefmZmJX0zvZvCuN3qr3Lp1S+ebb76x+fvvv7O4XO7TjIwMrffff59wdnZu9Pb2lnW0fTc3N+mlS5eyWy5bsGBBu19eBwBYvXq1ubm5edOJEydyAQDu379P19LSUgEA0Ol0JX73snvDESXqEq3FrMnlcrCxsREplUooKyujkMlk8ZkzZ/QBAMRiMSclJYW+bt06yzlz5hRxudynAABcLvfpnDlzitevX28BAODu7s65evWqLgBAUVER1draWgTwbOQoFos5fD6fx+fzea8S2jtnzpxey5Yta24/PDzcWiQS8VgslvDs2bP6/9sXzdraukm9jYuLS6OOjg7O1ughcETZnRybbgulaZ0aswbmfBl8vLXTYtYcHBwa7t27p/3gwQM6n8+XXb58WX/QoEF1xcXFWkKhsDErK0t74cKFxS3b6tevX9327dvbTT7v1auX/Nq1a1m6urqq5ORk+oQJExxTUlLSn18vPj5en8vl8gEARowYUbFu3bri59eRy+Wk5OTk9IMHDxquWLGi15AhQ7KmTZtWNnToUOL48ePGAwcOrPn888/LRSJRIwBAY2MjWd2mra1t4/nz53Ne9f1CbzcslKhLtBWz1r9//9oLFy4wcnNz6fPnzy/6/fffza5evSp1cXGpA3iW2kMm//NE52Wm2T59+pQ0depU+7S0NB0ymQz5+fmtziNv7dT7eWPGjKn8X1/r5s+fr/W/v9fn5uYmHzt2zOD8+fMG/fv35125ciWjb9++DXjq3f1hoexOXmPk928bNGiQNDIy0qykpERr06ZNkh9//NHywoULDC8vr1oAAIIg6m/duqXr4eFRr97mzp07uupCSqVSVQqFAgAAZDJZc6TaqlWrLMzNzZv+/PPPXKVSCTo6OuLX7WOL2DZQKBTN+zA0NFSGhoZWhYaGVoWEhMDx48cN+/bt29B2S6i7wGuUqEu0FbM2aNCgunv37umTyWSVrq6uSiAQyKKjo818fX2lAAALFy4s/vHHH63Ud44zMzO1IiMjLRYvXlwM8OzU9s6dO3oAAHv37m2+G15dXU2xsrJqolAoEBkZaaIupp3l77//1nvy5AkFAKChoYGUlZWlzWKxnnbqTtBbC0eUqMPUMWvq1+Hh4SXbtm17FBoayvrpp58sTUxM5NHR0XkAADo6OipLS8unbm5udQAA3t7e0hMnTjDd3d3rAZ6d4q5YseLxsGHDnJ8+fUqWSCRap06dynRxcWkEAFi0aFHJuHHjHA8cOGDi7e1do95nREREaVBQkNOxY8eMvby8anV0dJSdeYxZWVnaM2bMsAcAUCqVpICAgOrQ0NA2H4iGuheMWXvHdfeYtS+//NI6ISFB78qVKw/Up8To7YExawi9BSIjI/FRDOiNw2uUCCGkARZKhBDSAAslQghpgIUSIYQ0wEKJEEIaYKFEHUYikcQff/yxg/p1U1MTGBsbu/j6+joDAOzdu9dw8eLFlq/S5pw5c3pNnz7duuWymzdv6jg6Ogra265laAZCnQULJeowHR0dZWZmpo5UKiUBABw9etTAwsKiOWln4sSJ1atXr34hfKI9oaGh5cePH2e2XLZnzx5mUFBQRef0GqGXh4USdQp/f//qw4cPGwEA7N+//x8FbcuWLSYhISF2AAB//PGHMZvNFnA4HL6bmxsHAEAul8O0adNsCILgEwTBX7VqlbmLi0ujgYGB/OLFi81xaSdOnGCGhIRUAABMnDjRTigU8pydnQWzZ8/u9e8eLepp8Avn3cjSG0ttsyuzO/W009nYWbZywEqNYRuTJ0+u+Pbbb63GjRtXlZ6erjt16tTymzdv6j+/3tq1a63+/vvvLAcHh6aysjIKAMDGjRvN8vPz6ampqWk0Gg1KSkooAABBQUEVe/fuZfr5+dVduHBBz8jISK6ONtu0aZPEwsJCIZfLoX///pzbt2/rtAzSQKgz4YgSdQoPD4/6x48f03fs2MEMCAiobms9Nzc36cSJE1kbN240lcvlAABw8eJFg7CwsCc0Gg0AACwsLBQAAKGhoRWnTp0yVigUsHfvXubo0aObR6m7d+9m/i+kl//gwQPt+/fva3ftEaKeDEeU3cjLjPy60pAhQ6q+/fZb27///juztLS01c/Wvn37Hl28eFHvxIkThn369BH897//TVWpVEAikV6Yx+3s7NxkbW3dePr0acbp06eNb9y4kQ4AkJGRofXLL79YJCQkpJuZmSmCgoJYDQ0N+EsfdRn8cKFOEx4eXjZ37txCdRJQa1JTU+l+fn51mzdvLjQ2NpY/fPhQKyAgoCYqKsqsqenZ/R/1qTcAwJgxYyrmz59va2dn1+jk5NQEAFBZWUnR0dFRMplMRUFBAfXy5cuGXX5wqEfDQok6jZOTU9PSpUtL21tn9uzZNuonGfbr16+2X79+9bNnz35iY2PzlMvlCjgcDv/3339vvtsdEhJSmZ2drd3ytNvT07NeKBTK2Gy2YPLkySyxWCztyuNCCGPW3nHdPWYNvd16SswajigRQkgDLJQIIaQBFkqEENIACyVCCGmAhRIhhDTAQokQQhpgoUQdpilmrS0FBQVUX19fZw6Hw3dychL4+Pi0u35mZqYWm81uNWYN49VQV8IpjKjDWsas6evrq56PWWvLwoULrf38/GrUX1K/ffu2Ttf3FqFXhyNK1Cnai1krKSmhBAQEOBEEwXdxceGqC2JxcTHN1tb2qXo9dfqPUqmEL774wobNZgsIguDv2LHD+Pn9SaVS0tChQx0JguB/9NFHjg0NDaSuP0rUU+GIshspXPyNbeODB516+klns2W9Vq/qUMzaggULerm4uMji4uJyTpw4wQgNDXXIyMhImz59eumUKVMct23bJhs0aFBNeHh4OYvFaoqOjjZKTk7WSU9PTy0qKqK6u7vzBg8e/I9pij/88IO5jo6OMisrK+327ds6AwYM4HfmcSPUEo4oUadoL2btzp07jKlTp5YDAAwfPry2qqqKWl5eTgkKCqrJzs5O/uSTT8oyMzN1xGIxv7CwkHrt2jXG2LFjK6hUKtja2so9PDyk169f/8cvgOvXr+tPnjy5XL1vgiBk/97Rop4GR5TdyMuM/LpSWzFrreUJqGPVLCwsFGFhYRVhYWEVvr6+zn///bf+y+YPkEh4to3+HTiiRJ2mrZi1fv361e7cudMEAODkyZMMY2NjOZPJVJ44cYJRW1tLBgCorKwk5+fn0x0cHJ76+PjUxsbGMuVyORQWFlLv3Lmj7+3tXdeyTS8vL+mePXuYAAB3797VzsrKwjveqMvgiBJ1mrZi1tatW1cYHBzMIgiCr6Ojo9y1a1cuAMDdu3d1Z8+ebUehUFQqlYo0efLkMh8fH5m3t7fs5s2b+jweT0AikVTffffdYzs7O3lmZqaWus158+aVjh8/3oEgCL5AIJCJRKK65/eLUGfBmLV3HMasoTcJY9YQQggBABZKhBDSCAslQghpgIUSIYQ0wEKJEEIaYKFECCENsFCiDsvJyaH5+/s72dvbC21tbYWffPKJbWeHVMyZM6eXubl5by6Xy2ez2YK9e/d2yrO8dXV1XVtbfv/+fbq7uzuHy+XyHR0dBRMmTLAHePaFeQaD0YfL5fK5XC6/f//+RGf0A73dsFCiDlEqlfDxxx87Dx8+vCo/Pz8lNzc3pa6ujjxr1izrzt5XWFhYSUZGRtrBgwdzZsyYwVIoFC+1XVOTxsS3F0yfPt3uq6++KsnIyEh7+PBh6uzZs5u/SO/m5ibNyMhIy8jISLt582bWKzeO3jlYKFGH/PXXXww6na6cNWtWOQAAlUqFqKiogoMHD5quXbvWzN/f38nb25vNYrGEc+fOtVJvFxkZyRSJRDwul8sPDg62l8vlAPBshDdz5kxrDofDd3Fx4RYUFLwwe6xv374NFAoFiouLqVlZWVqenp4EQRB8T09P4sGDB1oAAEFBQazPPvvMxsPDg/jyyy9tqquryaNHj2YRBMEnCIK/a9cuI3V7re2vtLSUZm9v3xwB9/y0TNSz4BTGbuRCdLpthUTaqXOemdb6Mv8QXpthG8nJyTouLi7/SO5hMplKKyurp3K5nJSUlKSXnJycqq+vr3R1deWPGDGiWl9fXxkbG8uMj4/PoNPpqkmTJtlFRUWZzJgxo7y+vp7s6ekp/fnnnyVhYWE2P//8s9n69euLWrZ/8eJFPTKZrLKyspIHBAQ4BwcHl8+cObN88+bNJuHh4bZxcXE5AAA5OTnaN27cyKJSqRAeHm5tYGCgyMrKSgMAePLkCQUAoK39TZ8+veTDDz8kXF1d6/z9/aunT59ebmpqqgAAiI+P1+dyuXwAgBEjRlSsW7euuDPfc/T2wUKJOkSlUjUnAbWyHLy8vGosLS0VAAAfffRR5eXLl/WpVKoqJSVF18XFhQcA0NDQQDY3N5cDANBoNNX48eOrAQDEYnFdXFycgbrNqKgoi0OHDpno6ekpoqOjH5LJZEhMTNQ7c+ZMDgBAeHh4xXfffWejXn/UqFGVVOqzj/jVq1cNDhw48FD9MzMzM0V7+5s1a1b5iBEjao4dO2bw119/Ge3atcssLS0tDeDZqfelS5eyO/WNRG81LJTdSHsjv64iEonqjx8//o8E8oqKCnJxcbEWhUJRPR+FRiKRQKVSkcaMGVO+detWyfPtUalUFZlMVv8d5HJ5cwNhYWElK1asKHnZvunr6yvVf1cX7lfZH4vFaoqIiCiPiIgoZ7PZgvj4eHxURQ+F1yhRhwwfPry2oaGB/Msvv5gAAMjlcvjyyy9tx4wZU6arq6u8fv26QUlJCUUqlZJOnz5t5OPjIx0yZEjNyZMnjSUSCRXg2aMisrKytNrfU+tcXV3rfvvtN2MAgO3btzPd3Nykra03aNCgmk2bNpmrX6tPvdsSGxtr0NjYSAIAePToEbWqqorS8pol6lmwUKIOIZPJcOzYsewjR44Y29vbCx0cHIR0Ol25ZcsWCcCz09Rx48Y5CIVCwbBhwyoHDhwoE4vFDUuWLJH4+/sTBEHw/fz8iIKCAtrr7H/btm2PYmJiTAmC4O/fv98kMjKy1VH1mjVriqqqqihsNlvA4XD4p0+fZrTX7tmzZw04HI6Aw+Hw33//fUId9fY6fUTvPoxZe8e9zTFrW7ZsMYmPj9eLjo5+9Kb7groGxqwhhBACALyZg7rQV199VQ4A5W+6Hwh1FI4oEUJIAyyUCCGkARZKhBDSAAslQghpgIUSddi/EbP2PHU8WmZmphabzRaol1+6dEnXzc2Nw2KxhA4ODoJx48bZq58d3hFz5szptWzZMouOtoPeTVgoUYd0Vcza60SjFRQUUCdOnOi0du3ax3l5eSk5OTmpQ4YMqamqqsLPOeoQ/AChDmkvZk0oFPLi4+O11eu6u7tzrl27pltTU0MeM2YMSygU8ng8Hn/Pnj1GAM++oB4YGOjo5+fn7O3tTVRXV5M9PT0JPp/PIwiieb22bNy40Xzs2LHlAQEBdQDPZg198sknlba2tvKSkhJKQECAE0EQfBcXF+7t27d1AJ6NFMeMGcNyd3fn2NjYiL7//vvmaY4LFy60ZLFYwv79+xMPHjygd8X7h94N+D3KbuTcts22ZQX5nRqzZmprL/sgPOK1YtY++OCDqr179zLd3NwK8/PzaaWlpTRvb2/ZjBkzrH19fWsOHz6cV1ZWRnFzc+MNHz68BgDg3r17+klJSakWFhaKpqYmOHXqVDaTyVQWFRVRPTw8uMHBwVXqEIvnpaWl6YSEhLT6vc0FCxb0cnFxkcXFxeWcOHGCERoa6pCRkZEGAJCdna198+bNzKqqKgqPxxPOnz//yZ07d3SOHj3KTE5OTmtqaoI+ffrwXV1dZa21jbo/HFGiDmkvZs3Pz6/2xIkTxgAA0dHRxsOGDasEALh8+bLBjz/+aMXlcvleXl6cxsZGUnZ2thYAgLe3d42FhYUCAECpVJIiIiJsCILg+/r6EqWlpVqPHz9+rV/ud+7cYUydOrUc4FmQR1VVFbW8vJwCADB48OAqHR0dlZWVlZzJZDY9fvyYeunSJf0PP/ywisFgKJlMpnLw4MFVr/cOoe4AR5TdSHsjv67SXszawIEDZUZGRvLbt2/rHDlyhLl9+/Z8gGdFNDY2NtvFxaWx5XbXr1/X09XVbY5G2759O7O8vJyanJycTqfTVdbW1qL6+vo2f7nzeLz6+Ph43UmTJr1Q1FrLNFAXeDqd3vxDCoXSHLXWWiwb6plwRIk6pL2YNQaDoRw9enTF6tWrLWtraynqxyn4+vrWbNy40UKpfFYTb9y40WrOY3V1NcXU1LSJTqer/vrrL0ZhYWG7UWzz5s0rPXTokMnFixf11MsiIyOZjx49ovbr1692586dJgDPHhBmbGwsZzKZyrba8vPzk546dcpIKpWSKisryefPn2/3+ijq3rBQog7RFLM2adKkylOnTjFHjBhRod5m7dq1hXK5nKR+ouKSJUtavUP+2WefVdy/f19PKBTy9uzZw3RwcGhory+2trby6Ojoh/Pnz7dhsVhCR0dHwfXr1xnGxsbKdevWFd67d0+XIAj+N998Y71r167c9try8vKSjRw5skIoFAqGDh3q5O7u3mrOJeoZMGbtHfc2x6yh7g9j1hBCCAEAFkqEENIICyVCCGmAhRIhhDTAQokQQhpgoUQIIQ2wUKIOe5ti1s6dO6cvEol4Dg4OAhaLJVyzZo1ZZ+wH9WxYKFGHvE0xa48ePaJOmTLFYdu2bfm5ubmpt27dytizZ49pdHQ0zqpBHYKFEnXI2xazNm7cuHIvLy8ZAICVlZV89erVj3/88UdLAICgoCDWzp07m+elq0eLr7of1PNgKEY3UhGbZdtUXNepMWs0Sz0ZczTxTsSspaenvxCz5uXlJcvOztZudYP/0dXVVb7KflDPg58E1CFvU8xaW33RpDPj3FD3hB+GbqS9kV9Xedti1u7evas3ceLEavWyGzdu6IpEIhkAAJVKVSkUCgB4dm21qamJ9Dr7QT3jv4sTAAAgAElEQVQPfhhQh7xNMWtz5859cvDgQZObN2/qAAAUFxdTli1bZv3NN98UAgDY29s/TUhI0AUA2Lt3r5E6d/JV94N6HiyUqEPeppg1e3v7pj/++CP3iy++YLFYLKGdnZ1LeHh46UcffSQFAJg5c+aTmzdvMkQiEe8///mPno6OjvJ19oN6HoxZe8dhzFrb1qxZY7Zz506zGzduZJqZmSnedH+6I4xZQ+gd9/XXXz/JyspKwyKJOgoLJUIIaYCFEiGENMBCiRBCGmChRAghDbBQIoSQBlgoUYdRKBSx+juRgYGBjrW1ta/1uXo+0uy7774zp9PpfcvLyymd01OEXg8WStRhdDpdmZGRkfbgwYNUGo2m2rhxY4cyINViY2NNhEJh3d69e1tN83mdKDaEXgcWStSpvLy8pNnZ2XQAgOXLl1uw2WwBm80WrFixwly9TlvLW0pNTaXLZDLyihUrJIcOHWKqlz8fxQYAsHTpUguhUMgjCII/e/bsXup1AwICnAQCAc/Z2Vnwww8/mHbdUaPuDkMxupFjx47ZlpaWdmrMmrm5uezjjz9+qbCNpqYmOHfunMHgwYNrrl27prtv3z6ThISEdJVKBWKxmOfv71+rVCpJrS0fMGBAfcu2du/ezRw1alTFkCFDpNOmTdOWSCRUa2trOcA/o9iOHDlikJ2drZ2UlJSuUqkgICDA+cyZM/qBgYHSvXv35llYWCikUinJ1dWVP2nSpEpLS0v88jl6ZTiiRB3W2NhI5nK5fJFIxLexsXk6a9asssuXL+t/+OGHVQYGBkpDQ0PlRx99VHnp0iVGW8ufb/Po0aPMkJCQCgqFAoGBgZXR0dHNCUUto9jOnj1rcPXqVQM+n88XCAT8nJwc7YyMDG0AgHXr1llwOBy+WCzmFRcX01JTU9vNpUSoLTii7EZeduTX2dTXKFsuaytD4GWyBW7fvq2Tn59PHzJkCAEA0NTURLK1tW38+uuvnwA8C9pt2V5ERETR/Pnz/zHf/eTJk4wrV64w4uPjMxgMhtLd3Z2D0WnodeEHB3UJPz8/6enTp41qa2vJNTU15NOnTxv7+vrWtrW85bbR0dHMuXPnFkokkmSJRJJcWlqaVFxcrJWVlfVC/FlgYGBNTEyMaXV1NRkAIDc3lyaRSKhVVVUUQ0NDBYPBUCYmJmrfv39f7986dtT94IgSdQkvLy9ZcHBwed++fXkAAJMnT36ivg7Z1nK1Y8eOMU+ePPmg5bLAwMDK3bt3My0sLP5xq3vUqFE1qamp2u+99x4X4Nloc+/evblBQUHVv/76qxlBEHwnJ6cGFxeXuq48XtS9YczaOw5j1tCbhDFrCCGEAAALJUIIaYSFEiGENMBCiRBCGmChRAghDbBQIoSQBlgoUYd1RcxafHy8dr9+/QgWiyW0tbUVzp49u5dC8f/TtA8dOmQgFAp5jo6OAgcHB8G0adNsOuFQEGoVFkrUYZ0dsyaVSkkjR450XrBgQXFeXl5KWlpaWkJCgt73339vDgBw9+5d7blz59rFxMTkPnz4MDUrKyvV0dGxsXOOBqEXYaFEnaozYtZ27Nhh4ubmJh01alQNAACDwVBu27bt0datWy0BAFavXm05d+7cIldX1wYAABqNBosWLXry7xwh6olwCmM3kpa+0LZOmtWpMWt6+oSMz1v3r8aspaamavft21fWsm2BQNDY0NBALisro2RmZuosWLCgpDOPE6H2YKFEHaaOWQMA8PDwqJ01a1bZhg0bzNRxagAA6jg1lUoFrS1vWShVKhWJRCK9sB+cboveFCyU3cjLjvw6W2fHrAkEgvpr167pt1yWlpamZWxsLDc1NVUQBNFw+/ZtXU9Pz/q22kCoM+E1StQlOhKzNm3atPK7d+8yjh07xgB4dnNn+vTpdl9//XUhAMDXX39dvGnTJqukpCQ6AIBCoYDly5db/PtHiXoKHFGiLtGRmDV9fX3V0aNHH8yYMcMuIiLCvqSkhBYREVEUHh5eAQDg4eFRv27duoIJEyY41tfXk0kkEgQEBFT/28eIeg6MWXvH9YSYtZiYGKOvv/7a9uLFi5kEQTx90/1B/w9j1hB6S0yePLnq8ePHyVgk0ZuChRIhhDTAQokQQhpgoUQIIQ2wUCKEkAZYKBFCSAMslKhTLFy40NLZ2VlAEASfy+XyL168+K8/R3vOnDm9zM3Ne6sj3/bu3WvYGe22jH9DPRN+4Rx1WFxcnN65c+eMkpOT03R0dFRFRUXUxsbGFydrP6epqQloNFqn9iUsLKxkxYoVJffu3dP29/fnjB8//j6FQtG4XVf0BXUfOKJEHSaRSGhMJlOuo6OjAgCwsrKSs1ispitXrui6urpyORwOXyQS8SorK8lbtmwxCQwMdPTz83P29vYmAACWLl1qIRQKeQRB8GfPnt1L3W5kZCRTJBLxuFwuPzg42F4ulwPAsxHezJkzrTkcDt/FxYVbUFDwwi/8vn37NlAoFCguLqZmZWVpeXp6EgRB8D09PYkHDx5oAQAEBQWxPvvsMxsPDw/iyy+/tKmuriaPHj2aRRAEnyAI/q5du4zU7WnaH+re8B+8G4lIf2SbUdfQqTFrXD1t2WaeXbthGx9//HHNmjVrerFYLKGXl1fNhAkTKvz9/esmTpzotHfv3hwfHx9ZRUUFWV9fXwkAcO/ePf2kpKRUCwsLxZEjRwyys7O1k5KS0lUqFQQEBDifOXNG38LCQh4bG8uMj4/PoNPpqkmTJtlFRUWZzJgxo7y+vp7s6ekp/fnnnyVhYWE2P//8s9n69euLWvbp4sWLemQyWWVlZSUPCAhwDg4OLp85c2b55s2bTcLDw23j4uJyAABycnK0b9y4kUWlUiE8PNzawMBAkZWVlQYA8OTJEwoAwMvsD3VvWChRhxkaGipTUlLSzp49y7hw4QIjNDTUafbs2UXm5uZNPj4+MgAAJpOpVK/v7e1dY2FhoQAAOHv2rMHVq1cN+Hw+HwBAJpORMzIytBMTE0kpKSm6Li4uPACAhoYGsrm5uRwAgEajqcaPH18NACAWi+vi4uIM1G1HRUVZHDp0yERPT08RHR39kEwmQ2Jiot6ZM2dyAADCw8Mrvvvuu+bHRowaNaqSSn323+Dq1asGBw4ceKj+mZmZmULT/lDPgIWyG9E08utKVCoVhg4dWjt06NDa3r1710dFRZmRSKRWgwR0dXWbi6ZKpYKIiIii+fPn/2O++qpVq8zHjBlTvnXrVkkr+1KRyeTm/crl8ubroeprlC/bb/UoV92X1nIw29sf6hnwGiXqsPv379OTk5Pp6teJiYk6bDa7oaSkROvKlSu6AACVlZXkpqamF7YNDAysiYmJMa2uriYDAOTm5tIkEgl1yJAhNSdPnjSWSCRUAICSkhJKVlaW1uv0z9XVte63334zBgDYvn07083NTdraeoMGDarZtGlT86Mp1KfeCOGIEnVYTU0N5auvvrKrqamhUCgUFYvFaty9e3d+VlZW2VdffWXX0NBA1tbWVl69ejXr+W1HjRpVk5qaqv3ee+9xAZ6NNvfu3ZsrFosblixZIvH39yeUSiXQaDTVli1bHr1OMMa2bdsehYaGsn766SdLExMTeXR0dF5r661Zs6bok08+sWOz2QIymaxavHhxYWhoaNUrvyGo28GYtXdcT4hZQ28vjFlDCCEEAFgoEUJIIyyUCCGkARZKhBDSAAslQghpgIUSIYQ0wEKJOqS4uJjC5XL5XC6Xb2pq6qKOOeNyufyGhoYXZrCUlJRQ1q9fb6Z+nZKSQtfW1u7L5XL5jo6OgqCgIFZrX0x/XYMGDXIWi8WclstGjBjhEBMTY9TWNq05ePCgoVAo5Dk4OAi4XC5/2LBhDjk5ORrjhpqamoDBYPR51X6jtwsWStQhlpaWioyMjLSMjIy0kJCQJ2FhYSXq19ra2i98SffJkyfUP/74w6zlMhaL1ZCRkZGWmZmZ+ujRI/quXbuMO6NvxcXFlMzMTJ3y8nKaOjHoddy6dUtn4cKFtnv37n2Ym5ubmpaWljZ27NjKnJycF9rszCKP3h5YKFGXWbJkiQWbzRaw2WzBqlWrzAEA5s2bZ52Xl6fN5XL5X375pXXL9Wk0Gri6utZJJBItAIBNmzaZDh482MnX19fZ2tpatG7dOrOlS5da8Hg8vqurK7esrIwCAPDdd9+ZOzk5CTgcDn/EiBEO6vZiYmKMP/jgg6oRI0ZUREdH/6P4nj171kAsFnNYLJbw0KFDBgAAQqGQd//+/eapmGKxmHPr1i2d1atXW86fP7/QxcWlEQCATCbD5MmTqwYPHlynXm/mzJnWbm5unDVr1pinpqbSe/fuzRUKhby5c+f2AvTOwymM3cj82Pu2WcW1nRqzRlgyZBtGu7xy2MalS5d0Dx8+bHLv3r10uVwOYrGYFxAQUPvDDz9IRo8erZ2RkZEG8OzUW72NVColJSYm6k2bNi1fvSwrK0snKSkprbq6mszj8UQrVqwoSE9PTwsNDbX99ddfmYsXL37yyy+/WBYUFCRra2ur1MUTAODw4cMmq1evfmxsbKyYPHmyw8qVK5vDMgoLC7Xu3LmTmZKSQv/ggw84w4YNSx45cmTFnj17mC4uLkU5OTm0yspKqqenZ31mZqbOt99+226sWk1NDTk+Pj4TAMDHx8f5yy+/LA0LC6tYuXKleXvboXcDjihRl7h8+TJj2LBhlQwGQ2lsbKwMDAysunTpkn5r66pHmGZmZn0cHBwa3NzcGtQ/GzBgQI2BgYHS1tZWrqurqxgzZkwVAIBIJKrPy8ujAwCw2eyGUaNGOWzbto2ppaWlAngWrlFYWKjl5+dXJxaLGxQKBSkxMVFb3W5QUFAlhUIBFxeXRisrq6cpKSn0yZMnVx4/ftwYACA6Opo5YsSIyuf7KpFIqFwul29vby9csWJFcxGcOHFihfrviYmJ+p999lkFAMC0adPKO/peojcPR5TdyOuM/LrKq2QIqK9R5uXl0QYOHMg5ePCg4bhx46oBAOh0enNDJBIJ1CnqZDK5Oe7s6tWrWadPn2YcPXrUaMOGDVZZWVmpu3fvZlZXV1NsbW1FAAC1tbWUmJgYpqura+H/2vpHB0kkEhAE8VRPT0+ZkJCgfeTIEeauXbtyAQA4HE797du3dd3c3Bqsra3lGRkZaYsXL7aUSqXNo9eWcW0kEqnVuDb07sIRJeoSvr6+tadOnTKWSqWk6upq8tmzZ438/PykhoaGirq6ulY/dywWq2n58uWS9evXW77sfuRyOTx8+FBr+PDhtdu2bXtcWVlJra2tJcfGxjJPnjyZJZFIkiUSSfKNGzfSjxw5wlRv9+effzKVSiUkJSXRi4qKtIRCYSMAwKhRoypWrlxp9fTpU5JYLG4AAFi0aFHx+vXre7W8fimTydr8v9OnTx/p77//bgwA8Ntvv5m87LGgtxcWStQlfH19ZUFBQeWurq58Nzc33qeffvrE3d293tbWVt67d28ZQRAv3MwBAJgyZUpldXU1NS4u7qWe4tjU1EQaP368I0EQfJFIxJ8xY0ZxYWEhraysjObt7S1TrycSiRq1tLSU165d0wUAcHR0bHjvvfc4w4cPZ2/ZsiVPfYd+8uTJlX/99Rfz448/bj6VHjBgQP2aNWsKgoODHVkslrBv377cnJwcekhISMWLPQLYunVrwS+//GIhEol4UqkU/491Axiz9o7DmDX0JmHMGkIIIQDAQokQQhphoUQIIQ2wUCKEkAZYKBFCSAMslAghpAEWStQhHY1Za0vLeLKWUWwcDofft29fbsvniL+uEydOMC5cuND8fc179+5pv/feexx15NvEiRPtAACOHTvGYDAYfdTH5eXlxe7ovtG7Bacwog5Rx6wBAMyZM6eXvr6+YsWKFSVtra+OWVuwYMGTV9mPepojAMCaNWvMVq5caXno0KF8Tdu1Jy4ujmFqair39/evAwCYPn263dy5c4vHjx9frVQqIT4+Xke9roeHR21cXFxOR/aH3l04okRd5mVi1ioqKsj9+vUj+Hw+jyAI/v79+w01tVtTU0MxMjJSAADcuXNHRygU8rhcLp8gCH5aWppWSkoKnc1mC8aMGcNydnYWjBw5kvXnn38auLq6clkslvDq1au6qamp9H379pn98ssvllwul3/+/Hm90tJSmr29/VOAZ3PJ3d3d67v2HULvChxRdifHpttCaVqnxqyBOV8GH2/tspi1xsZG0pkzZ7KNjY2VEomE2r9/f+6ECROqn29PXVylUinl6dOnpFu3bqUDAPz0009ms2bNKv78888r6+vrSSqVCh4+fKiVm5tLP3DgQE6fPn0aBAIB//Dhw6rExMSMXbt2Ga1evdry7NmzD4ODg5+YmprKly1bVgoAMH369JLBgwdz+vbtK/X396+ZPn16uYmJiQIA4Pbt2wwul8sHeDYffPXq1cUdeVvRuwVHlKhLvGzMmkqlgpkzZ9oQBMH39/cniouLtYqKil74Ba4+9X78+HHyd9999/izzz6zBwDo37+/dMOGDVZLliyxyMnJ0dLV1VUBANjZ2TWKxeIGCoUCbDa73t/fvwYAoG/fvvWPHz9u9frmnDlzypKSklJHjhxZefnyZQN3d3eu+jqrh4dHrTq5HYtkz4Mjyu7kNUZ+XeVlMwQiIyNNampqKKmpqWk0Gg0sLCx6y2SydjPKxo8fXzV//nx7AIDp06dX+Pj41B09etTwgw8+IH777bdcW1vbJnUuJcCz02h16EXLeLbWODg4NEVERJRHRESUOzg4CFpmWKKeC0eUqEu8bMxadXU1xczMTE6j0eDo0aMGpaWlGh/Ydf78eX1bW9tGAIC0tDQtoVDYuHTp0lJ/f//qxMREHU3bqzEYDGVtbW1zpmRsbKyB+pk3eXl5tJqaGqq9vT0+BAfhiBJ1jZYxawAA6pg1AAB1zFpAQED1N998UxIYGOgsFAp5IpFIZm9v39hae+prlCqVCrS0tFRRUVF5AAC7du0yOXLkCJNKpaosLCyebtq0SVJcXPxSn+vRo0dXjRs3zvHkyZPGW7ZsyT916pThvHnz7Oh0upJEIsHq1asLevXqJe+ktwS9wzBm7R2HMWvoTcKYNYQQQgCAhRIhhDTCQokQQhpgoUQIIQ2wUCKEkAZYKBFCSAMslKhDpk6dartixQpz9WsvLy/2uHHj7NWvP//8c5vly5dbdGQfQUFBrJ07dxoDALi7u3NYLJaQIAi+g4ODICQkxK6srIyiqY3WzJkzp9eyZcte6NuFCxf0evfuzVXHrc2ZM6cXAMCWLVtMjI2NXdRxayNHjmR15LjQuwO/cI46ZMCAAdLY2FhjAChVKBRQWVlJlUqlzYXr7t27+hMmTOjUqZXR0dEPBw4cKGtoaCDNnDnTOjAw0Pnu3buZndX+1KlTHfbv35/j6elZL5fL4f79+83TGIcNG1YZHR39qLP2hd4NOKJEHeLn5ydNSEjQBwBISEjQ4XA49Xp6eoonT55Q6uvrSTk5Odqenp6yL774wobNZgsIguDv2LHDGABAqVRCW8tDQkLsnJycBIMGDXIuKytr9Re6tra2atu2bY8LCwu1bt26pQMAEBkZyRSJRDwul8sPDg62l8ufTayJjY014PP5PA6Hw/f09CSeb2vjxo2mAwcOZEulUlJFRQXVzs6uCQCASqWCWCxu6JI3D70zcETZjSy9sdQ2uzK7U2PWnI2dZSsHrGxzRMhisZqoVKrqwYMHWleuXNHr169fnUQioV28eFHf2NhYzuFw6g8ePGiYnJysk56enlpUVER1d3fnDR48WHrp0iW91pZfvnxZLzs7m56ZmZn6+PFjmkgkEkyZMqW8tf1TqVTg8XiylJQUbTqdroqNjWXGx8dn0Ol01aRJk+yioqJMRo0aVT1jxgzW5cuXM7hc7tOSkpJ/nKqvXr3aLC4uzvDcuXPZOjo6qmnTppXweDyhh4dH7eDBg6unT59erk4l+uuvv4y5XK4+AEB4eHjJrFmzWu0X6l6wUKIOE4vF0kuXLundunVLf/78+SWPHj3SunHjhp6hoaHC3d1deu3aNcbYsWMrqFQq2Nrayj08PKTXr1/XbWv5lStXmpezWKwmT0/P2vb2r56Ge/bsWUZKSoqui4sLDwCgoaGBbG5uLr98+bKeu7t7LZfLfQoAYGFhoVBve/DgQRMrK6un586dy6HT6SoAgB9++KHok08+qTh58qTBoUOHTA4fPmxy586dTAA89e6psFB2I+2N/LqSp6en9ObNm/oZGRk67733Xr2jo+PTzZs3W+jr6ys++eSTsri4OIPWtmsvZ4BEajdprZlcLofMzEzd3r17F8bFxTHGjBlTvnXrVknLdfbu3WvYVnscDqc+LS1NNzc3l6YupAAAAoGgUSAQPJkzZ84TExOTPsXFxa91wwh1D3iNEnWYj4+PNC4uzsjIyEhBpVLBwsJCUVNTQ0lMTNT39fWt8/HxqY2NjWXK5XIoLCyk3rlzR9/b27vd5YcPH2bK5XLIz8+n/ec//2G0tt/GxkbSjBkzbKysrJ56eHjUDxkypObkyZPGEomECvDsQWZZWVlavr6+dbdv32ZkZGRoqZer2+jTp49s69at+cOHD3fOy8ujAQAcOHDAUKlUAgBAcnKyNoVCUZmamipa6QLqIXBEiTrM3d29vqqqijpq1Kjm63VcLre+rq6OYmVlJZ88eXLVzZs39Xk8noBEIqm+++67x3Z2du0uv3DhggGHwxE4ODg0uLu7/+PUOyQkxFFLS0v59OlTsre3d82ZM2eyAQDEYnHDkiVLJP7+/oRSqQQajabasmXLI39//7otW7bkjRw50lmpVIKJiUnTzZs3H6jb++CDD6Rr1qx5HBgYyL548WLWnj17TBYtWmSrra2tpFKpqt9++y2XSsX/Kj0Zxqy94zBmDb1JGLOGEEIIALBQIoSQRlgoEUJIAyyUCCGkARZKhBDSAAslQghpgIUSdYhSqQSxWMw5dOhQ8+yb3377zdjb25vd0bZHjBjhYG1tLeJyuXwHBwfBggULrDRtEx0dbbR06VILAICvvvqqlzoCbvPmzSaPHj3CL0Oi14IfHNQhZDIZoqKi8seNG+c0dOjQNLlcTlq5cqX16dOnH2jeum1NTU0AALB27dqCyZMnV0mlUhJBEMJp06aVOTs7N7W1XUhISFVry2NiYkzd3d1ldnZ2+Jxu9MpwRIk67L333msYPHhw9dKlSy0XLFjQa+zYseUCgaDx559/NlFHnk2aNMlOoXg2C3DChAn2QqGQ5+zsLJg3b17zKNHCwqL3/Pnzrfr27cuNiYkxbrmPuro6MolEAn19faV6XXVg74ULF/T69+9PAABs2rTJ9NNPP7Vtue2OHTuM09PTdYODg524XC6/oaHh5SaSI/Q/OKLsRgoXf2Pb+OBBp8as0dlsWa/VqzSGbaxfv76wd+/efC0tLeX9+/fT7969q338+HGje/fupdNoNJgwYYL9jh07mGFhYRWbN29+bGFhoWhqaoJ+/fpxEhISKtWZj3p6esp79+5lAAAcP37caNGiRbarVq3qlZ+fT//iiy9KLC0tX3nO9eeff14ZFRVl/vPPPz/q379//au/C6inw0KJOoWBgYHy448/rtDX11fo6Oiozpw5Y5CUlKQnEon4AM8iz2xsbJ4CAPzxxx/MmJgYU7lcTnry5AktKSlJR10oQ0NDK1q2qz71rqysJHt7e3MuXbpU5evrK/v3jxD1ZFgou5GXGfl1JTKZDGTys6s5KpUKJkyYUPbTTz8VtlwnOTmZvn37dov4+Ph0U1NTxYgRIxzq6+ubT4UZDIaytbaNjY2Vnp6etVeuXGH4+vrKqFSqSn0qX19fj5eQUJfCDxjqEoGBgbXHjx9nFhUVUQEAiouLKQ8ePNCqqqqi6OnpKYyNjRX5+fm0q1evtppV+bzGxkbSvXv39JydnRsBAKytrZ/evHlTDwDg8OHDRpq219PTU9bU1GCmJHotWChRl3B3d69ftGhRoa+vL0EQBN/f358oLCykDhgwQMZmsxsIghBMmTLFXiwWS9trZ9GiRbZcLpfP4/H4Li4usuDg4CoAgGXLlhVGRETYicVijpaWlsYIrJCQkLKwsDAW3sxBrwNj1t5xGLOG3iSMWUMIIQQAWCgRQkgjLJQIIaQBFkqEENIACyVCCGmAhRIhhDTAQok65N+KWeNwOPy//vqr1ed7d5WWMW0AAA0NDSRDQ8M+s2bN6tXWNseOHWMEBAQ4tfazlkEe6N2ChRJ1iDpmbdGiRbYymYxUU1NDXrlypXVUVNSjjrTbMmYtIyMjbe3atY9nzZpl1ymdfk1//vmngbOzc/3x48eZb7If6N+HhRJ12L8Rs+bn5yctLS3VUr++cuWK7nvvvccRCAS8gQMHsgsKCqgAAGKxmPPZZ5/ZiMVijpOTk+Dq1au677//vpO9vb1wzpw5zSPBJUuWWLDZbAGbzRasWrWqedQ4b948KxaLJezfvz87JydHu2UfDhw4wPzqq69KTExMmq5cuaLbYrkhi8USisViztGjR5unUxYWFlL79+/P5vP5vIkTJ9rh5I53F4ZidCMXotNtKyTSTo1ZY1rry/xDeG8sZk3d/pEjRwzff//9SgCA+vp6UkREhN3p06ezrays5Nu2bWMuWLDAev/+/fkAADo6OqqEhITMb7/91mLMmDHO8fHxaSYmJgoWiyVavHhxSXJyMv3w4cMm9+7dS5fL5SAWi3kBAQG1MpmM9NdffxmnpKSkNjY2knv37s338PCQAgDU1NSQb9++zTh48GBeUVERLSYmhunj4yOrra0lz5o1y/7ixYuZPB6vMTAwsPm0e8GCBb0GDhxYu3bt2uI9e/YY7du3z6wz/23QvwcLJeoUXRWztmjRIttFixbZVlVVUa9du5YOAJCYmKidnZ2t7evrSwA8u05qaWnZnHo+cuTIKgAAFxeXeg6HU29raysHeBakkdg5u6gAACAASURBVJubS7t8+TJj2LBhleqkosDAwKpLly7py2Qy8rBhwyr19fVV+vr6ivfff785LX3//v1GXl5eNbq6uqqQkJBKNzc3nkKheJyYmKjt4ODQIBAIGgEAgoODy2NiYkwAAG7fvs349ttvHwAATJo0qSosLKzVZCT09sNC2Y28zMivK3VFzNratWsLJkyYULVixQqLKVOmsJKSkjJUKhUQBFGfkJCQ2Vo/tLW1lf/rj0pLS6u5PTKZrGpqamo334BEaj0v4+DBg8z79+/rWVtbiwAAKioqaGfOnGEYGBgo2trmf+3h+XY3gNcoUZfozJg1KpUKy5cvL2loaCAfP36c0bdv34aSkhKtS5cu6QI8uxsdHx+vrakdNV9f39pTp04ZS6VSUnV1Nfns2bNGfn5+Ul9f39qTJ08ay2QyUkVFBTkuLs4IAODJkyeU+/fv6xUWFiZJJJJkiUSSvGrVqkf79u1jurq6NuTm5mpnZGRoKZVKOHDgQPONHg8Pj9o//vjDBABg3759hnV1dfj/7R2FI0rUJVrGrCmVSqDRaKrIyMh8b2/v5pg1Ozu7Rk0xa2pkMhnmz59ftGHDBssRI0Y8OHDgQM6sWbNspVIpRaFQkGbMmFHs5ubW8DJt+fr6yoKCgspdXV35AACffvrpE3d393oAgI8++qiSz+cLbGxsGj08PGoBAGJiYoy9vLxq6HR68+hwwoQJVatWrbLW0tJ6tHnz5vzAwEA2k8mUu7u7Sx88eKAN8Oy67ejRox35fL7xgAEDas3Nzdt8KBp6u2HM2jsOY9bQm4QxawghhAAACyVCCGmEhRIhhDTAQokQQhpgoUQIIQ2wUCKEkAZYKFGHvErM2ubNm00IguATBMFns9mCPXv2tPs87qCgINbOnTuNn19+8uRJhq+vr3PnHAFCmuEXzlGHqGPWxo0b5zR06NA0uVxOWrlypfXp06cfqNdRKpWQk5OjtXHjRqv//ve/6SYmJorq6mqyetYOQm87/KCiDmsZs1ZXV0cZO3ZsOZVKVTk6Ogr69+9fm5CQoP/DDz880tPTUxoaGioAAAwNDZWGhoZPAQBu3rypEx4ebl9fX0+2t7dv3LdvX56ZmZmi5T5iY2MN5s+fb8tkMuUikUj2Jo4T9VxYKLuRc9s225YV5HdqzJqprb3sg/CIV45Ze/ToES0vL097x44deXv27Hkkl8thzZo1Tba2tqIBAwbUjho1qjI4OLgaAGDKlCkOP/7446OPPvpIGhER0WvhwoW9/vjjj+Z9ymQy0owZM1jnz5/PFAgEjUOHDnXszGNESBO8Rok6hTpmbezYseU6OjoqAAArK6un/v7+dQDPgi2uXr36YN++fTlsNrth0aJFtnPmzOlVXl5Oqa2tpXz00UdSAIDPP/+8/D//+Y9+y7b/+9//atvY2DSKRKJGMpkMEydOLP/3jxD1ZDii7EZeZuTXlVrGrAEA6OrqKp//ua+vr8zX11cWGBhY89lnn7G++eabkpdpu70oM4S6Go4o0b8iLy+Pdv369ebLAvHx8brW1tZPTUxMFAYGBoqzZ8/qAwD8/vvvJp6env9IFOrTp0/D48ePtVJTU+kA8I8oM4T+DTiiRP+Kp0+fkubNm2dTUlJCo9PpKiaT2bRjx45HAAA7d+7MDQ8Pt//qq6/IdnZ2jfv3789rua2urq7q559/zh86dKgzk8mUe3h4SNPT03XexHGgnglj1t5xGLOG3iSMWUMIIQQAWCgRQkgjLJQIIaQBFkqEENIACyVCCGmAhRIhhDTAQokQQhpgoUQdRqFQxFwul6/+s3jxYsv21l+0aFG7P2/LuHHj7BMSErRfr5f/VFhYSKVSqX03bNhgqmldd3d3ztWrV18IGykoKKD6+vo6czgcvpOTk8DHx8cZAPMyuyOcmYM6jE6nKzMyMtJedv0tW7ZYrV27tvhV9iGXy+HgwYP5r7oNldr6Rzw6OtrYxcWl7vDhwybz589/rS/sL1y40NrPz69m6dKlpQAAt2/fxtlC3RQWym6kIjbLtqm4rlNj1miWejLmaOKVwzbKy8spYrGYd/z48QcuLi6Nw4YNcxg0aFBtTk4OvbGxkczlcvkEQdSfOHEiNzIykrlt2zaLpqYmUt++feuio6PzqVQq6Orquk6bNq3k4sWLBhs2bHi8dOlS6x9++KFg4MCBsu3btzM3btxoqVKpSAEBAVXbtm2TAMAL23zwwQfS1vp3+PBh5g8//FAQGhrqmJubS3NwcGiSy+Uwbtw4VlJSkh6JRFJNnDix7Ntvvy0FANi1a5fJrFmz7KRSKeXXX3/N9fX1lRUXF9MGDx5crW7Tw8OjXv33uro6ypAhQxwzMzN1RCKR7NixY7lkMhmsra1FY8eOLT937pyhXC4nHTx48KGrq2tDYWEhdfTo0Q5VVVXUPn36yC5fvmyQkJCQbmVlJX/1fzXU2fDUG3WYuvCp/+zYscPYxMRE8eOPPz4KDQ11+PXXX42rqqqoc+fOLYuMjJSoR6AnTpzIvXfvnnZsbCwzPj4+IyMjI41MJquioqJMAADq6+vJQqGwPikpKaNlwcvLy6MtX77c+vLly1lpaWmpiYmJejExMUbtbdNSdnY2raysjObr6ysbPnx45e7du5kAALdu3dItKiqiPXjwIDUrKytt+vTpzXFuMpmMnJiYmLFly5b8adOmOQAATJ8+vXTmzJksDw8PYuHChZZ5eXk09frp6ek6W7duLcjOzk599OgR/fz5883RcaampvK0tLT0Tz/99MnatWstAAAWLVrUy8fHpzYtLS191KhRlUVFRVqd+6+EOgJHlN3I64z8OkNbp94jR46sOXTokPGCBQvsExISUlvb9uzZs4yUlBRdFxcXHgBAQ0MD2dzcXA4AQKFQYMqUKZXPb3P9+nW9fv361fbq1UsOADBu3LiKK1eu6E+ePLmqrW1a2r17N3P48OGVAACTJ0+umDp1Kmv58uUlXC63saCggB4aGmo7bNiw6pEjR9aotwkODq4AAAgMDJRKpVJyWVkZJSgoqMbLyyv56NGjhmfPnjUUi8X85OTkVAAAkUhU5+Tk1AQAIBAIZDk5OVot2qoEAHB3d5edOHHCGADgzp07+seOHcsGABg9enSNgYHBPxLe0ZuFhRJ1GYVCAVlZWdp0Ol1ZVlZGVReOllQqFWnMmDHlW7dulTz/My0tLWVr1xjbC3Jpa5uW/vzzT2ZZWRntyJEjTACA0tJSWnJyMl0kEjWmpKSkHT161CAyMtL84MGDzMOHD+cBvJiHqX5tYWGhCAsLqwgLC6vw9fV1/vvvv/VNTU0VdDq9uZMUCgXkcnlzA9ra2ioAACqVqlIvx3CatxueeqMus2LFCguCIBp27979cOrUqazGxkYSwLMCof77kCFDak6ePGkskUioAAAlJSWUrKysdk87Bw4cWHf79m1GUVERVS6Xw+HDh5mDBg1q9TT7effv36fLZDJKaWlpkkQiSZZIJMkzZswojo6OZhYVFVEVCgVMmTKl6vvvv5ckJyc3X+/dv3+/MQDAuXPn9BkMhsLExERx4sQJRm1tLRkAoLKykpyfn093cHB4+jrvlbu7uzQmJoYJAHDkyBGDmpoayuu0g7oGjihRh6mvUapf+/n5VYeFhZXFxMSYJiQkpBsbGytjY2NrFy1aZPXjjz8WTpw48QmPx+MLhULZiRMncpcsWSLx9/cnlEol0Gg01ZYtWx4RBNFmwbG3t29atmyZxMfHh1CpVCR/f//qSZMmVb1MX3fv3m3y4Ycf/uPUfPz48ZXBwcGOo0aNqpo6dSpLqVSSAABWrFjxWL2OsbGxwtXVlau+mQMAcPfuXd3Zs2fbUSgUlUqlIk2ePLnMx8dHdvLkScarvodr164tHD16tCOfzzf29PSUmpmZNRkZGeHp91sC8yjfcZhH2T3U19eTqFSqikajQVxcnN6MGTPsX+UrV29KT8mjxBElQm+B7OxsrbFjxzqpR9Xbt2/Pe9N9Qv8PCyXqtt5//32ngoICestlq1atehwUFFTT1jZvikgkakxPT3/rR5A9FRZK1G2dP38+5033AXUPeNcbIYQ0wEKJEEIaYKFECCENsFCiDuvuMWttmTNnTq9ly5ZZtPazhQsXWjo7OwsIguBzuVz+xYsX9QAArK2tRUVFRXhv4B2D/2Cow3pqzFpb4uLi9M6dO2eUnJycpqOjoyoqKqKqZyKhdxOOKFGXKC8vp7BYLOH9+/fpAADDhg1z2Lhxo+mXX35prZ7JM3z4cAcAgMjISKZIJOJxuVx+cHCwvVz+LFlMV1fXNSIiolfv3r25Fy5c0G8ZoLt9+3YmQRB8Npst+D/27jusybP7A/jJIoMEJOwRNiGDpdSAKEUQqbjqi9rXOsBaF9TiHrXWDrVqK9ViXXW0Fam1tXW3to4KKlXrRqYgQ2XJTghg1u8P3vBDBKImCInnc11eV3nyjDtgj+d+Hu5vYmNj7dXXbX9MZ+NTx6yVlZVRCgoKKAAthXXs2LHOHh4eQi6XK/j000+tAFqCe6dNm8bp27cvz8PDQ/j333+3Lm3Mysqii0QiTwcHB+/Vq1dbAQA8fPiQwmaz5XQ6XQUAYGtrK3d2dm5d5/7FF19YCQQCPpfLFdy4cYMG0NKdjh8/3rn9uVDvgB2lATl8+DCnoqJCp3mUVlZW0jFjxnSZStR+CePChQtLZ8yYUaOOWYuLiytXx6wBAHz//fdW6g60bcwalUpVTZ482XH79u3mc+bMqVJHpm3atKkEAOCjjz4CgP+PWbt27VqWpaWlPDg4mJuUlNRnypQpte2P6UhHMWuffPJJeduYNQCAysrK1vXW6pi1P/74gzlz5kwX9T55eXm0tLS0nNraWhKfz/davHjxozFjxtSvXbvWztnZ2WvQoEH1b7/9dvWIESNa16KrY9bWrVtnuW7dOmt1p9zRudqGa6Cegx0l0pp66q3+M2PGjBqAlpg1Pp/fuGTJEqfvv/++sKNj28as8Xg8wYULF0zu3btHBXi2mDUKhdIas9bVMW21j1k7ePAgGwCgbczawYMHTczMzFrXWncUswYAEBERUUun01W2trZyNpste/DgAdnU1FR5586dzG+++abI0tJSHhMT45aYmGje5lytMWttfyG+o3M90w8AdTv8QRgQTZ3fy/YqxKx1FqdGJpNh5MiR4pEjR4p9fHwak5KSzOPj46sAOo5Z6+pcqOdhR4m6jaHHrHV1jfT09NZO8caNG3QHB4cXil9DvQN2lEhrr2rMWmfq6+tJ8fHxjvX19SQSiaRydnZu/uGHH57riT3qXTBmTc9hzFr3E4lEnuoPNevpsfQ2r0rMGk69EUJIA5x6I4Olq5i1K1eu5Oh2ZEjfYKFEBgtj1pCu4NQbIYQ0wEKJEEIaYKFECCENsFAihJAGWCiR1vQtj1IkEnk6Ozt78Xg8gaurq3DDhg2dZlJ2lh9ZXFxMHjlypCuHw/Fyc3MThoSEuN++fZva0TmQ/sOn3khreppHee/111+XlpeXkzw8PLznzJlTpV6D3fb4jiiVShg9erT7xIkTq44fP34PACAtLY1eUlJC8fHxaX6eMSL9gB0l6ha9PY9Srb6+nkSn05VkMlnV1fESiYQQHBzskZCQYHH8+HEWmUxWLVmy5JH69aCgoMZhw4ZJlEolzJo1y0Gdablz504zAIDjx4+z+vfv7zl8+HBXZ2dnr7i4OPtt27axvb29+VwuV5CRkUEFABg7dqzzpEmTHAMCArgODg7eJ06cYI4fP97Z1dVVOHbsWGftfzLoRWBHaUAys5ZyGiS5Os2jNGZypQL+eoPKowQAiI6OdjUyMlIWFxfTVq1aVazuPDs6vr6+njh27FjXiRMnVs2ZM6dq9erVVr6+vh0uZ9y7d2+f9PR0elZWVkZpaSlZJBLxIyIiJAAA2dnZ9IMHD96zsrKSOzk5eVOp1Mr09PSsVatWWSUkJFjt2bPnPgBAXV0d+Z9//sn98ccf+/z3v//1OHv2bLa/v3+jj48PPy0tjR4UFNSo6eeGdAsLJdJaZ1Pv//znP/U///yz2ZIlS5yuXbuW0dGxbfMoAQCampqIVlZWcoBny6MEgNY8yilTptQ+Sx4lwP9PvUtKSsgDBgzgvfnmm/VcLvdxR8ePHj3afd68eWWxsbHVms57/vx51ltvvVVNJpOBw+HIAwICJBcuXGCYmpoqvb29G5ycnGQAAI6Ojs2RkZF1AAC+vr6NKSkpLPU5RowYUUskEqFfv35Sc3NzmUgkagQA4HK5jfn5+VQslC8fFkoDoqnze9l6ax5lW3Z2dnIvLy9pamqqMZfLfdzR8f3795ecPHnSdNasWdVEIhG8vb0bDx8+bNbR+boaW9u8SSKR2JpLSSQSQaFQtGZPqreTSCQwMjJ64hjMqOwZeI8SdZvemEfZnlgsJmZkZDA8PT07fQjz5ZdflrDZbPmUKVMcAQBGjRolfvz4MSEhIaH1aXlKSgrjxIkTzJCQEPHBgwfZcrkcSkpKyFeuXGEGBwc3vMjYUO+BhRJpTX2PUv0nLi7O/vbt29SkpCSLrVu33h82bJgkMDBQvGzZMlsAAHUe5ejRo138/f2b1HmUXC5XEBYWxr1//z6lq+u1zaPk8/lCHx8f6bPmUapFR0e78ng8ga+vL3/ChAmVwcHBXUao7d69+35zczNx9uzZDkQiEY4ePZp/5swZEw6H4+Xu7i78+OOP7RwdHWVTpkypFQqFjXw+Xzh48GDup59++sDR0bHjx+dIb2AepZ7DPErUkzCPEiGEEADgwxxkwHSVR4kQFkpksDCPEukKTr0RQkgDLJQIIaQBFkqEENIACyVCCGmAhRJp7VXMo2z/nnNycrpcTYT0Gz71Rlp71fIoAZ7/PSP9hh0l6haGnEfZ2blycnKM/P39PQUCAV8gEPBPnTplrH5txYoV1lwuV+Dp6SmIi4uzBwDIyMigBgcHewiFQr6/v7/njRs3tO6WUffAjtKAzMsq5mQ3NOk0j5JnTJNu4jtiHmWbPMr275nD4TSfOnUq387OTn7+/PlcBoOhSk9Pp7799tuud+7cyfr5559NTpw4YXbt2rVsFoulLC8vJwEATJ8+3enbb78t8vb2bj579qxxbGys46VLl3Kf92eEuh8WSqS1VzGPsqP3/PjxY8K7777rlJmZSScSiVBUVEQFADh16pTJ5MmTK1kslhIAwNraWlFXV0e8ceMGc/z48W5tj9c0btQzsFAaEE2d38tmqHmUnVmzZo21lZWV7Ndffy1QKpVAp9P91eMlEJ6sgQqFAlgslhzvc+oHvEeJuo2h5lF2pq6ujmRraysjkUiwdetWc4VCAQAt7zEpKclCLBYTAVreI5vNVjo4ODzes2ePGUDLB5b9888/9Bd5D6j7YaFEWnvV8ig722fevHkV+/fvN/f19eXl5ubS6HS6EgBg3Lhx9ZGRkbV+fn58Ho8nWLVqlQ0AwP79++999913Fp6engIPDw/hr7/+2ud53gN6eTCPUs9hHiXqSZhHiRBCCADwYQ4yYJhHiXQFCyUyWJhHiXQFp94IIaQBFkqEENIACyVCCGmAhRIhhDTAQom0pm95lM3NzYS4uDh7JycnLw8PD6G3tzf/559/NgFoyZ/kcrkCHo8n4HK5gn379rX+EjiDwej7Itdrm3qE9BM+9UZa07c8yvnz59uVlZVRsrOzM+h0uur+/fvkP//8k6V+PSUlJdfW1lZ+69YtamRkJPd5V/0gw4MdJeoWvTWPUiwWE3/88UfLXbt2FdPpdBUAAIfDkU+fPv2pxKHa2lqSiYmJov12pVIJs2bNcvDw8BByuVzBzp07zdSvdZQ7qaZQKCAqKso5Pj7e7gW/raiHYEdpQBYfvMXJLRPrdIrHtWFJvxznazB5lJmZmVRbW9vHbDZb2dn7CQkJ4apUKsKDBw+M9uzZc6/963v37u2Tnp5Oz8rKyigtLSWLRCJ+RESE5PLly/SOcicBAGQyGWHMmDEuAoGgcf369c/VTaOeh4USaU0f8yi7op56Z2RkUCMiIrjDhw/PMDU1bS2s58+fZ7311lvVZDIZOByOPCAgQHLhwgXGuXPnWO1zJ9XHxMXFOY0ZM6Yai6R+wkJpQDR1fi9bb8yjFAgEzaWlpUY1NTVEMzOzTrtKAAChUNhsbm4uu379Oi00NLQ1Xaiz63eUO6n22muvSc6fP28ilUrLGQwGJtHoGbxHibpNb8yjZLFYygkTJlTOmDHDsampiQAAUFRURNm6dSu7/b4PHz4kP3jwgOru7v647faQkBDxwYMH2XK5HEpKSshXrlxhBgcHN3SUO6k+ZtasWZURERF1I0eOdJPJnvr3AvVy2FEirbW/RxkWFlY3e/bsyqSkJItr165lmZmZKQ8ePChetmyZ7caNG0vUeZReXl7So0ePFqjzKJVKJVAoFFViYmIxl8t93Nn12uZRqlQqwpAhQ+qe58n0pk2bHs6bN8+ey+UKqVSqik6nKz7++OPWe5ohISFcIpEIcrmcsHLlygccDueJj2OcMmVKbVpaGpPP5wsJBILq008/feDo6Ch3dHSsv379OsPPz49PoVBU4eHhdd98801rp/zJJ5+Uz58/nxQVFeVy+PDhAhKJBEg/YB6lnsM8StSTMI8SIYQQAODUGxkwzKNEuoKFEhkszKNEuoJTb4QQ0gALJUIIaYCFEiGENMBCiRBCGmChRFozpDzK7vLZZ59ZqVfsAHSde9nWggUL7FauXGndnWNDmuFTb6Q1Q8uj7A47duywnjFjRrU6MAMAcy/1CXaUqFvoax7lb7/9ZuLn58cTCAT8yMhI17q6OiJASwc4Z84cez8/P56Xlxf/woULjEGDBnlwOByvL774whIA4Pjx4yyRSOQ5bNgwVxcXF+Ho0aNdlEolrF692qqiooISEhLCDQgI4LYfU/vcy6VLl9o4Ozt7BQUFce/evUttvz96+bCjNCSH3+NARaZuP3LASiCFMVteiTzK0tJS8ueff26bmpqaa2Jiovzwww9tVq1aZb1hw4ZSAAAOh/P45s2b2e+++y5n2rRpzpcvX87+3/WES5YseQQAkJWVRb958+Y9Z2dnmb+/P+/UqVPMFStWVGzbts1a3UGqr9dR7uX58+cZhw4dYqenp2fKZDLw8/MT9O3bV9p+rOjlwkKJtGYoeZTnzp0zzs/Pp4lEIh5AS9iuv79/ayrRW2+9VQsA4O3tLW1oaCCamZkpzczMlP+LkSP977UGdZycUCiU5ufnd5qE1FHu5d9//80cPnx4rXqKHhERgdPxXgALpSHR0Pm9bPqWR6lSqWDQoEH1x44dK+joWBqNpgIAIBKJYGRk1DoIIpEIMpmMAABApVJbt5NIJJDL5R0HVLbRNvcSADrNtEQ9B+9Rom6jb3mUgwcPbrh69Srzzp07VICW+5m3b9/WyT1CY2Njhfp+Z3ttcy/DwsIkJ06c6CORSAg1NTXEU6dOdfg0HL1c2FEirRlKHqWdnZ18x44dhRMmTHB9/PgxAQDg448/fujj49Os3XcIICYmpjIyMtLDyspKdvny5VyAjnMvORyO/D//+U+1l5eX0N7evlkkEj3TPwCoe2EepZ7DPErUkzCPEiGEEADg1BsZMMyjRLqChRIZLMyjRLqCU2+EENIACyVCCGmAhRIhhDTAQokQQhpgoURa07c8yrYpRAAAOTk5Rh4eHkJN+z5rhqS2jh8/zgoNDXXvjnOjF4NPvZHW9C2PUhvdkSHZHeNEuoU/HQPy0cWPOHk1eTqNWXM3c5euGrjqucM2qqqqSP7+/vwjR47c9fX1bR41apTL4MGDxfn5+VT1kkcul9t49OjRgq1bt7K3bdtmLZPJCP369WvYu3dvEZlMBgaD0XfmzJnlZ8+eNfnyyy8ffPTRR/YbNmy4//rrr0t37NjBTkhIsFGpVITw8PDabdu2PQSAp4554403nmsJoEQiIUyYMMElNzeX5uHh0aReD95e+wzJTz75xDo5OdkCAGDKlCmPVq5cWQEAEB4e7lZaWmrU3NxMnD17dvmiRYsqOxqnWCwmLl68mMNms+Xe3t4Yq9bLYKFEWtOnPEq16OhoVxqNpgRoiVMjElvuQm3YsMGKTqcrc3NzMy9fvkwfOHCgoO1xnWVI/vjjj+bXrl3LUqlU4O/vzx8yZIh44MCBjcnJyYXW1tYKiURC6Nu3r2Dy5Mk1NjY2irbjlEqlBFdXV+9Tp07lCIXC5pEjR7rq6meDdAMLpQF5kc5PF/Qxj3Lv3r33Xn/9dSlAyz3KkSNHevzv3Mz4+PgKAICAgIBGLpf7RHfXUYbkuXPnmMOHD681MTFRAgCMGDGi5u+//2YNHDiwcf369dYnTpzoAwBQVlZGycjIoNnY2DS0HefNmzdpDg4Ozd7e3s0AAJMmTaratWuXpab3gF4eLJSo2/TGPMpn8Sx5kG0zJDsbz/Hjx1kpKSmsq1evZrNYLKVIJPJsbGwkdjROzKDs3fCpN+o2vTGPUpNBgwZJ9u3bxwYA+Pfff2m5ubkd3vNtnyH5+++/9xGLxcT6+nri77//bhYaGiqura0lmZqaKlgslvLGjRu0W7duGXd0Lj8/v6YHDx4YZWRkUAEAfvrpJ7Yu3gvSHewokdb0LY+yK4sWLaqYMGGCC5fLFQiFQqm3t3dD29c7y5CcOHFiVb9+/fgALQ9zBg4c2NivX7+mb7/91pLL5Qrc3NyafH19Gzq6JoPBUG3evLlo5MiR7mw2Wx4QECDJysqi6+L9IN3APEo9h3mUqCdhHiVCCCEAwKk3MmCYR4l0BQslMliYR4l0BafeCCGkARZKhBDSAAslQghpgIUSIYQ0wEKJtKbveZSaiMVi4ujRo124XK7Aw8ND6O/v71lXV0esrKwkrVu3DtdkvwLwqTfSmqHnUX7++edWVlZWsqNHjxYAANy6dYtqZGSkKisrI+/evdtq2bJlj7S+COrVU+tOxAAAIABJREFUsFAakJLlH3Ka797VaR4l1cNDavf5GoPNo2QwGH1jYmIqUlNTTUxNTRVr1qx5sHTpUk5JSYnR+vXriydNmlRXWlpKcXJyal1S6evr2wwAsHDhQof79+9TeTyeICQkpH7UqFF1CQkJ1n///XceAEB0dLTja6+91hAfH1+VkpLCmDdvnqNUKiUaGRmpUlNTc1gsljIuLs7h3LlzJgAAMTExlR9++GHF836vUffDqTfSmrrwqf/s3LnTzNzcXKHOo/z222/N1HmUW7dufajuQI8ePVrQNo8yOzs7k0gkqrZv324OAKDObLx9+3Z224KnzqM8d+5cbmZmZsaNGzeMk5KS+nR1TGcaGxuJoaGh4oyMjCxjY2PFihUr7M+fP5/7yy+/5K1atcoeAGDmzJmVmzdvtvHz8+PFx8fbpaenUwEAEhISHnA4nObs7OzMHTt2POjsGk1NTYRJkya5bdq0qTgnJyczJSUlh8lkKhMSEiyLioqoGRkZmbm5uZnTp0+v0vZngboHdpQG5EU6P13QxzxKNQqFoho3blw9AIBQKGykUqlKKpWqEolEjQ8fPjQCAAgKCmosKChIP3z4sMmpU6dMgoKC+CkpKdnGxsbKZ7nG7du3aVZWVrKQkBApAACbzVYCAJw9e9Zk9uzZjygUCgAAWFtbK7o4DepBWChRt9GHPEoymaxSp5sTiUSgUqkqgJYirVAoWkMiTU1NlTExMbUxMTG10dHRcOTIEdOJEyc+UZApFIpKqfz/2qmOklOpVEAgEJ4adGfbUe+DU2/UbfQxj7Ijf/31l/GjR49IAC3T6NzcXJqzs/NjU1NTRUNDQ+v/Q25ubs15eXn0xsZGQlVVFenChQsmAAC+vr5N5eXlRikpKQwAgJqaGqJMJoPw8PD67du3W8pkLf9+lJeXk7rrPSDtYEeJtGZIeZQdyc3Npc2ZM8cJAECpVBLCw8PrYmJiaohEIvj7+0s8PDyEYWFhdTt27HgwatSoGj6fL3RxcWkSCoVSAAAajaZKTk7Oj4+Pd2xqaiLSaDRlampq7vz58x/l5uZSeTyekEwmq2JiYh4tX74cn6D3QphHqecwjxL1JMyjRAghBAA49UYGDPMoka5goUQGC/Moka7g1BshhDTAQokQQhpgoUQIIQ2wUCKEkAZYKJHW9DGP0tbW1rvtcsPw8HA3BoPRt6vjnid/Un0uhUIBU6dO5Xh4eAi5XK7Ay8uLn52d/dTKo8TERPPo6GjH53wr6CXBp95Ia/qYR8lisRSnTp1ivvHGG5LKykpSRUUFRdP5qqqqSM+bP7lr1y52WVkZJTs7O4NEIkF+fj7FxMTkmcI0UO+BhdKAnNmbxal+KNFpHiXbnikdEs03uDzKqKio6uTkZPYbb7wh2bdvX59Ro0bVbty4ka5+/aOPPrI+dOgQ+/Hjx4QRI0bUbty4saR9/uQXX3xRMmzYMPe6ujqSXC4nrFy5sqT9UsrS0lKKtbW1jERqWcbdNhjk66+/Nt+4caOtpaWlzM3NrcnIyAiXyfVSOPVGWtPHPMqIiAjxpUuXmOpQjejo6Gr1a7/99ptJXl4e7fbt21lZWVmZN2/eZPzxxx/M9vmTDAZDeeLEibzMzMyslJSU3OXLlzu0nc4DAEyZMqX69OnTfXg8nmDGjBkOFy9epAMAFBUVUdatW2eXlpaWff78+dzc3Fw6oF4LO0oD8iKdny7oYx4lmUxWiUQiya5du9hNTU1ET0/P1hCOkydPmqSmppoIBAIBAIBUKiVmZ2fTXF1dnwjqUCqVhHnz5jlcunSJSSQSoaKiwujBgwdkR0dHuXofNzc3WV5e3p1jx46xzpw5YzJ8+HDPvXv35tfX15PavoeoqKjq3Nxcre+/ou6BhRJ1m96eRzlp0qTqt99+233x4sUl7c8/b9680sWLFz8RNpKTk/PEQ5gdO3awq6qqyOnp6VlUKlVlb2/v3djY+NQsjU6nq9566636t956q97a2lr222+/9QkPDxcTCIT2u6JeCqfeqNv09jzKN954QxIfH186bdq06rbbIyMj65OSkizq6uqIAAAFBQWUhw8fktvnT9bV1ZEsLCxkVCpVdezYMVZJSclT475w4QKjsLCQAtDyD0d6ejrdycnp8euvv95w6dIlVllZGam5uZlw6NAhs+cdP3p5sKNEWtPXPEoikQifffZZefvtUVFR9RkZGbT+/fvzAAAYDIYyOTm5QCgUNrfNn/zkk0/KIiMj3b28vPhCoVDq4uLS1P5cZWVl5FmzZjk9fvyYCADg5+fXsGzZsgoGg6FaunRpSWBgIN/S0lLm4+MjbZuojnoXzKPUc5hHiXoS5lEihBACAJx6IwOGeZRIV7BQIoOFeZRIV3DqjRBCGmChRAghDbBQIoSQBlgoEUJIAyyUSGv6lkfZ1NREmDZtGofD4Xg5OTl5DRkyxC0/P58C8HTm5PHjx1mhoaHu2l4T6TcslEhr6lAM9Z/PP/+8y6zJxMRE2+e9hjqP0t/f/6nVL10d05H4+Hh7iURCLCgouFNUVHRn9OjRtWPGjHFXKpWtmZPPO77OyGRPLW9Hegh/PciA/LltE6fyfpFO8ygtOE7SN2LnGUwepVgsJv78888W9+7du60Oz5g7d27V3r17LY4dO8bauXOnZdvMyVGjRtU1NDSQhg0b5pqTk0P39vaWHj58uIBIJML58+cZCxYs4EilUqKZmZk8OTm50MnJSSYSiTxFIpHk8uXLzOHDh9d++umnTy2TRPoFO0qkNX3Ko8zMzKTa2to+ZrPZTwRH+vn5SdPT0+ntMycBALKysuhbtmy5n5eXl1FcXEw9deoUs7m5mRAfH+945MiR/IyMjKyYmJjKRYsW2avPV1tbS/r3339zsEgaBuwoDciLdH66oE95lEqlEggEwlMBByqVCjqLPfP29m5QR8QJhUJpfn6+EZvNlt+9e5ceFhbGVZ/X0tKydZ799ttvV3d4MqSXsFCibtMb8yiFQmFzSUkJtaamhmhmZtbaVd6+fZvx5ptvdphARKVSWy9IIpFALpcTVCoVwd3dvfHmzZvZHR3DYrHwc3EMCE69UbfpjXmUJiYmynHjxlXGxsZy1A97vvnmG/OmpibiqFGjxO0zJzvj4+PTVF1dTT59+rQxAEBzczPh6tWrmFBuoLCjRFrTtzzKzZs3P5w9e7aDi4uLF5FIBDc3t6bDhw/nEYlEsLGxUbTNnBw1alRdR+eg0Wiqn376KT8+Pt5RLBaTFAoFITY2tvy111575qfySH9gHqWewzxK1JMwjxIhhBAA4NQbGTDMo0S6goUSGSzMo0S6glNvhBDSAAslQghpgIUSIYQ0wEKJEEIaYKFEWtO3PEqRSOTp7Ozs5enpKejXrx/v1q1bVPX21NRUnaYvPY/ExETz6Ohox566PuocPvVGWussFKMziYmJtuvWresys7I9dR7l8x7T2brvvXv33nv99delGzZssJg/fz7n7Nmzec9zbvRqwUJpQKoP5nJkZQ067YgoNsZS9jiuweRRtjdkyBDJtm3brNtu27hxo8WdO3fou3fvvg8AkJCQYJGVlUWzsbGR0Wg01YoVKyreffddTkZGBv3SpUu5R44cYe3Zs8fiyJEjBZ2Nq7PtX3/9tfnGjRttLS0tZW5ubk1GRka4VK4Xwqk30po+5VG299tvv5nyeLzGttvefffd6r/++stUHdyxb98+i5kzZ1aFhoZKLl68yAQAuHnzJqOhoYHU3NxMSE1NZQ4aNEjc2bg6215UVERZt26dXVpaWvb58+dzc3Nz6br8uSDdwY7SgLxI56cL+pRHqRYdHe1Ko9GUDg4Ozdu3by9u+5qJiYly4MCB4gMHDph6e3s3yWQygkgkamxubibExMQY19TUEKlUqsrHx0dy/vx5xj///MPavHlzcWfjIhAI0NF2AHhie1RUVHVubi4mEPVCWChRt+mNeZRq6nuUnb0+c+bMyjVr1thwudymyZMnVwK05FI6ODg0b9myxUIkEkl8fX0bT58+zSoqKqL27du3KTMzs8Mi19V4OwsLRr0LTr1Rt+mNeZTPKiwsrKG0tNTo0KFD5u+++25rWnlQUJBky5Yt1oMHDxaHh4eLf/jhB0uBQCAlEomdjqur7ZcuXWKVlZWRmpubCYcOHTLT5XtAuoMdJdKavuVRPqsxY8bU3L59m2FpaalQbwsJCREnJibahIWFNZiYmCipVKpq4MCBEk3j6mz70qVLSwIDA/mWlpYyHx8fqUKhwBazF8I8Sj2HeZTdJzQ01H3evHnlb775prinx9JbYR4lQq+oyspKkrOzsxeNRlNikUQAOPVGBuxF8ygtLCwUhYWFd7p3dEifYKFEBgvzKJGu4NQbIYQ0wEKJEEIaYKFECCENsFAirelbzNr+/ftN+Xy+wNPTU+Dm5ib88ssvLQAAkpKS+uji/Mjw4MMcpDV9illrbm4mzJ071+mff/7JcnNzkzU2NhLUK4EOHz7cRy6X1/n7+zc9z3WQ4cOOEnWLqqoqkrOzs5c6FHfUqFEuCQkJFnFxcfbqlTyjR492AQDYunUr29vbm8/j8QQTJ050ksvlANASmTZv3jw7Hx8f3pkzZ5htg3V37NjB5nK5Ag8PD2FsbKy9+rrtj2k/rtraWqJcLidYW1vLAQDodLrK19e3+dSpU8anT5/us2LFCgcejyfIyMigpqWl0X19fXlcLlcwdOhQt0ePHpEAWgJ+Y2Nj7b29vfnOzs5eJ0+eZAK0FOZZs2Y5eHl58blcrkDdqSL9hx2lATl8+DCnoqJCp3mUVlZW0jFjxnSZStR+CePChQtLZ8yYUaOOWYuLiytXx6wBAHz//fdW6g60bcwalUpVTZ482XH79u3mc+bMqVJHpm3atKkEAOCjjz4CgP+PWbt27VqWpaWlPDg4mJuUlNRnypQpte2Pac/a2loxdOjQWkdHR5+BAwfWDx8+vG7mzJnVQ4cObQgPD68dOXJk3TvvvFMDAMDlcgUbN24sHjFihGTevHl2S5cutduzZ899AAC5XE5IT0/POnDggOlnn31mN2zYsNxNmzZZmJqaKu7cuZPV2NhI6N+/P2/UqFH1PB6v0+WYSD9goURa07eYtQMHDhRduXKl4o8//mAlJibanD592uTXX38tbLtPVVUVSSwWk0aMGCEBAJgxY0bV+PHjXdWvjx8/vgYAICgoqGHx4sVGAACnT582yc7OZhw9etQMAEAsFpMyMzNpWCj1HxZKA6Kp83vZenPMmkgkahSJRI0zZ86sdnd39waAQo0HtUGj0VQAAGQyGdRBFiqVipCQkFCsaeUP0j94jxJ1m94Ys1ZXV0c8fvw4S/315cuX6XZ2do8BAJhMpqK+vp4IAGBubq4wMTFRqO8/7t6923zAgAFdXmPo0KF127Zts1S/t9u3b1PV50P6DTtKpDV9illTKpXw5ZdfWs+ZM8eJRqMpGQyGcvfu3QUAAJMmTaqOjY113r59u/XBgwfzv/vuu4LY2Fin+Ph4oqOjY/P+/fsLuzr3/PnzKwsLC6ne3t58lUpFYLPZst9//x2XURoAjFnTcxizhnoSxqwhhBACAJx6IwP2ojFrCLWHhRIZLIxZQ7qCU2+EENIACyVCCGmAhRIhhDTAQokQQhpgoURawzxKZOjwqTfSGuZRIkOHHSXqFoaQR9n2eqWlpWR7e3tvgJYCPHPmTAculyvgcrmCNWvWWAEApKSkMPr27cvz9PQUeHt782tqaohSqZQwbtw4Zy6XK+Dz+YJjx46x2o8J9X7YURqQzKylnAZJrk7zKI2ZXKmAv/6VzKPsTEJCgmVRURE1IyMjk0KhQHl5OampqYkwadIkt+Tk5PyQkBBpdXU1kclkKlevXm0NAJCbm5t548YN2vDhwz3y8/PvMBgMXDusR7BQIq0ZYh5lV86ePWsye/bsRxQKBQBaiu+VK1foVlZWspCQECkAAJvNVgIApKWlMd9///0KAIC+ffs22dnZPU5PT6cFBAQ0Puv1UM/DQmlANHV+L5u+51GSyWSVQqEAAACpVEpoe30CgfDEIDrapmmsSH/gPUrUbfQ5jxIAgMPhNF+5csUYACA5OdlMvT08PLx++/btljJZS90vLy8n+fr6NpWXlxulpKQwAABqamqIMpkMBg0aJNm3bx8boCWfsrS01MjHxwcfFukZLJRIa+p7lOo/cXFx9rdv36YmJSVZbN269f6wYcMkgYGB4mXLltkCAKjzKEePHu3i7+/fpM6j5HK5grCwMO79+/cpXV2vbR4ln88X+vj4SJ83j9LZ2dmLx+MJPvvsM/u2eZSJiYk2fD5fkJGRQV22bFn57t27Lfv27currKxsbVPnz5//yMHB4TGPxxN6enoKdu/ezabRaKrk5OT8+Ph4R09PT8HgwYO5UqmUuGTJkgqFQkHgcrmC//73v247duwopNPp2GbqGcyj1HOYR4l6EuZRIoQQAgB8mIMMGOZRIl3BQokMFuZRIl3BqTdCCGmAhRIhhDTAQokQQhpgoUQIIQ2wUCKt6Vsepb29vXdpaWnrg8zjx4+zQkND3bs6Jicnx8jDw0Oo7bWRfsKn3khr+pRHidCLwI4SdYvemkepyYIFC+zGjx/vLBKJPB0cHLxXr15t1X6fzMxMIz6fL0hJSWEkJiaaR0REuAUHB3s4OTl5zZ4920G9X0dj3LVrl9n06dMdAABWrVpl5eDg4A0AkJGRQfX39/cEaOl458+fbycQCPhcLldw48YNTF3vYfjPrQGZl1XMyW5o0mkeJc+YJt3EdzSYPMpnkZeXR0tLS8upra0l8fl8r8WLFz9Sv3br1i3qhAkT3Hbv3l0QFBTUeOvWLXpmZibj1q1bmXQ6Xenu7u61aNGicjKZDB2NMSIiQrxp0yYbAICLFy8y+/TpIy8oKKCcPXuWGRgY2BrsYWFhIc/MzMxat26d5bp166yft5tGuoWFEmlN3/IoO0IgtKaoQURERC2dTlfR6XQ5m82WPXjwgAwAUF1dTR4zZoz7L7/8kv/aa6+1JgANGjSo3tzcXAEA4O7u3pSfn0999OgRubMxSqVSYk1NDbGkpMRo/PjxVX/99RfrwoULzKioqNZgj4kTJ9YAAIhEIunRo0dbk4tQz8BCaUA0dX4vW2/NozQzM5NXVlaSbG1t5QAttwnYbLZc/TqVSm29AIlEArlcTgAAYLFYCltb28fnzp1jti2URkZGbfdXyWSyLsNm/P39G7Zs2WLh5ubWFBoaKvn2228trl27xty6desD9T40Gk0F0BJJp74+6jl4jxJ1m96YRwkAEBQUJN69e7c5QMsDn+TkZPPBgweLNR1HoVBUJ0+ezN+/f7/59u3b2S86xuDgYPGWLVusg4ODJUFBQdK0tDSWkZGRUt2Vot4HO0qktfb3KMPCwupmz55dmZSUZHHt2rUsMzMz5cGDB8XLli2z3bhxY4k6j9LLy0t69OjRAnUepVKpBAqFokpMTCzmcrmPO7te2zxKlUpFGDJkSN2z5lECAKxdu7Z06tSpjp6engKVSgVhYWH1sbGxVc9yrImJifLPP//MGzx4MJfJZCpfZIxDhgyRzJ071yg8PFxMJpPB1tb2sYeHB4b59mKYR6nnMI8S9STMo0QIIQQAOPVGBgzzKJGuYKFEBgvzKJGu4NQbIYQ0wEKJEEIaYKFECCENsFAihJAGWCiR1vQpj3LBggV27733nn3bbWlpaXRXV1chAEBISIh7ZWUl6UXO3TbdaNOmTeZcLlegTg/at29fn/b7Y8al/sCn3khr+pRHGRMTUzVixAhu27Xl+/btY48dO7YaACAlJSXvea7Rkfz8fEpCQoLtzZs3s8zNzRV1dXXEtkHBSP9gR4m6RW/No/T19W02MTGRnz171li97ejRo+zo6OhqgP9PP8/JyTFydXUVTpgwwcnd3V04cOBAD4lEQsjIyKAKBAK++tj09HSqUCjkt71GaWkpxdjYWGlqaqoAADA1NVXyeLzHAADnz59neHp6Cvz8/HhfffXVU1mXqHfCf+UMyOKDtzi5ZWKd5lFybVjSL8f5GlQe5dixY6uTk5PZYWFhDWfOnDHu06eP3Nvbu7n9fsXFxbR9+/bdCwoKKho+fLjr3r17zeLi4qpZLJYiLS2NHhQU1Lhjxw6LiRMnPrFOPDAwUGphYSHjcDjeAwcOFEdFRdVMnDixDgDg3Xffdd64cWPxiBEjJLNmzXJof03UO2GhRFrTtzzKmJiY6kGDBvEVCsX95ORk9rhx46o72s/e3r45KCioEQCgb9++0sLCQioAwNSpUyt37txpIRKJ7h85csTs33//zWp7HJlMhtTU1LspKSmMv/76y2TZsmWcq1evGn/44YflYrGYNGLECAkAwLRp06rOnj1r2tVYUe+AhdKAaOr8Xrbemkfp7u4us7e3b/79999Zv//+u9nFixezOtqvfc5kY2MjEQAgJiamZv369XY//fST2NvbW2pjY/NUPBqRSITQ0FBpaGioNDIysn769OnOy5cvL28bEIz0B96jRN2mt+ZRAgCMHz++evHixRxHR8fmjgp4VxgMhiokJKRuwYIFjlOnTn0quamwsJBy4cKF1lsgV69eZdjb2z+2sLBQMJlMxZ9//skEAPj++++7zLREvQd2lEhr+pZHCQAQHR1ds2LFCs7nn3/+Ql14dHR09R9//GEWFRX1VMDG48ePCYsWLXIoLy+nUKlUFZvNlu3cubMYAGD37t2F06dPd6bT6cqwsDAM59ATmEep5zCPsmesXLnSuq6ujvT111+/8IeYGYJXJY8SO0qEntPQoUPdioqKqCkpKbk9PRb0cmChRAaru/IoMb7t1YOFEhksLGhIV/CpN0IIaYCFEiGENMBCiRBCGmChRAghDbBQIq3pUx7lvn37+oSHh7upv/7ggw9sHB0dvdRf//jjj6ZhYWHu7Y9LTEw0j46Odmy//f79++TQ0FB3T09PgZubmzAkJOSpYwEAxo4d6/zdd9+ZaTN21HPwqTfSmj7lUYaFhUnmzp3rpP768uXLTCaTqXj48CHZ3t5efvHiReaAAQOeeTnk0qVL7cPCwuo/+uijiv+dj/48Y0T6ATtK1C16ax6lnZ2dnMViKe7cuUMFACgvL6eMGjWq5uzZs0wAgCtXrjCDg4MlAABff/21ubOzs1f//v0909LSnjoXAEBZWRmFw+G0LrcMCAhoBABQKpUQHR3t6ObmJhw8eLB7ZWVla8W2t7f3nj9/vp1AIOBzuVzBjRs3aAAAJSUl5KCgIA+BQMCfOHGik52dnTcG/vYO+EMwJIff40BFpk7zKMFKIIUxWwwqj9Lf319y7tw5pkKhABcXl+agoKCGP/74w3TChAm1OTk59Ndff72hqKiIsm7dOrtr165lsdlsRVBQkKeXl5e0/bnee++9iqlTp7pu27ZNOnjw4PrY2NgqZ2dnWVJSUp+8vDxqTk5OxoMHDyje3t7CqVOntuZWWlhYyDMzM7PWrVtnuW7dOusDBw4ULVu2zC4kJES8du3asoMHD5rs37/f4jl/WqibYKFEWtO3PMqgoCBJWlqasUKhgICAAMnrr7/esHr1aru0tDSGi4tLE4PBUKWmpj5xjaioqOrc3Nyn7o+OHTu2ftCgQemHDh0yPXnypKm/v78gPT09IyUlhfXWW29Vk8lkcHZ2lg0YMEDc9riJEyfWAACIRCLp0aNHzQBautnDhw/nAQCMGzeu3sTE5Kn4NtQzsFAaEg2d38vWW/MoQ0JCJDt27LBSKpWEWbNmPTIzM1M2NzcTTp8+zRKJRK33J581O9La2loxe/bs6tmzZ1eHhoa6//XXX0xNx9NoNBVAS+ScXC4naHpfqGfhPUrUbXprHmW/fv2aHj16RLl8+TJTnWDu5eXV+P3331sOHDhQor7GpUuXWGVlZaTm5mbCoUOHOnxiffToUZZYLCYCANTU1BCLioqoLi4uj0NCQsS//PILWy6XQ1FREeXSpUssTeMSiUSSpKQkNgDAb7/9ZlJfX/9CnwaJdA87SqQ1fcujJBKJ4Ovr2yAWi0lUKlUFABAYGCjZv3+/RWhoaIP6GkuXLi0JDAzkW1paynx8fKQKheKpFvHff/9lzJ8/35FEIqlUKhVhypQplSEhIdLg4GDpmTNnTDw9PYUuLi5NIpFI3P7Y9tatW1cybtw4V4FAYDZgwACJpaWlrE+fPjj97gUwj1LPYR6l4WhsbCSQyWQVhUKB06dPG8+ZM8fpeX7tqidgHiVC6KXKy8szeuutt9zUnfWOHTsKe3pMqAUWSmSwuiuPsrt4e3s3Z2Vl9eoO8lWFhRIZLMyjRLqCT70RQkgDLJQIIaQBFkqEENIACyVCCGmAhRJp7VXLo7x16xZVJBJ58ng8gaurq/Dtt992ar9PeyEhIe6VlZW40kZP4VNvpLVXLY/yvffec4yPjy9Xrwa6cuWKxgzKlJSUvOcZO+pdsKNE3cKQ8ygrKiooTk5OrUssRSJRo/q/y8rKKMHBwR5OTk5es2fPdlBvt7e39y4tLSXn5OQYubq6CidMmODk7u4uHDhwoIdEIiEAAKSkpDC4XK7Az8+PN2vWLAcPDw+hTn4YSGvYURqQjy5+xMmrydNpHqW7mbt01cBVmEfZJo/yvffeKx8+fDi3b9++DUOGDKl77733qiwsLBQAAJmZmYxbt25l0ul0pbu7u9eiRYvK3d3dn0hNKi4upu3bt+9eUFBQ0fDhw1337t1rFhcXVz19+nSXrVu3Fg4dOrQhLi7OvqOxo56BHSXSmnrqrf4zY8aMGoCWPEo+n9+4ZMkSp++//76wo2Pb5lHyeDzBhQsXTO7du0cFeLY8SgqF0ppH2dUxbanzKM+dO8dU51Feu3bNuLM8ShqNpoqKiqpWHz937tyq9PT0jKioqOrU1FRW//79eY2NjQQAgEGDBtWbm5srGAyGyt3dvSk/P5/a/vr29vbN6tTl/snUAAAgAElEQVSivn37SgsLC6mVlZWkhoYG4tChQxsAAGJiYqrbH4d6DnaUBkRT5/eyGXIepbOzs2zevHlV8+bNq/Lw8BBevXqV/r/rtw6ORCKpZDLZUydpv09jYyMRw2l6N+woUbcx1DzKgwcPmqjHX1xcTK6trSW1vWf5IiwtLRXGxsbKM2fOGAMAqHMpUe+AHSXS2quWR3ny5EmTRYsWOVKpVCUAwKeffvrA0dFR/qLfP7UdO3YUzp4924nBYCgHDhwoZrFYmEXZS2AepZ7DPErDUVdXRzQ1NVUCACxfvtymtLSU8t133/Wq2yntYR4lQuil+vnnn00TEhJsFQoFwd7evvnHH38s7OkxoRZYKJHB0rc8yhkzZtSof2MA9S5YKJHBwjxKpCv41BshhDTAQokQQhpgoUQIIQ2wUCKtlJWVkdTxahYWFr5WVlY+6q+bmpqeWpVSXl5O+uKLLyw1nVcmkwGLxfIDALhz5w6VRqP1axvlJpfL4auvvrKYNm0ap6Pji4qKKCEhIe6enp4CNzc3oTo6rbNzIdQVfJiDtGJjY6NQB1wsWLDAjslkKj777LPyzvZ/9OgRec+ePZZLlix59DzXcXZ2bnrWKDeZTAaLFy+2GzZsWN0HH3zwCADg8uXLrVFoz3MuhACwo0TdaMWKFdYeHh5CDw8P4Zo1a6wAABYtWmRfWFhI4/F4gri4OPvq6mpiYGAgVyAQ8LlcrmD//v2mL3KtN99802XGjBkOAQEB3Pfff9+hvLycwuFwWteWBwQENHZ1PEJdwY7SgJQs/5DTfPeuTmPWqB4eUrvP1zz36pC///6b8csvv5hfv349Sy6Xg7+/Pz88PFy8YcOGh+PGjaOpO7rm5mbCH3/8kWdmZqZ8+PAhOSgoiPf222/XtT+furgCAAQGBoq///77p8ZUUFBATUtLyyWRSHDgwAHT6dOnu3zzzTfSwYMH18fGxlY5OTnJnvVcCLWFhRJ1i3PnzrFGjRpVw2KxlAAAkZGRtX///Tdz5MiRT/yyt0qlgvfff9/hypUrTCKRCGVlZUalpaVkCwuLJ24cPst0eezYsTUkUsunLfz3v/+tCwkJST906JDpyZMnTf39/QV37ty586znQqgtLJQG5EU6v+7yrBkCW7duNa+vrydlZGRkUigUsLa29pFKpZ3nm3WByWQq235tY2OjiI2NrY6Nja0ODg72OH36NMvPzw+n4Oi54T1K1C1CQ0PFJ06cMJNIJIS6ujriyZMn+4SFhUlMTU0VDQ0NrX/v6urqSJaWlnIKhQKHDh0yqaiooOji+keOHGGpP2KhurqaeP/+faqLi0uzLs6NXj3YUaJuERoaKh07dmxV3759BQAA06ZNe6T+bBkfHx8pl8sVhIeH13344YflkZGR7l5eXnxvb2+pk5OTTorZ5cuXjefPn+9IJpNVKpWKMG3atIqBAwc2qj8rB6HngTFreg5j1lBPelVi1nDqjRBCGmChRAghDbBQIoSQBlgoEUJIAyyUCCGkARZKhBDSAAslQghpgIUSaY1EIvnzeDyBp6enQCAQ8E+dOmWs7TnT0tLoBw4ceKYkoSFDhrj5+fnx2m5bsGCB3cqVK63b75ufn08ZMmSIm5OTk5eDg4N3dHS0Y2Nj4wstmUSvDiyUSGtUKlWZnZ2dmZOTk7lq1aqHy5cvd9D2nFevXmWcOHFCY6GsrKwkZWRkGNfX15Oys7ONutpXqVTCmDFj3EePHl1bVFR0p7CwML2pqYkQFxen9XiRYcMljAbkzN4sTvVDiU5j1tj2TOmQaP4zh23U1dWRTE1N5QAtKeNjx451lUgkJIVCQdi8eXPRsGHDJAwGo29MTExFamqqiampqWLNmjUPli5dyikpKTFav3598dixY+vXrl1r19TUROTxeMyFCxeWdvYxrklJSWbh4eG11tbWsh9++IG9du3ass7GduzYMRaVSlXOnTu3CgCATCbD9u3b7zs7O/ts2rTpoampqbKzY9GrDTtKpLXm5mYij8cTuLi4COfOnev08ccflwIA7Nmzhz1kyJC67OzszKysrIyAgAApAEBjYyMxNDRUnJGRkWVsbKxYsWKF/fnz53N/+eWXvFWrVtnTaDTVBx98UDJq1Kia7OzszK4+6/qXX35hT548uTomJqb6119/ZXc1zvT0dLqvr6+07TY2m620t7d/nJGRgWvAUaewozQgz9P56ZJ66g0AcPr0aeN33nnHJTc3NyMwMLBh1qxZzjKZjDhu3LiaoKCgRgAACoWiGjduXD0AgFAobKRSqUoqlaoSiUSNDx8+7HL63Nb9+/fJRUVF1IiICAmRSAQymaz6999/af3792/qaH+VSgUEAuGpcAPMO0CaYEeJdCo8PLyhpqaGXFpaSo6MjJSkpqbm2NvbP546darLN998Yw4AQCaTVURiy189IpEIVCpVBQBAIpFAoVA884OVH374gV1fX0/icDje9vb23g8fPqQmJSV12lV6e3s33rx584kHTdXV1cSqqiqyj49Ph8UVIQAslEjHbty4QVMqlWBtbS3Pzc01sre3ly1cuLBy8uTJldevX3/m+6cmJiYKiUTS5d/PgwcPsg8dOnT34cOH6Q8fPky/fPly5uHDhzstlKNHjxY3NTUR1QVbLpdDXFwcZ9q0aRVMJhPbStQpLJRIa+p7lDweTzBhwgTXbdu2FZLJZPjzzz9ZAoFAyOfzBUeOHDFbsmRJp5/O2F5kZKQ4NzeXzuPxBDt37jRr/3pOTo5RSUmJUVhYWIN6G4/He8xkMhVnz541BgDYuHGjrbW1tY/6D5FIhMOHD+f99ttvZk5OTl5mZmZ+RCIR1q9f3+kDIIQAMI9S72Ee5Ys7deqUcUxMjOuBAwfyg4ODpZqPQO29KnmU+DAHvbKGDh3aUFJSkt7T40C9HxZK1Ot9/fXX5tu2bXtilU3//v0lSUlJxT01JvRqwam3nsOpN+pJr8rUGx/mIISQBlgoEUJIAyyUCCGkARZKhBDSAAsl0lpP5VEmJiaam5mZ+fJ4PIG7u7tw2LBhrmKxmAiAeZRIt7BQIq31ZB6lOmEoLy8vg0KhqPbs2fPUKh41zKNELwp/j9KA/LltE6fyfpFO8ygtOE7SN2Ln9do8SjWZTAZSqZTIZrMVne2DeZToRWGhRFpTr/Vubm4mVFZWUn7//fdcgP/Po1y/fn2ZXC4H9bRYnUe5bdu2h0OHDnVT51Fev36d9s4777hMmjSp7oMPPii5evWq8d69e7v8pfJjx46Z8Xg85qNHjyjOzs5Nb7/9dm1n+2rKo1THwCHUHhZKA/I8nZ8u9VQeJUDL1Hvv3r3FSqUSoqOjHVeuXGnz+eefdxhygXmU6EXhPUqkUy8zj7ItIpEIo0ePrr148SKzs30wjxK9KCyUSKdeZh5le+fPn2c5Ozs3d/Y65lGiF4WFEmmtJ/Io1f53j1LA5XIFt2/fpn/++eel6tcwjxLpCoZi6DkMxXhxmEepvVclFAMf5qBXFuZRomeFhRL1ephHiXoaTr31HE69UU96Vabe+DAHIYQ0wEKJEEIaYKFECCENsFAihJAGWCiR1noqjxIA4ODBgybe3t58FxcXIY/HE4wYMcL17t27RgAAY8eOdf7uu++e+mX1q1ev0gIDA7nOzs5eHA7Ha/78+XYKRaehQwhhoUTa66k8yn///Ze2cOFCxx9++KGgoKAgIzs7O3PixIlVeXl5nQZrSCQSwn/+8x/3JUuWlBUWFt7JzMzMvHbtmvHq1auttB0zMlz4e5QGpPpgLkdW1qDTPEqKjbGUPY7bK/Mo16xZY7tgwYLSfv36tQZaTJo0qa6r8e3cudP8tddek0RFRdUDALBYLOW2bduKQ0NDPT/++OOKZ//OoFcJFkqktZ7Ko8zNzaUtXbr0udZpZ2Rk0Pr16/fEckWhUNjc1NRErKysJFlYWOAcHD0FC6UBeZ7OT5d6Mo9SraysjDR48GDPpqYmYnR09KPPPvuswwAOlUpFIBCeTnLDhReoK3iPEunUy8yj5HK5TVeuXGEAANjY2Ciys7Mzo6OjH0kkElJnxwiFwsZr1649cXsiMzPTyMzMTI7dJOoMFkqkUy8zj3L58uVlCQkJttevX6ept0ml0i6PmTlzZtW///7LOnz4MAug5eHOe++95/jBBx+UPOvY0KsHCyXSWk/lUYpEosYvvvjifnR0tIuLi4uwX79+vJycHNrUqVOr1PvMnz/fSZ1H6efnx2MymapDhw7dXbt2ra2zs7OXpaWlX2BgoCQ2NrZaF98LZJgwFEPPYSiGdpKSkvp88MEHnLNnz+ZwudzHPT0efYOhGAi9AqZMmVL74MGDdCySqCv41Bv1ephHiXoaTr31HE69UU/CqTdCCCEAwEKJEEIaYaFECCENsFAihJAGWCiR1noqjzIxMdE8Ojra8XnP3dTURJg2bRqHw+F4OTo6eoWGhrqrMywR6ggWSqS1nsqjfFHx8fH2EomEWFBQcKe4uPjOmDFjakaPHu2O4b2oM/h7lAbk8OHDnIqKCp3mUVpZWUnHjBnTK/MoO7Jr1y6zS5cuGe/atevBqlWrrHbs2GH94MGD9IyMDGp0dLTzuXPn7v78888W9+7du00mt/z1nzt3btXevXstjhw5YqLOqUSoLSyUSGs9lUfZkYiICPGmTZtsAAAuXrzI7NOnj7ygoIBy9uxZZmBgoCQzM5Nqa2v7mM1mK9se5+fnJ71z5w4NCyXqCBZKA/I8nZ8u9YY8SjVHR0e5VCol1tTUEEtKSozGjx9f9ddff7EuXLjAjIqKqlUqlUAgEJ5aZYELL1BX8B4l0qmXmUfZGX9//4YtW7ZYuLm5NYWGhkrOnz/PvHbtGjM8PFwiFAqbS0pKqDU1NU/83b99+zYjICBA2tk50asNCyXSqZeZR9mZ4OBg8ZYtW6yDg4MlQUFB0rS0NJaRkZHS3NxcYWJiohw3blxlbGwsRy6XAwDAN998Y06lUpVDhw6VvMj1kOHDqTfSmvoeJUDLFLZtHmViYqINmUxWMRgMRXJycsGznjMyMlK8YcMGWx6PJ+jqYc7BgwfN//zzzz7qr9PS0rKGDBkimTt3rlF4eLiYTCaDra3tYw8Pj9YPINu8efPD2NhYB1dXV6+mpiYim82WX716NUvd5SLUHoZi6DkMxdBOcXExOSIigjt9+vSKRYsW4ffxOb0qoRjYUaJXmqOjo1z9IAqhzmChRL0e5lGinoZTbz2HU2/Uk16VqTfevUYIIQ2wUCKEkAZYKBFCSAMslAghpAEWSqS1nsyjNDMz8+XxeAIXFxfhp59+aqXtdRHqCBZKpLWezKMcNWpUTXZ2duY///yTvWnTJtu8vDyKttdGqD38PUoDkpm1lNMgydVpHqUxkysV8Nf3+jxKGxsbhaOjY/P9+/cp7u7uspKSEvI777zjpE4j+uqrr4ojIiIa6urqiO+++67j7du3GQAAy5cvL5k6dWqtNt8jZPiwUCKt9YY8yrt37xo1NzcTAwICGgEAZs2axVmwYEH5G2+8Ibl7967RG2+84XHv3r2MZcuW2ZqYmChyc3MzAQAePXpE6q7vCzIcWCgNyPN0frrUk3mUx44dM3N3d2cVFhbSEhISChkMhgoA4OLFiyZ3796lq/eTSCSkmpoaYmpqqslPP/10T73d0tISP/8BaYT3KJFOvew8ylGjRtXk5eVlnDx5MnvlypWc4uJiMkBLitHVq1ezsrOzM7OzszMrKipum5mZKVUqFRAIWkdeolcMFkqkUz2VRxkeHt4QFRVVtX79emsAgEGDBtWvX7++9Sl4WloaHQBg8ODB9V999VXrdpx6o2eBhRJpTX2PksfjCSZMmODaNo9SIBAI+Xy+4MiRI2ZLliwpf9ZzRkZGinNzc+k8Hk+wc+dOs2c55uOPPy47cOCARU1NDfHbb7+9f/36dWMulytwc3MTfvPNN5YAAGvXri2tra0leXh4CD09PQW///4760XfN3p1YCiGnsNQDNSTMBQDIYQQAOBTb6QHMI8S9TSceus5nHqjnoRTb4QQQgCAhRIhhDTCQokQQhpgoURa66mYtQULFtitXLnyiYc89vb23qWlpV0+pLxx4waNx+MJ+Hy+ICMjg6rtWJHhw0KJtNaTMWsv4pdffukTGRlZm5WVlSkUCpu74xrIsGChRDrVPmbttdde8+TxeAIPDw/hyZMnmQAADAajb2xsrL1QKOQHBQVx//77b4ZIJPJ0cHDwTk5ONm1qaiKsXbvW7tixY2bPszKnrZycHCNXV1fhhAkTnNzd3YUDBw70kEgkhAMHDph+++231snJyRYBAQFcXb9/ZJjw9ygNyLysYk52Q5NO8yh5xjTpJr5jl6lEvSFmrSPFxcW0ffv23QsKCioaPny46969e83i4uKqL1++/IjJZCo+++yzZ15SiV5tWCiR1noqZo1AIHT4S8Dq7fb29s3qa/bt21daWFiI9yPRC8FCaUA0dX4vQ0cxa7/++qvp1KlTXeLj48vnzJlTpauYNXNzc3lpaekThbWhoYFkYWGhqKurIxkZGbUWUhKJpGpsbMRbTeiFYKFEOtU+Zs3FxeXxwoULKxsaGoj/i1mrepbzPEvM2pAhQyRTpkxxqampKTUzM1P+8MMPfXg8npRMxr/WSLfwbxTSmvoeJUBLYG7bmLXExEQbMpmsYjAYiuTk5IJnPWdkZKR4w4YNtjweT9DZZ+YEBAQ0zpgxoyIwMJBHIBDA3NxctmfPnkIdvjWEAADXeus9XOuNehKu9UYIIQQAOPVGegBj1lBPw6m3nsOpN+pJOPVGCCEEAFgoEUJIIyyUCCGkARZKhBDSAAsl0pq+5VGKRCLP1NRUnYaHIMOGhRJpTd/yKBF6XlgokU719jzKtvsoFAqIiopyjo+Pt1OP6/3337f39PQU+Pr68u7fv08GAMjNzTUaMGAAl8vlCgYMGMC9e/eukVwuBwcHB2+lUgmVlZUkIpHo/8cffzABAPz9/T3v3LlDXbBggd348eOd1e9t9erVVtp+f1HPwF84NyCLD97i5JaJdTql5NqwpF+O8zWoPEoAAJlMRhgzZoyLQCBoXL9+fZl6XAMGDJBs3rz54ezZsx02b95s+cUXX5TOnj3bceLEiVXvv/9+1aZNm8xjY2M5p0+fzndxcWm6fv067e7du1SBQCA9d+4cc/DgwQ1lZWVGXl5ezQAAeXl5tLS0tJza2loSn8/3Wrx48SN1WhLSH9hRIq2pp94FBQUZhw4duvvOO++4KJVKCAwMbNi/f7/FggUL7K5cuUI3MzNTAjydRzlo0CDxy86jjIuLc2pbJNXjmjBhQh0AgL+/f0NRUZERAMCNGzeMZ86cWQ0AEBsbW33t2jUmAEBQUJD4zJkzrJSUFNbixYtL//nnH1Zqaqqxr69vg/qcERERtXQ6XWVraytns9myBw8eYHOih/CHZkA0dX4vg77kUb722muS8+fPm0il0nIGg6ECAGg7LjKZDHK5vMuxDB48WLJ161bL8vJyo6+++urhxo0bbc6cOcMaNGiQWL1P2+6RRCJpPCfqnbCjRDrVPo/S3t5etnDhwsrJkydX/i+P8pk8ax7ln3/+aVpTU0MEAHiePMpZs2ZVRkRE1I0cOdJNJpN1uW/fvn0bdu3aZQYAsGPHDvb/tXfncU2c+R/Av5kkEGICBlDEcAqZJBMOEUSlsB71gFUpigoeUKm+ivLbHovna7WLtbW7bt1dl/WsrtuCStFV8XpppcoKXmsRUYSEQ0VEueSGcOT6/WGHZa0QlKgkft//9MVk5plngv3yPHN8xs/PrwUAYMKECa05OTk8giB0XC5XJ5PJlElJSUMmTpzY0tfjRMYBCyXqN/ocpUQioSIjI0d0z6OkKEomlUqp48ePC1avXt3nd9SEhIQ0FxUVWfR2Mad7HqVEIqG++eabIS+SR7lhw4Yqb29v5ezZs101Gk2P6+3cubMsOTnZliRJKiUlxWbHjh0PAQAsLCx0w4YN6/Tz82sFAAgKCmppbW0l/P392/raB2QcMBTDyGEoBnqTMBQDIYQQAODFHGQEMI8SvWk49TZyOPVGbxJOvRFCCAEAFkqEENILCyVCCOmBhRL12+uIWUtMTLQRCATeEomEcnd3lwUHB4+gnx3vyalTp/jd+xIeHu7yz3/+84UDNhDCQon67XXFrM2cObNeoVAUlJSU5LPZbN2+fft6LXoXLlzgZ2Vl8frbF4SwUCKDeh0xayqVCpRKJWFtba0BADh48KCVl5eXRCqVUgEBAeTDhw9ZhYWFZklJSUN27dplJ5FIKHrfFy9e5Pn4+EgcHBw8cXSJ+gpvDzJyA+H2ICaT6SsSidq6x6wFBQUpExIS7Nrb2xndY9YEAoGWwWD4pqamFs+bN69pypQpbkqlkrhw4UIJHbOmUCgKEhMTbbrHrCUmJtokJCQ42NnZqWpqatguLi7t//nPfwpZLBbU1NQwbWxsNARBwF/+8hdbuVzO2bNnT3l8fPxwHo+n2bhxYxXA06m3UqkkTp06dS83N5cza9Ys97Kysjtv8rszdm/L7UF4w7kpSfs/R6guMOwrDoZSSgjb3msqET31BgD48ccfB8XExLgWFRXljx07tjU2NtZFpVIRc+bMqacjz56NWTM3N9f2JWZt5syZ9UlJSWVarRaio6Odfv/73w/76quvKu/fv28WFhbmUFNTw+7s7CQcHR07emojNDS0gclkgq+vb3ttbS375b4U9LbBqTcyqOfFrAmFws7Fixe7btu2zQbgf+PMXiZmjSAICA0Nbbh8+TIPAOA3v/mNU1xcXHVRUVHBtm3bHnR0dPT475rD4XRNoXA2hfoKR5SmRM/I73V4NmbN1dW1c8WKFU9aW1uJn2PWavvSjr6YtaysLL6Li0sHAEBzczPTyclJBQDw7bff2tDr8Pl8TVNTE7Ofh4QQFkrUf3TMGsDTUVr3mLXExMRhLBZLx+VyNQcOHLjf1zZDQkKat2zZYi+RSKgVK1ZUAAD8fHGHp9Vqwd7evvPgwYOlAADr1q17PH/+fDc7O7tOPz+/1rKyMnMAgPDw8IY5c+a4nTlzZvDWrVvxuXD00vBijpEbCBdz0NvrbbmYg+coEUJIDyyUCCGkBxZKhBDSAwslQgjpgYUSIYT0wEKJEEJ6YKFE/VJZWcmkX1Vra2vrPXToUC/65/b29l88ZVNVVcX805/+NERfuyqVCvh8/kgAgDt37phzOJxREomEcnNzk0VFRTlptdp+9/29995zTU5OHvzscrVaDVFRUU4ikUhGkiTl6ekpLSoqMgMAsLOz8yJJkqKP8cKFC/2OlEMDH95wjvpl2LBhGvo572dDKJ6npqaGtW/fviGrV6+ueZH9uLi4tCsUioKOjg7GuHHjxCkpKVYLFy5spD9XqVTAZhvm0e3du3db19fXsxQKRT6TyYTi4mIzgUDQ9eLvK1euFNra2vb8InBkcnBEiV6Z9evX24lEIplIJJJt2rRpKADAypUrhaWlpRyJRELFxcUJ6+rqiLFjx5IURUlJkqRSUlKsemvT3Nxc5+fn11JcXMxJS0vjBwQEkDNmzBghk8monvYJ8PRNjiRJUmKxmJozZ44LvfzChQt8OnYtKSlpMABARUUF287OTsVkPn36USQSdWJhfLvhiBK9EhkZGdzDhw/b5OTkyNVqNfj6+konT57cvGXLlkdz5szh0KPQjo4OxpkzZ0oEAoH20aNHrICAAMn8+fMbe2q3qamJuHTpEn/Tpk2PAAByc3MH3bp1K18kEnX2tE+tVgtbt24ddu3aNYWdnZ2mqqqq6/nvJ0+esG7cuKH46aefLCIjI0dER0c3vP/++/Xjx48XS6VSflBQUNPixYtr6eQjAICAgAAxQRBgYWGhvXnzpuJVfo9oYMBCaUI+u/yZY0l9iUFj1twF7sov3vnihcM2/v3vf/NnzpxZz+fztQAAISEhDRkZGbwZM2Y0dV9Pp9PBRx995HD9+nUeQRBQWVlpVlFRwbK1tVV3X48ehRIEoZs+fXrDrFmzmtLS0vgjR45sEYlEnb3ts6OjgxEWFlZvZ2enAQCg/wvwNHaNIAgYM2ZMW3V1tRnA0xFkSUnJnZMnT1qeP3+eHxwcLD548ODdGTNmNAPg1PtthIUSvRJ9zRDYsWOHTVNTEzM/P7+AzWaDnZ2dl1Kp/MVFIPoc5bPLuVxu11Wdnvap0+kYDMbz09t6il3jcrm6iIiIxoiIiEZbW1v1sWPHBtOFEr19sFCakJcZ+b0qEydObI6Li3PZsGFDpUajYZw9e3ZwSkrKPSsrK01ra2vXufHGxkbmkCFD1Gw2G44dO2ZZXV390ldketqnTqeDyMjIEWvWrKmip97dR5XPysrK4jo5OamcnZ1VGo0G7ty5Y+Hn59f6sv1Cxg8LJXolJk6cqAwPD6/18fGhAAA++OCDGn9//zYAAC8vLyVJktTkyZMb161bVxUSEuLu4eEh9fT0VDo7O/eYTt6ffX7yySeVgYGBEiaTqfPy8mo9dOjQg57aefToETs2NtZZpVIROp0OfH19W1atWvVCV+mRacGYNSOHMWvoTcKYNYQQQgCAhRIhhPTCQokQQnpgoUQIIT2wUCKEkB5YKBFCSA8slAghpAcWStRvDAbDNywszJX+WaVSgUAg8J44caL7i7bl7+8vPnLkiGX3ZRs3bhy6aNEip/72Mzw83EUoFHpKJBLK1dVVtmLFCvve+pGZmfmL5+YbGxuJBQsWODs6Onq4u7vL/Pz8xJhJafqwUKJ+s7Cw0BYWFlq0tLQwAACOHTtmaWdnp3qZtubOnVubkpJi3X3ZkSNHrBctWlTX1zbUanWPn3355ZflCoWiID8/vyA1NdVWoVCYvcj2CxcudBEIBOrS0tI7JSUl+UlJSferq6vxCTcTh4USGcS7777bePjw4cEAACkpKdbh4eFdhS0jI4Pr4+MjkUqllI+Pj+TWrVsLLGcAABE5SURBVFvmAADZ2dkcT09PqUQioUiSpPLy8syjoqLqz58/b9XW1sYAACgsLDSrrq5mT506teXUqVN8f39/cXBw8AhXV1dZaGioK510LhQKPVeuXGnv6+sr3rdvn0Bff5VKJQEAQCcN9bS9RqOB2bNnu3z88cfD8/PzzW/evDnob3/72yM6q5KiqM7IyMjGwsJCM1dXV1lERISzSCSShYaGuqalpfFHjRolcXZ29sjIyOACPA03njt3rou/v7/YwcHB88svvxz6nO6hAQb/EpqQx79b59hRXGzQmDVzkUg5/KtNesM2oqKi6hISEuwjIiIa5HI5d8mSJbVXrlzhAQB4e3u3X79+XcFmsyEtLY2/evVqhx9++OHu3//+9yFxcXFVy5cvr2tvb2eo1Wrg8Xg6b2/v1iNHjlgtWrSo4bvvvrMODQ2tJ4inf9PlcrlFbm7uPRcXF5Wvr68kPT2dN23atBYAAA6Ho71x40Zhb/1cv369w+bNm+3LysrMP/jgg2qhUNg1fOy+/d69e4eqVCpGWFiYK0VRbZs3b648cOCAFUVRShbr+f/bPHz4kJOamnrP19f3gZeXl/TAgQM22dnZioMHDw7etGmT/cSJE+8CAJSUlHCuXLlS2NDQwJRKpR6rVq2qMTc3x2eJBzAcUSKDGDNmTFt5ebn5nj17rCdPnvw/wbt1dXXMX//6124ikUi2evVqx6KiIg4AwLhx41r//Oc/269bt25YcXGxGY/H0wEAzJs3ry41NVUAAHD06FHrqKiortGpp6dnq5ubm4rJZIJMJlPevXu3a+ocHR1dr6+f9NS7oqLiVmZmJj89Pb3r/OKz28fFxTnTRbIv34FQKOzw9/dvYzKZQJJk26RJk5oIgoBRo0Ypy8vLzen1pk6d2mBhYaGzt7dXW1tbq8rLy3HAMsDhL8iE9GXk9yoFBwc3JCQkOJ47d66w+3m7NWvWCMePH9+cnp5+t7Cw0GzSpEliAIBly5bVBQUFtR47dswqJCSE3LFjR2loaGjzwoULG9avX+946dIlbnt7OxEYGKik2+o+8mIymaBWq7uCJulpdF9YWVlp33nnneaLFy/ypkyZ0vq87f38/FqysrIslUplFZfL1Y0cObJdLpdzNRoN0FPv7szMzLr6RhBEV9Ylk8kEjUbT1c/ejgENTDiiRAazfPnyJytWrHhMR5vRmpqamA4ODp0AALt377allxcUFJhJpdKO9evXV0+dOrUhNzfXAuBpERs7dmzz0qVLXWbPnt3nizgvQqVSwY0bN3ju7u49xrrFxsY+mTp1auOMGTPcVCoVyGSyDi8vr9b4+Pjh9LnRvLw88/379//iTY7ItGChRAbj5uam+uyzz6qfXb5mzZrKDRs2OIwaNUqi0fw3Lzc5OdmaJEmZRCKhiouLObGxsbX0Z5GRkXWFhYUW3afdhrB+/XqHn181K5NKpcro6OiG3tbfsGFDlbe3t3L27NmuGo0G9u/fX1pVVcV2dnb2IEmSWrJkiYujo2OnIfuIBh7MozRymEeJ3iTMo0QIIQQAeDEHmaCoqCinn376idd92fLly6s++eST2p62Qag3WCiRyUlOTi57031ApgWn3gghpAcWSoQQ0gMLJUII6YGFEiGE9MBCifrtbcqjFAqFniRJUj/ftE51f1YcmS4slKjf3qY8SgCAixcvFikUigKFQlFAPyeOTBsWSmQQb0MeZU9tNTY2EuPGjSMpipKSJEl1f/Z727ZtNiRJUmKxmKJH3Y8fP2ZNmzbNzcPDQ+rh4SE9d+4cjkoHOLyP0oScT5I71j1qMWgepbWQp3w3Wop5lN2i1saPH08SBAFmZmba27dvK7hcrvb06dMl1tbW2oqKCtaYMWMkCxYsaMjJyeFs2bLF/urVqwp7e3t1VVUVEwAgNjbWMT4+vmratGktxcXFZtOmTRPdu3cv/yV+Peg1wUKJDEJfHmVERIRraWkph8Fg6FQqFQPgaR7lli1b7MvLy80iIyPrPT09OwD+m0e5aNGihqNHj1rv3bu3lG6LzqMEgJfOo4yJialvbGwkgoKCyPT09EH09Pl5eZRhYWF1z+ZRXrx4scje3r6rwGq1Wsann37qcO3aNR5BEFBdXW1WXl7O+uGHHyxnzpxZT69rZ2enAQC4fPmyZXFxsQW9fUtLC7O+vp4QCAR9jolDrxcWShPSl5Hfq2TqeZQ9tbV7927r2tpaVl5entzc3FwnFAo929raCJ1OBwwG4xfb6XQ6yM7OltNBxWjgw3OUyGBMPY+yJ42NjUxbW1uVubm57uTJk/zHjx+bAQAEBwc3nThxwrqyspIJAEBPvQMDA5s2b97c9a6cK1euWDy/ZTRQYKFEBvM25FE+z9KlS+tu3bo1yMPDQ7p//35rV1fXdgAAPz+/9hUrVlQEBQVJxGIxFRcX5wgA8M033zzMyckZRJIk5ebmJtu2bdsQQx4jMjzMozRymEeJ3iTMo0QIIQQAeDEHmSDMo0SGhoUSmRzMo0SGhlNvhBDSAwslQgjpgYUSIYT0wEKJEEJ6YKFE/WZMeZRDhw71opOJKioqWEKh0LO/7SLTh4US9Zsx5VEymUxdYmKibY8rIPQcWCiRQRhLHmVsbGz1zp077Z59dlur1UJsbKyDSCSSkSRJ7dmzRwAA0Ns+s7KyuKNHjxbLZDJpYGCg6MGDB2yDfqlowMD7KE3IDzu3Oj55+MCgeZS2js7Kacs/NZk8Smdn587Ro0e37Nixw2bevHldcXBJSUmD8/LyLORyeX5FRQXL399fOnXq1Jae9jlhwoTWjz/+2On06dMlw4cPV+/Zs0ewcuVK4eHDh0tf/ttGAxUWSmQQxpJHCQCQkJBQ8d5777nPmTOnq59ZWVn8efPm1bFYLHB0dFSPGTOm5dKlS1wrKyvt8/ZpbW2tLi4utpg0aRIJ8HREOmTIkJc63YAGPiyUJqQvI79XyVjyKD08PDooilJ+9913XVP03sJhnrdPnU7HcHd3b8vNzVX0ZZ/IuOE5SmQwxpRHmZCQULF9+/Zh9M/jx49v/te//mWtVqvh8ePHrOvXr/OCgoJ6fHGYl5dXe11dHevHH38cBADQ0dHByM7O5ryKvqI3DwslMhhjyKOk+fn5tctksq6RalRUVINMJmuTSqWyCRMmkJ9//nm5k5NTj5fPORyO7vvvv7+7du1aB7FYTMlkMurixYu8ntZHxg3zKI0c5lGiNwnzKBFCCAEAXsxBJgjzKJGhYaFEJgfzKJGh4dQbIYT0wEKJEEJ6YKFECCE9sFAihJAeWChRvxlLHuX58+cHeXl5SSQSCTVixAhZfHz8cICnCUHp6emD+ts+Ml1YKFG/GUse5ZIlS1x37979QKFQFBQVFeUvXLiwDgDgwoUL/KysLHyqBvUIbw8yIXX/KnJUVbYaNGaNPWyQ0noOqTdsg86jjImJqafzKOmYtYyMDG58fLxTe3s7weFwtN9+++19b2/vjuzsbE5MTIyrSqViaLVaOHLkyN2oqKj6r776StjW1sawsLDQPZtHuXHjxuHW1taqwsJCC09PT2VaWtp9giBAKBR6zp8//0lGRoZlbGxs9YcffviLJKG6ujqWk5OTCgCAxWKBr69ve2FhoVlSUtIQgiB0hw4dstm6dWvZiBEjOt9//32X2tpalo2NjTopKalUJBJ1hoeHu/D5fM2tW7cG1dTUsL/44ovymJiYegCAzz77zO7YsWPWnZ2djOnTpzf89a9/fWzI3wN6s3BEiQwiKiqqLjU1VaBUKhlyuZw7bty4rkAJOo9SLpcXJCQkPFq9erUDAACdR6lQKApu374td3V17Rw2bJiGzqMEAHheHuX27dsflpSU5JeVlZmnp6d3jQTpPMrnFUkAgA8//LBKKpV6TJkyxe3rr7+2VSqVDLFY3BkdHV2zbNmyKoVCURAcHNyybNkypwULFtQWFRUVRERE1C5fvtyRbqOqqoqdnZ2tOH78eHFCQoIQAODo0aOWJSUlnNu3b8vlcnlBbm4u98yZMzhCNSE4ojQhfRn5vSrGkEe5ZcuWipiYmLpTp05ZHjp0yObw4cM2169f/0XQ782bNwedOXPmLgDA8uXL6z7//HMH+rPQ0NAGJpMJvr6+7bW1tWwAgLNnz1pmZmZaUhRFAQAolUpCoVBwQkJCWl74i0QDEo4okcHQeZTR0dH/cz6RzqMsLi7OP3nyZElnZycB8DSP8vjx4yUWFhbakJAQ8sSJE3wAgIULFzZcvnzZ8lXkUcpkso41a9bUXLlypVChUFhUVlYyX+QYORxO1/7pQBmdTgeffvpphUKhKFAoFAVlZWV3fvvb32JQiQnBQokMZqDnUX7//fdW9Ptu8vLyOEwmU2dra6vh8/ma5ubmroLp4+PTunfvXsHP/bX28/PrdWQYEhLSlJycbNvY2EgAANy/f5/96NEjnK2ZECyUyGAGeh7l/v37bUaMGOEhkUio6Oho1717995nsVgQHh7ecPr06cESiYQ6e/Ysb+fOnWXJycm2JElSKSkpNjt27Oj1lMbs2bOb5s6dWzd69GgJSZLUrFmz3BoaGl5opIoGNsyjNHKYR4neJMyjRAghBAB41RuZIMyjRIaGhRKZHMyjRIaGU2+EENIDCyVCCOmBhRIhhPTAQokQQnpgoUT9Zix5lP7+/uLMzMyudKXCwkIzkUgk07cdl8v16e++kXHDQon6zVjyKBF6WXh7kAlJS0tzrK6uNmge5dChQ5VhYWEmkUfZm8TERJtTp04NbmtrI8rKysxDQkIadu3aVd59nYqKClZISIj72rVrK3g8nranvhw/fpy/du1aR41GA97e3sqkpKQH165ds/jDH/5gf+7cubv79+8fvHTp0hENDQ03tVotkCTpUV5enufv7y/29fVtuXTpkmVzczNz165dpcHBwZhANADgiBIZhDHkUepTUFDATUtLuyeXy/NPnDghKCkpYdOfPXz4kDVt2jT3hISEx5GRkY099UWpVDJiY2NdU1NT7xYVFRWo1Wr4+uuvhwQGBirz8/O5AACZmZk8d3f3tszMTG5GRsYgHx+frmKoVqsZeXl58s2bNz/cuHHj8Jc5DmR4OKI0IX0Z+b0qxpBHyWAwfhFs0H1ZYGBgk42NjQYAwN3dvf3u3bvm7u7uKrVazZg0aZJ469atD6ZPn95V1J7XF0tLS42Dg0OHl5dXBwDA4sWLa7dv3z6UzWZXOzs7t+fk5HBycnIGffTRR1UZGRl8jUbDeOedd7ranDt3bj0AQEBAQOuqVau6jg29WTiiRAYz0PMoBQKBura2tmtwUFNTwxIIBF0nNM3MzLq33VXQmUymztPTs/XMmTNW3dt7Xl96C5kJCAhoOXHihBWbzdbNnDmz6erVq7yrV6/y3n333WZ6HTrvksVigUajYfTYGHqtsFAigxnoeZS/+tWvmpOTk63pTMp//OMfNkFBQc16NgMGgwGHDh0qLSoq4vzud78b1tu6I0eObH/06JHZnTt3zAEAkpKSuvYxYcKElt27dw8dPXp0y/Dhw9X19fWse/fucXx9fdsNcHjoFcJCiQxmoOdRxsfHP+HxeFqJREKJxWKqtbWVSEhIqOrLtiwWC06cOHEvMzOT/8c//nFIT+txuVzdrl27SufOnetGkiRFEASsXLmyBuBpoaytrWVPmDChBQCAoqg2sVjcRp9/RQMX5lEaOcyjRG8S5lEihBACALzqjUwQ5lEiQ8NCiUwO5lEiQ8Opt/HTarVavI0EvXY//7vT+4pgU4CF0vjdqampscJiiV4nrVbLqKmpsQKAO2+6L68DTr2NnFqtXlpZWbm3srLSA/APH3p9tABwR61WL33THXkd8PYghBDSA0cgCCGkBxZKhBDSAwslQgjpgYUSIYT0wEKJEEJ6/D/ROadG84z/kwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "alpha_100 = np.logspace(-4, 2, 100)\n", - "coef = []\n", - "for i in alpha_100:\n", - " ridge.set_params(alpha = i)\n", - " ridge.fit(x_onehot, y_log)\n", - " coef.append(ridge.coef_)\n", - "\n", - "df_coef = pd.DataFrame(coef, index=alpha_100, columns=x_onehot.columns)\n", - "title = 'Ridge coefficients as a function of the regularization'\n", - "axes = df_coef.plot(logx=True, title=title)\n", - "axes.set_xlabel('alpha')\n", - "axes.set_ylabel('coefficients')\n", - "plt.rcParams['figure.figsize'] = 16, 4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### test prediction" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "best_ridge_test_y = para_search_ridge.best_estimator_.predict(test_onehot)\n", - "best_ridge_test_y = np.expm1(best_ridge_test_y)\n", - "best_ridge_test_y\n", - "sub = pd.DataFrame()\n", - "sub['SalePrice'] = best_ridge_test_y\n", - "sub = sub.set_index(np.arange(1461, 2920))\n", - "sub.to_csv('(1) ridge_log(y)_onehot_submission.csv')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Lasso regularization" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Find best lambda" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'alpha': 0.0001232846739442066}\n", - "Lowest RMSE found: 0.14547505715137055\n" - ] - } - ], - "source": [ - "lasso = linear_model.Lasso(normalize=True) # create a ridge regression instance\n", - "\n", - "# find the best alpha (lambda) for lasso \n", - "grid_param = [{'alpha': np.logspace(-4.5, 2, 100)}]\n", - "para_search_lasso = GridSearchCV(estimator=lasso, param_grid=grid_param, scoring='neg_mean_squared_error', cv=5, return_train_score=True)\n", - "para_search_lasso.fit(x_onehot, y_log)\n", - "\n", - "print(para_search_lasso.best_params_)\n", - "print(\"Lowest RMSE found: \", np.sqrt(np.abs(para_search_lasso.best_score_)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### RMSE for all train data using best lasso" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 0.12111931813978087\n" - ] - } - ], - "source": [ - "# fit best lasso equation to all train data \n", - "best_lasso_y = para_search_lasso.best_estimator_.predict(x_onehot)\n", - "print(\"RMSE: \", np.sqrt(np.mean((y_log-best_lasso_y)**2)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### RMSE across different grid search lambda" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", - " DeprecationWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", - " DeprecationWarning)\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "error = pd.DataFrame(para_search_lasso.grid_scores_)['mean_validation_score']\n", - "error = np.sqrt(np.abs(error))\n", - "alpha = pd.DataFrame(para_search_lasso.grid_scores_)['parameters'].apply(lambda x: x.get('alpha'))\n", - "plt.scatter(alpha, error)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Variable importance for best lambda" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "alpha_100 = np.logspace(-4.5, 2, 100)\n", - "coef = []\n", - "for i in alpha_100:\n", - " lasso.set_params(alpha = i)\n", - " lasso.fit(x_onehot, y_log)\n", - " coef.append(lasso.coef_)\n", - "\n", - "df_coef = pd.DataFrame(coef, index=alpha_100, columns=x_onehot.columns)\n", - "title = 'Lasso coefficients as a function of the regularization'\n", - "axes = df_coef.plot(logx=True, title=title)\n", - "axes.set_xlabel('alpha')\n", - "axes.set_ylabel('coefficients')\n", - "plt.rcParams['figure.figsize'] = 16, 4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### test prediction" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "best_lasso_test_y = para_search_lasso.best_estimator_.predict(test_onehot)\n", - "best_lasso_test_y = np.expm1(best_lasso_test_y)\n", - "sub = pd.DataFrame()\n", - "sub['SalePrice'] = best_lasso_test_y\n", - "sub = sub.set_index(np.arange(1461, 2920))\n", - "sub.to_csv('(2) lasso_log(y)_onehot_submission.csv')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Elastic net regularization" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'alpha': 0.000200923300256505, 'l1_ratio': 0.6000000000000001}\n", - "Lowest RMSE found: 0.14284287262599676\n" - ] - } - ], - "source": [ - "elasticnet = linear_model.ElasticNet(normalize=True) # create a ridge regression instance\n", - "\n", - "# find the best alpha (lambda) for lasso \n", - "grid_param = [{'alpha': np.logspace(-10, 6, 100), 'l1_ratio': np.arange(0, 1.1, 0.1)}]\n", - "para_search_elas = GridSearchCV(estimator=elasticnet, param_grid=grid_param, scoring='neg_mean_squared_error', cv=5, return_train_score=True)\n", - "para_search_elas.fit(x_onehot, y_log)\n", - "\n", - "print(para_search_elas.best_params_)\n", - "print(\"Lowest RMSE found: \", np.sqrt(np.abs(para_search_elas.best_score_)))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### RMSE for all train data using best lasso" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE: 1.361468015951288e-15\n" - ] - } - ], - "source": [ - "# fit best elastic net equation to all train data \n", - "best_elas_y = para_search_elas.best_estimator_.predict(x_onehot)\n", - "print(\"RMSE: \", np.sqrt(np.mean(y_log-best_elas_y)**2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### RMSE across different grid search lambda" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", - " DeprecationWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", - " DeprecationWarning)\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "error = pd.DataFrame(para_search_elas.grid_scores_)['mean_validation_score']\n", - "error = np.sqrt(np.abs(error))\n", - "alpha = pd.DataFrame(para_search_elas.grid_scores_)['parameters'].apply(lambda x: x.get('alpha'))\n", - "plt.scatter(alpha, error)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "/Users/kellyho/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "data": { - "image/png": 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qqZd2sBadNThYnUUt/FqlRyoYVERuQSwYFKlcDIRLyO33h9/4pQgI1lah79daLtl7f/bnefbmr2c9n/2BiL755htOWFiYSCwWS8LCwkRlZWWs3t5exvbt232OHz/uJhKJJHv37nXr3yWOi4vjJycn+4WFhYkmTZoU+sknn7gREZlMJlq5ciUvMDBQOnv27MDo6OhA67WE9H2sAAAgAElEQVSBOjs7bT7//PMn9u3bd43D4ViIiNzc3Mw7d+68QURUU1NjP3nyZOnKlSt5UqlUUldXZ79ixQpeSEiIODAwUPrqq6/6EBH94x//mPDcc89Nts574sQJ55iYmMCf922OH+jsAgAAAADAiN3405t++tpazsOckxUU1O2zbatmuDF6vd5GJBJJrMdarZapUCi0RESrV6/2S09Pb543b56utrbWft68eUFXr16tkMlkvSUlJdV2dnaUm5vrvH79+kknT56se+ONN26oVCrHrKysa0R3lkT3j9Xc3GynUqmqL1686PD8888HpqSktGdlZblpNBr7mpqaiuvXr9uGhISEJCcntw6Wa2VlJcvb27vPzc3NPNTz1NfXO+zdu7f+s88+u0ZEtHPnzutcLtdkNBopKioquLi4mP388893rF271r+jo8NmwoQJ5uzsbLelS5e2jfzN/rqh2AUAAAAAgMcei8UyV1dXV1qPlUqlh0qlciQiOnPmzITa2lq29ZpOp2O2t7fbtLW1MePj4wPq6+sdGAyGxWAwMEYSa/HixbeZTCaFh4f3tra22hERffvtt05LlixpZzKZxOPxjJGRkZ0jzX3Xrl0ee/bs4d6+fdv2f//3f6uIiLy9vfvmzJnTZR3z97//3f3TTz/1NBqNjFu3btmVlZU5TJs2rWfWrFkdhw8fdklJSWn/+uuvXXbv3t040ri/dih2AQAAAABgxO7XgX0ULBYLqVSqKicnJ0v/8y+99BIvOjq6Mz8/v66mpsY+JiYmeCTzOTg43J3HYrH86P+RkEgk+qamJvv29nYbNzc389q1a1vXrl3bGhQUJDWZTAwiIg6Hc7frW11dbb97925uaWlp1RNPPGGKi4vj9/b22hARLV++vO1vf/vbRE9PT9OUKVO6h+sWw4/hm10AAAAAABjTZsyY0ZGRkTHRelxUVMQmIuro6GBOmjSpj4goMzPT03p9woQJJp1ON6paaObMmbrc3Fw3k8lEGo3Gtri42Hmosc7Ozubly5e3/P73v+d1d3cziIiMRiMN1Vlub29nstlss7u7u0mj0dieOnXq7k7RCxYs6KyoqODs3bvXc9myZVjCPAoodgEAAAAAYEz7+OOPNefPn3cUCoUSgUAg3b179xNERBs2bLj5zjvvTJo6darIZDLdHR8bG9upVqvZ1g2qRhIjKSmp3dvbu08oFEpTUlL8ZTJZl6urq2mo8bt27bru5eVlEIlEUrFYLHnqqadE8fHxLf7+/oaBY6dPn94TEhLSHRQUJH3xxRf54eHhOus1W1tbmjNnjvb06dMu1t2jYWQYo2nHAwAAAADAr09ZWVm9TCZredR5PGpardbGxcXFfPPmTeZTTz0lPnPmTDWPxzM+6rzGs7KyMk+ZTMZ/kHvxzS4AAAAAAMAIKBSKoI6ODqbBYGC8/vrrTSh0H28odgEAAAAAAEagpKSkZuA5hUIh0Gg0rP7ntm7d2hgXF9fxy2UGg0GxCwAAAAAA8IDy8/PrHnUOMDhsUAUAAAAAAADjDopdAAAAAAAAGHdQ7AIAAAAAAMC4g2IXAAAAAAAAxh0UuwAAAAAA8NjjcDhh/Y+VSqVHYmIi70HmKioqYh85csTFenzo0CGXP/3pT14/Ncf+ent7Gb/73e/8/Pz8Qvz9/UPmzJkjqKursyMiamlpYf75z39+wjr2xIkTzrNnzw4cOMdnn33mOnfuXIH1+I033vDi8Xgh1uPPP//cJSYm5p774A4UuwAAAAAA8KuiUqk4//73v+8WuytWrNBu27bt5sOM8fLLL/vqdDqb77///nJDQ8PlxYsX3/7tb38baDabqbW1lbl///6J95sjJiZGd+HCBSfrcXFxsZOTk5Pp+vXrtkREZ86ccZo+fbruYeY9nqDYBQAAAACAMe3GjRu28+bNE4SEhIhDQkLEX331lSMR0TfffMMJCwsTicViSVhYmKisrIzV29vL2L59u8/x48fdRCKRZO/evW79u8RxcXH85ORkv7CwMNGkSZNCP/nkEzciIpPJRCtXruQFBgZKZ8+eHRgdHR1ovTZQZ2enzT/+8Q/Pjz76SGNre+evva5du7bV3t7efPz4ced169ZN0mg0LJFIJFm9evUkIqKuri7m/PnzJwcEBEgXL14cYDabycfHx+js7Gy6fPkyi4ioubnZbtGiRe1ff/21ExFRSUmJ08yZM1HsDgF/ZxcAAAAAAEbs/8uq8mu7ruM8zDndfZ265ySKNcON0ev1NiKRSGI91mq1TIVCoSUiWr16tV96enrzvHnzdLW1tfbz5s0Lunr1aoVMJustKSmptrOzo9zcXOf169dPOnnyZN0bb7xxQ6VSOWZlZV0jurMkun+s5uZmO5VKVX3x4kWH559/PjAlJaU9KyvLTaPR2NfU1FRcv37dNiQkJCQ5Obl1sFwrKytZ3t7efe7u7ub+55988snu8vJy9l//+tfGhQsXsqurqyuJ7ixjrqqqYl+8ePEqn883hIeHi/Lz853mzZunCw8P1506dcrJZDJRQECAPioqqisvL89l+fLlt2tqatjPPPNM14O99fEPxS4AAAAAADz2WCyW2VocEt0pUFUqlSMR0ZkzZybU1tayrdd0Oh2zvb3dpq2tjRkfHx9QX1/vwGAwLAaDgTGSWIsXL77NZDIpPDy8t7W11Y6I6Ntvv3VasmRJO5PJJB6PZ4yMjOwc6n6z2UwMBsMy8LzFYiEGY/AUQkNDuwQCgYGISCqVdtfV1dkTEUVFRemKioocTSYTTZs2TffMM890bdmyxaeoqIgTEBDQy+Fw7okDd6DYBQAAAACAEbtfB/ZRsFgspFKpqpycnH5U+L300ku86Ojozvz8/Lqamhr7mJiY4JHM5+DgcHcei8Xyo/9HQiqV6m/cuMFqb2+3cXNzu9vdvXTpEuc3v/nN7cHuYbFYdwMwmUwyGo0MIqLo6GhdZmbmRLPZzFi9evUtNzc3s16vZxQUFDjL5XIsYR4GvtkFAAAAAIAxbcaMGR0ZGRl3N3wqKipiExF1dHQwJ02a1EdElJmZ6Wm9PmHCBJNOpxtVLTRz5kxdbm6um8lkIo1GY1tcXOw81NgJEyaYly5d2pKWluZnNBqJiGj37t0evb29NosWLep0cXExdXV1jSj+1KlTe2/dumVXXFzsFBUV1UNEFBIS0vPpp58+8fTTT6PYHQaKXQAAAAAAGNM+/vhjzfnz5x2FQqFEIBBId+/e/QQR0YYNG26+8847k6ZOnSoymUx3x8fGxnaq1Wq2dYOqkcRISkpq9/b27hMKhdKUlBR/mUzW5erqahpq/AcffHCdxWKZAwICQvz9/UOOHj3qlpube8XGxoa8vLxM4eHhuqCgIKl1g6qh2NjYkEwm63J3dzdau7+RkZG6xsZG1uzZs/G97jAYo2nHAwAAAADAr09ZWVm9TCZredR5PGpardbGxcXFfPPmTeZTTz0lPnPmTDWPxzM+6rzGs7KyMk+ZTMZ/kHvxzS4AAAAAAMAIKBSKoI6ODqbBYGC8/vrrTSh0H28odgEAAAAAAEagpKSkZuA5hUIh0Gg0rP7ntm7d2hgXF9fxy2UGg0GxCwAAAAAA8IDy8/PrHnUOMDhsUAUAAAAAAADjDopdAAAAAAAAGHdQ7AIAAAAAAMC4g2IXAAAAAAAAxh0UuwAAAAAA8NjjcDhh/Y+VSqVHYmIi70HmKioqYh85csTFenzo0CGXP/3pT14/NUerzz77zHXu3LkC6/Ebb7zhxePxQqzHn3/+uUtMTEzgcHPI5fLgwsJCDhGRr69vaFNTEzYXHiUUuwAAAAAA8KuiUqk4//73v+8WuytWrNBu27bt5sOaPyYmRnfhwgUn63FxcbGTk5OT6fr167ZERGfOnHGaPn267mHFg8Gh2AUAAAAAgDHtxo0btvPmzROEhISIQ0JCxF999ZUjEdE333zDCQsLE4nFYklYWJiorKyM1dvby9i+fbvP8ePH3UQikWTv3r1u/bvEcXFx/OTkZL+wsDDRpEmTQj/55BM3IiKTyUQrV67kBQYGSmfPnh0YHR0daL02kI+Pj9HZ2dl0+fJlFhFRc3Oz3aJFi9q//vprJyKikpISp5kzZ+qIiFasWMELCQkRBwYGSl999VWfX+J9/VqgFQ4AAAAAACN2cs/7fi2aBs7DnNPTz797XtormuHG6PV6G5FIJLEea7VapkKh0BIRrV692i89Pb153rx5utraWvt58+YFXb16tUImk/WWlJRU29nZUW5urvP69esnnTx5su6NN964oVKpHLOysq4R3VkS3T9Wc3OznUqlqr548aLD888/H5iSktKelZXlptFo7GtqaiquX79uGxISEpKcnNw6VL7h4eG6U6dOOZlMJgoICNBHRUV15eXluSxfvvx2TU0N+5lnnukiItq5c+d1LpdrMhqNFBUVFVxcXMyeNm1az095n3AHil0AAAAAAHjssVgsc3V1daX1WKlUeqhUKkciojNnzkyora1lW6/pdDpme3u7TVtbGzM+Pj6gvr7egcFgWAwGA2MksRYvXnybyWRSeHh4b2trqx0R0bfffuu0ZMmSdiaTSTwezxgZGdk53BxRUVG6oqIiR5PJRNOmTdM988wzXVu2bPEpKiriBAQE9HI4HAsR0d///nf3Tz/91NNoNDJu3bplV1ZW5oBi9+FAsQsAAAAAACN2vw7so2CxWEilUlU5OTlZ+p9/6aWXeNHR0Z35+fl1NTU19jExMcEjmc/BweHuPBaL5Uf/j1R0dLQuMzNzotlsZqxevfqWm5ubWa/XMwoKCpzlcrmOiKi6utp+9+7d3NLS0qonnnjCFBcXx+/t7cWnpg8JXiQAAAAAAIxpM2bM6MjIyJhoPS4qKmITEXV0dDAnTZrUR0SUmZnpab0+YcIEk06nG1UtNHPmTF1ubq6byWQijUZjW1xc7Dzc+KlTp/beunXLrri42CkqKqqHiCgkJKTn008/feLpp5/WERG1t7cz2Wy22d3d3aTRaGxPnTrlMtycMDoodgEAAAAAYEz7+OOPNefPn3cUCoUSgUAg3b179xNERBs2bLj5zjvvTJo6darIZDLdHR8bG9upVqvZ1g2qRhIjKSmp3dvbu08oFEpTUlL8ZTJZl6urq2mo8TY2NiSTybrc3d2NLBbLQkQUGRmpa2xsZM2ePbuLiGj69Ok9ISEh3UFBQdIXX3yRHx4ejh2aHyLGaNvxAAAAAADw61JWVlYvk8laHnUej5pWq7VxcXEx37x5k/nUU0+Jz5w5U83j8YyPOq/xrKyszFMmk/Ef5F58swsAAAAAADACCoUiqKOjg2kwGBivv/56EwrdxxuKXQAAAAAAgBEoKSmpGXhOoVAINBoNq/+5rVu3NsbFxXX8cpnBYFDsAgAAAAAAPKD8/Py6R50DDA4bVAEAAAAAAMC4g2IXAAAAAAAAxh0UuwAAAAAAADDuoNgFAAAAAACAcQfFLgAAAAAAPPY4HE5Y/2OlUumRmJjIe5C5ioqK2EeOHHGxHh86dMjlT3/6k9dPzREeL9iNGQAAAAAAflVUKhVHpVI5xsfHa4mIVqxYoSUi7SNOCx4yFLsAAAAAADBibf9U+xludnEe5px2Xo7d7kuFmge9/8aNG7YpKSn+169ftyci2rlz57Vnn32265tvvuGkp6fzent7bRwcHMyffvrp98HBwX3bt2/36e3ttRGJRE7r1q1r6unpsVGpVI5ZWVnX4uLi+M7OzqaysjLHW7du2W3evLkxJSWl3WQyUVJSEu/s2bPOfn5+erPZTMnJya0pKSntg+Xk6+sb+sILL7SePHnSxWg0Mo4cOXI1LCysd7CcZDKZXqlUepw4ccK1p6fH5tq1a6zY2NjbH330UeODvhPAMmYAAAAAABgD9Hq9jUgkklj/bd++3cd6bfXq1X7p6enNly9frvrXv/5Vl5qayicikslkvSUlJdVVVVWVGzduvL5+/fpJDg4OljfeeOPGokWL2qurqyv/8Ic/3FOsNjc326lUqupjx47Vbty40ZeIKCsry02j0djX1NRU/P3vf6+/cOGC0/1y9vT0NFZWVlb97ne/u/XnP/+ZO1RO1vGVlZWc3Nzcq1VVVRVffPGF25UrV+wewqv71UJnFwAAAAAARuyndGB/ChaLZa6urq60HiuVSg+VSuVIRHTmzJkJtbW1bOs1nU7HbG9vt2lra2PGx8cH1NfXOzAYDIvBYGCMJNbixYtvM5lMCg8P721tbbUjIvr222+dlixZ0s5kMonH4xkjIyM77zdPQkJCOxGRXC7v/uKLL9yIiIbLacaMGR0eHh4mIqLAwMDeuro6VmBgoGFkbwgGQrELAAAAAABjmsViIZVKVeXk5GTpf/6ll17iRUdHd+bn59fV1NTYx8TEBI9kPgcHh7vzWCyWH/0/GtZ5bG1tLUajkUFEtGHDBt+hcrK3t78bhMlkjrg4h8FhGTMAAAAAAIxpM2bM6MjIyJhoPS4qKmITEXV0dDAnTZrUR0SUmZnpab0+YcIEk06nG1UtNHPmTF1ubq6byWQijUZjW1xc7PwguQ6VEzx8KHYBAAAAAGBM+/jjjzXnz593FAqFEoFAIN29e/cTREQbNmy4+c4770yaOnWqyGQy3R0fGxvbqVar2SKRSLJ37163kcRISkpq9/b27hMKhdKUlBR/mUzW5erqarr/nT82VE7w8DEepB0PAAAAAAC/HmVlZfUymazlUefxqGm1WhsXFxfzzZs3mU899ZT4zJkz1Twez/io8xrPysrKPGUyGf9B7sU3uwAAAAAAACOgUCiCOjo6mAaDgfH66683odB9vKHYBQAAAAAAGIGSkpKagecUCoVAo9Gw+p/bunVrY1xcXMcvlxkMBsUuAAAAAADAA8rPz6971DnA4LBBFQAAAAAAAIw7KHYBAAAAAABg3EGxCwAAAAAAAOMOil0AAAAAAAAYd1DsAgAAAADAY4/D4YT1P1YqlR6JiYm8XyJ2dna2i1gslgQHB0sEAoF0x44dnkREBw8edC0tLXW43/1yuTy4sLCQM9q427Zte4LH44UwGIzwpqamITcXZjKZ4SKRSCISiSQxMTGB1vO+vr6h/e87ceKE8+zZswMHn+X/2bJly8TJkydLFy9eHDDanB8n2I0ZAAAAAABgCHq9nrF27Vr/7777rkogEBh6enoYarXanogoNzfX1Wg0asPDw3t/jtjR0dG6uLg4bUxMTPBw41gslrm6urryYcXdv3//E3l5ebUikajvYc35KKDYBQAAAACAEcvNzfX74YcfRt2lHM7EiRO7f/vb32oe9H61Wm2flJTEb21ttfXw8DBmZWXVBwUF9cXFxfEXLlyoTUlJaSe60x3u7u6+0NDQYBcXFzdZp9MxTSYT44MPPmiYP3++7ujRoxM2bdrk09fXx/D399cfPny4vre3l2E0GhlcLtdIRMRmsy0ymUyfn5/vWFBQ4Hr27FnnjIwM75ycnLply5ZNrqysrCIiKi8vZy1fvnxyRUVFVf9cB4vh4uJiHuy5nn766Z4HfSf3k56e7qPRaOwbGhpYN27csE9NTW1+6623fkhISOA1NjayFi9eHLhixYqWjRs3/vBz5fBzwzJmAAAAAAB47On1ehvrUl2RSCTZvn27j/VaamoqLyEhoVWtVlfGx8e3pqWl+Q0314EDB9znzJmjra6urqyqqqqYNm1ad1NTk+22bdu8CwsL1ZWVlVVTp07t3rx5M5fL5ZoUCsVtHo83ZdGiRQF79uxxN5lMpFAouubOnXt7y5YtjdXV1ZVSqVTv7OxsKioqYhMRZWZmeiYkJLT2jztUjJ/6bvr6+mxCQkLEMplMdPDgQdeR3nflyhWH06dPq8+dO1f13nvv+ej1esbnn39+beLEiYbTp0+rx3KhS4TOLgAAAAAAjMJP6cD+FAOX6iqVSg+VSuVIRHThwgXHvLy8OiKitLS0tnfffXfScHNFRkZ2rV69mm8wGGyWLl3aHhUV1ZOdne1cV1fnIJfLRUREBoOBER4eriMiOnLkSENJSckPeXl5zkql0qugoGBCTk5O/cB5k5OTW/bu3espl8s1x44dczt37tyPurqnTp1yHCrGT3HlypVLfD7fUFlZaa9QKIKnTp3aI5VK9YONZTAYd39+9tlnb7PZbAubzTa6u7sbGhsbbQUCgeGn5vO4QLELAAAAAADjkq2trcVkMhERkdlsJoPBwCAiio2N1RUWFtbk5OS4JCcnB7z88svN7u7uxhkzZnQcP378+8HmksvlPXK5vGfVqlVtgYGBoURUP3BMUlJSe0ZGhs/hw4c7Q0NDu728vEz9r1ssFhouxoPi8/kGIiKJRNIXGRnZWVJSwpFKpXo3NzdjS0sL09vb20hE1NraynR3dzda72OxWBbrz0wmk4xGI+Pe2ccuLGMGAAAAAIAxLSwsrGvfvn1uRESZmZnuEREROiIif3//vtLSUg4R0aFDh1ytxZxarbb39fU1rFu3rmXlypUt58+f58yaNatLpVI5Xb58mUVE1NnZaXPp0iWWVqu1OXHihLM1VnFxMdvHx6ePiMjJycnU0dFxt6bicDiW6OhobXp6Oi85ObllYJ5Dxfgpz37r1i1mT08Pg+jOMmmVSuU0ZcqUHiKiqKiozv3793sQERmNRjp06JDHrFmzOn9KvLEExS4AAAAAAIxpe/bsuXbw4EFPoVAoyc7O9vjwww81RERr1qy5VVRU5BwaGio+e/asI5vNNhMRnTx50lkikUjFYrHk2LFjbuvXr2/28fExZmZm1i9fvnyyUCiUhIeHi8rLyx3MZjPt2LGDy+fzQ0QikWTTpk2++/fv/56IaMWKFW1KpdJLLBZLKioqWEREiYmJbURES5Ys6RiY51AxhnquLVu2TORyuVOam5vtZTKZJD4+3p+IqLCwkGP9+eLFiw4ymUwcHBwsiY6OFr7yyis3rbtDb9++vamuro4VHBwskUgkksmTJ+vT0tJah4o33jAsFsv9RwEAAAAAwK9WWVlZvUwmu6dTCfd6++23uVqtlrlr164bjzqX8aCsrMxTJpPxH+RefLMLAAAAAADwECgUCkFDQwPr9OnT6kedC6DYBQAAAAAAeCjy8/PrHuQ+hUIh0Gg0P/p2d+vWrY1xcXH3LIWGkUOxCwAAAAAA8Ag9aJEMw8MGVQAAAAAAADDuoNgFAAAAAACAcQfFLgAAAAAAAIw7KHYBAAAAAABg3EGxCwAAAAAAjz0OhxPW/1ipVHokJibyfonY2dnZLmKxWBIcHCwRCATSHTt2eBIRHTx40LW0tNThfvfL5fLgwsJCzmjjbtu27QkejxfCYDDCm5qa7m4urNFobGfPnh1ozSc6OjqQiKimpsb+o48+ch9tnPEKxS4AAAAAAMAQ9Ho9Y+3atf4nTpyorampqbx8+XLls88+20lElJub63rp0iX2zxU7Ojpal5+fr/bx8enrf37Dhg2+MTExHTU1NZV1dXUVf/nLX64TEdXW1rKOHDkyqmLXYDA8zJQfK/jTQwAAAAAAMGKVVRv8unTqUXcph+PoJOyWiDM0D3q/Wq22T0pK4re2ttp6eHgYs7Ky6oOCgvri4uL4Cxcu1KakpLQT3ekOd3d3X2hoaLCLi4ubrNPpmCaTifHBBx80zJ8/X3f06NEJmzZt8unr62P4+/vrDx8+XN/b28swGo0MLpdrJCJis9kWmUymz8/PdywoKHA9e/asc0ZGhndOTk7dsmXLJldWVlYREZWXl7OWL18+uaKioqp/roPFcHFxMQ/2XE8//XTPYOdv3rxp9+yzz2qtx9OmTeshInrzzTd9r1696iASiST/8R//0fL666/fSkxM9L906RKHyWTSX/7yF82iRYs6lUqlR15enoter7fp7u628fLyMixdurR95cqVt4mIFi9eHBAfH9+2YsUK7WDxxwp0dgEAAAAA4LGn1+ttRCKRxPpv+/btPtZrqampvISEhFa1Wl0ZHx/fmpaW5jfcXAcOHHCfM2eOtrq6urKqqqpi2rRp3U1NTbbbtm3zLiwsVFdWVlZNnTq1e/PmzVwul2tSKBS3eTzelEWLFgXs2bPH3WQykUKh6Jo7d+7tLVu2NFZXV1dKpVK9s7OzqaioiE1ElJmZ6ZmQkNDaP+5QMUb7Lv74xz/+sGbNGv60adOEGzZs8Kqvr7cjItq6dev1iIgIXXV1deXGjRt/yMjImEhEpFarKz///POrq1at4nd3dzOIiM6fP++UnZ39/dmzZ9V/+MMfbn366aceREStra3M0tJSpxdeeGFMF7pE6OwCAAAAAMAo/JQO7E/BYrHM1dXVldZjpVLpoVKpHImILly44JiXl1dHRJSWltb27rvvThpursjIyK7Vq1fzDQaDzdKlS9ujoqJ6srOznevq6hzkcrmIiMhgMDDCw8N1RERHjhxpKCkp+SEvL89ZqVR6FRQUTMjJyakfOG9ycnLL3r17PeVyuebYsWNu586d+1FX99SpU45DxRiNuLi4jhkzZpT/61//cvnyyy9dwsPDJeXl5RUDxxUVFTmtWbPmByKisLCwXh8fn77y8nIHIqKZM2d2cLlcExHRggULdK+88or/9evXbQ8dOuS2YMGCdjs7u9Gm9dhBsQsAAAAAAOOSra2txWQyERGR2Wwmg8HAICKKjY3VFRYW1uTk5LgkJycHvPzyy83u7u7GGTNmdBw/fvz7weaSy+U9crm8Z9WqVW2BgYGhRFQ/cExSUlJ7RkaGz+HDhztDQ0O7vby8TP2vWywWGi7GaHC5XFNqampbampq2+zZswO/+uorJ09Pz3viDYXD4fxo6fQLL7zQum/fPvecnBz3AwcO1P/U/B4HWMYMAAAAAABjWlhYWNe+ffvciIgyMzPdIyIidERE/v7+faWlpRwiokOHDrkajUYG0Z1vfH19fQ3r1q1rWblyZcv58+c5s2bN6lKpVE6XL19mERF1dnbaXN+AQYMAACAASURBVLp0iaXVam1OnDjhbI1VXFzMtm4Y5eTkZOro6LhbU3E4HEt0dLQ2PT2dl5yc3DIwz6FijPZ5v/jiC+fOzk4bIqL29nabhoYGVkBAQJ+Li4tJp9MxreNmzJih++yzz9yJiC5dusRqamqynzJlSu9gc6amprZkZmZyiYgiIiIGHTPWoNgFAAAAAIAxbc+ePdcOHjzoKRQKJdnZ2R4ffvihhohozZo1t4qKipxDQ0PFZ8+edWSz2WYiopMnTzpLJBKpWCyWHDt2zG39+vXNPj4+xszMzPrly5dPFgqFkvDwcFF5ebmD2WymHTt2cPl8fohIJJJs2rTJd//+/d8TEa1YsaJNqVR6icViSUVFBYuIKDExsY2IaMmSJR0D8xwqxlDPtWXLlolcLndKc3OzvUwmk8THx/sTEZ07d47z5JNPioVCoUQul4tffPHFlujo6G65XN5ja2trCQ4Olrz77rsT169f/4PJZGIIhUJJfHy8IDMzs57NZg/a7vXz8zMKBILelStXtg52fSxiDNfaBgAAAAAAKCsrq5fJZPd0KuFeb7/9Nler1TJ37dp141HnMhqdnZ02EolEcvHixSoPDw/T/e/4ZZSVlXnKZDL+g9yLb3YBAAAAAAAeAoVCIWhoaGCdPn1a/ahzGY3c3FzntLQ0flpaWvPjVOj+VOjsAgAAAADAsNDZ/XkpFAqBRqP50be7W7dubYyLi7tnKfSvDTq7AAAAAAAAY1R+fn7do85hPMIGVQAAAAAAADDuoNgFAAAAAACAcQfFLgAAAAAAAIw7KHYBAAAAAABg3EGxCwAAAAAAjz0OhxPW/1ipVHokJibyfonY2dnZLmKxWBIcHCwRCATSHTt2eBIRHTx40LW0tNThfvfL5fLgwsJCzmjjLl68OIDP54cEBQVJly1bxtfr9YzBxjGZzHCRSCQRiUSSmJiYwP5x+Xx+iPXaJ5984kZEFBYWJhptLmMRdmMGAAAAAAAYgl6vZ6xdu9b/u+++qxIIBIaenh6GWq22JyLKzc11NRqN2vDw8N6fI/aKFSvacnNzvyci+s1vfhPw/vvve27YsOHWwHEsFstcXV1dOdgcWVlZV5955pnu/ucuXLhQ/XPk+7hBsQsAAAAAACP2StU1v+qu3lF3KYcjcnTofl/M0zzo/Wq12j4pKYnf2tpq6+HhYczKyqoPCgrqi4uL4y9cuFCbkpLSTnSnO9zd3X2hoaHBLi4ubrJOp2OaTCbGBx980DB//nzd0aNHJ2zatMmnr6+P4e/vrz98+HB9b28vw2g0MrhcrpGIiM1mW2QymT4/P9+xoKDA9ezZs84ZGRneOTk5dcuWLZtcWVlZRURUXl7OWr58+eSKioqq/rkOFsPFxcU82HPFx8drrT9HRER0NTY22j/oO+rP+h5OnDjhvGnTJh93d3dDTU0NOzQ0tDs3N/d7G5vxsQB4fDwFAAAAAACMa3q93sa6HFckEkm2b9/uY72WmprKS0hIaFWr1ZXx8fGtaWlpfsPNdeDAAfc5c+Zoq6urK6uqqiqmTZvW3dTUZLtt2zbvwsJCdWVlZdXUqVO7N2/ezOVyuSaFQnGbx+NNWbRoUcCePXvcTSYTKRSKrrlz597esmVLY3V1daVUKtU7OzubioqK2EREmZmZngkJCa394w4VYwTPzjhy5IjHggULtINd7+vrswkJCRHLZDLRwYMHXftfS0xMnGx9Zzdv3mQOvLeqqor9t7/9TXPlypWKa9eusfLz853ul89Ygc4uAAAAAACM2E/pwP4UA5fqKpVKD5VK5UhEdOHCBce8vLw6IqK0tLS2d999d9Jwc0VGRnatXr2abzAYbJYuXdoeFRXVk52d7VxXV+cgl8tFREQGg4ERHh6uIyI6cuRIQ0lJyQ95eXnOSqXSq6CgYEJOTk79wHmTk5Nb9u7d6ymXyzXHjh1zO3fu3I+6uqdOnXIcKsZwkpKSeJGRkbr58+cPOvbKlSuX+Hy+obKy0l6hUARPnTq1RyqV6okGX8bcX2hoaJdAIDAQEUml0u66urqH0j1+HKDYBQAAAACAccnW1tZiMpmIiMhsNpPBYGAQEcXGxuoKCwtrcnJyXJKTkwNefvnlZnd3d+OMGTM6jh8//v1gc8nl8h65XN6zatWqtsDAwFAiqh84JikpqT0jI8Pn8OHDnaGhod1eXl6m/tctFgsNF2Mw69at825pabE9efJk3VBj+Hy+gYhIIpH0RUZGdpaUlHCsxe79sFgsi/VnJpNJRqNx0E2wxiIsYwYAAAAAgDEtLCysa9++fW5ERJmZme4RERE6IiJ/f/++0tJSDhHRoUOHXK2FnFqttvf19TWsW7euZeXKlS3nz5/nzJo1q0ulUjldvnyZRUTU2dlpc+nSJZZWq7U5ceKEszVWcXEx28fHp4+IyMnJydTR0XG3puJwOJbo6Ghteno6Lzk5uWVgnkPFGOq5du7c6fn111+75ObmXmUy71mBTEREt27dYvb09DCI7iyTVqlUTlOmTOkZ5Sscl1DsAgAAAADAmLZnz55rBw8e9BQKhZLs7GyPDz/8UENEtGbNmltFRUXOoaGh4rNnzzqy2WwzEdHJkyedJRKJVCwWS44dO+a2fv36Zh8fH2NmZmb98uXLJwuFQkl4eLiovLzcwWw2044dO7jWP+GzadMm3/37939PdGe3ZKVS6SUWiyUVFRUsIqLExMQ2IqIlS5Z0DMxzqBhDPdf69ev9W1pabCMiIsQikUjy2muveRMRFRYWcuLj4/2JiC5evOggk8nEwcHBkujoaOErr7xy8+faHXqsYVgslvuPAgAAAACAX62ysrJ6mUx2T6cS7vX2229ztVotc9euXTcedS7jQVlZmadMJuM/yL34ZhcAAAAAAOAhUCgUgoaGBtbp06fVjzoXQLELAAAAAADwUOTn5w+5idRwFAqFQKPR/Ojb3a1btzbGxcXdsxQaRg7FLgAAAAAAwCP0oEUyDA8bVAEAAAAAAMC4g2IXAAAAAAAAxh0UuwAAAAAAADDuoNgFAAAAAACAcQfFLgAAAAAAPPY4HE5Y/2OlUumRmJjI+yViZ2dnu4jFYklwcLBEIBBId+zY4UlEdPDgQdfS0lKH+90vl8uDCwsLOaONu3jx4gA+nx8SFBQkXbZsGV+v1zMGG8dkMsNFIpFEJBJJYmJiAq3nfX19Q5uamu5uSnzixAnn2bNnBw42R39btmyZOHnyZOnixYsDRpvz4wS7MQMAAAAAAAxBr9cz1q5d6//dd99VCQQCQ09PD0OtVtsTEeXm5roajUZteHh4788Re8WKFW25ubnfExH95je/CXj//fc9N2zYcGvgOBaLZa6urq58WHH379//RF5eXq1IJOp7WHM+Cih2AQAAAABgxF7/Z5mf+mbnqLuUwxF6OXfvWCrTPOj9arXaPikpid/a2mrr4eFhzMrKqg8KCuqLi4vjL1y4UJuSktJOdKc73N3dfaGhocEuLi5usk6nY5pMJsYHH3zQMH/+fN3Ro0cnbNq0yaevr4/h7++vP3z4cH1vby/DaDQyuFyukYiIzWZbZDKZPj8/37GgoMD17NmzzhkZGd45OTl1y5Ytm1xZWVlFRFReXs5avnz55IqKiqr+uQ4Ww8XFxTzYc8XHx2utP0dERHQ1NjbaP+g7Gig9Pd1Ho9HYNzQ0sG7cuGGfmpra/NZbb/2QkJDAa2xsZC1evDhwxYoVLRs3bvzhYcX8pWEZMwAAAAAAPPb0er2NdamuSCSSbN++3cd6LTU1lZeQkNCqVqsr4+PjW9PS0vyGm+vAgQPuc+bM0VZXV1dWVVVVTJs2rbupqcl227Zt3oWFherKysqqqVOndm/evJnL5XJNCoXiNo/Hm7Jo0aKAPXv2uJtMJlIoFF1z5869vWXLlsbq6upKqVSqd3Z2NhUVFbGJiDIzMz0TEhJa+8cdKsYInp1x5MgRjwULFmgHu97X12cTEhIilslkooMHD7qO7I0SXblyxeH06dPqc+fOVb333ns+er2e8fnnn1+bOHGi4fTp0+qxXOgSobMLAAAAAACj8FM6sD/FwKW6SqXSQ6VSORIRXbhwwTEvL6+OiCgtLa3t3XffnTTcXJGRkV2rV6/mGwwGm6VLl7ZHRUX1ZGdnO9fV1TnI5XIREZHBYGCEh4friIiOHDnSUFJS8kNeXp6zUqn0KigomJCTk1M/cN7k5OSWvXv3esrlcs2xY8fczp0796Ou7qlTpxyHijGcpKQkXmRkpG7+/PmDjr1y5colPp9vqKystFcoFMFTp07tkUql+sHGMhj/77PfZ5999jabzbaw2Wyju7u7obGx0VYgEBjul89YgWIXAAAAAADGJVtbW4vJZCIiIrPZTAaDgUFEFBsbqyssLKzJyclxSU5ODnj55Zeb3d3djTNmzOg4fvz494PNJZfLe+Ryec+qVavaAgMDQ4mofuCYpKSk9oyMDJ/Dhw93hoaGdnt5eZn6X7dYLDRcjMGsW7fOu6WlxfbkyZN1Q43h8/kGIiKJRNIXGRnZWVJSwpFKpXo3NzdjS0sL09vb20hE1NraynR3dzda72OxWBbrz0wmk4xG46AbYI1VWMYMAAAAAABjWlhYWNe+ffvciIgyMzPdIyIidERE/v7+faWlpRwiokOHDrlaizm1Wm3v6+trWLduXcvKlStbzp8/z5k1a1aXSqVyunz5MouIqLOz0+bSpUssrVZrc+LECWdrrOLiYraPj08fEZGTk5Opo6Pjbk3F4XAs0dHR2vT0dF5ycnLLwDyHijHUc+3cudPz66+/dsnNzb3KZDIHHXPr1i1mT08Pg+jOMmmVSuU0ZcqUHiKiqKiozv3793sQERmNRjp06JDHrFmzOkfxasc0FLsAAAAAADCm7dmz59rBgwc9hUKhJDs72+PDDz/UEBGtWbPmVlFRkXNoaKj47Nmzjmw220xEdPLkSWeJRCIVi8WSY8eOua1fv77Zx8fHmJmZWb98+fLJQqFQEh4eLiovL3cwm820Y8cOLp/PDxGJRJJNmzb57t+//3uiO7slK5VKL7FYLKmoqGARESUmJrYRES1ZsqRjYJ5DxRjqudavX+/f0tJiGxERIRaJRJLXXnvNm4iosLCQEx8f709EdPHiRQeZTCYODg6WREdHC1955ZWb1t2ht2/f3lRXV8cKDg6WSCQSyeTJk/VpaWmtQ8UbbxgWi+X+owAAAAAA4FerrKysXiaT3dOphHu9/fbbXK1Wy9y1a9eNR53LeFBWVuYpk8n4D3IvvtkFAAAAAAB4CBQKhaChoYF1+vRp9aPOBVDsAgAAAAAAPBT5+flDbiI1HIVCIdBoND/6dnfr1q2NcXFx9yyFhpFDsQsAAAAAAPAIPWiRDMPDBlUAAAAAAAAw7qDYBQAAAAAAgHEHxS4AAAAAAACMOyh2AQAAAADgscfhcML6HyuVSo/ExETeLxE7OzvbRSwWS4KDgyUCgUC6Y8cOTyKigwcPupaWlg75d3Kt5HJ5cGFhIWe0cRcvXhzA5/NDgoKCpMuWLePr9XoGEZFGo7GdPXt2oDWf6OjoQCKimpoa+48++sh9tHHGKxS7AAAAAAAAQ9Dr9Yy1a9f6nzhxorampqby8uXLlc8++2wnEVFubq7rpUuX2D9X7BUrVrRdvXr1ck1NTUVvby/j/fff9yQi2rBhg29MTExHTU1NZV1dXcVf/vKX60REtbW1rCNHjoyq2DUYDD9H6o8FFLsAAAAAADCmqdVq++nTpwuFQqFk+vTpwtraWnsiori4OP4nn3ziZh1n7Q43NDTYRUREBItEIklQUJD0yy+/dCIiOnr06IQnn3xSJJFIxLGxsZO1Wq3N7du3bYxGI4PL5RqJiNhstkUmk+nz8/MdCwoKXN96661JIpFIUlFRwZJIJGJrrPLycpZUKhXTAIPFGOq54uPjtTY2NmRjY0MRERFdjY2N9kREN2/etPPz8+uzjps2bVoPEdGbb77pq1KpnEQikeTdd9+d2N3dzVi6dClfKBRKxGKx5Pjx485Ed7risbGxk2NiYgJnzpwp/O1vfxvw2WefuVrnW7x4ccChQ4dcHvT38bjAnx4CAAAAAICRy/2jH/1QOeolucOaKOmm3/5NM9wQvV5vIxKJJNZjrVbLVCgUWiKi1NRUXkJCQuuaNWta33//fY+0tDS/goKCIf+cz4EDB9znzJmjzcjIuGk0Gqmzs9OmqanJdtu2bd6FhYXqCRMmmN98802vzZs3c997770mhUJxm8fjTXn66ac7nnvuOe2qVavaFApF19y5c28vXLhQm5KS0k5E5OzsbCoqKmJHRUX1ZGZmeiYkJLT2jztcjPs8O+PIkSMeO3fu1BAR/fGPf/whOTl58p49e7pnzZrVkZaW1srn8w1bt269/te//pX7zTffXCEi2rhxI5eISK1WV164cMHhueeeC6qrq7tMRHT+/HmnS5cuVXC5XNO///1vp//5n//hrly58nZrayuztLTUKScn5/vhchoL0NkFAAAAAIDHHovFMldXV1da/73xxhs3rNcuXLjguGrVqjYiorS0tLbS0lKn4eaKjIzsys7O9kxPT/cpKSlhu7m5mU+dOuVYV1fnIJfLRSKRSHL48GGPa9eu2RMRHTlypOHLL79UR0REdCmVSq8XXniBP9i8ycnJLXv37vU0Go107Ngxt9///vc/KnaHizGcpKQkXmRkpG7+/Pk6IqK4uLiOK1eulKekpLTU1NSww8PDJTdu3LinkVlUVOSUmJjYSkQUFhbW6+Pj01deXu5ARDRz5swOLpdrIiJasGCBrqGhweH69eu2+/fvd1+wYEG7nZ3d/dJ67KGzCwAAAAAAI3efDuzjxNbW1mIymYiIyGw2k8FgYBARxcbG6goLC2tycnJckpOTA15++eVmd3d344wZMzqOHz8+aEdTLpf3yOXynlWrVrUFBgaGElH9wDFJSUntGRkZPocPH+4MDQ3t9vLyMvW/brFYaLgYg1m3bp13S0uL7cmTJ3/UqeZyuabU1NS21NTUttmzZwd+9dVXTp6envfEGwqHwzH3P37hhRda9+3b556Tk+N+4MCBe55tLEJnFwAAAAAAxrSwsLCuffv2uRERZWZmukdEROiIiPz9/ftKS0s5RESHDh1yNRqNDKI73/j6+voa1q1b17Jy5cqW8+fPc2bNmtWlUqmcLl++zCIi6uzstLl06RJLq9XanDhxwtkaq7i4mO3j49NHROTk5GTq6Oi4W1NxOBxLdHS0Nj09nZecnNwyMM+hYgz1XDt37vT8+uuvXXJzc68ymcy757/44gvnzs5OGyKi9vZ2m4aGBlZAQECfi4uLSafT3R04Y8YM3WeffeZORHTp0iVWU1OT/ZQpU3oHi5WamtqSmZnJJSKKiIgYdMxYg84uAAAAAACMaXv27LmWlJTE37Vrl5eHh4cxKyurnohozZo1txYuXBgYGhoqfuaZZzrYbLaZiOjkyZPOSqXSy9bW1sLhcEyHDh363sfHx5iZmVm/fPnyyX19fQwioo0bN1738/Mz7Nixg/tf//Vf/g4ODmYOh2Pev3//90R3dktOS0vjf/TRR9x//vOfdVKpVJ+YmNiWl5fntmTJko6BeQ4VY8qUKfrBnmv9+vX+3t7e+oiICDER0cKFC9vfe++9pnPnznFeffVVHpPJtFgsFsaLL77YEh0d3a3X6xm2traW4OBgSUJCQsv69et/ePHFF/2FQqGEyWRSZmZmPZvNHrTd6+fnZxQIBL2LFi26/RB+JY8FxnCtbQAAAAAAgLKysnqZTHZPpxLu9fbbb3O1Wi1z165dN+4/+vHR2dlpI5FIJBcvXqzy8PAw3f+OX0ZZWZmnTCbjP8i96OwCAAAAAAA8BAqFQtDQ0MA6ffq0+lHnMhq5ubnOaWlp/LS0tObHqdD9qdDZBQAAAACAYaGz+/NSKBQCjUbzo293t27d2hgXF3fPUuhfG3R2AQAAAAAAxqj8/Pwh/yYwPDjsxgwAAAAAAADjDopdAAAAAAAAGHdQ7AIAAAAAAMC4g2IXAAAAAAAAxh0UuwAAAAAA8NjjcDhh/Y+VSqVHYmIi75eInZ2d7SIWiyXBwcESgUAg3bFjhycR0cGDB11LS0sd7ne/XC4PLiws5DysuOnp6T4TJ06cIhKJJCKRSPKf//mfvqN/qvEPuzEDAAAAAAAMQa/XM9auXev/3XffVQkEAkNPTw9DrVbbExHl5ua6Go1GbXh4eO8vGZeIKDU1tXnTpk3NDzvuQEajkWxtx2bZiM4uAAAAAACMaWq12n769OlCoVAomT59urC2ttaeiCguLo7/ySefuFnHWbvDDQ0NdhEREcEikUgSFBQk/fLLL52IiI4ePTrhySefFEkkEnFsbOxkrVZrc/v2bRuj0cjgcrlGIiI2m22RyWT6/Px8x4KCAte33nprkkgkklRUVLAkEonYGqu8vJwllUrFNMBgMQZ7pqHiDvcejh075iwWiyVCoVCybNkyfk9PD+PYsWPOCoVCYB3zr3/9a8Kzzz4rGC4XX1/f0Ndee807PDw8+MCBA25DxXvcjc0SHQAAAAAAHon/c+b/+F1pvzLqJbnDCXQL7N789GbNcGP0er2NSCSSWI+1Wi1ToVBoiYhSU1N5CQkJrWvWrGl9//33PdLS0vwKCgqG/Nu1Bw4ccJ8zZ442IyPjptFopM7OTpumpibbbdu2eRcWFqonTJhgfvPNN702b97Mfe+995oUCsVtHo835emnn+547rnntKtWrWpTKBRdc+fOvb1w4UJtSkpKOxGRs7OzqaioiB0VFdWTmZnpmZCQ0No/7nAxBubI5XJNg8VlMplERPTRRx9x//GPf3gQEW3durUxNja2c/Xq1QFfffVVzZQpU/TPP/88f8eOHU+89dZbP7zyyiu8Gzdu2Pr4+BgPHDjgkZyc3HK/XBwcHMylpaU1I/4lPoZQ7AIAAAAAwGOPxWKZq6urK63HSqXSQ6VSORIRXbhwwTEvL6+OiCgtLa3t3XffnTTcXJGRkV2rV6/mGwwGm6VLl7ZHRUX1ZGdnO9fV1TnI5XIREZHBYGCEh4friIiOHDnSUFJS8kNeXp6zUqn0KigomJCTk1M/cN7k5OSWvXv3esrlcs2xY8fczp07V9X/+qlTpxyHijGY4eIOXMb83XffsSdNmqSfMmWK/v/m0vq3v/1too2NzQ8vvPBC6969e93/+Mc/tp4/f97p6NGj3//zn/90GS6XxMTE9uHe4ViAYhcAAAAAAEbsfh3Yx4mtra3FZDIREZHZbCaDwcAgIoqNjdUVFhbW5OTkuCQnJwe8/PLLze7u7sYZM2Z0HD9+/PvB5pLL5T1yubxn1apVbYGBgaFEVD9wTFJSUntGRobP4cOHO0NDQ7u9vLxM/a9bLBYaLsaDxrXOPZS0tLTWBQsWBDo4OFgWLVrUbmdnd99cnJ2dzSPN8XGFb3YBAAAAAGBMCwsL69q3b58bEVFmZqZ7RESEjojI39+/r7S0lENEdOjQIVej0cgguvONr6+vr2HdunUtK1eubDl//jxn1qxZXSqVyuny5cssIqLOzk6bS5cusbRarc2JEyecrbGKi4vZPj4+fURETk5Opo6Ojrs1FYfDsURHR2vT09N5ycnJLQPzHCrGYM80XNzBPPnkk73Xr1+3t86dlZXlMXPmzE4iIj6fb+ByuYa//vWv3n/4wx9aRpvLWIViFwAAAAAAxrQ9e/ZcO3jwoKdQKJRkZ2d7fPjhhxoiojVr1twqKipyDg0NFZ89e9aRzWabiYhOnjzpLJFIpGKxWHLs2DG39evXN/v4+BgzMzPrly9fPlkoFErCw8NF5eXlDmazmXbs2MHl8/khIpFIsmnTJt/9+/d/T0S0YsWKNqVS6SUWiyUVFRUsIqLExMQ2IqIlS5Z0DMxzqBiDPdNwcQfD4XAsH330Uf2yZcsEQqFQYmNjQ6+99tot6/Xly5e3ent791l3jh5NLmMVY7h2NwAAAAAAQFlZWb1MJrunUwn3evvtt7larZa5a9euG486l/4SExN5YWFh3a+++uqY+j2WlZV5ymQy/oPci292AQAAAAAAHgKFQiFoaGhgnT59Wv2oc+lPKpWK2Wy2OTMzc8x8b/0woNgFAAAAAAB4CPLz84f8c0fDUSgUAo1G86PvZbdu3doYFxd3z1LoB1FRUVF1/1HjD4pdAAAAAACAR+hBi2QYHjaoAgAAAAAAgHEHxS4AAAAAAACMOyh2AQAAAAAAYNxBsQsAAAAAAADjDopdAAAAAAB47HE4nLD+x0ql0iMxMZH3S8TOzs52EYvFkuDgYIlAIJDu2LHDk4jo4MGDrqWlpQ73u18ulwcXFhZyHlbc9PR0n7fffps7kjlqamrsg4KCpKONPR5gN2YAAAAAAIAh6PV6xtq1a/2/++67KoFAYOjp6WGo1Wp7IqLc3FxXo9GoDQ8P7/0l48LIoLMLAAAAAABjmlqttp8+fbpQKBRKpk+fLqytrbUnIoqLi+N/8sknbtZx1u5wQ0ODXURERLBIJJIEBQVJv/zySycioqNHj0548sknRRKJRBwbGztZq9Xa3L5928ZoNDK4XK6RiIjNZltkMpk+Pz/fsaCgwPWtt96aJBKJJBUVFSyJRCK2xiovL2dJpVIxDTBYjMGeaai4Q72DtLQ03z//+c9PWI/T09N9Nm7cOKLu73iFzi4AAAAAAIzYjT+96aevrR31ktzhsIKCun22bdUMN0av19uIRCKJ9Vir1TIVCoWWiCg1NZWXkJDQumbNmtb333/fIy0tza+goGDIv1174MAB9zlz5mgzMjJuGo1G6uzstGlqarLdtm2bd2FhoXrChAnmN99802vz5s3c9957r0mhUNzm8XhTnn766Y7nnntOu2rVqjaFQtE1d+7c2wsXLtSmpKS0ExE5OzubioqK2FFRUT2ZmZmeCQkJrf3jDhdjYI5cLtc0WFwmkznoiIMSLwAAIABJREFUM61cubLtlVde4f33f//3LSKiY8eOuX355Ze1ZrN5uNc6rqHY/f/Zu/e4Ju/zf/xXDhASiECCBjmjEEICRISl06p1trjW6aZFq2IH2q5KrLRKVfxOv2zSdaWFVuRjRSb91GI94GpbFcss1GPrVhUxgiFEqRw8oJggp0DI6fdHv/HnqCJQWgVfz8fDx4Pkfbiu++5fV6/3fQcAAAAAAB55HA7HqtFo1PbP2dnZwjNnzjgTEZWVlTkXFRVVExEplUr9+vXrfXra69e//nX7kiVLAkwmE3P27NlN48eP79i1axe/urraSaFQSIiITCYTIyoqqo2IqKCgoPbUqVM3i4qK+NnZ2Z4lJSXD9u7dW9N934ULF97aunWrh0KhqN+3b5/76dOnK+8eP3r0qPP9YtxLb+MSET355JMdOp2OXVNT43D9+nW2q6urJTg4uKuqquqxPfqMYhcAAAAAAHrtQR3YRwmbzbZZLBYiIrJarWQymRhERM8991zb8ePHq/bu3eu6cOHCwNdee+2GQCAwT5gwoeXAgQOX77WXQqHoUCgUHYsXL9YHBQWFE1FN9zkJCQlN77zzjtfu3btbw8PDDZ6enpa7x202G/UUo79x7WbMmNH0ySefuDc0NDjExsbqextjqMIzuwAAAAAAMKhFRka25+XluRMR5ebmCqKjo9uIiPz9/btKS0t5REQ7duxwM5vNDKIfnvH19vY2vfHGG7defPHFW2fPnuVNnjy5/cyZMy4VFRUcIqLW1lbm+fPnOc3NzczCwkK+PdZ3333H9fLy6iIicnFxsbS0tNypqXg8nu2pp55qTk5O9lu4cOGt7nneL8a9rqmnuPfzxz/+Ub93715BYWGh+4svvtjU2/s3VKHYBQAAAACAQS0nJ6du+/btHmKxWLpr1y7h5s2b64mIkpKSGk+ePMkPDw8P/c9//uPM5XKtRESHDh3iS6VSWWhoqHTfvn3uq1evvuHl5WXOzc2tmTdv3iixWCyNioqSlJeXO1mtVsrIyBAFBASESSQSaVpamveHH354mYhowYIF+uzsbM/Q0FDphQsXOERE8fHxeiKi559/vqV7nveLca9r6ikuEdGGDRtGikSiCPs/IqLo6OjO9vZ2pkgk6vL39zcN9H0ebBg2m+1h5wAAAAAAAI8wlUpVI5fLf9SphB9LTU0VNTc3szZu3HjtYecyFKhUKg+5XB7Qn7V4ZhcAAAAAAGAAxMTEjK6treUcO3ZM+7BzARS7AAAAAAAAA6K4uPi+P3fUk5iYmNH19fX/9ezuW2+9dSU2NvZHR6Gh91DsAgAAAAAAPET9LZKhZ3hBFQAAAAAAAAw5KHYBAAAAAABgyEGxCwAAAAAAAEMOil0AAAAAAAAYclDsAgAAAADAI4/H40Xe/Tk7O1sYHx/v90vE3rVrl2toaKg0JCREOnr0aFlGRoYHEdH27dvdSktLnR60XqFQhBw/fpz382cKd8PbmAEAAAAAAO7DaDQyXn/9df9///vflaNHjzZ1dHQwtFqtIxHRF1984WY2m5ujoqI6H3ae8GPo7AIAAAAAwKCm1Wodx40bJxaLxdJx48aJL1686EhEFBsbG/DRRx+52+fZu8O1tbUO0dHRIRKJRBocHCz717/+5UJE9Nlnnw0bM2aMRCqVhj733HOjmpubmbdv32aazWaGSCQyExFxuVybXC43FhcXO5eUlLitW7fORyKRSC9cuMCRSqWh9ljl5eUcmUwWSt3cK8b9rsvb2zt8xYoVXlKpNFQsFkvLysqciIiOHDnCi4yMlISGhkojIyMlKpWKQ/RDt3vq1KmjJ06cGOzv7x+WmJjoMzB3eHBCZxcAAAAAAHrt6/xKX/3VtgE9kivwdjE8HR9a39Mco9HIlEgkUvvn5uZmVkxMTDMRUWJiol9cXJwuKSlJl5WVJVQqlb4lJSX3/e3a//3f/xU8/fTTze+8806D2Wym1tZW5vXr19l///vfRx4/flw7bNgw69q1az3ffPNNUWZm5vWYmJjbfn5+EU8++WTLtGnTmhcvXqyPiYlpf+aZZ25Pnz69edGiRU1ERHw+33Ly5Enu+PHjO3Jzcz3i4uJ0d8ftKcb9cvXw8DCr1erK9PT04enp6aKCgoJauVzeeerUKY2DgwN98cUX/NWrV/scOnSomohIrVbzVCqVmsvlWoOCgsJWrlx5IygoyNS7/xJDC4pdAAAAAAB45HE4HKtGo1HbP2dnZwvPnDnjTERUVlbmXFRUVE1EpFQq9evXr++xo/nrX/+6fcmSJQEmk4k5e/bspvHjx3fs2rWLX11d7aRQKCRERCaTiREVFdVGRFRQUFB76tSpm0VFRfzs7GzPkpKSYXv37q3pvu/ChQtvbd261UOhUNTv27fP/fTp05V3jx89etT5fjHuJy4uromISKFQGPbv3+9ORKTX61lz584NrKmpcWIwGDaTycSwz58wYUKLUCi0EBEFBQV1VldXc1DsAgAAAAAAPMCDOrCPEjabbbNYLEREZLVayV4UPvfcc23Hjx+v2rt3r+vChQsDX3vttRsCgcA8YcKElgMHDly+114KhaJDoVB0LF68WB8UFBRORDXd5yQkJDS98847Xrt3724NDw83eHp6Wu4et9ls1FOMe3FycrLZr8VsNjOIiFJSUryfeuqp1uLi4uqqqirHKVOmhNjnOzo62ux/s1is/yqEHzd4ZhcAAAAAAAa1yMjI9ry8PHciotzcXEF0dHQbEZG/v39XaWkpj4hox44dbvZiUavVOnp7e5veeOONWy+++OKts2fP8iZPntx+5swZl4qKCg4RUWtrK/P8+fOc5uZmZmFhId8e67vvvuN6eXl1ERG5uLhYWlpa7tRUPB7P9tRTTzUnJyf7LVy48Fb3PO8Xo6/X29LSwvLx8en6f9fr0df1jws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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "alphas_elastic = np.logspace(-10, 6, 100)\n", - "coef_elastic = []\n", - "for i in alphas_elastic:\n", - " elastic = linear_model.ElasticNet(l1_ratio =0.6)\n", - " elastic.set_params(alpha = i)\n", - " elastic.fit(x_onehot, y_log)\n", - " coef_elastic.append(elastic.coef_)\n", - "\n", - "df_coef = pd.DataFrame(coef_elastic, index=alphas_elastic, columns=x_onehot.columns)\n", - "title = 'ElasticNet coefficients as a function of the regularization'\n", - "df_coef.plot(logx=True, title=title)\n", - "plt.xlabel('alpha')\n", - "plt.ylabel('coefficients')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### test prediction" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "best_elas_test_y = para_search_elas.best_estimator_.predict(test_onehot)\n", - "best_elas_test_y = np.expm1(best_elas_test_y)\n", - "sub = pd.DataFrame()\n", - "sub['SalePrice'] = best_elas_test_y\n", - "sub = sub.set_index(np.arange(1461, 2920))\n", - "sub.to_csv('(3) elas_log(y)_onehot_submission.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Kelly/Kaggle_house_price/Untitled.ipynb b/Kelly/Kaggle_house_price/Untitled.ipynb deleted file mode 100644 index 2fd6442..0000000 --- a/Kelly/Kaggle_house_price/Untitled.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - 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a/Kelly/Kaggle_house_price/preprocess1.py b/Kelly/Kaggle_house_price/preprocess1.py deleted file mode 100644 index b9fbf58..0000000 --- a/Kelly/Kaggle_house_price/preprocess1.py +++ /dev/null @@ -1,214 +0,0 @@ -class LabelCountEncoder(object): - def __init__(self): - self.count_dict = {} - self.rev_count_dict = {} - def fit(self, column): - # We want to rank the key by its value and use the rank as the new value - count = column.value_counts() - self.count_dict = dict( list( zip (count.index, reversed(range(len(count)+1 ) ) ) ) ) - self.rev_count_dict = dict( list( zip ( reversed(range(len(count)+1 ) ) , count.index ) ) ) - def transform(self, column): - # If a category only appears in the test set, we will assign the value to zero. - missing = 0 - return column.map(lambda x: self.count_dict.get(x, missing)) - def fit_transform(self, column): - self.fit(column) - return self.transform(column) - - - -import numpy as np -import pandas as pd -import math -import re - -def impute( inputDF, onehot = False): - - #input: pd.dataframe - #one-hot or label encoding for the categorical field - #return: - # Imputed pd.DataFrame - # label-encoded dictionary - - - encodedDic = {} - - inputDF.Exterior1st = inputDF.Exterior1st.str.replace("Brk Cmn", "BrkComm") - inputDF.Exterior2nd = inputDF.Exterior2nd.str.replace("Brk Cmn", "BrkComm") - inputDF.Exterior1st = inputDF.Exterior1st.str.replace("CmentBd", "CemntBd") - inputDF.Exterior2nd = inputDF.Exterior2nd.str.replace("CmentBd", "CemntBd") - inputDF.Exterior1st = inputDF.Exterior1st.str.replace("Wd Shng", "WdShing") - inputDF.Exterior2nd = inputDF.Exterior2nd.str.replace("Wd Shng", "WdShing") - - ############################### purposelyEncodeData ######################################## - ## Start to purposely encode the information based on our best understanding. - ## Combine Exterior1st and Exterior2nd to Exterior - ## Add TotalFlrSF = 1stFlrSF + 2ndFlrSF + TotalBsmtSF - ## BsmtFinType1 and BsmtFinType2 to Bsmt -Replace each type to it's actually square feet BsmtFinSF1, BsmtFinSF2 -For Unf in type 1 and type2, replace it with the BsmtUnfSF - ## Combine BsmtFullBath, BsmtHalfBath to BsmtBath - ## Add all different PorchSF to TotalProchSF - ## Dummy MasVnrType to MasVnr and replace the value with MasVnrArea - ############################################################################################### - - preProcessCatField = ["MasVnrType", "Exterior1st", "Exterior2nd", "BsmtFinType1", "BsmtFinType2"] - preProcessNumFiled = ["1stFlrSF", "2ndFlrSF", "MasVnrArea", "BsmtFinSF1", "BsmtFinSF2", "BsmtUnfSF", "BsmtFullBath", "BsmtHalfBath"] - inputDF[preProcessCatField] = inputDF[preProcessCatField].fillna("Unknown") - inputDF[preProcessNumFiled] = inputDF[preProcessNumFiled].fillna(-1) - - # Exterior1st, Exterior2nd (Exterior covering on house) - var1_dummy_columns = pd.get_dummies(inputDF['Exterior1st'], prefix= "Exterior") - var2_dummy_columns = pd.get_dummies(inputDF['Exterior2nd'], prefix= "Exterior") - var_dummy_columns = pd.concat([var1_dummy_columns,var2_dummy_columns]).groupby(level=0).sum() - var_dummy_columns = var_dummy_columns.replace(2, 1) - - inputDF = pd.concat([inputDF, var_dummy_columns], axis=1) - inputDF = inputDF.drop( columns=['Exterior1st', 'Exterior2nd'] ) - - #TotalBsmtFinSF = "BsmtFinSF1"+ "BsmtFinSF2" ( + "TotalBsmtSF" ) - inputDF["TotalFlrSF"] = inputDF["1stFlrSF"].fillna(0) + inputDF["2ndFlrSF"].fillna(0) + inputDF["TotalBsmtSF"].fillna(0) - inputDF["TotalFlrSF"].replace(0, math.nan) - # BsmtFinType1, BsmtFinType2, BsmtFinSF1 (Type 1 finished square feet), BsmtFinSF2 (Type 1 finished square feet), BsmtUnfSF: Unfinished square feet of basement area - var1_dummy_columns = pd.get_dummies(inputDF['BsmtFinType1'], prefix= "Bsmt") - var1_dummy_columns = var1_dummy_columns.mul( inputDF['BsmtFinSF1'] , axis=0) - tmp = var1_dummy_columns['Bsmt_Unf'] - tmp [ inputDF.loc[inputDF['BsmtFinType1'] == "Unf"].index ] = 1 - tmp = tmp.mul( inputDF['BsmtUnfSF'] , axis=0) - var1_dummy_columns['Bsmt_Unf'] = tmp - - var2_dummy_columns = pd.get_dummies(inputDF['BsmtFinType2'], prefix= "Bsmt") - var2_dummy_columns = var2_dummy_columns.mul( inputDF['BsmtFinSF2'] , axis=0) - tmp = var2_dummy_columns['Bsmt_Unf'] - tmp [ inputDF.loc[inputDF['BsmtFinType2'] == "Unf"].index ] = 1 - tmp = tmp.mul( inputDF['BsmtUnfSF'] , axis=0) - var2_dummy_columns['Bsmt_Unf'] = tmp - - var_dummy_columns = pd.concat([var1_dummy_columns,var2_dummy_columns]).groupby(level=0).sum() - inputDF = pd.concat([inputDF, var_dummy_columns], axis=1) - inputDF = inputDF.drop( columns=['BsmtFinType1', 'BsmtFinType1', 'BsmtFinType2', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF'] ) - - #BsmtFullBath, BsmtHalfBath (number of type of bathroom in the basement) - inputDF['BsmtBath'] = inputDF["BsmtFullBath"] + 0.5* inputDF["BsmtHalfBath"] - inputDF = inputDF.drop( columns=['BsmtFullBath', 'BsmtHalfBath'] ) - - inputDF["TotalProchSF"] = inputDF["OpenPorchSF"] + inputDF["EnclosedPorch"] + inputDF["3SsnPorch"] + inputDF["ScreenPorch"] - - #MasVnrType, MasVnrArea - var_dummy_columns = pd.get_dummies(inputDF['MasVnrType'], prefix= "MasVnr") - var_dummy_columns = var_dummy_columns.mul( inputDF['MasVnrArea'] , axis=0) - inputDF = pd.concat([inputDF, var_dummy_columns], axis=1) - inputDF = inputDF.drop( columns=['MasVnrType', 'MasVnrArea'] ) - - ############################### Ordinal Features Label encoding ####################### - - ord_cols = ['ExterQual', 'ExterCond','BsmtCond','HeatingQC', 'KitchenQual', - 'FireplaceQu', 'GarageQual', 'GarageCond', 'PoolQC', 'BsmtQual'] - ord_dic = {'Ex': 5, 'Gd': 4, 'TA': 3, 'Fa':2, 'Po':1} - - for col in ord_cols: - inputDF[col] = inputDF[col].map(lambda x: ord_dic.get(x, 0)) - - - ############################### Median Impute ####################### - impute_cols = ['GarageArea', 'GarageCars','GarageYrBlt','LotFrontage', 'TotalBsmtSF', 'TotalFlrSF'] - for i, c in enumerate ( impute_cols ): - if inputDF[c].isnull().any(): - inputDF[c] = inputDF[c].fillna( inputDF[c].median() ) - - ############################### Label frequency encoding ####################### - inputDF.MSSubClass = inputDF.MSSubClass.astype('object') - if onehot: - - object_feats = inputDF.dtypes[inputDF.dtypes == "object"].index - numeric_feats = inputDF.dtypes[inputDF.dtypes != "object"].index - objEnc = pd.get_dummies(inputDF[object_feats], drop_first=True, dummy_na=True) - numEnc = inputDF[numeric_feats] - inputDF = pd.concat( [objEnc,numEnc], axis=1) - - else: - for i, c in enumerate ( inputDF.columns ): - if inputDF[c].dtype == 'object': - lce = LabelCountEncoder() - inputDF[c] = lce.fit_transform(inputDF[c]) - encodedDic[inputDF.columns[i]] = lce.rev_count_dict #add reversed dic back to the global variable - - return inputDF, encodedDic - - -##################################This is from Zeyu's original file. ######################## -###########???? Should we also include those ???????????????????????????????################## -# # LotFrontage : Since the area of each street connected to the house property most likely have a similar area to other houses in its neighborhood , we can fill in missing values by the median LotFrontage of the neighborhood. -# # Group by neighborhood and fill in missing value by the median LotFrontage of all the neighborhood -# all_data["LotFrontage"] = all_data.groupby("Neighborhood")["LotFrontage"].transform(lambda x: x.fillna(x.median())) -# -# # GarageYrBlt, GarageArea and GarageCars : Replacing missing data with 0 (Since No garage = no cars in such garage.) -# for col in ('GarageYrBlt', 'GarageArea', 'GarageCars'): -# all_data[col] = all_data[col].fillna(0) -# -# # Functional : data description says NA means typical -# all_data["Functional"] = all_data["Functional"].fillna("Typ") -# -# # Electrical : It has one NA value. Since this feature has mostly 'SBrkr', we can set that for the missing value. -# all_data['Electrical'] = all_data['Electrical'].fillna(all_data['Electrical'].mode()[0]) -# -# # KitchenQual: Only one NA value, and same as Electrical, we set 'TA' (which is the most frequent) for the missing value in KitchenQual. -# all_data['KitchenQual'] = all_data['KitchenQual'].fillna(all_data['KitchenQual'].mode()[0]) -# -# # SaleType : Fill in again with most frequent which is "WD" -# all_data['SaleType'] = all_data['SaleType'].fillna(all_data['SaleType'].mode()[0]) -# -# # Transforming some numerical variables that are really categorical -# #Year and month sold are transformed into categorical features. -# all_data['YrSold'] = all_data['YrSold'].astype(str) -# all_data['MoSold'] = all_data['MoSold'].astype(str) -# -################################################################################################ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/Kelly/Kaggle_house_price/preprocess2_label.py b/Kelly/Kaggle_house_price/preprocess2_label.py deleted file mode 100644 index f7d7a1f..0000000 --- a/Kelly/Kaggle_house_price/preprocess2_label.py +++ /dev/null @@ -1,214 +0,0 @@ -class LabelCountEncoder(object): - def __init__(self): - self.count_dict = {} - self.rev_count_dict = {} - def fit(self, column): - # We want to rank the key by its value and use the rank as the new value - count = column.value_counts() - self.count_dict = dict( list( zip (count.index, reversed(range(len(count)+1 ) ) ) ) ) - self.rev_count_dict = dict( list( zip ( reversed(range(len(count)+1 ) ) , count.index ) ) ) - def transform(self, column): - # If a category only appears in the test set, we will assign the value to zero. - missing = 0 - return column.map(lambda x: self.count_dict.get(x, missing)) - def fit_transform(self, column): - self.fit(column) - return self.transform(column) - - - -import numpy as np -import pandas as pd -import math -import re - -def impute( inputDF, onehot = False): - - #input: pd.dataframe - #one-hot or label encoding for the categorical field - #return: - # Imputed pd.DataFrame - # label-encoded dictionary - - - encodedDic = {} - - inputDF.Exterior1st = inputDF.Exterior1st.str.replace("Brk Cmn", "BrkComm") - inputDF.Exterior2nd = inputDF.Exterior2nd.str.replace("Brk Cmn", "BrkComm") - inputDF.Exterior1st = inputDF.Exterior1st.str.replace("CmentBd", "CemntBd") - inputDF.Exterior2nd = inputDF.Exterior2nd.str.replace("CmentBd", "CemntBd") - inputDF.Exterior1st = inputDF.Exterior1st.str.replace("Wd Shng", "WdShing") - inputDF.Exterior2nd = inputDF.Exterior2nd.str.replace("Wd Shng", "WdShing") - - ############################### purposelyEncodeData ######################################## - ## Start to purposely encode the information based on our best understanding. - ## Combine Exterior1st and Exterior2nd to Exterior - ## Add TotalFlrSF = 1stFlrSF + 2ndFlrSF + TotalBsmtSF - ## BsmtFinType1 and BsmtFinType2 to Bsmt -Replace each type to it's actually square feet BsmtFinSF1, BsmtFinSF2 -For Unf in type 1 and type2, replace it with the BsmtUnfSF - ## Combine BsmtFullBath, BsmtHalfBath to BsmtBath - ## Add all different PorchSF to TotalProchSF - ## Dummy MasVnrType to MasVnr and replace the value with MasVnrArea - ############################################################################################### - - preProcessCatField = ["MasVnrType", "Exterior1st", "Exterior2nd", "BsmtFinType1", "BsmtFinType2"] - preProcessNumFiled = ["1stFlrSF", "2ndFlrSF", "MasVnrArea", "BsmtFinSF1", "BsmtFinSF2", "BsmtUnfSF", "BsmtFullBath", "BsmtHalfBath"] - inputDF[preProcessCatField] = inputDF[preProcessCatField].fillna("Unknown") - inputDF[preProcessNumFiled] = inputDF[preProcessNumFiled].fillna(-1) - - # Exterior1st, Exterior2nd (Exterior covering on house) - var1_dummy_columns = pd.get_dummies(inputDF['Exterior1st'], prefix= "Exterior") - var2_dummy_columns = pd.get_dummies(inputDF['Exterior2nd'], prefix= "Exterior") - var_dummy_columns = pd.concat([var1_dummy_columns,var2_dummy_columns]).groupby(level=0).sum() - var_dummy_columns = var_dummy_columns.replace(2, 1) - - inputDF = pd.concat([inputDF, var_dummy_columns], axis=1) - inputDF = inputDF.drop( columns=['Exterior1st', 'Exterior2nd'] ) - - #TotalBsmtFinSF = "BsmtFinSF1"+ "BsmtFinSF2" ( + "TotalBsmtSF" ) - inputDF["TotalFlrSF"] = inputDF["1stFlrSF"].fillna(0) + inputDF["2ndFlrSF"].fillna(0) + inputDF["TotalBsmtSF"].fillna(0) - inputDF["TotalFlrSF"].replace(0, math.nan) - # BsmtFinType1, BsmtFinType2, BsmtFinSF1 (Type 1 finished square feet), BsmtFinSF2 (Type 1 finished square feet), BsmtUnfSF: Unfinished square feet of basement area - var1_dummy_columns = pd.get_dummies(inputDF['BsmtFinType1'], prefix= "Bsmt") - var1_dummy_columns = var1_dummy_columns.mul( inputDF['BsmtFinSF1'] , axis=0) - tmp = var1_dummy_columns['Bsmt_Unf'] - tmp [ inputDF.loc[inputDF['BsmtFinType1'] == "Unf"].index ] = 1 - tmp = tmp.mul( inputDF['BsmtUnfSF'] , axis=0) - var1_dummy_columns['Bsmt_Unf'] = tmp - - var2_dummy_columns = pd.get_dummies(inputDF['BsmtFinType2'], prefix= "Bsmt") - var2_dummy_columns = var2_dummy_columns.mul( inputDF['BsmtFinSF2'] , axis=0) - tmp = var2_dummy_columns['Bsmt_Unf'] - tmp [ inputDF.loc[inputDF['BsmtFinType2'] == "Unf"].index ] = 1 - tmp = tmp.mul( inputDF['BsmtUnfSF'] , axis=0) - var2_dummy_columns['Bsmt_Unf'] = tmp - - var_dummy_columns = pd.concat([var1_dummy_columns,var2_dummy_columns]).groupby(level=0).sum() - inputDF = pd.concat([inputDF, var_dummy_columns], axis=1) - inputDF = inputDF.drop( columns=['BsmtFinType1', 'BsmtFinType1', 'BsmtFinType2', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF'] ) - - #BsmtFullBath, BsmtHalfBath (number of type of bathroom in the basement) - inputDF['BsmtBath'] = inputDF["BsmtFullBath"] + 0.5* inputDF["BsmtHalfBath"] - inputDF = inputDF.drop( columns=['BsmtFullBath', 'BsmtHalfBath'] ) - - inputDF["TotalProchSF"] = inputDF["OpenPorchSF"] + inputDF["EnclosedPorch"] + inputDF["3SsnPorch"] + inputDF["ScreenPorch"] - - #MasVnrType, MasVnrArea - var_dummy_columns = pd.get_dummies(inputDF['MasVnrType'], prefix= "MasVnr") - var_dummy_columns = var_dummy_columns.mul( inputDF['MasVnrArea'] , axis=0) - inputDF = pd.concat([inputDF, var_dummy_columns], axis=1) - inputDF = inputDF.drop( columns=['MasVnrType', 'MasVnrArea'] ) - - ############################### Ordinal Features Label encoding ####################### - - #ord_cols = ['ExterQual', 'ExterCond','BsmtCond','HeatingQC', 'KitchenQual', - # 'FireplaceQu', 'GarageQual', 'GarageCond', 'PoolQC', 'BsmtQual'] - #ord_dic = {'Ex': 5, 'Gd': 4, 'TA': 3, 'Fa':2, 'Po':1} - - #for col in ord_cols: - # inputDF[col] = inputDF[col].map(lambda x: ord_dic.get(x, 0)) - - - ############################### Median Impute ####################### - impute_cols = ['GarageArea', 'GarageCars','GarageYrBlt','LotFrontage', 'TotalBsmtSF', 'TotalFlrSF'] - for i, c in enumerate ( impute_cols ): - if inputDF[c].isnull().any(): - inputDF[c] = inputDF[c].fillna( inputDF[c].median() ) - - ############################### Label frequency encoding ####################### - inputDF.MSSubClass = inputDF.MSSubClass.astype('object') - if onehot: - - object_feats = inputDF.dtypes[inputDF.dtypes == "object"].index - numeric_feats = inputDF.dtypes[inputDF.dtypes != "object"].index - objEnc = pd.get_dummies(inputDF[object_feats], drop_first=True, dummy_na=True) - numEnc = inputDF[numeric_feats] - inputDF = pd.concat( [objEnc,numEnc], axis=1) - - else: - for i, c in enumerate ( inputDF.columns ): - if inputDF[c].dtype == 'object': - lce = LabelCountEncoder() - inputDF[c] = lce.fit_transform(inputDF[c]) - encodedDic[inputDF.columns[i]] = lce.rev_count_dict #add reversed dic back to the global variable - - return inputDF, encodedDic - - -##################################This is from Zeyu's original file. ######################## -###########???? Should we also include those ???????????????????????????????################## -# # LotFrontage : Since the area of each street connected to the house property most likely have a similar area to other houses in its neighborhood , we can fill in missing values by the median LotFrontage of the neighborhood. -# # Group by neighborhood and fill in missing value by the median LotFrontage of all the neighborhood -# all_data["LotFrontage"] = all_data.groupby("Neighborhood")["LotFrontage"].transform(lambda x: x.fillna(x.median())) -# -# # GarageYrBlt, GarageArea and GarageCars : Replacing missing data with 0 (Since No garage = no cars in such garage.) -# for col in ('GarageYrBlt', 'GarageArea', 'GarageCars'): -# all_data[col] = all_data[col].fillna(0) -# -# # Functional : data description says NA means typical -# all_data["Functional"] = all_data["Functional"].fillna("Typ") -# -# # Electrical : It has one NA value. Since this feature has mostly 'SBrkr', we can set that for the missing value. -# all_data['Electrical'] = all_data['Electrical'].fillna(all_data['Electrical'].mode()[0]) -# -# # KitchenQual: Only one NA value, and same as Electrical, we set 'TA' (which is the most frequent) for the missing value in KitchenQual. -# all_data['KitchenQual'] = all_data['KitchenQual'].fillna(all_data['KitchenQual'].mode()[0]) -# -# # SaleType : Fill in again with most frequent which is "WD" -# all_data['SaleType'] = all_data['SaleType'].fillna(all_data['SaleType'].mode()[0]) -# -# # Transforming some numerical variables that are really categorical -# #Year and month sold are transformed into categorical features. -# all_data['YrSold'] = all_data['YrSold'].astype(str) -# all_data['MoSold'] = all_data['MoSold'].astype(str) -# -################################################################################################ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/Kelly/Kaggle_house_price/preprocess3_yrmonthsold.py b/Kelly/Kaggle_house_price/preprocess3_yrmonthsold.py deleted file mode 100644 index e9cd554..0000000 --- a/Kelly/Kaggle_house_price/preprocess3_yrmonthsold.py +++ /dev/null @@ -1,216 +0,0 @@ -class LabelCountEncoder(object): - def __init__(self): - self.count_dict = {} - self.rev_count_dict = {} - def fit(self, column): - # We want to rank the key by its value and use the rank as the new value - count = column.value_counts() - self.count_dict = dict( list( zip (count.index, reversed(range(len(count)+1 ) ) ) ) ) - self.rev_count_dict = dict( list( zip ( reversed(range(len(count)+1 ) ) , count.index ) ) ) - def transform(self, column): - # If a category only appears in the test set, we will assign the value to zero. - missing = 0 - return column.map(lambda x: self.count_dict.get(x, missing)) - def fit_transform(self, column): - self.fit(column) - return self.transform(column) - - - -import numpy as np -import pandas as pd -import math -import re - -def impute( inputDF, onehot = False): - - #input: pd.dataframe - #one-hot or label encoding for the categorical field - #return: - # Imputed pd.DataFrame - # label-encoded dictionary - - - encodedDic = {} - - inputDF.Exterior1st = inputDF.Exterior1st.str.replace("Brk Cmn", "BrkComm") - inputDF.Exterior2nd = inputDF.Exterior2nd.str.replace("Brk Cmn", "BrkComm") - inputDF.Exterior1st = inputDF.Exterior1st.str.replace("CmentBd", "CemntBd") - inputDF.Exterior2nd = inputDF.Exterior2nd.str.replace("CmentBd", "CemntBd") - inputDF.Exterior1st = inputDF.Exterior1st.str.replace("Wd Shng", "WdShing") - inputDF.Exterior2nd = inputDF.Exterior2nd.str.replace("Wd Shng", "WdShing") - - ############################### purposelyEncodeData ######################################## - ## Start to purposely encode the information based on our best understanding. - ## Combine Exterior1st and Exterior2nd to Exterior - ## Add TotalFlrSF = 1stFlrSF + 2ndFlrSF + TotalBsmtSF - ## BsmtFinType1 and BsmtFinType2 to Bsmt -Replace each type to it's actually square feet BsmtFinSF1, BsmtFinSF2 -For Unf in type 1 and type2, replace it with the BsmtUnfSF - ## Combine BsmtFullBath, BsmtHalfBath to BsmtBath - ## Add all different PorchSF to TotalProchSF - ## Dummy MasVnrType to MasVnr and replace the value with MasVnrArea - ############################################################################################### - - preProcessCatField = ["MasVnrType", "Exterior1st", "Exterior2nd", "BsmtFinType1", "BsmtFinType2"] - preProcessNumFiled = ["1stFlrSF", "2ndFlrSF", "MasVnrArea", "BsmtFinSF1", "BsmtFinSF2", "BsmtUnfSF", "BsmtFullBath", "BsmtHalfBath"] - inputDF[preProcessCatField] = inputDF[preProcessCatField].fillna("Unknown") - inputDF[preProcessNumFiled] = inputDF[preProcessNumFiled].fillna(-1) - - # Exterior1st, Exterior2nd (Exterior covering on house) - var1_dummy_columns = pd.get_dummies(inputDF['Exterior1st'], prefix= "Exterior") - var2_dummy_columns = pd.get_dummies(inputDF['Exterior2nd'], prefix= "Exterior") - var_dummy_columns = pd.concat([var1_dummy_columns,var2_dummy_columns]).groupby(level=0).sum() - var_dummy_columns = var_dummy_columns.replace(2, 1) - - inputDF = pd.concat([inputDF, var_dummy_columns], axis=1) - inputDF = inputDF.drop( columns=['Exterior1st', 'Exterior2nd'] ) - - #TotalBsmtFinSF = "BsmtFinSF1"+ "BsmtFinSF2" ( + "TotalBsmtSF" ) - inputDF["TotalFlrSF"] = inputDF["1stFlrSF"].fillna(0) + inputDF["2ndFlrSF"].fillna(0) + inputDF["TotalBsmtSF"].fillna(0) - inputDF["TotalFlrSF"].replace(0, math.nan) - # BsmtFinType1, BsmtFinType2, BsmtFinSF1 (Type 1 finished square feet), BsmtFinSF2 (Type 1 finished square feet), BsmtUnfSF: Unfinished square feet of basement area - var1_dummy_columns = pd.get_dummies(inputDF['BsmtFinType1'], prefix= "Bsmt") - var1_dummy_columns = var1_dummy_columns.mul( inputDF['BsmtFinSF1'] , axis=0) - tmp = var1_dummy_columns['Bsmt_Unf'] - tmp [ inputDF.loc[inputDF['BsmtFinType1'] == "Unf"].index ] = 1 - tmp = tmp.mul( inputDF['BsmtUnfSF'] , axis=0) - var1_dummy_columns['Bsmt_Unf'] = tmp - - var2_dummy_columns = pd.get_dummies(inputDF['BsmtFinType2'], prefix= "Bsmt") - var2_dummy_columns = var2_dummy_columns.mul( inputDF['BsmtFinSF2'] , axis=0) - tmp = var2_dummy_columns['Bsmt_Unf'] - tmp [ inputDF.loc[inputDF['BsmtFinType2'] == "Unf"].index ] = 1 - tmp = tmp.mul( inputDF['BsmtUnfSF'] , axis=0) - var2_dummy_columns['Bsmt_Unf'] = tmp - - var_dummy_columns = pd.concat([var1_dummy_columns,var2_dummy_columns]).groupby(level=0).sum() - inputDF = pd.concat([inputDF, var_dummy_columns], axis=1) - inputDF = inputDF.drop( columns=['BsmtFinType1', 'BsmtFinType1', 'BsmtFinType2', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF'] ) - - #BsmtFullBath, BsmtHalfBath (number of type of bathroom in the basement) - inputDF['BsmtBath'] = inputDF["BsmtFullBath"] + 0.5* inputDF["BsmtHalfBath"] - inputDF = inputDF.drop( columns=['BsmtFullBath', 'BsmtHalfBath'] ) - - inputDF["TotalProchSF"] = inputDF["OpenPorchSF"] + inputDF["EnclosedPorch"] + inputDF["3SsnPorch"] + inputDF["ScreenPorch"] - - #MasVnrType, MasVnrArea - var_dummy_columns = pd.get_dummies(inputDF['MasVnrType'], prefix= "MasVnr") - var_dummy_columns = var_dummy_columns.mul( inputDF['MasVnrArea'] , axis=0) - inputDF = pd.concat([inputDF, var_dummy_columns], axis=1) - inputDF = inputDF.drop( columns=['MasVnrType', 'MasVnrArea'] ) - - ############################### Ordinal Features Label encoding ####################### - - ord_cols = ['ExterQual', 'ExterCond','BsmtCond','HeatingQC', 'KitchenQual', - 'FireplaceQu', 'GarageQual', 'GarageCond', 'PoolQC', 'BsmtQual'] - ord_dic = {'Ex': 5, 'Gd': 4, 'TA': 3, 'Fa':2, 'Po':1} - - for col in ord_cols: - inputDF[col] = inputDF[col].map(lambda x: ord_dic.get(x, 0)) - - - ############################### Median Impute ####################### - impute_cols = ['GarageArea', 'GarageCars','GarageYrBlt','LotFrontage', 'TotalBsmtSF', 'TotalFlrSF'] - for i, c in enumerate ( impute_cols ): - if inputDF[c].isnull().any(): - inputDF[c] = inputDF[c].fillna( inputDF[c].median() ) - - ############################### Label frequency encoding ####################### - inputDF.MSSubClass = inputDF.MSSubClass.astype('object') - inputDF.YrSold = inputDF.YrSold.astype('object') - inputDF.MoSold = inputDF.MoSold.astype('object') - if onehot: - - object_feats = inputDF.dtypes[inputDF.dtypes == "object"].index - numeric_feats = inputDF.dtypes[inputDF.dtypes != "object"].index - objEnc = pd.get_dummies(inputDF[object_feats], drop_first=True, dummy_na=True) - numEnc = inputDF[numeric_feats] - inputDF = pd.concat( [objEnc,numEnc], axis=1) - - else: - for i, c in enumerate ( inputDF.columns ): - if inputDF[c].dtype == 'object': - lce = LabelCountEncoder() - inputDF[c] = lce.fit_transform(inputDF[c]) - encodedDic[inputDF.columns[i]] = lce.rev_count_dict #add reversed dic back to the global variable - - return inputDF, encodedDic - - -##################################This is from Zeyu's original file. ######################## -###########???? Should we also include those ???????????????????????????????################## -# # LotFrontage : Since the area of each street connected to the house property most likely have a similar area to other houses in its neighborhood , we can fill in missing values by the median LotFrontage of the neighborhood. -# # Group by neighborhood and fill in missing value by the median LotFrontage of all the neighborhood -# all_data["LotFrontage"] = all_data.groupby("Neighborhood")["LotFrontage"].transform(lambda x: x.fillna(x.median())) -# -# # GarageYrBlt, GarageArea and GarageCars : Replacing missing data with 0 (Since No garage = no cars in such garage.) -# for col in ('GarageYrBlt', 'GarageArea', 'GarageCars'): -# all_data[col] = all_data[col].fillna(0) -# -# # Functional : data description says NA means typical -# all_data["Functional"] = all_data["Functional"].fillna("Typ") -# -# # Electrical : It has one NA value. Since this feature has mostly 'SBrkr', we can set that for the missing value. -# all_data['Electrical'] = all_data['Electrical'].fillna(all_data['Electrical'].mode()[0]) -# -# # KitchenQual: Only one NA value, and same as Electrical, we set 'TA' (which is the most frequent) for the missing value in KitchenQual. -# all_data['KitchenQual'] = all_data['KitchenQual'].fillna(all_data['KitchenQual'].mode()[0]) -# -# # SaleType : Fill in again with most frequent which is "WD" -# all_data['SaleType'] = all_data['SaleType'].fillna(all_data['SaleType'].mode()[0]) -# -# # Transforming some numerical variables that are really categorical -# #Year and month sold are transformed into categorical features. -# all_data['YrSold'] = all_data['YrSold'].astype(str) -# all_data['MoSold'] = all_data['MoSold'].astype(str) -# -################################################################################################ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/Kelly/Machine_Learning_Lab/ML_lab_Kelly.ipynb b/Kelly/Machine_Learning_Lab/ML_lab_Kelly.ipynb deleted file mode 100644 index 1b3bff9..0000000 --- a/Kelly/Machine_Learning_Lab/ML_lab_Kelly.ipynb +++ /dev/null @@ -1,3004 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd \n", - "import numpy as np\n", - "orders = pd.read_csv(\"./data/Orders.csv\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Part I: Preprocessing and EDA" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Problem 1: Dataset Import & Cleaning\n", - "Check **\"Profit\"** and **\"Sales\"** in the dataset, convert these two columns to numeric type." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "orders.dtypes\n", - "\n", - "# convert sales and profit to numeric\n", - "orders.Sales = orders.Sales.str.replace('$', '').str.replace(',' , '').astype(float).round(2)\n", - "orders.Profit = orders.Profit.str.replace('$', '').str.replace(',', '').astype(float).round(2)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Problem 2: Inventory Management\n", - "- Retailers that depend on seasonal shoppers have a particularly challenging job when it comes to inventory management. Your manager is making plans for next year's inventory.\n", - "- He wants you to answer the following questions:\n", - " 1. Is there any seasonal trend of inventory in the company?\n", - " 2. Is the seasonal trend the same for different categories?\n", - "\n", - "- ***Hint:*** For each order, it has an attribute called `Quantity` that indicates the number of product in the order. If an order contains more than one product, there will be multiple observations of the same order." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import datetime\n", - "import seaborn as sns\n", - "from matplotlib import pyplot as plt\n", - "\n", - "# convert order date/ship date to datetime object\n", - "orders['Order.Date.mod'] = pd.to_datetime(orders['Order.Date']).dt.to_period('M')\n", - "orders['Ship.Date.mod'] = pd.to_datetime(orders['Ship.Date']).dt.to_period('M')\n", - "\n", - "# 1. Is there any seasonal trend of inventory in the company?\n", - "season = orders[['Quantity', 'Order.Date.mod']].groupby('Order.Date.mod').sum().reset_index()\n", - "sns.barplot(x='Order.Date.mod', y='Quantity', data=season)\n", - "plt.xticks(rotation=90)\n", - "plt.rcParams['figure.figsize']=16,4\n", - "\n", - "# sales seems to be better during winter months and June" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n", - " 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n", - " 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47]),\n", - " )" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# 2. Is the seasonal trend the same for different categories?\n", - "\n", - "catseason = orders[['Quantity', 'Category', 'Order.Date.mod']].groupby(['Order.Date.mod','Category']).sum().reset_index()\n", - "sns.barplot(x='Order.Date.mod', y='Quantity',hue='Category',data=catseason)\n", - "plt.xticks(rotation=90)\n", - "\n", - "# Sale of office supplies increased. Sales of furniture and technology items decreased." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Problem 3: Why did customers make returns?\n", - "- Your manager required you to give a brief report (**Plots + Interpretations**) on returned orders.\n", - "\n", - "\t1. How much profit did we lose due to returns each year?\n", - "\n", - "\n", - "\t2. How many customer returned more than once? more than 5 times?\n", - "\n", - "\n", - "\t3. Which regions are more likely to return orders?\n", - "\n", - "\n", - "\t4. Which categories (sub-categories) of products are more likely to be returned?\n", - "\n", - "- ***Hint:*** Merge the **Returns** dataframe with the **Orders** dataframe using `Order.ID`." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "returns = pd.read_csv(\"./data/Returns.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

" - ], - "text/plain": [ - " Profit\n", - "Order.Date.year \n", - "2012 17477.26\n", - "2013 9269.89\n", - "2014 17510.63\n", - "2015 17112.97" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "combine = pd.merge(orders, returns, left_on='Order.ID', right_on='Order ID', how='outer')\n", - "\n", - "# 1. How much profit did we lose due to returns each year?\n", - "# new df that drop all NaN in returns\n", - "combine['Order.Date.year'] = pd.to_datetime(combine['Order.Date']).dt.year\n", - "returninfo = combine[combine['Returned']=='Yes']\n", - "returninfo[['Order.Date.year','Profit']].groupby('Order.Date.year').sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(547, 2)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 2. How many customer returned more than once? more than 5 times?\n", - "\n", - "returnnum = returninfo[['Returned','Customer.ID']].groupby('Customer.ID').count().reset_index()\n", - "returnnum[returnnum['Returned']>5].shape\n", - "returnnum[returnnum['Returned']>1].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Region_yReturned
2Central America248
22Western Europe233
23Western US180
12Oceania154
14Southeastern Asia140
9Eastern US134
13South America133
6Eastern Asia131
17Southern Europe112
16Southern Asia111
20Western Asia108
18Southern US83
11Northern Europe76
4Central US71
0Caribbean69
19Western Africa60
10North Africa51
8Eastern Europe42
15Southern Africa25
5Eastern Africa18
1Central Africa17
7Eastern Canada10
3Central Asia9
21Western Canada5
\n", - "
" - ], - "text/plain": [ - " Region_y Returned\n", - "2 Central America 248\n", - "22 Western Europe 233\n", - "23 Western US 180\n", - "12 Oceania 154\n", - "14 Southeastern Asia 140\n", - "9 Eastern US 134\n", - "13 South America 133\n", - "6 Eastern Asia 131\n", - "17 Southern Europe 112\n", - "16 Southern Asia 111\n", - "20 Western Asia 108\n", - "18 Southern US 83\n", - "11 Northern Europe 76\n", - "4 Central US 71\n", - "0 Caribbean 69\n", - "19 Western Africa 60\n", - "10 North Africa 51\n", - "8 Eastern Europe 42\n", - "15 Southern Africa 25\n", - "5 Eastern Africa 18\n", - "1 Central Africa 17\n", - "7 Eastern Canada 10\n", - "3 Central Asia 9\n", - "21 Western Canada 5" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 3. Which regions are more likely to return orders?\n", - "returninfo[['Returned','Region_y']].groupby('Region_y').count().reset_index().sort_values('Returned', ascending=False)\n", - "# Central America" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Sub.CategoryReturned
3Binders269
2Art217
14Storage212
12Paper150
5Chairs147
13Phones145
0Accessories138
10Labels137
9Furnishings135
4Bookcases104
15Supplies103
8Fasteners102
7Envelopes99
6Copiers99
11Machines63
1Appliances59
16Tables41
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" - ], - "text/plain": [ - " Sub.Category Returned\n", - "3 Binders 269\n", - "2 Art 217\n", - "14 Storage 212\n", - "12 Paper 150\n", - "5 Chairs 147\n", - "13 Phones 145\n", - "0 Accessories 138\n", - "10 Labels 137\n", - "9 Furnishings 135\n", - "4 Bookcases 104\n", - "15 Supplies 103\n", - "8 Fasteners 102\n", - "7 Envelopes 99\n", - "6 Copiers 99\n", - "11 Machines 63\n", - "1 Appliances 59\n", - "16 Tables 41" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 4. Which categories (sub-categories) of products are more likely to be returned?\n", - "returninfo[['Returned','Sub.Category']].groupby('Sub.Category').count().reset_index().sort_values('Returned', ascending=False)\n", - "# Binders\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Part II: Machine Learning and Business Use Case" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Problem 4: Feature Engineering" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# step 1 & step 2\n", - "combine.Returned = pd.get_dummies(combine.Returned)\n", - "combine['Order.Month'] = pd.to_datetime(combine['Order.Date']).dt.month\n", - "combine['Process.Time'] = pd.to_datetime(combine['Ship.Date']) - pd.to_datetime(combine['Order.Date'])\n", - "combine['Process.Time'] = combine['Process.Time'].dt.days" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# step 3 Let us generate a feature indictes how many times the product has been returned before\n", - "combine ['Return.Freq'] = combine.groupby('Product.ID')['Returned'].transform('sum')" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Row.IDOrder.IDOrder.DateShip.DateShip.ModeCustomer.IDCustomer.NameSegmentPostal.CodeCity...Order.PriorityOrder.Date.modShip.Date.modReturnedOrder IDRegion_yOrder.Date.yearOrder.MonthProcess.TimeReturn.Freq
040098CA-2014-AB10015140-4195411/11/1411/13/14First ClassAB-100151402Aaron BergmanConsumer73120.0Oklahoma City...High2014-112014-110NaNNaN20141120
140099CA-2014-AB10015140-4195411/11/1411/13/14First ClassAB-100151402Aaron BergmanConsumer73120.0Oklahoma City...High2014-112014-110NaNNaN20141120
226341IN-2014-JR162107-416752/5/142/7/14Second ClassJR-162107Justin RitterCorporateNaNWollongong...Critical2014-022014-020NaNNaN2014222
326339IN-2014-JR162107-416752/5/142/7/14Second ClassJR-162107Justin RitterCorporateNaNWollongong...Critical2014-022014-020NaNNaN2014221
426340IN-2014-JR162107-416752/5/142/7/14Second ClassJR-162107Justin RitterCorporateNaNWollongong...Critical2014-022014-020NaNNaN2014220
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" - ], - "text/plain": [ - " Row.ID Order.ID Order.Date Ship.Date Ship.Mode \\\n", - "0 40098 CA-2014-AB10015140-41954 11/11/14 11/13/14 First Class \n", - "1 40099 CA-2014-AB10015140-41954 11/11/14 11/13/14 First Class \n", - "2 26341 IN-2014-JR162107-41675 2/5/14 2/7/14 Second Class \n", - "3 26339 IN-2014-JR162107-41675 2/5/14 2/7/14 Second Class \n", - "4 26340 IN-2014-JR162107-41675 2/5/14 2/7/14 Second Class \n", - "\n", - " Customer.ID Customer.Name Segment Postal.Code City \\\n", - "0 AB-100151402 Aaron Bergman Consumer 73120.0 Oklahoma City \n", - "1 AB-100151402 Aaron Bergman Consumer 73120.0 Oklahoma City \n", - "2 JR-162107 Justin Ritter Corporate NaN Wollongong \n", - "3 JR-162107 Justin Ritter Corporate NaN Wollongong \n", - "4 JR-162107 Justin Ritter Corporate NaN Wollongong \n", - "\n", - " ... Order.Priority Order.Date.mod Ship.Date.mod Returned Order ID \\\n", - "0 ... High 2014-11 2014-11 0 NaN \n", - "1 ... High 2014-11 2014-11 0 NaN \n", - "2 ... Critical 2014-02 2014-02 0 NaN \n", - "3 ... Critical 2014-02 2014-02 0 NaN \n", - "4 ... Critical 2014-02 2014-02 0 NaN \n", - "\n", - " Region_y Order.Date.year Order.Month Process.Time Return.Freq \n", - "0 NaN 2014 11 2 0 \n", - "1 NaN 2014 11 2 0 \n", - "2 NaN 2014 2 2 2 \n", - "3 NaN 2014 2 2 1 \n", - "4 NaN 2014 2 2 0 \n", - "\n", - "[5 rows x 33 columns]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "combine.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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261207.5640.0020278.340120.0000...0000100000
271061.0480.0021162.5101253.0400...0000100000
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5128961.3830.00401.00251.8000...0001001000
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51290 rows × 45 columns

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" - ], - "text/plain": [ - " Sales Quantity Discount Process.Time Return.Freq Shipping.Cost \\\n", - "0 221.98 2 0.00 2 0 40.770 \n", - "1 341.96 2 0.00 2 0 25.270 \n", - "2 3709.40 9 0.10 2 2 923.630 \n", - "3 344.68 2 0.10 2 1 65.350 \n", - "4 133.92 5 0.10 2 0 41.640 \n", - "5 70.79 2 0.10 2 0 10.480 \n", - "6 5175.17 9 0.10 1 2 915.490 \n", - "7 333.15 3 0.10 1 1 71.020 \n", - "8 64.15 6 0.10 1 2 5.980 \n", - "9 16.04 2 0.10 1 3 2.250 \n", - "10 27.27 1 0.10 1 2 1.620 \n", - "11 2892.51 5 0.10 2 0 910.160 \n", - "12 2832.96 8 0.00 1 0 903.040 \n", - "13 2862.68 5 0.10 3 1 897.350 \n", - "14 687.85 4 0.10 3 1 166.980 \n", - "15 233.77 2 0.10 3 0 55.480 \n", - "16 90.83 2 0.10 3 2 17.500 \n", - "17 26.73 3 0.10 3 0 8.740 \n", - "18 1822.08 4 0.00 2 5 894.770 \n", - "19 53.16 4 0.00 2 1 10.060 \n", - "20 5244.84 6 0.00 4 0 878.380 \n", - "21 48.71 1 0.20 1 1 11.130 \n", - "22 17.94 3 0.00 1 0 4.290 \n", - "23 242.94 3 0.00 1 0 1.280 \n", - "24 4626.15 5 0.00 3 0 835.570 \n", - "25 2616.96 4 0.00 2 0 832.410 \n", - "26 1207.56 4 0.00 2 0 278.340 \n", - "27 1061.04 8 0.00 2 1 162.510 \n", - "28 54.00 4 0.00 2 1 11.080 \n", - "29 25.29 1 0.00 2 1 10.030 \n", - "... ... ... ... ... ... ... \n", - "51260 43.92 3 0.00 5 0 2.780 \n", - "51261 4.02 1 0.60 6 2 1.060 \n", - "51262 15.28 2 0.00 5 0 2.030 \n", - "51263 8.73 1 0.00 5 0 1.570 \n", - "51264 5.68 2 0.00 5 0 1.190 \n", - "51265 6.90 1 0.50 4 1 1.050 \n", - "51266 48.93 3 0.50 2 2 1.050 \n", - "51267 2.78 1 0.70 5 1 1.050 \n", - "51268 25.83 1 0.00 4 2 1.050 \n", - "51269 4.51 3 0.20 6 12 1.440 \n", - "51270 12.54 1 0.20 6 0 1.380 \n", - "51271 1.08 2 0.70 6 0 1.060 \n", - "51272 36.68 7 0.00 2 1 1.042 \n", - "51273 4.57 4 0.70 6 0 1.280 \n", - "51274 823.96 5 0.20 0 0 69.660 \n", - "51275 15.98 2 0.20 0 0 2.010 \n", - "51276 213.48 3 0.20 5 0 14.750 \n", - "51277 22.72 4 0.20 5 1 3.210 \n", - "51278 8.56 2 0.00 5 0 1.580 \n", - "51279 47.14 2 0.10 5 1 1.030 \n", - "51280 259.96 4 0.00 6 0 11.840 \n", - "51281 71.12 4 0.00 6 0 7.300 \n", - "51282 49.30 3 0.45 1 0 1.030 \n", - "51283 61.44 3 0.00 3 1 8.470 \n", - "51284 9.61 2 0.70 5 0 1.020 \n", - "51285 84.00 5 0.00 2 2 1.019 \n", - "51286 58.05 5 0.10 5 1 1.010 \n", - "51287 26.94 2 0.00 0 0 1.010 \n", - "51288 16.72 5 0.20 4 0 1.930 \n", - "51289 61.38 3 0.00 4 0 1.002 \n", - "\n", - " Order.Month Profit Segment_Corporate Segment_Home Office \\\n", - "0 11 62.15 0 0 \n", - "1 11 54.71 0 0 \n", - "2 2 -288.77 1 0 \n", - "3 2 34.42 1 0 \n", - "4 2 -6.03 1 0 \n", - "5 2 25.13 1 0 \n", - "6 10 919.97 0 0 \n", - "7 10 88.80 0 0 \n", - "8 10 22.03 0 0 \n", - "9 10 -1.30 0 0 \n", - "10 10 9.09 0 0 \n", - "11 1 -96.54 0 1 \n", - "12 11 311.52 0 0 \n", - "13 6 763.28 1 0 \n", - "14 6 213.97 1 0 \n", - "15 6 20.77 1 0 \n", - "16 6 35.27 1 0 \n", - "17 6 -2.97 1 0 \n", - "18 11 564.84 0 0 \n", - "19 11 26.52 0 0 \n", - "20 4 996.48 0 0 \n", - "21 3 5.48 0 0 \n", - "22 3 4.66 0 0 \n", - "23 3 4.86 0 0 \n", - "24 4 647.55 1 0 \n", - "25 12 1151.40 0 0 \n", - "26 12 0.00 0 0 \n", - "27 12 53.04 0 0 \n", - "28 12 17.28 0 0 \n", - "29 12 1.26 0 0 \n", - "... ... ... ... ... \n", - "51260 5 12.74 0 0 \n", - "51261 12 -1.11 0 0 \n", - "51262 11 7.49 0 0 \n", - "51263 11 2.97 0 0 \n", - "51264 11 1.76 0 0 \n", - "51265 11 -0.84 0 0 \n", - "51266 12 -2.97 1 0 \n", - "51267 12 -3.25 0 0 \n", - "51268 7 9.03 0 0 \n", - "51269 9 0.85 0 0 \n", - "51270 9 4.23 0 0 \n", - "51271 9 -0.79 0 0 \n", - "51272 12 16.80 1 0 \n", - "51273 6 -3.81 0 0 \n", - "51274 7 51.50 0 0 \n", - "51275 7 5.00 0 0 \n", - "51276 8 16.01 0 0 \n", - "51277 8 7.38 0 0 \n", - "51278 8 2.48 0 0 \n", - "51279 11 8.86 1 0 \n", - "51280 4 124.78 0 0 \n", - "51281 4 22.05 0 0 \n", - "51282 8 -18.83 0 1 \n", - "51283 6 16.59 0 0 \n", - "51284 3 -21.17 1 0 \n", - "51285 6 9.20 0 0 \n", - "51286 8 19.95 0 1 \n", - "51287 5 1.86 1 0 \n", - "51288 5 3.34 0 0 \n", - "51289 5 1.80 0 0 \n", - "\n", - " ... 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"27 0 0 0 \n", - "28 0 0 0 \n", - "29 0 0 0 \n", - "... ... ... ... \n", - "51260 0 0 0 \n", - "51261 0 0 0 \n", - "51262 0 0 0 \n", - "51263 0 0 0 \n", - "51264 0 0 0 \n", - "51265 0 0 0 \n", - "51266 0 0 0 \n", - "51267 0 0 0 \n", - "51268 0 0 0 \n", - "51269 0 0 0 \n", - "51270 0 0 0 \n", - "51271 0 0 0 \n", - "51272 0 1 0 \n", - "51273 0 0 0 \n", - "51274 0 0 0 \n", - "51275 0 0 0 \n", - "51276 0 1 0 \n", - "51277 0 1 0 \n", - "51278 0 1 0 \n", - "51279 0 0 0 \n", - "51280 0 0 0 \n", - "51281 0 0 0 \n", - "51282 0 0 0 \n", - "51283 0 1 0 \n", - "51284 0 0 0 \n", - "51285 0 1 0 \n", - "51286 0 0 0 \n", - "51287 0 1 0 \n", - "51288 0 1 0 \n", - "51289 0 1 0 \n", - "\n", - " Order.Priority_Medium Order.Priority_nan \n", - "0 0 0 \n", - "1 0 0 \n", - "2 0 0 \n", - "3 0 0 \n", - "4 0 0 \n", - "5 0 0 \n", - "6 1 0 \n", - "7 1 0 \n", - "8 1 0 \n", - "9 1 0 \n", - "10 1 0 \n", - "11 1 0 \n", - "12 0 0 \n", - "13 0 0 \n", - "14 0 0 \n", - "15 0 0 \n", - "16 0 0 \n", - "17 0 0 \n", - "18 0 0 \n", - "19 0 0 \n", - "20 0 0 \n", - "21 0 0 \n", - "22 0 0 \n", - "23 0 0 \n", - "24 0 0 \n", - "25 0 0 \n", - "26 0 0 \n", - "27 0 0 \n", - "28 0 0 \n", - "29 0 0 \n", - "... ... ... \n", - "51260 1 0 \n", - "51261 1 0 \n", - "51262 1 0 \n", - "51263 1 0 \n", - "51264 1 0 \n", - "51265 1 0 \n", - "51266 1 0 \n", - "51267 1 0 \n", - "51268 1 0 \n", - "51269 1 0 \n", - "51270 1 0 \n", - "51271 1 0 \n", - "51272 0 0 \n", - "51273 1 0 \n", - "51274 1 0 \n", - "51275 1 0 \n", - "51276 0 0 \n", - "51277 0 0 \n", - "51278 0 0 \n", - "51279 1 0 \n", - "51280 1 0 \n", - "51281 1 0 \n", - "51282 1 0 \n", - "51283 0 0 \n", - "51284 1 0 \n", - "51285 0 0 \n", - "51286 1 0 \n", - "51287 0 0 \n", - "51288 0 0 \n", - "51289 0 0 \n", - "\n", - "[51290 rows x 45 columns]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "use_columns = ['Sales', 'Quantity', 'Discount', 'Process.Time', 'Return.Freq', 'Shipping.Cost', 'Segment',\n", - " 'Ship.Mode', 'Region_x', 'Category', 'Order.Month', 'Order.Priority','Profit']\n", - "x = pd.get_dummies(combine[use_columns], drop_first=True, dummy_na=True)\n", - "y = combine['Returned']\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Problem 5: Fitting Models" - ] - }, - { - "cell_type": "code", - "execution_count": 372, - "metadata": {}, - "outputs": [], - "source": [ - "# factorized categorical col\n", - "class LabelCountEncoder(object):\n", - " def __init__(self):\n", - " self.count_dict = {}\n", - " \n", - " def fit(self, column):\n", - " # This gives you a dictionary with level as the key and counts as the value\n", - " count = column.value_counts().to_dict()\n", - " # We want to rank the key by its value and use the rank as the new value\n", - " # Your code here\n", - " self.count_dict = dict([(key[0], rank+1) for rank, key in enumerate(sorted(count.items(), key = lambda x: x[1]))])\n", - " \n", - " def transform(self, column):\n", - " # If a category only appears in the test set, we will assign the value to zero.\n", - " missing = 0\n", - " # Your code here\n", - " return column.map(lambda x: self.count_dict.get(x, missing)) # if the key:value pair is not found in the dictionary, fill the value with missing(0)\n", - " \n", - " def fit_transform(self, column):\n", - " self.fit(column)\n", - " return self.transform(column)" - ] - }, - { - "cell_type": "code", - "execution_count": 373, - "metadata": {}, - "outputs": [], - "source": [ - "# factorized categorical col with obj dtype\n", - "\n", - "label_count_df = x.copy()\n", - "\n", - "key_val_rank ={}\n", - "for c in label_count_df.columns:\n", - " if label_count_df[c].dtype == 'object':\n", - " lce = LabelCountEncoder() # objects are instances of a class. We create an instance by calling the class.\n", - " label_count_df[c] = lce.fit_transform(label_count_df[c])\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 375, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[array([[-2.51553479e-04, -8.25943771e-03, 2.54445203e-01,\n", - " -1.97819035e-02, 8.48438718e-01, 4.26019393e-04,\n", - " 1.08786757e-02, 5.07857175e-04, -8.04051138e-02,\n", - " -1.01120425e-01, 0.00000000e+00, -6.28253534e-02,\n", - " 5.00434738e-02, 1.93926159e-01, 0.00000000e+00,\n", - " 0.00000000e+00, -3.49255920e-01, 1.78376185e-01,\n", - " -4.40778573e-01, 2.58725020e-01, -3.81088213e-01,\n", - " 2.21462348e-01, -3.31578097e-01, 7.77755606e-01,\n", - " -1.58219367e-01, -1.24900481e-02, -9.08547805e-02,\n", - " 7.44040174e-02, -6.34059968e-02, 1.15770466e-01,\n", - " 1.26441257e-01, 8.92452176e-02, 8.27573649e-01,\n", - " -7.67702384e-03, -2.55500207e-02, -4.06010057e-01,\n", - " 9.58363378e-01, 0.00000000e+00, -2.32181129e-01,\n", - 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"[[7077 2737]\n", - " [ 136 308]]\n", - "0.7074031949210267\n", - "[array([[-1.81259379e-04, -1.86833488e-02, 2.57891712e-01,\n", - " 2.93833056e-02, 8.28197087e-01, 1.27748713e-03,\n", - " 1.03883344e-02, 1.29682361e-05, -5.67122194e-02,\n", - " -1.41226095e-01, 0.00000000e+00, -1.82527518e-02,\n", - " -2.46871280e-01, 2.58407833e-02, 0.00000000e+00,\n", - " 2.66415780e-01, 6.27822459e-02, 3.92957847e-01,\n", - " -3.40609797e-01, 3.81138063e-01, -1.76218554e-01,\n", - " 6.43303631e-01, -4.43809000e-02, 1.08969851e+00,\n", - " 1.06132463e-01, 3.97095255e-02, 3.57027649e-01,\n", - " 4.36587157e-01, 2.68130632e-01, 5.57120278e-01,\n", - " 3.05175133e-01, 5.34316157e-01, 9.31302974e-01,\n", - " 1.15646711e-02, 3.31338441e-01, 2.40771832e-01,\n", - " 1.19820024e+00, 0.00000000e+00, -2.35901336e-01,\n", - " 1.27904296e-01, 0.00000000e+00, -1.62764618e-01,\n", - " -1.04216714e-01, -1.73115159e-01, 0.00000000e+00]]), array([-1.41423413])]\n", - "0.7200477675960226\n", - "[[7201 2613]\n", - " [ 126 318]]\n", - "0.7249819617865267\n" - ] - } - ], - "source": [ - "np.random.seed(0)\n", - "from sklearn.model_selection import StratifiedKFold\n", - "from sklearn import linear_model\n", - "from sklearn.metrics import confusion_matrix, roc_auc_score\n", - "\n", - "skf = StratifiedKFold(n_splits=5)\n", - "\n", - "LR = linear_model.LogisticRegression(class_weight='balanced')\n", - "LR.set_params(penalty='l1')\n", - "\n", - "\n", - "for train_index, test_index in skf.split(label_count_df, y):\n", - " x_train, x_test = label_count_df.iloc[train_index], label_count_df.iloc[test_index]\n", - " y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n", - " \n", - " LR.fit(x_train, y_train)\n", - " y_predict = LR.predict(x_test)\n", - " \n", - " print([LR.coef_, LR.intercept_])\n", - " print(LR.score(x_train, y_train))\n", - " print(confusion_matrix(y_test, y_predict))\n", - " print(roc_auc_score(y_test, y_predict))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Problem 6: Evaluating Models" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Kelly/Machine_Learning_Lab/Machine_Learning_Lab.md b/Kelly/Machine_Learning_Lab/Machine_Learning_Lab.md deleted file mode 100644 index 0167a60..0000000 --- a/Kelly/Machine_Learning_Lab/Machine_Learning_Lab.md +++ /dev/null @@ -1,106 +0,0 @@ -# Machine Learning Lab - -This lab is aimed to walk you through the complete workflow of a machine learning project; from data wrangling, exploratory data analysis (EDA), model training and model evaluation/comparison. - -You will work with your machine project teamates for this lab and your team needs to decide whether to use either R or Python as the main programming language. **Each team memeber needs to work on his/her own submission.** - -We will use Github for team collaboration. There is a [TL;DR](https://gist.github.com/Chaser324/ce0505fbed06b947d962) of how do programmers work together on Github or we can break it down into following steps: - -1. The team leader creates a public Github repository under his/her account first. - -2. All the other team members fork the repo so you will have a COPY of the repo under your account - -3. Git clone YOUR OWN repo otherwise you won't be able to push later. - -4. Create a subfolder under your name and finish your code. Push the changes to Github. - -5. Go to the Github page of YOUR OWN repository and click the "Pull Request" tab. You can find the details [here](https://help.github.com/articles/creating-a-pull-request-from-a-fork/) - -6. Submit the pull request so you can see it under team leader's repository. - -7. Pair review each other's code before merging it to the master branch. - - -**Homework** -To understand fork, pull request and branch better, review [this video](https://youtu.be/_NrSWLQsDL4) in 1.25X speed. - - -## Part I: Preprocessing and EDA - -- The data comes from a global e-retailer company, including orders from 2012 to 2015. Import the **Orders** dataset and do some basic EDA. -- For problem 1 to 3, we mainly focus on data cleaning and data visualizations. You can use all the packages that you are familiar with to conduct some plots and also provide **brief interpretations** about your findings. - -### Problem 1: Dataset Import & Cleaning -Check **"Profit"** and **"Sales"** in the dataset, convert these two columns to numeric type. - - -### Problem 2: Inventory Management -- Retailers that depend on seasonal shoppers have a particularly challenging job when it comes to inventory management. Your manager is making plans for next year's inventory. -- He wants you to answer the following questions: - 1. Is there any seasonal trend of inventory in the company? - 2. Is the seasonal trend the same for different categories? - -- ***Hint:*** For each order, it has an attribute called `Quantity` that indicates the number of product in the order. If an order contains more than one product, there will be multiple observations of the same order. - - -### Problem 3: Why did customers make returns? -- Your manager required you to give a brief report (**Plots + Interpretations**) on returned orders. - - 1. How much profit did we lose due to returns each year? - - - 2. How many customer returned more than once? more than 5 times? - - - 3. Which regions are more likely to return orders? - - - 4. Which categories (sub-categories) of products are more likely to be returned? - -- ***Hint:*** Merge the **Returns** dataframe with the **Orders** dataframe using `Order.ID`. - - -## Part II: Machine Learning and Business Use Case - -Now your manager has a basic understanding of why customers returned orders. Next, he wants you to use machine learning to predict which orders are most likely to be returned. In this part, you will generate several features based on our previous findings and your manager's requirements. - -### Problem 4: Feature Engineering -#### Step 1: Create the dependent variable -- First of all, we need to generate a categorical variable which indicates whether an order has been returned or not. -- ***Hint:*** the returned orders’ IDs are contained in the dataset “returns” - - -#### Step 2: -- Your manager believes that **how long it took the order to ship** would affect whether the customer would return it or not. -- He wants you to generate a feature which can measure how long it takes the company to process each order. -- ***Hint:*** Process.Time = Ship.Date - Order.Date - - -#### Step 3: - -- If a product has been returned before, it may be returned again. -- Let us generate a feature indictes how many times the product has been returned before. -- If it never got returned, we just impute using 0. -- ***Hint:*** Group by different Product.ID - - -### Problem 5: Fitting Models - -- You can use any binary classification method you have learned so far. -- Use 80/20 training and test splits to build your model. -- Double check the column types before you fit the model. -- Only include useful features. i.e all the `ID`s should be excluded from your training set. -- Not that there are only less than 5% of the orders have been returned, so you should consider using the `createDataPartition` function from `caret` package that does a **stratified** random split of the data. Scikit-learn also has a [StratifiedKfold](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedKFold.html#sklearn-model-selection-stratifiedkfold) function that does similar thing. -- Do forget to `set.seed()` before the spilt to make your result reproducible. -- **Note:** We are not looking for the best tuned model in the lab so don't spend too much time on grid search. Focus on model evaluation and the business use case of each model. - - -### Problem 6: Evaluating Models -- What is the best metric to evaluate your model. Is accuracy good for this case? -- Now you have multiple models, which one would you pick? -- Can you get any clue from the confusion matrix? What is the meaning of precision and recall in this case? Which one do you care the most? How will your model help the manager make decisions? -- **Note:** The last question is open-ended. Your answer could be completely different depending on your understanding of this business problem. - -### Problem 7: Feature Engineering Revisit -- Is there anything wrong with the new feature we generated? How should we fix it? -- ***Hint***: For the real test set, we do not know it will get returned or not. diff --git a/Kelly/Kaggle_house_price/__pycache__/preprocessfinal.cpython-36.pyc b/Kelly/__pycache__/preprocessfinal.cpython-36.pyc similarity index 93% rename from Kelly/Kaggle_house_price/__pycache__/preprocessfinal.cpython-36.pyc rename to Kelly/__pycache__/preprocessfinal.cpython-36.pyc index 3fefa3aa4381fc05da2a94b51f32036b233cc959..216562b61aa671c5d5573d3ad982d06602fcea8f 100644 GIT binary patch delta 18 ZcmeyT{91W~1tX)`WJ^Ys%>|5o0sukj1_J;9 delta 37 scmaE@{7-p<1tX*9WJ^XBA@9WW^qkcAjQrB#)cAs;%;eO~F^v5J0R8F>RR910 diff --git a/Kelly/Kaggle_house_price/housing price data manipulation.ipynb b/Kelly/housing_price_data_preprocess.ipynb similarity index 100% rename from Kelly/Kaggle_house_price/housing price data manipulation.ipynb rename to Kelly/housing_price_data_preprocess.ipynb diff --git a/Kelly/linreg_regularization_models.ipynb b/Kelly/linreg_regularization_models.ipynb new file mode 100644 index 0000000..5c59caa --- /dev/null +++ b/Kelly/linreg_regularization_models.ipynb @@ -0,0 +1,697 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multiple linear regression " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "## import data\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "train = pd.read_csv(\"train.csv\").set_index(\"Id\")\n", + "test = pd.read_csv(\"test.csv\").set_index(\"Id\")\n", + "combine = pd.concat([train, test])\n", + "\n", + "# process data before model fitting\n", + "from preprocessfinal import impute\n", + "onehot, encodedDic = impute(combine, True) # process data and onehot encode\n", + "\n", + "# seperate onehot data into train and test\n", + "train_onehot = onehot.iloc[0:1460,]\n", + "test_onehot = onehot.iloc[1460:2919,].drop('SalePrice', axis = 1) # salesprice col were all NA \n", + "\n", + "# split train data frame into x var and y var for model testing\n", + "x_onehot = train_onehot.drop('SalePrice', axis=1)\n", + "y_log = np.log(train_onehot.SalePrice) # convert y to normal distribution for regression models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### original y vs log(y) distirbution" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 5., 11., 13., 61., 58., 126., 165., 180., 122., 130., 121.,\n", + " 78., 61., 64., 49., 36., 36., 25., 13., 25., 16., 11.,\n", + " 4., 11., 9., 5., 4., 4., 4., 2., 1., 1., 1.,\n", + " 0., 1., 0., 2., 0., 1., 0., 2., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 2.]),\n", + " array([ 34900., 49302., 63704., 78106., 92508., 106910., 121312.,\n", + " 135714., 150116., 164518., 178920., 193322., 207724., 222126.,\n", + " 236528., 250930., 265332., 279734., 294136., 308538., 322940.,\n", + " 337342., 351744., 366146., 380548., 394950., 409352., 423754.,\n", + " 438156., 452558., 466960., 481362., 495764., 510166., 524568.,\n", + " 538970., 553372., 567774., 582176., 596578., 610980., 625382.,\n", + " 639784., 654186., 668588., 682990., 697392., 711794., 726196.,\n", + " 740598., 755000.]),\n", + "
)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "plt.hist(train_onehot.SalePrice, bins=50) # original y" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 2., 2., 1., 0., 0., 0., 2., 3., 4., 3., 5.,\n", + " 1., 5., 21., 22., 23., 18., 29., 58., 56., 65., 100.,\n", + " 122., 93., 90., 82., 108., 91., 64., 55., 58., 51., 46.,\n", + " 42., 23., 29., 22., 13., 13., 13., 7., 5., 4., 1.,\n", + " 2., 2., 2., 0., 0., 2.]),\n", + " array([10.46024211, 10.52172673, 10.58321134, 10.64469596, 10.70618058,\n", + " 10.7676652 , 10.82914982, 10.89063444, 10.95211906, 11.01360367,\n", + " 11.07508829, 11.13657291, 11.19805753, 11.25954215, 11.32102677,\n", + " 11.38251138, 11.443996 , 11.50548062, 11.56696524, 11.62844986,\n", + " 11.68993448, 11.75141909, 11.81290371, 11.87438833, 11.93587295,\n", + " 11.99735757, 12.05884219, 12.12032681, 12.18181142, 12.24329604,\n", + " 12.30478066, 12.36626528, 12.4277499 , 12.48923452, 12.55071913,\n", + " 12.61220375, 12.67368837, 12.73517299, 12.79665761, 12.85814223,\n", + " 12.91962684, 12.98111146, 13.04259608, 13.1040807 , 13.16556532,\n", + " 13.22704994, 13.28853455, 13.35001917, 13.41150379, 13.47298841,\n", + " 13.53447303]),\n", + " )" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(y_log, bins=50) # log(y)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ridge regularization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Find best lambda" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'alpha': 0.2848035868435802}\n", + "Lowest RMSE found: 0.13372256831658905\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import GridSearchCV # search for the best lambda\n", + "from sklearn import linear_model\n", + "\n", + "ridge = linear_model.Ridge(normalize=True) # create a ridge regression instance\n", + "\n", + "# find the best alpha (lambda) for ridge\n", + "grid_param = [{'alpha': np.logspace(-4, 2, 100)}]\n", + "para_search_ridge = GridSearchCV(estimator=ridge, param_grid=grid_param, scoring='neg_mean_squared_error', cv=5, return_train_score=True)\n", + "para_search_ridge.fit(x_onehot, y_log)\n", + "\n", + "print(para_search_ridge.best_params_)\n", + "print(\"Lowest RMSE found: \", np.sqrt(np.abs(para_search_ridge.best_score_)))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### RMSE for all train data using best ridge" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.11292062271587874\n" + ] + } + ], + "source": [ + "# fit best ridge equation to all train data \n", + "best_ridge_y = para_search_ridge.best_estimator_.predict(x_onehot)\n", + "print(\"RMSE: \", np.sqrt(np.mean((y_log-best_ridge_y)**2)))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### RMSE across different grid search lambda" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "error = pd.DataFrame(para_search_ridge.grid_scores_)['mean_validation_score'] # or para_search_ridge.cv_results_['mean_test_score']\n", + "error = np.sqrt(np.abs(error))\n", + "alpha = pd.DataFrame(para_search_ridge.grid_scores_)['parameters'].apply(lambda x: x.get('alpha'))\n", + "plt.scatter(alpha, error)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Variable importance for best lambda" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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tLS0tN7Y+t7W1eZSXl+suXbok+MUvfiF97rnnupKSkoy1tbWeZrOZc+bMGVFKSkrPt99+y7948aLg/PnzwpkzZxqIiObMmWP4/PPPRRERERaWZZ3nzp0TERGdO3dO9Jvf/Oa74Xjm4wWKWQAAAAAAGFGubcb9x06ePDlRp9MJDx486ENE1NPTw2q1WgGPx3PGxsb2hoeHW4mIoqOjjfX19TwfHx+7v7+/NTk52UhEJBaLHe7iuCtmFy5c2L1x48agKVOmWBcuXHjt1vn58+dfZ1mWVCqVubOz04OIyNPT0xkREWE+c+aMsLy83OvNN9/8oa6ujl9aWiq6ePGiMDExsZeI6NFHH+3505/+NGX69Ol9jz/+uP7UqVMTe3p6mObmZr5SqbQMzxMdH1DMAgAAAAAAERHdroP6r+R0Ojl5eXmNCxcu7O4/XlxcPIHP5ztdxyzLks1m4zidTuJwOM47jeOOQCBwxsXFGXfv3h1QWVlZ+cknn0y6db5f/BvjDz74oOGLL74Q9fb2spMnT7YnJSX1/ulPf/KvrKwUrlq16ioR0SOPPGJctmyZsKyszPLzn/+8u6Ojg7t9+3a/2NjY3jvND/4J35kFAAAAAID7Tmpqqn737t2TLRYLh4hIo9Hwu7u7B61flEqlua2tjVdaWiokIurq6mKsVuuQ47i89tprP/zud79rDggIsN9pzklJSYYPP/xwskKhMBIRzZo1y3jhwgWv1tZWnkqlMhH9sxCeOnWq9eDBgz4pKSm9Dz/8cM+7774bMGfOHLwcaojQmQUAAAAAgPvOyy+/3NHQ0MCPjY2NcjqdHLFYbD1y5Ej9YOcLBALnvn376l966aUQs9nMCAQCR1lZWe1Q47jMnDnTPHPmzNu+xbi/xx57zNDc3Mx/6KGHfiAi8vDwIF9fX9vUqVP7WJa9cV5iYmJPWVnZxAkTJjhSU1MNL7zwggfedDx0nP5tcQAAAAAAGF/UanWDUqnsGOk8YHxSq9V+SqUy9G7WYpsxAAAAAAAAjDrYZgwAAAAAAOPK+fPnPbOyssL6j/F4PIdGo9GNVE4wdChmAQAAAABgXElISDDd+lNAMPpgmzEAAAAAAACMOihmAQAAAAAAYNRBMQsAAAAAAACjDopZAAAAAAAAGHXwAigAAAAAABhRLMuqIiIiTK7jv//973UymaxvJHOC+x+KWQAAAAAAGFF8Pt+BtwvDUKGYBQAAAACAfzrw7xJq1wqHNaa/wkgL3m0a6jKbzUb//u//HnzmzJkJfX19nOeff779lVde6SguLp6wcePGQLFYbK2pqfGMjY01Hjhw4FuGYai0tFS4Zs2aEKPRyPB4PGdZWVnNhAkTHAPFGeia7mKvXbt26rFjxyZZLBZm5syZhn379n3HMAwlJCTIVCqV4csvv5zY09PD7tmzpyEtLc3w0x8c3A6+MwsAAAAAACPKYrEwcrlcIZfLFampqeFERNu3b/fz9va2V1ZWVqvV6uoPP/xwsk6n4xERVVdXe7777rtNdXV1VY2NjfySkhKR2WzmLF68OHz79u2NNTU12tLS0hqRSORwF2cgA8UmInrllVfaKysrqy9fvlxlMpmYjz76yNu1xmazcSoqKqq3bNnStHHjxsB7/bzgn9CZBQAAAACAf7qLDupwGGib8cmTJyfqdDrhwYMHfYiIenp6WK1WK+DxeM7Y2Nje8PBwKxFRdHS0sb6+nufj42P39/e3JicnG4mIxGKxw10cuVw+4HdyB4pNRHT06NEJ27ZtCzCbzcz169e5CoXCRER6IqJf/vKXXUREs2fP7n3llVcGLZRheKGYBQAAAACA+47T6eTk5eU1Lly4sLv/eHFx8QQ+n+90HbMsSzabjeN0OonD4TjvNM5gBoptNBo5ubm507766iutVCq15uTkBJrN5hu7XAUCgZOIiMvlkt1u59zN/cLQYZsxAAAAAADcd1JTU/W7d++ebLFYOEREGo2G393dPWj9olQqzW1tbbzS0lIhEVFXVxdjtVqHHGcgRqORISIKCAiw6fV65tChQz53f2cwXNCZBQAAAACA+87LL7/c0dDQwI+NjY1yOp0csVhsPXLkSP1g5wsEAue+ffvqX3rppRCz2cwIBAJHWVlZ7VDjDMTPz8++ePHiqwqFIjo4OLhPqVT2/vQ7hJ+K43T+qBMPAAAAAADjhFqtblAqlQO+3RfgXlOr1X5KpTL0btZimzEAAAAAAACMOthmDAAAAAAA48r58+c9s7KywvqP8Xg8h0aj0Y1UTjB0KGYBAAAAAGBcSUhIMN36U0Aw+mCbMQAAAAAAAIw6KGYBAAAAAABg1EExCwAAAAAAAKMOilkAAAAAAAAYdVDMAgAAAADAiGJZViWXyxWuT01NDe9eXk8oFMa7m+/o6GA3b9482XXc0NDgkZaWNn04c9Dr9cyiRYumSSSSGKlUGj1z5kzZ559/7kX0/5+HTCZTKBSKqJKSEi8iopqaGl5ERET03VwvKCgotrW1dUy9AHhM3QwAAAAAAIw+fD7fcT+9Xbizs5Pdu3ev/7p1664SEYWGhlqPHTt2ZTivsXjx4tBp06ZZGhoaKlmWJa1Wy9NoNJ5ENz+Pzz77bOL69euDU1NTa4bz+mMBilkAAAAAACAiov975v9K6rrqhMMZU+ojNW6as6lpqOuMRiMnKytrmkajEbIsS1u3bm2aN29ez44dO3zLy8u9CgsLG4mIUlJSpLm5uW0ZGRk9QqEwftmyZe0nTpzwFggEjuLi4jqJRGLT6XS8zMzM6TabjTN37ly96xp6vZ5JS0uT6vV61mazcTZs2NCyZMmS67m5ucFNTU18uVyuSE5O7s7JyWnPyMiIuHz5cpW7vIqLiyeZTCamsbGRn56efn3Pnj3NA91bVVUV/+LFi14HDhy4wrIsEREpFIo+hULRd+u5er2e9fb2tt3p87HZbLRy5crgU6dOTSQi+vWvf93x+uuvt7vWGQwGTnp6unTBggVdubm5HUP9d7mfoJgFAAAAAIARZbFYGLlcriAikkgklpKSkvotW7b4ExHV1tZqL168KHjiiSci6uvrK93FMZlMTGJiomHnzp3fr1ixInjnzp2Tt27d2rpy5cqQ5cuXX33xxRc733rrrRvbh4VCoePw4cN1YrHY0drayp01a5Z80aJF1/Py8pozMjI8Xd3R/tue3eWl1WqFarVa6+np6ZBKpTFr165tk0ql1lvzvHTpkkChUBi53IHLMdfzsFgsnI6ODo8jR47U3nrOYHns3LnT77vvvuNXVVVpPTw8qK2tjXWt6e7uZhYuXDh90aJFnS+++GKnu2c5GqCYBQAAAAAAIiK6mw7qcBhom/HZs2dFq1ataiciio+PNwcGBvZVVFQI3MXx8PBwZmZm6omIVCpV78mTJycSEV24cEF09OjReiKi7Ozszk2bNgUTETkcDs6aNWuCz507J2IYhtrb23nNzc1uayR3eSUlJXX7+vraiYikUqm5vr6eP1AxO5TncfLkSa/nnnsurLa2tupO8vj8888nrlix4qqHhwcREU2ZMsXuWjN//nzpmjVrfvjtb397bag53Y/wAigAAAAAALjvOJ3OAce5XK7T4XDcOLZYLEz/OYZhXH+TzWbjuOYYhvlRwPz8fHFnZye3oqKiWqfTaX19fa0mk8ltjTRYXkREPB7vxiTLsk6r1coZ6LwHHnjAXF1dLbTb7QNN3+RnP/tZb1dXF/fWlzcNlofT6SQOhzPg5IMPPmg4duyYd//nN5qhmAUAAAAAgPtOUlKSoaioSExEpNFo+K2trby4uDhzeHh4X1VVldBut1NdXZ2HRqPxul2sGTNmGAoKCsRERAUFBb6ucb1ez/r5+Vn5fL7z0KFDE1paWnhERN7e3vbe3t4Ba6XB8hrKvUVHR1vi4uJ6c3JyAl2FZUVFBb+oqGjSredevHhR4HA4aMqUKTd9b3awPH72s59179mzZ7LV+s+GcP9txm+//XaLWCy2LV26NGQo+d6vUMwCAAAAAMB959VXX2232+2cyMhIxdNPPx2en5/f4Onp6UxNTTVIJBKLTCaLXr16tUShUBhvF2vXrl2N7733nn9MTEyUXq+/UdwtX778mlqt9oqJiYkqKioSh4WFmYmIAgIC7CqVyhARERGdnZ0dfCd5DfX+ioqKGtra2jymTZsWExkZqVi2bFmoRCLpI/r/35mVy+WKzMzM6bt372649fu1g+Xx8ssvXw0ODu6Ty+XRMplMsXfvXnH/dXv37m2yWCzMihUrbrqv0Yjjrk0OAAAAAABjm1qtblAqlaP6rbYweqnVaj+lUhl6N2vRmQUAAAAAAIBRB28zBgAAAAAAuEfi4uLkfX19NzURCwsLv01ISDCNVE5jBYpZAAAAAACAe0Sj0ehGOoexCtuMAQAAAAAAYNRBMQsAAAAAAACjDopZAAAAAAAAGHVQzAIAAAAAAMCog2IWAAAAAABGFMuyKrlcrnB9ampqePfyekKhMN7dfEdHB7t58+bJruOGhgaPtLS06cOZQ1BQUGxkZKRCJpMp5syZE9HY2MglItLr9cyiRYumSSSSGKlUGj1z5kzZ559/7jWc1x4rUMwCAAAAAMCI4vP5Dp1Op3V9ZDJZ30jm09nZye7du9ffdRwaGmo9duzYleG+TmlpaW1NTY02Pj7euGHDhqlERIsXLw718fGxNTQ0VNbV1VUVFhZ+297ejl+hGQAeCgAAAAAAEBFRy/rXJZbLl4XDGZMfEWEM/M/fNw11ndFo5GRlZU3TaDRClmVp69atTfPmzevZsWOHb3l5uVdhYWEjEVFKSoo0Nze3LSMjo0coFMYvW7as/cSJE94CgcBRXFxcJ5FIbDqdjpeZmTndZrNx5s6dq3ddQ6/XM2lpaVK9Xs/abDbOhg0bWpYsWXI9Nzc3uKmpiS+XyxXJycndOTk57RkZGRGXL1+ucpdXcXHxJJPJxDQ2NvLT09Ov79mzp/lO7vXRRx/t2blzp39VVRX/4sWLXgcOHLjCsiwRESkUij6FQtFHRPTmm29O2bdvnx8R0dKlS69u2LChvaamhpeenh6RkJBgKC8vF02ZMqXv+PHjdSKRyFlZWcl/4YUXpnV2dnJZlnX+93//95Xo6GjLUP8t7lfozAIAAAAAwIiyWCyMa4txampqOBHRli1b/ImIamtrtfv377/ywgsvhBqNRo67OCaTiUlMTDTU1NRoExMTDTt37pxMRLRy5cqQ5cuXX62srKwOCAiwus4XCoWOw4cP12m12urS0tLa9evXBzscDsrLy2uWSCQWnU6nzc/Pv6kgdZeXVqsVHjhw4Ep1dXXVwYMHferq6jzu5P4PHjw4SaFQmC5duiRQKBRGLvfHPcfTp08L9+/f7/vNN99Ul5eXVxcWFk4+c+aMJxFRY2Oj4KWXXmqvq6ur8vb2thcWFvoQES1atChsxYoV7TU1Ndry8nJdSEiI9UeBRzF0ZgEAAAAAgIiI7qaDOhxc24z7j509e1a0atWqdiKi+Ph4c2BgYF9FRYSh+qQAACAASURBVIXAXRwPDw9nZmamnohIpVL1njx5ciIR0YULF0RHjx6tJyLKzs7u3LRpUzARkcPh4KxZsyb43LlzIoZhqL29ndfc3Oy2RnKXV1JSUrevr6+diEgqlZrr6+v5Uql00AIyOTk5kmEYioqKMm7btu3748ePiwY799SpU6Innnji+sSJEx1ERE8++WTXF198MeGXv/zl9aCgIMvs2bNN/5uTsaGhgd/V1cW0tbXxsrKyrhMRCYVCJxE53d3baINiFgAAAAAA7jtO58B1F5fLdTocjhvHFouF6T/HMIzrb7LZbDc6uQzD/Chgfn6+uLOzk1tRUVHN5/OdQUFBsSaTye3u1cHyIiLi8Xg3JlmWdVqtVred5NLS0tqpU6faXMcPPPCAubq6Wmi328m1zfhurmsymRh3548V2GYMAAAAAAD3naSkJENRUZGYiEij0fBbW1t5cXFx5vDw8L6qqiqh3W6nuro6D41Gc9s3/c6YMcNQUFAgJiIqKCjwdY3r9XrWz8/PyufznYcOHZrQ0tLCIyLy9va29/b2DlgrDZbXcNxzdHS0JS4urjcnJyfQVbBXVFTwi4qKJj322GOGI0eOTOrp6WG6u7uZI0eO+KSkpPQMFkssFjsCAgL6/vznP08iIjKZTJyenp4xVf+NqZsBAAAAAICx4dVXX2232+2cyMhIxdNPPx2en5/f4Onp6UxNTTVIJBKLTCaLXr16tUShUBhvF2vXrl2N7733nn9MTEyUXq+/0fJcvnz5NbVa7RUTExNVVFQkDgsLMxMRBQQE2FUqlSEiIiI6Ozs7+E7yGq77Lioqamhra/OYNm1aTGRkpGLZsmWhEomkLykpybho0aLOGTNmRKlUqqilS5denTNnjuk2sb599913/SMjIxUzZ86UNzU1jamduZzx0H4GAAAAAICBqdXqBqVS2THSecD4pFar/ZRKZejdrEVnFgAAAAAAAEadMdVmBgAAAAAAuJ/ExcXJ+/r6bmoiFhYWfpuQkOB2izDcHopZAAAAAACAe0Sj0ehGOoexCtuMAQAAAAAAYNRBMQsAAAAAAACjDopZAAAAAAAAGHVQzAIAAAAAAMCog2IWAAAAAABGFMuyKrlcrnB9ampqePfyekKhMN7dfEdHB7t58+bJruOGhgaPtLS06cOZw/bt230jIyMVkZGRioiIiOiioqJJREQLFy4Mff/9933crU1ISJCVlZUJhzOf0QhvMwYAAAAAgBHF5/MdOp1OO9J5uHR2drJ79+71X7du3VUiotDQUOuxY8euDFf8+vp6j7y8vKmXLl2q9vX1tev1eqa1tRW12RDhgQEAAAAAABER/U9hteTa94Zh7fiJg0TGuVlRTUNdZzQaOVlZWdM0Go2QZVnaunVr07x583p27NjhW15e7lVYWNhIRJSSkiLNzc1ty8jI6BEKhfHLli1rP3HihLdAIHAUFxfXSSQSm06n42VmZk632WycuXPn6l3X0Ov1TFpamlSv17M2m42zYcOGliVLllzPzc0Nbmpq4svlckVycnJ3Tk5Oe0ZGRsTly5er3OVVXFw8yWQyMY2Njfz09PTre/bsaR7o3lpbWz28vLwc3t7ediIib29vh7e3d9+t561du3bqsWPHJlksFmbmzJmGffv2fccw/9xc+8EHH/iuXr06xGAwsO+99963KSkpxqE+49EO24wBAAAAAGBEWSwWxrXFODU1NZyIaMuWLf5ERLW1tdr9+/dfeeGFF0KNRiPHXRyTycQkJiYaampqtImJiYadO3dOJiJauXJlyPLly69WVlZWBwQEWF3nC4VCx+HDh+u0Wm11aWlp7fr164MdDgfl5eU1SyQSi06n0+bn599UkLrLS6vVCg8cOHClurq66uDBgz51dXUeA+X50EMPGf38/KwSiST2qaeeCt2/f7/3QOe98sor7ZWVldWXL1+uMplMzEcffXTjPKPRyFy8eFG3Y8eO71544YWwO3nOYw06swAAAAAAQEREd9NBHQ4DbTM+e/asaNWqVe1ERPHx8ebAwMC+iooKgbs4Hh4ezszMTD0RkUql6j158uREIqILFy6Ijh49Wk9ElJ2d3blp06ZgIiKHw8FZs2ZN8Llz50QMw1B7ezuvubnZbY3kLq+kpKRuX19fOxGRVCo119fX86VSqfXWGFwul8rKyi6XlpYKT5w4MXHdunWS8vJyr23btrX0P+/o0aMTtm3bFmA2m5nr169zFQqFiYj0RESLFi26RkSUnp5uMBgMTEdHB+vn52d3l/tYg84sAAAAAADcd5xO54DjXC7X6XA4bhxbLBam/5xrGy6XyyWbzXajk8swzI8C5ufnizs7O7kVFRXVOp1O6+vrazWZTG5rpMHyIiLi8Xg3JlmWdVqt1kE7yQzDUEpKivGtt976oaio6EpxcfGk/vNGo5GTm5s77a9//Wt9bW2tdsmSJR1ms/lGbhzOzaFvPR4PUMwCAAAAAMB9JykpyVBUVCQmItJoNPzW1lZeXFycOTw8vK+qqkpot9uprq7OQ6PReN0u1owZMwwFBQViIqKCggJf17her2f9/PysfD7feejQoQktLS08IiJvb297b2/vgLXSYHkN5d4aGho8vvzyyxvfTS4vLxcGBQXd9J1Zo9HIEBEFBATY9Ho9c+jQoZvecPyXv/zFh4jo+PHjogkTJthdHeHxBNuMAQAAAADgvvPqq6+2L126dFpkZKSCZVnKz89v8PT0dKamphreffddi0wmi5bJZCaFQnHbFx/t2rWrMTMzc/quXbumzJ8/v8s1vnz58mvp6enSmJiYqOjoaGNYWJiZiCggIMCuUqkMERER0Y899pg+Jyen/XZ5DeXe+vr6OGvXrg1ua2vz4PP5TrFYbC0oKGjsf46fn5998eLFVxUKRXRwcHCfUqns7T/v4+Njj4+Pl7teADWU648VHHdtcgAAAAAAGNvUanWDUqnsGOk8YHxSq9V+SqUy9G7WYpsxAAAAAAAAjDrYZgwAAAAAAHCPxMXFyfv6+m5qIhYWFn6bkJBgGqmcxgoUswAAAAAAAPeIRqPRjXQOYxW2GQMAAAAAAMCog2IWAAAAAAAARh0UswAAAAAAADDqoJgFAAAAAACAUQfFLAAAAAAAjCiWZVVyuVzh+tTU1PDu5fWEQmG8u/mOjg528+bNk13HDQ0NHmlpadOHM4ft27f7RkZGKiIjIxURERHRRUVFk4iIduzY4dvQ0OAxnNcaq/A2YwAAAAAAGFF8Pt+h0+m0I52HS2dnJ7t3717/devWXSUiCg0NtR47duzKcMWvr6/3yMvLm3rp0qVqX19fu16vZ1pbW7lEREVFRX4PPPCAKTQ01Dpc1xurUMwCAAAAAAARER3fvV3S0fSdcDhj+kmmGX/+2zVNQ11nNBo5WVlZ0zQajZBlWdq6dWvTvHnzenbs2OFbXl7uVVhY2EhElJKSIs3NzW3LyMjoEQqF8cuWLWs/ceKEt0AgcBQXF9dJJBKbTqfjZWZmTrfZbJy5c+fqXdfQ6/VMWlqaVK/XszabjbNhw4aWJUuWXM/NzQ1uamriy+VyRXJycndOTk57RkZGxOXLl6vc5VVcXDzJZDIxjY2N/PT09Ot79uxpHujeWltbPby8vBze3t52IiJvb2+Ht7d33/vvv+9TWVkpzMrKmi4QCBzl5eXV//M//yNat26dxG63k1KpNBYWFn7n6enpDAoKiv3Vr37Vefz4cW+bzcb5+OOPr8THx5u7u7uZZcuWhVRXV3va7XbO66+/3rJkyZLrd/vvdz/DNmMAAAAAABhRFouFcW0xTk1NDSci2rJliz8RUW1trXb//v1XXnjhhVCj0chxF8dkMjGJiYmGmpoabWJiomHnzp2TiYhWrlwZsnz58quVlZXVAQEBNzqeQqHQcfjw4TqtVltdWlpau379+mCHw0F5eXnNEonEotPptPn5+TcVpO7y0mq1wgMHDlyprq6uOnjwoE9dXd2A24Ufeugho5+fn1UikcQ+9dRTofv37/cmInruuee6YmJijIWFhVd0Op2WYRjKzs4O+/jjj+tra2u1NpuN3n777Rvbn/38/Gxarbb6N7/5zdXNmzdPISJav3791JSUlO7Kysrq06dP17zxxhvB3d3dY7LuQ2cWAAAAAACIiOhuOqjDYaBtxmfPnhWtWrWqnYgoPj7eHBgY2FdRUSFwF8fDw8OZmZmpJyJSqVS9J0+enEhEdOHCBdHRo0friYiys7M7N23aFExE5HA4OGvWrAk+d+6ciGEYam9v5zU3N7utkdzllZSU1O3r62snIpJKpeb6+nq+VCr90XZhLpdLZWVll0tLS4UnTpyYuG7dOkl5ebnXtm3bWvqfp1arBcHBwZa4uDgLEdGzzz7b+e677/oTUTsR0aJFi7qIiBISEowHDx70ISI6derUxOPHj0/asWNHABGRxWLh1NXV8WbMmGF2d1+jEYpZAAAAAAC47zidzgHHuVyu0+Fw3Di2WCxM/zmGYVx/k81mu9HJZRjmRwHz8/PFnZ2d3IqKimo+n+8MCgqKNZlMbruYg+VFRMTj8W5MsizrtFqtg3aSGYahlJQUY0pKijE9Pb17+fLlobcWs+6uRUQkEAicRP+8b9e9Op1O+vTTT+uUSqXF7eIxYEy2mwEAAAAAYHRLSkoyFBUViYmINBoNv7W1lRcXF2cODw/vq6qqEtrtdqqrq/PQaDRet4s1Y8YMQ0FBgZiIqKCgwNc1rtfrWT8/Pyufz3ceOnRoQktLC4+IyNvb297b2ztgrTRYXkO5t4aGBo8vv/zyxneTy8vLhUFBQX1ERCKRyK7X61kiogceeMD8/fff8yorK/lERIWFhb4PP/xwj7vYKSkp3Xl5eVNcBf+ZM2c8h5LbaIJiFgAAAAAA7juvvvpqu91u50RGRiqefvrp8Pz8/AZPT09namqqQSKRWGQyWfTq1aslCoXCeLtYu3btanzvvff8Y2JiolyFIhHR8uXLr6nVaq+YmJiooqIicVhYmJmIKCAgwK5SqQwRERHR2dnZwXeS11Dura+vj7N27drgsLCwaLlcrvj000993nnnnSYioqysrI5Vq1ZNk8vlCofDQXv27Gn45S9/GR4ZGalgGIbWrl171V3szZs3t9hsNo5cLldEREREv/HGG0FDyW004dyudQ0AAAAAAGOXWq1uUCqVHSOdB4xParXaT6lUht7NWnRmAQAAAAAAYNTBC6AAAAAAAADukbi4OHlfX99NTcTCwsJvExISTCOV01iBYhYAAAAAAOAe0Wg0upHOYazCNmMAAAAAAAAYdVDMAgAAAAAAwKiDYhYAAAAAAABGHRSzAAAAAAAAMOqgmAUAAAAAgBHFsqxKLpcrXJ+amhrevbyeUCiMdzff0dHBbt68ebLruKGhwSMtLW36cF1/06ZN/r/5zW8kruNFixZNmz17dqTr+Pe//73/s88+Kxl49Z3LyckJ3LBhw5SfGud+hWIWAAAAAABGFJ/Pd+h0Oq3rI5PJ+kYyn87OTnbv3r3+ruPQ0FDrsWPHrgxX/EceecTw9ddfi1zHlZWVnt3d3azNZiMionPnznnNmTPHMFzXG6vw0zwAAAAAAEBERNc+rZVYf+gVDmdMjwAvo/ipyKahrjMajZysrKxpGo1GyLIsbd26tWnevHk9O3bs8C0vL/cqLCxsJCJKSUmR5ubmtmVkZPQIhcL4ZcuWtZ84ccJbIBA4iouL6yQSiU2n0/EyMzOn22w2zty5c/Wua+j1eiYtLU2q1+tZm83G2bBhQ8uSJUuu5+bmBjc1NfHlcrkiOTm5Oycnpz0jIyPi8uXLVe7yKi4unmQymZjGxkZ+enr69T179jQPdG+zZ882NjQ08A0GA8disTCenp6OsLAwy/nz5z1nz55t+uabb0R//OMfm4mI3nzzzSn79u3zIyJaunTp1Q0bNrS7G3/ttdcCPv74Y7/AwMA+X19fa3x8vHGoz360QDELAAAAAAAjymKxMHK5XEFEJJFILCUlJfVbtmzxJyKqra3VXrx4UfDEE09E1NfXV7qLYzKZmMTERMPOnTu/X7FiRfDOnTsnb926tXXlypUhy5cvv/riiy92vvXWWze2DwuFQsfhw4frxGKxo7W1lTtr1iz5okWLrufl5TVnZGR46nQ6LRFR/23P7vLSarVCtVqt9fT0dEil0pi1a9e2SaVS6615enh4UFRUlOn06dNeRqORUalUvREREZaysjJRYGCgzel0klQqtZ4+fVq4f/9+32+++aba6XSSSqWKmjt3bo/D4eAMNv63v/1NXFFRobVarfTAAw8oUMwCAAAAAMCYdzcd1OHg2mbcf+zs2bOiVatWtRMRxcfHmwMDA/sqKioE7uJ4eHg4MzMz9UREKpWq9+TJkxOJiC5cuCA6evRoPRFRdnZ256ZNm4KJiBwOB2fNmjXB586dEzEMQ+3t7bzm5ma3NZK7vJKSkrp9fX3tRERSqdRcX1/PH6iYJSKaNWuW4fTp0yKTycTMnj27Nyoqyrxx48ap/v7+NpVK1UtEdOrUKdETTzxxfeLEiQ4ioieffLLriy++mOB0OmmgcYfDQU888cT1CRMmOIiIHn/88evu7mW0w3dmAQAAAADgvuN0Ogcc53K5TofDcePYYrEw/ecYhnH9TTabjeOaYxjmRwHz8/PFnZ2d3IqKimqdTqf19fW1mkwmtzXSYHkREfF4vBuTLMs6rVYrZ7Bz58yZYzh37pzo/PnzXikpKYb4+Hjz5cuXBWfOnBElJiYa3F3LXQ4czqCXHHNQzAIAAAAAwH0nKSnJUFRUJCYi0mg0/NbWVl5cXJw5PDy8r6qqSmi326murs5Do9F43S7WjBkzDAUFBWIiooKCAl/XuF6vZ/38/Kx8Pt956NChCS0tLTwiIm9vb3tvb++AtdJgeQ31/ubOnWu4dOmS17Vr1zyCgoJsDMOQWCy2HT9+fNIjjzxiICJ67LHHDEeOHJnU09PDdHd3M0eOHPFJSUnpcTd++PDhSQaDgdPV1cWUlJRMGmpeowm2GQMAAAAAwH3n1VdfbV+6dOm0yMhIBcuylJ+f3+Dp6elMTU01vPvuuxaZTBYtk8lMCoXitt8J3bVrV2NmZub0Xbt2TZk/f36Xa3z58uXX0tPTpTExMVHR0dHGsLAwMxFRQECAXaVSGSIiIqIfe+wxfU5OTvvt8hrq/U2ePNkuFottkZGRJtdYQkJC74ULF0QPPfSQkYgoKSnJuGjRos4ZM2ZEEf3zRU9z5swxERENNv6LX/ziWkxMTHRQUJAlISFhTL8RmeOuRQ0AAAAAAGObWq1uUCqVHSOdB4xParXaT6lUht7NWmwzBgAAAAAAgFEH24wBAAAAAADukbi4OHlfX99NTcTCwsJvExISTIOtgTuDYhYAAAAAAOAe0Wg0upHOYazCNmMAAAAAAAAYdVDMAgAAAAAAwKiDYhYAAAAAAABGHRSzAAAAAAAAMOqgmAUAAAAAgBHFsqxKLpcrXJ+amhrevbyeUCiMdzff0dHBbt68ebLruKGhwSMtLW36vcwJhg5vMwYAAAAAgBHF5/MdOp1OO9J5uHR2drJ79+71X7du3VUiotDQUOuxY8eujHRecDMUswAAAAAAQEREBw4ckLS3twuHM6a/v79xwYIFTUNdZzQaOVlZWdM0Go2QZVnaunVr07x583p27NjhW15e7lVYWNhIRJSSkiLNzc1ty8jI6BEKhfHLli1rP3HihLdAIHAUFxfXSSQSm06n42VmZk632WycuXPn6l3X0Ov1TFpamlSv17M2m42zYcOGliVLllzPzc0Nbmpq4svlckVycnJ3Tk5Oe0ZGRsTly5er3OVVXFw8yWQyMY2Njfz09PTre/bsaR7s/gbLdf/+/d6bN2+earVaGR8fH9vHH398RSKR2HJycgKbmpp43333Hb+lpYW3YsWKtjfeeKP97v5VxgZsMwYAAAAAgBFlsVgY1xbj1NTUcCKiLVu2+BMR1dbWavfv33/lhRdeCDUajRx3cUwmE5OYmGioqanRJiYmGnbu3DmZiGjlypUhy5cvv1pZWVkdEBBgdZ0vFAodhw8frtNqtdWlpaW169evD3Y4HJSXl9cskUgsOp1Om5+ff1NB6i4vrVYrPHDgwJXq6uqqgwcP+tTV1XkMNdfU1FTDpUuXdNXV1dqnnnrq2saNGwNca+rq6gSlpaW1X3/9dfUf/vCHQIvF4vZ5jHXozAIAAAAAABER3U0HdTgMtM347NmzolWrVrUTEcXHx5sDAwP7KioqBO7ieHh4ODMzM/VERCqVqvfkyZMTiYguXLggOnr0aD0RUXZ2duemTZuCiYgcDgdnzZo1wefOnRMxDEPt7e285uZmtzWSu7ySkpK6fX197UREUqnUXF9fz5dKpdaB4gyW67fffstbsGBB8NWrVz36+voYiURica15/PHHr3t6ejo9PT1tYrHY2tzczA0PDx8w/niAziwAAAAAANx3nE7ngONcLtfpcDhuHFssFqb/HMMwrr/JZrPd6FwyDPOjgPn5+eLOzk5uRUVFtU6n0/r6+lpNJpPbGmmwvIiIeDzejUmWZZ1Wq3XQzulgub744oshK1eubK+trdW+88473/W/Pz6f3z/+Tfc3HqGYBQAAAACA+05SUpKhqKhITESk0Wj4ra2tvLi4OHN4eHhfVVWV0G63U11dnYdGo/G6XawZM2YYCgoKxEREBQUFvq5xvV7P+vn5Wfl8vvPQoUMTWlpaeERE3t7e9t7e3gFrpcHyGo57JiLq6elhQ0JCrEREH3zwge/tzh/PUMwCAAAAAMB959VXX2232+2cyMhIxdNPPx2en5/f4Onp6UxNTTVIJBKLTCaLXr16tUShUBhvF2vXrl2N7733nn9MTEyUXq9nXePLly+/plarvWJiYqKKiorEYWFhZiKigIAAu0qlMkRERERnZ2cH30lew3Xfr7/+esszzzwTrlKpZL6+vrbhijsWcdy1yQEAAAAAYGxTq9UNSqWyY6TzgPFJrVb7KZXK0LtZi84sAAAAAAAAjDp4mzEAAAAAAMA9EhcXJ+/r67upiVhYWPhtQkKCaaRyGitQzAIAAAAAANwjGo1GN9I5jFXYZgwAAAAAAACjDopZAAAAAAAAGHVQzAIAAAAAAMCog2IWAAAAAABGFMuyKrlcrnB9ampqeMMVu6Ojg928efNk13FDQ4NHWlra9OGK75KQkCArKysTDjYfFBQUq1KpZP3H5HK5IiIiItpd3P75/u1vf5sYHR0dFRkZqYiOjo46ePDghOHJfnTCC6AAAAAAAGBE8fl8h06n096L2J2dnezevXv9161bd5WIKDQ01Hrs2LEr9+Jat9Pb28vW1dV5SKVS64ULFwR3sqZ/vv7+/tbDhw/XhYaGWr/++mvBk08+Gdne3q65t1nfv9CZBQAAAACA+86OHTt8s7KyQlzHKSkp0uLi4glEREKhMH7VqlVBMplMoVQq5U1NTVwioqamJm5qamq4TCZTyGQyRUlJiVdubm5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LF3tlZGR4MAwjq6io4Fr6165dK2IYRlZaWmp3qzjr1q0TdXZ29udLnp6e4Wq1esAX6DIMI0tISHjA8ruvr48EAoF08uTJAXe6l+tFRkYG3W7N9xskswAAAAAAMKy4XK5JqVQqampqKp2cnAwbN250vdNYe/bscS0vL1dkZ2c3EBGJxWJdTk6O0NJ/8OBBob+/f8/t4mRnZ7tptdpB50u2tram6upqW61WyxARffXVVw5ubm59d7KHO9XX91+dbtjh0zwAAAAAAEBERCvy5N6q5s67Wv0LdOd3b0yS1g92fHR0dFdZWZktEZHJZKL09HSvwsJCR4ZhzCtWrFCnpaVpBmqPi4sL0Ol0rIiIiODly5eriYhmzJjRdvjwYacNGzaoFQqFNZ/PN3A4HLNlvjlz5vjI5XL7np4e1syZMzVbt25tWr9+vejq1atWsbGxgQKBwHD69GnVYNY+ZcqU9s8//9xpwYIFmr179woTExNbT548ySMi6ujoYC1cuNCnqqrK1mg0MitXrmyaO3duW1ZWlvPXX3/tZDKZmOrqatvnn3++ube3l7Vv3z5na2tr07/+9a8aNzc3IxHRrl27nJcuXeqj1WrZH3744U+TJ0/uzsjI8FCr1VaXL1+2FgqFhk2bNjXOnj37AZ1OxyIieu+99y5PnTq1Kz8/n79u3ToPoVDYV11dbRseHt594MCBn1is0VvfHL0rBwAAAACAe4rBYKCioiJ+QkJCGxFRTk6OU3l5uW1VVVXlsWPHVK+99prXpUuXrAZqLywsrLVUedPS0jRERA4ODkYPD4/es2fP2uzevVuYlJSkuX7OLVu2NFZUVFQplcrKEydO8E+fPm27atWqqyKRqK+kpEQ12ESWiCg1NbV13759gu7ubqaqqspu3LhxXZa+V199dczkyZM7Kioqqo4fP169atUqr46ODhYRkUqlsv3iiy9+PHv2bNXbb7/taWdnZ6qqqlI88sgjXdnZ2c6WGN3d3awffvhBmZWVdWnRokX9R5rLysrsCgoKar/55pufPDw8DMePH1cpFIqqffv2/fjCCy/4WMZVVVXZbt++vb62trby8uXL3KNHj/Lu5P80UqAyCwAAAAAAREQ0lArq3aTX61kSiSSksbHROiwsrDshIaGDiOj48eP85OTkVg6HQ97e3oaoqCjtd999ZzdQu6+vb/vN4icnJ7fm5uYKCwsLHUtLS6tzc3NdLH27d+8W7tq1y8VgMDDXrl2zksvlNlFRUbo72UdUVJSuoaGB+9FHHwkff/zxX62luLjYoaCgwCkrK8v9//bM1NbWWhMRjR8/vlMgEJgEAoGJx+MZZ82a1UZEFB4e3l1WVtZfKZ89e3YrEVF8fLxWq9WyLM8WT58+vY3H45mJiHp7e5mFCxf6KhQKWxaLRZcuXep/Xjg8PLzL39+/j4goNDS0++LFi9Z3ss+RApVZAAAAAAAYVpZqal1dXXlvby+TmZkpIiIym803HT9Q+0BSUlLa8vLynD09PXuFQqHJ0q5UKq23bdvmVlJSolKpVIq4uLj2np6e/yhHmj59etvrr7/uPW/evNYb15yXl1erVCoVSqVSoVaryx9++OEeIiJra+v+DbFYLLKxsTFb/jYYDIylj2GY60P2/7a3t+/f05tvvukmEon6qqqqFOXl5Yq+vr7+/XC53P552Gz2r2KPRkhmAQAAAABgRHB2djZmZWVd3r59u5ter2diY2M78/LyhAaDgZqamjhnzpzhTZw4sWug9oHi8ng885o1axpWr16tvr5do9GwbW1tTUKh0FhfX88pLi52tPTZ29sb29vbh5wvpaentyxfvrwpMjLyV9XdyZMnd2zevNnNZPol7zxx4oTtUGPv3btXQERUUFDA4/P5RmdnZ+ONY9rb29ljxozpY7PZ9P777zsbjb8Zcs/AMWMAAAAAABgxJkyYoAsODtbt3LlTkJ6e3nry5ElecHBwKMMw5rVr1zb4+PgYUlNT227Wfqu4ixYt0tzYNm7cOF1YWFi3WCwO9fHx0ctkMq2lb/78+S3x8fFikUjUN5TnZv39/ftWr1599cb2zMzMpkWLFvlIJJIQs9nMeHl56YuKimoHG5eISCAQGCMiIiSWF0DdbMyyZcuuJiYm+h84cEAQExPTaWtra7rZuHsBM9QSPQAAAAAA3DvkcnmdVCptGe51wP1JLpe7SKVSvzu5FseMAQAAAAAAYNTBMWMAAAAAAIBBaG5uZk+aNCnoxvbi4uJqd3f3e/fh1BEKySwAAAAAAMAguLu7G5VKpWK41wG/wDFjAAAAAAAAGHWQzAIAAAAAAMCog2QWAAAAAAAARh0kswAAAAAAADDqIJkFAAAAAIBhxWazZRKJJEQsFofGxcUFtLS0sO801uLFi70CAgJCFy9e7JWRkeHBMIysoqKCa+lfu3atiGEYWWlpqd2t4qxbt07U2dnZny95enqGq9XqAV+gyzCMLCEh4QHL776+PhIIBNLJkycH3GqekydP2u7bt8/xdvvKz8/n3y7W/QbJLAAAAAAADCsul2tSKpWKmpqaSicnJ8PGjRtd7zTWnj17XMvLyxXZ2dkNRERisViXk5MjtPQfPHhQ6O/v33O7ONnZ2W5arXbQ+ZKtra2purraVqvVMkREX331lYObm1vf7a47d+6c3aFDh26bzMJv4dM8AAAAAADwiwPPe9NVxS0rlkMmCummhO31gx0eHR3dVVZWZktEZDKZKD093auwsNCRYRjzihUr1GlpaZqB2uPi4gJ0Oh0rIiIiePny5WoiohkzZrQdPnzYacOGDWqFQmHN5/MNHA7HbJlvzpw5PnK53L6np4c1c+ZMzdatW5vWr18vunr1qlVsbGygQCAwnD59WjWYtU+ZMqX9888/d1qwYIFm7969wsTExNaTJ0/yiIg6OjpYCxcu9KmqqrI1Go3MypUrm5KSktrffvttj56eHpZEIuEtX75cHRAQoM/IyPDp6elh2djYmHbt2vWTVCrVD+2m3x9QmQUAAAAAgBHBYDBQUVERPyEhoY2IKCcnx6m8vNy2qqqq8tixY6rXXnvN69KlS1YDtRcWFtZaqrxpaWkaIiIHBwejh4dH79mzZ212794tTEpK0lw/55YtWxorKiqqlEpl5YkTJ/inT5+2XbVq1VWRSNRXUlKiGmwiS0SUmpraum/fPkF3dzdTVVVlN27cuC5L36uvvjpm8uTJHRUVFVXHjx+vXrVqlVdvby/zyiuvNM2cOVNjWbNUKu05c+aMsqqqSvH66683vvTSS1536/7ea1CZBQAAAACAXwyhgno36fV6lkQiCWlsbLQOCwvrTkhI6CAiOn78OD85ObmVw+GQt7e3ISoqSvvdd9/ZDdTu6+vbfrP4ycnJrbm5ucLCwkLH0tLS6tzcXBdL3+7du4W7du1yMRgMzLVr16zkcrlNVFSU7k72ERUVpWtoaOB+9NFHwscff/xXaykuLnYoKChwysrKcv+/PTO1tbXWN8ZobW1lP/vssw/U1dXZMAxj7uvrY+5kLfcDVGYBAAAAAGBYWaqpdXV15b29vUxmZqaIiMhsNt90/EDtA0lJSWnLy8tz9vT07BUKhSZLu1KptN62bZtbSUmJSqVSKeLi4tp7enr+oxxp+vTpba+//rr3vHnzWm9cc15eXq1SqVQolUqFWq0uf/jhh3/z7O7LL7/sGRsb21lTU1P5zTff1Pb29iJnGwBuDAAAAAAAjAjOzs7GrKysy9u3b3fT6/VMbGxsZ15entBgMFBTUxPnzJkzvIkTJ3YN1D5QXB6PZ16zZk3D6tWr1de3azQatq2trUkoFBrr6+s5xcXF/S9isre3N7a3tw85X0pPT29Zvnx5U2Rk5K+qu5MnT+7YvHmzm8n0Sy594sQJW6JfjkFf/6Kpjo4OtpeXVy8RUXZ2tgvBgJDMAgAAAADAiDFhwgRdcHCwbufOnYLU1NS20NBQXXBwcOikSZMC165d2+Dj42MYqP1WcRctWqSJiYnpvr5t3LhxurCwsG6xWByamprqJ5PJtJa++fPnt8THx4ujoqICh7J+f3//vtWrV1+9sT0zM7PJYDAwlk8QrVq1ypOIKD4+vlOlUtlKJJKQjz76SPDyyy83r1mzxuvhhx+WGI3GoUx932GGWqIHAAAAAIB7h1wur5NKpS3DvQ64P8nlchepVOp3J9eiMgsAAAAAAACjDt5mDAAAAAAAMAjNzc3sSZMmBd3YXlxcXO3u7o4zwf9lSGYBAAAAAAAGwd3d3ahUKhXDvQ74BY4ZAwAAAAAAwKiDZBYAAAAAAABGHSSzAAAAAAAAMOogmQUAAAAAAIBRB8ksAAAAAAAMKzabLZNIJCFisTg0Li4uoKWlhX2nsRYvXuwVEBAQunjxYq+MjAwPhmFkFRUVXEv/2rVrRQzDyEpLS+1uFWfdunWizs7O/nzJ09MzXK1WD/gC3aHuobq62losFocSEeXn5/MnT54cMPhdAhGSWQAAAAAAGGZcLtekVCoVNTU1lU5OToaNGze63mmsPXv2uJaXlyuys7MbiIjEYrEuJydHaOk/ePCg0N/fv+d2cbKzs920Wu2g86W7uQcYHHyaBwAAAAAAiIho9YnV3rWa2ltWLIcqQBDQ/caEN+oHOz46OrqrrKzMlojIZDJRenq6V2FhoSPDMOYVK1ao09LSNAO1x8XFBeh0OlZERETw8uXL1UREM2bMaDt8+LDThg0b1AqFwprP5xs4HI7ZMt+cOXN85HK5fU9PD2vmzJmarVu3Nq1fv1509epVq9jY2ECBQGA4ffq0aih7HswehhIPbg7JLAAAAAAAjAgGg4GKior4CxcubCEiysnJcSovL7etqqqqVKvVnMjIyOBp06Zpi4qK7G/WXlhYWGtnZxdh+RZsRkaGrYODg9HDw6P37NmzNnl5eU5JSUma3NxcF8ucW7ZsaXRzczMaDAYaP3580OnTp21XrVp19YMPPnArKSlRjRkzxvB77OFu3rf7FZJZAAAAAAAgIqKhVFDvJr1ez5JIJCGNjY3WYWFh3QkJCR1ERMePH+cnJye3cjgc8vb2NkRFRWm/++47u4HafX19228WPzk5uTU3N1dYWFjoWFpaWn19Mrt7927hrl27XAwGA3Pt2jUruVxuExUVpfu99/DII48MeQ74NTwzCwAAAAAAw8ryvGldXV15b28vk5mZKSIiMpvNNx0/UPtAUlJS2vLy8pw9PT17hUKhydKuVCqtt23b5lZSUqJSqVSKuLi49p6enjvKkYa6B/jPIZkFAAAAAIARwdnZ2ZiVlXV5+/btbnq9nomNje3My8sTGgwGampq4pw5c4Y3ceLEroHaB4rL4/HMa9asaVi9erX6+naNRsO2tbU1CYVCY319Pae4uNjR0mdvb29sb28fcr402D0MNS78Fo4ZAwAAAADAiDFhwgRdcHCwbufOnYL09PTWkydP8oKDg0MZhjGvXbu2wcfHx5Camtp2s/ZbxV20aNFvXro0btw4XVhYWLdYLA718fHRy2Sy/mdZ58+f3xIfHy8WiUR9Q30B1GD2UF1dbT2UmPBbDMreAAAAAAD3L7lcXieVSluGex1wf5LL5S5SqdTvTq7FMWMAAAAAAAAYdXDMGAAAAAAAYBCam5vZkyZNCrqxvbi4uNrd3d04HGu6nyGZBQAAAAAAGAR3d3ej5Ru2MPxwzBgAAAAAAABGHSSzAAAAAAAAMOogmQUAAAAAAIBRB8ksAAAAAAAAjDpIZgEAAAAAYFix2WyZRCIJEYvFoXFxcQEtLS3sO421ePFir4CAgNDFixd7ZWRkeDAMI6uoqOBa+teuXStiGEZWWlpqd6s469atE3V2dvbnS56enuFqtfo3L9Btbm5mSySSEIlEEuLi4iIViURjLb97enqYwa7bZDJRcnKyr7+/f2hgYGBIUVHRr9an0WhYs2fP9vX29g4LCQkJDgsLC3733XedBxv/XoRkFgAAAAAAhhWXyzUplUpFTU1NpZOTk2Hjxo2udxprz549ruXl5Yrs7OwGIiKxWKzLyckRWvoPHjwo9Pf377ldnOzsbDetVnvbfMnyhmOlUqmYN2/etSVLllyx/LaxsTEPZs0Gg4EOHTrEb2ho4F68eLHy/PnzysDAwN7rx/zxj3/0E4lEfXV1dRUKhaKqoKCgpqWl5TfJdV9f32CmvCfg0zwAAAAAAEBERE2vrvTW19TcsmI5VFyxuNvjrTfrBzs+Ojq6q6yszJbol2plenq6V2FhoSPDMOYVK1ao09LSNAO1x8XFBeh0OlZERETw8uXL1UREM2bMaDt8+LDThg0b1AqFwprP5xs4HE5/kjlnzhwfuVxu39PTw5o5c6Zm69atTevXrxddvXrVKjY2NlAgEBhOnz6tutP9x8XFBVy5csVKr9ez/vKXv1zJyMho6evrI6FQ+NCCBQuuFhUVOW7duvUyl8s1tbS0cPR6PcPn8018Pt9kiSGXy7lVVVV2hw4d+pHN/qVo7enpaVi/fv0VIqIDBw7wN2zYMEYoFBpUKpVNbGxsR1BQUM+LL77YQkT0t7/9zcPV1dWwevXqq3e6j5EIlVkAAAAAABgRDAYDFRUV8RMSEtqIiHJycpzKy8ttq6qqKo8dO6Z67bXXvC5dumQ1UHthYWGtpcqblpamISJycHAwenh49J49e9Zm9+7dwqSkJM31c27ZsqWxoqKiSqlUVp44cYJ/+vRp21WrVl0ViUR9JSUlqv8kkSUi2rt370+VlZVVP/zwQ9X27dvdrl27xiYi0mq1bJlM1l1eXl71+OOPd3l5efW1tbVxnn32WT+TyfSrGBcuXLANCQnptiSyN3PhwgX7rVu3NqhUKsXcuXNbv/jii/5q9Ndffy1MTU3VDHjxKIXKLAAAAAAAEBHRUCqod5Ner2dJJJKQxsZG67CwsO6EhIQOIqLjx4/zk5OTWzkcDnl7exuioqK03333nd1A7b6+vu03i5+cnNyam5srLCwsdCwtLa3Ozc11sfTt3r1buGvXLheDwcBcu3bNSi6X20RFRenu1t7eeusttyNHjjgREV25csW6qqqKO27cuG4rKytzampqG9EvFeikpCT/wsLC6tdff33MkiVLvD788MOGP/7xj743Jt9ERC+++OKY/Px8QVtbG6e5ubmMiOihhx7SisXiXiKi2NjYbrVabV1fX8+pq6uzdnV17fPz87vnzh+jMgsAAAAAAMPKUk2tq6sr7+3tZTIzM0VERGbzzR85Hah9ICkpKW15eXnOnp6evUKhsL/sqVQqrbdt2+ZWUlKiUqlUiri4uPaenp67liMdOHCAf/LkSf758+erqqurFUFBQd06nY5F9MueWaxfprp8+bKVVqtlh4WF6T/77LO62tpam5deemlMRUWF3RNPPNH50EMP6RQKhZ3RaCQiok2bNqmVSqWio6Ojv1RrZ2f3q3Luk08+qdmzZ49gz549wqSkpNa7taeRBMksAAAAAACMCM7OzsasrKzL27dvd9Pr9UxsbGxnXl6e0GAwUFNTE+fMmTO8iRMndg3UPlBcHo9nXrNmTcPq1avV17drNBq2ra2tSSgUGuvr6znFxcWOlj57e3tje3v7f5QvtbW1sZ2cnAw8Hs987tw5m/LycvubjfP29u7r7e1l/fOf/+RZWVnRrl276j744AO3iIiILh6PZ5ZKpfqgoCBdRkaGhyWh7e7uZm6V1M+dO7c1Ly9PePjwYae5c+fec0eMiXDMGAAAAAAARpAJEybogoODdTt37hSkp6e3njx5khccHBzKMIx57dq1DT4+PobU1NS2m7XfKu6iRYt+k9CNGzdOFxYW1i0Wi0N9fHz0MplMa+mbP39+S3x8vFgkEvXd6XOzycnJ7Tt37nQNCgoKCQgI6Bk7duxNE242m0179+6tfeGFF3yWLVvGsrOzM23cuPHyli1b3HNycpzmzZvXtnfv3rrnn3/ey8fHJ1wgEBi4XK5p7dq1DQPNPW7cOF1rayvHy8tL7+npect7M1rdMpsHAAAAAIB7m1wur5NKpS3DvQ64P8nlchepVOp3J9fimDEAAAAAAACMOjhmDAAAAAAAMAjNzc3sSZMmBd3YXlxcXO3u7m4cjjXdz5DMAgAAAAAADIK7u7tRqVQqhnsd8AscMwYAAAAAAIBRB8ksAAAAAAAAjDpIZgEAAAAAAGDUQTILAAAAAAAAow6SWQAAAAAAGFZsNlsmkUhCxGJxaFxcXEBLSwv7TmMtXrzYKyAgIHTx4sVeGRkZHgzDyCoqKriW/rVr14oYhpGVlpba3SrOunXrRJ2dnf35kqenZ7harb7pC3QXLlzovW7dOpHld0xMjPjZZ5/1tfxOS0vzWrNmjduN1yUmJvp9+umnAiKivXv3OgYHB4cEBQWF+Pv7h27cuNHlxjGDUV1dbS0Wi0MHO340QzILAAAAAADDisvlmpRKpaKmpqbSycnJsHHjRtc7jbVnzx7X8vJyRXZ2dgMRkVgs1uXk5Agt/QcPHhT6+/v33C5Odna2m1arHVS+NGHCBO2pU6d4RERGo5E0Gg2nurra1tJ/9uxZ3mOPPaYd6Hq9Xs8sXbrUNz8/v6a6ulpRUVGhmDZtWudg5r6f4dM8AAAAAABARETHcqq8Wxu1t6xYDpXQk9c9ZV5w/WDHR0dHd5WVldkSEZlMJkpPT/cqLCx0ZBjGvGLFCnVaWppmoPa4uLgAnU7HioiICF6+fLmaiGjGjBlthw8fdtqwYYNaoVBY8/l8A4fDMVvmmzNnjo9cLrfv6elhzZw5U7N169am9evXi65evWoVGxsbKBAIDKdPn1bdas1xcXHaV155xZuI6Pz587ZBQUG6K1euWF27do3N4/FMFy9etBk/fny3yWSi5557zufEiRN8b29vvdn8yzLa2tpYBoOBcXNzMxAR2dramqVSqd4Sv6SkhJeVleV27do1qzfeeKNhwYIFmvb2dtb06dMD2tvb2QaDgXnttdea5s6d23b9uhQKhXViYmLAjh07yndLsgAAIABJREFU6iZMmND9/PPPe504cYLf29vLpKWlXV2xYkXLYP8vIxGSWQAAAAAAGBEMBgMVFRXxFy5c2EJElJOT41ReXm5bVVVVqVarOZGRkcHTpk3TFhUV2d+svbCwsNbOzi7C8i3YjIwMWwcHB6OHh0fv2bNnbfLy8pySkpI0ubm5LpY5t2zZ0ujm5mY0GAw0fvz4oNOnT9uuWrXq6gcffOBWUlKiGjNmjOF26/bz8+vjcDjmmpoa65KSEvvo6OiuxsZGq8LCQp5AIDAEBQXpbGxszLt373aqra3lVldXVzY0NFiFh4eHPvfccz+7ubkZp06d2ubj4zN2woQJHTNmzGhftGhRK5v9y2nrK1euWJ07d0554cIFm6effjpgwYIFGjs7O9OhQ4dqhUKhSa1Wc6KioiSzZ8/uT2blcjk3JSXF/+OPP/5p/Pjxuk2bNrk4OjoaKyoqqnQ6HfPoo49KZs6c2SGRSHrv+j/yvwTJLAAAAAAAEBHRUCqod5Ner2dJJJKQxsZG67CwsO6EhIQOIqLjx4/zk5OTWzkcDnl7exuioqK03333nd1A7b6+vu03i5+cnNyam5srLCwsdCwtLa2+PpndvXu3cNeuXS4Gg4G5du2alVwut4mKitINdQ8ymUxbVFRk/+9//5u3YsWKK5cvX7Y+ceKEvaOjozEyMlJLRFRSUtK/bj8/v75x48b1HyXet2/fpTNnzlz95z//yc/KynL/9ttvHb744os6IqKnnnqqjc1mk0wm6/n555+tiIhMJhOzbNkyr1OnTvFYLBZdvXrVuqGhgUNE1NrayklISAj4/PPPLz7yyCM9RETffvutg1KptPv6668FRESdnZ1shUJhM5qTWTwzCwAAAAAAw8ryzGxdXV15b28vk5mZKSIishzDvdFA7QNJSUlpy8vLc/b09OwVCoUmS7tSqbTetm2bW0lJiUqlUini4uLae3p67ihHGjdunPbkyZM8pVJp++ijj+omTZqkPXv2LO/UqVO8mJiY/udlGYYZMEZkZKTu9ddfv1pYWKg6cuRI/0ufbGxs+jds2Xt2drbw559/5pSXl1cplUqFs7Nzn06nYxER8fl845gxY3qLi4t5113HbN68+bJSqVQolUpFY2Nj+TPPPNNxJ3sdKZDMAgAAAADAiODs7GzMysq6vH37dje9Xs/ExsZ25uXlCQ0GAzU1NXHOnDnDmzhxYtdA7QPF5fF45jVr1jSsXr1afX27RqNh29ramoRCobG+vp5TXFzsaOmzt7c3tre3Dzpfio2N1X777bdOTk5ORg6HQ25ubsaOjg72Dz/8wJs8eXLX/43p/Pzzz4UGg4EuXbpkderUKT4RUXt7Oys/P59viXX69GlbDw+PW1ZM29vb2S4uLn1cLtf8zTff8JuamqwtfVZWVuYjR45c3Lt3r/OOHTuERERTp05t/+CDD1z1ej1DRFRWVsbt6OgY1fkgjhkDAAAAAMCIMWHCBF1wcLBu586dgvT09NaTJ0/ygoODQxmGMa9du7bBx8fHkJqa2naz9lvFXbRokebGtnHjxunCwsK6xWJxqI+Pj14mk/VXUOfPn98SHx8vFolEfbd7ARTRL1XVtrY2zjPPPPOzpU0ikei6urrYluduU1NT244dO+YQFBQU+sADD/RERkZ2Ev3yoquNGze6/fWvf/W1sbEx2dnZmT7++OOfbjXfn//859b4+PiAsLCw4NDQ0O4HHnjgV29odnBwMBUUFNROmjQpkMfjmV544YWWuro6bnh4eLDZbGaEQmHf4cOHL95uXyMZM9QSPQAAAAAA3DvkcnmdVCod1W+1hdFLLpe7SKVSvzu5dlSXlQEAAAAAAOD+hGPGAAAAAAAAg9Dc3MyeNGlS0I3txcXF1e7u7sbhWNP9DMksAAAAAADAILi7uxst37CF4YdjxgAAAAAAADDqIJkFAAAAAACAUQfJLAAAAAAAAIw6SGYBAAAAAABg1EEyCwAAAAAAw4rNZsskEkmIWCwOjYuLC2hpaWHfaazFixd7BQQEhC5evNgrIyPDg2EYWUVFBdfSv3btWhHDMLLS0lK7W8VZt26dqLOzsz9f8vT0DFer1Td9ge7ChQu9161bJ7L8jomJET/77LO+lt9paWlea9ascbvxusTERL9PP/1UQES0d+9ex+Dg4JCgoKAQf3//0I0bN7rcOOY/lZWV5Txv3jyfuxFrJEAyCwAAAAAAw4rL5ZqUSqWipqam0snJybBx40bXO421Z88e1/LyckV2dnYDEZFYLNbl5OQILf0HDx4U+vv799wuTnZ2tptWqx1UvjRhwgTtqVOneERERqORNBoNp7q62tbSf/bsWd5jjz2mHeh6vV7PLF261Dc/P7+murpaUVFRoZg2bVrnYOa+n+HTPAAAAAAAQEREBR+8691Sf+mWFcuhcvH27X4ifVn9YMdHR0d3lZWV2RIRmUwmSk9P9yosLHRkGMa8YsUKdVpammag9ri4uACdTseKiIgIXr58uZqIaMaMGW2HDx922rBhg1qhUFjz+XwDh8MxW+abM2eOj1wut+/p6WHNnDlTs3Xr1qb169eLrl69ahUbGxsoEAgMp0+fVt1qzXFxcdpXXnnFm4jo/PnztkFBQborV65YXbt2jc3j8UwXL160GT9+fLfJZKLnnnvO58SJE3xvb2+92fzLMtra2lgGg4Fxc3MzEBHZ2tqapVKp3hK/pKSEl5WV5Xbt2jWrN954o2HBggWa/Px8/rp16zyEQmFfdXW1bXh4ePeBAwd+YrFYtG/fPsf/+Z//8RIKhYbw8PDuS5cucYuKimqH8G8bFZDMAgAAAADAiGAwGKioqIi/cOHCFiKinJwcp/LyctuqqqpKtVrNiYyMDJ42bZq2qKjI/mbthYWFtXZ2dhGWb8FmZGTYOjg4GD08PHrPnj1rk5eX55SUlKTJzc11scy5ZcuWRjc3N6PBYKDx48cHnT592nbVqlVXP/jgA7eSkhLVmDFjDLdbt5+fXx+HwzHX1NRYl5SU2EdHR3c1NjZaFRYW8gQCgSEoKEhnY2Nj3r17t1NtbS23urq6sqGhwSo8PDz0ueee+9nNzc04derUNh8fn7ETJkzomDFjRvuiRYta2exfTltfuXLF6ty5c8oLFy7YPP300wELFizQEBFVVVXZXrhw4Uc/P78+mUwmOXr0KG/ixIldS5cu9S0uLlZKJJLemTNnPvC7/LNGACSzAAAAAABARERDqaDeTXq9niWRSEIaGxutw8LCuhMSEjqIiI4fP85PTk5u5XA45O3tbYiKitJ+9913dgO1+/r6tt8sfnJycmtubq6wsLDQsbS0tPr6ZHb37t3CXbt2uRgMBubatWtWcrncJioqSjfUPchkMm1RUZH9v//9b96KFSuuXL582frEiRP2jo6OxsjISC0RUUlJSf+6/fz8+saNG9d/lHjfvn2Xzpw5c/Wf//wnPysry/3bb791+OKLL+qIiJ566qk2NptNMpms5+eff7ayXBMeHt7l7+/fR0QUGhraffHiRWs+n2/09vbWSySSXiKilJSU1p07d97xse2RDM/MAvw/9u40Kqoz2xv4rkGgmIQClLlAq6AGtFLBLhyjTVZI8CZgtMXgHCckJg44xqRJTNItRmPu5Wq3AxcIxo4kpFs06Wi3gqKNSMqXlAgUODSgDCJTMdQAp6j3Q255kYAD0o3o/7cWa6We85z97Of4aWefAQAAAAAGleWZ2fLy8sKOjg5WQkLCCCIiy224PfU13pc33nijOSMjw8XLy6uDz+d3WcY1Go3Vnj17Rp49e7asrKysODQ0VGswGPpVI02YMKEtNzfXXqPR8H71q1/pp02b1vbjjz/a5+Xl2U+ePPnu87IsFqvPGEqlUv/BBx/UZWVllZ04ceLuS59sbGzubrj73q2tre/+4HA4xDAM61GvzVCGYhYAAAAAAJ4ILi4upsTExMq9e/eONBqNrKlTp7ZmZGTwGYah6upqbn5+vv2UKVPa+xrvK669vb35ww8/vPXb3/62pvt4U1MTh8fjdfH5fNPNmze5Z86cGW45ZmdnZ9JqtQ9dL02dOrXt1KlTTk5OTiYul0sjR440tbS0cAoKCux//etft//vnNZvvvmGzzAMVVRUDMvLy3MgItJqtezvvvvOwRLr4sWLPE9Pz45HuXYWcrnccPPmTevS0lIrIqL09HT+g84ZqnCbMQAAAAAAPDEmTZqkl0gk+qSkJOfY2NjG3Nxce4lEImOxWOZt27bd8vX1ZRYsWNDc2/j94q5YsaKp59iECRP0QUFBOpFIJPP19TUGBwff7aAuWrSoPjw8XDRixIjOB70AiujnrmpzczN35syZDZYxsVisb29v51ieu12wYEHz6dOnHQMDA2X+/v4GpVLZSvTzi6527tw58u233xbY2Nh02dradv3P//zPPx/lulnY29ubd+/eXfHKK6+I+Hw+o1Ao+izyh7pnqg0NAAAAAAD3UqvV5XK5vH6w84CBo9Vq2cOHD+/q6uqihQsX+opEIsMHH3xQN9h59UatVrvK5XK//pyL24wBAAAAAACeIv/5n//pKhaLpSKRSNbS0sKJi4t7Kv9nBTqzAAAAAADPMHRmH15tbS1n2rRpgT3Hz5w5U+ru7m4ajJyGusfpzOKZWQAAAAAAgIfg7u5usnzDFgYfbjMGAAAAAACAIQfFLAAAAAAAAAw5KGYBAAAAAABgyEExCwAAAAAAg4rD4QRb3r4bGhoqrK+v5/Q3VkxMjLdQKJTFxMR4x8XFebJYrOArV65YW45v27ZtBIvFCs7JybG9X5yPPvpoRGtr6916ycvLa0xNTQ3eOfQEQTELAAAAAACDytraukuj0RRfvXq1yMnJidm5c6dbf2MdPnzYrbCwsHj//v23iIhEIpE+LS2NbzmemZnJHz16tOFBcfbv3z+yra0N9dITDP84AAAAAADwxBg/fnx7VVWVFRFRV1cXxcTEeItEIllAQID04MGDzvcbDw0NFer1erZCoZBYxqZPn97817/+1YmIqLi42MrBwYHh8/mMZb158+b5BgUFSYRCoWzdunWeRESffPLJiLq6umFTp04NCAkJCXhQzqWlpVajRo2SvfHGGwKhUCibNGmSqK2tjUVE9Nlnn7kGBQVJAgMDpS+//PJoS7d31qxZfosXL/ZRKBRib2/vMSkpKc4DeyWffmiTAwAAAAAAERE1ZpT5dNa23/f220c1zN1Ox/9NwM2HmcswDGVnZzssXbq0nogoLS3NqbCwkFdSUlJUU1PDVSqVkrCwsLbs7Gy73sazsrKu2draKiyfz4mLi+M5OjqaPD09O3788UebjIwMp9/85jdNhw4dcrWsuXv37qqRI0eaGIahiRMnBl68eJH3/vvv1/3xj38cefbs2TIPDw+mr3y7q6ystPnyyy9vTJw4sWL69Omj0tLSnN96663GefPmNa1fv76eiGj16tWeiYmJru+9914dEdHt27eHqVQqzU8//WTz+uuvC998882mR72+zzJ0ZgEAAAAAYFAZjUa2WCyWOjs7P9fc3MydMWNGCxHRuXPnHKKiohq5XC75+PgwISEhbefPn7fta7yv+FFRUY2HDh3if//9987z5s27p2D84osv+FKpVCKVSqVXr161UavVNv3Zg5eXl3HixIl6IiKFQqErLy+3JiK6dOkSLzg4ODAgIED67bffuhQVFd2NHxER0czhcCg4ONjQ0NAwrD/rPsvQmQUAAAAAACIietgO6kCzPDPb0NDACQsLEyYkJIx4//3368xmc6/z+xrvyxtvvNEcHx/vPWbMGB2fz++yjGs0Gqs9e/aMvHTpUombm5tp1qxZfgaDoV8NPysrq7tJcTgcs16vZxMRrVixwj8jI+PahAkT9ImJiS5nz551sMyzsbG5e86j7gnQmQUAAAAAgCeEi4uLKTExsXLv3r0jjUYja+rUqa0ZGRl8hmGourqam5+fbz9lypT2vsb7imtvb2/+8MMPb/32t7+t6T7e1NTE4fF4XXw+33Tz5k3umTNnhluO2dnZmbRa7WPXSzqdju3r69tpNBpZR44c4T/4DHhY6MwCAAAAAMATY9KkSXqJRKJPSkpyjo2NbczNzbWXSCQyFotl3rZt2y1fX19mwYIFzb2N3y/uihUrfvE86oQJE/RBQUE6kUgk8/X1NQYHB7dZji1atKg+PDxcNGLEiM6LFy+W9Xc/W7ZsqVYqlRIvL68OiUSia2tr6/dnh+BeLLSzAQAAAACeXWq1ulwul9cPdh7wbFKr1a5yudyvP+fiNmMAAAAAAAAYcnCbMQAAAAAAwEOora3lTJs2LbDn+JkzZ0rd3d1Ng5HTswzFLAAAAAAAwENwd3c3Wb5hC4MPtxkDAAAAAADAkINiFgAAAAAAAIYcFLMAAAAAAAAw5KCYBQAAAAAAgCEHxSwAAAAAAAwqDocTLBaLpSKRSBYaGiqsr6/n9DdWTEyMt1AolMXExHir1WprpVIZKBaLpaNGjZJFR0cLiIhyc3N56enpwx8UKy4uzjM+Pn5kf/L4wx/+wA8ICJAKhUJZYGCgdM6cOYIH7UupVAbm5OTY9hxPTEx0WbhwoW9/8nia4W3GAAAAAAAwqKytrbssbwmeOXOm386dO9127NhR259Yhw8fdrtz585PPB7PPHnyZNHq1atvz58/v5mIKD8/n0dEpFKpbFUqld2cOXO0A7eL/5ORkeG4d+/ekSdPnrzq7+/fyTAM7dmzx6Wqqorr6uqKT/gMEHRmAQAAAADgiTF+/Pj2qqoqKyKirq4uiomJ8RaJRLKAgADpwYMHne83HhoaKtTr9WyFQiE5ePCgc11d3TCBQNBhia1UKvUGg4G1fft2z+PHjzuLxWLpwYMHnQUCQVB1dTWXiMhkMpGvr29QTU3NPY2/oqIi6ylTpohkMpkkODg4sKCgwKavPWzfvt0jISHhlr+/fycREZfLpbVr1zbI5XIjEdGGDRs8goKCJCKRSBYdHS3o6uq6e25qaqqLQqEQi0QiWXZ29i+6tNXV1dyXX355dFBQkCQoKEjyt7/9ze4xLveQhs4sAAAAAAAQEdHRo0d96urqflFAPY4RI0boZsyYcfNh5jIMQ9nZ2Q5Lly6tJyJKS0tzKiws5JWUlBTV1NRwlUqlJCwsrC07O9uut/GsrKxrtra2CkuXV6fTsadPnx6gUCjaX3zxRe2qVasaXF1dTe+++261SqWyS0tLqyQi0mg0NklJSfz4+Pi6zMxMR4lEovfw8GC657Zs2TLBgQMHKsaMGWPMysqyi42N9c3LyyvrbR/Xrl3jTZw4UdfXPjdu3Fi3a9euGiKiGTNm+B85cmT43LlztZacCwoKND/88IP9ihUr/K9evVrU/dyYmBifuLi42y+//HLb1atXrV5++WXRjRs3inpb52mHziwAAAAAAAwqo9HIFovFUmdn5+eam5u5M2bMaCEiOnfunENUVFQjl8slHx8fJiQkpO38+fO2fY33jLtmzZqGwsLCopkzZzbm5OQ4/OpXvxLr9XpWz3mxsbH1R44ccSEiSk5Odl28eHF99+NarZZdUFBgP3v27NFisVj61ltvCerq6oY9zN7y8/N5YrFY6uPjE2TpIP/www8OY8eOFQcEBEhzc3Mdrly5wrPMnzt3biMRUXh4eFtbWxu753O2//jHPxzXrFnjKxaLpa+99pqwra2N09TU9EzWdejMAgAAAAAAERE9bAd1oFmemW1oaOCEhYUJExISRrz//vt1ZrO51/l9jffGz8+vc+3atQ1r165tEIlEMpVKxes5RygUdrq6ujLHjh1zKCgosDt69OiN7sdNJhM5ODgwlo7vgwiFQn1ubq7ta6+91qpUKvUajaZ44cKFvnq9nq3T6Vjr168XXLx4sVgoFHbGxcV5GgyGu8Uoi3Vvrd3zt9lsJpVKVWJvb//wF+Ep9UxW8AAAAAAA8ORxcXExJSYmVu7du3ek0WhkTZ06tTUjI4PPMAxVV1dz8/Pz7adMmdLe13jPeBkZGY5Go5FFRFRZWcltbm7mCASCDkdHR1NbW9s9tdCSJUvuLFu2zD8iIqKRy72358fn87u8vb07kpOT7z6ze+HChV8UxRabNm2q3bJli/f169fvdm8NBgOL6OfbiImI3N3dGa1Wyz5+/Lhz93O/+uorZyKikydP2js4OJhcXFzueWHU5MmTW3bs2DHC8js3N7fPPJ526MwCAAAAAMATY9KkSXqJRKJPSkpyjo2NbczNzbWXSCQyFotl3rZt2y1fX19mwYIFzb2N94x14sQJxw0bNvhaW1t3ERFZ5oWHh7fu2rXLQywWS9evX1+zfPnypujoaO3bb7/NWbFiRUNveX311Vc3li9fLtixY4cHwzCs119/vXHChAn63ubOmTNHW1dXxw0PDxeZTCaWo6OjSSwW6yMjI1tcXV1N8+bNuyOVSmXe3t4dcrn8niLc2dnZpFAoxG1tbZwDBw78s2fsAwcO3Fy2bJlvQECA1GQysUJCQlonTpxY2b+rPbSxHqVFDwAAAAAATxe1Wl0ul8vrHzzz6ZaTk2O7bt06n0uXLpUOdi7PErVa7SqXy/36cy46swAAAAAA8EzbunWre2pqqltKSsovOqHw5EJnFgAAAADgGYbObP9t3rzZPTMzk999LDIysnHHjh21g5XTUPM4nVkUswAAAAAAzzAUszCYHqeYxduMAQAAAAAAYMhBMQsAAAAAAABDDopZAAAAAAAAGHJQzAIAAAAAAMCQg2IWAAAAAAAGFYfDCRaLxVKRSCQLDQ0V1tfXc/obKyYmxlsoFMpiYmK81Wq1tVKpDBSLxdJRo0bJoqOjBUREubm5vPT09OEPihUXF+cZHx8/sj95/OEPf+AHBARIhUKhLDAwUDpnzhzB4+zLorS01EokEskeJ8bj7OtJgu/MAgAAAADAoLK2tu7SaDTFREQzZ87027lzp1t/P29z+PBhtzt37vzE4/HMkydPFq1evfr2/Pnzm4mI8vPzeUREKpXKVqVS2c2ZM0c7cLv4PxkZGY579+4defLkyav+/v6dDMPQnj17XKqqqriurq6mf8WaPTEMQ1zu013uoTMLAAAAAABPjPHjx7dXVVVZERF1dXVRTEyMt0gkkgUEBEgPHjzofL/x0NBQoV6vZysUCsnBgwed6+rqhgkEgg5LbKVSqTcYDKzt27d7Hj9+3FksFksPHjzoLBAIgqqrq7lERCaTiXx9fYNqamruqQSLioqsp0yZIpLJZJLg4ODAgoICm772sH37do+EhIRb/v7+nUREXC6X1q5d2yCXy41ERBs2bPAICgqSiEQiWXR0tKCrq8uSX+DSpUt9xo0bFzhq1CjZ2bNnbcPCwkYLBIKg1atXe1riMwxDM2fO9AsICJC+8soro1pbW9lERF5eXmM2bNjgERwcHJicnOz8KDkPRU93qQ4AAAAAAA+tuGSzT3tbme1AxrSzD9BJJTtuPsxchmEoOzvbYenSpfVERGlpaU6FhYW8kpKSopqaGq5SqZSEhYW1ZWdn2/U2npWVdc3W1lZh6fLqdDr29OnTAxQKRfuLL76oXbVqVYOrq6vp3XffrVapVHZpaWmVREQajcYmKSmJHx8fX5eZmekokUj0Hh4eTPfcli1bJjhw4EDFmDFjjFlZWXaxsbG+eXl5Zb3t49q1a7yJEyfq+trnxo0b63bt2lVDRDRjxgz/I0eODJ87d66WiMjKyqpLpVKVfvzxxyNmz54t/PHHH0tGjBjB+Pn5jdm6dettIqLy8nKb/fv3l4eFhbXPnj3bb+fOnW4fffTRbSIiGxubrkuXLpUSEU2YMCHgYXMeitCZBQAAAACAQWU0GtlisVjq7Oz8XHNzM3fGjBktRETnzp1ziIqKauRyueTj48OEhIS0nT9/3rav8Z5x16xZ01BYWFg0c+bMxpycHIdf/epXYr1ez+o5LzY2tv7IkSMuRETJycmuixcvru9+XKvVsgsKCuxnz549WiwWS9966y1BXV3dsIfZW35+Pk8sFkt9fHyCLB3kH374wWHs2LHigIAAaW5ursOVK1d4lvmvv/56MxGRXC7XC4VCvUAg6OTxeGYfHx/jjRs3rIiI3N3dO8LCwtqJiBYsWNCQm5trbzl/4cKFTY+b81CBziwAAAAAABAR0cN2UAea5ZnZhoYGTlhYmDAhIWHE+++/X2c2m3ud39d4b/z8/DrXrl3bsHbt2gaRSCRTqVS8nnOEQmGnq6src+zYMYeCggK7o0eP3uh+3GQykYODA2Pp+D6IUCjU5+bm2r722mutSqVSr9FoihcuXOir1+vZOp2OtX79esHFixeLhUJhZ1xcnKfBYLjbZLSxsTETEbHZbLK2tr67UTabTQzDsIiIWKx76/Huvx0cHLr6k/NQhM4sAAAAAAA8EVxcXEyJiYmVe/fuHWk0GllTp05tzcjI4DMMQ9XV1dz8/Hz7KVOmtPc13jNeRkaGo9FoZBERVVZWcpubmzkCgaDD0dHR1NbWdk8ttGTJkjvLli3zj4iIaOz54iQ+n9/l7e3dkZycfPeZ3QsXLvyiKLbYtGlT7ZYtW7yvX79+txNqMBhYRD/f+kxE5O7uzmi1Wvbx48edH/U61dTUWJ06dcqOiOhPf/oTf+LEiW095zxqzkMRilkAAAAAAHhiTJo0SS+RSPRJSUnOCxYsaJbJZHqJRCKbNm1awLZt2275+voyfY33jHXixAnHwMBAWWBgoPSll166Oy88PLy1rKyMZ3kBFBFRdHS0VqfTcVasWNHQW15fffXVjZSUFNfAwECpSCSSffvtt0597WHOnDnalStX1oWHh4tGjx4tUygUYg6HQ5GRkS2urq6mefPm3ZFKpbLw8HChXC7/RRH+IKNGjTIkJye7BAQESJuamrgbNmy487g5D0WsR2nRAwAAAADA00WtVpfL5fL6B898uuXk5NiuW7fOx/LyJPj3UKvVrnJaVGQ5AAAgAElEQVS53K8/5+KZWQAAAAAAeKZt3brVPTU11S0lJeWfg50LPDx0ZgEAAAAAnmHozPbf5s2b3TMzM/ndxyIjIxt37NhRO1g5DTWP05lFMQsAAAAA8AxDMQuD6XGKWbwACgAAAAAAAIYcFLMAAAAAAAAw5KCYBQAAAAAAgCEHxSwAAAAAAAAMOShmAQAAAABgUHE4nGCxWCwViUSy0NBQYX19Pae/sWJiYryFQqEsJibGW61WWyuVykCxWCwdNWqULDo6WkBElJuby0tPTx/+oFhxcXGe8fHxIx81h97O8/LyGlNTU8MlIlIoFOJHjQm/hO/MAgAAAADAoLK2tu7SaDTFREQzZ87027lzp1t/P29z+PBhtzt37vzE4/HMkydPFq1evfr2/Pnzm4mI8vPzeUREKpXKVqVS2c2ZM0c7cLt4eAUFBZrBWPdpg2IWAAAAAACIiGhtSaWPpt1gO5AxxXY2uv+U+N582Pnjx49vv3z5Mo+IqKuri2JjY72zsrKGs1gs88aNG2uWL1/e1Nd4aGioUK/XsxUKhWT9+vU1dXV1wwQCQYcltlKp1BsMBtb27ds9DQYDWywW269fv77mk08+8bpw4YLG09OTMZlM5O/vH3Tx4sV7Cs6ioiLrlStX+jY2NnJtbGy6kpKSKhQKhaE/18TW1lah0+kKvvvuO4cPP/zQ09nZmblx44ZNSEhI66FDhyo5nH43pp8pKGYBAAAAAOCJwDAMZWdnOyxdurSeiCgtLc2psLCQV1JSUlRTU8NVKpWSsLCwtuzsbLvexrOysq7Z2toqLF1enU7Hnj59eoBCoWh/8cUXtatWrWpwdXU1vfvuu9UqlcouLS2tkohIo9HYJCUl8ePj4+syMzMdJRKJ3sPDg+me27JlywQHDhyoGDNmjDErK8suNjbWNy8vr6yvvezbt2/k119/7WL5XVdXN6y3eYWFhXYFBQVXAgICOl544QVRWlqa85tvvtk0ENfzaYdiFgAAAAAAiIjoUTqoA8loNLLFYrG0qqrKKigoSDdjxowWIqJz5845REVFNXK5XPLx8WFCQkLazp8/b9vXuEAguOe24TVr1jRERka2HD161PH48eNOqampbsXFxcU914+Nja2PiIgQxsfH1yUnJ7suXry4vvtxrVbLLigosJ89e/Zoy1hHRwfrfntauXLl7Y8++ui25beXl9eY3uaNGTOmXSqVdhARRUVFNZ47d84exezDwQugAAAAAABgUFmemS0vLy/s6OhgJSQkjCAiMpvNvc7va7w3fn5+nWvXrm04ffr0dS6XSyqVitdzjlAo7HR1dWWOHTvmUFBQYDd79ux7imKTyUQODg6MRqMptvzduHGj6NF22TsWi3Xf39A3FLMAAAAAAPBEcHFxMSUmJlbu3bt3pNFoZE2dOrU1IyODzzAMVVdXc/Pz8+2nTJnS3td4z3gZGRmORqORRURUWVnJbW5u5ggEgg5HR0dTW1vbPbXQkiVL7ixbtsw/IiKikcu99wZWPp/f5e3t3ZGcnOxM9POzvBcuXPhFUdwfhYWFdhqNxspkMlFGRgZ/ypQprQMR91mAYhYAAAAAAJ4YkyZN0kskEn1SUpLzggULmmUymV4ikcimTZsWsG3btlu+vr5MX+M9Y504ccIxMDBQFhgYKH3ppZfuzgsPD28tKyvjicVi6cGDB52JiKKjo7U6nY6zYsWKht7y+uqrr26kpKS4BgYGSkUikezbb791Goj9Pvfcc23r16/3DggIkPn6+hoXLFjQPBBxnwWsR2nRAwAAAADA00WtVpfL5fL6B898uuXk5NiuW7fO59KlS6X/rjW/++47h88++2xkdnb2tX/Xmk8atVrtKpfL/fpzLl4ABQAAAAAAz7StW7e6p6amuqWkpPxzsHOBh4fOLAAAAADAMwyd2f7bvHmze2ZmJr/7WGRkZOOOHTtqByunoeZxOrMoZgEAAAAAnmEoZmEwPU4xixdAAQAAAAAAwJCDYhYAAAAAAACGHBSzAAAAAAAAMOSgmAUAAAAAAIAhB8UsAAAAAAAMKg6HEywWi6UikUgWGhoqrK+v5/Q3VkxMjLdQKJTFxMR4q9Vqa6VSGSgWi6WjRo2SRUdHC4iIcnNzeenp6cMfFCsuLs4zPj5+5KPmEBcX58lisYKvXLlibRnbtm3bCBaLFZyTk2P7qPEGWmJiosvChQt9BzuPx4ViFgAAAAAABpW1tXWXRqMpvnr1apGTkxOzc+dOt/7GOnz4sFthYWHx/v37b61atcp39erVtzUaTfGNGzeK1q1bV0dEpFKpbL///vsHFrOPQyQS6dPS0u5+ticzM5M/evRow79yzd50dXWRyWT6dy/7b8Ed7AQAAAAAAODJsDFD7VNW2zqgncMAdwfdzt/Ibz7s/PHjx7dfvnyZR/RzIRYbG+udlZU1nMVimTdu3FizfPnypr7GQ0NDhXq9nq1QKCTr16+vqaurGyYQCDossZVKpd5gMLC2b9/uaTAY2GKx2H79+vU1n3zyideFCxc0np6ejMlkIn9//6CLFy9quudVVFRkvXLlSt/GxkaujY1NV1JSUoVCoeizOJ0+fXrzX//6V6dPP/20pri42MrBwYHhcrl3v4s6b948X7VabWcwGNivvfZa0+eff15NROTl5TUmKiqq4eTJk8MZhmGlp6ffUCgUhu+//95+/fr1vkRELBaLcnNzNWw2m1555RWhVqvlMAzDio+Pr54/f35zaWmpVXh4uGjixImtly5dss/MzLz2ww8/OHz++ecebm5unaNHjzZYWVkN+W+0opgFAAAAAIAnAsMwlJ2d7bB06dJ6IqK0tDSnwsJCXklJSVFNTQ1XqVRKwsLC2rKzs+16G8/Kyrpma2ur0Gg0xUREOp2OPX369ACFQtH+4osvaletWtXg6upqevfdd6tVKpVdWlpaJRGRRqOxSUpK4sfHx9dlZmY6SiQSvYeHB9M9t2XLlgkOHDhQMWbMGGNWVpZdbGysb15eXllfe3F0dDR5enp2/PjjjzYZGRlOv/nNb5oOHTrkajm+e/fuqpEjR5oYhqGJEycGXrx4kRcSEqInInJ1dWWKi4tLEhIS3BISEkamp6dXfPbZZ+6JiYkVYWFh7Vqtlm1ra9tFRPT9999f4/P5XTU1NdyQkBDx3Llzm4mIysvLbQ4ePFj+5ZdfVlZUVAxLSEjwvHTpUgmfzzdNnDgxMCgoSDfQ/37/bihmAQAAAACAiIgepYM6kIxGI1ssFkurqqqsgoKCdDNmzGghIjp37pxDVFRUI5fLJR8fHyYkJKTt/Pnztn2NCwQCbfe4a9asaYiMjGw5evSo4/Hjx51SU1PdiouLi3uuHxsbWx8RESGMj4+vS05Odl28eHF99+NarZZdUFBgP3v27NGWsY6ODtaD9hUVFdV46NAhflZW1vCcnJzS7sXsF198wU9NTXVlGIZ1586dYWq12sZSzM6dO7eJiEipVOqOHTvmTEQ0fvz4tg0bNvhERUU1RkdHN40ePbrLaDSy1q5d652Xl2fPZrOprq7O6tatW1wiIg8Pj44XX3yxnYgoJyfHbvz48a2enp4MEdHMmTMby8rKbB723+dJhWdmAQAAAABgUFmemS0vLy/s6OhgJSQkjCAiMpt7vxO2r/He+Pn5da5du7bh9OnT17lcLqlUKl7POUKhsNPV1ZU5duyYQ0FBgd3s2bPvKYpNJhM5ODgwGo2m2PJ348aNoget/cYbbzRnZGS4eHl5dfD5/C7LuEajsdqzZ8/Is2fPlpWVlRWHhoZqDQbD3drMxsbGTETE5XLNDMOwiIh+//vf1yYlJVXo9Xr2xIkTJQUFBTb79+/nNzQ0cAsLC0s0Gk2xi4tLp16vZxMRWTq3FizWA2vvIQfFLAAAAAAAPBFcXFxMiYmJlXv37h1pNBpZU6dObc3IyOAzDEPV1dXc/Px8+ylTprT3Nd4zXkZGhqPRaGQREVVWVnKbm5s5AoGgw9HR0dTW1nZPLbRkyZI7y5Yt84+IiGjkcu+9gZXP53d5e3t3JCcnOxP9/CzvhQsXflEU92Rvb2/+8MMPb/32t7+t6T7e1NTE4fF4XXw+33Tz5k3umTNnHvgyqqKiImulUqn/3e9+VztmzJj2K1eu2Gi1Wo6rq2untbW1+fjx4w7V1dVWvZ37wgsvtOfl5TnU1tZyjEYj6y9/+Yvzg9YbCnCbMQAAAAAAPDEmTZqkl0gk+qSkJOfY2NjG3Nxce4lEImOxWOZt27bd8vX1ZRYsWNDc23jPWCdOnHDcsGGDr7W1dRcRkWVeeHh4665duzzEYrF0/fr1NcuXL2+Kjo7Wvv3225wVK1Y09JbXV199dWP58uWCHTt2eDAMw3r99dcbJ0yYoH/QflasWNHUc2zChAn6oKAgnUgkkvn6+hqDg4PbHhTn008/HZGbm+vIZrPNAQEB+t/85jfa5uZmTnh4uDAoKEgik8l0/v7+vb6QSiAQdG7evLl6/PjxEjc3t86xY8fqTCbTkG/Vsh6lRQ8AAAAAAE8XtVpdLpfL6x888+mWk5Nju27dOp9Lly6VDnYuzxK1Wu0ql8v9+nMuOrMAAAAAAPBM27p1q3tqaqpbSkrKPwc7F3h46MwCAAAAADzD0Jntv82bN7tnZmbyu49FRkY27tixo3awchpqHqczi2IWAAAAAOAZhmIWBtPjFLN4mzEAAAAAAAAMOShmAQAAAAAAYMhBMQsAAAAAAABDDopZAAAAAAAAGHJQzAIAAAAAwKDicDjBYrFYKhKJZKGhocL6+npOf2PFxMR4C4VCWUxMjLdarbZWKpWBYrFYOmrUKFl0dLSAiCg3N5eXnp4+/EGx4uLiPOPj40c+ag59rZuYmOiycOFC30ff1c++++47h1//+tfC/p7/tMF3ZgEAAAAAYFBZW1t3aTSaYiKimTNn+u3cudOtv5+3OXz4sNudO3d+4vF45smTJ4tWr159e/78+c1ERPn5+TwiIpVKZatSqezmzJmjHbhd/J9Vq1b59rYuDCwUswAAAAAA8LOjq3yorth2QGOOkOpoxt6bDzt9/Pjx7ZcvX+YREXV1dVFsbKx3VlbWcBaLZd64cWPN8uXLm/oaDw0NFer1erZCoZCsX7++pq6ubphAIOiwxFYqlXqDwcDavn27p8FgYIvFYvv169fXfPLJJ14XLlzQeHp6MiaTifz9/YMuXryo6Z5XUVGR9cqVK30bGxu5NjY2XUlJSRUKhcLQ2x56W9fy37W1tcOmTJkiqqystA4PD2/et2/fLSKiP//5z44fffSRZ0dHB0sgEBiPHDlSPnz48K6MjAzHjRs3+vD5fGbMmDG6h7/wTz8UswAAAAAA8ERgGIays7Mdli5dWk9ElJaW5lRYWMgrKSkpqqmp4SqVSklYWFhbdna2XW/jWVlZ12xtbRWWLq9Op2NPnz49QKFQtL/44ovaVatWNbi6uprefffdapVKZZeWllZJRKTRaGySkpL48fHxdZmZmY4SiUTv4eHBdM9t2bJlggMHDlSMGTPGmJWVZRcbG+ubl5dX1ts+Vq1adbu3dYmIiouLbdVqdTGPx+sSCoVBGzZsuG1nZ2f+/e9/75GTk1Pm6OjY9d5777l//PHHIz/66KPat99+2+/vf/97qUwmM7766quj/rX/AkMLilkAAAAAAPjZI3RQB5LRaGSLxWJpVVWVVVBQkG7GjBktRETnzp1ziIqKauRyueTj48OEhIS0nT9/3ravcYFAcM9tw2vWrGmIjIxsOXr0qOPx48edUlNT3YqLi4t7rh8bG1sfEREhjI+Pr0tOTnZdvHhxfffjWq2WXVBQYD979uzRlrGOjg5WX/u537qTJ09ucXFxMRERCYVCw/Xr160bGxs5169ft1EqlWIios7OTlZwcHDbTz/9ZOPt7W0cM2aMkYho3rx5DUlJSW79v9JPF7wACgAAAAAABpXlmdny8vLCjo4OVkJCwggiIrPZ3Ov8vsZ74+fn17l27dqG06dPX+dyuaRSqX7x/KpQKOx0dXVljh075lBQUGA3e/bse4pik8lEDg4OjEajKbb83bhxo6g/61pZWd1NnsPhmDs7O1lms5kmT57cYol9/fr1oq+//rqCiIjF6rNmfuahmAUAAAAAgCeCi4uLKTExsXLv3r0jjUYja+rUqa0ZGRl8hmGourqam5+fbz9lypT2vsZ7xsvIyHA0Go0sIqLKykpuc3MzRyAQdDg6Opra2truqYWWLFlyZ9myZf4RERGNXO69N7Dy+fwub2/vjuTkZGein5/lvXDhQp8vdepr3b7mT5s2rV2lUtlfuXLFmoiotbWVffnyZevnnnvOcOvWLauioiJrIqIjR47wH/piPgNQzAIAAAAAwBNj0qRJeolEok9KSnJesGBBs0wm00skEtm0adMCtm3bdsvX15fpa7xnrBMnTjgGBgbKAgMDpS+99NLdeeHh4a1lZWU8sVgsPXjwoDMRUXR0tFan03FWrFjR0FteX3311Y2UlBTXwMBAqUgkkn377bdOfe2hr3X7mu/p6cns37+//I033hgVEBAgDQ4OFhcWFtrY2tqa//u//7vi1VdfFQYHBwf6+Pj0WRA/i1iP0qIHAAAAAICni1qtLpfL5fUPnvl0y8nJsV23bp3PpUuXSgc7l2eJWq12lcvlfv05Fy+AAgAAAACAZ9rWrVvdU1NT3VJSUv452LnAw0NnFgAAAADgGYbObP9t3rzZPTMz857nWCMjIxt37NhRO1g5DTWP05lFMQsAAAAA8AxDMQuD6XGKWbwACgAAAAAAAIYcFLMAAAAAAAAw5KCYBQAAAAAAgCEHxSwAAAAAAAAMOShmAQAAAABgUHE4nGCxWCwViUSy0NBQYX19Pae/sWJiYryFQqEsJibGW61WWyuVykCxWCwdNWqULDo6WkBElJuby0tPTx/+oFhxcXGe8fHxI/ubC/xr4TuzAAAAAAAwqKytrbs0Gk0xEdHMmTP9du7c6dbfz9scPnzY7c6dOz/xeDzz5MmTRatXr749f/78ZiKi/Px8HhGRSqWyValUdnPmzNEO3C7g3w3FLAAAAAAAEBHRb//xW59rTddsBzKm0Fmo+3jSxzcfdv748ePbL1++zCMi6urqotjYWO+srKzhLBbLvHHjxprly5c39TUeGhoq1Ov1bIVCIVm/fn1NXV3dMIFA0GGJrVQq9QaDgbV9+3ZPg8HAFovF9uvXr6/55JNPvC5cuKDx9PRkTCYT+fv7B128eFHTPa+ioiLrlStX+jY2NnJtbGy6kpKSKhQKhaG3PcyaNcvPwcHBpFar7e7cuTPs448/vvXmm282abVa9iuvvCLUarUchmFY8fHx1fPnz28uLS21Cg8PFymVyjaVSmU/cuTIjpMnT16zt7fHd1TvA8UsAAAAAAA8ERiGoezsbIelS5fWExGlpaU5FRYW8kpKSopqamq4SqVSEhYW1padnW3X23hWVtY1W1tbhaXLq9Pp2NOnTw9QKBTtL774onbVqlUNrq6upnfffbdapVLZpaWlVRIRaTQam6SkJH58fHxdZmamo0Qi0Xt4eDDdc1u2bJngwIEDFWPGjDFmZWXZxcbG+ubl5ZX1tZfbt28PU6lUmp9++snm9ddfF7755ptNtra2Xd9///01Pp/fVVNTww0JCRHPnTu3mYiosrLS5ssvv7wxceLEiunTp49KS0tzfuuttxr/dVd76EMxCwAAAAAARET0KB3UgWQ0GtlisVhaVVVlFRQUpJsxY0YLEdG5c+ccoqKiGrlcLvn4+DAhISFt58+ft+1rXCAQ3HPb8Jo1axoiIyNbjh496nj8+HGn1NRUt+Li4uKe68fGxtZHREQI4+Pj65KTk10XL15c3/24VqtlFxQU2M+ePXu0Zayjo4N1vz1FREQ0czgcCg4ONjQ0NAwjIurq6mKtXbvWOy8vz57NZlNdXZ3VrVu3uEREXl5exokTJ+qJiBQKha68vNy6v9fzWYEXQAEAAAAAwKCyPDNbXl5e2NHRwUpISBhBRGQ2936XbV/jvfHz8+tcu3Ztw+nTp69zuVxSqVS8nnOEQmGnq6src+zYMYeCggK72bNn31MUm0wmcnBwYDQaTbHl78aNG0X3W9fGxuZukpZ89+/fz29oaOAWFhaWaDSaYhcXl069Xs8mIrKysro7n8PhmBmGuW+xDChmAQAAAADgCeHi4mJKTEys3Lt370ij0ciaOnVqa0ZGBp9hGKqurubm5+fbT5kypb2v8Z7xMjIyHI1GI4uIqLKyktvc3MwRCAQdjo6Opra2tntqoSVLltxZtmyZf0RERCOXe+8NrHw+v8vb27sjOTnZmejnZ3kvXLjwi6L4QbRaLcfV1bXT2trafPz4cYfq6mqrR40B/wfFLAAAAAAAPDEmTZqkl0gk+qSkJOcFCxY0y2QyvUQikU2bNi1g27Ztt3x9fZm+xnvGOnHihGNgYKAsMDBQ+tJLL92dFx4e3lpWVsYTi8XSgwcPOhMRRUdHa3U6HWfFihUNveX11Vdf3UhJSXENDAyUikQi2bfffuv0qHtbtmxZo1qttgsKCpJ8+eWXfH9//15fIAUPh/UoLXoAAAAAAHi6qNXqcrlcXv/gmU+3nJwc23Xr1vlcunSpdLBzeZao1WpXuVzu159z8QIoAAAAAAB4pm3dutU9NTXVLSUl5Z+DnQs8PHRmAQAAAACeYejM9t/mzZvdMzMz+d3HIiMjG3fs2FE7WDkNNY/TmUUxCwAAAADwDEMxC4PpcYpZvAAKAAAAAAAAhhwUswAAAAAAADDkoJgFAAAAAACAIQfFLAAAAAAAAAw5KGYBAAAAAGDQbd682V0oFMoCAgKkYrFYmpWVZdfX3FmzZvmlpKQ4PyhmfHz8SH9/f5lIJJIFBgZK9+zZ4zIQuXp5eY2pqanhEhEpFAoxEVFpaanVvn377r7ZOCcnx3bx4sU+A7Fed2lpaU4sFiu4oKDApq853a/PnDlzBJcuXbLpmfdASUxMdFm4cKHvQMZ8WPjOLAAAAAAADKpTp07ZnTx50qmwsLCYx+OZa2pquEajkfU4MT/99FO3rKwsx0uXLpXw+fyuhoYGzp/+9CengcrZoqCgQENEdPXqVev09HT+ypUrG4mIXnjhBd0LL7ygG+j1jhw5wn/++efbDh06xFcoFNUPmp+enl4x0Dk8KVDMAgAAAAAAERFVb33Px3j1qu1AxrQWiXSev//dzfvNqaqqGsbn8xkej2cmIvLw8GCIiDZs2OBx4sQJJ6PRyB43blzb4cOHK9jse28uPXfunG1cXJyPTqdjOzs7M4cPHy4XCASdn3/+ufupU6fK+Hx+FxGRi4uL6Z133mkgIsrMzHTYsmWLj8lkIrlcrktLS6vg8XhmLy+vMVFRUQ0nT54czjAMKz09/YZCoTDU1tZyZs2aNaqxsXGYQqFo7/55U1tbW4VOpyt47733vG7cuGEjFoul0dHR9cHBwfrPPvtsZHZ29rXbt29z5s2b51dZWWnN4/G6Dhw4UBESEqKPi4vzvHnzplVFRYV1dXW11cqVK2+///77dX1dJ61Wy1apVPanTp0qjYyMFO7evbuaiKirq4sWL17s+49//MPBx8fH2D0/pVIZuGvXrpt9FdbZ2dm2cXFxvgaDgW1jY9OVmpr6T7lcbkxMTHT57rvvnPR6PbuystI6PDy8ed++fbeIiP7rv/7L5fPPP/dwc3PrHD16tMHKympQvveK24wBAAAAAGBQzZgxo6W6utrKz88vaP78+b7ff/+9PRHRxo0b665cuVJy9erVIr1ezz5y5Mjw7ucZjUbW6tWrfTMzM68XFRWVLFq0qH7Dhg1eTU1N7Pb2do5MJjP2XEun07FiYmL809PTr5eVlRUzDEM7d+50sxx3dXVliouLS5YsWXInISFhJBHRli1bPCdMmNBWUlJSHBER0VxTU2PVM+7vfve7qnHjxrVpNJriDz744J6CdNOmTZ5yuVxXVlZW/PHHH1ctWrTI33Ls2rVrNmfPni378ccfS3bt2uV5v4704cOHnaZNm6YdO3as0cnJyXT+/HlbIqJDhw45Xbt2zbq0tLQoNTW14v/9v/9n/7DXXi6XG/Lz8zUlJSXFH3zwQdWmTZu8LceKi4ttjx49eqOkpKTo2LFjzteuXRtWUVExLCEhwTM3N1dz7ty5srKyMt7DrjXQ0JkFAAAAAAAiInpQB/VfZfjw4V1XrlwpPnHihMPp06cdFi1aNDo+Pv6Wo6Ojaffu3e4Gg4Hd3NzMlUqleiLSWs67fPmy9dWrV3mhoaEBRD93KN3c3DrNZjOxWL3XhGq12sbb29s4duxYIxHR4sWLG/bu3TuCiOqIiObOndtERKRUKnXHjh1zJiLKy8tz+POf/3yNiOiNN97QxsTEmB5lf/n5+Q7ffvvtNSKiiIiI1hUrVnAbGho4RERhYWHNPB7PzOPxGD6f33nr1i3u6NGjO3uL8/XXX/PXrFlTR0Q0a9asxkOHDvEnT56sO3v2rENUVFQjl8slPz+/zgkTJrQ+bG6NjY2cOXPm+JeXl9uwWCxzZ2fn3Qs3efLkFhcXFxMRkVAoNFy/ft26rq6OO378+FZPT0+GiGjmzJmNZWVlfT6/+6+EYhYAAAAAAAYdl8ulV199tfXVV19tHTt2rP7gwYOupaWlthcvXiwWCoWdcXFxngaD4Z47S81mM0soFOp/+uknTc94PB6vq7i42EoqlXb0OOe+edjY2Jj/Nx8zwzB3C7uetzc/it7WZLFYZiIia2vruwc5HA51X7O72tpaTl5enmNZWRnv7bffJpPJxGKxWOY//vGPt/43Xr9y27x5s9fUqVNb//73v18vLS21Cg0NDbQc6377MIfDuVvo9netgYbbjAEAAAAAYFCp1WrrwsJCa8vvgoICnlAoNBIRubu7M1qtln38+PFfvL147NixhsbGRu6pU6fsiH6+7VilUtkQEa1duyoCxsAAACAASURBVLZm5cqVgsbGRjYRUWNjI3vXrl2uzz33nKGqqsrqypUr1kREaWlpLlOmTLlvJ3P8+PGtycnJLkREX3/9tWNLSwun55zhw4eb2trafjFuOT8lJcWFiOi7775zcHZ2ZizP8j6sQ4cOOc+cObOhurq6sKqqqrC2tvayt7d3x9/+9jf7qVOntn7zzTd8hmGooqJiWF5ensPDxm1paeF4e3t3EBHt37/f9UHzX3jhhfa8vDyH2tpajtFoZP3lL3954Ful/1XQmQUAAAAAgEHV0tLCWb16tW9LSwuHw+GY/fz8jF988UWFk5MTI5VKZd7e3h1yuby953k2NjbmI0eOXF+9erVva2srx2QysWJjY2+PGzfOsGnTpjttbW3s559/Xjps2DAzl8s1v/POO7W2trbmffv2lc+ePXu05QVQGzZsuHO//BISEqpnzZo1SiqVSiZMmNDm4eHR0XOOUqnUc7lcc2BgoHTu3Ln1wcHBesuxHTt2VM+dO9cvICBAyuPxulJTU//5qNfom2++cdm0aVNN97HIyMimQ4cO8Q8dOlR5+vRpx8DAQJm/v79BqVTeU5x376TK5XKp5fdrr73WuHnz5tply5b5JyYmuk+ZMqXlQXkIBILOzZs3V48fP17i5ubWOXbsWJ3JZBqUVi3rQW12AAAAAAB4eqnV6nK5XF4/2HnAv0ZAQID02LFj18Ri8S8K8CeBWq12lcvlfv05F7cZAwAAAAAAPIUmTpwoCgwM1D+phezjwm3GAAAAAAAAT4ja2lrOtGnTAnuOnzlzptTd3f2R3qKcm5t7deAye/KgmAUAAAAAAHhCuLu7mzQaTfFg5zEU4DZjAAAAAAAAGHJQzAIAAAAAAMCQg2IWAAAAAAAAhhwUswAAAAAAADDkoJgFAAAAAIBBt3nzZnehUCgLCAiQisViaVZWll1fc2fNmuWXkpLi/KCY8fHxI/39/WUikUgWGBgo3bNnj8tA5Orl5TWmpqaGS0SkUCjERESlpaVW+/bt41vm5OTk2C5evNhnINbrLi0tzYnFYgUXFBTYWMZKS0utRCKRjIjou+++c/j1r38tHOh1n0QoZgEAAAAAYFCdOnXK7uTJk06FhYXFZWVlxdnZ2WWjRo16rG+jfvrpp25ZWVmOly5dKrl69WpRbm5uqdlsHqiU7yooKNAQEV29etU6PT39bjH7wgsv6FJTU28O9HpHjhzhP//8822HDh3iP3j20w2f5gEAAAAAACIiOp1W4tNY1WY7kDH5Xva6FxdK7lvUVVVVDePz+QyPxzMTEXl4eDBERBs2bPA4ceKEk9FoZI8bN67t8OHDFWz2vf24c+fO2cbFxfnodDq2s7Mzc/jw4XKBQND5+eefu586daqMz+d3ERG5uLiY3nnnnQYioszMTIctW7b4mEwmksvlurS0tAoej2f28vIaExUV1XDy5MnhDMOw0tPTbygUCkNtbS1n1qxZoxobG4cpFIr27kWxra2tQqfTFbz33nteN27csBGLxdLo6Oj64OBg/WeffTYyOzv72u3btznz5s3zq6ystObxeF0HDhyoCAkJ0cfFxXnevHnTqqKiwrq6utpq5cqVt99///26vq6TVqtlq1Qq+1OnTpVGRkYKd+/eXX2/69rXulqtlr106VLfy5cv2xIRbd26tXrx4sXN8+bN81Wr1XYGg4H92muvNX3++ef3jT/Y0JkFAAAAAIBBNWPGjJbq6morPz+/oPnz5/t+//339kREGzdurLty5UrJ1atXi/R6PfvIkSPDu59nNBpZq1ev9s3MzLxeVFRUsmjRovoNGzZ4NTU1sdvb2zkymczYcy2dTseKiYnxT09Pv15WVlbMMAzt3LnTzXLc1dWVKS4uLlmyZMmdhISEkUREW7Zs8ZwwYUJbSUlJcURERHNNTY1Vz7i/+93vqsaNG9em0WiKP/jgg3sK0k2bNnnK5XJdWVlZ8ccff1y1aNEif8uxa9eu2Zw9e7bsxx9/LNm1a5en0Whk9XWdDh8+7DRt2jTt2LFjjU5OTqbz58/f93889LXuli1bPBwdHU1lZWXFZWVlxf/xH//RSkS0e/fuqitXrpRoNJqif/zjHw4XL17k3S/+YENnFgAAAAAAiIjoQR3Uf5Xhw4d3XblypfjEiRMOp0+fdli0aNHo+Pj4W46Ojqbdu3e7GwwGdnNzM1cqleqJSGs57/Lly9ZXr17lhYaGBhARdXV1kZubW6fZbCYWq/eaUK1W23h7exvHjh1rJCJavHhxw969e0cQUR0R0dy5c5uIiJRKpe7YsWPORER5eXkOf/7zn68REb3xxhvamJgY06PsLz8/3+Hbb7+9RkQUERHRumLFCm5DQwOHiCgsLKyZx+OZeTwew+fzO2/dusUdPXp0Z29xvv76a/6aNWvqiIhmzZrVeOjQIf7kyZN1j7puTk6O45EjR25Y5rm5uZmIiL744gt+amqqK8MwrDt37gxTq9U2ISEh+kfZ678TilkAAAAAABh0XC6XXn311dZXX321dezYsfqDBw+6lpaW2l68eLFYKBR2xsXFeRoMhnvuLDWbzSyhUKj/6aefND3j8Xi8ruLiYiupVNrR45z75mFjY2P+33zMDMPcrYh73t78KHpbk8VimYmIrK2t7x7kcDjUfc3uamtrOXl5eY5lZWW8t99+m0wmE4vFYpn/+Mc/3nrUdXsr9jUajdWePXtGXrp0qcTNzc00a9Ysv57X+0nzRCcHAAAAAABPP7VabV1YWGht+V1QUMATCoVGIiJ3d3dGq9Wyjx8//ou3F48dO9bQ2NjIPXXqlB3Rz7cdq1QqGyKitWvX1qxcuVLQ2NjIJiJqbGxk79q1y/W5554zVFVVWV25csWaiCgtLc1lypQprffLb/z48a3JyckuRERff/21Y0tLC6fnnOHDh5va2tp+MW45PyUlxYXo57cNOzs7M5ZneR/WoUOHnGfOnNlQXV1dWFVVVVhbW3vZ29u7429/+5v9/fLubd1p06a17N69e4Rl3p07dzhNTU0cHo/XxefzTTdv3uSeOXNmeF9xnxTozAIAAAAAwKBqaWnhrF692relpYXD4XDMfn5+xi+++KLCycmJkUqlMm9v7w65XN7e8zwbGxvzkSNHrq9evdq3tbWVYzKZWLGxsbfHjRtn2LRp0522tjb2888/Lx02bJiZy+Wa33nnnVpbW1vzvn37ymfPnj3a8gKoDRs23LlffgkJCdWzZs0aJZVKJRMmTGjz8PD4xZuWlUqlnsvlmgMDA6Vz586tDw4Ovnt77o4dO6rnzp3rFxAQIOXxeF2pqan/fNRr9M0337hs2rSppvtYZGRk06FDh/jx8fG1vZ3T17rbt2+vefPNN31FIpGMzWabt27dWr1o0aLmoKAgnUgkkvn6+hqDg4PbHjXHfzfWv+L11AAAAAAAMDSo1epyuVxeP9h5wLNJrVa7yuVyv/6ci9uMAQAAAAAAYMjBbcYAAAAAAABPiNraWs60adMCe46fOXOm1N3d/ZHeovy0QzELAAAAAADwhHB3dzdpNJriwc5jKMBtxgAAAAAAADDkoJgFAAAAAACAIQfFLAAAAAAAAAw5KGYBAAAAAABgyEExCwAAAAAAg27z5s3uQqFQFhAQIBWLxdKsrCy7vubOmjXLLyUlxflBMePj40f6+/vLRCKRLDAwULpnzx6XgcjVy8trTE1NDZeISKFQiImISktLrfbt28e3zMnJybFdvHixz0CsZ8HhcILFYrHU8ldaWmr1uDE//fRTN8t1edjr+qTA24wBAAAAAGBQnTp1yu7kyZNOhYWFxTwez1xTU8M1Go2sx4n56aefumVlZTleunSphM/ndzU0NHD+9Kc/OQ1UzhYFBQUaIqKrV69ap6en81euXNlIRPTCCy/oXnjhBd1ArmVtbd010G863rRp052BjPfvhGIWAAAAAACIiOjkH//Tp/5mhe1AxnT1Eehejl17835zqqqqhvH5fIbH45mJiDw8PBgiog0bNnicOHHCyWg0sseNG9d2+PDhCjb73ptLz507ZxsXF+ej0+nYzs7OzOHDh8sFAkHn559/7n7q1KkyPp/fRUTk4uJieueddxqIiDIzMx22bNniYzKZSC6X69LS0ip4PJ7Zy8trTFRUVMPJkyeHMwzDSk9Pv6FQKAy1tbWcWbNmjWpsbBymUCjazWbz3fVtbW0VOp2u4L333vv/7N17XFNntjfwlQsk4U4MEhAxlBjCjiQoiCj2RjudzpShjhx7lEFD8YxCe5rTckA87fva6lSrI9BOTztaaOvtaGOPtrXojM4g1DJarVqICeEmrTdAlGuIJJGEvH848aUKeKktUn7fz6d/sJ/9rOfJbv9ZXWs/e8K3337Ll8vlzIIFC9piYmIsBQUFgeXl5adbW1s5v/vd7yTnzp3jCQSC/qKiorMzZsywZGdnB58/f9797NmzvObmZvfMzMzW//N//s+lO3m+dXV17qmpqWEWi4VNRPSnP/3p3C9+8Ysre/fu9V65cmVwQEBAn9Fo9Pj1r3/dGRUVZfnzn/8caLPZWJ9++mmjQqGwZWdnB3t5eTlWrVrV6oq5Z88e73feeWf83//+90Yiok8//dRnw4YNAX/7298a72RvPza0GQMAAAAAwIiaM2eOqbm52V0ikUxJS0sL3bdvnxcRUW5u7iWDwVDT0NBQbbFY2Fqt1nfgPJvNxtJoNKF79uxprK6urlGr1W05OTkTOjs72VeuXOEoFArbjWv19vayli5dGrZz587G+vp6o91up/Xr1we4xkUikd1oNNZkZGRcXrt2bSAR0fLly4NnzpxprqmpMSYnJ3e1tLTc1N67evXqptjYWHNtba3x1Vdf/V5CumzZsmCVStVbX19v/MMf/tCkVqvDXGOnT5/mHzp0qP748eM1+fn5wcNVpG02G9vVYvyLX/winIgoODjYXlFRUW80Gmt27tz57UsvvRTqur+2tlawYcOG8zU1NdW7du0aV19fz9fr9TULFy5sKygoGD/UOr/5zW96Tp8+zW9ubuYSEX344Yfj0tPT24a6f6SgMgsAAAAAAEREdKsK6o/F19e332AwGPfv3+998OBBb7VaHb5ixYoLPj4+jsLCQrHVamV3dXVxGYaxEFG3a96pU6d4DQ0NgsTERBkRUX9/PwUEBPQ5nU5isQbPCXU6HT8kJMSmVCptRETp6ent77777ngiukRElJqa2klEFBcX1/v555/7ExEdPXrU+5NPPjlNRDR//vzupUuXOu7k93399dfeu3fvPk1ElJyc3LNkyRJue3s7h4joiSee6BIIBE6BQGAXCoV9Fy5c4IaHh/cNFmewNuOrV6+yFi9ePMloNArYbDadPXuW5xqLioq6MmnSpD4iotDQUNuvfvWrbiIilUplOXTokPdQ+2Wz2fTMM8+0FxcXC59//vn2b775xuuTTz757k5+808BySwAAAAAAIw4LpdLSUlJPUlJST1KpdJSXFwsqqur8zh27JhRKpX2ZWdnB1ut1u91ljqdTpZUKrVUVVXV3hhPIBD0G41Gd4Zhrt4wZ9h98Pl85z/347Tb7dcz4hvbm+/EYGuyWCwnERGPx7s+yOFwaOCat2P16tWB48eP79u9e/d3/f39JBAIYlxjA2Oz2ezrv43NZpPD4Rh2naysrPannnpKyufznb/5zW863dzc7mRbPwm0GQMAAAAAwIjS6XQ8vV5/vaJYWVkpkEqlNiIisVhs7+7uZpeUlNx0yq5SqbR2dHRwS0tLPYmutR2fOHGCT0T04osvtmRmZk7q6OhgExF1dHSw8/PzRdHR0dampiZ3g8HAIyLaunXruAcffLBnuP3Fx8f3fPjhh+OIiD7++GMfk8nEufEeX19fh9lsvum6a/6mTZvGERHt3bvX29/f3+56l/eH6u7u5gQFBfVxOBz685//PM7huKOi8ZAkEklfYGBgX0FBQdDvf//7+67FmAiVWQAAAAAAGGEmk4mj0WhCTSYTh8PhOCUSiW3Lli1n/fz87AzDKEJCQq6qVKorN87j8/lOrVbbqNFoQnt6ejgOh4OVlZXVGhsba122bNlls9nMnjZtGuPm5ubkcrnOF1544aKHh4dz48aNZ+bNmxfuOgAqJydn2BN9165d25ySkvIAwzCRM2fONAcFBV298Z64uDgLl8t1RkREMKmpqW0xMTEW19i6deuaU1NTJTKZjBEIBP2bN2++Zy27L7744qWUlJTwzz77zH/27Nk9AoHgniTJRETz589vf/fdd7kxMTHWexXzXmLdqswOAAAAAAA/Xzqd7oxKpbovK28wshYtWhQ6derU3pdeeulH++9Dp9OJVCqV5G7mojILAAAAAAAA36NQKCIFAkH/e++9NyKHgt0OJLMAAAAAAAD3iYsXL3IeeeSRiBuvf/HFF3VisfjevBB7G6qrq2t+qrXuFpJZAAAAAACA+4RYLHbc+PkdGBxOMwYAAAAAAIBRB8ksAAAAAAAAjDpIZgEAAAAAAGDUQTILAAAAAAAjLi8vTyyVShUymYyRy+VMWVmZ51D3pqSkSDZt2uR/q5grVqwIDAsLU0yePFkRERHBvPPOO+PuxV4nTJgQ1dLSwiUimjp1qpyIqK6uzn3jxo1C1z1ffvmlR3p6+sR7sZ4Li8WK+f3vfx/i+nvFihWB2dnZwfdyjVu53Wf/U8ABUAAAAAAAMKJKS0s9Dxw44KfX640CgcDZ0tLCtdlsrB8S849//GNAWVmZz8mTJ2uEQmF/e3s7Z8eOHX73as8ulZWVtUREDQ0NvJ07dwozMzM7iIgeeuih3oceeqj3Xq7l7u7u/Mtf/uLf0tJyMSgoyH6n8/v6+sjNze1ebmlEoTILAAAAAAAjqqmpyU0oFNoFAoGTiCgoKMgukUj6cnJygqZMmRI5efJkxYIFCyb19/ffNLeiosJj+vTpEQqFInL27NmTz54960ZE9Oabb4rfe++9c0KhsJ+IaNy4cY4XXnihnYhoz5493pGRkYxMJmPmzZsnsVgsLKJrFdeXXnopmGGYSJlMxlRWVvKJrn0uJyEhYXJkZCSTmpo6yel0Xl/fw8NjKhHRK6+8MuHEiRNecrmcWbly5fi9e/d6P/roo1IiotbWVs7jjz8eLpPJGJVKJT927JiAiCg7Ozt43rx5kri4uIiQkJCo119/ffxwz4nD4TgXLVp0ec2aNYE3jtXX17vPnDlTJpPJmJkzZ8oaGhrcia5VUv/t3/4tZMaMGbLnnnsuJDs7O3ju3LmShISEyRMmTIjasmWLX2ZmZohMJmMefPDBya7/iXA7z36kIZkFAAAAAAAiIurYVT+x9Z3KiHv5T8eu+lu22s6ZM8fU3NzsLpFIpqSlpYXu27fPi4goNzf3ksFgqGloaKi2WCxsrVbrO3CezWZjaTSa0D179jRWV1fXqNXqtpycnAmdnZ3sK1eucBQKhe3GtXp7e1lLly4N27lzZ2N9fb3RbrfT+vXrA1zjIpHIbjQaazIyMi6vXbs2kIho+fLlwTNnzjTX1NQYk5OTu1paWtxvjLt69eqm2NhYc21trfHVV1+9NHBs2bJlwSqVqre+vt74hz/8oUmtVoe5xk6fPs0/dOhQ/fHjx2vy8/ODb1WRzs3NvfTJJ58I29vbOQOvZ2ZmhqamprbX19cb//Vf/7U9Kyvr+nNvbGzkHz58uL64uPgCEdHZs2d5ZWVlp3ft2nU6MzMzLDEx0VRfX2/k8/n9H3/8se/tPPv7AZJZAAAAAAAYUb6+vv0Gg8H4zjvvnA0ICLCr1erwt99+e9xf//pXb6VSKZfJZMyRI0e8DQaDYOC8U6dO8RoaGgSJiYkyuVzOrF+/Pqi5udnN6XQSizV4TqjT6fghISE2pVJpIyJKT09v/8c//uHtGk9NTe0kIoqLi+s9f/48j4jo6NGj3hkZGe1ERPPnz+/28fFx3Mnv+/rrr70XL17cTkSUnJzc09XVxXUlo0888USXQCBwBgUF2YVCYd+FCxeGfRVUKBT2z5s3r33t2rXfq+JWVlZ6LlmypIOIKCsrq+PkyZNerrG5c+d2crn/P+zjjz/ezePxnHFxcRaHw8H6l3/5FxMRkUKhsHz33XfuRES3evb3A7wzCwAAAAAAREQk/BfZ+ZFam8vlUlJSUk9SUlKPUqm0FBcXi+rq6jyOHTtmlEqlfdnZ2cFWq/V7xTin08mSSqWWqqqq2hvjCQSCfqPR6M4wzNUb5gy7Dz6f7/znfpx2u/16Rsxm330dcLA1WSyWk4iIx+NdH+RwODRwzaH813/9V+u0adOY+fPnt93O+l5eXt/rEXatyeFwiMvlOl2/jc1mk91uZ/X29rL+8z//c9Jwz/5+cN9tCAAAAAAAxhadTsfT6/U819+VlZUCqVRqIyISi8X27u5udklJyU0n6CqVSmtHRwe3tLTUk+ha2/GJEyf4REQvvvhiS2Zm5qSOjg42EVFHRwc7Pz9fFB0dbW1qanI3GAw8IqKtW7eOe/DBB3uG2198fHzPhx9+OI6I6OOPP/YxmUycG+/x9fV1mM3mm6675m/atGkcEdHevXu9/f397a53ee9GYGCg4ze/+U3njh07RK5rU6dOvfL+++/7ExG99957wtjYWPPdxu/t7WUTDf/s7weozAIAAAAAwIgymUwcjUYTajKZOBwOxymRSGxbtmw56+fnZ2cYRhESEnJVpVJduXEen893arXaRo1GE9rT08NxOBysrKys1tjYWOuyZcsum81m9rRp0xg3Nzcnl8t1vvDCCxc9PDycGzduPDNv3rxwh8NBKpWqNycn5/Jw+1u7dm1zSkrKAwzDRM6cOdMcFBR09cZ74uLiLFwu1xkREcGkpqa2xcTEWFxj69ata05NTZXIZDJGIBD0b968+bsf+sxeeeWVi1u2bLn+ru+GDRvOqdVqyZ/+9CfxuHHj7Fu3bj1zt7FFIpHjd7/73eXhnv39gHWrMjsAAAAAAPx86XS6MyqV6rbaVQHuNZ1OJ1KpVJK7mYs2YwAAAAAAABh10GYMAAAAAABwn7h48SLnkUceibjx+hdffFEnFovv6BTlnzskswAAAAAAAPcJsVjsqK2tNY70PkYDtBkDAAAAAADAqINkFgAAAAAAAEYdJLMAAAAAAAAw6iCZBQAAAAAAgFEHySwAAAAAAIy4vLw8sVQqVchkMkYulzNlZWWeQ92bkpIi2bRpk/+tYq5YsSIwLCxMMXnyZEVERATzzjvvjLsXe50wYUJUS0sLl4ho6tSpciKiuro6940bNwpd93z55Zce6enpE+/Fei4cDidGLpczkydPVvzqV796oKen547yuVWrVo0fOOfhhx+WtrW1cYabM/C33m+QzAIAAAAAwIgqLS31PHDggJ9erzfW19cby8vL6x944IGrPyTmH//4x4CysjKfkydP1jQ0NFQfOXKkzul03qstX1dZWVlLRNTQ0MDbuXPn9WT2oYce6t28efP5e7kWj8frr62tNTY0NFS7ubk5CwoKAm53rt1up/feey/QbDZfzwEPHTp0WiQSjdrP/SCZBQAAAACAEdXU1OQmFArtAoHASUQUFBRkl0gkfTk5OUFTpkyJnDx5smLBggWT+vv7b5pbUVHhMX369AiFQhE5e/bsyWfPnnUjInrzzTfF77333jmhUNhPRDRu3DjHCy+80E5EtGfPHu/IyEhGJpMx8+bNk1gsFhbRtSrkSy+9FMwwTKRMJmMqKyv5RNe+/ZqQkDA5MjKSSU1NnTQwKfbw8JhKRPTKK69MOHHihJdcLmdWrlw5fu/evd6PPvqolIiotbWV8/jjj4fLZDJGpVLJjx07JiAiys7ODp43b54kLi4uIiQkJOr1118ff7vPbPbs2ebTp0/ziIgef/zxcIVCESmVShX5+fmigXt78cUXg5VKpXz58uVBly5dcnv44YdlM2bMkLl+r6vqOlSM+9l9WS4GAAAAAICf3meffTbx0qVLHvcy5vjx43vnzJkzbIVyzpw5pjfeeCNYIpFMmT17tmnBggUdTz31lDk3N/dSfn5+yz/vCdNqtb6pqandrnk2m42l0WhC9+3bdzo4ONheXFzsn5OTM6GoqOjclStXOAqFwnbjWr29vaylS5eG/e1vf6tTKpW23/72t5L169cHrFix4hIRkUgkshuNxpq1a9cGrF27NnDnzp1nly9fHjxz5kxzfn5+i1ar9f3oo49uSvZWr17dVFBQEFheXn6aiGjv3r3errFly5YFq1Sq3tLS0sbPP//cW61Wh7m+JXv69Gn+kSNH6rq6ujiRkZFTcnNzL/N4vGFLyH19fXTgwAGfJ554wkREtH379jOBgYEOs9nMmjp1KpOWltYpFosdFouFPWXKFMtbb73VTET00UcfiQ4dOlQfFBRkvzHmUDGG28dIQzILAAAAAAAjytfXt99gMBj379/vffDgQW+1Wh2+YsWKCz4+Po7CwkKx1Wpld3V1cRmGsRDR9WT21KlTvIaGBkFiYqKMiKi/v58CAgL6nE4nsVisQdfS6XT8kJAQm1KptBERpaent7/77rvjiegSEVFqamonEVFcXFzv559/7k9EdPToUe9PPvnkNBHR/Pnzu5cuXXpHSd7XX3/tvXv37tNERMnJyT1Llizhtre3c4iInnjiiS6BQOAUCAR2oVDYd+HCBW54eHjfYHFsNhtbLpczREQzZszo+Y//+I82IqJ169YF7tu3z4+I6OLFi27V1dV8sVh8hcPhUHp6euft7HGoGHfyO39qSGYBAAAAAICIiG5VQf0xcblcSkpK6klKSupRKpWW4uJiUV1dncexY8eMUqm0Lzs7O9hqtX7vNUmn08mSSqWWqqqq2hvjCQSCfqPR6M4wzNUb5gy7Dz6f7/znfpx2u/16Rsxm3/0bmoOtyWKxnEREA6uwHA6HBq55I9c7swOv7d271/vQoUPeJ06cqPX29u6Pi4uLsFgsbCIid3f3fi73POViaAAAIABJREFU1infcDHuZ/f9BgEAAAAA4OdNp9Px9Ho9z/V3ZWWlQCqV2oiIxGKxvbu7m11SUnLT6cVKpdLa0dHBLS0t9SS61nZ84sQJPhHRiy++2JKZmTmpo6ODTUTU0dHBzs/PF0VHR1ubmprcDQYDj4ho69at4x588MGe4fYXHx/f8+GHH44jIvr44499TCbTTScA+/r6Osxm86AnA8fHx/ds2rRpHNG1xNHf39/uepf3h+rq6uL4+vo6vL29+ysrK/k6nW7IU6A9PT0d3d3dN+WAdxLjfoLKLAAAAAAAjCiTycTRaDShJpOJw+FwnBKJxLZly5azfn5+doZhFCEhIVdVKtVNLa98Pt+p1WobNRpNaE9PD8fhcLCysrJaY2NjrcuWLbtsNpvZ06ZNY9zc3JxcLtf5wgsvXPTw8HBu3LjxzLx588IdDgepVKrenJycy8Ptb+3atc0pKSkPMAwTOXPmTHNQUNBNJy3HxcVZuFyuMyIigklNTW2LiYmxuMbWrVvXnJqaKpHJZIxAIOjfvHnzd/fmyRGlpKR0FxUVBchkMiY8PNw62HNyUavVbb/61a8mjx8/vu/YsWP1dxPjfsL6MY6nBgAAAACA0UGn051RqVRtI70PGJt0Op1IpVJJ7mYu2owBAAAAAABg1EGbMQAAAAAAwH3i4sWLnEceeSTixutffPFF3f3+qZyfGpJZAAAAAACA+4RYLHbceGIxDA5txgAAAAAAADDqIJkFAAAAAACAUQfJLAAAAAAAAIw6SGYBAAAAAABg1EEyCwAAAAAAIy4vL08slUoVMpmMkcvlTFlZmedQ96akpEg2bdrkf6uYK1asCAwLC1NMnjxZERERwbzzzjvj7sVeJ0yYENXS0sIlIpo6daqciKiurs5948aNQtc9X375pUd6evrEe7EeDA6nGQMAAAAAwIgqLS31PHDggJ9erzcKBAJnS0sL12azsX5IzD/+8Y8BZWVlPidPnqwRCoX97e3tnB07dvjdqz27VFZW1hIRNTQ08Hbu3CnMzMzsICJ66KGHeh966KHee70e/H+ozAIAAAAAwIhqampyEwqFdoFA4CQiCgoKskskkr6cnJygKVOmRE6ePFmxYMGCSf39/TfNraio8Jg+fXqEQqGInD179uSzZ8+6ERG9+eab4vfee++cUCjsJyIaN26c44UXXmgnItqzZ493ZGQkI5PJmHnz5kksFguL6FrF9aWXXgpmGCZSJpMxlZWVfKJr335NSEiYHBkZyaSmpk5yOp3X1/fw8JhKRPTKK69MOHHihJdcLmdWrlw5fu/evd6PPvqolIiotbWV8/jjj4fLZDJGpVLJjx07JiAiys7ODp43b54kLi4uIiQkJOr1118fP9Qzqqurc3/ggQcU8+fPnySVShUJCQmTzWYzi4iooKBANGXKlMiIiAjml7/8ZXhPTw+b6FoFOz09feLUqVPlISEhUbdTzR5NUJkFAAAAAAAiIjLW5E28Yq73uJcxPb1kvUzkuvPD3TNnzhzTG2+8ESyRSKbMnj3btGDBgo6nnnrKnJubeyk/P7/ln/eEabVa39TU1G7XPJvNxtJoNKH79u07HRwcbC8uLvbPycmZUFRUdO7KlSschUJhu3Gt3t5e1tKlS8P+9re/1SmVSttvf/tbyfr16wNWrFhxiYhIJBLZjUZjzdq1awPWrl0buHPnzrPLly8Pnjlzpjk/P79Fq9X6fvTRR6Ib465evbqpoKAgsLy8/DQR0d69e71dY8uWLQtWqVS9paWljZ9//rm3Wq0Oc31L9vTp0/wjR47UdXV1cSIjI6fk5uZe5vF4zhvjExGdO3eO/z//8z/fzpo16+yvf/3rB7Zu3er/3HPPdfzud7/r/M///M82IiKNRhP89ttvi1555ZVLREStra1uJ06cqK2qquL/9re/lT777LOdt/63NjqgMgsAAAAAACPK19e332AwGN95552zAQEBdrVaHf7222+P++tf/+qtVCrlMpmMOXLkiLfBYBAMnHfq1CleQ0ODIDExUSaXy5n169cHNTc3uzmdTmKxBu9S1ul0/JCQEJtSqbQREaWnp7f/4x//uJ54pqamdhIRxcXF9Z4/f55HRHT06FHvjIyMdiKi+fPnd/v4+Dju5Pd9/fXX3osXL24nIkpOTu7p6uritre3c4iInnjiiS6BQOAMCgqyC4XCvgsXLgxZcJwwYYJt1qxZFiKiqVOn9p45c4ZHRHTy5ElBTExMhEwmY3bv3j2uurqa75qTnJzcxeFwKCYmxtre3u52J/u+36EyCwAAAAAARER0qwrqj4nL5VJSUlJPUlJSj1KptBQXF4vq6uo8jh07ZpRKpX3Z2dnBVqv1e8U4p9PJkkqllqqqqtob4wkEgn6j0ejOMMzVG+YMuw8+n+/8536cdrv9ekbMZt99HXCwNVkslpOIaGAVlsPh0MA1b+Tu7j7wXqfFYmETES1ZsiRs165dp2fOnGl5++23xx06dOh6cu76PUPtYzRDZRYAAAAAAEaUTqfj6fV6nuvvyspKgVQqtRERicVie3d3N7ukpOSm9z2VSqW1o6ODW1pa6kl0re34xIkTfCKiF198sSUzM3NSR0cHm4ioo6ODnZ+fL4qOjrY2NTW5GwwGHhHR1q1bxz344IM9w+0vPj6+58MPPxxHRPTxxx/7mEwmzo33+Pr6Osxm803XXfM3bdo0juha+7G/v7/d9S7vvdDb28sODQ3ts9lsLK1WK7z1jJ8HVGYBAAAAAGBEmUwmjkajCTWZTBwOh+OUSCS2LVu2nPXz87MzDKMICQm5qlKprtw4j8/nO7VabaNGownt6enhOBwOVlZWVmtsbKx12bJll81mM3vatGmMm5ubk8vlOl944YWLHh4ezo0bN56ZN29euMPhIJVK1ZuTk3N5uP2tXbu2OSUl5QGGYSJnzpxpDgoKunrjPXFxcRYul+uMiIhgUlNT22JiYiyusXXr1jWnpqZKZDIZIxAI+jdv3vzdvXly1yxfvrw5Li4ucsKECVcjIyN7h0qqf25YP7dSMwAAAAAA3D6dTndGpVK1jfQ+YGzS6XQilUoluZu5aDMGAAAAAACAUQdtxgAAAAAAAPeJixcvch555JGIG69/8cUXdWKx+I5OUf65QzILAAAAAABwnxCLxQ7XN2hheGgzBgAAAAAAgFEHySwAAAAAAACMOkhmAQAAAAAAYNRBMgsAAAAAAACjDpJZAAAAAAAYcXl5eWKpVKqQyWSMXC5nysrKPIe6NyUlRbJp0yb/ocYXLlwYKpfLmfDwcAWfz58ml8sZuVzODDfnXtFqtb4KhSIyPDxcERYWpsjKyprQ0tLCFQqFKtc9+/fv92KxWDHnzp3jEhG1trZy/P39Vf39/fT000+HTZgwISoiIoKRSCRT5s6dKzlz5ozbj73v0QinGQMAAAAAwIgqLS31PHDggJ9erzcKBAJnS0sL12azse423rZt284REdXV1bknJSVN/qlOB/7qq68EeXl5E0tKShqUSqWtr6+PCgsLA4KCguw+Pj6OU6dO8ZRKpa2iosIrMjKyt7y83EutVneVl5d7TZ069Qqbfa3WuHbt2vMLFy7scjgctHLlysBHH31UVltba+TxeM6f4neMFqjMAgAAAADAiGpqanITCoV2gUDgJCIKCgqySySSvpycnKApU6ZETp48WbFgwYJJ/f39N82tqKjwmD59eoRCoYicPXv25LNnzw5ZxdTpdLyoqKhI19/ffPMN3/V3YGCg8rnnnpsQFRUVqVQq5Uaj0Z2I6Pz589wnnngifMqUKZFRUVGRBw8eHLJivGbNGnFubm6LUqm0ERG5ublRXl7eZSKi6dOnm7/44gsvIqKjR496Pv/8862HDx/2IiI6fPiw14wZM8w3xuNwOLRq1apWPz8/x6effupzWw9zDEFlFgAAAAAAiIjoxZpzE2uvWD3uZUy5J7/3rcjQ88PdM2fOHNMbb7wRLJFIpsyePdu0YMGCjqeeesqcm5t7KT8/v+Wf94RptVrf1NTUbtc8m83G0mg0ofv27TsdHBxsLy4u9s/JyZnwv//7v2cGW0elUtl4PF7/8ePH+dOnT7cWFRWJ0tLS2lzj/v7+Dr1eX/PWW2+N02g0E0tLSxszMzND8/LyLj722GNXXJXehoaG6sHi19XVCV599dWWwcZmzpxp/uqrr7w0Gk17c3Mzb9GiRV2JiYkBRERff/2152uvvdY81POJiorqramp4RNR91D3jEVIZgEAAAAAYET5+vr2GwwG4/79+70PHjzorVarw1esWHHBx8fHUVhYKLZareyuri4uwzAWGpDQnTp1itfQ0CBITEyUERH19/dTQEBA33BrqdXqtqKiIlF0dPSFkpISf51Od70FOT09vYOIaOnSpR2rVq0KISI6fPiwT2NjI991T3d3N8dsNrO8vLzuqOX3kUceMW/YsCFQr9fzJBKJ1dvbu7+vr49lMpnYNTU1Hg8++OCVoeY6neguHgySWQAAAAAAICKiW1VQf0xcLpeSkpJ6kpKSepRKpaW4uFhUV1fncezYMaNUKu3Lzs4Otlqt33tN0ul0sqRSqaWqqqr2dtdJT0/vjIqKCtqxY4d52rRpZpFI5HCNsVism7JGp9NJVVVVNXw+/5YZZUREhOXo0aOesbGx1hvHoqOjrW1tbdzPPvvMd8aMGVeIiKZMmdL73//93yKJRGIdLjmurq72SEpKQlX2BnhnFgAAAAAARpROp+Pp9Xqe6+/KykqBVCq1ERGJxWJ7d3c3u6Sk5KaTiJVKpbWjo4NbWlrqSXSt7fjEiRP8G+8byNvbuz8hIcGUm5sbmpGR0T5wbOvWrUIiouLiYmFMTIyZiCghIcG0bt26ANc9R44cEQwVe/ny5Rfz8/ODDAYDj4jIbrfTa6+9FkhExGazKTo6+kpRUdH42bNnm4mutR5v3Lhx/PTp0wetyvb399PKlSvHd3Z2cubMmWMa7neNRajMAgAAAADAiDKZTByNRhNqMpk4HA7HKZFIbFu2bDnr5+dnZxhGERISclWlUt2U8PH5fKdWq23UaDShPT09HIfDwcrKymodrDI60KJFizrKy8t9k5OTv5cg9vb2sqOioiJZLJZTq9V+S0T0/vvvn8vIyAiVyWQih8PBmjVrVs+sWbPODRY3ISHBsnr16vPPPPPMA1arlc1isejJJ5/sco3Hx8ebDx8+7JOQkNBLRPTwww9f+fd//3deQkLC9w5/Wr58+cTXX3892GazsadNm2YuLy+vx0nGN2Oh/xoAAAAAYOzS6XRnVCpV263v/Pl4+eWXxTabjVVQUHD9sKbAwEBldXV19cC2Y/jx6XQ6kUqlktzNXFRmAQAAAABgzEhMTJQ2Nze7Hzp0qG6k9wI/DJJZAAAAAAAYM8rKyk4Pdr21tfXU7cYoLCwUFRUVjR94LT4+vmfz5s0jdoDWWIQ2YwAAAACAMWwsthnD/eOHtBnjNGMAAAAAAAAYdZDMAgAAAAAAwKiDZBYAAAAAAABGHSSzAAAAAAAAMOogmQUAAAAAgBGXl5cnlkqlCplMxsjlcqasrMxzqHtTUlIkmzZt8h9qfOHChaFyuZwJDw9X8Pn8aXK5nJHL5cxwc+4VrVbrq1AoIsPDwxVhYWGKrKysCT/2mmMVPs0DAAAAAAAjqrS01PPAgQN+er3eKBAInC0tLVybzca623jbtm07R0RUV1fnnpSUNLm2ttZ473Y7tK+++kqQl5c3saSkpEGpVNr6+vqosLAw4KdYeyxCMgsAAAAAAERElLtLN7H+Yo/HvYwpE3v3rv8X1bDfX21qanITCoV2gUDgJCIKCgqyExHl5OQE7d+/389ms7FjY2PN27dvP8tmf7+5tKKiwiM7O3tib28v29/f3759+/YzkyZN6htsHZ1Ox0tLS3tAr9fXEBF98803fLVaHabX62sCAwOVKSkp7RUVFT4sFsup1Wq/ZRjm6vnz57mLFy+e1Nzc7M5iseitt94699hjj10ZLP6aNWvEubm5LUql0kZE5ObmRnl5eZeJiGpra93VarWks7OTKxKJ+rZt23YmPDy87+mnnw4TCoX2qqoqz8uXL7utWbPm/KJFi7ru8DGPSWgzBgAAAACAETVnzhxTc3Ozu0QimZKWlha6b98+LyKi3NzcSwaDoaahoaHaYrGwtVqt78B5NpuNpdFoQvfs2dNYXV1do1ar23JycoZs61WpVDYej9d//PhxPhFRUVGRKC0t7fo3dv39/R16vb4mIyPjskajmUhElJmZGZqXl3fRYDDU7Nq1qzEzM1MyVPy6ujpBfHz8oInukiVLJqWnp7fV19cb586d2/n8889PdI21tbVxT548Wbt79+7Tr776KtqSbxMqswAAAAAAQEREt6qg/lh8fX37DQaDcf/+/d4HDx70VqvV4StWrLjg4+PjKCwsFFutVnZXVxeXYRgLEXW75p06dYrX0NAgSExMlBER9ff3U0BAwKBVWRe1Wt1WVFQkio6OvlBSUuKv0+mutyCnp6d3EBEtXbq0Y9WqVSFERIcPH/ZpbGzku+7p7u7mmM1mlpeXl/NOfqNOp/MsKytrICJ67rnn2t94443rSWtycnIXm82mGTNmWC5duuR+J3HHMiSzAAAAAAAw4rhcLiUlJfUkJSX1KJVKS3Fxsaiurs7j2LFjRqlU2pednR1stVq/11nqdDpZUqnUUlVVVXu766Snp3dGRUUF7dixwzxt2jSzSCRyuMZYLNZNCarT6aSqqqoaPp9/y+Q1IiLCcvToUc/Y2Fjr7e6HiGhgbKfzjnLkMQ1txgAAAAAAMKJ0Oh1Pr9fzXH9XVlYKpFKpjYhILBbbu7u72SUlJTedRKxUKq0dHR3c0tJST6JrbccnTpzg33jfQN7e3v0JCQmm3Nzc0IyMjPaBY1u3bhUSERUXFwtjYmLMREQJCQmmdevWXT/E6ciRI4KhYi9fvvxifn5+kMFg4BER2e12eu211wKJiKKjo80ffPCBkIho48aN4+Li4npu9VxgeKjMAgAAAADAiDKZTByNRhNqMpk4HA7HKZFIbFu2bDnr5+dnZxhGERISclWlUt30Liqfz3dqtdpGjUYT2tPTw3E4HKysrKzWW1VGFy1a1FFeXu6bnJxsGni9t7eXHRUVFek6AIqI6P333z+XkZERKpPJRA6HgzVr1qyeWbNmnRssbkJCgmX16tXnn3nmmQesViubxWLRk08+2UVEtGHDhnPPPvuspKCgQOw6AOquHxgQERELZWwAAAAAgLFLp9OdUalUbbe+8+fj5ZdfFttsNlZBQUGL61pgYKCyurq6emDbMfz4dDqdSKVSSe5mLiqzAAAAAAAwZiQmJkqbm5vdDx06VDfSe4EfBsksAAAAAACMGWVlZacHu97a2nrqdmMUFhaKioqKxg+8Fh8f37N58+YROQ16rEKbMQAAAADAGDYW24zh/vFD2oxxmjEAAAAAAACMOkhmAQAAAAAAYNRBMgsAAAAAAACjDpJZAAAAAAAAGHWQzAIAAAAAwIjLy8sTS6VShUwmY+RyOVNWVuY51L0pKSmSTZs2+Q81vnDhwlC5XM6Eh4cr+Hz+NLlczsjlcma4OfeKVqv1VSgUkeHh4YqwsDBFVlbWhLuJYzAYeHK5nLnx+tNPPx02YcKEqIiICEYikUyZO3eu5MyZM24/fOejDz7NAwAAAAAAI6q0tNTzwIEDfnq93igQCJwtLS1cm83Gutt427ZtO0dEVFdX556UlDS5trbWeO92O7SvvvpKkJeXN7GkpKRBqVTa+vr6qLCwMOBer7N27drzCxcu7HI4HLRy5crARx99VFZbW2vk8Xhj6lM1SGYBAAAAAOCaz56fSJeMHvc05niml+a8O+z3V5uamtyEQqFdIBA4iYiCgoLsREQ5OTlB+/fv97PZbOzY2Fjz9u3bz7LZ328uraio8MjOzp7Y29vL9vf3t2/fvv3MpEmT+gZbR6fT8dLS0h7Q6/U1RETffPMNX61Wh+n1+prAwEBlSkpKe0VFhQ+LxXJqtdpvGYa5ev78ee7ixYsnNTc3u7NYLHrrrbfOPfbYY1cGi79mzRpxbm5ui1KptBERubm5UV5e3mUiotraWne1Wi3p7OzkikSivm3btp0JDw/ve/rpp8OEQqG9qqrK8/Lly25r1qw5v2jRoq7bebQcDodWrVrVWlJS4v/pp5/6zJ8/v/t25v1coM0YAAAAAABG1Jw5c0zNzc3uEolkSlpaWui+ffu8iIhyc3MvGQyGmoaGhmqLxcLWarW+A+fZbDaWRqMJ3bNnT2N1dXWNWq1uy8nJGbKtV6VS2Xg8Xv/x48f5RERFRUWitLS069/Y9ff3d+j1+pqMjIzLGo1mIhFRZmZmaF5e3kWDwVCza9euxszMTMlQ8evq6gTx8fGDJrpLliyZlJ6e3lZfX2+cO3du5/PPPz/RNdbW1sY9efJk7e7du0+/+uqrd9yWHBUV1VtTU8O/03mjHSqzAAAAAABwzS0qqD8WX1/ffoPBYNy/f7/3wYMHvdVqdfiKFSsu+Pj4OAoLC8VWq5Xd1dXFZRjGQkTXq4+nTp3iNTQ0CBITE2VERP39/RQQEDBoVdZFrVa3FRUViaKjoy+UlJT463S66y3I6enpHURES5cu7Vi1alUIEdHhw4d9GhsbryeK3d3dHLPZzPLy8rqjll6dTudZVlbWQET03HPPtb/xxhvXk9bk5OQuNptNM2bMsFy6dMn9TuISETmdY6q7+DokswAAAAAAMOK4XC4lJSX1JCUl9SiVSktxcbGorq7O49ixY0apVNqXnZ0dbLVav9dZ6nQ6WVKp1FJVVVV7u+ukp6d3RkVFBe3YscM8bdo0s0gkcrjGWCzWTVmh0+mkqqqqGj6ff8uMMSIiwnL06FHP2NhY6+3uh4hoYOy7SUyrq6s9kpKSxlSLMRHajAEAAAAAYITpdDqeXq/nuf6urKwUSKVSGxGRWCy2d3d3s0tKSm46iVipVFo7Ojq4paWlnkTX2o5PnDgxbLutt7d3f0JCgik3Nzc0IyOjfeDY1q1bhURExcXFwpiYGDMRUUJCgmndunXXD3E6cuSIYKjYy5cvv5ifnx9kMBh4RER2u51ee+21QCKi6Oho8wcffCAkItq4ceO4uLi4nls9l1vp7++nlStXju/s7OTMmTPH9EPjjTaozAIAAAAAwIgymUwcjUYTajKZOBwOxymRSGxbtmw56+fnZ2cYRhESEnJVpVLd9C4qn893arXaRo1GE9rT08NxOBysrKys1ltVRhctWtRRXl7um5yc/L0EsLe3lx0VFRXpOgCKiOj9998/l5GRESqTyUQOh4M1a9asnlmzZp0bLG5CQoJl9erV55955pkHrFYrm8Vi0ZNPPtlFRLRhw4Zzzz77rKSgoEDsOgDqVs+lsbGRHxgYqHT9vX79+nNERMuXL5/4+uuvB9tsNva0adPM5eXl9WPtJGMiItZY7a8GAAAAAAAinU53RqVStd36zp+Pl19+WWyz2VgFBQUtrmuBgYHK6urq6oFtx/Dj0+l0IpVKJbmbuajMAgAAAADAmJGYmChtbm52P3ToUN1I7wV+GCSzAAAAAAAwZpSVlZ0e7Hpra+up241RWFgoKioqGj/wWnx8fM/mzZtH5DTosQptxgAAAAAAY9hYbDOG+8cPaTPGacYAAAAAAAAw6iCZBQAAAAAAgFEHySwAAAAAAACMOkhmAQAAAAAAYNRBMgsAAAAAACMuLy9PLJVKFTKZjJHL5UxZWZnnUPempKRINm3a5D/U+MKFC0PlcjkTHh6u4PP50+RyOSOXy5nh5twrWq3WV6FQRIaHhyvCwsIUWVlZE+4mjsFg4MnlcubG608//XTYtm3b/H74Tkc/fJoHAAAAAABGVGlpqeeBAwf89Hq9USAQOFtaWrg2m411t/G2bdt2joiorq7OPSkpaXJtba3x3u12aF999ZUgLy9vYklJSYNSqbT19fVRYWFhwE+x9liEZBYAAAAAAIiI6P8e/r8TT3ee9riXMaX+0t4/JPxh2O+vNjU1uQmFQrtAIHASEQUFBdmJiHJycoL279/vZ7PZ2LGxsebt27efZbO/31xaUVHhkZ2dPbG3t5ft7+9v3759+5lJkyb1DbaOTqfjpaWlPaDX62uIiL755hu+Wq0O0+v1NYGBgcqUlJT2iooKHxaL5dRqtd8yDHP1/Pnz3MWLF09qbm52Z7FY9NZbb5177LHHrgwWf82aNeLc3NwWpVJpIyJyc3OjvLy8y0REtbW17mq1WtLZ2ckViUR927ZtOxMeHt739NNPhwmFQntVVZXn5cuX3dasWXN+0aJFXXf4mMcktBkDAAAAAMCImjNnjqm5udldIpFMSUtLC923b58XEVFubu4lg8FQ09DQUG2xWNhardZ34DybzcbSaDShe/bsaayurq5Rq9VtOTk5Q7b1qlQqG4/H6z9+/DifiKioqEiUlpZ2/Ru7/v7+Dr1eX5ORkXFZo9FMJCLKzMwMzcvLu2gwGGp27drVmJmZKRkqfl1dnSA+Pn7QRHfJkiWT0tPT2+rr641z587tfP755ye6xtra2rgnT56s3b179+lXX331rtqSxyJUZgEAAAAAgIiIblVB/bH4+vr2GwwG4/79+70PHjzorVarw1esWHHBx8fHUVhYKLZareyuri4uwzAWIup2zTt16hSvoaFBkJiYKCMi6u/vp4CAgEGrsi5qtbqtqKhIFB0dfaGkpMRfp9Ndb0FOT0/vICJaunRpx6pVq0KIiA4fPuzT2NjId93T3d3NMZvNLC8vL+ed/EadTudZVlbWQET03HPPtb/xxhvXk9bk5OQuNptNM2bMsFy6dMn9TuKOZUhmAQAAAABgxHG5XEpKSupJSkrqUSqVluLiYlFdXZ3HsWPHjFKptC87OzvYarV+r7PU6XSypFKppaqqqvZ210lPT++MiooK2rFjh3nvUwRRAAAgAElEQVTatGlmkUjkcI2xWKybElSn00lVVVU1fD7/lslrRESE5ejRo56xsbHW290PEdHA2E7nHeXIYxrajAEAAAAAYETpdDqeXq/nuf6urKwUSKVSGxGRWCy2d3d3s0tKSm46iVipVFo7Ojq4paWlnkTX2o5PnDjBv/G+gby9vfsTEhJMubm5oRkZGe0Dx7Zu3SokIiouLhbGxMSYiYgSEhJM69atu36I05EjRwRDxV6+fPnF/Pz8IIPBwCMistvt9NprrwUSEUVHR5s/+OADIRHRxo0bx8XFxfXc6rnA8FCZBQAAAACAEWUymTgajSbUZDJxOByOUyKR2LZs2XLWz8/PzjCMIiQk5KpKpbrpXVQ+n+/UarWNGo0mtKenh+NwOFhZWVmtt6qMLlq0qKO8vNw3OTnZNPB6b28vOyoqKtJ1ABQR0fvvv38uIyMjVCaTiRwOB2vWrFk9s2bNOjdY3ISEBMvq1avPP/PMMw9YrVY2i8WiJ598souIaMOGDeeeffZZSUFBgdh1ANStnktjYyM/MDBQ6fp7/fr1g647VrFQxgYAAAAAGLt0Ot0ZlUrVdus7fz5efvllsc1mYxUUFLS4rgUGBiqrq6urB7Ydw49Pp9OJVCqV5G7mojILAAAAAABjRmJiorS5udn90KFDdSO9F/hhkMwCAAAAAMCYUVZWdnqw662traduN0ZhYaGoqKho/MBr8fHxPZs3bx6R06DHKrQZAwAAAACMYWOxzRjuHz+kzRinGQMAAAAAAMCog2QWAAAAAAAARh0kswAAAAAAADDqIJkFAAAAAACAUQfJLAAAAAAAjLi8vDyxVCpVyGQyRi6XM2VlZZ5D3ZuSkiLZtGmT/1DjCxcuDJXL5Ux4eLiCz+dPk8vljFwuZ4abc69otVpfhUIRGR4erggLC1NkZWVNuJs4BoOBJ5fLmXu9v58TfJoHAAAAAABGVGlpqeeBAwf89Hq9USAQOFtaWrg2m411t/G2bdt2joiorq7OPSkpaXJtba3x3u12aF999ZUgLy9vYklJSYNSqbT19fVRYWFhwE+x9liEZBYAAAAAAIiIqPnlVybaGho87mVM3uTJvcFrVg/7/dWmpiY3oVBoFwgETiKioKAgOxFRTk5O0P79+/1sNhs7NjbWvH379rNs9vebSysqKjyys7Mn9vb2sv39/e3bt28/M2nSpL7B1tHpdLy0tLQH9Hp9DRHRN998w1er1WF6vb4mMDBQmZKS0l5RUeHDYrGcWq32W4Zhrp4/f567ePHiSc3Nze4sFoveeuutc4899tiVweKvWbNGnJub26JUKm1ERG5ubpSXl3eZiKi2ttZdrVZLOjs7uSKRqG/btm1nwsPD+55++ukwoVBor6qq8rx8+bLbmjVrzi9atKhrsPhnzpxxS05ODj916lRtRUWFx0MPPRT53XffnZJIJH0TJkyIamhoMHh4eIyZb6+izRgAAAAAAEbUnDlzTM3Nze4SiWRKWlpa6L59+7yIiHJzcy8ZDIaahoaGaovFwtZqtb4D59lsNpZGownds2dPY3V1dY1arW7LyckZsq1XpVLZeDxe//Hjx/lEREVFRaK0tLTr39j19/d36PX6moyMjMsajWYiEVFmZmZoXl7eRYPBULNr167GzMxMyVDx6+rqBPHx8YMmukuWLJmUnp7eVl9fb5w7d27n888/P9E11tbWxj158mTt7t27T7/66qtD7l8ikfT19PRwTCYTu7y83EuhUPT+/e9/96quruaJxeKrYymRJUJlFgAAAAAA/ulWFdQfi6+vb7/BYDDu37/f++DBg95qtTp8xYoVF3x8fByFhYViq9XK7urq4jIMYyGibte8U6dO8RoaGgSJiYkyIqL+/n4KCAgYtCrrolar24qKikTR0dEXSkpK/HU63fUW5PT09A4ioqVLl3asWrUqhIjo8OHDPo2NjXzXPd3d3Ryz2czy8vK6o8RRp9N5lpWVNRARPffcc+1vvPHG9aQ1OTm5i81m04wZMyyXLl1yHy5OTEzMlYMHD3odPnzYe9myZS1///vffSwWC3vmzJnmO9nPzwGSWQAAAAAAGHFcLpeSkpJ6kpKSepRKpaW4uFhUV1fncezYMaNUKu3Lzs4Otlqt3+ssdTqdLKlUaqmqqqq93XXS09M7o6Kignbs2GGeNm2aWSQSOVxjLBbrpgTV6XRSVVVVDZ/Pv2XyGhERYTl69KhnbGys9Xb3Q0Q0MLbTOfwys2fP7vniiy+8Ll686LZgwYKuN998U3z16lXWvHnzOu9kzZ8DtBkDAAAAAMCI0ul0PL1ez3P9XVlZKZBKpTYiIrFYbO/u7maXlJTcdBKxUqm0dnR0cEtLSz2JrrUdnzhxgn/jfQN5e3v3JyQkmHJzc0MzMjLaB45t3bpVSERUXFwsjImJMRMRJSQkmNatW3f9EKcjR44Ihoq9fPnyi/n5+UEGg4FHRGS32+m1114LJCKKjo42f/DBB0Iioo0bN46Li4vrudVzGczjjz9u/vjjj8dJpVKrm5sbeXp6Or788kufxMREVGYBAAAAAAB+SiaTiaPRaEJNJhOHw+E4JRKJbcuWLWf9/PzsDMMoQkJCrqpUqpveReXz+U6tVtuo0WhCe3p6OA6Hg5WVldV6q8rookWLOsrLy32Tk5NNA6/39vayo6KiIl0HQBERvf/+++cyMjJCZTKZyOFwsGbNmtUza9asc4PFTUhIsKxevfr8M88884DVamWzWCx68sknu4iINmzYcO7ZZ5+VFBQUiF0HQN3quTQ2NvIDAwOVrr/Xr19/btGiRV0Oh4P14IMP9hARxcfHmzs6OrhCobD/VvF+bli3KmMDAAAAAMDPl06nO6NSqdpufefPx8svvyy22WysgoKCFte1wMBAZXV1dfXAtmP48el0OpFKpZLczVxUZgEAAAAAYMxITEyUNjc3ux86dKhupPcCPwySWQAAAAAAGDPKyspOD3a9tbX11O3GKCwsFBUVFY0feC0+Pr5n8+bNI3Ia9FiFNmMAAAAAgDFsLLYZw/3jh7QZ4zRjAAAAAAAAGHWQzAIAAAAAAMCog2QWAAAAAAAARh0kswAAAAAAADDqIJkFAAAAAIARl5eXJ5ZKpQqZTMbI5XKmrKzMc6h7U1JSJJs2bfIfanzhwoWhcrmcCQ8PV/D5/GlyuZyRy+XMcHPuhcLCQhGbzY45ceIE33UtLCxM0djY6PZjrjtW4dM8AAAAAAAwokpLSz0PHDjgp9frjQKBwNnS0sK12Wysu423bdu2c0REdXV17klJSZNra2uN9263wwsMDLy6atWqoM8///y7n2rNsQrJLAAAAAAAEBHRwa01EzuazB73MqZwglfvY4sih/3+alNTk5tQKLQLBAInEVFQUJCdiCgnJydo//79fjabjR0bG2vevn37WTb7+82lFRUVHtnZ2RN7e3vZ/v7+9u3bt5+ZNGlS32Dr6HQ6Xlpa2gN6vb6GiOibb77hq9XqML1eXxMYGKhMSUlpr6io8GGxWE6tVvstwzBXz58/z128ePGk5uZmdxaLRW+99da5xx577MpQv+WXv/xl15dffuljMBh4U6ZMsQ0c+/jjj33WrFkTfPXqVVZYWJjto48+OnPs2DHBm2++GfiXv/zl282bN/tlZWWFdXV1VdlsNlZUVBRz9uxZw2096DEIbcYAAAAAADCi5syZY2pubnaXSCRT0tLSQvft2+dFRJSbm3vJYDDUNDQ0VFssFrZWq/UdOM9ms7E0Gk3onj17Gqurq2vUanVbTk7OhKHWUalUNh6P13/8+HE+EVFRUZEoLS3t+jd2/f39HXq9viYjI+OyRqOZSESUmZkZmpeXd9FgMNTs2rWrMTMzUzLcb2Gz2aTRaC6uWrVKPPB6U1MTd/369UEVFRX1RqOxZsqUKb1r1qwZ/9BDD/Xq9XoPIqIvv/zSOzw83Hr48GGP8vJyz6lTpw6ZNAMqswAAAAAA8E+3qqD+WHx9ffsNBoNx//793gcPHvRWq9XhK1asuODj4+MoLCwUW61WdldXF5dhGAsRdbvmnTp1itfQ0CBITEyUERH19/dTQEDAoFVZF7Va3VZUVCSKjo6+UFJS4q/T6a63IKenp3cQES1durRj1apVIUREhw8f9mlsbLz+Dmx3dzfHbDazvLy8nEOtkZWV1fHmm28GNTQ0uLuulZWVeZ0+fZo/ffp0ORFRX18fKy4uzszj8ZzBwcFX9Xo9T6fTeTz//POt5eXlXleuXOHMnj27544f5hiCZBYAAAAAAEYcl8ulpKSknqSkpB6lUmkpLi4W1dXVeRw7dswolUr7srOzg61W6/c6S51OJ0sqlVqqqqpqb3ed9PT0zqioqKAdO3aYp02bZhaJRA7XGIvFuilBdTqdVFVVVcPn84dMXm/E4/GcWVlZrX/4wx+uV2edTic9/PDDps8+++ymd2lnzpxp/vTTT335fH7/U089ZUpPT5dYrVb2u+++e+521xyL0GYMAAAAAAAjSqfT8fR6Pc/1d2VlpUAqldqIiMRisb27u5tdUlJy00nESqXS2tHRwS0tLfUkutZ2PPAk4cF4e3v3JyQkmHJzc0MzMjLaB45t3fr/2LvTuKbPrG/gJwshgUAw7FuICiEkhEgBxa0WnKqt1NuPFDuuoK1VrGLr6GCVMq1ttfYWx2WmOkqlFoq0xd61Wh9tEbeK4wiFyBZALSCgWEFIIBDI8rzoxKFWcR+k/L6v5Ppfy8n/3fGcXPlUSES0a9cuYUhISBsR0ejRozUbNmxwtszJy8vj3ctnWrZs2fVjx47Zt7a2somIIiIi2s6ePcsvKyvjEBFpNBqm5TM/88wz2u3bt7uOGDGiXSQSGX7++Wermpoa6+Dg4M57OWugQmUWAAAAAAD6lEajYSUkJIg0Gg2LxWKZxWKxfs+ePTUODg4GmUwm9/Ly6lIqlb/5/iiXyzVnZWVdTEhIEGm1WpbRaGTEx8c3hoaG9poEzp07t/nYsWOCKVOmaHqO63Q6pkKhCLBcAEVElJqaWjt//nyRRCJxMhqNjFGjRmlHjRp114opj8czv/zyyz+/8847XkRE3t7eho8++qhm+vTpQ7u7uxlERO+88069QqHQR0ZGtv/8889WzzzzjJaISCqVdrS2trLu/Q0OTAyz+Z6r5QAAAAAA8DujUqmqlUrl9bvP/P1YvXq1m16vZ6SkpFyxjLm6ugaVlpaW9mw7hsdPpVI5KZVK8YOsRWUWAAAAAAAGjMjISN+GhgbOiRMnKvo6Fng4SGYBAAAAAGDAyM3NvXC78cbGxvP3usemTZucdu7c6dJzLDw8XPvJJ5/0yW3QAxXajAEAAAAABrCB2GYMT46HaTPGbcYAAAAAAADQ7yCZBQAAAAAAgH4HySwAAAAAAAD0O0hmAQAAAAAAoN9BMgsAAAAAAH0uMTHRzdfXVy6RSGRSqVSWm5tre6e50dHR4rS0tEF3ej5nzhyRVCqVDR06VM7lcp+SSqUyqVQq623No7Jnzx4HiUQiGzJkiFwikcgyMzMFlmebN292rK2tvfmLMq6urkHXr19nPe6Yfq/w0zwAAAAAANCncnJybI8cOeJQXFxcxuPxzFeuXGHr9XrGg+6Xnp5eS0RUUVHBiYqK8lOr1WWPLto7++GHH2ySk5O9vv/++0qJRNJVWlpqPXHiRIlEIqkKDQ3tTE9Pdxo+fLhOJBIZ/hvx/N4hmQUAAAAAACIiOrJ9s/f1yzU2j3JPJ28f3cT413v9/dX6+noroVBo4PF4ZiIid3d3AxHRihUr3A8fPuyg1+uZoaGhbZ999lkNk/nr5tJTp07ZLF++3Fun0zEHDRpk+Oyzz6p9fHy6b3eOSqWynj179pDi4uJyIqIff/yRGxsbO7i4uLjc1dU1KDo6uunUqVP2DAbDnJWVdUkmk3VdvnyZ/fLLL/s0NDRwGAwGbd68uXb8+PHtt9t/w4YNritXrrwikUi6iIjkcrl+2bJlVz744AO3iRMntpaXl9vMnDlzKJfLNRUVFZUTEa1bt871//2//+dgNBopOzv7YlBQkP4+X/GAhTZjAAAAAADoU1OnTtU0NDRwxGJx4OzZs0Xffvstn4ho5cqV10pKSsqrqqpKOzo6mFlZWYKe6/R6PSMhIUG0f//+i6WlpeWxsbHXV6xY4Xmnc5RKpd7a2tp07tw5LhHRzp07nWbPnn3zN3YHDRpkLC4uLp8/f/7PCQkJ3kREixYtEiUmJl4tKSkpz87Ovrho0SLxnfavqKjghYeH/yrRDQ8P11VUVPAWLFhwIyAgQJeZmXlRrVaXcblcMxGRq6trd3l5edncuXOvf/DBB64P8PoGLFRmAQAAAACAiIjuVkF9XAQCgamkpKTs8OHDdkePHrWLjY0dmpycXGdvb2/ctGmTW2dnJ7OlpYUtk8k6iKjVsu78+fPWVVVVvMjISAkRkclkImdn59tWZS1iY2Ov79y502nYsGF1Bw4cGKRSqW62IMfFxTUTES1cuLB57dq1XkREp0+ftr948SLXMqe1tZXV1tbG4PP55tvtz2D8ujvabDYTg8G47VwiopkzZ94gIho+fHj7kSNHBHeaB7+FZBYAAAAAAPocm82mqKgobVRUlDYoKKhj165dThUVFTZnz54t8/X17V6+fLlHZ2fnrzpLzWYzw9fXt6OoqEh9r+fExcXdUCgU7pmZmW1PPfVUm5OTk9Hy7HZJp9lspqKionJLJbU3Eomk88yZM7YhISGdlrF//etfNhKJpPNOayyt1SwWi4xG4wN/T3ggQpsxAAAAAAD0KZVKZV1cXGxt+buwsJDn6+urJyJyc3MztLa2Mg8cOPCbm4iDgoI6m5ub2Tk5ObZEv7Qd5+fnc2+d15OdnZ1p9OjRmpUrV4rmz5/f1PPZp59+KiQi2rVrlzAkJKSNiGj06NGaDRs2OFvm5OXl8e6095///OerGzdudK+qquIQEZWVlXG2bNnilpiYeJWIyNbW1qTRaHB78SOCyiwAAAAAAPQpjUbDSkhIEGk0GhaLxTKLxWL9nj17ahwcHAwymUzu5eXVpVQqf3PpEpfLNWdlZV1MSEgQabValtFoZMTHxzeGhobesRJKRDR37tzmY8eOCaZMmaLpOa7T6ZgKhSLAcgEUEVFqamrt/PnzRRKJxMloNDJGjRqlHTVqVO3t9n366ad1ycnJ9c8//7yfwWAgKysr87p16+rCwsI6/33u9UWLFol7XgAFD45hNt+1Wg4AAAAAAL9TKpWqWqlUXr/7zN+P1atXu+n1ekZKSsoVy5irq2tQaWlpac+2Y3j8VCqVk1KpFD/IWlRmAQAAAABgwIiMjPRtaGjgnDhxoqKvY4GHg2QWAAAAAAAGjNzc3Au3G29sbDx/r3ts2rTJaefOnS49x8LDw7WffPJJn9wGPVChzRgAAAAAYAAbiG3G8OR4mDZj3GYMAAAAAAAA/Q6SWQAAAAAAAOh3kMwCAAAAAABAv4NkFgAAAAAAAPodJLMAAAAAANDnEhMT3Xx9feUSiUQmlUplubm5tneaGx0dLU5LSxt0p+dz5swRSaVS2dChQ+VcLvcpqVQqk0qlst7WPCyj0UgCgWBYc3Mzk4jo4sWLVgwGI+To0aO2REQmk4kcHByGXb9+nZWQkODh4uISJJVKZT4+PoETJ04cWlhYyH1csf1e4ad5AAAAAACgT+Xk5NgeOXLEobi4uIzH45mvXLnC1uv1jAfdLz09vZaIqKKighMVFeWnVqvLHl20t8disUihULQfP36cP23aNE1ubi4/ICBAd+rUKf748ePbf/zxR66Li0uXk5OTkYhoyZIlV5OTk68REf3jH/8QTpgwQVJcXFzq5uZmfNyx/l4gmQUAAAAAACIias6u9O6+2m7zKPe0crPVCV+U9Pr7q/X19VZCodDA4/HMRETu7u4GIqIVK1a4Hz582EGv1zNDQ0PbPvvssxom89fNpadOnbJZvny5t06nYw4aNMjw2WefVfv4+HTf7hyVSmU9e/bsIcXFxeVERD/++CM3NjZ2cHFxcbmrq2tQdHR006lTp+wZDIY5Kyvrkkwm67p8+TL75Zdf9mloaOAwGAzavHlz7fjx49tvt394eHjbDz/8wJ82bZomLy+P/9prrzUeOHDAgYjoxIkT/NDQ0NuuW7hwYfPBgwcFaWlpwjfffPPnu7xS+De0GQMAAAAAQJ+aOnWqpqGhgSMWiwNnz54t+vbbb/lERCtXrrxWUlJSXlVVVdrR0cHMysoS9Fyn1+sZCQkJov37918sLS0tj42Nvb5ixQrPO52jVCr11tbWpnPnznGJiHbu3Ok0e/bsm7+xO2jQIGNxcXH5/Pnzf05ISPAmIlq0aJEoMTHxaklJSXl2dvbFRYsWie+0/5gxY9rOnj1rS0RUVFRkGxsbe6Ours6aiOjMmTP8UaNGtd1pbXBwsE6tVqPV+D6gMgsAAAAAAEREdLcK6uMiEAhMJSUlZYcPH7Y7evSoXWxs7NDk5OQ6e3t746ZNm9w6OzuZLS0tbJlM1kFErZZ158+ft66qquJFRkZKiH75Xqqzs/Ntq7IWsbGx13fu3Ok0bNiwugMHDgxSqVQ3W5Dj4uKaiX6plK5du9aLiOj06dP2Fy9evJlktra2stra2hh8Pt98694RERHtsbGxtq2trUwiIj6fb/bw8OiqqKjg5Ofn899+++0rd4rLbP7NdnAXSGYBAAAAAKDPsdlsioqK0kZFRWmDgoI6du3a5VRRUWFz9uzZMl9f3+7ly5d7dHZ2/qqz1Gw2M3x9fTuKiorU93pOXFzcDYVC4Z6Zmdn21FNPtVm+w0pExGAwfpNRms1mKioqKudyuXfNNgUCgcnDw6Prb3/7m5NSqWwnIho+fHhbdna2Q1tbGyswMFB/p7VFRUU2o0ePvmPlFn4LbcYAAAAAANCnVCqVdXFxsbXl78LCQp6vr6+eiMjNzc3Q2trKPHDgwG9uIg4KCupsbm5m5+Tk2BL90nacn5/fa6uunZ2dafTo0ZqVK1eK5s+f39Tz2aeffiokItq1a5cwJCSkjYho9OjRmg0bNjhb5uTl5fF62z8sLKxtx44dLiNHjmwnIhozZkz7jh07XIKDg++YqKampg765z//aWepDMO9QWUWAAAAAAD6lEajYSUkJIg0Gg2LxWKZxWKxfs+ePTUODg4GmUwm9/Ly6rJUOnvicrnmrKysiwkJCSKtVssyGo2M+Pj4xtDQ0M7ezps7d27zsWPHBFOmTNH0HNfpdEyFQhFguQCKiCg1NbV2/vz5IolE4mQ0GhmjRo3Sjho1qvZOe48ePbotPT3d+Zlnnmkj+iWZvXr1KicuLu5XFzv97W9/c8vMzHTq6Ohg+vv7d3z33XeVuMn4/jDQmw0AAAAAMHCpVKpqpVJ5/e4zfz9Wr17tptfrGSkpKTe/w+rq6hpUWlpa2rPtGB4/lUrlpFQqxQ+yFpVZAAAAAAAYMCIjI30bGho4J06cqOjrWODhIJkFAAAAAIABIzc398LtxhsbG8/f6x6bNm1y2rlzp0vPsfDwcO0nn3zSJ7dBD1RoMwYAAAAAGMAGYpsxPDkeps0YtxkDAAAAAABAv4NkFgAAAAAAAPodJLMAAAAAAADQ7yCZBQAAAACAPpeYmOjm6+srl0gkMqlUKsvNzbW909zo6GhxWlraoDs9nzNnjkgqlcqGDh0q53K5T0mlUplUKpX1tgb6H9xmDAAAAAAAfSonJ8f2yJEjDsXFxWU8Hs985coVtl6vZzzofunp6bVERBUVFZyoqCg/tVpd9uiihScFKrMAAAAAANCn6uvrrYRCoYHH45mJiNzd3Q1isbh7xYoV7oGBgQF+fn7yGTNm+JhMpt+sPXXqlE1YWJi/XC4PGDNmjF9NTY3Vnc5RqVTWCoUiwPL3jz/+yLX87erqGrR48WJPhUIREBQUJC0rK+MQEV2+fJk9YcKEoYGBgQEKhSLg6NGjd6wYJyQkeEyfPt0nLCzM38vLS7F+/Xpny7PIyEhfuVwe4OvrK9+0aZMTEVF3dzfZ2dkNW7x4sae/v79s2LBh0vr6ehQc7xFeFAAAAAAAEBHR119/7X3t2jWbR7mni4uLburUqb3+/urUqVM169ev9xCLxYFjxozRzJgxo3ny5MltK1euvLZx48Yr/54zOCsrSzBz5sxWyzq9Xs9ISEgQffvttxc8PDwMu3btGrRixQrPL7/8svp25yiVSr21tbXp3Llz3LCwsM6dO3c6zZ49++bPEg0aNMhYXFxcvnnzZseEhATvnJyci4sWLRIlJiZeHT9+fLul0ltVVVV6p89y8eJF7unTpyubmppYcrk8cOXKlT+z2Wzau3fvT66urkatVsscNmxYwJw5c244ODgY29raWM8884z2o48+qn/llVe8/v73vzutW7fu6n2/6AEIySwAAAAAAPQpgUBgKikpKTt8+LDd0aNH7WJjY4cmJyfX2dvbGzdt2uTW2dnJbGlpYctksg4iupnMnj9/3rqqqooXGRkpISIymUzk7Ozc3dtZsbGx13fu3Ok0bNiwugMHDgxSqVQ3W5Dj4uKaiYgWLlzYvHbtWi8iotOnT9tfvHiRa5nT2trKamtrY/D5fPPt9p80aVIrl8s1e3p6GgQCgaGhoYEtEokM69atcz18+LADEVFjYyOnvLzceuTIkToul2uaPn26hogoJCREd+rUKf4Dv8gBBsksAAAAAAAQEdHdKqiPE5vNpqioKG1UVJQ2KCioY9euXU4VFRU2Z8+eLfP19e1evny5R2dn56++Jmk2mxm+vr4dRUVF6ns9Jy4u7oZCoXDPzMxse+qpp9qcnJyMlmcMBuM3CarZbKaioqJyLpd72+T1VtbW1jd7oZlMprm7u5vx9ddf2+Xl5dkVFBSU8/l8c0hIiH9HRwfz35/75r4sFstsNBof+LvCAw2+MwsAAAAAAH1KpVJZFxcXW1v+Liws5Pn6+uqJiMBBCbUAACAASURBVNzc3Aytra3MAwcO/OYm4qCgoM7m5mZ2Tk6OLdEvbcf5+fncW+f1ZGdnZxo9erRm5cqVovnz5zf1fPbpp58KiYh27dolDAkJaSMiGj16tGbDhg03v/ual5fHu9/P19LSwnJwcDDw+Xxzfn4+t7i4+I7fu4V7h8osAAAAAAD0KY1Gw0pISBBpNBoWi8Uyi8Vi/Z49e2ocHBwMMplM7uXl1aVUKttvXcflcs1ZWVkXExISRFqtlmU0Ghnx8fGNoaGhnb2dN3fu3OZjx44JpkyZouk5rtPpmAqFIoDBYJizsrIuERGlpqbWzp8/XySRSJyMRiNj1KhR2lGjRtXez+ebPn16a2pqqrO/v7/M19e3Mygo6DefBe4fw2y+p2o5AAAAAAD8DqlUqmqlUnn97jN/P1avXu2m1+sZKSkpVyxjrq6uQaWlpaU9247h8VOpVE5KpVL8IGtRmQUAAAAAgAEjMjLSt6GhgXPixImKvo4FHg6SWQAAAAAAGDByc3Mv3G68sbHx/L3usWnTJqedO3e69BwLDw/XfvLJJ312gdZAhDZjAAAAAIABbCC2GcOT42HajHGbMQAAAAAAAPQ7SGYBAAAAAACg30EyCwAAAAAAAP0OklkAAAAAAADod5DMAgAAAABAn0tMTHTz9fWVSyQSmVQqleXm5tquXbvWRavVPrKcJT093aGgoIDb25zo6Gixp6enQiqVymQyWUBOTo7tozofHi0kswAAAAAA0KdycnJsjxw54lBcXFxWWVlZduzYscohQ4Z0/eMf/3Bta2u7bc5iMBju+5yvv/7a4fz587y7zXvvvffq1Gp12XvvvVe/ePFin/s+CP4rkMwCAAAAAECfqq+vtxIKhQYej2cmInJ3dzdkZGQMunbtmtW4ceMkI0aMkBAR2djYBL/++useQUFB0qNHj/JPnTplExYW5i+XywPGjBnjV1NTY0VEVFpaaj127Fg/uVweEBIS4l9YWMj9/vvvbXNychySkpK8pFKprLS01PpucU2aNEl7+fJlayKilJQUp8DAwAB/f3/ZxIkTh2q1WmZTUxPL09NTYTQaiYhIq9Uy3dzcgvR6PeN2MTy2FzhAsfs6AAAAAAAAeDKUlSd6t7dV2jzKPW35Ep0sYMPl3uZMnTpVs379eg+xWBw4ZswYzYwZM5qTkpKubd++3fXEiROV7u7uBiKijo4OZmBgYMfmzZsb9Ho9Izw83P/bb7+94OHhYdi1a9egFStWeH755ZfVr7zyis/OnTtrFAqFPjc31zY+Pl70z3/+s/IPf/hDS1RUVOu8efNu3EvsWVlZDn5+fh1ERLN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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "alpha_100 = np.logspace(-4, 2, 100)\n", + "coef = []\n", + "for i in alpha_100:\n", + " ridge.set_params(alpha = i)\n", + " ridge.fit(x_onehot, y_log)\n", + " coef.append(ridge.coef_)\n", + "\n", + "df_coef = pd.DataFrame(coef, index=alpha_100, columns=x_onehot.columns)\n", + "title = 'Ridge coefficients as a function of the regularization'\n", + "axes = df_coef.plot(logx=True, title=title)\n", + "axes.set_xlabel('alpha')\n", + "axes.set_ylabel('coefficients')\n", + "plt.rcParams['figure.figsize'] = 16, 4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### test prediction" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "best_ridge_test_y = para_search_ridge.best_estimator_.predict(test_onehot)\n", + "best_ridge_test_y = np.expm1(best_ridge_test_y)\n", + "best_ridge_test_y\n", + "sub = pd.DataFrame()\n", + "sub['SalePrice'] = best_ridge_test_y\n", + "sub = sub.set_index(np.arange(1461, 2920))\n", + "sub.to_csv('(1) ridge_log(y)_onehot_submission.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lasso regularization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Find best lambda" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'alpha': 0.0001232846739442066}\n", + "Lowest RMSE found: 0.12730800492203845\n" + ] + } + ], + "source": [ + "lasso = linear_model.Lasso(normalize=True) # create a ridge regression instance\n", + "\n", + "# find the best alpha (lambda) for lasso \n", + "grid_param = [{'alpha': np.logspace(-4.5, 2, 100)}]\n", + "para_search_lasso = GridSearchCV(estimator=lasso, param_grid=grid_param, scoring='neg_mean_squared_error', cv=5, return_train_score=True)\n", + "para_search_lasso.fit(x_onehot, y_log)\n", + "\n", + "print(para_search_lasso.best_params_)\n", + "print(\"Lowest RMSE found: \", np.sqrt(np.abs(para_search_lasso.best_score_)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### RMSE for all train data using best lasso" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.11676135911841964\n" + ] + } + ], + "source": [ + "# fit best lasso equation to all train data \n", + "best_lasso_y = para_search_lasso.best_estimator_.predict(x_onehot)\n", + "print(\"RMSE: \", np.sqrt(np.mean((y_log-best_lasso_y)**2)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### RMSE across different grid search lambda" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "error = pd.DataFrame(para_search_lasso.grid_scores_)['mean_validation_score']\n", + "error = np.sqrt(np.abs(error))\n", + "alpha = pd.DataFrame(para_search_lasso.grid_scores_)['parameters'].apply(lambda x: x.get('alpha'))\n", + "plt.scatter(alpha, error)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Variable importance for best lambda" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pd.DataFrame(list(zip(x_onehot.columns, para_search_lasso.best_estimator_.coef_))).set_index(0).sort_values(1).plot.barh()\n", + "plt.rcParams['figure.figsize'] = 4, 36" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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fz7oWi4XxdL03wDZmAAAAAAAYl1JSUkwlJSViIiKtVstvbW3lJSQk9EVFRfXX1NSIHA4HGQwGrlarveebimfMmGE6cOCAmIjowIEDEvd5o9HIBgYG2vh8vuvYsWMTbty4wSMi8vf3d/T29g5aTw2V12jcc1xcnDUhIaE3Ly8v1F3QV1VV8UtKSiY98cQTphMnTkzq6elhuru7mRMnTgSkpaX1DBVLLBY7Q0JC+v/0pz9NIiKyWCycnp4er6kRveZGAAAAAADg4bJx48Z2h8PBiYmJUT733HNRRUVFjUKh0JWenm6SSqVWuVwet27dOqlSqTTfK9aePXua9u/fHxwfHx9rNBrvtExXrVp1S6PR+MbHx8eWlJSIp06d2kdEFBIS4lCr1abo6Oi43Nzc8OHkNVr3XVJS0tjW1saNiIiIj4mJUa5cuTJSKpX2p6SkmJcuXdo5Y8aMWLVaHbtixYqbc+bMsdwj1vX33nsvOCYmRjlz5kxFc3Oz1+z+5Xh76xoAAAAAAP53NBpNo0ql6hjrPODho9FoAlUqVeT9zEVnFwAAAAAAALyO17SoAQAAAAAAxqOEhARFf3//dxqRxcXF15OSkjxuQQbPUOwCAAAAAACMIa1Wqx/rHLwRtjEDAAAAAACA10GxCwAAAAAAAF4HxS4AAAAAAAB4HRS7AAAAAAAA4HVQ7AIAAAAAwAOPZVm1QqFQuj91dXW8H3I9kUiU6Gm8o6OD3bZtW5D7uLGxkZuRkTFtNHPYtWuXJCYmRhkTE6OMjo6OKykpmURElJWVFfnHP/4xwNPcpKQkeUVFhWg08xlv8DZmAAAAAAB44PH5fKder9eNdR5unZ2d7MGDB4M3bdp0k4goMjLSdurUqWujFb+hoYFbUFDwyJUrV2olEonDaDQyra2tqN9GAA8LAAAAAACG7X+Ka6W3vjGNasdQHOZnnp8T2zzSeWazmZOTkxOh1WpFLMvSjh07mhcvXtyze/duSWVlpW9xcXETEVFaWposPz+/LTMzs0ckEiWuXLmy/cyZM/4CgcBZWlpqkEqldr1ez8vOzp5mt9s58+fPN7rXMBqNTEZGhsxoNLJ2u52zefPmG8uXL7+dn58f3tzczFcoFMrU1NTuvLy89szMzOirV6/WeMqrtLR0ksViYZqamvgLFy68vW/fvpbB7q21tZXr6+vr9Pf3dxAR+fv7O/39/fvvvm7Dhg2PnDp1apLVamVmzpxpOnTo0NcM888NvO+//75k3bp1U0wmE7t///7raWlp5pE+4/EM25gBAAAAAOCBZ7VaGfcW5vT09Cgiou3btwcTEdXX1+sOHz587eWXX440m80cT3EsFguTnJxsqqur0yUnJ5sKCwuDiIjWrFkzZdWqVTerq6trQ0JCbO7rRSKR8/jx4wadTldbXl5e/+abb4Y7nU4qKChokUqlVr1erysqKvpOweopL51OJzpy5Mi12tramqNHjwYYDAbuYHk+/vjj5sDAQJtUKp2+ZMmSyMOHD/sPdt0bb7zRXl1dXXv16tUai8XC/OUvf7lzndlsZi5fvqzfvXv31y+//PLU4Txnb4LOLgAAAAAADNv9dGBHw2DbmC9cuOC3du3adiKixMTEvtDQ0P6qqiqBpzhcLteVnZ1tJCJSq9W9Z8+enUhEdOnSJb+TJ082EBHl5uZ2bt26NZyIyOl0ctavXx9+8eJFP4ZhqL29ndfS0uKxjvKUV0pKSrdEInEQEclksr6Ghga+TCaz3R3Dx8eHKioqrpaXl4vOnDkzcdOmTdLKykrfnTt33hh43cmTJyfs3LkzpK+vj7l9+7aPUqm0EJGRiGjp0qW3iIgWLlxoMplMTEdHBxsYGOjwlLs3QWcXAAAAAADGJZfLNeh5Hx8fl9PpvHNstVqZgWPubb4+Pj5kt9vvdIIZhvlewKKiInFnZ6dPVVVVrV6v10kkEpvFYvFYRw2VFxERj8e7M8iyrMtmsw3ZiWYYhtLS0sy/+93vvi0pKblWWlo6aeC42Wzm5OfnR/z3f/93Q319vW758uUdfX19d3LjcL4b+u5jb4diFwAAAAAAxqWUlBRTSUmJmIhIq9XyW1tbeQkJCX1RUVH9NTU1IofDQQaDgavVan3vFWvGjBmmAwcOiImIDhw4IHGfNxqNbGBgoI3P57uOHTs24caNGzwiIn9/f0dvb++g9dRQeY3k3hobG7n/+Mc/7nw3urKyUhQWFvad7+yazWaGiCgkJMRuNBqZY8eOfecNzX/+858DiIhOnz7tN2HCBIe7o/ywwDZmAAAAAAAYlzZu3Ni+YsWKiJiYGCXLslRUVNQoFApd6enppvfee88ql8vj5HK5RalU3vPFTHv27GnKzs6etmfPnslPPfVUl/v8qlWrbi1cuFAWHx8fGxcXZ546dWofEVFISIhDrVaboqOj45544gljXl5e+73yGsm99ff3czZs2BDe1tbG5fP5LrFYbDtw4EDTwGsCAwMdy5Ytu6lUKuPCw8P7VSpV78DxgIAAR2JiosL9gqqRrO8NOJ5a7AAAAAAAABqNplGlUnWMdR7w8NFoNIEqlSryfuZiGzMAAAAAAAB4HWxjBgAAAAAAGEMJCQmK/v7+7zQii4uLryclJVnGKidvgGIXAAAAAABgDGm1Wv1Y5+CNsI0ZAAAAAAAAvA6KXQAAAAAAAPA6KHYBAAAAAADA66DYBQAAAAAAAK+DYhcAAAAAAB54LMuqFQqF0v2pq6vj/ZDriUSiRE/jHR0d7LZt24Lcx42NjdyMjIxpo5nDrl27JDExMcqYmBhldHR0XElJySQiot27d0saGxu5o7mWN8LbmAEAAAAA4IHH5/Oder1eN9Z5uHV2drIHDx4M3rRp000iosjISNupU6eujVb8hoYGbkFBwSNXrlyplUgkDqPRyLS2tvoQEZWUlAQ++uijlsjISNtoreeNUOwCAAAAAMCwnd67S9rR/LVoNGMGSiPMP/nF+uaRzjObzZycnJwIrVYrYlmWduzY0bx48eKe3bt3SyorK32Li4ubiIjS0tJk+fn5bZmZmT0ikShx5cqV7WfOnPEXCATO0tJSg1Qqtev1el52dvY0u93OmT9/vtG9htFoZDIyMmRGo5G12+2czZs331i+fPnt/Pz88ObmZr5CoVCmpqZ25+XltWdmZkZfvXq1xlNepaWlkywWC9PU1MRfuHDh7X379rUMdm+tra1cX19fp7+/v4OIyN/f3+nv79//xz/+MaC6ulqUk5MzTSAQOCsrK2v/53/+x2/Tpk1Sh8NBKpXKXFxc/LVQKHSFhYVN/9nPftZ5+vRpf7vdzvnwww+vJSYm9nV3dzMrV66cUltbK3Q4HJxf/epXN5YvX377fv/9HlTYxgwAAAAAAA88q9XKuLcwp6enRxERbd++PZiIqL6+Xnf48OFrL7/8cqTZbOZ4imOxWJjk5GRTXV2dLjk52VRYWBhERLRmzZopq1atulldXV0bEhJyp2MqEomcx48fN+h0utry8vL6N998M9zpdFJBQUGLVCq16vV6XVFR0XcKVk956XQ60ZEjR67V1tbWHD16NMBgMAy6Hfnxxx83BwYG2qRS6fQlS5ZEHj582J+I6MUXX+yKj483FxcXX9Pr9TqGYSg3N3fqhx9+2FBfX6+z2+30zjvv3NleHRgYaNfpdLUvvfTSzW3btk0mInrzzTcfSUtL666urq797LPP6n7961+Hd3d3e11tiM4uAAAAAAAM2/10YEfDYNuYL1y44Ld27dp2IqLExMS+0NDQ/qqqKoGnOFwu15WdnW0kIlKr1b1nz56dSER06dIlv5MnTzYQEeXm5nZu3bo1nIjI6XRy1q9fH37x4kU/hmGovb2d19LS4rGO8pRXSkpKt0QicRARyWSyvoaGBr5MJvvedmQfHx+qqKi4Wl5eLjpz5szETZs2SSsrK3137tx5Y+B1Go1GEB4ebk1ISLASEb3wwgud7733XjARtRMRLV26tIuIKCkpyXz06NEAIqJz585NPH369KTdu3eHEBFZrVaOwWDgzZgxo8/TfY03KHYBAAAAAGBccrlcg5738fFxOZ3OO8dWq5UZOMYwjPtvstvtdzrBDMN8L2BRUZG4s7PTp6qqqpbP57vCwsKmWywWj13QofIiIuLxeHcGWZZ12Wy2ITvRDMNQWlqaOS0tzbxw4cLuVatWRd5d7Hpai4hIIBC4iP553+57dblc9PHHHxtUKpXV4+Rxzuta1QAAAAAA8HBISUkxlZSUiImItFotv7W1lZeQkNAXFRXVX1NTI3I4HGQwGLhardb3XrFmzJhhOnDggJiI6MCBAxL3eaPRyAYGBtr4fL7r2LFjE27cuMEjIvL393f09vYOWk8NlddI7q2xsZH7j3/84853oysrK0VhYWH9RER+fn4Oo9HIEhE9+uijfd988w2vurqaT0RUXFwsmTt3bo+n2Glpad0FBQWT3f8hcP78eeFIchsvUOwCAAAAAMC4tHHjxnaHw8GJiYlRPvfcc1FFRUWNQqHQlZ6ebpJKpVa5XB63bt06qVKpNN8r1p49e5r2798fHB8fH+suJImIVq1adUuj0fjGx8fHlpSUiKdOndpHRBQSEuJQq9Wm6OjouNzc3PDh5DWSe+vv7+ds2LAhfOrUqXEKhUL58ccfB7z77rvNREQ5OTkda9eujVAoFEqn00n79u1rfPbZZ6NiYmKUDMPQhg0bbnqKvW3btht2u52jUCiU0dHRcb/+9a/DRpLbeMG5V9sbAAAAAAAebhqNplGlUnWMdR7w8NFoNIEqlSryfuaiswsAAAAAAABeBy+oAgAAAAAAGEMJCQmK/v7+7zQii4uLryclJVnGKidvgGIXAAAAAABgDGm1Wv1Y5+CNsI0ZAAAAAAAAvA6KXQAAAAAAAPA6KHYBAAAAAADA66DYBQAAAAAAAK+DYhcAAAAAAB54LMuqFQqF0v2pq6vj/ZDriUSiRE/jHR0d7LZt24Lcx42NjdyMjIxpo7X+1q1bg1966SWp+3jp0qURs2fPjnEfv/3228EvvPCCdPDZw5eXlxe6efPmyf/bOA8iFLsAAAAAAPDA4/P5Tr1er3N/5HJ5/1jm09nZyR48eDDYfRwZGWk7derUtdGKP2/ePNOXX37p5z6urq4Wdnd3s3a7nYiILl686DtnzhzTaK3njfDTQwAAAAAAMGy3Pq6X2r7tFY1mTG6Ir1m8JKZ5pPPMZjMnJycnQqvViliWpR07djQvXry4Z/fu3ZLKykrf4uLiJiKitLQ0WX5+fltmZmaPSCRKXLlyZfuZM2f8BQKBs7S01CCVSu16vZ6XnZ09zW63c+bPn290r2E0GpmMjAyZ0Whk7XY7Z/PmzTeWL19+Oz8/P7y5uZmvUCiUqamp3Xl5ee2ZmZnRV69erfGUV2lp6SSLxcI0NTXxFy5ceHvfvn0tg93b7NmzzY2NjXyTycSxWq2MUCh0Tp061frFF18IZ8+ebfnqq6/8fv/737cQEb311luTDx06FEhEtGLFipubN29u93T+l7/8ZciHH34YGBoa2i+RSGyJiYnmkT778QDFLgAAAAAAPPCsViujUCiURERSqdRaVlbWsH379mAiovr6et3ly5cFixYtim5oaKj2FMdisTDJycmmwsLCb1avXh1eWFgYtGPHjtY1a9ZMWbVq1c1XX32183e/+92d7ckikch5/Phxg1gsdra2tvrMmjVLsXTp0tsFBQUtmZmZQr1eryMiGrit2lNeOp1OpNFodEKh0CmTyeI3bNjQJpPJbHfnyeVyKTY21vLZZ5/5ms1mRq1W90ZHR1srKir8QkND7S6Xi2Qyme2zzz4THT58WPLVV1/VulwuUqvVsfPnz+9xOp2coc7/9a9/FVdVVelsNhs9+uijShS7AAAAAADw0LufDuxocG9jHnjuwoULfmvXrm0nIkpMTOwLDQ3tr6qqEniKw+VyXdnZ2UYiIrVa3Xv27NmJRESXLl3yO3nyZAMRUW5ubufWrVvDiYicTidn/fr14RcvXvRjGIba29t5LS0tHusoT3mlpKR0SyQSBxGRTCbra2ho4A9W7BIRzZo1y/TZZ5/5WSwWZvbs2b2xsbF9W7ZseSQ4ONiuVqt7iYjOnTvnt2jRotsTJ050EhE9+eSTXX//+98nuFwuGuy80+mkRYsW3Z4wYYKTiGjBggW3Pd3LeIbv7AIAAAAAwLjkcrkGPe/j4+NyOp13jq1WKzNwjGEY999kt9s57jGGYb4XsKioSNzZ2elTVVVVq9frdRKJxGaxWDzWUUPlRUTE4/HuDLIs67LZbJyhrp0zZ47p4sWLfl988YVvWlqaKTExse/q1auC8+fP+yUnJ5s8reUpBw5nyCW9CopdAAAAAAAYl1JSUkwlJSViIiKtVstvbW3lJSQk9EVFRfXX1NSIHA4HGQwGrlar9b1XrBkzZpgOHDggJiI6cOCAxH3eaDSygYGBNj6f7zp27NiEGzdu8IiI/P39Hb29vYPWU0PlNdL7mz9/vunKlSu+t27d4oaFhdkZhiGxWGw/ffr0pHnz5pmIiJ544gnTiRMnJvX09DDd3d3MiRMnAtLS0no8nT9+/Pgkk8nE6erqYsrKyiaNNK/xAtuYAQAAAABgXNq4cWP7ihUrImJiYpQsy1JRUVGjUCh0paenm9577z2rXC6Pk8vlFqVSec/vpO7Zs6cpOzt72p49eyY/9dRTXe7zq1aturVw4UJZfHx8bFxcnHnq1Kl9REQhISEOtVptio6OjnviiSeMeXl57ffKa6T3FxQU5BCLxfaYmBiL+1xSUlLvpUuX/B5//HEzEVFKSop56dKlnTNmzIgl+ueLqObMmWMhIhrq/E9/+tNb8fHxcWFhYdakpCSvfaMzx1N7GwAAAAAAQKPRNKpUqo6xzgMePhqNJlClUkXez1xsYwYAAAAAAACvg23MAAAAAAAAYyghIUHR39//nUZkcXHx9aSkJMtQc+DeUOwCAAAAAACMIa1Wqx/rHLwRtjEDAAAAAACA10GxCwAAAAAAAF4HxS4AAAAAAAB4HRS7AAAAAAAA4HVQ7AIAAAAAwAOPZVm1QqFQuj91dXW8H3I9kUiU6Gm8o6OD3bZtW5D7uLGxkZuRkTHth8wJRgZvYwYAAAAAgAcen8936vV63Vjn4dbZ2ckePHgweNOmTTeJiCIjI22nTp26NtZ5wb+g2AUAAAAAgGE7cuSItL29XTSaMYODg81PP/1080jnmc1mTk5OToRWqxWxLEs7duxoXrx4cc/u3bsllZWVvsXFxU1ERGlpabL8/Py2zMzMHpFIlLhy5cr2M2fO+AsEAmdpaalBKpXa9Xo9Lzs7e5rdbufMnz/f6F7DaDQyGRkZMqPRyNrtds7mzZtvLF++/HZ+fn54c3MzX6FQKFNTU7vz8vLaMzMzo69evVrjKa/S0tJJFouFaWpq4i9cuPD2vn37Woa6v6FyPXz4sP+2bdsesdlsTEBAgP3DDz+8JpVK7Xl5eaHNzc28r7/+mn/jxg3e6tWr237961+339+/yviHbcwAAAAAAPDAs1qtjHsLc3p6ehQR0fbt24OJiOrr63WHDx++9vLLL0eazWaOpzgWi4VJTk421dXV6ZKTk02FhYVBRERr1qyZsmrVqpvV1dW1ISEhNvf1IpHIefz4cYNOp6stLy+vf/PNN8OdTicVFBS0SKVSq16v1xUVFX2nYPWUl06nEx05cuRabW1tzdGjRwMMBgN3pLmmp6ebrly5oq+trdUtWbLk1pYtW0LccwwGg6C8vLz+yy+/rP3P//zPUKvV6vF5eDN0dgEAAAAAYNjupwM7GgbbxnzhwgW/tWvXthMRJSYm9oWGhvZXVVUJPMXhcrmu7OxsIxGRWq3uPXv27EQiokuXLvmdPHmygYgoNze3c+vWreFERE6nk7N+/frwixcv+jEMQ+3t7byWlhaPdZSnvFJSUrolEomDiEgmk/U1NDTwZTKZbbA4Q+V6/fp13tNPPx1+8+ZNbn9/PyOVSq3uOQsWLLgtFApdQqHQLhaLbS0tLT5RUVGDxvd26OwCAAAAAMC45HK5Bj3v4+Pjcjqdd46tViszcIxhGPffZLfb73Q+GYb5XsCioiJxZ2enT1VVVa1er9dJJBKbxWLxWEcNlRcREY/HuzPIsqzLZrMN2XkdKtdXX311ypo1a9rr6+t177777tcD74/P5w+M/537e9ig2AUAAAAAgHEpJSXFVFJSIiYi0mq1/NbWVl5CQkJfVFRUf01NjcjhcJDBYOBqtVrfe8WaMWOG6cCBA2IiogMHDkjc541GIxsYGGjj8/muY8eOTbhx4waPiMjf39/R29s7aD01VF6jcc9ERD09PeyUKVNsRETvv/++5F7XP6xQ7AIAAAAAwLi0cePGdofDwYmJiVE+99xzUUVFRY1CodCVnp5ukkqlVrlcHrdu3TqpUqk03yvWnj17mvbv3x8cHx8fazQaWff5VatW3dJoNL7x8fGxJSUl4qlTp/YREYWEhDjUarUpOjo6Ljc3N3w4eY3Wff/qV7+68fOf/zxKrVbLJRKJfbTiehuOpxY7AAAAAACARqNpVKlUHWOdBzx8NBpNoEqliryfuejsAgAAAAAAgNfB25gBAAAAAADGUEJCgqK/v/87jcji4uLrSUlJDZ2rAgAAIABJREFUlrHKyRug2AUAAAAAABhDWq1WP9Y5eCNsYwYAAAAAAACvg2IXAAAAAAAAvA6KXQAAAAAAAPA6KHYBAAAAAOCBx7KsWqFQKN2furo63mjF7ujoYLdt2xbkPm5sbORmZGRMG634bklJSfKKigrRUONhYWHT1Wq1fOA5hUKhjI6OjvMUd2C+f/3rXyfGxcXFxsTEKOPi4mKPHj06YXSyH3/wgioAAAAAAHjg8fl8p16v1/0QsTs7O9mDBw8Gb9q06SYRUWRkpO3UqVPXfoi17qW3t5c1GAxcmUxmu3TpkmA4cwbmGxwcbDt+/LghMjLS9uWXXwqefPLJmPb2du0Pm/WDCZ1dAAAAAAAYl3bv3i3JycmZ4j5OS0uTlZaWTiAiEolEiWvXrg2Ty+VKlUqlaG5u9iEiam5u9klPT4+Sy+VKuVyuLCsr883Pzw9vbm7mKxQKZW5ubnhdXR3P3U01m82cJUuWRMbExChjY2OVx44dm+Bee8GCBVFz586NjoiIiF+9enW4O49ly5ZNiY+Pj5XJZHGvv/566Eju6emnn75VXFwsJiIqLi4WZ2Vl3XKP1dXV8dRqtVypVMYqlcrYsrIyX/d5d75z5syxREZG2oiI1Gp1X39/P2OxWDj394THN3R2AQAAAABg2HS1v5T2muqH3Ip7P3z9YszK2O3Nnq6xWq2MQqFQEhFJpVJrWVlZg6frLRYLk5ycbCosLPxm9erV4YWFhUE7duxoXb169ZS5c+f2bN68ucFut5PRaGQLCgpaMjMzhe7O8cAt0tu3bw8mIqqvr9ddvnxZsGjRouiGhoZqIiKdTifSaDQ6oVDolMlk8Rs2bGiTyWS2nTt3fjN58mSH3W6n2bNnyz///HPhrFmzhvWbuUuXLu16/vnnp27ZsqXt9OnTk0pKSq599NFHEiKi0NBQ+2effVYvEolcVVVV/J///OfTqqura4eK9cEHHwQolUqzUCh0DWdtb4NiFwAAAAAAHngj3cbM5XJd2dnZRiIitVrde/bs2YlERBcuXJjw8ccfXyci8vHxIYlE4ujo6GCHinPhwgW/tWvXthMRJSYm9oWGhvZXVVUJiIhSUlK6JRKJg4hIJpP1NTQ08GUyme2DDz4Qv//++4F2u51z8+ZNrkajEQy32A0KCnL4+/vb9+/fHyCTySx+fn5O91h/fz9n5cqVETqdTsgwDH399df8oeJUVlYKNm/eHHbq1Kmrw1nXG6HYBQAAAACAYbtXB/bfycfHx+V03qkFyWq1MgPHGIZx/012u/2+tvK6XEM3RXk83p1BlmVdNpuNo9free++++7kr776qjYoKMiRlZUV2dfXN6Kvjy5ZsqRr48aNEXv27Lk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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "alpha_100 = np.logspace(-4.5, 2, 100)\n", + "coef = []\n", + "for i in alpha_100:\n", + " lasso.set_params(alpha = i)\n", + " lasso.fit(x_onehot, y_log)\n", + " coef.append(lasso.coef_)\n", + "\n", + "df_coef = pd.DataFrame(coef, index=alpha_100, columns=x_onehot.columns)\n", + "title = 'Lasso coefficients as a function of the regularization'\n", + "axes = df_coef.plot(logx=True, title=title)\n", + "axes.set_xlabel('alpha')\n", + "axes.set_ylabel('coefficients')\n", + "plt.rcParams['figure.figsize'] = 16, 4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### test prediction" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "best_lasso_test_y = para_search_lasso.best_estimator_.predict(test_onehot)\n", + "best_lasso_test_y = np.expm1(best_lasso_test_y)\n", + "sub = pd.DataFrame()\n", + "sub['SalePrice'] = best_lasso_test_y\n", + "sub = sub.set_index(np.arange(1461, 2920))\n", + "sub.to_csv('(2) lasso_log(y)_onehot_submission.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Elastic net regularization" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'alpha': 0.00013848863713938745, 'l1_ratio': 0.9}\n", + "Lowest RMSE found: 0.12738724030490256\n" + ] + } + ], + "source": [ + "elasticnet = linear_model.ElasticNet(normalize=True) # create a ridge regression instance\n", + "\n", + "# find the best alpha (lambda) for lasso \n", + "grid_param = [{'alpha': np.logspace(-10, 6, 100), 'l1_ratio': np.arange(0, 1.1, 0.1)}]\n", + "para_search_elas = GridSearchCV(estimator=elasticnet, param_grid=grid_param, scoring='neg_mean_squared_error', cv=5, return_train_score=True)\n", + "para_search_elas.fit(x_onehot, y_log)\n", + "\n", + "print(para_search_elas.best_params_)\n", + "print(\"Lowest RMSE found: \", np.sqrt(np.abs(para_search_elas.best_score_)))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### RMSE for all train data using best lasso" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 9.076453439675252e-16\n" + ] + } + ], + "source": [ + "# fit best elastic net equation to all train data \n", + "best_elas_y = para_search_elas.best_estimator_.predict(x_onehot)\n", + "print(\"RMSE: \", np.sqrt(np.mean(y_log-best_elas_y)**2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### RMSE across different grid search lambda" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "error = pd.DataFrame(para_search_elas.grid_scores_)['mean_validation_score']\n", + "error = np.sqrt(np.abs(error))\n", + "alpha = pd.DataFrame(para_search_elas.grid_scores_)['parameters'].apply(lambda x: x.get('alpha'))\n", + "plt.scatter(alpha, error)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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Her3eTqlU6omI4uPjvaKjo1sSEhJa9u7dK1CpVJ6FhYWD/nbr4cOH+QsXLtSnpKTcNJlM1NHRwWxqamLt2rVrSlFRkW7ixImWN99802379u3CPXv2NCmVyrteXl7BzzzzTPsLL7ygX7duXatSqex6/vnn7y5dulS/du3aNiIiHo9nLi4u5syZM6dHo9G4RkdHtzwYd6gYw9w7IzMzU/Dee+8N+Iz6+vqY06dPl9rZ2Vlff/31m6+++upd27mYmJhpDg4OFiKiU6dO1bq5uZkfHFtdXc25cOHC9yKRyBgaGiopKChw+o//+I/OofIZT1DMAgAAAADAqOq/lFatVgtKSkociYjKysoc8/Ly6oiIVCpV67Zt26YONdfs2bO71q9fLzIajcwVK1a0zZkzpycjI4NXV1fnoFAoJERERqORERoa2klElJmZ2XD27NnbeXl5PLVa7VZYWDgxKyurvv+8cXFxzQcOHHBVKBSNOTk5LufOnat+8PypU6ccB4sxlNjYWK/Zs2d3Ll68eMBrL1++XCESiYxardZeqVQGzJw5sycwMNBANPAy4wcFBQV1+fr6GomIAgMDu+vq6h5L93esQDELAAAAAABERDRcB3UsYbFYVrP5XiPSYrGQ0WhkEBEtWbKks6ioqDYrK8s5Li7OZ8OGDbf4fL5p7ty57SdOnLgy0FwKhaJHoVD0rFu3rtXPzy+IiOr7XxMbG9uWkpLifuzYsY6goKDu/l1Qq9VKQ8UYyObNm6c0Nzez8vPzB+00i0QiIxGRTCbrmz17dsfZs2e5tmJ2OGw222r7bGdnRyaTacBNpsYrvDMLAAAAAABjVkhISNfBgwddiIg0Gg0/LCysk4jI29u7r7S0lEtEdPTo0Um2Qk2n09l7eHgYN2/e3LxmzZrm8+fPc+fPn99VUlLidPHiRTYRUUdHB7OiooKt1+uZubm5PFus7777juPu7t5HROTk5GRub2+/Xy9xuVxreHi4PjEx0SsuLq65f56DxRjsvt577z3XkydPOmdnZ39vZ2c34DV37tyx6+npYRDdW8ZcUlLiFBwc3DPCR/jEQjELAAAAAABjVlpa2tUjR464isViWUZGhuCjjz5qJCJKSEi4U1xczAsKCpKeOXPGkcPhWIiI8vPzeTKZLFAqlcpycnJctmzZcsvd3d2k0Wjqo6KiponFYlloaKiksrLSwWKxUGpqqtD2EzfJyckehw4dukJ0b7dhtVrtJpVKZVVVVWwiopiYmFYiouXLl7f3z3OwGIPd15YtW7ybm5tZYWFhUolEInv99denEBEVFRVxIyMjvYmILly44CCXy6UBAQGy8PBw8aZNm27+XLsrj0cMq9U6/FUAAAAAAPBEKi8vr5fL5Q91GuFhSUlJQr1eb7dv374bo53Lk6K8vNxVLpeLHmUs3pkFAAAAAAAYhlKp9G1oaGCfPn1aN9q5wD0oZgEAAAAAAIZRUFAw6CZNQ1Eqlb6NjY0/eHd2586d1yIiIh5aqgwjg2IWAAAAAADgZ/KoRTAMDxtAAQAAAAAAwLiDYhYAAAAAAADGHRSzAAAAAAAAMO6gmAUAAAAAAIBxB8UsAAAAAACMKi6XG/LgsVqtFsTExHj9ErEzMjKcpVKpLCAgQObr6xuYmprqSkR05MiRSaWlpQ7DjVcoFAFFRUXckcZ96aWXfEQi0XR/f//AlStXigwGA2Og6+zs7EIlEolMIpHIFixY4Gf73sPDI6ipqen+hr65ubm85557zm+gOR60Y8eOydOmTQt86aWXfEaa81iD3YwBAAAAAOBXyWAwMDZu3Oj97bffVvv6+hp7enoYOp3OnogoOzt7kslk0oeGhvb+HLFXr17dmp2dfYWI6De/+Y3P3r17Xbdu3Xqn/3VsNttSU1OjfVxxDx069FReXt4liUTS97jmHC0oZgEAAAAAgIiIbvx/b3oaLl0acZdxKGx//273XTsbH3W8Tqezj42NFbW0tLAEAoEpPT293t/fvy8iIkK0dOlS/dq1a9uI7nV3u7u7yxoaGiZERERM6+zstDObzYwPPvigYfHixZ3Hjx+fmJyc7N7X18fw9vY2HDt2rL63t5dhMpkYQqHQRETE4XCscrncUFBQ4FhYWDjpzJkzvJSUlClZWVl1K1eunKbVaquJiCorK9lRUVHTqqqqqh/MdaAYzs7OloHuKzIyUm/7HBYW1nXt2jX7R31G/SUmJro3NjbaNzQ0sG/cuGEfHx9/66233rodHR3tde3aNfZLL73kt3r16ua333779uOKORqwzBgAAAAAAEaVwWBg2pbSSiQS2e7du91t5+Lj472io6NbdDqdNjIyskWlUnkONdfhw4f5Cxcu1NfU1Girq6urZs2a1d3U1MTatWvXlKKiIp1Wq62eOXNm9/bt24VCodCsVCrvenl5BS9btswnLS2NbzabSalUdj3//PN3d+zYca2mpkYbGBho4PF45uLiYg4RkUajcY2Ojm55MO5gMX7EvTMyMzMFL774on6g8319fczp06dL5XK55MiRI5N+3BMlunz5ssPp06d1586dq96zZ4+7wWBgfPrpp1cnT55sPH36tG68F7JE6MwCAAAAAMC//ZQO6k/RfymtWq0WlJSUOBIRlZWVOebl5dUREalUqtZt27ZNHWqu2bNnd61fv15kNBqZK1asaJszZ05PRkYGr66uzkGhUEiIiIxGIyM0NLSTiCgzM7Ph7Nmzt/Py8nhqtdqtsLBwYlZWVn3/eePi4poPHDjgqlAoGnNyclzOnTv3g67sqVOnHAeLMZTY2Fiv2bNndy5evHjAay9fvlwhEomMWq3WXqlUBsycObMnMDDQMNC1DMb/e+120aJFdzkcjpXD4Zj4fL7x2rVrLF9fX+Nw+YwnKGYBAAAAAGDcYbFYVrPZTEREFouFjEYjg4hoyZIlnUVFRbVZWVnOcXFxPhs2bLjF5/NNc+fObT9x4sSVgeZSKBQ9CoWiZ926da1+fn5BRFTf/5rY2Ni2lJQU92PHjnUEBQV1u7m5mR88b7VaaagYA9m8efOU5uZmVn5+ft1g14hEIiMRkUwm65s9e3bH2bNnuYGBgQYXFxdTc3Oz3ZQpU0xERC0tLXZ8Pt9kG8dms622z3Z2dmQymQbcYGo8wzJjAAAAAAAYs0JCQroOHjzoQkSk0Wj4YWFhnURE3t7efaWlpVwioqNHj06yFWs6nc7ew8PDuHnz5uY1a9Y0nz9/njt//vyukpISp4sXL7KJiDo6OpgVFRVsvV7PzM3N5dlifffddxx3d/c+IiInJydze3v7/XqJy+Vaw8PD9YmJiV5xcXHN/fMcLMZg9/Xee++5njx50jk7O/t7Ozu7Aa+5c+eOXU9PD4Po3jLmkpISp+Dg4B4iojlz5nQcOnRIQERkMpno6NGjgvnz53eM4NGOeyhmAQAAAABgzEpLS7t65MgRV7FYLMvIyBB89NFHjURECQkJd4qLi3lBQUHSM2fOOHI4HAsRUX5+Pk8mkwVKpVJZTk6Oy5YtW265u7ubNBpNfVRU1DSxWCwLDQ2VVFZWOlgsFkpNTRWKRKLpEolElpyc7HHo0KErRPd2G1ar1W5SqVRWVVXFJiKKiYlpJSJavnx5e/88B4sx2H1t2bLFu7m5mRUWFiaVSCSy119/fQoRUVFRETcyMtKbiOjChQsOcrlcGhAQIAsPDxdv2rTppm135d27dzfV1dWxAwICZDKZTDZt2jSDSqVqGSzek4hhtVqHvwoAAAAAAJ5I5eXl9XK5/KFOIzwsKSlJqNfr7fbt23djtHN5UpSXl7vK5XLRo4zFO7MAAAAAAADDUCqVvg0NDezTp0/rRjsXuAfFLAAAAAAAwDAKCgoG3aRpKEql0rexsfEH787u3LnzWkRExENLlWFkUMwCAAAAAAD8TB61CIbhYQMoAAAAAAAAGHdQzAIAAAAAAMC4g2IWAAAAAAAAxh0UswAAAAAAADDuoJgFAAAAAIBRxeVyQx48VqvVgpiYGK9fInZGRoazVCqVBQQEyHx9fQNTU1NdiYiOHDkyqbS01GG48QqFIqCoqIg70rgvvfSSj0gkmu7v7x+4cuVKkcFgYBARNTY2sp577jk/Wz7h4eF+RES1tbX2+/fv5480zpMMxSwAAAAAAPwqGQwGxsaNG71zc3Mv1dbWai9evKhdtGhRBxFRdnb2pIqKCs7PFXv16tWt33///cXa2tqq3t5ext69e12JiLZu3eqxYMGC9traWm1dXV3Vu+++e52I6NKlS+zMzMwRFbNGo/HnSH3MwE/zAAAAAAAAERH9M73as/V654i7jEPhezh1L4yRNj7qeJ1OZx8bGytqaWlhCQQCU3p6er2/v39fRESEaOnSpfq1a9e2Ed3r7nZ3d5c1NDRMiIiImNbZ2WlnNpsZH3zwQcPixYs7jx8/PjE5Odm9r6+P4e3tbTh27Fh9b28vw2QyMYRCoYmIiMPhWOVyuaGgoMCxsLBw0pkzZ3gpKSlTsrKy6lauXDlNq9VWExFVVlayo6KiplVVVVU/mOtAMZydnS0D3VdkZKTe9jksLKzr2rVr9kREN2/enLBo0aL752bNmtVDRPTmm296fP/99w4SiUT2yiuvNP/pT3+6ExMT411RUcG1s7Ojd999t3HZsmUdarVakJeX52wwGJjd3d1MNzc344oVK9rWrFlzl+heRzgyMrJ19erV+v45jTfozAIAAAAAwKgyGAxMiUQis/3t3r3b3XYuPj7eKzo6ukWn02kjIyNbVCqV51BzHT58mL9w4UJ9TU2Ntrq6umrWrFndTU1NrF27dk0pKirSabXa6pkzZ3Zv375dKBQKzUql8q6Xl1fwsmXLfNLS0vhms5mUSmXX888/f3fHjh3XampqtIGBgQYej2cuLi7mEBFpNBrX6OjolgfjDhbjR9w7IzMzU/Diiy/qiYj+8Ic/3E5ISBDNmjVLvHXrVrf6+voJREQ7d+68HhYW1llTU6N9++23b6ekpEwmItLpdNpPP/30+3Xr1om6u7sZRETnz593ysjIuHLmzBndf/3Xf935+OOPBURELS0tdqWlpU6rVq0a94UsETqzAAAAAADwbz+lg/pTsNlsS01NjdZ2rFarBSUlJY5ERGVlZY55eXl1REQqlap127ZtU4eaa/bs2V3r168XGY1G5ooVK9rmzJnTk5GRwaurq3NQKBQSIiKj0cgIDQ3tJCLKzMxsOHv27O28vDyeWq12KywsnJiVlVXff964uLjmAwcOuCoUisacnByXc+fO/aAre+rUKcfBYgwlNjbWa/bs2Z2LFy/uJCKKiIhonzt3buVnn33m/OWXXzqHhobKKisrq/qPKy4udkpISLhNRBQSEtLr7u7eV1lZ6UBENG/evHahUGgmInrxxRc7N23a5H39+nXW0aNHXV588cW2CRMmDJfWuIBiFgAAAAAAxh0Wi2U1m81ERGSxWMhoNDKIiJYsWdJZVFRUm5WV5RwXF+ezYcOGW3w+3zR37tz2EydOXBloLoVC0aNQKHrWrVvX6ufnF0RE9f2viY2NbUtJSXE/duxYR1BQULebm5v5wfNWq5WGijGQzZs3T2lubmbl5+fXPfi9UCg0x8fHt8bHx7c+99xzfl999ZWTq6vrQ/EGw+Vyf7C0edWqVS0HDx7kZ2Vl8Q8fPvzQvY1XWGYMAAAAAABjVkhISNfBgwddiIg0Gg0/LCysk4jI29u7r7S0lEtEdPTo0Ukmk4lBdO8dWw8PD+PmzZub16xZ03z+/Hnu/Pnzu0pKSpwuXrzIJiLq6OhgVlRUsPV6PTM3N5dni/Xdd99x3N3d+4iInJyczO3t7ffrJS6Xaw0PD9cnJiZ6xcXFNffPc7AYg93Xe++953ry5Enn7Ozs7+3s7O5///nnn/M6OjqYRERtbW3MhoYGto+PT5+zs7O5s7Pz/oVz587t/OSTT/hERBUVFeympib74ODg3oFixcfHN2s0GiERUVhY2IDXjEfozAIAAAAAwJiVlpZ2NTY2VrRv3z432wZQREQJCQl3li5d6hcUFCR99tln2zkcjoWIKD8/n6dWq91YLJaVy+Wajx49esXd3d2k0Wjqo6KipvX19TGIiN5+++3rnp6extTUVOFrr73m7eDgYOFyuZZDhw5dIbq327BKpRLt379f+I9//KMuMDDQEBMT05qXl+eyfPny9v55DhYjODjYMNB9bdmyxXvKlCmGsLAwKRHR0qVL2/bs2dN07tw57h//+EcvOzs7q9VqZbz66qvN4eHh3QaDgcFisawBAQGy6Ojo5i1bttx+9dVXvcVisczOzo40Gk09h8MZsF3r6elp8vX17V22bNndx/AvGTMYQ7WnAQAAAADgyVZeXl4vl8sf6jTCw5KSkoR6vd5u3759N0Y7l5Ho6OhgymQy2YULF6oFAoF5+BG/nPLycle5XC56lLHozAIAAAAAAAxDqVT6NjQ0sE+fPq0b7VxGIjs7m6dSqUQqlerWWCtkfyp0ZgEAAAAAfsXQmf15KZVK38bGxh+8O7tz585rERERDy1V/jVCZxYAAAAAAGAMKigoqBv+KngU2M0YAAAAAAAAxh0UswAAAAAAADDuoJgFAAAAAACAcQfFLAAAAAAAAIw7KGYBAAAAAGBUcbnckAeP1Wq1ICYmxuuXiJ2RkeEslUplAQEBMl9f38DU1FRXIqIjR45MKi0tdRhuvEKhCCgqKuI+rriJiYnukydPDpZIJDKJRCL7/e9/7zHyu/p1wG7GAAAAAADwq2QwGBgbN270/vbbb6t9fX2NPT09DJ1OZ09ElJ2dPclkMulDQ0N7f8m4RETx8fG3kpOTbz3uuP2ZTCZiscZvSTh+MwcAAAAAgMcqP22vZ3Njw4i7jENx9fTu/g/VpsZHHa/T6exjY2NFLS0tLIFAYEpPT6/39/fvi4iIEC1dulS/du3aNqJ73d3u7u6yhoaGCREREdM6OzvtzGYz44MPPmhYvHhx5/HjxycmJye79/X1Mby9vQ3Hjh2r7+3tZZhMJoZQKDQREXE4HKtcLjcUFBQ4FhYWTjpz5gwvJSVlSlZWVt3KlSunabXaaiKiyspKdlRU1LSqqqrqB3MdKIazs7Ol/z3dvXuXOVDcoZ5DTk4O74033vA0m80kl8u709PTG7766iunDz/8cLLt538+++yziWlpaU999dVXdYPl4uHhEfTKK680f/311xPXr19/e926dW2P+r8ZbVhmDAAAAAAAo8pgMDBty2olEols9+7d7rZz8fHxXtHR0S06nU4bGRnZolKpPIea6/Dhw/yFCxfqa2pqtNXV1VWzZs3qbmpqYu3atWtKUVGRTqvVVs+cObN7+/btQqFQaFYqlXe9vLyCly1b5pOWlsY3m82kVCq7nn/++bs7duy4VlNTow0MDDTweDxzcXExh4hIo9G4RkdHtzwYd7AYA+U4WFyb/fv3C23PIisra2J3dzdj/fr1PpmZmXU6nU5rMpkoNTX1qWXLlnVcvnzZ4caNG6x/37sgLi6uebhcHBwcLKWlpbXjuZAlQmcWAAAAAAD+7ad0UH8KNpttqamp0dqO1Wq1oKSkxJGIqKyszDEvL6+OiEilUrVu27Zt6lBzzZ49u2v9+vUio9HIXLFiRducOXN6MjIyeHV1dQ4KhUJCRGQ0GhmhoaGdRESZmZkNZ8+evZ2Xl8dTq9VuhYWFE7Oysur7zxsXF9d84MABV4VC0ZiTk+Ny7ty5H3RlT5065ThYjIEMFbf/MuNvv/2WM3XqVENwcLDh37m0/OUvf5nMZDJvr1q1quXAgQP8P/zhDy3nz593On78+JV//OMfzkPlEhMTM66LWBsUswAAAAAAMO6wWCyrrZtpsVjIaDQyiIiWLFnSWVRUVJuVleUcFxfns2HDhlt8Pt80d+7c9hMnTlwZaC6FQtGjUCh61q1b1+rn5xdERPX9r4mNjW1LSUlxP3bsWEdQUFC3m5ub+cHzVquVhorxqHFtcw9GpVK1vPjii34ODg7WZcuWtU2YMGHYXHg83kNLn8cjLDMGAAAAAIAxKyQkpOvgwYMuREQajYYfFhbWSUTk7e3dV1payiUiOnr06CSTycQguveOrYeHh3Hz5s3Na9asaT5//jx3/vz5XSUlJU4XL15kExF1dHQwKyoq2Hq9npmbm8uzxfruu+847u7ufURETk5O5vb29vv1EpfLtYaHh+sTExO94uLimvvnOViMge5pqLgDmTFjRu/169ftbXOnp6cL5s2b10FEJBKJjEKh0PjnP/95yn/91381jzSX8QzFLAAAAAAAjFlpaWlXjxw54ioWi2UZGRmCjz76qJGIKCEh4U5xcTEvKChIeubMGUcOh2MhIsrPz+fJZLJAqVQqy8nJcdmyZcstd3d3k0ajqY+KipomFotloaGhksrKSgeLxUKpqalCkUg0XSKRyJKTkz0OHTp0hYho9erVrWq12k0qlcqqqqrYREQxMTGtRETLly9v75/nYDEGuqeh4g6Ey+Va9+/fX79y5UpfsVgsYzKZ9Prrr9+xnY+KimqZMmVKn23n5ZHkMp4xhmpZAwAAAADAk628vLxeLpc/1GmEhyUlJQn1er3dvn37box2Lg+KiYnxCgkJ6f7jH/847v6P5eXlrnK5XPQoY/HOLAAAAAAAwDCUSqVvQ0MD+/Tp07rRzuVBgYGBUg6HY9FoNKOyeddoQjELAAAAAAAwDNtvuY6UUqn0bWxs/MH7qjt37rwWERHx0FLlR9H/t25/TVDMAgAAAAAA/EwetQiG4WEDKAAAAAAAABh3UMwCAAAAAADAuINiFgAAAAAAAMYdFLMAAAAAAAAw7qCYBQAAAACAUcXlckMePFar1YKYmBivXyJ2RkaGs1QqlQUEBMh8fX0DU1NTXYmIjhw5Mqm0tNRhuPEKhSKgqKiI+7jiJiYmuiclJQl/zBy1tbX2/v7+gSON/aTAbsYAAAAAAPCrZDAYGBs3bvT+9ttvq319fY09PT0MnU5nT0SUnZ09yWQy6UNDQ3t/ybjw46GYBQAAAAAAIiJq/YfO03iza8RdxqFMcHPs5q8QNz7qeJ1OZx8bGytqaWlhCQQCU3p6er2/v39fRESEaOnSpfq1a9e2Ed3r7nZ3d5c1NDRMiIiImNbZ2WlnNpsZH3zwQcPixYs7jx8/PjE5Odm9r6+P4e3tbTh27Fh9b28vw2QyMYRCoYmIiMPhWOVyuaGgoMCxsLBw0pkzZ3gpKSlTsrKy6lauXDlNq9VWExFVVlayo6KipvX/jdeBYjg7O1v639Pdu3eZA8UdYrdU9AAAIABJREFU7BmoVCoPb2/vvjfeeOMO0b3uLY/HM0dHR7c96nN9EmCZMQAAAAAAjCqDwcCUSCQy29/u3bvdbefi4+O9oqOjW3Q6nTYyMrJFpVJ5DjXX4cOH+QsXLtTX1NRoq6urq2bNmtXd1NTE2rVr15SioiKdVqutnjlzZvf27duFQqHQrFQq73p5eQUvW7bMJy0tjW82m0mpVHY9//zzd3fs2HGtpqZGGxgYaODxeObi4mIOEZFGo3GNjo5ueTDuYDEGynGwuINZs2ZNa1ZWFt92nJOT47JmzZpfdSFLhM4sAAAAAAD820/poP4UbDbbUlNTo7Udq9VqQUlJiSMRUVlZmWNeXl4dEZFKpWrdtm3b1KHmmj17dtf69etFRqORuWLFirY5c+b0ZGRk8Orq6hwUCoWEiMhoNDJCQ0M7iYgyMzMbzp49ezsvL4+nVqvdCgsLJ2ZlZdX3nzcuLq75wIEDrgqFojEnJ8fl3LlzP+jKnjp1ynGwGAP5sXGJiJ555pmelpYWVn19/YSmpiaWs7Oz2d/fv6+2tvZXvTQZxSwAAAAAAIw7LBbLautmWiwWMhqNDCKiJUuWdBYVFdVmZWU5x8XF+WzYsOEWn883zZ07t/3EiRNXBppLoVD0KBSKnnXr1rX6+fkFEVF9/2tiY2PbUlJS3I8dO9YRFBTU7ebm9oNWqtVqpaFiPGpcm2XLlrV98sknLjdv3pwQERHR+mNjPMmwzBgAAAAAAMaskJCQroMHD7oQEWk0Gn5YWFgnEZG3t3dfaWkpl4jo6NGjk0wmE4Po3ju2Hh4exs2bNzevWbOm+fz589z58+d3lZSUOF28eJFNRNTR0cGsqKhg6/V6Zm5uLs8W67vvvuO4u7v3ERE5OTmZ29vb79dLXC7XGh4erk9MTPSKi4tr7p/nYDEGuqeh4g7m1Vdfbc3KyuLn5uZiifG/oZgFAAAAAIAxKy0t7eqRI0dcxWKxLCMjQ/DRRx81EhElJCTcKS4u5gUFBUnPnDnjyOFwLERE+fn5PJlMFiiVSmU5OTkuW7ZsueXu7m7SaDT1UVFR08RisSw0NFRSWVnpYLFYKDU1VSgSiaZLJBJZcnKyx6FDh64QEa1evbpVrVa7SaVSWVVVFZuIKCYmppWIaPny5e398xwsxkD3NFRcIqL3339/ilAoDLb9ERGFhYX1dnV1MYVCYZ+3t7fxcT/n8YhhtVpHOwcAAAAAABgl5eXl9XK5/KFOIzwsKSlJqNfr7fbt23djtHN5UpSXl7vK5XLRo4zFO7MAAAAAAADDUCqVvg0NDezTp0/rRjsXuAfFLAAAAAAAwDAKCgrqHmWcUqn0bWxs/MG7szt37rwWERHx0FJlGBkUswAAAAAAAD+TRy2CYXjYAAoAAAAAAADGHRSzAAAAAAAAMO6gmAUAAAAAAIBxB8UsAAAAAAAAjDsoZgEAAAAAYFRxudyQB4/VarUgJibG65eInZGR4SyVSmUBAQEyX1/fwNTUVFcioiNHjkwqLS11GG68QqEIKCoq4v78mUJ/2M0YAAAAAAB+lQwGA2Pjxo3e3377bbWvr6+xp6eHodPp7ImIsrOzJ5lMJn1oaGjvaOcJA0MxCwAAAAAARESUnZ3tefv27cfaZZw8eXL3yy+/3Pio43U6nX1sbKyopaWFJRAITOnp6fX+/v59ERERoqVLl+rXrl3bRnSvu9vd3V3W0NAwISIiYlpnZ6ed2WxmfPDBBw2LFy/uPH78+MTk5GT3vr4+hre3t+HYsWP1vb29DJPJxBAKhSYiIg6HY5XL5YaCggLHwsLCSWfOnOGlpKRMycrKqlu5cuU0rVZbTURUWVnJjoqKmlZVVVX9YK4DxXB2drYMdF8eHh5Bq1atasnPz3c2mUyMzMzM70NCQnq//vprbmJioldvby/TwcHB8vHHH1+Ry+UGtVotyM3NndTT08O8evUqe8mSJXf3799/7VGf65MAy4wBAAAAAGBUGQwGpkQikdn+du/e7W47Fx8f7xUdHd2i0+m0kZGRLSqVynOouQ4fPsxfuHChvqamRltdXV01a9as7qamJtauXbumFBUV6bRabfXMmTO7t2/fLhQKhWalUnnXy8sreNmyZT5paWl8s9lMSqWy6/nnn7+7Y8eOazU1NdrAwEADj8czFxcXc4iINBqNa3R0dMuDcQeLMVSurq6uJq1WW/273/3uzjvvvCMkIpLL5b1nz56tqa6u1r799tvXt2zZMtV2vVar5WZnZ39fXV1d9fnnn7tcvnx5wqM87ycFOrMAAAAAAEBERD+lg/pTsNlsS01NjdZ2rFarBSUlJY5ERGVlZY55eXl1REQqlap127ZtUwebh4ho9uzZXevXrxcZjUbmihUr2ubMmdOTkZHBq6urc1AoFBIiIqPRyAgNDe0kIsrMzGw4e/bs7by8PJ5arXYrLCycmJWVVd9/3ri4uOYDBw64KhSKxpycHJdz5879oCt76tQpx8FiDCY6OrqNiEihUHR//vnnLkREra2tdpGRkT719fUODAbDajQaGbbr586d2y4QCMxERH5+fr11dXVsPz8/41AxnmQoZgEAAAAAYNxhsVhWs9lMREQWi4VsRd+SJUs6i4qKarOyspzj4uJ8NmzYcIvP55vmzp3bfuLEiSsDzaVQKHoUCkXPunXrWv38/IKIqL7/NbGxsW0pKSnux44d6wgKCup2c3MzP3jearXSUDEG4uDgYLXdi8lkYhARbd261SM8PLyjoKCgrra21n7BggUBtuvt7e2tts92dnY/KHR/jbDMGAAAAAAAxqyQkJCugwcPuhARaTQaflhYWCcRkbe3d19paSmXiOjo0aOTbMWgTqez9/DwMG7evLl5zZo1zefPn+fOnz+/q6SkxOnixYtsIqKOjg5mRUUFW6/XM3Nzc3m2WN999x3H3d29j4jIycnJ3N7efr9e4nK51vDwcH1iYqJXXFxcc/88B4sx0vttb2+3mzp1at+/79d1pON/TVDMAgAAAADAmJWWlnb1yJEjrmKxWJaRkSH46KOPGomIEhIS7hQXF/OCgoKkZ86cceRwOBYiovz8fJ5MJguUSqWynJwcly1bttxyd3c3aTSa+qioqGlisVgWGhoqqaysdLBYLJSamioUiUTTJRKJLDk52ePQoUNXiIhWr17dqlar3aRSqayqqopNRBQTE9NKRLR8+fL2/nkOFmOk97t169ab//M//zN15syZElvnGQbGsFqtw18FAAAAAABPpPLy8nq5XP5QpxEelpSUJNTr9Xb79u27Mdq5PCnKy8td5XK56FHG4p1ZAAAAAACAYSiVSt+Ghgb26dOndaOdC9yDYhYAAAAAAGAYBQUFdY8yTqlU+jY2Nv7g3dmdO3dei4iIeGipMowMilkAAAAAAICfyaMWwTA8bAAFAAAAAAAA4w6KWQAAAAAAABh3UMwCAAAAAADAuINiFgAAAAAAAMYdFLMAAAAAADCquFxuyOOeMzEx0T0pKUloO05KShL6+PgE+vv7BwYEBMg+/PBDwaPMm5ubyysoKHB8fJkOrP8zUavVgpiYGC+ih+/NZuvWrW5+fn6BYrFYJpFIZCdPnvzZ8xxN2M0YAAAAAACeaO++++5TJ0+enFhaWlrN5/MtLS0tdp9++umkR5nr5MmTPCcnJ7NSqez6qXmZTCZisR5PSVZYWOiYn58/qbKyUsvhcKxNTU0sg8HAeCyTj1EoZgEAAAAAgIiItNVbPbs6ddzHOaejk7hbJk1pHOm4Tz/91Pmdd96ZYjQamS4uLqbMzMzvPT09TYmJie6NjY32DQ0N7Bs3btjHx8ffeuutt24T3etMZmZmurq7u/cJBAJjSEhINxHR+++/71ZYWKjj8/kWIiKBQGBOSEhoISLKycnhvfHGG55ms5nkcnl3enp6A4fDsXp4eAStWrWqJT8/39lkMjEyMzO/53K5lvT09KeYTKb173//u2Dv3r1Xp02b1hcbGytqaWlhCQQCU3p6er2/v39fRESEaOnSpfq1a9e2Ed3rtHZ3d5fl5ubytm/fPmXy5MlGrVbLraurq3ocz/n69esT+Hy+icPhWImIpkyZYnoc845lWGYMAAAAAABjjlKp7Lxw4UJNdXW1dsWKFa3JyclutnOXL192OH36tO7cuXPVe/bscTcYDIxvvvmG+9lnn/ErKyu1ubm5l8vLyx2JiNra2phdXV12gYGBhv4xuru7GevXr/fJzMys0+l0WpPJRKmpqU/Zzru6upq0Wm317373uzvvvPOOMCAgoC8mJuZOfHz8rZqaGu3ixYs74+PjvaKjo1t0Op02MjKyRaVSeQ53bxUVFY6pqanXhypkDQYDUyKRyGx/u3fvdh9qzpdffrn9xo0b9iKRaPqaNWu8vvjiC6fh8hjv0JkFAAAAAAAiInqUDurP5cqVK/Yvv/zy1Dt37kzo6+tjenp63i9GFy1adJfD4Vg5HI6Jz+cbr127xvr666+dXnjhhbs8Hs9iu4aIyGq1EoMx8Grb8vJyh6lTpxqCg4MNRERxcXEtf/nLXyYT0W0ioujo6DYiIoVC0f3555+7DDRHWVmZY15eXh0RkUqlat22bdvU4e4tODi4SyKR9A11DZvNttTU1Ghtx2q1WlBSUjLoO7DOzs6Wixcvar/88kveP//5T15sbKxvUlLStQ0bNrQMl894hc4sAAAAAACMOa+99prX73//+9s6nU774YcfNhgMhvu1C5vNtto+29nZkclkYhDRgEUrn8+3cDgci1arte9/zmq1PnT9gxwcHKxERCwWy2qL8WOxWCyr2WwmIiKLxUJGo/H+eC6XaxnJXCOISUuXLu14//33b6Smpl7Nzs4esAB/UqCYBQAAAACAMaejo8POy8vLSET08ccfD7vz8IIFCzq/+OKLSZ2dnYy2tjZmQUHB/Q2eNm3a1BQfH+/d2trKJCJqbW1l7tmzx3XGjBm9169ft7948SKbiCg9PV0wb968jqHi8Hg8c0dHh53tOCQkpOvgwYMuREQajYYfFhbWSUTk7e3dV1payiUiOnr06KSRFsMjVV5ezq6srGTbjsvKyjhTp04dsvs73mGZMQAAAAAAjKre3l6mUCgMth2rVKpbb7755o1XXnnFVygU9oWFhXVdvXqVPdQcc+fO7f7tb3/bOn369EAPDw+DQqHotJ3bsmXLnc7OTubMmTNlEyZMsLJYLGtCQsJNLpdr3b9/f/3KlSt9bRtAvf7663eGihMREXF3xYoVvnl5eZP27t17NS0t7WpsbKxo3759brYNoIiIEhIS7ixdutQvKChI+uyzz7ZzOJzH2o19//33p2g0mvs/z/OPf/zj8oYNG7za29vt7OzsrCKRyPC3v/2t4XHGHGsYw7XWAQAAAADgyVVeXl4vl8ubRzsP+HUqLy93lcvlokcZi2XGAAAAAAAAMO5gmTEAAAAAAMAouHnzpt38+fMD+n9/6tSpWjc3N/No5DSeoJgFAAAAAAAYBW5ubuYHf34HRgbLjAEAAAAAAGDcQTELAAAAAAAA4w6KWQAAAAAAABh3UMwCAAAAAADAuINiFgAAAAAARhWXyw153HMmJia6JyUlCW3HSUlJQh8fn0B/f//AgIAA2Ycffih4lHlzc3N5BQUFjo8v04H9HM/kSYNiFgAAAAAAnmjvvvvuUydPnpxYWlpafenSpari4uJaq9X6SHOdPHmS98033zg9jrxMJtPjmOZXCz/NAwAAAAAARES0qfqqZ01XL/dxzilxdOjeK/VqHOm4Tz/91Pmdd96ZYjQamS4uLqbMzMzvPT09TYmJie6NjY32DQ0N7Bs3btjHx8ffeuutt24TEW3dutUtMzPT1d3dvU8gEBhDQkK6iYjef/99t8LCQh2fz7cQEQkEAnNCQkILEVFOTg7vjTfe8DSbzSSXy7vT09MbOByO1cPDI2jVqlUt+fn5ziaTiZGZmfk9l8u1pKenP8VkMq1///vfBXv37r06bdq0vtjYWFFLSwtLIBCY0tPT6/39/fsiIiJES5cu1a9du7aN6F6ntbu7uyw3N5e3ffv2KZMnTzZqtVpuXV1d1Y99Jjqdzr5/LB8fnz6RSBR09erVytbWVrvJkyfP+OKLL2qXLFnSGRoaGvC3v/2tfvr06YaRPv/xAJ1ZAAAAAAAYc5RKZeeFCxdqqqurtStWrGhNTk52s527fPmyw+nTp3Xnzp2r3rNnj7vBYGB888033M8++4xfWVmpzc3NvVxeXu5IRNTW1sbs6uqyCwwMfKig6+7uZqxfv94nMzOzTqfTaU0mE6Wmpj5lO+/q6mrSarXVv/vd7+688847woCAgL6YmJg78fHxt2pqarSLFy/ujI+P94qOjm7R6XTayMjIFpVK5TncvVVUVDimpqZeH0khS0Q0UCwWi0U+Pj6958+fdygoKHCSyWTdp06dcurp6WHcvHnT/kktZInQmQUAAAAAgH97lA7qz+XKlSv2L7/88tQ7d+5M6OvrY3p6et4vyhYtWnSXw+FYORyOic/nG69du8b6+uuvnV544YW7PB7PYruGiMhqtRKDwRgwRnl5ucPUqVMNwcHBBiKiuLi4lr/85S+Tieg2EVF0dHQbEZFCoej+/PPPXQaao6yszDEvL6+OiEilUrVu27Zt6nD3Fhwc3CWRSPpG8jyGijVnzpyOf/7zn7wrV66w//SnPzUdOnToqaKiok65XN410hjjCTqzAAAAAAAw5rz22mtev//972/rdDrthx9+2GAwGO7XLmw2+/4Lr3Z2dmQymRhENGDRyufzLRwOx6LVau37nxvuvVkHBwcrERGLxbLaYvxYLBbLajabiYjIYrGQ0Wi8P57L5VpGMtdw5s+f3/mvf/3L6fz5844rV67Ut7e32/3zn//kzZ07t+NxxhlrUMwCAAAAAMCY09HRYefl5WUkIvr444+H3Xl4wYIFnV988cWkzs5ORltbG7OgoGCS7dymTZua4uPjvVtbW5lERK2trcw9e/a4zpgxo/f69ev2Fy9eZBMRpaenC+bNmzdkAcjj8cwdHR12tuOQkJCugwcPuhARaTQaflhYWCcRkbe3d19paSmXiOjo0aOTRloMD2SwWPPnz+86f/68E5PJtHK5XGtgYGB3enr6U88991znT405lmGZMQAAAAAAjKre3l6mUCgMth2rVKpbb7755o1XXnnFVygU9oWFhXVdvXqVPdQcc+fO7f7tb3/bOn369EAPDw+DQqG4X8ht2bLlTmdnJ3PmzJmyCRMmWFksljUhIeEml8u17t+/v37lypW+tg2gXn/99TtDxYmIiLi7YsUK37y8vEl79+69mpaWdjU2Nla0b98+N9umTERECQkJd5YuXeoXFBQkffbZZ9s5HM6IurEDPZPBYnE4HKubm1tfWFhYFxHRvHnzOj///HO+QqHoGUnM8YbxqFtSAwAAAADA+FdeXl4vl8ubRzsP+HUqLy93lcvlokcZi2XGAAAAAAAAMO5gmTEAAAAAAMAouHnzpt38+fMD+n9/6tSpWjc3N/No5DSeoJgFAAAAAAAYBW5ubuaamhrtaOcxXmGZMQAAAAAAAIw7KGYBAAAAAABg3EExCwAAAAAAAOMOilkAAAAAABhVXC435HHPmZiY6J6UlCS0HSclJQl9fHwC/f39AwMCAmQffvih4FHmzc3N5RUUFDg+vkwHNpJnEhERIfrrX//q8nPmMxahmAUAAAAAgCfau++++9TJkycnlpaWVl+6dKmquLi41mq1PtJcJ0+e5H3zzTdOjyMvk8n0OKb51UIxCwAAAAAAY86nn37qHBwcLJFKpbI5c+aIGxsbWUT3Oq4rV64UKRSKgKlTpwbt2LFjsm3M1q1b3UQi0fQ5c+aIL126xLZ9//7777tpNJqrfD7fQkQkEAjMCQkJLUREOTk5PKlUKhOLxbKVK1eKenp6GEREHh4eQX/84x/dZTKZVCwWy8rKyhxqa2vt09PTn9q/f79QIpHIvvzySyedTmf/9NNPi8Visezpp58WX7p0yZ7o4W6prdOam5vLmzVrlnjZsmU+AQEBgT/2ebS0tNh5eHgEmc33frGno6OD6ebmFmwwGBg/4TGPa/hpHgAAAAAAICKiP/2j3FN3s4P7OOcUu/G6U1fIG0c6TqlUdkZFRdUwmUx67733XJOTk90OHDhwjYjo8uXLDsXFxbV37961k0ql0//0pz/dOXv2LOezzz7jV1ZWao1GI82YMUMWEhLS3dbWxuzq6rILDAw09I/R3d3NWL9+vc9XX31VGxwcbPjtb38rSk1NfSopKek2EZGrq6tJq9VWv/POO0+98847wszMzIaYmJg7Tk5O5uTk5FtERAsWLPCLjo5uSUhIaNm7d69ApVJ5FhYW1g11bxUVFY5lZWVVEomk78c+D4FAYJZIJN3/+7//y1u2bFnHsWPHnMPDw/VsNvvRWsxPAHRmAQAAAABgzLly5Yr9vHnz/MVisUytVrvV1NRwbOcWLVp0l8PhWKdMmWLi8/nGa9eusb7++munF1544S6Px7Pw+XzLokWL7hIRWa1WYjAGbl6Wl5c7TJ061RAcHGwgIoqLi2v517/+xbOdj46ObiMiUigU3Y2NjeyB5igrK3Nct25dKxGRSqVqLS0tHXYJcnBwcNdIClmblStXtmVkZLgQEf3973/nR0VFtY10jicJOrMAAAAAAEBERI/SQf25vPbaa14bN268uXr1an1ubi4vOTnZ3XbuwW6knZ0dmUwmBhENWLTy+XwLh8OxaLVae5lM9oMCcrj3Zh0cHKxERCwWy2qL8WOxWCyrbUmwxWIho9F4fzyXy7WMZC6bV1555W5ycrLHrVu37C5evMhdtmxZ+6PM86RAZxYAAAAAAMacjo4OOy8vLyMR0ccffzzszsMLFizo/OKLLyZ1dnYy2tramAUFBZNs5zZt2tQUHx/v3drayiQiam1tZe7Zs8d1xowZvdevX7e/ePEim4goPT1dMG/evI6h4vB4PHNHR4ed7TgkJKTr4MGDLkREGo2GHxYW1klE5O3t3VdaWsolIjp69OikkRbDA3F2drbI5fKu9evXe/3/7N17VJN3tjfwnQuQRBBJkCA3ASGEBIgIjS8oTk8LtnbaqkMRCw4X64XY0ipVcdUeOjqeU6m0iqMClZl6ovXW0oracTqiHcfWUUaKUQkxSIuAimLCPVxye//oiYvDeGVsEfl+1upaPs/vtp+nf+21dx6effbZNjZ7ZNcmR/bTAwAAAADAkOvp6WEKhcIw27VCobixevXqa6+++uoEoVDYFxkZ2VVfX3/HNl+bqVOnGmbPnq0PCQmRenp69srl8k7b2MqVK5s7OzuZkyZNktjZ2VnZbLY1MzOzicfjWQsLC+sSEhImmM1mkslkhuXLlzff65z4+PjWV155ZcKRI0fGbNq0qb6goKA+NTXVNz8/310gEJiUSmUdEVFmZmbziy++GBAaGho8bdq0di6X+1DV2Du9k9/97nc35syZ0zJ//nz/w4cPX3qY/Z5EjMF+khoAAAAAAIY/lUpVJ5PJbg11HDAyqVQqV5lM5juYtWgzBgAAAAAAgGEHbcYAAAAAAABDoKmpifX0008HDbz/t7/97ZK7u7t5KGIaTpDMAgAAAAAADAF3d3ezRqNRD3UcwxXajAEAAAAAAGDYQTILAAAAAAAAww6SWQAAAAAAABh2kMwCAAAAAADAsINkFgAAAAAAhhSPxwt/1HtmZWV55OTkCG3XOTk5Qj8/P2lgYKA0KChIsmXLFsFg9j18+LDT0aNHRz26SGGwkMwCAAAAAMAT7YMPPhh7/Pjx0RUVFdU1NTVVp06dumS1Wge11/Hjx51Onjzp+CjiMplMj2KbEQvJLAAAAAAAPHZ2797tHBYWJg4ODpZER0eLGhoa2EQ/VVwTEhJ85XJ5kJeXV+i6devcbGuys7PdfX19Q6Kjo0U1NTUOtvsbN250Lyoqqufz+RYiIoFAYM7MzNQREZWWljoFBwdLRCKRJCEhwbe7u5tBROTp6Rm6bNkyD4lEEiwSiSSVlZWcS5cu2SuVyrGFhYVCsVgs+ctf/uKo1Wrto6KiRCKRSBIVFSWqqamxJyKKj4/3/eSTT1xsMdiqz4cPH3aaPHmy6KWXXvILCgqS3unZL126ZO/v7y+dO3fu+ICAAOmUKVMCOzs7GUREH374oWtISEhwUFCQ5LnnnpvQ0dHBtJ2XlpbmHR4eLvby8grtf/aTCn9nFgAAAAAAfnLgdW+6qeY90j3dJAaatbXhYZfFxcV1zp07V8NkMumjjz5yXbt2rfv27dsbiYguX77MOXXq1KXW1lZWcHBwyIoVK5rLy8u5X375Jf/ChQtqo9FIEydOlISHhxtaWlqYXV1dLKlU2jvwDIPBwFi8eLHfX//610thYWG9s2fP9t2wYcPYnJycm0RErq6uJrVaXb1+/fqx69evF+7bt+9KSkpKs6Ojo3nt2rU3iIieeeaZgKSkJF1mZqZu06ZNAoVC4V1WVlZ7r2c7f/78qMrKyiqxWNx3tzn19fWcXbt2/RAdHX3lhRde8FcqlS5LlizRJycnt7z99tu3iIjefPNNj82bN7uuXr36JhHRjRs37M6ePas5d+4cZ/bs2QHp6ektD/vehxMkswAAAAAA8Nj58ccf7WfNmuXV3Nxs19fXx/T29r6djE6fPr2Vy+VauVyuic/nGxsbG9nffPON4wsvvNDq5ORksc0hIrJarcRgMO54hkql4nh5efWGhYX1EhGlpaXptm7d6kZEN4mIkpKSWoiI5HK54eDBg3esdFZWVo46cuRILRGRQqHQr1mzxut+zxYWFtZ1r0SWiMjT07M3Ojq6m4goPDzcUFdX50BEVFFRwc3JyfHs6OhgdXV1sX71q1+12da8/PLLrSwWiyIiInp0Op3d/eIY7pDMAgAAAADATwZRQf25vPHGGz5vvfVWU3Jyctvhw4ed1q5d62Ebc3Crk30zAAAgAElEQVRwuP2DVxaLRSaTiUFEd0xa+Xy+hcvlWtRqtb1EIvk/CeT9fjfL4XCsRERsNttqO+NBsdlsq9lsJiIii8VCRqPx9noej2e533p7e/v+z2jt7u5mEhEtWrTI7/PPP78cFRXVvXnzZsGJEyecBsZLdP9nexLgN7MAAAAAAPDY6ejoYPn4+BiJiHbs2HHfLw8/88wznV999dWYzs5ORktLC/Po0aNjbGNLly69npGRMV6v1zOJiPR6PTMvL8914sSJPVevXrW/ePGiAxGRUqkUxMTEdNzrHCcnJ3NHRwfLdh0eHt5VXFzsQkRUVFTEj4yM7CQiGj9+fF9FRQWPiOjTTz8d87DJ8N0YDAamj4+Psbe3l7F3717+o9hzuEJlFgAAAAAAhlRPTw9TKBSG2a4VCsWN1atXX3v11VcnCIXCvsjIyK76+nqHe+0xdepUw+zZs/UhISFST0/PXrlc3mkbW7lyZXNnZydz0qRJEjs7OyubzbZmZmY28Xg8a2FhYV1CQsIEs9lMMpnMsHz58uZ7nRMfH9/6yiuvTDhy5MiYTZs21RcUFNSnpqb65ufnuwsEApNSqawjIsrMzGx+8cUXA0JDQ4OnTZvWzuVy71uNfRCrVq26JpfLgz09PfuCg4MNnZ2drPuvejIxRkL5GQAAAAAA7kylUtXJZLJbQx0HjEwqlcpVJpP5DmYt2owBAAAAAABg2EGbMQAAAAAAwBBoampiPf3000ED7//tb3+75O7ubh6KmIYTJLMAAAAAAABDwN3d3azRaNRDHcdwhTZjAAAAAAAAGHaQzAIAAAAAAMCwg2QWAAAAAAAAhh0kswAAAAAAADDsIJkFAAAAAIAhxePxwh/1nllZWR45OTlCIqJjx46NCgsLE4vFYom/v780KyvLg4ho8+bNgpSUFJ9HffZAly5dsmcwGBFvvfWWh+3e9evX2Ww2e9LDnv9zvKvhCl8zBgAAAACAJ9prr73mt2fPntqoqKhuk8lEKpWK80vH4OXl1fvXv/51DBFdIyJSKpUuAQEBPb90HE8SVGYBAAAAAOCxs3v3buewsDBxcHCwJDo6WtTQ0MAm+qnimpCQ4CuXy4O8vLxC161b52Zbk52d7e7r6xsSHR0tqqmpcbDd1+v1bB8fHyMREZvNpoiIiH9JIrVarX1UVJRIJBJJoqKiRDU1NfZERPHx8b5JSUk+ERERQb6+viF79uxxJiIymUy0ePFir5CQkGCRSCTZsGGD672eh8PhWAMCArr//ve/84iISkpK+LNmzdLf73yNRmM/ceJEcUhISHD/yi6gMgsAAAAAAP/rP7/7T+/LLZd5j3LPAJcAw++n/L7hYdfFxcV1zp07V8NkMumjjz5yXbt2rfv27dsbiYguX77MOXXq1KXW1lZWcHBwyIoVK5rLy8u5X375Jf/ChQtqo9FIEydOlISHhxuIiBYtWnQjODg4ZPLkyR3Tp09ve/3113U8Hs/a/7yMjAyfpKQkXWZmpm7Tpk0ChULhXVZWVktE1NDQ4FBeXn5JrVY7xMbGBs2cOfPCtm3bBM7OzuaLFy9Wd3d3M5566inxSy+91C4Wi/vu9kxz587V79q1i+/p6WlksVhWDw8P47Vr1+zvdf6SJUt8FixY0PzGG2/o3n///bEP+x6fZKjMAgAAAADAY+fHH3+0j4mJCRSJRJLNmze7azQarm1s+vTprVwu1zpu3DgTn883NjY2sr/55hvHF154odXJycnC5/Mt06dPb7XNz8vLu/6Pf/yjOjY2tn3//v2Cp59+WjTwvMrKylGLFi3SExEpFAp9RUWFo20sPj5ez2KxKDQ0tNfb27v33LlznLKystH79+8XiMViSXh4eHBLSwtbrVbfs305Pj6+/cSJE6N37NjBj4+P1/cfu9v533//vePChQv1RESLFy/WDe5tPplQmQUAAAAAACIiGkwF9efyxhtv+Lz11ltNycnJbYcPH3Zau3bt7RZbBweH21VVFotFJpOJQUTEYDDuup9UKu2VSqXNWVlZzQKBYGJTUxPrQWMZuC+DwSCr1cr48MMP6+Pj49sfdB8Oh2MNCwszFBQUuF+8ePHi/v37xzzIOiaTab3/rJEHlVkAAAAAAHjsdHR0sGy/c92xY4fgfvOfeeaZzq+++mpMZ2cno6WlhXn06NHbieLevXudLRYLERFduHCBw2KxrK6urub+68PDw7uKi4tdiIiKior4kZGRnbaxL774wsVsNlNVVZVDQ0ODg0wm64mLi2srKCgY29vbyyAiOn/+vEN7e/t986vs7Oym9957r9Hd3f2Bzp80aVLn9u3b+URE27dvv+97GElQmQUAAAAAgCHV09PDFAqFYbZrhUJxY/Xq1ddeffXVCUKhsC8yMrKrvr7e4V57TJ061TB79mx9SEiI1NPTs1cul99ORnft2iVYtWqVN4fDsbDZbGtxcfGPbPb/TYUKCgrqU1NTffPz890FAoFJqVTW2cYCAgJ65XJ5kE6ns9u0adMVHo9nXbZs2a26ujqH0NDQYKvVyuDz+cY///nPtfd71sjIyJ7IyMh/+QDV3c7ftm1b/dy5c/23bdsmfPnll1vut/9IwrBaUbEGAAAAABipVCpVnUwmuzXUcTyu4uPjfV988cW29PR0JJI/A5VK5SqTyXwHsxZtxgAAAAAAADDsoM0YAAAAAADgLkpKSuoedG55eTk3JSXFr/89e3t7y/nz5zWPPDBAMgsAAAAAAPAoyOXybo1Gox7qOEYKtBkDAAAAAADAsINkFgAAAAAAAIYdJLMAAAAAAAAw7CCZBQAAAAAAgGEHySwAAAAAAAwpHo8X/qj3zMrK8sjJyRESER07dmxUWFiYWCwWS/z9/aVZWVkeRESbN28WpKSk+Dzqswcym82UlpbmHRgYKBWJRJKQkJBgjUZj/3Of+6TD14wBAAAAAOCJ9tprr/nt2bOnNioqqttkMpFKpeL8kucXFxfzm5qa7DQaTRWLxaLa2lq70aNHW37JGJ5EqMwCAAAAAMBjZ/fu3c5hYWHi4OBgSXR0tKihoYFN9FPFNSEhwVculwd5eXmFrlu3zs22Jjs7293X1zckOjpaVFNT42C7r9fr2T4+PkYiIjabTRERET0Dz9NqtfZRUVEikUgkiYqKEtXU1NgTEcXHx/smJSX5REREBPn6+obs2bPHmYjIZDLR4sWLvUJCQoJFIpFkw4YNrnd7luvXr9sJhUIji8UiIqIJEyYYx44dayYi+uKLL0ZPnDhRLJFIgmfMmOHf1tbG3L9//+gXXnjB37b+8OHDTs8880zAv/lKnziozAIAAAAAABERXXtntXdvTQ3vUe7pEBho8Pjv/2p42HVxcXGdc+fO1TCZTProo49c165d6759+/ZGIqLLly9zTp06dam1tZUVHBwcsmLFiuby8nLul19+yb9w4YLaaDTSxIkTJeHh4QYiokWLFt0IDg4OmTx5csf06dPbXn/9dR2Px7P2Py8jI8MnKSlJl5mZqdu0aZNAoVB4l5WV1RIRNTQ0OJSXl19Sq9UOsbGxQTNnzrywbds2gbOzs/nixYvV3d3djKeeekr80ksvtYvF4r6Bz/Lb3/5WP23aNLFYLHaKiYlpT0tL002ZMqX7+vXr7P/+7/8e9/e//107evRoy+rVq91///vfC99///3rb7311vj29nbm6NGjLXv27HF55ZVX9IP7P/DkQjILAAAAAACPnR9//NF+1qxZXs3NzXZ9fX1Mb2/vXtvY9OnTW7lcrpXL5Zr4fL6xsbGR/c033zi+8MILrU5OThbbHNv8vLy86+np6frDhw+P3r9/v+Czzz4TlJeXX+p/XmVl5agjR47UEhEpFAr9mjVrvGxj8fHxehaLRaGhob3e3t69586d45SVlY3WaDS8gwcPuhARdXR0sNRqNedOyeyECROMly9fvnjo0CGnY8eOjX7hhReClEplrcFgYNbW1nLkcrmYiMhoNDIiIiI67ezs6Omnn27fu3evc3p6esvx48edt2zZ0vio3/Fwh2QWAAAAAACIiGgwFdSfyxtvvOHz1ltvNSUnJ7cdPnzYae3atR62MQcHh9tVVRaLRSaTiUFExGAw7rqfVCrtlUqlzVlZWc0CgWBiU1MT60FjGbgvg8Egq9XK+PDDD+vj4+PbH2QPLpdrnTNnTvucOXPahUKh8Ysvvhjz3HPPtU+dOrX90KFDPw6cP3fuXP3WrVvdXF1dzWFhYQYXFxf8xnYA/GYWAAAAAAAeOx0dHSzb71x37NghuN/8Z555pvOrr74a09nZyWhpaWEePXp0jG1s7969zhbLT7nghQsXOCwWy+rq6mruvz48PLyruLjYhYioqKiIHxkZ2Wkb++KLL1zMZjNVVVU5NDQ0OMhksp64uLi2goKCsb29vQwiovPnzzu0t7ffMb/69ttveXV1dXZEP33Z+MKFC9zx48f3Pf30011nz551vHjxosP/PjPz/PnzDkREv/71rzuqqqp427dvd01ISECL8R2gMgsAAAAAAEOqp6eHKRQKw2zXCoXixurVq6+9+uqrE4RCYV9kZGRXfX29w732mDp1qmH27Nn6kJAQqaenZ69cLr+djO7atUuwatUqbw6HY2Gz2dbi4uIf2ez/mwoVFBTUp6am+ubn57sLBAKTUqmss40FBAT0yuXyIJ1OZ7dp06YrPB7PumzZslt1dXUOoaGhwVarlcHn841//vOfa+8UW1NTE3vx4sXj+/r6mEREEydO7Fq1atVNHo9nLSoqqps7d65/X18fg4jovffeuxoWFtbLZrPp2Wefbfv8888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GKGYBAAAAAOCJZzQaHTq+CTgnJ4fv6+vb5urqanFxcbFkZWUJiIh27NjhGhUV1dhx7aBBg1rLy8s5BQUFjkRE6enpos72Kikp4cTHxwfs3LnzSkhIiMk+Hh0d3WQ0Grl6vZ7T2trK+Prrr0VxcXG1ncV64YUXav/617+6Wq1WOnr0qJOzs7MFz8t2DxwzBgAAAACAJ15bWxtjyZIlfpWVlQ6Ojo42kUjUvm3btjIiol27dl1JTEyULFiwgCkWi01paWnGjmv5fL5t8+bNpbGxsYEikcg8ePDgxqKiIt7d9iEiSkpK8q6trWXPnz9fQkTEZrNt+fn5RQ4ODpScnFw2btw4ucVioalTp94MDw9vJSJas2aNx+bNm72qq6sdtFqtKiYmpm7Pnj2lkydPrvvuu++EEokkmMfjWbdv3268177wcBh3O8MNAAAAAAB9g06nM2q12ps9nQf0TTqdzk2r1Uq7shbHjAEAAAAAAKDXwTFjAAAAAADokzZu3Oi6ZcsWz45jERERjSkpKWU9lRM8OBwzBgAAAADow3DMGHoSjhkDAAAAAABAn4JiFgAAAAAAAHodFLMAAAAAAADQ66CYBQAAAAAAgF4HxSwAAAAAAPQYq9VKYWFhir1797rYx7Zv395/xIgRsjvnbtiwwVUul6vkcrlKJpOpU1NT+3UWOy4uTrpr167+d45nZmY6x8TEBHbPN4Cegp/mAQAAAACAHsNkMmnr1q2lL730UkBsbGyh2WxmrF692vfgwYMl9jlWq5UuXbrESU5O9s7LyytydXW11NXVMW/cuIF6pg/DPz4AAAAAABARUU2Gwb+9oonfnTEdvJyaRS/Kr3Y2JyIionXMmDF177zzjldTUxNr8uTJ1Ww22zZw4ED10KFDG3JzcwXr168vc3JysgqFQgsRkVAotAqFwjYiotOnT/MSExMlLS0tTIlEYtq9e7fR3d3d0nGPjIwMl6VLl/qLRCKzRqNp7iyfxYsX+1y9epVTWlrqeP36dc6cOXMqk5KSqoiInn322YAbN25wTCYTc86cOZVLliy5SUTE5/NDX3vttapDhw4JuVyuNTMz86K/v7/5Ue4ddA7HjAEAAAAAoMd99NFH1/ft2+d67Ngxl1WrVlUQERmNRu7MmTOri4qKCseOHdvo5ubW7u/vr3nxxRelu3fvFtrXJiQkDFi7dm25wWAoVKvVLcuWLfPpGLu5uZkxb9486TfffHPx7NmzxVVVVQ73y+fixYvc7Oxsw9mzZ4vWr1/vYzKZGEREf/3rX40FBQVFeXl5hZ9++qlnRUUFi4iopaWFGRUV1VhcXFwYFRXVuHnzZvfuvUNwJ3RmAQAAAACAiIju10F9nFxcXKwTJ06sEQgEFh6PZyMi8vb2bhs1alQTERGbzaYTJ06UZGdn8w8dOuSyfPly/5ycHKcVK1ZUNjQ0sMaPH99IRDRr1qzq+Pj4gR1j5+Xlcf38/EwajcZERDRt2rTq7du3d1psjhkzppbH49l4PJ5ZJBK1l5eXswMCAto//PBDz++++64fEVFFRYVDQUEB18vLq8nBwcH28ssv1xERhYWFNR05csSls/jw6NCZBQAAAACAJwKTySQm858lCp/Pt955PSYmpnndunUVqamplzMzMzt9AVRHDAbjoXJxdHS02f9msVhkNpsZmZmZztnZ2c45OTn64uLiwqCgoJaWlhYmERGbzbbZc2ez2WQ2mx9uQ3hoKGYBAAAAAOCJZzQaHU6ePHn7ed6cnBy+r69vm6urq8XFxcWSlZUlICLasWOHa1RUVGPHtYMGDWotLy/nFBQUOBIRpaeni7qSQ21tLUsoFFqcnZ2t586d4+p0OqdH+U7waHDMGAAAAAAAnnhtbW2MJUuW+FVWVjo4OjraRCJR+7Zt28qIiHbt2nUlMTFRsmDBAqZYLDalpaUZO67l8/m2zZs3l8bGxgaKRCLz4MGDG4uKingPm0NcXFzdZ5995i6Xy1UBAQGtWq22qXu+HXQFw2az3X8WAAAAAAD8R9LpdEatVnuzp/OAvkmn07lptVppV9bimDEAAAAAAAD0OjhmDAAAAAAAfdLGjRtdt2zZ4tlxLCIiojElJaWsp3KCB4djxgAAAAAAfRiOGUNPwjFjAAAAAAAA6FNQzAIAAAAAAECvg2IWAAAAAAAAeh0UswAAAAAA0OPi4+OlIpFIK5PJ1J3Ny8zMdD58+LCT/fPixYt9PDw8QpRKpUqpVKrmzp3rS0QUGRmpOHHiBP9uMdLS0oRBQUEqhUKhCggIUH/88cduncWCJxPeZgwAAAAAAD3u1Vdfvblw4cKqmTNnDuhs3rFjx5wFAoFl9OjRTfaxOXPmVK5ataryQfZpaWlhLFy4UHLmzJmigICA9paWFobBYOB0JRb0LHRmAQAAAACgxz333HON7u7u5o5ja9as8QgICFDL5XJVbGzswOLiYs6XX37pvnXrVk+lUqnKysoSPEhsPp8fumjRIp+QkBDl8ePHncxmM8PT09NMRMTj8Wxardb0OL4TPF7ozAIAAAAAABER7d+/37+qququR3O7ysPDo3nixIlXu7J206ZNXqWlpRd4PJ7t5s2bLDc3N8v06dN/EQgEFnv39NChQy5bt2713Lt3rysR0fvvv18eFxdX3zFOS0sLMzg4uGXDhg3XiYhGjx5dKxaLQ4YNG1b//PPP173xxhs1LBaLiIjuFwueHOjMAgAAAADAE0mhULT813/PO8hpAAAgAElEQVT914BPPvlE5ODgYLvXvDlz5lTq9fpCvV5feLfik8ViUUJCwi375z179pRmZWUZwsPDmzZt2uQ1efJk6YPGgicHOrMAAAAAAEBERF3toD4uP/zwQ8nf/vY35/379/f76KOPfEpKSvK7EofD4VjZ7H8tfSIjI1siIyNb3njjjZrAwEANERm7IWX4N0JnFgAAAAAAnjgWi4UuXbrEmTBhQsMnn3xS3tDQwKqrq2M5OztbGhoaWF2NW1dXx8zMzHS2f/7pp594Pj4+bd2TNfw7oTMLAAAAAAA9bsKECQP+/ve/O9+6dYvt6ekZsmTJkuvp6emuDQ0NLJvNxpg9e3alm5ubJS4urvbFF18M+Nvf/tZvw4YNZQ+7j9VqpY8//thz3rx5Ei6Xa+Xz+dYdO3ZceRzfCR4vhs12z6PnAAAAAADwH06n0xm1Wu3Nns4D+iadTuem1WqlXVmLY8YAAAAAAADQ66CYBQAAAAAAgF4HxSwAAAAAAAD0OihmAQAAAAAAoNdBMQsAAAAAAAC9DopZAAAAAAAA6HVQzAIAAAAAQI+6ePGiw+DBg+UDBw5UBwYGqlevXu3xMOsjIyMVJ06c4BMR+fr6auRyuUqpVKqUSqXq8OHDTsXFxRyZTKa+21qLxUIJCQn+MplMLZfLVcHBwUF6vZ5zr1iP/m2hu7B7OgEAAAAAAOjbHBwcKDk5uXz48OHNt27dYoaGhqqef/75+rCwsNauxMvOzjZ4e3ub7Z+Li4s5d5vX3t5OO3fuFFVUVDjo9foCFotFly5dcnBxcbHeKxY8OVDMAgAAAABAj5JIJO0SiaSdiKh///7WgICAlrKyMk5iYqIkLCys8eTJky4NDQ2srVu3GseNG9fY2NjIePnllwcYDAauTCZrbW1tZTzoXps2bXL929/+JjSZTMzm5mbmuHHj6jw9PdtZLBYREQUEBLQ/pq8J3QzFLAAAAAAAEBFRYdEy/6ZGA787YzoJ5M2qoA+vPuj84uJiTmFhIT86Orpx3bp1ZDabGRcuXCjas2ePcNWqVT7jxo0zrF+/3oPH41kNBkPhTz/9xBs2bJiqY4zo6Gg5k8kkDodjPX/+vP7OPX7++WfB+fPnCzw9PS2XLl1yePrpp5VKpdJ5xIgR9QkJCdXDhg1redBY0HPwzCwAAAAAADwR6urqmJMmTQr44IMPropEIisRUXx8/C0ioqFDhzaVl5dziIhOnjwpeOWVV6qJiAYPHtwil8ubO8bJzs426PX6wnsVnyNGjKj39PS0EP3aib148WL+qlWryplMJj3//POKAwcOOD9oLOg56MwCAAAAAAARET1MB7W7mUwmxvjx4wPi4+NrZsyYUWsf53K5NiIiNptNFovl9nFiBuOBTxb/Bp/Pt3b8zOPxbJMnT66fPHlyvaenZ/vXX3/d73e/+11DlzeAfwt0ZgEAAAAAoEdZrVZ6+eWXJXK5vPXdd9+tvN/84cOHN6ampoqIiM6ePcs1GLp+NPrkyZN8o9HoQPTrm40vXLjAk0gkbV2NB/8+6MwCAAAAAECPOnz4sGD//v2uMpmsRalUqoiI3nvvvWv3mr9kyZKql19+eYBcLlep1epmjUbT1NW9Kyoq2LNnz5a0tbUxiYgGDRrUtHz58qquxoN/H4bNZuvpHAAAAAAAoIfodDqjVqu92dN5QN+k0+nctFqttCtrccwYAAAAAAAAeh0UswAAAAAAANDroJgFAAAAAACAXgfFLAAAAAAAAPQ6KGYBAAAAAACg10ExCwAAAAAAAL0OilkAAAAAAOhRzc3NDI1GE6RQKFSBgYHqN99804eIKC0tTRgUFKRSKBSqgIAA9ccff+zWlfi+vr4auVyuUigUqmHDhsnKysrYj5rzpk2bXKdPny5+1DjQdY/8jwgAAAAAAPAouFyu7eTJk8VCodBqMpkYERERiqysrPqFCxdKzpw5UxQQENDe0tLCMBgMnK7ukZ2dbfD29jbPmzfPd+XKld6ff/751QdZZzabic1G2fQkQmcWAAAAAAB6FJPJJKFQaCUiamtrY5jNZgaHw7GZzWaGp6enmYiIx+PZtFqtiYho586d/WUymVqhUKjCw8MVRL92SseMGRMwYsQImUQiCZ4zZ47f3fZ65plnGq5cueJIRPTpp5+K5HK5SiaTqRMTE33tc/h8fuiiRYt8QkJClEePHhVkZ2fzQ0NDlQqFQqXRaIJu3brFJCKqqKhwuN9+8PjgvxgAAAAAAICIiBYVlfnrm1r53RlT6cRt3hA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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "alphas_elastic = np.logspace(-10, 6, 100)\n", + "coef_elastic = []\n", + "for i in alphas_elastic:\n", + " elastic = linear_model.ElasticNet(l1_ratio =0.6)\n", + " elastic.set_params(alpha = i)\n", + " elastic.fit(x_onehot, y_log)\n", + " coef_elastic.append(elastic.coef_)\n", + "\n", + "df_coef = pd.DataFrame(coef_elastic, index=alphas_elastic, columns=x_onehot.columns)\n", + "title = 'ElasticNet coefficients as a function of the regularization'\n", + "df_coef.plot(logx=True, title=title)\n", + "plt.xlabel('alpha')\n", + "plt.ylabel('coefficients')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### test prediction" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "best_elas_test_y = para_search_elas.best_estimator_.predict(test_onehot)\n", + "best_elas_test_y = np.expm1(best_elas_test_y)\n", + "sub = pd.DataFrame()\n", + "sub['SalePrice'] = best_elas_test_y\n", + "sub = sub.set_index(np.arange(1461, 2920))\n", + "sub.to_csv('(3) elas_log(y)_onehot_submission.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Kelly/Kaggle_house_price/Neighborhood feature engineering (regression).ipynb b/Kelly/neighborhood_feature_engineering_lasso.ipynb similarity index 98% rename from Kelly/Kaggle_house_price/Neighborhood feature engineering (regression).ipynb rename to Kelly/neighborhood_feature_engineering_lasso.ipynb index 8e9056a..12be478 100644 --- a/Kelly/Kaggle_house_price/Neighborhood feature engineering (regression).ipynb +++ b/Kelly/neighborhood_feature_engineering_lasso.ipynb @@ -14,7 +14,7 @@ "test = pd.read_csv(\"test.csv\").set_index(\"Id\")\n", "combine = pd.concat([train, test])\n", "\n", - "# seperate based on neighborhoood median SalesPrice\n", + "# seperate based on neighborhoood median SalesPrice \n", "worst_neighbor_df = combine[combine.Neighborhood.isin(['MeadowV', 'IDOTRR', 'BrDale', 'OldTown','Edwards', 'BrkSide', 'Sawyer', 'Blueste'])]\n", "med_neighbor_df = combine[combine.Neighborhood.isin(['SWISU', 'NAmes', 'NPkVill', 'Mitchel', 'SawyerW', 'Gilbert', 'NWAmes', 'Blmngtn'])]\n", "best_neighbor_df = combine[combine.Neighborhood.isin(['CollgCr', 'ClearCr', 'Crawfor', 'Veenker', 'Somerst', 'Timber', 'StoneBr', 'NoRidge', 'NridgHt'])]\n" @@ -227,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -261,7 +261,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -279,16 +279,16 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "lasso_neighbor_pred_y.to_csv('(16) lasso_neighbor_submission.csv')" + "lasso_neighbor_pred_y.to_csv('(6) lasso_neighbor_submission.csv')" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -637,7 +637,7 @@ "[1459 rows x 1 columns]" ] }, - "execution_count": 33, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } diff --git a/Kelly/Kaggle_house_price/preprocessfinal.py b/Kelly/preprocessfinal.py similarity index 100% rename from Kelly/Kaggle_house_price/preprocessfinal.py rename to Kelly/preprocessfinal.py diff --git a/Kelly/tree_based_models.ipynb b/Kelly/tree_based_models.ipynb new file mode 100644 index 0000000..f82589b --- /dev/null +++ b/Kelly/tree_based_models.ipynb @@ -0,0 +1,358 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "## import data\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "train = pd.read_csv(\"train.csv\").set_index(\"Id\")\n", + "test = pd.read_csv(\"test.csv\").set_index(\"Id\")\n", + "combine = pd.concat([train, test])\n", + "\n", + "# process data before model fitting\n", + "from preprocessfinal import impute\n", + "fact, encodedDic = impute(combine, False) # process data and label encode \n", + "\n", + "# seperate factorized data into train and test\n", + "train_label = fact.iloc[0:1460,]\n", + "test_label = fact.iloc[1460:2919,].drop('SalePrice', axis = 1) # salesprice col were all NA \n", + "\n", + "# split train data frame into x var and y var for model testing\n", + "x_label = train_label.drop('SalePrice', axis=1)\n", + "y_log = np.log(train_label.SalePrice)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "GridSearchCV(cv=5, error_score='raise',\n", + " estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n", + " max_features='auto', max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n", + " oob_score=False, random_state=None, verbose=0, warm_start=False),\n", + " fit_params=None, iid=True, n_jobs=-1,\n", + " param_grid=[{'n_estimators': array([100, 200, 300]), 'min_samples_leaf': range(10, 15), 'min_samples_split': array([ 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30]), 'random_state': [42]}],\n", + " pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n", + " scoring='neg_mean_squared_error', verbose=0)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn import ensemble\n", + "from sklearn.model_selection import GridSearchCV\n", + "\n", + "randomForest = ensemble.RandomForestRegressor()\n", + "grid_para_forest = [{\n", + " \"n_estimators\": np.arange(100, 400, 100),\n", + " \"min_samples_leaf\": range(10,15),\n", + " \"min_samples_split\": np.linspace(start=2, stop=30, num=15, dtype=int),\n", + " \"random_state\": [42]}]\n", + "grid_search_forest = GridSearchCV(randomForest, grid_para_forest, scoring='neg_mean_squared_error', cv=5, n_jobs=-1)\n", + "grid_search_forest.fit(x_label, y_log)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters found: {'min_samples_leaf': 10, 'min_samples_split': 2, 'n_estimators': 300, 'random_state': 42}\n", + "Lowest RMSE found: 0.1503675297419843\n" + ] + } + ], + "source": [ + "print(\"Best parameters found: \", grid_search_forest.best_params_)\n", + "print(\"Lowest RMSE found: \", np.sqrt(np.abs(grid_search_forest.best_score_)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### RMSE for all train data using best random forest" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.00018805264791066581\n" + ] + } + ], + "source": [ + "# fit best random forest to all train data \n", + "best_forest_y = grid_search_forest.best_estimator_.predict(x_label)\n", + "print(\"RMSE: \", np.sqrt(np.mean(y_log-best_forest_y)**2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Feature importance" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "pd.DataFrame(list(zip(x_label.columns, grid_search_forest.best_estimator_.feature_importances_ ))).set_index(0).sort_values(1).plot.barh()\n", + "plt.rcParams['figure.figsize'] = 4, 32" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### test prediction" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "best_forest_test_y = grid_search_forest.best_estimator_.predict(test_label) \n", + "best_forest_test_y = np.expm1(best_forest_test_y)\n", + "sub = pd.DataFrame() \n", + "sub['SalePrice'] = best_forest_test_y\n", + "sub = sub.set_index(np.arange(1461, 2920))\n", + "sub.to_csv('(4) forest_log(y)_label_submission.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Extreme Gradient Boosting Random Forest (regression)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# !pip install xgboost" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GridSearchCV(cv=5, error_score='raise',\n", + " estimator=XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n", + " colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n", + " max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n", + " n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n", + " reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n", + " silent=True, subsample=1),\n", + " fit_params=None, iid=True, n_jobs=-1,\n", + " param_grid=[{'colsample_bytree': array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]), 'max_depth': [2, 4, 6], 'n_estimators': array([500, 600, 700, 800, 900]), 'learning_rate': [0.001, 0.01, 0.1, 0.2, 0.3], 'random_state': [42]}],\n", + " pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n", + " scoring='neg_mean_squared_error', verbose=0)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "from sklearn.model_selection import GridSearchCV\n", + "import xgboost as xgb\n", + "xgbforest = xgb.XGBRegressor()\n", + "grid_para_xgbforest = [{\n", + " \"colsample_bytree\": np.linspace(0.1, 0.9, 9),\n", + " 'max_depth':[2, 4, 6],\n", + " \"n_estimators\": np.arange(500, 1000, 100),\n", + " \"learning_rate\": [0.001, 0.01, 0.1, 0.2, 0.3],\n", + " \"random_state\": [42]}]\n", + "grid_search_xgbforest = GridSearchCV(estimator = xgbforest, param_grid = grid_para_xgbforest, \n", + " scoring = 'neg_mean_squared_error', cv = 5, n_jobs=-1)\n", + "\n", + "grid_search_xgbforest.fit(x_label, y_log)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters found: {'colsample_bytree': 0.2, 'learning_rate': 0.1, 'max_depth': 4, 'n_estimators': 500, 'random_state': 42}\n", + "Lowest RMSE found: 0.11953516580561195\n" + ] + } + ], + "source": [ + "print(\"Best parameters found: \", grid_search_xgbforest.best_params_)\n", + "print(\"Lowest RMSE found: \", np.sqrt(np.abs(grid_search_xgbforest.best_score_)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### RMSE for all train data using best gradient boost" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 7.743956133943185e-06\n" + ] + } + ], + "source": [ + "# fit best XGB to all train data \n", + "best_xgbforest_y = grid_search_xgbforest.best_estimator_.predict(x_label)\n", + "print(\"RMSE: \", np.sqrt(np.mean(y_log-best_xgbforest_y)**2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Feature importance" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "pd.DataFrame(list(zip(x_label.columns, grid_search_xgbforest.best_estimator_.feature_importances_ ))).set_index(0).sort_values(1).plot.barh()\n", + "plt.rcParams['figure.figsize'] = 4, 32" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### test prediction" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "best_xgb_test_y = grid_search_xgbforest.best_estimator_.predict(test_label) \n", + "best_xgb_test_y = np.expm1(best_xgb_test_y)\n", + "sub = pd.DataFrame() \n", + "sub['SalePrice'] = best_xgb_test_y\n", + "sub = sub.set_index(np.arange(1461, 2920))\n", + "sub.to_csv('(5) xgb_log(y)_label_submission.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 28698f735ce06d17fcb0996c3a4c7e3fae84f040 Mon Sep 17 00:00:00 2001 From: kellyho15 <40176918+kellyho15@users.noreply.github.com> Date: Fri, 28 Sep 2018 08:03:29 -0400 Subject: [PATCH 7/8] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index a9e3eba..9231de6 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@ # 4sigma ML project -The goal of this project was aimed to utilize supervised machine learning techniques to predict the price of houses located in Ames, Iowa. This dataset was provided by Kaggle. This dataset provided around 80 different features, including multiple aspects of the house that would help or may not help predict the fluctuation of the house prices. +The goal of this project was aimed to utilize supervised machine learning techniques to predict the price of houses located in Ames, Iowa. This dataset was provided by Kaggle. This dataset provided around 80 different features, including multiple aspects of the house that would help or may not help predict the fluctuation of the house prices. The algorithm used in this project includes linear regression models and treee based regression models. https://nycdatascience.com/blog/student-works/predictive-modeling-on-house-prices-ames-iowa/ From 20033af99add7ff2d658a5ea927f3c5939528653 Mon Sep 17 00:00:00 2001 From: kellyho15 <40176918+kellyho15@users.noreply.github.com> Date: Fri, 28 Sep 2018 08:04:09 -0400 Subject: [PATCH 8/8] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 9231de6..7876293 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@ # 4sigma ML project -The goal of this project was aimed to utilize supervised machine learning techniques to predict the price of houses located in Ames, Iowa. This dataset was provided by Kaggle. This dataset provided around 80 different features, including multiple aspects of the house that would help or may not help predict the fluctuation of the house prices. The algorithm used in this project includes linear regression models and treee based regression models. +The goal of this project was aimed to utilize supervised machine learning techniques to predict the price of houses located in Ames, Iowa. This dataset was provided by Kaggle. This dataset provided around 80 different features, including multiple aspects of the house that would help or may not help predict the fluctuation of the house prices. The algorithms used in this project include linear regression models and treee based regression models. https://nycdatascience.com/blog/student-works/predictive-modeling-on-house-prices-ames-iowa/