From 646bc7b4be49352932b3a6c6cbedb197c87d74f9 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Fri, 27 Jun 2025 15:40:24 -0700 Subject: [PATCH 1/7] change docs to myst --- docs/examples/Test_design.ipynb | 619 -------- docs/examples/Test_design.md | 2 +- docs/examples/bikeshare.Rmd | 300 ---- docs/examples/bikeshare.ipynb | 1765 ----------------------- docs/examples/bikeshare_new.Rmd | 329 ----- docs/examples/bikeshare_new.ipynb | 1923 ------------------------- docs/examples/cross_validation.ipynb | 1777 ----------------------- docs/examples/cross_validation.md | 181 +++ docs/examples/diabetes_logistic.ipynb | 535 ------- docs/examples/diabetes_logistic.md | 82 ++ docs/examples/elnet_compare.ipynb | 203 --- docs/examples/elnet_compare.md | 66 + docs/examples/glmnet_path.ipynb | 281 ---- docs/requirements.txt | 7 + 14 files changed, 337 insertions(+), 7733 deletions(-) delete mode 100644 docs/examples/Test_design.ipynb delete mode 100644 docs/examples/bikeshare.Rmd delete mode 100644 docs/examples/bikeshare.ipynb delete mode 100644 docs/examples/bikeshare_new.Rmd delete mode 100644 docs/examples/bikeshare_new.ipynb delete mode 100644 docs/examples/cross_validation.ipynb create mode 100644 docs/examples/cross_validation.md delete mode 100644 docs/examples/diabetes_logistic.ipynb create mode 100644 docs/examples/diabetes_logistic.md delete mode 100644 docs/examples/elnet_compare.ipynb create mode 100644 docs/examples/elnet_compare.md delete mode 100644 docs/examples/glmnet_path.ipynb create mode 100644 docs/requirements.txt diff --git a/docs/examples/Test_design.ipynb b/docs/examples/Test_design.ipynb deleted file mode 100644 index 0f0468b..0000000 --- a/docs/examples/Test_design.ipynb +++ /dev/null @@ -1,619 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "1caa3787-e6cb-44d6-9956-b0f4678d57d8", - "metadata": {}, - "source": [ - "Implementing this code here: https://github.com/trevorhastie/glmnet/blob/3b268cebc7a04ff0c7b22931cb42b4c328ede307/R/glmnetFlex.R#L217C1-L233C6\n", - "\n", - "'''\n", - "# standardize x if necessary\n", - " if (intercept) {\n", - " xm <- meansd$mean\n", - " } else {\n", - " xm <- rep(0.0, times = nvars)\n", - " }\n", - " if (standardize) {\n", - " xs <- meansd$sd\n", - " } else {\n", - " xs <- rep(1.0, times = nvars)\n", - " }\n", - " if (!inherits(x, \"sparseMatrix\")) {\n", - " x <- scale(x, xm, xs)\n", - " }\n", - "'''" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "6b9c16cb-e6b5-42f0-a3ab-98134d14fd08", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from glmnet.base import Design\n", - "from glmnet import GLMNet\n", - "import rpy2\n", - "%load_ext rpy2.ipython\n", - "\n", - "n, p = 100, 50\n", - "rng = np.random.default_rng(0)\n", - "X = rng.standard_normal((n, p))\n", - "Y = rng.standard_normal(n)\n", - "beta = rng.standard_normal(p)\n", - "W = rng.uniform(1, 2, size=n)\n", - "W /= W.mean()\n", - "%R -i X,Y" - ] - }, - { - "cell_type": "markdown", - "id": "0f8fa935-bd2c-454e-ae9d-e551bd8ca799", - "metadata": {}, - "source": [ - "# Without weights (all 1's)\n", - "\n", - "## `standardize=True`, `intercept=True`" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "920a6de3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.2820689073452428" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "standardize, intercept = True, True\n", - "W = np.ones(n)\n", - "xm = (X * W[:,None]).sum(0) / W.sum()\n", - "x2 = (X**2 * W[:,None]).sum(0) / W.sum()\n", - "xs = np.sqrt(x2 - xm**2)\n", - "if not standardize:\n", - " xs = np.ones_like(xs)\n", - "if not intercept:\n", - " xm = np.zeros_like(xm)\n", - "X_ = (X - xm[None,:]) / xs[None,:]\n", - "D = Design(X, intercept=intercept, standardize=standardize)\n", - "assert np.allclose(D.centers_, xm)\n", - "assert np.allclose(D.scaling_, xs)\n", - "assert np.allclose(D @ np.hstack([0, beta]), X_ @ beta)\n", - "G = GLMNet(lambda_values=np.linspace(0.05,1,51)[::-1],\n", - " fit_intercept=intercept, standardize=standardize)\n", - "G.fit(X, Y)\n", - "assert np.allclose(G.lambda_max_, np.fabs(X_.T @ (W * (Y - Y.mean()))).max() / n)\n", - "G.lambda_max_" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "46fd4253", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1] 28.20689\n" - ] - }, - { - "data": { - "text/plain": [ - "Loading required package: Matrix\n", - "Loaded glmnet 4.1-7\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%%R -i standardize,intercept,W\n", - "library(glmnet)\n", - "G=glmnet(X, Y, intercept=intercept, standardize=standardize)\n", - "print(max(G$lambda)*nrow(X))" - ] - }, - { - "cell_type": "markdown", - "id": "d2c190df-cbef-4c5f-870d-94e0987fe1f5", - "metadata": {}, - "source": [ - "## `standardize=True`, `intercept=False`" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "bab1d6d4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.2910602370580807" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "standardize, intercept = True, False\n", - "xm = (X * W[:,None]).sum(0) / W.sum()\n", - "x2 = (X**2 * W[:,None]).sum(0) / W.sum()\n", - "xs = np.sqrt(x2 - xm**2)\n", - "if not standardize:\n", - " xs = np.ones_like(xs)\n", - "if not intercept:\n", - " xm = np.zeros_like(xm)\n", - "X_ = (X - xm[None,:]) / xs[None,:]\n", - "D = Design(X, intercept=intercept, standardize=standardize)\n", - "assert np.allclose(D.centers_, xm)\n", - "assert np.allclose(D.scaling_, xs)\n", - "assert np.allclose(D @ np.hstack([0, beta]), X_ @ beta)\n", - "G = GLMNet(lambda_values=np.linspace(0.05,1,51)[::-1],\n", - " fit_intercept=intercept, standardize=standardize)\n", - "G.fit(X, Y)\n", - "assert np.allclose(G.lambda_max_, np.fabs(X_.T @ (W * Y)).max()/n)\n", - "G.lambda_max_" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "30eeeb45", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1] 29.10602\n" - ] - } - ], - "source": [ - "%%R -i standardize,intercept,W\n", - "library(glmnet)\n", - "G=glmnet(X, Y, intercept=intercept, standardize=standardize)\n", - "print(max(G$lambda)*nrow(X))" - ] - }, - { - "cell_type": "markdown", - "id": "40094536-baed-462e-9500-dded96f903b5", - "metadata": {}, - "source": [ - "## `standardize=False`, `intercept=True`" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "3d1ae7f4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.27946460564487424" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "standardize, intercept = False, True\n", - "xm = (X * W[:,None]).sum(0) / W.sum()\n", - "x2 = (X**2 * W[:,None]).sum(0) / W.sum()\n", - "xs = np.sqrt(x2 - xm**2)\n", - "if not standardize:\n", - " xs = np.ones_like(xs)\n", - "if not intercept:\n", - " xm = np.zeros_like(xm)\n", - "X_ = (X - xm[None,:]) / xs[None,:]\n", - "D = Design(X, intercept=intercept, standardize=standardize)\n", - "assert np.allclose(D.centers_, xm)\n", - "assert np.allclose(D.scaling_, xs)\n", - "assert np.allclose(D @ np.hstack([0, beta]), X_ @ beta)\n", - "G = GLMNet(lambda_values=np.linspace(0.05,1,51)[::-1],\n", - " fit_intercept=intercept, standardize=standardize)\n", - "G.fit(X, Y)\n", - "assert np.allclose(G.lambda_max_, np.fabs(X_.T @ (W * (Y - Y.mean()))).max()/n)\n", - "G.lambda_max_" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "950fd3c3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1] 27.94646\n" - ] - } - ], - "source": [ - "%%R -i standardize,intercept,W\n", - "library(glmnet)\n", - "G=glmnet(X, Y, intercept=intercept, standardize=standardize)\n", - "print(max(G$lambda)*nrow(X))" - ] - }, - { - "cell_type": "markdown", - "id": "f4345993-7843-4031-b701-56c686aa3b6a", - "metadata": {}, - "source": [ - "## `standardize=False`, `intercept=False`" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "30d670d7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.2883729197021402" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "standardize, intercept = False, False\n", - "xm = (X * W[:,None]).sum(0) / W.sum()\n", - "x2 = (X**2 * W[:,None]).sum(0) / W.sum()\n", - "xs = np.sqrt(x2 - xm**2)\n", - "if not standardize:\n", - " xs = np.ones_like(xs)\n", - "if not intercept:\n", - " xm = np.zeros_like(xm)\n", - "X_ = (X - xm[None,:]) / xs[None,:]\n", - "D = Design(X, intercept=intercept, standardize=standardize)\n", - "assert np.allclose(D.centers_, xm)\n", - "assert np.allclose(D.scaling_, xs)\n", - "assert np.allclose(D @ np.hstack([0, beta]), X_ @ beta)\n", - "G = GLMNet(lambda_values=np.linspace(0.05,1,51)[::-1],\n", - " fit_intercept=intercept, standardize=standardize)\n", - "G.fit(X, Y)\n", - "assert np.allclose(G.lambda_max_, np.fabs(X_.T @ (W * Y)).max()/n)\n", - "G.lambda_max_" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "e05d8072", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1] 28.83729\n" - ] - } - ], - "source": [ - "%%R -i standardize,intercept,W\n", - "library(glmnet)\n", - "G=glmnet(X, Y, intercept=intercept, standardize=standardize)\n", - "print(max(G$lambda)*nrow(X))" - ] - }, - { - "cell_type": "markdown", - "id": "b165fb47", - "metadata": {}, - "source": [ - "# With weights\n", - "\n", - "## `standardize=True`, `intercept=True`" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "259762b1-adfa-4473-affc-a15ba7380126", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.2820689073452428" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "standardize, intercept = True, True\n", - "xm = (X * W[:,None]).sum(0) / W.sum()\n", - "x2 = (X**2 * W[:,None]).sum(0) / W.sum()\n", - "xs = np.sqrt(x2 - xm**2)\n", - "if not standardize:\n", - " xs = np.ones_like(xs)\n", - "if not intercept:\n", - " xm = np.zeros_like(xm)\n", - "X_ = (X - xm[None,:]) / xs[None,:]\n", - "D = Design(X, W, intercept=intercept, standardize=standardize)\n", - "assert np.allclose(D.centers_, xm)\n", - "assert np.allclose(D.scaling_, xs)\n", - "assert np.allclose(D @ np.hstack([0, beta]), X_ @ beta)\n", - "G = GLMNet(lambda_values=np.linspace(0.05,1,51)[::-1],\n", - " fit_intercept=intercept, standardize=standardize)\n", - "G.fit(X, Y, sample_weight=W)\n", - "assert np.allclose(G.lambda_max_, np.fabs(X_.T @ (W * (Y - Y.mean()))).max()/n)\n", - "G.lambda_max_" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "b5c72297-6d4b-4f0c-b9f6-46fefd2596df", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1] 28.20689\n" - ] - } - ], - "source": [ - "%%R -i standardize,intercept,W\n", - "library(glmnet)\n", - "G=glmnet(X, Y, intercept=intercept, standardize=standardize, weights=W)\n", - "print(max(G$lambda)*nrow(X))" - ] - }, - { - "cell_type": "markdown", - "id": "64459b13", - "metadata": {}, - "source": [ - "## `standardize=True`, `intercept=False`" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "a9bd7eca-4088-4e4b-9539-757cd91c4f8f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.2910602370580807" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "standardize, intercept = True, False\n", - "xm = (X * W[:,None]).sum(0) / W.sum()\n", - "x2 = (X**2 * W[:,None]).sum(0) / W.sum()\n", - "xs = np.sqrt(x2 - xm**2)\n", - "if not standardize:\n", - " xs = np.ones_like(xs)\n", - "if not intercept:\n", - " xm = np.zeros_like(xm)\n", - "X_ = (X - xm[None,:]) / xs[None,:]\n", - "D = Design(X, W, intercept=intercept, standardize=standardize)\n", - "assert np.allclose(D.centers_, xm)\n", - "assert np.allclose(D.scaling_, xs)\n", - "assert np.allclose(D @ np.hstack([0, beta]), X_ @ beta)\n", - "G = GLMNet(lambda_values=np.linspace(0.05,1,51)[::-1],\n", - " fit_intercept=intercept, standardize=standardize)\n", - "G.fit(X, Y, sample_weight=W)\n", - "assert np.allclose(G.lambda_max_, np.fabs(X_.T @ (W * Y)).max()/n)\n", - "G.lambda_max_" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "14542f52-8f58-4ac9-869f-8b3b82bf5d3d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1] 29.10602\n" - ] - } - ], - "source": [ - "%%R -i standardize,intercept,W\n", - "library(glmnet)\n", - "G=glmnet(X, Y, intercept=intercept, standardize=standardize, weights=W)\n", - "print(max(G$lambda)*nrow(X))" - ] - }, - { - "cell_type": "markdown", - "id": "9a129c0b", - "metadata": {}, - "source": [ - "## `standardize=False`, `intercept=True`" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "a5cc832f-fafd-4c73-86f3-8b7617cc7def", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.27946460564487424" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "standardize, intercept = False, True\n", - "xm = (X * W[:,None]).sum(0) / W.sum()\n", - "x2 = (X**2 * W[:,None]).sum(0) / W.sum()\n", - "xs = np.sqrt(x2 - xm**2)\n", - "if not standardize:\n", - " xs = np.ones_like(xs)\n", - "if not intercept:\n", - " xm = np.zeros_like(xm)\n", - "X_ = (X - xm[None,:]) / xs[None,:]\n", - "D = Design(X, W, intercept=intercept, standardize=standardize)\n", - "assert np.allclose(D.centers_, xm)\n", - "assert np.allclose(D.scaling_, xs)\n", - "assert np.allclose(D @ np.hstack([0, beta]), X_ @ beta)\n", - "G = GLMNet(lambda_values=np.linspace(0.05,1,51)[::-1],\n", - " fit_intercept=intercept, standardize=standardize)\n", - "G.fit(X, Y, sample_weight=W)\n", - "assert np.allclose(G.lambda_max_, np.fabs(X_.T @ (W * (Y - Y.mean()))).max()/n)\n", - "G.lambda_max_" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "5beb2f1f-1947-4494-82c5-573c146a752a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1] 27.94646\n" - ] - } - ], - "source": [ - "%%R -i standardize,intercept,W\n", - "library(glmnet)\n", - "G=glmnet(X, Y, intercept=intercept, standardize=standardize, weights=W)\n", - "print(max(G$lambda)*nrow(X))" - ] - }, - { - "cell_type": "markdown", - "id": "588838bd", - "metadata": {}, - "source": [ - "## `standardize=False`, `intercept=False`" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "28d7fe36-c498-4728-a8c0-5311b2256a3d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.2883729197021402" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "standardize, intercept = False, False\n", - "xm = (X * W[:,None]).sum(0) / W.sum()\n", - "x2 = (X**2 * W[:,None]).sum(0) / W.sum()\n", - "xs = np.sqrt(x2 - xm**2)\n", - "if not standardize:\n", - " xs = np.ones_like(xs)\n", - "if not intercept:\n", - " xm = np.zeros_like(xm)\n", - "X_ = (X - xm[None,:]) / xs[None,:]\n", - "D = Design(X, W, intercept=intercept, standardize=standardize)\n", - "assert np.allclose(D.centers_, xm)\n", - "assert np.allclose(D.scaling_, xs)\n", - "assert np.allclose(D @ np.hstack([0, beta]), X_ @ beta)\n", - "G = GLMNet(lambda_values=np.linspace(0.05,1,51)[::-1],\n", - " fit_intercept=intercept, standardize=standardize)\n", - "G.fit(X, Y, sample_weight=W)\n", - "assert np.allclose(G.lambda_max_, np.fabs(X_.T @ (W * Y)).max()/n)\n", - "G.lambda_max_" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1025cb84-ac9b-4c20-a746-a9af8c265d6f", - "metadata": {}, - "outputs": [], - "source": [ - "%%R -i standardize,intercept,W\n", - "library(glmnet)\n", - "G=glmnet(X, Y, intercept=intercept, standardize=standardize, weights=W)\n", - "print(max(G$lambda)*nrow(X))" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,md:myst" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/examples/Test_design.md b/docs/examples/Test_design.md index 40973ed..2fb1df8 100644 --- a/docs/examples/Test_design.md +++ b/docs/examples/Test_design.md @@ -5,7 +5,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.15.0 + jupytext_version: 1.17.2 kernelspec: display_name: Python 3 (ipykernel) language: python diff --git a/docs/examples/bikeshare.Rmd b/docs/examples/bikeshare.Rmd deleted file mode 100644 index 0402e49..0000000 --- a/docs/examples/bikeshare.Rmd +++ /dev/null @@ -1,300 +0,0 @@ ---- -jupyter: - jupytext: - cell_metadata_filter: -all - formats: ipynb,Rmd - text_representation: - extension: .Rmd - format_name: rmarkdown - format_version: '1.2' - jupytext_version: 1.15.0 - kernelspec: - display_name: Python 3 (ipykernel) - language: python - name: python3 ---- - - -# Chapter 4 - - - - - -# Lab: Logistic Regression, LDA, QDA, and KNN - - - -## The Stock Market Data - -In this lab we will examine the `Smarket` -data, which is part of the `ISLP` -library. This data set consists of percentage returns for the S&P 500 -stock index over 1,250 days, from the beginning of 2001 until the end -of 2005. For each date, we have recorded the percentage returns for -each of the five previous trading days, `Lag1` through - `Lag5`. We have also recorded `Volume` (the number of -shares traded on the previous day, in billions), `Today` (the -percentage return on the date in question) and `Direction` -(whether the market was `Up` or `Down` on this date). - -We start by importing our libraries at this top level; these are all imports we have seen in previous labs. - -```{python} -import numpy as np -import pandas as pd -from matplotlib.pyplot import subplots -import statsmodels.api as sm -from ISLP import load_data -from ISLP.models import (ModelSpec as MS, - summarize) -``` - -We also collect together the new imports needed for this lab. - -```{python} -from ISLP import confusion_table -from ISLP.models import contrast -from sklearn.discriminant_analysis import \ - (LinearDiscriminantAnalysis as LDA, - QuadraticDiscriminantAnalysis as QDA) -from sklearn.naive_bayes import GaussianNB -from sklearn.neighbors import KNeighborsClassifier -from sklearn.preprocessing import StandardScaler -from sklearn.model_selection import train_test_split -from sklearn.linear_model import LogisticRegression - -``` - - -## Linear and Poisson Regression on the Bikeshare Data -Here we fit linear and Poisson regression models to the `Bikeshare` data, as described in Section 4.6. -The response `bikers` measures the number of bike rentals per hour -in Washington, DC in the period 2010--2012. - -```{python} -Bike = load_data('Bikeshare') - -``` -Let's have a peek at the dimensions and names of the variables in this dataframe. - -```{python} -Bike.shape, Bike.columns - -``` - -### Linear Regression - -We begin by fitting a linear regression model to the data. - -```{python} -X = MS(['mnth', - 'hr', - 'workingday', - 'temp', - 'weathersit']).fit_transform(Bike) -Y = Bike['bikers'] -M_lm = sm.OLS(Y, X).fit() -summarize(M_lm) - -``` - -There are 24 levels in `hr` and 40 rows in all. -In `M_lm`, the first levels `hr[0]` and `mnth[Jan]` are treated -as the baseline values, and so no coefficient estimates are provided -for them: implicitly, their coefficient estimates are zero, and all -other levels are measured relative to these baselines. For example, -the Feb coefficient of $6.845$ signifies that, holding all other -variables constant, there are on average about 7 more riders in -February than in January. Similarly there are about 16.5 more riders -in March than in January. - -The results seen in Section 4.6.1 -used a slightly different coding of the variables `hr` and `mnth`, as follows: - -```{python} -hr_encode = contrast('hr', 'sum') -mnth_encode = contrast('mnth', 'sum') - -``` -Refitting again: - -```{python} -X2 = MS([mnth_encode, - hr_encode, - 'workingday', - 'temp', - 'weathersit']).fit_transform(Bike) -M2_lm = sm.OLS(Y, X2).fit() -S2 = summarize(M2_lm) -S2 -``` - -What is the difference between the two codings? In `M2_lm`, a -coefficient estimate is reported for all but level `23` of `hr` -and level `Dec` of `mnth`. Importantly, in `M2_lm`, the (unreported) coefficient estimate -for the last level of `mnth` is not zero: instead, it equals the -negative of the sum of the coefficient estimates for all of the -other levels. Similarly, in `M2_lm`, the coefficient estimate -for the last level of `hr` is the negative of the sum of the -coefficient estimates for all of the other levels. This means that the -coefficients of `hr` and `mnth` in `M2_lm` will always sum -to zero, and can be interpreted as the difference from the mean -level. For example, the coefficient for January of $-46.087$ indicates -that, holding all other variables constant, there are typically 46 -fewer riders in January relative to the yearly average. - -It is important to realize that the choice of coding really does not -matter, provided that we interpret the model output correctly in light -of the coding used. For example, we see that the predictions from the -linear model are the same regardless of coding: - -```{python} -np.sum((M_lm.fittedvalues - M2_lm.fittedvalues)**2) - -``` - -The sum of squared differences is zero. We can also see this using the -`np.allclose()` function: - -```{python} -np.allclose(M_lm.fittedvalues, M2_lm.fittedvalues) - -``` - - -To reproduce the left-hand side of Figure 4.13 -we must first obtain the coefficient estimates associated with -`mnth`. The coefficients for January through November can be obtained -directly from the `M2_lm` object. The coefficient for December -must be explicitly computed as the negative sum of all the other -months. We first extract all the coefficients for month from -the coefficients of `M2_lm`. - -```{python} -coef_month = S2[S2.index.str.contains('mnth')]['coef'] -coef_month - -``` -Next, we append `Dec` as the negative of the sum of all other months. - -```{python} -months = Bike['mnth'].dtype.categories -coef_month = pd.concat([ - coef_month, - pd.Series([-coef_month.sum()], - index=['mnth[Dec]' - ]) - ]) -coef_month - -``` -Finally, to make the plot neater, we’ll just use the first letter of each month, which is the $6$th entry of each of -the labels in the index. - -```{python} -fig_month, ax_month = subplots(figsize=(8,8)) -x_month = np.arange(coef_month.shape[0]) -ax_month.plot(x_month, coef_month, marker='o', ms=10) -ax_month.set_xticks(x_month) -ax_month.set_xticklabels([l[5] for l in coef_month.index], fontsize=20) -ax_month.set_xlabel('Month', fontsize=20) -ax_month.set_ylabel('Coefficient', fontsize=20); - -``` - -Reproducing the right-hand plot in Figure 4.13 follows a similar process. - -```{python} -coef_hr = S2[S2.index.str.contains('hr')]['coef'] -coef_hr = coef_hr.reindex(['hr[{0}]'.format(h) for h in range(23)]) -coef_hr = pd.concat([coef_hr, - pd.Series([-coef_hr.sum()], index=['hr[23]']) - ]) - -``` - -We now make the hour plot. - -```{python} -fig_hr, ax_hr = subplots(figsize=(8,8)) -x_hr = np.arange(coef_hr.shape[0]) -ax_hr.plot(x_hr, coef_hr, marker='o', ms=10) -ax_hr.set_xticks(x_hr[::2]) -ax_hr.set_xticklabels(range(24)[::2], fontsize=20) -ax_hr.set_xlabel('Hour', fontsize=20) -ax_hr.set_ylabel('Coefficient', fontsize=20); - -``` - -### Poisson Regression - -Now we fit instead a Poisson regression model to the -`Bikeshare` data. Very little changes, except that we now use the -function `sm.GLM()` with the Poisson family specified: - -```{python} -M_pois = sm.GLM(Y, X2, family=sm.families.Poisson()).fit() - -``` - -We can plot the coefficients associated with `mnth` and `hr`, in order to reproduce Figure 4.15. We first complete these coefficients as before. - -```{python} -S_pois = summarize(M_pois) -coef_month = S_pois[S_pois.index.str.contains('mnth')]['coef'] -coef_month = pd.concat([coef_month, - pd.Series([-coef_month.sum()], - index=['mnth[Dec]'])]) -coef_hr = S_pois[S_pois.index.str.contains('hr')]['coef'] -coef_hr = pd.concat([coef_hr, - pd.Series([-coef_hr.sum()], - index=['hr[23]'])]) -S_pois -``` -The plotting is as before. - -```{python} -fig_pois, (ax_month, ax_hr) = subplots(1, 2, figsize=(16,8)) -ax_month.plot(x_month, coef_month, marker='o', ms=10) -ax_month.set_xticks(x_month) -ax_month.set_xticklabels([l[5] for l in coef_month.index], fontsize=20) -ax_month.set_xlabel('Month', fontsize=20) -ax_month.set_ylabel('Coefficient', fontsize=20) -ax_hr.plot(x_hr, coef_hr, marker='o', ms=10) -ax_hr.set_xticklabels(range(24)[::2], fontsize=20) -ax_hr.set_xlabel('Hour', fontsize=20) -ax_hr.set_ylabel('Coefficient', fontsize=20); - -``` -We compare the fitted values of the two models. -The fitted values are stored in the `fittedvalues` attribute -returned by the `fit()` method for both the linear regression and the Poisson -fits. The linear predictors are stored as the attribute `lin_pred`. - -```{python} -fig, ax = subplots(figsize=(8, 8)) -ax.scatter(M2_lm.fittedvalues, - M_pois.fittedvalues, - s=20) -ax.set_xlabel('Linear Regression Fit', fontsize=20) -ax.set_ylabel('Poisson Regression Fit', fontsize=20) -ax.axline([0,0], c='black', linewidth=3, - linestyle='--', slope=1); - -``` - -The predictions from the Poisson regression model are correlated with -those from the linear model; however, the former are non-negative. As -a result the Poisson regression predictions tend to be larger than -those from the linear model for either very low or very high levels of -ridership. - -In this section, we fit Poisson regression models using the `sm.GLM()` function with the argument -`family=sm.families.Poisson()`. Earlier in this lab we used the `sm.GLM()` function -with `family=sm.families.Binomial()` to perform logistic regression. Other -choices for the `family` argument can be used to fit other types -of GLMs. For instance, `family=sm.families.Gamma()` fits a Gamma regression -model. - - diff --git a/docs/examples/bikeshare.ipynb b/docs/examples/bikeshare.ipynb deleted file mode 100644 index d701ffe..0000000 --- a/docs/examples/bikeshare.ipynb +++ /dev/null @@ -1,1765 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "5443fa92", - "metadata": {}, - "source": [ - "\n", - "# Chapter 4\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "3fc2dfec", - "metadata": {}, - "source": [ - "# Lab: Logistic Regression, LDA, QDA, and KNN\n" - ] - }, - { - "cell_type": "markdown", - "id": "0c96d561", - "metadata": {}, - "source": [ - "## The Stock Market Data\n", - "\n", - "In this lab we will examine the `Smarket` \n", - "data, which is part of the `ISLP`\n", - "library. This data set consists of percentage returns for the S&P 500\n", - "stock index over 1,250 days, from the beginning of 2001 until the end\n", - "of 2005. For each date, we have recorded the percentage returns for\n", - "each of the five previous trading days, `Lag1` through\n", - " `Lag5`. We have also recorded `Volume` (the number of\n", - "shares traded on the previous day, in billions), `Today` (the\n", - "percentage return on the date in question) and `Direction`\n", - "(whether the market was `Up` or `Down` on this date).\n", - "\n", - "We start by importing our libraries at this top level; these are all imports we have seen in previous labs." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "95d28c33", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "from matplotlib.pyplot import subplots\n", - "import statsmodels.api as sm\n", - "from ISLP import load_data\n", - "from ISLP.models import (ModelSpec as MS,\n", - " summarize)" - ] - }, - { - "cell_type": "markdown", - "id": "0c887b44", - "metadata": {}, - "source": [ - "We also collect together the new imports needed for this lab." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "f7fb5f2a", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "from ISLP import confusion_table\n", - "from ISLP.models import contrast\n", - "from sklearn.discriminant_analysis import \\\n", - " (LinearDiscriminantAnalysis as LDA,\n", - " QuadraticDiscriminantAnalysis as QDA)\n", - "from sklearn.naive_bayes import GaussianNB\n", - "from sklearn.neighbors import KNeighborsClassifier\n", - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.linear_model import LogisticRegression\n" - ] - }, - { - "cell_type": "markdown", - "id": "bb96ed25", - "metadata": {}, - "source": [ - "## Linear and Poisson Regression on the Bikeshare Data\n", - "Here we fit linear and Poisson regression models to the `Bikeshare` data, as described in Section 4.6.\n", - "The response `bikers` measures the number of bike rentals per hour\n", - "in Washington, DC in the period 2010--2012." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "def80d79", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], - "source": [ - "Bike = load_data('Bikeshare')\n" - ] - }, - { - "cell_type": "markdown", - "id": "146abe69", - "metadata": {}, - "source": [ - "Let's have a peek at the dimensions and names of the variables in this dataframe." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f899d5ab", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((8645, 15),\n", - " Index(['season', 'mnth', 'day', 'hr', 'holiday', 'weekday', 'workingday',\n", - " 'weathersit', 'temp', 'atemp', 'hum', 'windspeed', 'casual',\n", - " 'registered', 'bikers'],\n", - " dtype='object'))" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Bike.shape, Bike.columns\n" - ] - }, - { - "cell_type": "markdown", - "id": "b07be452", - "metadata": {}, - "source": [ - "### Linear Regression\n", - "\n", - "We begin by fitting a linear regression model to the data." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "76f4cea5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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coefstd errtP>|t|
intercept-68.63175.307-12.9320.000
mnth[Feb]6.84524.2871.5970.110
mnth[March]16.55144.3013.8480.000
mnth[April]41.42494.9728.3310.000
mnth[May]72.55715.64112.8620.000
mnth[June]67.81876.54410.3640.000
mnth[July]45.32457.0816.4010.000
mnth[Aug]53.24306.6408.0190.000
mnth[Sept]66.67835.92511.2540.000
mnth[Oct]75.83434.95015.3190.000
mnth[Nov]60.31004.61013.0830.000
mnth[Dec]46.45774.27110.8780.000
hr[1]-14.57935.699-2.5580.011
hr[2]-21.57915.733-3.7640.000
hr[3]-31.14085.778-5.3890.000
hr[4]-36.90755.802-6.3610.000
hr[5]-24.13555.737-4.2070.000
hr[6]20.59975.7043.6120.000
hr[7]120.09315.69321.0950.000
hr[8]223.66195.69039.3100.000
hr[9]120.58195.69321.1820.000
hr[10]83.80135.70514.6890.000
hr[11]105.42345.72218.4240.000
hr[12]137.28375.74023.9160.000
hr[13]136.03595.76023.6170.000
hr[14]126.63615.77621.9230.000
hr[15]132.08655.78022.8520.000
hr[16]178.52065.77230.9270.000
hr[17]296.26705.74951.5370.000
hr[18]269.44095.73646.9760.000
hr[19]186.25585.71432.5960.000
hr[20]125.54925.70422.0120.000
hr[21]87.55375.69315.3780.000
hr[22]59.12265.68910.3920.000
hr[23]26.83765.6884.7190.000
workingday1.26961.7840.7110.477
temp157.209410.26115.3210.000
weathersit[cloudy/misty]-12.89031.964-6.5620.000
weathersit[heavy rain/snow]-109.744676.667-1.4310.152
weathersit[light rain/snow]-66.49442.965-22.4250.000
\n", - "
" - ], - "text/plain": [ - " coef std err t P>|t|\n", - "intercept -68.6317 5.307 -12.932 0.000\n", - "mnth[Feb] 6.8452 4.287 1.597 0.110\n", - "mnth[March] 16.5514 4.301 3.848 0.000\n", - "mnth[April] 41.4249 4.972 8.331 0.000\n", - "mnth[May] 72.5571 5.641 12.862 0.000\n", - "mnth[June] 67.8187 6.544 10.364 0.000\n", - "mnth[July] 45.3245 7.081 6.401 0.000\n", - "mnth[Aug] 53.2430 6.640 8.019 0.000\n", - "mnth[Sept] 66.6783 5.925 11.254 0.000\n", - "mnth[Oct] 75.8343 4.950 15.319 0.000\n", - "mnth[Nov] 60.3100 4.610 13.083 0.000\n", - "mnth[Dec] 46.4577 4.271 10.878 0.000\n", - "hr[1] -14.5793 5.699 -2.558 0.011\n", - "hr[2] -21.5791 5.733 -3.764 0.000\n", - "hr[3] -31.1408 5.778 -5.389 0.000\n", - "hr[4] -36.9075 5.802 -6.361 0.000\n", - "hr[5] -24.1355 5.737 -4.207 0.000\n", - "hr[6] 20.5997 5.704 3.612 0.000\n", - "hr[7] 120.0931 5.693 21.095 0.000\n", - "hr[8] 223.6619 5.690 39.310 0.000\n", - "hr[9] 120.5819 5.693 21.182 0.000\n", - "hr[10] 83.8013 5.705 14.689 0.000\n", - "hr[11] 105.4234 5.722 18.424 0.000\n", - "hr[12] 137.2837 5.740 23.916 0.000\n", - "hr[13] 136.0359 5.760 23.617 0.000\n", - "hr[14] 126.6361 5.776 21.923 0.000\n", - "hr[15] 132.0865 5.780 22.852 0.000\n", - "hr[16] 178.5206 5.772 30.927 0.000\n", - "hr[17] 296.2670 5.749 51.537 0.000\n", - "hr[18] 269.4409 5.736 46.976 0.000\n", - "hr[19] 186.2558 5.714 32.596 0.000\n", - "hr[20] 125.5492 5.704 22.012 0.000\n", - "hr[21] 87.5537 5.693 15.378 0.000\n", - "hr[22] 59.1226 5.689 10.392 0.000\n", - "hr[23] 26.8376 5.688 4.719 0.000\n", - "workingday 1.2696 1.784 0.711 0.477\n", - "temp 157.2094 10.261 15.321 0.000\n", - "weathersit[cloudy/misty] -12.8903 1.964 -6.562 0.000\n", - "weathersit[heavy rain/snow] -109.7446 76.667 -1.431 0.152\n", - "weathersit[light rain/snow] -66.4944 2.965 -22.425 0.000" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X = MS(['mnth',\n", - " 'hr',\n", - " 'workingday',\n", - " 'temp',\n", - " 'weathersit']).fit_transform(Bike)\n", - "Y = Bike['bikers']\n", - "M_lm = sm.OLS(Y, X).fit()\n", - "summarize(M_lm)\n" - ] - }, - { - "cell_type": "markdown", - "id": "46d55b3d", - "metadata": {}, - "source": [ - "There are 24 levels in `hr` and 40 rows in all.\n", - "In `M_lm`, the first levels `hr[0]` and `mnth[Jan]` are treated\n", - "as the baseline values, and so no coefficient estimates are provided\n", - "for them: implicitly, their coefficient estimates are zero, and all\n", - "other levels are measured relative to these baselines. For example,\n", - "the Feb coefficient of $6.845$ signifies that, holding all other\n", - "variables constant, there are on average about 7 more riders in\n", - "February than in January. Similarly there are about 16.5 more riders\n", - "in March than in January.\n", - "\n", - "The results seen in Section 4.6.1\n", - "used a slightly different coding of the variables `hr` and `mnth`, as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "5778ada8", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], - "source": [ - "hr_encode = contrast('hr', 'sum')\n", - "mnth_encode = contrast('mnth', 'sum')\n" - ] - }, - { - "cell_type": "markdown", - "id": "06849d92", - "metadata": {}, - "source": [ - "Refitting again:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "c6da14b9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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coefstd errtP>|t|
intercept73.59745.13214.3400.000
mnth[Jan]-46.08714.085-11.2810.000
mnth[Feb]-39.24193.539-11.0880.000
mnth[March]-29.53573.155-9.3610.000
mnth[April]-4.66222.741-1.7010.089
mnth[May]26.47002.8519.2850.000
mnth[June]21.73173.4656.2720.000
mnth[July]-0.76263.908-0.1950.845
mnth[Aug]7.15603.5352.0240.043
mnth[Sept]20.59123.0466.7610.000
mnth[Oct]29.74722.70011.0190.000
mnth[Nov]14.22292.8604.9720.000
hr[0]-96.14203.955-24.3070.000
hr[1]-110.72133.966-27.9160.000
hr[2]-117.72124.016-29.3100.000
hr[3]-127.28284.081-31.1910.000
hr[4]-133.04954.117-32.3190.000
hr[5]-120.27754.037-29.7940.000
hr[6]-75.54243.992-18.9250.000
hr[7]23.95113.9696.0350.000
hr[8]127.51993.95032.2840.000
hr[9]24.43993.9366.2090.000
hr[10]-12.34073.936-3.1350.002
hr[11]9.28143.9452.3530.019
hr[12]41.14173.95710.3970.000
hr[13]39.89393.97510.0360.000
hr[14]30.49403.9917.6410.000
hr[15]35.94453.9958.9980.000
hr[16]82.37863.98820.6550.000
hr[17]200.12493.96450.4880.000
hr[18]173.29893.95643.8060.000
hr[19]90.11383.94022.8720.000
hr[20]29.40713.9367.4710.000
hr[21]-8.58833.933-2.1840.029
hr[22]-37.01943.934-9.4090.000
workingday1.26961.7840.7110.477
temp157.209410.26115.3210.000
weathersit[cloudy/misty]-12.89031.964-6.5620.000
weathersit[heavy rain/snow]-109.744676.667-1.4310.152
weathersit[light rain/snow]-66.49442.965-22.4250.000
\n", - "
" - ], - "text/plain": [ - " coef std err t P>|t|\n", - "intercept 73.5974 5.132 14.340 0.000\n", - "mnth[Jan] -46.0871 4.085 -11.281 0.000\n", - "mnth[Feb] -39.2419 3.539 -11.088 0.000\n", - "mnth[March] -29.5357 3.155 -9.361 0.000\n", - "mnth[April] -4.6622 2.741 -1.701 0.089\n", - "mnth[May] 26.4700 2.851 9.285 0.000\n", - "mnth[June] 21.7317 3.465 6.272 0.000\n", - "mnth[July] -0.7626 3.908 -0.195 0.845\n", - "mnth[Aug] 7.1560 3.535 2.024 0.043\n", - "mnth[Sept] 20.5912 3.046 6.761 0.000\n", - "mnth[Oct] 29.7472 2.700 11.019 0.000\n", - "mnth[Nov] 14.2229 2.860 4.972 0.000\n", - "hr[0] -96.1420 3.955 -24.307 0.000\n", - "hr[1] -110.7213 3.966 -27.916 0.000\n", - "hr[2] -117.7212 4.016 -29.310 0.000\n", - "hr[3] -127.2828 4.081 -31.191 0.000\n", - "hr[4] -133.0495 4.117 -32.319 0.000\n", - "hr[5] -120.2775 4.037 -29.794 0.000\n", - "hr[6] -75.5424 3.992 -18.925 0.000\n", - "hr[7] 23.9511 3.969 6.035 0.000\n", - "hr[8] 127.5199 3.950 32.284 0.000\n", - "hr[9] 24.4399 3.936 6.209 0.000\n", - "hr[10] -12.3407 3.936 -3.135 0.002\n", - "hr[11] 9.2814 3.945 2.353 0.019\n", - "hr[12] 41.1417 3.957 10.397 0.000\n", - "hr[13] 39.8939 3.975 10.036 0.000\n", - "hr[14] 30.4940 3.991 7.641 0.000\n", - "hr[15] 35.9445 3.995 8.998 0.000\n", - "hr[16] 82.3786 3.988 20.655 0.000\n", - "hr[17] 200.1249 3.964 50.488 0.000\n", - "hr[18] 173.2989 3.956 43.806 0.000\n", - "hr[19] 90.1138 3.940 22.872 0.000\n", - "hr[20] 29.4071 3.936 7.471 0.000\n", - "hr[21] -8.5883 3.933 -2.184 0.029\n", - "hr[22] -37.0194 3.934 -9.409 0.000\n", - "workingday 1.2696 1.784 0.711 0.477\n", - "temp 157.2094 10.261 15.321 0.000\n", - "weathersit[cloudy/misty] -12.8903 1.964 -6.562 0.000\n", - "weathersit[heavy rain/snow] -109.7446 76.667 -1.431 0.152\n", - "weathersit[light rain/snow] -66.4944 2.965 -22.425 0.000" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X2 = MS([mnth_encode,\n", - " hr_encode,\n", - " 'workingday',\n", - " 'temp',\n", - " 'weathersit']).fit_transform(Bike)\n", - "M2_lm = sm.OLS(Y, X2).fit()\n", - "S2 = summarize(M2_lm)\n", - "S2" - ] - }, - { - "cell_type": "markdown", - "id": "62d884bd", - "metadata": {}, - "source": [ - "What is the difference between the two codings? In `M2_lm`, a\n", - "coefficient estimate is reported for all but level `23` of `hr`\n", - "and level `Dec` of `mnth`. Importantly, in `M2_lm`, the (unreported) coefficient estimate\n", - "for the last level of `mnth` is not zero: instead, it equals the\n", - "negative of the sum of the coefficient estimates for all of the\n", - "other levels. Similarly, in `M2_lm`, the coefficient estimate\n", - "for the last level of `hr` is the negative of the sum of the\n", - "coefficient estimates for all of the other levels. This means that the\n", - "coefficients of `hr` and `mnth` in `M2_lm` will always sum\n", - "to zero, and can be interpreted as the difference from the mean\n", - "level. For example, the coefficient for January of $-46.087$ indicates\n", - "that, holding all other variables constant, there are typically 46\n", - "fewer riders in January relative to the yearly average.\n", - "\n", - "It is important to realize that the choice of coding really does not\n", - "matter, provided that we interpret the model output correctly in light\n", - "of the coding used. For example, we see that the predictions from the\n", - "linear model are the same regardless of coding:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "461d57c5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.0353919305327476e-18" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.sum((M_lm.fittedvalues - M2_lm.fittedvalues)**2)\n" - ] - }, - { - "cell_type": "markdown", - "id": "a5a50827", - "metadata": {}, - "source": [ - "The sum of squared differences is zero. We can also see this using the\n", - "`np.allclose()` function:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "05d33247", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.allclose(M_lm.fittedvalues, M2_lm.fittedvalues)\n" - ] - }, - { - "cell_type": "markdown", - "id": "41256e16", - "metadata": {}, - "source": [ - "To reproduce the left-hand side of Figure 4.13\n", - "we must first obtain the coefficient estimates associated with\n", - "`mnth`. The coefficients for January through November can be obtained\n", - "directly from the `M2_lm` object. The coefficient for December\n", - "must be explicitly computed as the negative sum of all the other\n", - "months. We first extract all the coefficients for month from\n", - "the coefficients of `M2_lm`." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "bee42b38", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "mnth[Jan] -46.0871\n", - "mnth[Feb] -39.2419\n", - "mnth[March] -29.5357\n", - "mnth[April] -4.6622\n", - "mnth[May] 26.4700\n", - "mnth[June] 21.7317\n", - "mnth[July] -0.7626\n", - "mnth[Aug] 7.1560\n", - "mnth[Sept] 20.5912\n", - "mnth[Oct] 29.7472\n", - "mnth[Nov] 14.2229\n", - "Name: coef, dtype: float64" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "coef_month = S2[S2.index.str.contains('mnth')]['coef']\n", - "coef_month\n" - ] - }, - { - "cell_type": "markdown", - "id": "976ff3e0", - "metadata": {}, - "source": [ - "Next, we append `Dec` as the negative of the sum of all other months." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "4aa60857", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "mnth[Jan] -46.0871\n", - "mnth[Feb] -39.2419\n", - "mnth[March] -29.5357\n", - "mnth[April] -4.6622\n", - "mnth[May] 26.4700\n", - "mnth[June] 21.7317\n", - "mnth[July] -0.7626\n", - "mnth[Aug] 7.1560\n", - "mnth[Sept] 20.5912\n", - "mnth[Oct] 29.7472\n", - "mnth[Nov] 14.2229\n", - "mnth[Dec] 0.3705\n", - "dtype: float64" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "months = Bike['mnth'].dtype.categories\n", - "coef_month = pd.concat([\n", - " coef_month,\n", - " pd.Series([-coef_month.sum()],\n", - " index=['mnth[Dec]'\n", - " ])\n", - " ])\n", - "coef_month\n" - ] - }, - { - "cell_type": "markdown", - "id": "39be6060", - "metadata": {}, - "source": [ - "Finally, to make the plot neater, we’ll just use the first letter of each month, which is the $6$th entry of each of\n", - "the labels in the index." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "894d3e2c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig_month, ax_month = subplots(figsize=(8,8))\n", - "x_month = np.arange(coef_month.shape[0])\n", - "ax_month.plot(x_month, coef_month, marker='o', ms=10)\n", - "ax_month.set_xticks(x_month)\n", - "ax_month.set_xticklabels([l[5] for l in coef_month.index], fontsize=20)\n", - "ax_month.set_xlabel('Month', fontsize=20)\n", - "ax_month.set_ylabel('Coefficient', fontsize=20);\n" - ] - }, - { - "cell_type": "markdown", - "id": "fe1d213c", - "metadata": {}, - "source": [ - "Reproducing the right-hand plot in Figure 4.13 follows a similar process." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "d636746e", - "metadata": {}, - "outputs": [], - "source": [ - "coef_hr = S2[S2.index.str.contains('hr')]['coef']\n", - "coef_hr = coef_hr.reindex(['hr[{0}]'.format(h) for h in range(23)])\n", - "coef_hr = pd.concat([coef_hr,\n", - " pd.Series([-coef_hr.sum()], index=['hr[23]'])\n", - " ])\n" - ] - }, - { - "cell_type": "markdown", - "id": "c181f106", - "metadata": {}, - "source": [ - "We now make the hour plot." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "ce6a1623", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig_hr, ax_hr = subplots(figsize=(8,8))\n", - "x_hr = np.arange(coef_hr.shape[0])\n", - "ax_hr.plot(x_hr, coef_hr, marker='o', ms=10)\n", - "ax_hr.set_xticks(x_hr[::2])\n", - "ax_hr.set_xticklabels(range(24)[::2], fontsize=20)\n", - "ax_hr.set_xlabel('Hour', fontsize=20)\n", - "ax_hr.set_ylabel('Coefficient', fontsize=20);\n" - ] - }, - { - "cell_type": "markdown", - "id": "8cafb6b7", - "metadata": {}, - "source": [ - "### Poisson Regression\n", - "\n", - "Now we fit instead a Poisson regression model to the\n", - "`Bikeshare` data. Very little changes, except that we now use the\n", - "function `sm.GLM()` with the Poisson family specified:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "9fb8b759", - "metadata": {}, - "outputs": [], - "source": [ - "M_pois = sm.GLM(Y, X2, family=sm.families.Poisson()).fit()\n" - ] - }, - { - "cell_type": "markdown", - "id": "9cc0ea6d", - "metadata": {}, - "source": [ - "We can plot the coefficients associated with `mnth` and `hr`, in order to reproduce Figure 4.15. We first complete these coefficients as before." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "ee272341", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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coefstd errzP>|z|
intercept4.11820.006683.9630.0
mnth[Jan]-0.67020.006-113.4450.0
mnth[Feb]-0.44410.005-91.3790.0
mnth[March]-0.29370.004-70.8860.0
mnth[April]0.02150.0036.8880.0
mnth[May]0.24050.00382.4620.0
mnth[June]0.22320.00462.8180.0
mnth[July]0.10360.00425.1210.0
mnth[Aug]0.15120.00441.2810.0
mnth[Sept]0.23350.00375.2810.0
mnth[Oct]0.26760.00396.0910.0
mnth[Nov]0.15030.00347.2480.0
hr[0]-0.75440.008-95.7440.0
hr[1]-1.22600.010-123.1730.0
hr[2]-1.56310.012-131.7020.0
hr[3]-2.19830.016-133.8460.0
hr[4]-2.83050.023-125.5860.0
hr[5]-1.81470.013-134.7750.0
hr[6]-0.42990.007-62.3410.0
hr[7]0.57520.004130.5440.0
hr[8]1.07690.004302.2200.0
hr[9]0.58180.004135.7270.0
hr[10]0.33690.00571.3720.0
hr[11]0.49410.004112.4940.0
hr[12]0.67960.004167.0400.0
hr[13]0.67360.004164.7220.0
hr[14]0.62490.004149.5700.0
hr[15]0.65380.004158.2050.0
hr[16]0.87430.004231.0400.0
hr[17]1.29460.003397.8480.0
hr[18]1.21230.003365.0840.0
hr[19]0.91400.004247.0650.0
hr[20]0.61620.004147.0450.0
hr[21]0.36420.00578.1730.0
hr[22]0.11750.00522.4880.0
workingday0.01470.0027.5020.0
temp0.78530.01168.4340.0
weathersit[cloudy/misty]-0.07520.002-34.5280.0
weathersit[heavy rain/snow]-0.92630.167-5.5540.0
weathersit[light rain/snow]-0.57580.004-141.9050.0
\n", - "
" - ], - "text/plain": [ - " coef std err z P>|z|\n", - "intercept 4.1182 0.006 683.963 0.0\n", - "mnth[Jan] -0.6702 0.006 -113.445 0.0\n", - "mnth[Feb] -0.4441 0.005 -91.379 0.0\n", - "mnth[March] -0.2937 0.004 -70.886 0.0\n", - "mnth[April] 0.0215 0.003 6.888 0.0\n", - "mnth[May] 0.2405 0.003 82.462 0.0\n", - "mnth[June] 0.2232 0.004 62.818 0.0\n", - "mnth[July] 0.1036 0.004 25.121 0.0\n", - "mnth[Aug] 0.1512 0.004 41.281 0.0\n", - "mnth[Sept] 0.2335 0.003 75.281 0.0\n", - "mnth[Oct] 0.2676 0.003 96.091 0.0\n", - "mnth[Nov] 0.1503 0.003 47.248 0.0\n", - "hr[0] -0.7544 0.008 -95.744 0.0\n", - "hr[1] -1.2260 0.010 -123.173 0.0\n", - "hr[2] -1.5631 0.012 -131.702 0.0\n", - "hr[3] -2.1983 0.016 -133.846 0.0\n", - "hr[4] -2.8305 0.023 -125.586 0.0\n", - "hr[5] -1.8147 0.013 -134.775 0.0\n", - "hr[6] -0.4299 0.007 -62.341 0.0\n", - "hr[7] 0.5752 0.004 130.544 0.0\n", - "hr[8] 1.0769 0.004 302.220 0.0\n", - "hr[9] 0.5818 0.004 135.727 0.0\n", - "hr[10] 0.3369 0.005 71.372 0.0\n", - "hr[11] 0.4941 0.004 112.494 0.0\n", - "hr[12] 0.6796 0.004 167.040 0.0\n", - "hr[13] 0.6736 0.004 164.722 0.0\n", - "hr[14] 0.6249 0.004 149.570 0.0\n", - "hr[15] 0.6538 0.004 158.205 0.0\n", - "hr[16] 0.8743 0.004 231.040 0.0\n", - "hr[17] 1.2946 0.003 397.848 0.0\n", - "hr[18] 1.2123 0.003 365.084 0.0\n", - "hr[19] 0.9140 0.004 247.065 0.0\n", - "hr[20] 0.6162 0.004 147.045 0.0\n", - "hr[21] 0.3642 0.005 78.173 0.0\n", - "hr[22] 0.1175 0.005 22.488 0.0\n", - "workingday 0.0147 0.002 7.502 0.0\n", - "temp 0.7853 0.011 68.434 0.0\n", - "weathersit[cloudy/misty] -0.0752 0.002 -34.528 0.0\n", - "weathersit[heavy rain/snow] -0.9263 0.167 -5.554 0.0\n", - "weathersit[light rain/snow] -0.5758 0.004 -141.905 0.0" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "S_pois = summarize(M_pois)\n", - "coef_month = S_pois[S_pois.index.str.contains('mnth')]['coef']\n", - "coef_month = pd.concat([coef_month,\n", - " pd.Series([-coef_month.sum()],\n", - " index=['mnth[Dec]'])])\n", - "coef_hr = S_pois[S_pois.index.str.contains('hr')]['coef']\n", - "coef_hr = pd.concat([coef_hr,\n", - " pd.Series([-coef_hr.sum()],\n", - " index=['hr[23]'])])\n", - "S_pois" - ] - }, - { - "cell_type": "markdown", - "id": "4a86df8c", - "metadata": {}, - "source": [ - "The plotting is as before." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "1f5bde07", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/dm/pr1j360n4_9g03p0vy7zfpmr0000gq/T/ipykernel_79312/3779905754.py:8: UserWarning: FixedFormatter should only be used together with FixedLocator\n", - " ax_hr.set_xticklabels(range(24)[::2], fontsize=20)\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig_pois, (ax_month, ax_hr) = subplots(1, 2, figsize=(16,8))\n", - "ax_month.plot(x_month, coef_month, marker='o', ms=10)\n", - "ax_month.set_xticks(x_month)\n", - "ax_month.set_xticklabels([l[5] for l in coef_month.index], fontsize=20)\n", - "ax_month.set_xlabel('Month', fontsize=20)\n", - "ax_month.set_ylabel('Coefficient', fontsize=20)\n", - "ax_hr.plot(x_hr, coef_hr, marker='o', ms=10)\n", - "ax_hr.set_xticklabels(range(24)[::2], fontsize=20)\n", - "ax_hr.set_xlabel('Hour', fontsize=20)\n", - "ax_hr.set_ylabel('Coefficient', fontsize=20);\n" - ] - }, - { - "cell_type": "markdown", - "id": "ff00b931", - "metadata": {}, - "source": [ - "We compare the fitted values of the two models.\n", - "The fitted values are stored in the `fittedvalues` attribute\n", - "returned by the `fit()` method for both the linear regression and the Poisson\n", - "fits. The linear predictors are stored as the attribute `lin_pred`." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "b0bd66a1", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = subplots(figsize=(8, 8))\n", - "ax.scatter(M2_lm.fittedvalues,\n", - " M_pois.fittedvalues,\n", - " s=20)\n", - "ax.set_xlabel('Linear Regression Fit', fontsize=20)\n", - "ax.set_ylabel('Poisson Regression Fit', fontsize=20)\n", - "ax.axline([0,0], c='black', linewidth=3,\n", - " linestyle='--', slope=1);\n" - ] - }, - { - "cell_type": "markdown", - "id": "a3b4b06e", - "metadata": {}, - "source": [ - "The predictions from the Poisson regression model are correlated with\n", - "those from the linear model; however, the former are non-negative. As\n", - "a result the Poisson regression predictions tend to be larger than\n", - "those from the linear model for either very low or very high levels of\n", - "ridership.\n", - "\n", - "In this section, we fit Poisson regression models using the `sm.GLM()` function with the argument\n", - "`family=sm.families.Poisson()`. Earlier in this lab we used the `sm.GLM()` function\n", - "with `family=sm.families.Binomial()` to perform logistic regression. Other\n", - "choices for the `family` argument can be used to fit other types\n", - "of GLMs. For instance, `family=sm.families.Gamma()` fits a Gamma regression\n", - "model.\n", - "\n" - ] - } - ], - "metadata": { - "jupytext": { - "cell_metadata_filter": "-all", - "formats": "ipynb,Rmd" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/examples/bikeshare_new.Rmd b/docs/examples/bikeshare_new.Rmd deleted file mode 100644 index c7aaeaf..0000000 --- a/docs/examples/bikeshare_new.Rmd +++ /dev/null @@ -1,329 +0,0 @@ ---- -jupyter: - jupytext: - cell_metadata_filter: -all - formats: ipynb,Rmd - text_representation: - extension: .Rmd - format_name: rmarkdown - format_version: '1.2' - jupytext_version: 1.15.0 - kernelspec: - display_name: Python 3 (ipykernel) - language: python - name: python3 ---- - - -# Chapter 4 - - - - - -# Lab: Logistic Regression, LDA, QDA, and KNN - - - -## The Stock Market Data - -In this lab we will examine the `Smarket` -data, which is part of the `ISLP` -library. This data set consists of percentage returns for the S&P 500 -stock index over 1,250 days, from the beginning of 2001 until the end -of 2005. For each date, we have recorded the percentage returns for -each of the five previous trading days, `Lag1` through - `Lag5`. We have also recorded `Volume` (the number of -shares traded on the previous day, in billions), `Today` (the -percentage return on the date in question) and `Direction` -(whether the market was `Up` or `Down` on this date). - -We start by importing our libraries at this top level; these are all imports we have seen in previous labs. - -```{python} -import numpy as np -import pandas as pd -from matplotlib.pyplot import subplots -import statsmodels.api as sm -from ISLP import load_data -from ISLP.models import (ModelSpec as MS, - summarize) -``` - -We also collect together the new imports needed for this lab. - -```{python} -from ISLP import confusion_table -from ISLP.models import contrast -from sklearn.discriminant_analysis import \ - (LinearDiscriminantAnalysis as LDA, - QuadraticDiscriminantAnalysis as QDA) -from sklearn.naive_bayes import GaussianNB -from sklearn.neighbors import KNeighborsClassifier -from sklearn.preprocessing import StandardScaler -from sklearn.model_selection import train_test_split -from sklearn.linear_model import LogisticRegression - -``` - - -```{python} -from glmnet.glm import GLM -``` - -## Linear and Poisson Regression on the Bikeshare Data -Here we fit linear and Poisson regression models to the `Bikeshare` data, as described in Section 4.6. -The response `bikers` measures the number of bike rentals per hour -in Washington, DC in the period 2010--2012. - -```{python} -Bike = load_data('Bikeshare') - -``` -Let's have a peek at the dimensions and names of the variables in this dataframe. - -```{python} -Bike.shape, Bike.columns - -``` - -### Linear Regression - -We begin by fitting a linear regression model to the data. - -```{python} -X = MS(['mnth', - 'hr', - 'workingday', - 'temp', - 'weathersit']).fit_transform(Bike) -X = X.drop(columns=['intercept']) -Y = Bike['bikers'] -OLS = GLM(summarize=True) -OLS.fit(X, Y) -OLS.summary_ - -``` - -There are 24 levels in `hr` and 40 rows in all. -In `M_lm`, the first levels `hr[0]` and `mnth[Jan]` are treated -as the baseline values, and so no coefficient estimates are provided -for them: implicitly, their coefficient estimates are zero, and all -other levels are measured relative to these baselines. For example, -the Feb coefficient of $6.845$ signifies that, holding all other -variables constant, there are on average about 7 more riders in -February than in January. Similarly there are about 16.5 more riders -in March than in January. - -The results seen in Section 4.6.1 -used a slightly different coding of the variables `hr` and `mnth`, as follows: - -```{python} -hr_encode = contrast('hr', 'sum') -mnth_encode = contrast('mnth', 'sum') - -``` -Refitting again: - -```{python} -X2 = MS([mnth_encode, - hr_encode, - 'workingday', - 'temp', - 'weathersit'], - intercept=False).fit_transform(Bike) -OLS2 = GLM(summarize=True) -OLS2.fit(X2, Y) -S2 = OLS2.summary_ -S2 -``` - -What is the difference between the two codings? In `M2_lm`, a -coefficient estimate is reported for all but level `23` of `hr` -and level `Dec` of `mnth`. Importantly, in `M2_lm`, the (unreported) coefficient estimate -for the last level of `mnth` is not zero: instead, it equals the -negative of the sum of the coefficient estimates for all of the -other levels. Similarly, in `M2_lm`, the coefficient estimate -for the last level of `hr` is the negative of the sum of the -coefficient estimates for all of the other levels. This means that the -coefficients of `hr` and `mnth` in `M2_lm` will always sum -to zero, and can be interpreted as the difference from the mean -level. For example, the coefficient for January of $-46.087$ indicates -that, holding all other variables constant, there are typically 46 -fewer riders in January relative to the yearly average. - -It is important to realize that the choice of coding really does not -matter, provided that we interpret the model output correctly in light -of the coding used. For example, we see that the predictions from the -linear model are the same regardless of coding: - -```{python} -fit1 = OLS.predict(X) -fit2 = OLS2.predict(X2) -np.sum((fit1 - fit2)**2) - -``` - -The sum of squared differences is zero. We can also see this using the -`np.allclose()` function: - -```{python} -np.allclose(fit1, fit2, rtol=1e-3) - -``` - - -To reproduce the left-hand side of Figure 4.13 -we must first obtain the coefficient estimates associated with -`mnth`. The coefficients for January through November can be obtained -directly from the `M2_lm` object. The coefficient for December -must be explicitly computed as the negative sum of all the other -months. We first extract all the coefficients for month from -the coefficients of `M2_lm`. - -```{python} -coef_month = S2[S2.index.str.contains('mnth')]['coef'] -coef_month - -``` -Next, we append `Dec` as the negative of the sum of all other months. - -```{python} -months = Bike['mnth'].dtype.categories -coef_month = pd.concat([ - coef_month, - pd.Series([-coef_month.sum()], - index=['mnth[Dec]' - ]) - ]) -coef_month - -``` -Finally, to make the plot neater, we’ll just use the first letter of each month, which is the $6$th entry of each of -the labels in the index. - -```{python} -fig_month, ax_month = subplots(figsize=(8,8)) -x_month = np.arange(coef_month.shape[0]) -ax_month.plot(x_month, coef_month, marker='o', ms=10) -ax_month.set_xticks(x_month) -ax_month.set_xticklabels([l[5] for l in coef_month.index], fontsize=20) -ax_month.set_xlabel('Month', fontsize=20) -ax_month.set_ylabel('Coefficient', fontsize=20); - -``` - -Reproducing the right-hand plot in Figure 4.13 follows a similar process. - -```{python} -coef_hr = S2[S2.index.str.contains('hr')]['coef'] -coef_hr = coef_hr.reindex(['hr[{0}]'.format(h) for h in range(23)]) -coef_hr = pd.concat([coef_hr, - pd.Series([-coef_hr.sum()], index=['hr[23]']) - ]) - -``` - -We now make the hour plot. - -```{python} -fig_hr, ax_hr = subplots(figsize=(8,8)) -x_hr = np.arange(coef_hr.shape[0]) -ax_hr.plot(x_hr, coef_hr, marker='o', ms=10) -ax_hr.set_xticks(x_hr[::2]) -ax_hr.set_xticklabels(range(24)[::2], fontsize=20) -ax_hr.set_xlabel('Hour', fontsize=20) -ax_hr.set_ylabel('Coefficient', fontsize=20); - -``` - -### Poisson Regression - -Now we fit instead a Poisson regression model to the -`Bikeshare` data. Very little changes, except that we now use the -function `sm.GLM()` with the Poisson family specified: - -```{python} -Poisson_GLM = GLM(family=sm.families.Poisson(), - summarize=True) -Poisson_GLM.fit(X2, Y) -``` - -```{python} -Poisson_GLM.score(X2, Y) -``` - -We can plot the coefficients associated with `mnth` and `hr`, in order to reproduce Figure 4.15. We first complete these coefficients as before. - -```{python} -S_pois = Poisson_GLM.summary_ -coef_month = S_pois[S_pois.index.str.contains('mnth')]['coef'] -coef_month = pd.concat([coef_month, - pd.Series([-coef_month.sum()], - index=['mnth[Dec]'])]) -coef_hr = S_pois[S_pois.index.str.contains('hr')]['coef'] -coef_hr = pd.concat([coef_hr, - pd.Series([-coef_hr.sum()], - index=['hr[23]'])]) -S_pois -``` -The plotting is as before. - -```{python} -fig_pois, (ax_month, ax_hr) = subplots(1, 2, figsize=(16,8)) -ax_month.plot(x_month, coef_month, marker='o', ms=10) -ax_month.set_xticks(x_month) -ax_month.set_xticklabels([l[5] for l in coef_month.index], fontsize=20) -ax_month.set_xlabel('Month', fontsize=20) -ax_month.set_ylabel('Coefficient', fontsize=20) -ax_hr.plot(x_hr, coef_hr, marker='o', ms=10) -ax_hr.set_xticklabels(range(24)[::2], fontsize=20) -ax_hr.set_xlabel('Hour', fontsize=20) -ax_hr.set_ylabel('Coefficient', fontsize=20); -np.diag(Poisson_GLM.covariance_) -``` -We compare the fitted values of the two models. -The fitted values are stored in the `fittedvalues` attribute -returned by the `fit()` method for both the linear regression and the Poisson -fits. The linear predictors are stored as the attribute `lin_pred`. - -```{python} -fig, ax = subplots(figsize=(8, 8)) -fit_pois = Poisson_GLM.predict(X2) -ax.scatter(fit2, - fit_pois, - s=20) -ax.set_xlabel('Linear Regression Fit', fontsize=20) -ax.set_ylabel('Poisson Regression Fit', fontsize=20) -ax.axline([0,0], c='black', linewidth=3, - linestyle='--', slope=1); - -``` - -The predictions from the Poisson regression model are correlated with -those from the linear model; however, the former are non-negative. As -a result the Poisson regression predictions tend to be larger than -those from the linear model for either very low or very high levels of -ridership. - -In this section, we fit Poisson regression models using the `sm.GLM()` function with the argument -`family=sm.families.Poisson()`. Earlier in this lab we used the `sm.GLM()` function -with `family=sm.families.Binomial()` to perform logistic regression. Other -choices for the `family` argument can be used to fit other types -of GLMs. For instance, `family=sm.families.Gamma()` fits a Gamma regression -model. - - - - -## Cross-validating a GLM - -```{python} -from sklearn.model_selection import cross_validate, KFold -cv = KFold(5, shuffle=True, random_state=0) -cross_validate(Poisson_GLM, X2, Y, cv=cv) -``` - -```{python} - -``` diff --git a/docs/examples/bikeshare_new.ipynb b/docs/examples/bikeshare_new.ipynb deleted file mode 100644 index c9d8762..0000000 --- a/docs/examples/bikeshare_new.ipynb +++ /dev/null @@ -1,1923 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "5443fa92", - "metadata": {}, - "source": [ - "\n", - "# Chapter 4\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "3fc2dfec", - "metadata": {}, - "source": [ - "# Lab: Logistic Regression, LDA, QDA, and KNN\n" - ] - }, - { - "cell_type": "markdown", - "id": "0c96d561", - "metadata": {}, - "source": [ - "## The Stock Market Data\n", - "\n", - "In this lab we will examine the `Smarket` \n", - "data, which is part of the `ISLP`\n", - "library. This data set consists of percentage returns for the S&P 500\n", - "stock index over 1,250 days, from the beginning of 2001 until the end\n", - "of 2005. For each date, we have recorded the percentage returns for\n", - "each of the five previous trading days, `Lag1` through\n", - " `Lag5`. We have also recorded `Volume` (the number of\n", - "shares traded on the previous day, in billions), `Today` (the\n", - "percentage return on the date in question) and `Direction`\n", - "(whether the market was `Up` or `Down` on this date).\n", - "\n", - "We start by importing our libraries at this top level; these are all imports we have seen in previous labs." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "95d28c33", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "from matplotlib.pyplot import subplots\n", - "import statsmodels.api as sm\n", - "from ISLP import load_data\n", - "from ISLP.models import (ModelSpec as MS,\n", - " summarize)" - ] - }, - { - "cell_type": "markdown", - "id": "0c887b44", - "metadata": {}, - "source": [ - "We also collect together the new imports needed for this lab." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "f7fb5f2a", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "from ISLP import confusion_table\n", - "from ISLP.models import contrast\n", - "from sklearn.discriminant_analysis import \\\n", - " (LinearDiscriminantAnalysis as LDA,\n", - " QuadraticDiscriminantAnalysis as QDA)\n", - "from sklearn.naive_bayes import GaussianNB\n", - "from sklearn.neighbors import KNeighborsClassifier\n", - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.linear_model import LogisticRegression\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "3e8dc8d3-d027-4d02-b4a9-c6f29ba118cb", - "metadata": {}, - "outputs": [], - "source": [ - "from glmnet.glm import GLM" - ] - }, - { - "cell_type": "markdown", - "id": "bb96ed25", - "metadata": {}, - "source": [ - "## Linear and Poisson Regression on the Bikeshare Data\n", - "Here we fit linear and Poisson regression models to the `Bikeshare` data, as described in Section 4.6.\n", - "The response `bikers` measures the number of bike rentals per hour\n", - "in Washington, DC in the period 2010--2012." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "def80d79", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], - "source": [ - "Bike = load_data('Bikeshare')\n" - ] - }, - { - "cell_type": "markdown", - "id": "146abe69", - "metadata": {}, - "source": [ - "Let's have a peek at the dimensions and names of the variables in this dataframe." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "f899d5ab", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((8645, 15),\n", - " Index(['season', 'mnth', 'day', 'hr', 'holiday', 'weekday', 'workingday',\n", - " 'weathersit', 'temp', 'atemp', 'hum', 'windspeed', 'casual',\n", - " 'registered', 'bikers'],\n", - " dtype='object'))" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Bike.shape, Bike.columns\n" - ] - }, - { - "cell_type": "markdown", - "id": "b07be452", - "metadata": {}, - "source": [ - "### Linear Regression\n", - "\n", - "We begin by fitting a linear regression model to the data." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "76f4cea5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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coefstd errtP>|t|
intercept-68.6317046.449716-10.6410432.795288e-26
mnth[Feb]6.8452035.2106981.3136821.889881e-01
mnth[March]16.5514385.2274743.1662401.549623e-03
mnth[April]41.4249076.0431146.8548957.628098e-12
mnth[May]72.5570846.85611610.5828275.170986e-26
mnth[June]67.8187497.9527788.5276801.746876e-17
mnth[July]45.3244608.6061225.2665371.423821e-07
mnth[Aug]53.2430418.0696956.5979004.416471e-11
mnth[Sept]66.6782777.2008389.2597942.544948e-20
mnth[Oct]75.8342526.01637712.6046374.145970e-36
mnth[Nov]60.3100035.60264710.7645557.498975e-27
mnth[Dec]46.4576725.1904418.9506214.274233e-19
hr[1]-14.5792676.926035-2.1049953.532070e-02
hr[2]-21.5791396.967538-3.0970971.960549e-03
hr[3]-31.1407567.022558-4.4343899.347992e-06
hr[4]-36.9075057.051804-5.2337681.700079e-07
hr[5]-24.1354816.971975-3.4617865.392119e-04
hr[6]20.5996826.9321582.9716122.970602e-03
hr[7]120.0931446.91885217.3573792.322155e-66
hr[8]223.6619376.91493632.3447581.057720e-216
hr[9]120.5819406.91846017.4290156.941207e-67
hr[10]83.8013386.93369612.0861002.328449e-33
hr[11]105.4234496.95430615.1594483.002355e-51
hr[12]137.2837226.97632019.6785302.314601e-84
hr[13]136.0359007.00044819.4324572.347541e-82
hr[14]126.6360717.02020818.0387911.978866e-71
hr[15]132.0865167.02472818.8030802.493708e-77
hr[16]178.5206197.01531125.4472878.595165e-138
hr[17]296.2669916.98651142.4055710.000000e+00
hr[18]269.4409166.97078238.6528971.579651e-301
hr[19]186.2558156.94448326.8206882.966091e-152
hr[20]125.5491696.93191618.1117565.532432e-72
hr[21]87.5537246.91946112.6532582.260416e-36
hr[22]59.1226436.9146368.5503621.437934e-17
hr[23]26.8375646.9124433.8825011.041609e-04
workingday1.2696012.1687340.5854115.582868e-01
temp157.20936612.47088112.6061164.070326e-36
weathersit[cloudy/misty]-12.8902662.387241-5.3996516.855075e-08
weathersit[heavy rain/snow]-109.74457793.176926-1.1778092.389055e-01
weathersit[light rain/snow]-66.4943653.603729-18.4515441.374932e-74
\n", - "
" - ], - "text/plain": [ - " coef std err t P>|t|\n", - "intercept -68.631704 6.449716 -10.641043 2.795288e-26\n", - "mnth[Feb] 6.845203 5.210698 1.313682 1.889881e-01\n", - "mnth[March] 16.551438 5.227474 3.166240 1.549623e-03\n", - "mnth[April] 41.424907 6.043114 6.854895 7.628098e-12\n", - "mnth[May] 72.557084 6.856116 10.582827 5.170986e-26\n", - "mnth[June] 67.818749 7.952778 8.527680 1.746876e-17\n", - "mnth[July] 45.324460 8.606122 5.266537 1.423821e-07\n", - "mnth[Aug] 53.243041 8.069695 6.597900 4.416471e-11\n", - "mnth[Sept] 66.678277 7.200838 9.259794 2.544948e-20\n", - "mnth[Oct] 75.834252 6.016377 12.604637 4.145970e-36\n", - "mnth[Nov] 60.310003 5.602647 10.764555 7.498975e-27\n", - "mnth[Dec] 46.457672 5.190441 8.950621 4.274233e-19\n", - "hr[1] -14.579267 6.926035 -2.104995 3.532070e-02\n", - "hr[2] -21.579139 6.967538 -3.097097 1.960549e-03\n", - "hr[3] -31.140756 7.022558 -4.434389 9.347992e-06\n", - "hr[4] -36.907505 7.051804 -5.233768 1.700079e-07\n", - "hr[5] -24.135481 6.971975 -3.461786 5.392119e-04\n", - "hr[6] 20.599682 6.932158 2.971612 2.970602e-03\n", - "hr[7] 120.093144 6.918852 17.357379 2.322155e-66\n", - "hr[8] 223.661937 6.914936 32.344758 1.057720e-216\n", - "hr[9] 120.581940 6.918460 17.429015 6.941207e-67\n", - "hr[10] 83.801338 6.933696 12.086100 2.328449e-33\n", - "hr[11] 105.423449 6.954306 15.159448 3.002355e-51\n", - "hr[12] 137.283722 6.976320 19.678530 2.314601e-84\n", - "hr[13] 136.035900 7.000448 19.432457 2.347541e-82\n", - "hr[14] 126.636071 7.020208 18.038791 1.978866e-71\n", - "hr[15] 132.086516 7.024728 18.803080 2.493708e-77\n", - "hr[16] 178.520619 7.015311 25.447287 8.595165e-138\n", - "hr[17] 296.266991 6.986511 42.405571 0.000000e+00\n", - "hr[18] 269.440916 6.970782 38.652897 1.579651e-301\n", - "hr[19] 186.255815 6.944483 26.820688 2.966091e-152\n", - "hr[20] 125.549169 6.931916 18.111756 5.532432e-72\n", - "hr[21] 87.553724 6.919461 12.653258 2.260416e-36\n", - "hr[22] 59.122643 6.914636 8.550362 1.437934e-17\n", - "hr[23] 26.837564 6.912443 3.882501 1.041609e-04\n", - "workingday 1.269601 2.168734 0.585411 5.582868e-01\n", - "temp 157.209366 12.470881 12.606116 4.070326e-36\n", - "weathersit[cloudy/misty] -12.890266 2.387241 -5.399651 6.855075e-08\n", - "weathersit[heavy rain/snow] -109.744577 93.176926 -1.177809 2.389055e-01\n", - "weathersit[light rain/snow] -66.494365 3.603729 -18.451544 1.374932e-74" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X = MS(['mnth',\n", - " 'hr',\n", - " 'workingday',\n", - " 'temp',\n", - " 'weathersit']).fit_transform(Bike)\n", - "X = X.drop(columns=['intercept'])\n", - "Y = Bike['bikers']\n", - "OLS = GLM(summarize=True)\n", - "OLS.fit(X, Y)\n", - "OLS.summary_\n" - ] - }, - { - "cell_type": "markdown", - "id": "46d55b3d", - "metadata": {}, - "source": [ - "There are 24 levels in `hr` and 40 rows in all.\n", - "In `M_lm`, the first levels `hr[0]` and `mnth[Jan]` are treated\n", - "as the baseline values, and so no coefficient estimates are provided\n", - "for them: implicitly, their coefficient estimates are zero, and all\n", - "other levels are measured relative to these baselines. For example,\n", - "the Feb coefficient of $6.845$ signifies that, holding all other\n", - "variables constant, there are on average about 7 more riders in\n", - "February than in January. Similarly there are about 16.5 more riders\n", - "in March than in January.\n", - "\n", - "The results seen in Section 4.6.1\n", - "used a slightly different coding of the variables `hr` and `mnth`, as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "5778ada8", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], - "source": [ - "hr_encode = contrast('hr', 'sum')\n", - "mnth_encode = contrast('mnth', 'sum')\n" - ] - }, - { - "cell_type": "markdown", - "id": "06849d92", - "metadata": {}, - "source": [ - "Refitting again:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "c6da14b9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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coefstd errtP>|t|
intercept73.5974286.23741211.7993546.921256e-32
mnth[Jan]-46.0870904.965230-9.2819642.071549e-20
mnth[Feb]-39.2418884.301213-9.1234468.931133e-20
mnth[March]-29.5356523.834686-7.7022351.485377e-14
mnth[April]-4.6621833.330716-1.3997541.616230e-01
mnth[May]26.4699933.4647457.6398092.408760e-14
mnth[June]21.7316584.2112995.1603212.520304e-07
mnth[July]-0.7626304.750037-0.1605538.724496e-01
mnth[Aug]7.1559504.2958691.6657759.579468e-02
mnth[Sept]20.5911863.7014375.5630242.730856e-08
mnth[Oct]29.7471623.2808159.0670021.493982e-19
mnth[Nov]14.2229133.4763204.0913704.327440e-05
hr[0]-96.1420414.807110-19.9999665.121650e-87
hr[1]-110.7213084.820311-22.9697432.286062e-113
hr[2]-117.7211804.881385-24.1163472.116054e-124
hr[3]-127.2827974.959529-25.6642904.923582e-140
hr[4]-133.0495465.003323-26.5922378.365081e-150
hr[5]-120.2775234.906320-24.5148162.407454e-128
hr[6]-75.5423594.851151-15.5720486.114527e-54
hr[7]23.9511024.8231914.9658216.972624e-07
hr[8]127.5198964.80057526.5634651.697530e-149
hr[9]24.4398994.7835525.1091533.305556e-07
hr[10]-12.3407044.783673-2.5797559.903485e-03
hr[11]9.2814074.7941811.9359735.290366e-02
hr[12]41.1416814.8092068.5547771.384411e-17
hr[13]39.8938584.8309428.2579871.701262e-16
hr[14]30.4940304.8504216.2868843.396638e-10
hr[15]35.9444754.8551447.4033791.452734e-13
hr[16]82.3785784.84708316.9954979.653660e-64
hr[17]200.1249494.81733641.5426610.000000e+00
hr[18]173.2988744.80795436.0442084.273690e-265
hr[19]90.1137744.78841518.8191241.864658e-77
hr[20]29.4071284.7838486.1471708.238708e-10
hr[21]-8.5883174.780187-1.7966497.242647e-02
hr[22]-37.0193994.781629-7.7420051.089496e-14
workingday1.2696012.1687340.5854115.582868e-01
temp157.20936612.47088112.6061164.070326e-36
weathersit[cloudy/misty]-12.8902662.387241-5.3996516.855075e-08
weathersit[heavy rain/snow]-109.74457793.176926-1.1778092.389055e-01
weathersit[light rain/snow]-66.4943653.603729-18.4515441.374932e-74
\n", - "
" - ], - "text/plain": [ - " coef std err t P>|t|\n", - "intercept 73.597428 6.237412 11.799354 6.921256e-32\n", - "mnth[Jan] -46.087090 4.965230 -9.281964 2.071549e-20\n", - "mnth[Feb] -39.241888 4.301213 -9.123446 8.931133e-20\n", - "mnth[March] -29.535652 3.834686 -7.702235 1.485377e-14\n", - "mnth[April] -4.662183 3.330716 -1.399754 1.616230e-01\n", - "mnth[May] 26.469993 3.464745 7.639809 2.408760e-14\n", - "mnth[June] 21.731658 4.211299 5.160321 2.520304e-07\n", - "mnth[July] -0.762630 4.750037 -0.160553 8.724496e-01\n", - "mnth[Aug] 7.155950 4.295869 1.665775 9.579468e-02\n", - "mnth[Sept] 20.591186 3.701437 5.563024 2.730856e-08\n", - "mnth[Oct] 29.747162 3.280815 9.067002 1.493982e-19\n", - "mnth[Nov] 14.222913 3.476320 4.091370 4.327440e-05\n", - "hr[0] -96.142041 4.807110 -19.999966 5.121650e-87\n", - "hr[1] -110.721308 4.820311 -22.969743 2.286062e-113\n", - "hr[2] -117.721180 4.881385 -24.116347 2.116054e-124\n", - "hr[3] -127.282797 4.959529 -25.664290 4.923582e-140\n", - "hr[4] -133.049546 5.003323 -26.592237 8.365081e-150\n", - "hr[5] -120.277523 4.906320 -24.514816 2.407454e-128\n", - "hr[6] -75.542359 4.851151 -15.572048 6.114527e-54\n", - "hr[7] 23.951102 4.823191 4.965821 6.972624e-07\n", - "hr[8] 127.519896 4.800575 26.563465 1.697530e-149\n", - "hr[9] 24.439899 4.783552 5.109153 3.305556e-07\n", - "hr[10] -12.340704 4.783673 -2.579755 9.903485e-03\n", - "hr[11] 9.281407 4.794181 1.935973 5.290366e-02\n", - "hr[12] 41.141681 4.809206 8.554777 1.384411e-17\n", - "hr[13] 39.893858 4.830942 8.257987 1.701262e-16\n", - "hr[14] 30.494030 4.850421 6.286884 3.396638e-10\n", - "hr[15] 35.944475 4.855144 7.403379 1.452734e-13\n", - "hr[16] 82.378578 4.847083 16.995497 9.653660e-64\n", - "hr[17] 200.124949 4.817336 41.542661 0.000000e+00\n", - "hr[18] 173.298874 4.807954 36.044208 4.273690e-265\n", - "hr[19] 90.113774 4.788415 18.819124 1.864658e-77\n", - "hr[20] 29.407128 4.783848 6.147170 8.238708e-10\n", - "hr[21] -8.588317 4.780187 -1.796649 7.242647e-02\n", - "hr[22] -37.019399 4.781629 -7.742005 1.089496e-14\n", - "workingday 1.269601 2.168734 0.585411 5.582868e-01\n", - "temp 157.209366 12.470881 12.606116 4.070326e-36\n", - "weathersit[cloudy/misty] -12.890266 2.387241 -5.399651 6.855075e-08\n", - "weathersit[heavy rain/snow] -109.744577 93.176926 -1.177809 2.389055e-01\n", - "weathersit[light rain/snow] -66.494365 3.603729 -18.451544 1.374932e-74" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X2 = MS([mnth_encode,\n", - " hr_encode,\n", - " 'workingday',\n", - " 'temp',\n", - " 'weathersit'],\n", - " intercept=False).fit_transform(Bike)\n", - "OLS2 = GLM(summarize=True)\n", - "OLS2.fit(X2, Y)\n", - "S2 = OLS2.summary_\n", - "S2" - ] - }, - { - "cell_type": "markdown", - "id": "62d884bd", - "metadata": {}, - "source": [ - "What is the difference between the two codings? In `M2_lm`, a\n", - "coefficient estimate is reported for all but level `23` of `hr`\n", - "and level `Dec` of `mnth`. Importantly, in `M2_lm`, the (unreported) coefficient estimate\n", - "for the last level of `mnth` is not zero: instead, it equals the\n", - "negative of the sum of the coefficient estimates for all of the\n", - "other levels. Similarly, in `M2_lm`, the coefficient estimate\n", - "for the last level of `hr` is the negative of the sum of the\n", - "coefficient estimates for all of the other levels. This means that the\n", - "coefficients of `hr` and `mnth` in `M2_lm` will always sum\n", - "to zero, and can be interpreted as the difference from the mean\n", - "level. For example, the coefficient for January of $-46.087$ indicates\n", - "that, holding all other variables constant, there are typically 46\n", - "fewer riders in January relative to the yearly average.\n", - "\n", - "It is important to realize that the choice of coding really does not\n", - "matter, provided that we interpret the model output correctly in light\n", - "of the coding used. For example, we see that the predictions from the\n", - "linear model are the same regardless of coding:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "461d57c5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.3360284202090695e-20" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fit1 = OLS.predict(X)\n", - "fit2 = OLS2.predict(X2)\n", - "np.sum((fit1 - fit2)**2)\n" - ] - }, - { - "cell_type": "markdown", - "id": "a5a50827", - "metadata": {}, - "source": [ - "The sum of squared differences is zero. We can also see this using the\n", - "`np.allclose()` function:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "05d33247", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.allclose(fit1, fit2, rtol=1e-3)\n" - ] - }, - { - "cell_type": "markdown", - "id": "41256e16", - "metadata": {}, - "source": [ - "To reproduce the left-hand side of Figure 4.13\n", - "we must first obtain the coefficient estimates associated with\n", - "`mnth`. The coefficients for January through November can be obtained\n", - "directly from the `M2_lm` object. The coefficient for December\n", - "must be explicitly computed as the negative sum of all the other\n", - "months. We first extract all the coefficients for month from\n", - "the coefficients of `M2_lm`." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "bee42b38", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "mnth[Jan] -46.087090\n", - "mnth[Feb] -39.241888\n", - "mnth[March] -29.535652\n", - "mnth[April] -4.662183\n", - "mnth[May] 26.469993\n", - "mnth[June] 21.731658\n", - "mnth[July] -0.762630\n", - "mnth[Aug] 7.155950\n", - "mnth[Sept] 20.591186\n", - "mnth[Oct] 29.747162\n", - "mnth[Nov] 14.222913\n", - "Name: coef, dtype: float64" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "coef_month = S2[S2.index.str.contains('mnth')]['coef']\n", - "coef_month\n" - ] - }, - { - "cell_type": "markdown", - "id": "976ff3e0", - "metadata": {}, - "source": [ - "Next, we append `Dec` as the negative of the sum of all other months." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "4aa60857", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/plain": [ - "mnth[Jan] -46.087090\n", - "mnth[Feb] -39.241888\n", - "mnth[March] -29.535652\n", - "mnth[April] -4.662183\n", - "mnth[May] 26.469993\n", - "mnth[June] 21.731658\n", - "mnth[July] -0.762630\n", - "mnth[Aug] 7.155950\n", - "mnth[Sept] 20.591186\n", - "mnth[Oct] 29.747162\n", - "mnth[Nov] 14.222913\n", - "mnth[Dec] 0.370582\n", - "dtype: float64" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "months = Bike['mnth'].dtype.categories\n", - "coef_month = pd.concat([\n", - " coef_month,\n", - " pd.Series([-coef_month.sum()],\n", - " index=['mnth[Dec]'\n", - " ])\n", - " ])\n", - "coef_month\n" - ] - }, - { - "cell_type": "markdown", - "id": "39be6060", - "metadata": {}, - "source": [ - "Finally, to make the plot neater, we’ll just use the first letter of each month, which is the $6$th entry of each of\n", - "the labels in the index." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "894d3e2c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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mE53Pqz8fxP4SHSKDlXj3rqFQyL0nggQq5Jh2aTIA4D8bj/N97ge8579OIiInVeiaUFLbCEEALknUuOU5BEHAtEuT8esTYzE8OQL1BjOeXbEPDyzZiQpdk1uek8hb/bC7BMu2FkIQgPm3D0E3TZDUJbXbPenJUAbIsKdYi+zTf4CT72JAJiKfY+8/7hcXitBAhVufKzkqGF8/nI45V/eHUi7DusMVmDx/A1btLXXr8xJ5i6MV9Zizch8A4LEr+mBcaozEFXVMdIgKNw/tAQD4dCO3n/Z1DMhE5HMc/cddtPudXCbg4fG98dNjl2FQ9zDU6k2YtXwXHvtyF041GLukBiJP1Gi04NEvcqA3WpDeKwqPT0yVuqROsZ+st/pgOQqqGySuhtyJAZmIfI47+48vpF98KP77yBj85co+kMsE/LSnFJPnb8D6wxVdWgeRp5j7w37kltchJlSFBXcOgdwFE2Wk1DcuFJf3i4EoAgtPj6oj38SATEQ+xWi2Ym+Jbbe7YUnhXf78ygAZZk/uh5UzR6N3TDAq6wzIWLwDz63Yi3qDucvrIZLKNzuL8G12MWQC8M4dQxEbGih1SS5h3376m53F0Oq5cYivYkAmIp9y8KQORrMVEWoFenZyg5DOuCQxHD//ZSzuv6wnBMG2C9dV8zdg6/FqyWoi6iqHy3SY+8N+AMBTk/shvXeUxBW5zpg+UegfH4pGkwXLtxdKXQ65CQMyEfkUe3vF0KQICIK0H+cGKuR44bqB+PLBS5EQEYTiU42489OteGXVQTSZLJLWRuQu9QYzHvkiB00mK8anxmDm+N5Sl+RSgiA4tp9evOUEjGZuHOKLGJCJyKfYJ1hI0V7Rmkt7ReHXJ8bhzpGJEEXgs00ncO07G7GnqFbq0ohcShRFzFm5D8crG9BNE4i3bx/ikp0sPc0Nl3RHbKgK5ToDft7HiTW+iAGZiHzKLjfsoOcKIaoAzLt5MBbNGIHYUBWOVTbg5g+34K3VuVyBIp/xxbZC/LSnFAEyAe/dNRSRwUqpS3ILZYAM00enAAA+3XCCG4f4IAZkIvIZ5ac3CJEJth5gT3RF/1j89sQ4XH9Jd1isIt75/Shu+mAzcsvqpC6NqFP2l2jx8k8HAQDPXd0facmRElfkXnePSkKQQo6DJ3XI4rkFPocBmYh8hr3/uF98GIJVARJX07qIYCXevXMo3rtrKMLVChwo1eH6dzfho8xjsFi5EkXeR9towiNf5MBosWLSwDjHvGBfFq5W4pa0BADAfzZy5JuvYUAmIp/hif3HF3Ld4O5Y/cQ4XNk/FkaLFa/9chi3f5yF/KrWNyAQRRE1DUYU1ehR02DkR7skOVEU8dfv9qCwRo/EyCC8ecslkp8g21XuOz2l5vfDFThaUS91OeRCnrvEQkTUTtkSbRDSGbFhgfhs+nB8u7MYL686iJ0Fp3D1go14/toBmDYqyRE0tI0mrMguxpIt+Sio0TuOT45UY/roFExNS4AmyL3bahOdz8LN+fjtQDmUchnev2sYNGr/+e+wZ3QwJg6Iw5qD5Vi4+QT+76aLpS6JXIQryETkEwxmC/aX6AB03RbTriIIAm4bkYhfHh+LS3tFotFkwQvf78e9C7fjpLYRmXmVSJ+3Dq+sOojCZuEYAApr9Hhl1UGkz1uHzLxKiV4B+aucwlOY979DAIAXrhuAwQnh0hYkgQdOt5OsyC5Gdb1B4mrIVRiQicgnHCjVwWixIjJYiZQotdTldEhipBrLH7gUL14/EKoAGTYeqcKVb/6BGYu2o9FkgQjg7IYK+3WNJgsyFm1nSKYuc6rBiFlf5MBsFXHd4G6Ydmmy1CVJYmTPSAxO0MBgtuKLbdw4xFcwIBORT3BsEJIY7tX9jzKZgIwxPfG/x8fioh5haDRZIYpAW63GomgLyjOXZUPbyO1vyb2sVhGzv9mNUm0TekUH47Wpg736fdcZgiA4TkpcmpXPTYB8BAMyEfkEx/xjL2uvaE3vmBDcOKRHu44RRaDRaMHKnGI3VUVk89GGY1ifWwlVgAzv3z0MIR48NaYrXHNxN3TTBKKq3ogfd3PjEF/AgExEPsE+wWKol0ywaIsoivg8qwAdWZNbvDmf0y3IbbYer8abv+UCAF7500UY0C1M4oqkp5DLkDEmBQDwn03H+f7zAQzIROT1TmobcVLbBLlMwCU+cpLQKb0JBTX6c3qO2yICKKjRo1bPNgtyvco6A/7y5S5YRWDqsATcOjxB6pI8xu0jkhCslCOvvB4bjlRJXQ51EgMyEXm9nIJaAED/+FCP3iCkPRoM5k4dX9/J48m/nW/etsUq4omvd6GizoDUuBC8cuMgv+07Ph9NkAK3j0gCAPxn43GJq6HO8o3fJETk185sEOIb/ccAOh30/b0nlDrmQvO2k6PU2Hy0GmqlHB/cPQxqJf8bO1vGmBQs3nICG49U4XCZDv3j2X7irbiCTERezxGQk8OlLcSFItQKJEeq292DLMAWZsL9aLMGco225m3b2wbuTU9Gn9hQKUr0eImRalx9UTcAwGfcftqrMSATkVczmC04YN8gxIdWkAVBwPTRKR06dsaYFH70Te2SmVeJjDbmbdt9suE4521fwP1jbSPffthdioq6JomroY5iQCYir7a/xLZBSFSwEkmR3rlBSGumpiUgSCmHs1lXJgBBSjluHsYTp8h52kYTZi7LtgVjJ84K5bztCxuWFIFhSeEwWqz4PKtA6nKogxiQicir7XKMd4vwuVVTTZACH05LgwC0GZLtt380LQ2aILZXkPNWZBej0WhxKhwDnLftjAfH9gIALNtagEYjNw7xRgzIROTVfLH/uLnxqTFYlDESQQq5LSif5z4CgCCFHIszRmJcakwXV0jeTBRFLNmS36FjOW+7dZMHxSMxMgin9Cas4B8SXokBmYi8mn3Emy/1H59tfGoMsuZMwNzrB563jWRqWg9sfX4CwzG1G+dtu4dcJuC+MbZe5IWbTsBq5R8S3sbnA/K8efMwYsQIhIaGIjY2FjfeeCNyc3Nb3KepqQmPPvoooqKiEBISgqlTp6K8vFyiionIWaW1jSjT2TYIGZygkboct9IEKZAxpif+eOZy7HphEjb+9QpMH50MADBZRIQFsq2C2o/ztt3n1uGJCA0MwPGqBvx+uELqcqidfD4gZ2Zm4tFHH8XWrVuxZs0amEwmTJ48GQ0NDY77PPnkk/jpp5/w7bffIjMzE6Wlpbj55pslrJqInGFvrxjQLdRvZrIKgoCIYCUSI9W4fnB3AMD6wxUwWawSV0beiPO23SdEFYC7Rp3eOGQTNw7xNj7/X/avv/7a4vvFixcjNjYW2dnZGDduHLRaLT777DMsX74cV155JQBg0aJFGDBgALZu3YpLL71UirKJyAn+0F5xIUOTIhAVrER1gxHbT9RgTJ9oqUsiL2Oft13YzjYLAUAS5223acboFHy28QS2Hq/B/hItLurh2590+RKfX0E+m1arBQBERkYCALKzs2EymTBx4kTHffr374+kpCRkZWVJUiMROSfbB3fQaw+5TMCEAbEAgDUH2RZG7cd52+7VTROEawfbNg7h9tPexa8CstVqxRNPPIExY8bgoosuAgCUlZVBqVQiPDy8xX3j4uJQVlbW6mMZDAbodLoWFyLqOk0mCw6W2v7g9deADACTBsYDsAVkThSgjuC8bfd64DLbyLdVe0/ipLZR4mrIWX4VkB999FHs378fX331Vacfa968edBoNI5LYmKiCyokImftL9HCZBERHaJEYmSQ1OVI5rI+0QhUyFBS24iDJ/mHOrVf83nbbeG87fa7OEGDUT0jYbaKWNzBkXrU9fwmIM+aNQurVq3C+vXrkZBw5q/e+Ph4GI1G1NbWtrh/eXk54uPjW328OXPmQKvVOi5FRUXuKp2IziPHhzcIaY8gpRxj+9rGu7HNgjpqfGoMBsSHOb4/+x1ln8HNedsdY984ZPm2wk5PDqGu4fMBWRRFzJo1C//973/x+++/o2fPni1uT0tLg0KhwLp16xzX5ebmorCwEOnp6a0+rkqlQlhYWIsLEXUd+wl6acn+215hN2lgHAAGZOq4rGPVOHBShwAZ8PiEvufM206KVGPu9QM5b7uDruwfi17RwahrMuObnVxQ8wY+P8Xi0UcfxfLly/HDDz8gNDTU0Ves0WgQFBQEjUaD+++/H7Nnz0ZkZCTCwsLw2GOPIT09nRMsiDyUKIpndtDz4/5juwn9YyETgAOlOpTUNqJHuP+2nFD7iaKIf6+27Q9w58hkPDkpFU9M7ItavQn1BjNCVAEIVyv8+pOazpLJBNx3WU/8/fv9WLj5BO5NT4Fcxv8/PZnPryB/+OGH0Gq1uPzyy9GtWzfH5euvv3bc5+2338Z1112HqVOnYty4cYiPj8fKlSslrJqILqSkthEVdQYE+MEGIc6IClE5VtLXchWZ2ikzrxI7C05BFSDDrCv7AGg5bzsiWMlw7AJThyUgXK1AUU0jVh9ofQgAeQafD8iiKJ73MmPGDMd9AgMD8f7776OmpgYNDQ1YuXLlBfuPiUhaOYW1AICB3cMQqJBLW4yHYJsFdYRt9TgPAHBvejLiwgIlrsh3BSnlmDbKtvvlfzadkLgaaovPB2Qi8j05BWyvOJt93NvW49XQNpokroa8xeqD5dhXooVaKcefx/eWuhyfd+/oZCjlMmQXnHK0iZFnYkAmIq+zyzHBIlzaQjxIz+hg9IkNgdkq4o/cCqnLIS9gtYp46/Tq8X1jeiIqRCVxRb4vNjQQNwyxbRH/2UauInsyBmQi8ipNJgsOlNrm/XIFuSW2WVB7/LS3FLnldQgNDHCMISP3e2CsbZrWL/tPoqhGL3E11BoGZCLyKvtKtDBbRcSEqpAQwWkNzdkDcmZuJYxmq8TVkCczW6yYv/YIAODhcb2gUXPTj67SPz4MY/tGwyoCizbnS10OtYIBmYi8ypn+43CeWX+WIQnhiAlVoc5gxtbj1VKXQx5s5a4SnKhqQGSwEjPG9Gz7AHKp+y+z/X/+9Y5C6Jp4zoAnYkAmIq/C+cetk8kETBwQC4BtFtQ6o9mKBadXj2eO740Qlc9vieBxxqfGoG9sCBqMFny1vVDqcug8GJCJyGvYNgipBQAM4w5652Vvs1h7qByiKEpcDXmir3cUoqS2EbGhKtyTnix1OX5JEARHL/LizfkwWdgS5WkYkInIaxSfakTl6Q1CLu7BDULOZ3TvaKiVcpzUNmF/iU7qcsjDNJksePf3owCAx67swzniEvrTkB6IDlGiVNuEX/Zz4xBPw4BMRF7D3l4xiBuEtCpQIce4vjEAgDUH+UuXWlq2tQAVdQb0CA/CbSMSpS7HrwUq5Ljn0hQAwH82HucnPh6GAZmIvIb9BL2h7D++IHubxWr2IVMzDQYzPvjjGADg8Ql9oQrgH5lSm3ZpElQBMuwt1mJHPjcO8SQMyETkNdh/7Jwr+8dCLhNwuKyOc1bJYfGWfNQ0GJESpcbNw3pIXQ4BiApR4eZhCQCATzcel7gaao4BmYi8QqPRgkMn7RuEhEtbjIeLCFZi+Ok/IjjNggBA22jCx5m21eMnJ6UiQM5f/57CPvJt7aFynKhqkLgasuM7hIi8wt7iWpitImJDVegRzg1C2sJd9ai5/2w8Dl2TGf3iQnH94O5Sl0PN9IkNwZX9YyGKwMJN3H7aUzAgE5FXsLdXpCVHcIMQJ0weGA8A2J5fg1q9UeJqSErV9QZH8HpyUipkMr5/PM0Dp1eRv80u4vvVQzAgE5FX4AYh7ZMUpUa/uFBYrCLW51ZIXQ5J6OMNx9FgtOCiHmGYMihO6nLoPNJ7R2FgtzA0maz4Yhs3DvEEDMhE5PFEUcQue0BODpe2GC/CNguq0DVhyZZ8AMBTk/vx0xcP1XzjkCVb8mE0c+MQqTEgE5HHK6ppRFW9EQq5gEHduUGIs+wBOTO3EgazReJqSArvrT8Kg9mKtOQIXJ4aI3U5dAHXDe6OuDAVKuoM+GlPqdTl+D0GZCLyeGc2CNFwg5B2uLiHBnFhKjQYLdhyrFrqcqiLFZ/S48vtto/rn+bqscdTBsgwfXQKANvIN24cIi0GZCLyeOw/7hiZTMDEAWyz8FfvrjsKk0XEmD5RSO8dJXU55IS7RiYhSCHH4bI6/lErMQZkIvJ4Oew/7jB7m8W6Q+WwWrki5S9OVDXgu5xiAMDsSf0kroacFa5W4rbh3DjEEzAgE5FH0xvNOHSyDgBXkDsivXcUQlQBKNcZsK9EK3U51EXmr82DxSriyv6xSOPOk14lY0xPCALwR24ljpTXSV2O32JAJiKPtrdYC4tVRHxYILpzg5B2UwXIMf70yVlss/APuWV1+PH0SV6zJ6VKXA21V0p0MCaf/uRn4WZuHCIVBmQi8mhsr+i8iQNjATAg+4u31+RBFIFrLo7HRT049cUbPTC2FwBgRU4JquoNElfjnxiQicij5RTUAmB7RWdc0S8WcpmA3PI6FFbrpS6H3GhfsRa/HiiDIABPTuTqsbcanhyBSxI0MJqtWLa1QOpy/BIDMhF5rOYbhAxlQO6wcLUSI1MiAQCrD5ZJXA2501trcgEANw7pgb5xoRJXQx1l2zjEtor8eVYBmkycY97VGJCJyGMV1uhR3WCEUi7DRT3CpC7Hq3FXPd+XXVCD9bmVkMsEPD6hr9TlUCddfVE8eoQHobrBiO93lUhdjt9hQCYij5VdcHqDkB5hUAVwg5DOsAfkHfk1ONVglLgacoc3f8sDANw2PAEp0cESV0OdFSCXIWNMCgDgP5tOcExjF2NAJiKPxQ1CXCcxUo3+8aGwisDvhyukLodcbMvRKmQdr4ZSLsOsK7l67CtuG5GIEFUAjlbUI/NIpdTl+BUGZCLyWDxBz7Ums83CJ4miiDdX23qP7xqVhB4ch+gzwgIVuGNEIgDgs40nIIoiahqMKKrRo6bByO2o3ShA6gKIiM6nwWDG4TIdAI54c5VJA+Pxzu9HseFIJZpMFgQq2LbiC9bnViCnsBaBChkeuby31OWQi80Yk4LPNp3ApqNVGP3a7zipbXLclhypxvTRKZialgBNkELCKn0PV5CJyCPtKa6FVQS6awLRTcMVMVe4qEcYumkCoTdasOVYldTlkAtYrSL+vdrWezw9PQWxYYESV0SudqyyATKZAAAtwjFgO5H5lVUHkT5vHTLz2ILhSgzIROSRdhXWAgCGcptclxEEARMHsM3Cl/x2oAwHSnUIUQXgz+O5euxrMvMqkbFoe6sn6ImnL40mCzIWbWdIdiEGZCLySDkFPEHPHezTLNYequBZ8V7OYhXx1hrb6vF9l/VERLBS4orIlbSNJsxclu0IwRciirb7zFyWDW2jqQuq830MyETkcURRxK6iWgDAsKRwSWvxNZf2ikKoKgCVdQbsLq6VuhzqhJ/2lOJIRT00QQrcf1lPqcshF1uRXYxGowXOnocnikCj0YKVOcXuLcxPMCATkcfJr7adoa0MkGFQd43U5fgUZYAM4/vFAGCbhTczWax4e61t9fihcb14gpaPEUURS7bkd+jYxZvzOd3CBRiQicjj2NsrLu6hgTKA/0y5GnfV834rsotRUK1HdIgSM0anSF0OudgpvQkFNfo2WyvOJgIoqNGjVs82i87ibx4i8jhnNggJl7YQH3V5v1gEyAQcrajHiaoGqcuhdjKYLXhn3REAwMzL+yBYxYmtvqbBYO7U8fWdPJ4YkInIA+WcnmDBE/TcQxOkwKW9ogAAaw6WSVwNtddX24tQqm1CfFgg7h6VJHU55Aad/aMnhH80dRoDMhF5lHqDGbmODUIYkN2FbRbeqdFowXvrjwIAZl3Zh5u9+KgItQLJkWoI7TxOgG3zkHA1e9I7iwGZiDzK3iLbBiE9woMQx00P3Gbi6YCcXXAK1fUGiashZy3NykdlnQEJEUG4bXii1OWQmwiCgOkd7C2fMSYFgtDeaE1nY0AmIo9i7z8eyv5jt+oRHoRB3cNgFYF1hyukLoecUNdkwkeZxwAAT0xM5QmsPm5qWgKClHI4m3VlAhCklOPmYQnuLcxP8N1FRB6F/cddh20W3mXR5nyc0pvQKyYYNw7pLnU55GaaIAU+nJYGAWgzJNtv/2haGkf+uQgDMhF5DFEUz0ywYP+x29kD8sYjlWg0WiSuhi6kVm/EpxuOAwCenJiKADl/ffuD8akxWJQxEkEKuS0ot3I/hVyGxRkjMS41pivL82l8hxGRxzhe1YBavQmqABkGdguTuhyfN7BbGHqEB6HJZMWmo1VSl0MX8MmG46gzmNE/PhTXXtxN6nKoC41PjUHWnAmYe/1AJEWqW9xmXy3uER6EMX2ipSjPZ3EOCBF5DG4Q0rUEQcCkgXFYvCUfaw6WOVaUybNU1RuwaHM+AGD2pFTIZDwBy99oghTIGNMTM0anoFZvQr3BjBBVAGQyYNzrf+BEVQNW7S3Fn4b0kLpUn8HfQETkMRz9x2yv6DL2ULzuUAUsVm5P64k+/OMYGk0WXJKg4R8xfk4QBEQEK5EYqUZEsBKaICUeuKwnAOCddUf4HnYhBmQi8hi7HDvoMSB3lZE9IxEWGIDqBqPj/3/yHGXaJny+tQAA8NTkfhzfReeYPiYFmiAFjlXaVpHJNRiQicgj1DWZkFteBwAYlhwubTF+RCGX4Yr+sQA4zcITvbf+CIxmK0amRGJsX/aY0rnCAhVcRXYDBmQi8gh7irQQRSAhIgixodwgpCtx3JtnKqrR46vtRQCApyancvWYWjWDq8gux4BMRB4hh+0VkhmfGgOFXMDxqgYcraiXuhw6bcG6IzBbRYztG41RvaKkLoc8WGigAg+O5SqyKzEgE5FHOBOQw6UtxA+FBiqQ3tv28T1XkT3Dscp6rMwpBmDrPSZqy/TRXEV2JQZkIpKc1SpiFydYSOpMm0WZxJUQAMxfewRWEZg4IA5DEsOlLoe8QPNV5AVcRe40BmQiktzxqgZoG00IVMgwgBuESGLSAFtA3lVUi8o6g8TV+LdDJ3X4aY9tBXD2pFSJqyFvMn10CsLVChznKnKnMSATkeTs7RWDe4RDwS10JRGvCcTgBA1EEfj9MNsspPTWmjwAwLWDu2Fgd/7BSM6zrSL3AsBV5M7ibyIikpx9/u5QjneT1MQBnGYhtT1FtVhzsBwyAXhyIlePqf3uTU92rCLbP4mg9mNAJiLJ5RTUAuAEC6nZ+5A3HqmC3miWuBr/9O/Tq8c3DU1An9gQiashb9R8FZkTLTqOAZmIJKVrMiGv4vQGIQzIkuofH4qEiCAYzFZsPFIldTl+Z/uJGmzIq0SATMDjE/pKXQ55MUcvchVXkTuKAZmIJLWnqBaiCCRGBiEmVCV1OX5NEARuGiIRURTx5upcAMBtIxKRFKWWuCLyZiGqgBaryGaLVeKKvA8DMhFJiu0VnsUekH8/XMGPZrvQpqNV2H6iBsoAGR67so/U5ZAPaLGKzIkW7caATESS4g56nmVkSiQ0QQrUNBiRXXBK6nL8gm312NZ7PG1UMrppgiSuiHxB81Xkd9cd5SpyOzEgE5FkrFaRAdnDBMhluLJ/LABuGtJV1h2qwJ6iWgQp5Jh5eW+pyyEfMn10CiK4itwhDMhEJJljlfWoazIjUCFD/26hUpdDpzXvQxZFtlm4k9V6pvd4xpgU9uGTS4WoAvDgOK4idwQDMhFJxrFBSAI3CPEk41JjoJTLkF+tx9GKeqnL8Wn/238Sh8vqEKoKwMOngwyRK92bfmYV+UdOtHCaX/xG2rBhA66//np0794dgiDg+++/b3G7KIqYO3cuunXrhqCgIEycOBFHjhyRplgiP2I/QS8tme0VniREFYDRfaIAAKs5zcJtzBarY9e8+8f2RLhaKXFF5ItarCL/zlVkZ/lFQG5oaMAll1yC999//7y3v/7663jnnXfw0UcfYdu2bQgODsaUKVPQ1NTUxZUS+Rf2H3sujntzvx92l+J4ZQPC1Qrcf1lPqcshHzb99CryCa4iO80vAvLVV1+NV199FTfddNM5t4miiPnz5+Pvf/87/vSnP2Hw4MFYunQpSktLz1lpJiLX0TaacOT0x/dDk8KlLYbOYd92endRLSp0XCxwNZPFivnrbKvHfx7fG6GBCokrIl8WrArAQ+NsJ4ByFdk5fhGQL+TEiRMoKyvDxIkTHddpNBqMGjUKWVlZElZG5Nt2F9UCAJKj1IgO4YlJniYuLBCXJIYDANYeqpC2GB/0zc4iFNU0IjpEhXvTk6Uuh/zAvenJjlXkH3ZzFbktfh+Qy8psY4zi4uJaXB8XF+e47XwMBgN0Ol2LCxE5L6eA7RWebrKjzYLj3lypyWTBu+uOAgAevaI31MoAiSsif9ByFZm767XF7wNyR82bNw8ajcZxSUxMlLokIq9ypv84XNpCqFX2PuTNx6rRYDBLXI3vWL6tEGW6JnTTBOLOkUlSl0N+5N70ZEQGK5Ffrecqchv8PiDHx8cDAMrLW56IUl5e7rjtfObMmQOtVuu4FBUVubVOIl9itYqOFouhXEH2WH1jQ5AcpYbRbMWGvEqpy/EJeqMZH/xhWz3+y4S+CFTIJa6I/IltFdk+0YKryBfi9wG5Z8+eiI+Px7p16xzX6XQ6bNu2Denp6a0ep1KpEBYW1uJCRM45enqDELVSjv7x3CDEUwmCgEkDOM3ClZZsKUBVvRFJkWrckpYgdTnkh+65lKvIzvCLgFxfX4/du3dj9+7dAGwn5u3evRuFhYUQBAFPPPEEXn31Vfz444/Yt28f7r33XnTv3h033nijpHUT+Sp7//HgBA0CuEGIR7O3WfyeW8HVpk7SNZnwUeYxAMATE/tycxySBFeRneMX786dO3di6NChGDp0KABg9uzZGDp0KObOnQsA+Otf/4rHHnsMDz30EEaMGIH6+nr8+uuvCAwMlLJsIp/F+cfeIy05AhFqBWr1JuzIPyV1OV7ts40noG00oU9sCP40pIfU5ZAfa96L/D1Xkc/LLwLy5ZdfDlEUz7ksXrwYgO1jxJdffhllZWVoamrC2rVrkZqaKm3RRD4sp7AWAAOyNwiQy3Blf7ZZdNapBiM+23QCADB7UirkMkHiisifqZVntjbnKvL5+UVAJiLPodWbcJQbhHgVx656h8ogiqLE1XinjzccR73BjIHdwnDVoNZPACfqKvecXkUu4CryeTEgE1GX2lVk+5g+JUqNKG4Q4hXGpUZDFSBDUU0jcsvrpC7H61TUNWHxFtvq8VOTUyHj6jF5AK4iXxgDMhF1KbZXeB+1MgCX9YkGAKw5wDaL9vpg/TE0mawYkhiOK/vHSl0OkcM96cmIOr2K/N9dJVKX41EYkImoS+06fYLe0GQGZG9yps2CAbk9SmsbsXxbIQDg6cn9IAhcPSbPoVYG4OHxtlXk99Yf5SpyMwzIRNRlLFYRuxwryOGS1kLtM2FAHAQB2FusRZm2SepyvMa7vx+F0WLFpb0iMaZPlNTlEJ1j2qVcRT4fBmQi6jJHKupQb7BtENIvjhuEeJOYUBWGJoYD4CqyswqqG/DtTtsuq09x9Zg8VPNV5Hd/PwoTV5EBMCATURfKKagFAAxJDOcGIV5o0kDb9AWOe3POgrVHYLaKGJ8agxEpkVKXQ9SqaZcmIzpEicIariLb8TcUEXUZbhDi3ex9yFnHqlDXZJK4Gs8iiiJqGowoqtGjpsGIvDId/rvbFjSemsy5+uTZbBMtegMA3uMqMgAgQOoCiMh/OAJycri0hVCH9IkNQa/oYByvakBmXiWuG9xd6pIkp200YUV2MZZsyUdBjd5xvVophygCV/SLweCEcOkKJHLS3Zcm4eMNxxyryLcNT5S6JElxBZmIukSt3ojjlQ0AgKGJXEH2Vo5pFmyzQGZeJdLnrcMrqw6isFk4BgC90QIAyDpejcy8SinKI2oXriK3xIBMRF3CPr2iV3QwIoKV0hZDHWYPyOsPV/j1L9DMvEpkLNqORpMFIoDW9hc0mK3IWLSdIZm8wt2XJp3pRc7x715kBmQi6hL29oqh7D/2akOTIhAVrISuyYztJ2qkLkcS2kYTZi7LtgXjNnbeFkVbeJ65LBvaRvZtk2dTKwPw5/G2VeR31x/x6z+CGZCJqEuw/9g3yGWCYzc4f22zWJFdjEajpc1wbCeKQKPRgpU5xe4tjMgF7h5lm2hRVNPo16vIDMhE5HYWq4jd3GLaZzTvQxadTYk+QhRFLNmS36FjF2/O97v/v8j7BCnlXEUGAzIRdYG88jo0GC0IUQUglRuEeL2xfWMQqJChpLYRh07WSV1OlzqlN6GgRt9qz3FrRAAFNXrU6tlmQZ7PtoqsQlFNo99+8sGATERuZ2+vuCRRA7mMu4l5uyClHJf1iQHgf20WDQZzp46v7+TxRF3Btors37vrMSATkdvZd9Bje4XvmGxvszhUJnElXStY1bntA0I6eTxRV7GvIhef8s9VZAZkInK7XdxBz+dcOSAWggDsL9GhtLZR6nK6TIRageRINdr7OYgAIDlSjXC1wh1lEbmcv68iMyATkVudajDieNXpDUKSwqUthlwmOkSFtNN/8Kw95D9tFoIgYProlA4dO2NMCgSBLUbkPfx5FZkBmYjcaleRbfW4V0wwwtXcIMSX+OuuelPTEqAIcP7Xp0ywrcbdPCzBjVURuV6QUo6Zl5+eaPH7URjN/rOKzIBMRG7F/mPfZQ/IW49XQ9fkP9MZDp3UwWp1bo6FfcH4o2lp0ASxvYK8z92jkhAT6n+ryAzIRORW2QXsP/ZVvWJC0DsmGCaLiD9y/WMr5SPldXho6U6YrSKGJ0dArZRDAM7pSbZfF6SQY3HGSIxLjen6YolcIFBxZi7ye+v9ZxWZAZmI3MZssWJPcS0AIC2ZAdkXTRoYD8A/2iwqdE2YsWgHdE1mpCVHYNkDo5A1ZwLmXj8QSZHqFvdNilRj7vUDsfX5CQzH5PX8cRWZ82aIyG1yy+ugN1oQqgpA39gQqcshN5g0MA4fZR7DH4crYDRboWxHb643aTCYcd+SHSipbUTP6GB8eu9wBCrkCFTIkTGmJ2aMTkGt3oR6gxkhqgCEqxU8IY98RqBCjpnje+PlVQfx7u9HcfOwBJ99r9v59qsjIknlnN5eekhSOGTcIMQnDU0MR3SICnUGM7adqJa6HLcwW6yYtTwH+0t0iApWYnHGCEQGtzzhVBAERAQrkRipRkSwkuGYfM5dp1eRS2obscIPVpEZkInIbXad7j8eyv5jnyWTCZg4IBaAb7ZZiKKIF37Yj/W5lQhUyPCf6cORHBUsdVlEXc6+igwA7/nBRAsGZCJymxzHBiHh0hZCbmWfZrH2YDlE0bnpDt7igz+O4cvtRRAE4J07hvKPPfJrd41KQqyfrCIzIBORW1TXG5BfrQcADE1kqPBlY/pEI0ghR6m2CQdKdVKX4zLf7yrBG7/lAgD+cf0gTB4UL3FFRNIKVJyZi+zrq8gMyETkFrtO9x/3iQ2Bhtvr+rRAhRzjUqMBAKt9pM1iy7EqPPPdHgDAg2N7dnj3PCJfc+fIM6vI32X77ioyAzIRuQXbK/yLL417yyuvw8OfZ8NkEXHtxd0w5+oBUpdE5DGaryK/78NzkRmQicgtzgRktlf4gyv7x0Im2HaZK6rRS11Oh5XrmjBj4XbUNZkxPDkC/77tEk5gITqLP6wiMyATkcuZLVbsKdICAIZxgxC/EBmsxPCUSADA2kPeuYpcbzAjY9EOlGqb0CvmzKxjImopUCHHIz6+isyATEQud7isDo0mC0IDA9AnhhuE+IvJp6dZeGObhclixSNf5ODgSR2iQ5RYPGMkIs6adUxEZ9zRbBX52+wiqctxOQZkInK5XafbK4YkcoMQf2If97btRA20epPE1ThPFEX8/b/7sSHPNuv4s+kjkBSlbvtAIj/WYhXZBydaMCATkcvZd9Bj/7F/SY4KRmpcCCxWEetzK6Qux2nv/X4UX+8sgkwA3rtzGC5JDJe6JCKvYF9FLtU2+dwqMgMyEbmc4wQ99h/7nUle1maxIrsY/16TBwB46YZBmHi6fiJqmy+vIjMgE5FLVdUbUHB6g5AhXInzO/Zxb3/kVsBgtkhczYVtPlqFZ1fsBQA8PL4X7klPkbYgIi90x8gkxIXZVpG/2ek7q8gMyETkUvYNQvrGhkATxA1C/M3gHhrEhqrQYLQg61i11OW06nCZDn/+PBtmq4jrBnfDs1P6S10SkVeyrSL3AQB8sP6ox/9h7CwGZCJyqewCzj/2ZzKZ4GhT8NQ2izJtEzIW7UCdwYyRPSPx5q2cdUzUGbePSHSsIn+70zfmIjMgE5FL2fuP09h/7LfsfchrD5XDahUlrqaluiYTMhbvwEltE3rHBOOTe9I465iok3xxFZkBmYhcxmSxYm9xLQBgWHK4pLWQdEb3jkKwUo5ynQH7SrRSl+Ngn3V86KQO0SEqLM4YiXA1Zx0TucLtIxIRHxZ4uhfZ+1eRGZCJyGUOn6xDk8mKsMAA9IrmBiH+ShUgx/h+MQA8p81CFEU8v3IfNh6pQpBCjoUzhiMxkrOOiVwlUCHHI1fYJlr4wioyAzIRuYy9vWJoUgR7Ov2cp417e2fdUXybXQyZALx/91AMTgiXuiQin3PbcNsq8kkfWEVmQCYil3HMP+YJen7vin6xkMsE5JbXofD02D+pfLuzCG+vtc06fuXGi3Blf846JnIHX1pFZkAmIpc5s0FIuLSFkOTC1UqMSLH9obT6YJlkdWw8Uok5K/cBAGZe3ht3j0qWrBYif2DvRfb2VWQGZCJyico6A4pqGiEI3CCEbOybhqw9JE2bxaGTOsxclgOzVcSfhnTHM5P7SVIHkT9RBcjxqA+sIjMgE5FL2FePU2NDERrIDUIImHy6D3lH/inU6o1d+twntY3IWLQD9QYzRvWMxOu3DGZfPFEXua35KvIO79xdjwGZiFyC7RV0tsRINfrHh8JiFfH74Youe15dkwkZi3agTNeEvrEh+OSe4VAFcNYxUVdpvor8/vpjXrmKzIBMRC6xq6AWgG2CBZFdV0+zMJqteGRZDg6X1SEmVIVFGSOgUfMTDaKudtuIRHTTBKJM552ryAzIRNRpJosVe0tqAXCCBbVkD8iZeZVoMrl3FUkURTy3ci82Ha2CWinHohkjkBDBWcdEUlAFyPHIFbbd9d5ff8zt739XY0Amok47dFKHJpMVmiAFekUHS10OeZCLe2gQHxYIvdGCrGPVbn2ut9cewcqcEshlAt6/exgu6qFx6/MR0YXdNjzhzCryTu9aRWZAJqJOyymwbxASzhOhqAVBEDBxYCwAYLUb2yy+2VGEd9YdAQC8euNFuKJfrNuei4ic03wV+QMvW0VmQCaiTssprAXA9go6v+bj3qxW0eWPn5lXiTn/tc06nnVFH9w5Msnlz0FEHXPb8AR098JVZAZkIuo07qBHF3Jpr0iEqAJQWWfAnuJalz72gVItHlmWDYtVxE1De+CpyakufXwi6hxvXUVmQCaiTqnQNaH4lG2DkEsS2fNJ51IFyDG+XwwA106zKKm1zTpuMFqQ3isK/5o6GILAFh8iT3Nrs1Xkr71kogUDMhF1in31uF8cNwih1k128bg3baMJGYu2o6LOgNS4EHx0TxqUAfyVRuSJWqwi/3HUK1aRXfKvydKlS7F06VLodDqnj6mvr3ccR0Tey9F/nMz2Cmrd5f1iESATcKSiHvlVDZ16LKPZij9/no288nrEhamwKGMkNEH844zIk902PBHdNYEo1xm8YhXZJQF5xowZyMjIQHFxsdPHlJeXY8aMGbjvvvtcUQIRScQ+wYL9x3QhmiAFRvWKBNC5VWRRFPHsir3IOl6NYKUcC2eMQI/wIFeVSURuogyQ4dErz6wiNxrNqGkwoqhGj5oGI0TR9SfwdkaA1AV42v8hROQ8o9mKvSVaAMCwpHBpiyGPN2lAHDYfrcaag+V4cFyvDj3Gv1fn4b+7bLOOP5iWhkHd2fdO5C1uTUvEu+uOoExnwOjXfscpvclxW3KkGtNHp2BqWoJHfCIkWcOWxWLrPwkIkDyjE1EHHTypg9FsRYRagZ7cIITaMPF0H/LOghrUNBjbffyX2wvx3vqjAIB5N12M8akxLq2PiNwr63g1ahpsobh5OAaAwho9Xll1EOnz1iEzr1KK8lqQLCDn5uYCACIjI6UqgYg66cwGIRGcHkBtSohQY2C3MFhFYN2h9rVZrM+twN+/3w8A+MuEvrhtRKI7SiQiN8nMq0TGou0wWa3nvV08fWk0WZCxaLvkIblDy7cbNmw47/U7duxAVVXVBY81GAw4duwY3nzzTQiCgCFDhnSkBCLyAGfmH4dLWwh5jUkD43DwpA5rDpbj1uHOhdz9JVo8+kUOLFYRNw/rgScn9nVzlUTkStpGE2Yuy7aF4DY6a0URgADMXJaNrDkTJGu36FBAvvzyy89ZLRJFsV0n3ImiCEEQ8PDDD3ekBCLyALu4gx6106SBcViw7gg2HqlCk8mCQIX8gvcvPqVHxuId0BstuKxPNF67mbOOibzNiuxiNBotcPasM1EEGo0WrMwpRsaYnm6trTUdbrEQRdFxOd91bV0SEhLw/vvv48Ybb3TF63CJ999/HykpKQgMDMSoUaOwfft2qUsi8ljluiaU1DZCJgCXJIZLXQ55iUHdw9AjPAiNJgs2HbnwJ45avQkzFu1AZZ0B/eND8cG0YZx1TORlRFHEki35HTp28eZ8yYY5dGgFef369Y6vRVHElVdeCUEQ8Nlnn6Fnz9aTviAICAwMRLdu3ZCY6Fn9Y19//TVmz56Njz76CKNGjcL8+fMxZcoU5ObmIjY2VuryiDyOvf+4X3wYglU82ZacIwgCJg6IxZKsAqw5WO44ce9sBrMFDy/biaMV9YgPC8SijBEI40Y0RF7nlN6Eghp9u48TARTU6FGrNyEiWOn6wtrQod9q48ePP+/1I0eOxMCBAztVkFTeeustPPjgg8jIyAAAfPTRR/j555+xcOFCPPfccxJXR+R52H9MHTVpYDyWZBVg7aEyVNb1Q5PJgmBVACLUCgiCAKtVxF+/24utx2sQogrAoowR6KbhrGMib9RgMHfq+HqD2XsC8tlOnDgBAOjRo4crHq7LGY1GZGdnY86cOY7rZDIZJk6ciKysrPMeYzAYYDAYHN+3ZxdBIl+Qw/5j6qAB3UKhCpChusGEEf9c67jePge15JQeP+wuRYBMwIfThmFAtzAJqyWizujsJ4whEn1C6ZJnTU5OdsXDSKaqqgoWiwVxcS0/6ouLi8Phw4fPe8y8efPw0ksvdUV5RB7HaLZin32DEG4xTe2QmVeJmcuyYTCfO+qpsEaPl1cddHw/7+aLMbYvZx0TebMItQLJkWoU1uidPkkPAAQASZFqhKulaa3i2Q4dNGfOHGi1WselqMjz9xUncpUDpVoYzVZEBiuREqWWuhzyEvY5qI0my3lvb/7LUwAQGxbYJXURkfsIgoDpo1M6dOyMMSmSTa1x6bq12WzGzz//jI0bN+L48eOoq6tz7JjXGkEQsG7dOleW0W7R0dGQy+UoL285uL68vBzx8fHnPUalUkGlUnVFeUQex95eMTQxnCO3yCntmYMKwCPmoBKRa0xNS8Cbq3PRaLI49f6XCUCgQo6bhyW4v7hWuCwgb9q0Cffccw8KCwsd111oNIcgCI5ZyFJTKpVIS0vDunXrHGPnrFYr1q1bh1mzZklbHJEHcpygx/YKcpI3zkElItfQBCnw4bQ0ZCzaDggX/iPZHgs/mpYm6R/HLgnIhw8fxlVXXYXGxkaIogilUom+ffsiMjISMpl3dHHMnj0b06dPx/DhwzFy5EjMnz8fDQ0NjqkWRHTGLscW0+HSFkJeobNzUGeMlu5jViJyjfGpMViUMRIzl2Wj0WjrLji7rQoAghRyfDQtDeNSpT3/wCUB+f/+7/+g1+shl8vx0ksv4S9/+QtCQkJc8dBd5vbbb0dlZSXmzp2LsrIyDBkyBL/++us5J+4R+buT2kaUapsglwm4JCFc6nLIC3jrHFQicq3xqTHImjMBK3OKsXhzfot/F5Ii1ZgxJgVT0xI8Yua5SwLy77//DkEQ8Pjjj+P55593xUNKYtasWWypIGpDTkEtAKB/fCg3CCGneOscVCJyPU2QAhljemLG6BTU6k2oN5gRogpA+Ok56J7CJb/dqqps24XedNNNrng4IvJgZzYIYf8xOcdb56ASkfsIgoCIYKXH/vHrkgbhmBhbn0hQEHc6IvJ1Z07QC5e2EPIa9jmo7V0bEmDbPESqOahE5L9cEpAvu+wyAMD+/ftd8XBE5KEMZgsOlNh2jeQKMjnLW+egEpH/cklAnj17NuRyORYsWACzuXO9ZkTkufaX6GC0WBEVrERSJDcIIedNTUtAkFIOZ7OuTACClNLOQSUi/+WSgDxixAjMnz8fe/bswc033+zoSSYi37Kr0D7eLYKretQu9jmoAtBmSPaUOahE5L9ccubDyy+/DAAYOXIkVq1aheTkZEyaNAn9+/eHWt32KtPcuXNdUQYRuRn7j6kzvG0OKhH5L0G80HZ3TpLJZC1Wk9q7Q15b21F7A51OB41GA61Wi7CwMKnLIXKLS/9vHcp0TfjqoUtxaa8oqcshL6VtNJ13Dmqyh81BJSLf42xec9nsnLNztgtyNxF5kNLaRpTpbBuEDE7QSF0OeTFvmYNKRP7LJQHZarW64mGIyIPZ2ysGdAuFWsm5tNR5nj4HlYj8l0tO0iMi32ffQY/j3YiIyNcxIBORU7iDHhER+QsGZCJqU5PJggOlWgAMyERE5PtcHpDXrVuHe+65B3369EFISAgCAgJw8ODBFvfZsGEDPvjgAyxbtszVT09EbnCgVAuTRUR0iBKJkdxSnoiIfJvLzrTR6/WYPn06Vq5cCeDMFIvznZEsl8sxa9YsCIKAUaNGoW/fvq4qg4jcwN5/zA1CiIjIH7hsBfm2227DypUrIYoiRowYgaeffrrV+44ZMwYXXXQRAGDFihWuKoGI3IT9x0RE5E9cEpBXrFiB//3vfwCATz75BFu3bsXrr79+wWNuvvlmiKKIzMxMV5RARG4iiqIjIKclMyATEZHvc0lAXrJkCQBg2rRpeOCBB5w6Ji0tDQBw6NAhV5RARG5SUtuIcp0BAdwghIiI/IRLAvLOnTshCAJuv/12p4/p1q0bAKCystIVJRCRm+QU1gIABnYPQ6BCLm0xREREXcAlAbm6uhoA0L17d+efWGZ7au7CR+TZcgrYf0xERP7FJQFZo7F97FpaWur0MSdOnAAAREdHu6IEInKTXaf7j4cmhUtbCBERURdxSUBOTU0FAOzZs8fpY77//nsAwNChQ11RAhG5kCiKqGkw4mhFHfaXcIMQIiLyLy4JyNdeey1EUcS7776LpqamNu+/ceNGfPXVVxAEAddff70rSiAiF9A2mrBw0wlc/sYfGPbKGkx8awMsIiATgDUHy6BtNEldIhERkdu5JCA/+uijiIyMRHl5OW655RbU1NSc935msxmffvoprrvuOlitViQmJmLGjBmuKIGIOikzrxLp89bhlVUHUVijb3GbVQReWXUI6fPWITOPJ9YSEZFvE0T7lnedtG7dOlxzzTUwm80IDAzE+PHj8euvv0IQBFx99dUwGo3YuXMntFotRFFEYGAg/vjjD4wcOdIVTy85nU4HjUYDrVaLsLAwqcshapfMvEpkLNoOEcCF/kUQBEAAsChjJManxnRVeURERC7hbF5zWUAGgM2bN2PatGkoKCiwPfhZW9LanyoxMRHffPMNRo0a5aqnlhwDMnkrbaMJ6fPWodFkuWA4thMEIEghR9acCdAEKdxfIBERkYs4m9dcttU0YNtC+siRI1i6dCluueUWJCcnIygoCEqlEt26dcO1116Ljz/+GEeOHPGpcEzkzVZkF6PR6Fw4BmwrzI1GC1bmFLu3MCIiIom4dAXZn3EFmbyRKIq4/I0/UFijR3v+IRAAJEWq8cczl5/zSREREZGnkmQFmYi8yym9CQXtDMcAIAIoqNGjVs+pFkRE5HsYkIn8WIPB3Knj6zt5PBERkSdiQCbyY8GqgE4dH9LJ44mIiDxRu3679erVC4BtOsWxY8fOub4jzn4sIuo6EWoFkiPVHe5BDldzigUREfmedgXk/Px8AOeOb7Nf3xE8wYdIOoIgYProFLyy6mC7j50xJoXvXyIi8kntCsjTp09v1/VE5PmmpiXgzdW5Ts9BlglAoEKOm4cluL84IiIiCXDMm4twzBt5s/bupLc4YyTGcSc9IiLyMhzzRkROG58ag0UZIxGkkJ/3duH0JUghZzgmIiKfx4BMRABsITlrzgR00wSec1tSpBpzrx+Irc9PYDgmIiKfxxlNRORgslhxUtsEAFjz5DgEKuQIUQUgXK3gCXlEROQ3XLKCvGXLFsjlcgQFBaGkpKTN+5eUlCAwMBABAQHIzs52RQlE5AKbjlQBAAZ2C0PfuFAkRqoREaxkOCYiIr/ikoD81VdfQRRFXHfddejRo0eb9+/Roweuv/56WK1WLF++3BUlEJELZOZVAgDG92MbBRER+S+XBORNmzZBEARcffXVTh9z7bXXAgA2bNjgihKIqJOsVhEbj9gC8ri+DMhEROS/XBKQ7TvhDRw40Olj+vfvDwA4evSoK0ogok46eFKHqnojgpVypCVHSF0OERGRZFwSkJuabCf1BAaee/Z7a1QqFQCgoaHBFSUQUSfZ2yvSe0dDGcABN0RE5L9c8lswMjISAFBYWOj0McXFxQCA8PBwV5RARJ20wd5/nBotcSVERETScklAtrdW/Pjjj04f8/333wMA+vXr54oSiKgT6ppMyC44BQAYnxorcTVERETScklAvuaaayCKIpYuXYqNGze2ef8NGzbg888/hyAIuO6661xRAhF1QtaxapitIlKi1EiKUktdDhERkaRcEpAffvhhREdHw2Kx4JprrsF7773n6EturqmpCe+88w6uvfZamM1mREREYObMma4ogYg6wTHejbvkERERuWYnvZCQECxfvhzXXHMN9Ho9Hn/8cTz//PNIS0tDt27dAAAnT57Ezp07odfrIYoiAgIC8OWXXyIsLMwVJRBRB4miiA328W4MyERERK7banrixIn47bffcM8996C0tBT19fXnzDgWRRGAbaOQzz//HJdffrmrnp6IOii/Wo+imkYo5AIu7RUldTlERESSc1lABoArrrgCx44dw9KlS7Fq1Srs2rULVVW2rWujo6MxbNgwXH/99Zg2bZpjzBsRSSsztwIAMCIlEsEql/6TQERE5JVc/ttQpVLhwQcfxIMPPujqhyYiN9hwxPZHLNsriIiIbLgbAJEfM5gtyDpWDYAn6BEREdkxIBP5sZ35p9BosiA2VIX+8aFSl0NEROQRGJCJ/Jh997xxqTEQBEHiaoiIiDxDu3qQe/XqBQAQBAHHjh075/qOOPuxiKjrZOZxvBsREdHZ2hWQ8/PzAeCclSb79R3BVSsiaZTrmnC4rA6CAIztEy11OURERB6jXQH53nvvPW+gnT59ussKIqKuYV89HpwQjohgpcTVEBEReY52BeTFixef9/pFixa5ohYi6kL2/uPxfbl6TERE1Fy7AvKPP/4IAJgwYQKCg4PdUhARuZ/FKmLj6fnH4/ux/5iIiKi5dk2xuPHGG3HTTTehoKCgxfX33Xcf7rvvPpw8edKlxRGRe+wtroW20YTQwABckhAudTlEREQexSVj3hYvXowlS5bg1KlTrng4InKzDXm21ePL+kQjQM5pj0RERM216zejSqUCANTX17ulGCLqGpl5FQC4ex4REdH5tCsg9+jRAwCwceNGtxRDRO6n1Zuwu6gWAOcfExERnU+7TtKbMGECPv30Uzz//PPYvn07UlNToVAoHLd/8MEHiI2NbXcRc+fObfcxRNQxm45WwSoCfWND0D08SOpyiIiIPI4giqLo7J2LioowbNgwVFdXt5iHbH+Ijm76YbFYOnScM/75z3/i559/xu7du6FUKlFbW3vOfQoLCzFz5kysX78eISEhmD59OubNm4eAAOf/ftDpdNBoNNBqtQgLC3PhKyByrWe/24uvdxbh/st64oXrBkpdDhERUZdxNq+1q8UiMTEROTk5eOCBB5CSkgKFQgFRFB3BWBTFDl3cyWg04tZbb8XMmTPPe7vFYsG1114Lo9GILVu2YMmSJVi8eDFXtckniaKIDUdOzz9mewUREdF5tWsFuTUymQyCIGDfvn0YONAzV6QWL16MJ5544pwV5F9++QXXXXcdSktLERcXBwD46KOP8Oyzz6KyshJKpXM7jHEFmbxBXnkdJr+9AaoAGfa8OBmBCrnUJREREXUZt6wg+6KsrCxcfPHFjnAMAFOmTIFOp8OBAwdaPc5gMECn07W4EHk6++55o3pFMRwTERG1ol0n6c2ePRsA8Nxzz7U4GW/RokUQBAEJCQmura4LlJWVtQjHABzfl5WVtXrcvHnz8NJLL7m1NiJXy8xjewUREVFb2rWCPH/+fCxYsABVVVUtrv/HP/6Bl156CRUVFS4trjXPPfccBEG44OXw4cNurWHOnDnQarWOS1FRkVufj6izGo0WbDtRAwAYnxotcTVERESeq10ryK0pKCiAIAgwGo2ueLg2PfXUU5gxY8YF79OrVy+nHis+Ph7bt29vcV15ebnjttaoVCrHxilE3mDbiWoYzVb0CA9C75gQqcshIiLyWO0KyGq1Go2NjeesIHe1mJgYxMS45iPi9PR0/POf/0RFRYWjbWTNmjUICwvz2BMOiTrC3l4xLjW6wyMZiYiI/EG7Wiz69OkDAFi6dKnbx7O5SmFhIXbv3o3CwkJYLBbs3r0bu3fvdmyXPXnyZAwcOBD33HMP9uzZg99++w1///vf8eijj3KFmHyK/QS9cX3Zf0xERHQh7VpBvummm7B3714sWrQIv/zyC3r16tViJ72MjAwEBwe3qwBBELBu3bp2HdMec+fOxZIlSxzfDx06FACwfv16XH755ZDL5Vi1ahVmzpyJ9PR0BAcHY/r06Xj55ZfdVhNRVys+pcexygbIZQJG92H/MRER0YW0aw5yU1MTJkyYgKysrM4/sSA4Nhlx5056XYVzkMmTLd9WiOf/uw/DkyPw3czRUpdDREQkCWfzWrtWkAMDA5GZmYlvv/0Wa9euRUlJCQwGAzIzMyEIAtLS0tq9gkxE7peZZ5sww/FuREREbWv3FIuAgADceeeduPPOOx3XyWS2VubFixfzxDYiD2OyWLHlaDUAYBwDMhERUZv8fic9Il+3u6gWdQYzItQKXNRDI3U5REREHs8lc5BPnDgBAOjRo4crHo6IXCgz1za9YmzfGMhlHO9GRETUFpcE5OTkZFc8DBG5wYYj9vnHbK8gIiJyhksCcnNarRbfffcdsrKyUFZWBr1ej0WLFrUI0aWlpaitrUVgYKDTO94RUftV1xuwr0QLABjXl+PdiIiInOHSgPzee+/hb3/7m2MTDvsYt4aGhhb3++OPPzBt2jQEBgaiuLgYkZGRriyDiE7bdLQKoggM6BaG2LBAqcshIiLyCi47Se/FF1/E448/jrq6OiiVSqSlpbV63zvuuAPx8fEwGAxYsWKFq0ogorPYt5fmeDciIiLnuSQgZ2dn49VXXwUATJs2DWVlZdi+fXvrTyqT4dZbb4UoilizZo0rSiCis1itIjbkVQEAxqWyvYKIiMhZLgnI7733HkRRRHp6OpYuXQqNpu1RUunp6QCAffv2uaIEIjrLoTIdquoNUCvlGJ7MNiYiIiJnuSQgb9iwAYIgYNasWU4fk5KSAgAoKSlxRQlEdBZ7e8Xo3lFQBnDkORERkbNc8lvz5MmTAIB+/fo5fUxgoO2EIYPB4IoSiOgsG/I43o2IiKgjXBKQlUolAKC2ttbpY8rLywEA4eHhriiBiJqpN5ixM/8UAJ6gR0RE1F4uCchJSUkAgCNHjjh9zO+//w6gfavOROScrGPVMFtFJEepkRwVLHU5REREXsUlAXnChAkQRREfffSRU/cvKSnBJ598AkEQMHnyZFeUQETNONor+nL1mIiIqL1cEpBnzZoFhUKBPXv24JVXXrngfXNzc3HVVVdBq9VCrVbj4YcfdkUJRNQM5x8TERF1nEt20uvduzf++c9/4q9//Sv+8Y9/4Oeff8bNN9/suP3bb7+FQqHA5s2bsXr1alitVgiCgPnz5yMmhr/AiVwpv6oBhTV6KOQC0ntHSV0OERGR13HZVtNPP/00RFHE3//+d2zfvh07duyAIAgAgJdfftlxP1EUIZfL8eabb+L+++931dMT0Wn21ePhyZEIVrl0N3kiIiK/4NLhqM888wx2796NjIwMREdHQxTFFpewsDDceeed2LVrFx5//HFXPjURncbxbkRERJ3j8uWlAQMG4LPPPgMAFBYWoqKiAhaLBVFRUejVqxdkMm5YQOQuBrMFWcerAbD/mIiIqKPc+vlrUlKSYwQcEblfdv4p6I0WxISqMKBbqNTlEBEReSUu5xL5kMwjtvaKsX2jHecAEBERUfu4ZQU5Ozsba9euxf79+1FTUwMAiIyMxEUXXYSJEyciLS3NHU9L5PcycznejYiIqLNcGpD37duHhx56CNu3b2/1Ps8//zxGjRqFjz/+GBdffLErn57Ir5XrmnC4rA6CAIzlBiFEREQd5rIWi7Vr12LkyJHYvn27Y2pFQEAA4uLiEBcXh4CAAMf1W7duxciRI7Fu3TpXPT2R37NPrxjcQ4PIYKXE1RAREXkvlwTkqqoq3HrrrTAYDBAEAQ888AC2bduGhoYGlJaWorS0FHq9Htu3b8eDDz4IuVwOg8GAW2+9FdXV1a4ogcjvbThSBYDj3YiIiDrLJQF5wYIF0Gq1UCqV+Pnnn/HJJ59gxIgRCAg408Ehl8sxfPhwfPzxx/j555+hUCig1WqxYMECV5RA5NcsVhGbjnD+MRERkSu4JCD//PPPEAQBs2bNwpQpU9q8/+TJk/HYY49BFEX8/PPPriiByK/tK9HilN6E0MAADE0Ml7ocIiIir+aSgHzixAkAwA033OD0Mfb7Hj9+3BUlEPk1e//xmN7RCJBzeiMREVFnuOQ3aVNTEwAgODjY6WPs9zUYDK4ogcivZZ4OyOP7sb2CiIios1wSkOPj4wEAu3btcvoY+33j4uJcUQKR39I2mrC7qBYA+4+JiIhcwSUBeezYsRBFEa+99hp0Ol2b96+rq8O//vUvCIKAsWPHuqIEIr+15WgVLFYRfWJD0CM8SOpyiIiIvJ5LAvLDDz8MwNaLPG7cOOzcubPV++7cuRPjx4/HsWPHWhxLRB1jb68Yx81BiIiIXMIlO+mNGTMGjzzyCD744APs27cPo0aNwqBBgzBq1CjExsZCEASUl5dj27ZtOHDggOO4Rx55BGPGjHFFCUR+SRRFxwl641KjJa6GiIjIN7hsq+l3330XarUab731FqxWK/bv398iDAO2X+YAIJPJ8PTTT+O1115z1dMT+aWjFfUo1TZBFSDDpb2ipC6HiIjIJ7hsHpQgCHj99dexe/duzJw5E3379nVsLW2/9O3bFzNnzsTu3bsdPchE1HH29oqRPSMRqJBLXA0REZFvcNkKst1FF12E999/HwBgNBpx6tQpAEBERASUSqWrn47IrznGu3F6BRERkcu4PCA3p1QqOcaNyE2aTBZsP1EDgAGZiIjIlTrUYvHLL79g2LBhGDZsGJYvX96uY5cvX+44du3atR15eiICsO1EDQxmK7ppAtEnNkTqcoiIiHxGuwOyKIp48sknsWfPHsTExOCuu+5q1/F33nknoqOjsXv3bjz11FPtfXoiOi0z90x7Bfv5iYiIXKfdAfn3339HXl4eZDIZ3n777XY/oSAImD9/PuRyOfbv34/MzMx2PwYRARuO2Me7sb2CiIjIldodkFesWAEAmDRpEgYOHNihJx04cCCmTJkCAPjuu+869BhE/qykthFHK+ohlwkY04fzj4mIiFyp3QF5+/btEAQB119/faee+LrrroMoiti6dWunHofIH9k3BxmSGA5NkELiaoiIiHxLuwNyQUEBAKBfv36deuLU1FQAQH5+fqceh8gfbeD20kRERG7T7oCs1WoBAJGRkZ16YvvxOp2uU49D5G/MFis2Ha0CAIzvx4BMRETkau0OyGFhYQCA2traTj2x/fjQ0NBOPQ6Rv9ldVIu6JjPC1Qpc3EMjdTlEREQ+p90BOSbGtmJ18ODBTj3xoUOHAACxsbGdehwif2PfPW9s3xjIZRzvRkRE5GrtDsgjR46EKIr46aefOvXEP/zwAwRBwIgRIzr1OET+5kz/MadXEBERuUO7A/LVV18NAFi9ejU2bdrUoSfdsGEDVq9e3eLxiKhtNQ1G7C2xnQfA7aWJiIjco90BeerUqUhJSYEoirj11ltx5MiRdh2fl5eH2267DYIgICUlBbfcckt7SyDyWxuPVEIUgf7xoYgNC5S6HCIiIp/U7oCsUCjw5ptvAgAqKiqQlpaGBQsWoKGh4YLH1dfXY/78+Rg+fDgqKioAAP/+978REBDQgbKJ/NOGvNPTK7h6TERE5DaCKIpiRw585ZVX8OKLL0IQbCcJBQcHY+zYsUhLS0NsbCyCg4PR0NCA8vJy5OTkYOPGjWhoaID96V5++WX8/e9/d90rkZhOp4NGo4FWq3VM+iByJVEUMfL/1qGyzoDlD4zCaO6gR0RE1C7O5rUOL9++8MILSEhIwGOPPQa9Xo/6+nr8+uuv+PXXX897f3swVqvVeO+99zBjxoyOPjWRXzp0sg6VdQYEKeRIS4mQuhwiIiKf1e4Wi+YyMjKQl5eH2bNnIzo6GqIotnqJjo7GU089hby8PIZjog6wj3cb3TsKqgC5xNUQERH5rk43AHfv3h1vvvkm3nzzTRw4cAB79uxBdXU16urqEBoaiqioKFxyySUYNGiQK+ol8luO8W7sPyYiInIrl54hN2jQIAZhIjdoMJixs6AGAAMyERGRu3WqxYKIukbWsWqYLCKSItVIiVJLXQ4REZFPY0Am8gIbjtjbK6Idk2OIiIjIPRiQibyA/QS98amxEldCRETk+xiQiTxcQXUDCqr1CJAJSO8dJXU5REREPo8BmcjD2adXDE+JQIiKO08SERG5GwMykYfL5Hg3IiKiLsWATOTBjGYrthyrBgCM68uATERE1BV8OiDn5+fj/vvvR8+ePREUFITevXvjxRdfhNFobHG/vXv3YuzYsQgMDERiYiJef/11iSomamlnQQ30RguiQ1QY2K31PeOJiIjIdXy6ofHw4cOwWq34+OOP0adPH+zfvx8PPvggGhoa8OabbwIAdDodJk+ejIkTJ+Kjjz7Cvn37cN999yE8PBwPPfSQxK+A/N2GvCoAwLi+0ZDJON6NiIioK/h0QL7qqqtw1VVXOb7v1asXcnNz8eGHHzoC8hdffAGj0YiFCxdCqVRi0KBB2L17N9566y0GZJKcY7xbP7ZXEBERdRWfbrE4H61Wi8jISMf3WVlZGDduHJRKpeO6KVOmIDc3F6dOnWr1cQwGA3Q6XYsLkStV1DXh0EkdBAG4rE+01OUQERH5Db8KyEePHsW7776Lhx9+2HFdWVkZ4uLiWtzP/n1ZWVmrjzVv3jxoNBrHJTEx0T1Fk9/aeLq94qLuGkSFqCSuhoiIyH94ZUB+7rnnIAjCBS+HDx9ucUxJSQmuuuoq3HrrrXjwwQc7XcOcOXOg1Wodl6Kiok4/JlFzZ3bPY3sFERFRV/LKHuSnnnoKM2bMuOB9evXq5fi6tLQUV1xxBUaPHo1PPvmkxf3i4+NRXl7e4jr79/Hx8a0+vkqlgkrFVT1yD4tVxMYjnH9MREQkBa8MyDExMYiJcS40lJSU4IorrkBaWhoWLVoEmazlonl6ejr+9re/wWQyQaFQAADWrFmDfv36ISIiwuW1Ezljf4kWp/QmhKoCMDQpXOpyiIiI/IpXtlg4q6SkBJdffjmSkpLw5ptvorKyEmVlZS16i++66y4olUrcf//9OHDgAL7++mssWLAAs2fPlrBy8nf27aVH94mCQu7Tb1MiIiKP45UryM5as2YNjh49iqNHjyIhIaHFbaIoAgA0Gg1Wr16NRx99FGlpaYiOjsbcuXM54o0ktYHtFURERJIRRHtSpE7R6XTQaDTQarUIC+OOZ9RxuiYThr68xtaH/NcrkBiplrokIiIin+BsXuNnt0QeZsvRKlisInrFBDMcExERSYABmcjDcLwbERGRtBiQiTyIKIrYcHqDEPYfExERSYMBmciDHKusR0ltI5QBMlzaM0rqcoiIiPwSAzKRB8k8vXo8qmckgpRyiashIiLyTwzIRB7EPv94XF+2VxAREUmFAZnIQzSZLNh6vBoAML4fAzIREZFUGJCJPMT2EzUwmK2IDwtE39gQqcshIiLyWwzIRB6i+Xg3QRAkroaIiMh/MSATeQhH/zHHuxEREUmKAZnIA5TWNuJIRT1kAnBZn2ipyyEiIvJrDMhEHsC+ejwkMRwatULiaoiIiPwbAzKRB9hwhO0VREREnoIBmUhiZosVG4/YNggZz4BMREQkOQZkIontKa5FXZMZ4WoFBieES10OERGR32NAJpJYZq6tveKyPtGQyzjejYiISGoMyEQSyzzdXsH+YyIiIs/AgEwkoVMNRuwtrgUAjOvLgExEROQJGJCJJLTxaBVEEegfH4p4TaDU5RAREREYkIkkxd3ziIiIPA8DMpFERFF0BGSOdyMiIvIcDMhEEjlcVoeKOgOCFHIMT4mQuhwiIiI6jQGZSCL21eNLe0VCFSCXuBoiIiKyY0Amkkgm2yuIiIg8EgMykQQaDGbszD8FgCfoEREReRoGZCIJbD1eDaPFisTIIPSMDpa6HCIiImqGAZlIAo7xbn1jIAjcXpqIiMiTMCATSWADt5cmIiLyWAzIRF2ssFqPE1UNCJAJGN07SupyiIiI6CwMyERdLPOIrb1iWHIEQgMVEldDREREZ2NAJupimbkc70ZEROTJGJCJupDRbEXWMVv/MQMyERGRZ2JAJupC2QWn0GC0IDpEiYHdwqQuh4iIiM6DAZmoC2043X88tm8MZDKOdyMiIvJEDMhEXcgx/zg1WuJKiIiIqDUMyERdpLLOgAOlOgC2FWQiIiLyTAzIRF1k4+n2iot6hCE6RCVxNURERNQaBmSiLpKZx/FuRERE3oABmagLWK0iNtq3l2Z7BRERkUdjQCbqAgdKdahpMCJEFYBhyRFSl0NEREQXwIBM1AUy8yoAAKN7R0Eh59uOiIjIk/E3NVEX2JB3ur2C/cdEREQejwGZyM10TSZkF54CwBP0iIiIvAEDMpGbbTlaDYtVRK/oYCRGqqUuh4iIiNrAgEzkZpmO3fO4ekxEROQNGJCJ3EgURcf20myvICIi8g4MyERudLyqASW1jVDKZRjVK1LqcoiIiMgJDMhEbpSZa1s9HtkzEmplgMTVEBERkTMYkIncaMMRe/9xtMSVEBERkbMYkIncpMlkwdbj1QCA8amxEldDREREzmJAJnKTHfk1aDJZER8WiNS4EKnLISIiIicxIBO5iX16xdi+0RAEQeJqiIiIyFkMyERuYp9/PL4fx7sRERF5EwZkIjc4qW1EXnk9ZAJwWR+eoEdERORNGJCJ3MDeXnFJYjjC1UqJqyEiIqL24GBWIhcRRRGn9CY0GMxYe7ACADCuL9sriIiIvA0DMlEnaRtNWJFdjCVb8lFQo29xW12TCdpGEzRBComqIyIiovZiQCbqhMy8Ssxclo1Go+W8ty/anI+vdhThw2lpGJ/K1WQiIiJvwB5kog7KzKtExqLtaDRZIAIQz3MfEUCjyYKMRdsdUy2IiIjIszEgE3WAttGEmcuybcH4fMm4GVG0BeWZy7KhbTR1RXlERETUCQzIRB2wIrsYjUZLm+HYThSBRqMFK3OK3VsYERERdRoDMlE7iaKIJVvyO3Ts4s35EJ1N1URERCQJBmSidjqlN6GgRn/enuMLEQEU1OhRq2ebBRERkSfz+YB8ww03ICkpCYGBgejWrRvuuecelJaWtrjP3r17MXbsWAQGBiIxMRGvv/66RNWSN2gwmDt1fH0njyciIiL38vmAfMUVV+Cbb75Bbm4uVqxYgWPHjuGWW25x3K7T6TB58mQkJycjOzsbb7zxBv7xj3/gk08+kbBq8mTBqs5NRwzp5PFERETkXj7/m/rJJ590fJ2cnIznnnsON954I0wmExQKBb744gsYjUYsXLgQSqUSgwYNwu7du/HWW2/hoYcekrBy8lQRagWSI9UobGebhQAgKVKNcDU3DSEiIvJkPr+C3FxNTQ2++OILjB49GgqFLaRkZWVh3LhxUCqVjvtNmTIFubm5OHXqVKuPZTAYoNPpWlzIPwiCgOmjUzp07IwxKRAEwbUFERERkUv5RUB+9tlnERwcjKioKBQWFuKHH35w3FZWVoa4uLgW97d/X1ZW1upjzps3DxqNxnFJTEx0T/HkkaamJSBIKYezUVcmAEFKOW4eluDWuoiIiKjzvDIgP/fccxAE4YKXw4cPO+7/zDPPYNeuXVi9ejXkcjnuvffeTo/amjNnDrRareNSVFTU2ZdFXkQTpMAHdw9zqsXCvmD80bQ0aILYXkFEROTpvLIH+amnnsKMGTMueJ9evXo5vo6OjkZ0dDRSU1MxYMAAJCYmYuvWrUhPT0d8fDzKy8tbHGv/Pj4+vtXHV6lUUKlUHX8R5PXKdU2Or+0ryc0Ds/26IIUcH01Lw7jUmK4qjYiIiDrBKwNyTEwMYmI6FjasVisAWw8xAKSnp+Nvf/ub46Q9AFizZg369euHiIgI1xRMPud4ZT3+8eNBAMATE/tCE6TA4s35KKjRO+6TFKnGjDEpmJqWgLBArhwTERF5C0H04W29tm3bhh07duCyyy5DREQEjh07hhdeeAHl5eU4cOAAVCoVtFot+vXrh8mTJ+PZZ5/F/v37cd999+Htt99u1xQLnU4HjUYDrVaLsLAwN74qkprRbMXUD7dgX4kWo3tHYdn9oyCTCRBFEbV6E+oNZoSoAhCuVvCEPCIiIg/ibF7zyh5kZ6nVaqxcuRITJkxAv379cP/992Pw4MHIzMx0tEdoNBqsXr0aJ06cQFpaGp566inMnTuXI96oVW+tycO+Ei3C1Qq8ddsQyGS2ECwIAiKClUiMVCMiWMlwTERE5KV8egW5K3EF2T9sOVaFu/+zDaJoO+nuqota71MnIiIiz8IVZCIXO9VgxOyv90AUgTtHJjIcExER+SgGZCIniKKIOSv3oUzXhF4xwXjhuoFSl0RERERuwoBM5ISvdxTh1wNlUMgFvHPHUKiVXjkAhoiIiJzAgEzUhmOV9XjpJ9tIt6cn98NFPTQSV0RERETuxIBMdAFGsxVPfLUbjSYLxvSJwoNje7V9EBEREXk1BmSiC/j3mlzHSLd/33pmpBsRERH5LgZkolZsOVqFTzYcBwD8a+pgxGsCJa6IiIiIugIDMtF5nGow4slvdp8e6ZaEKYM40o2IiMhfMCATnUUURTy3ci/KdYbTI90GSF0SERERdSEGZKKzfLWjCL8dKOdINyIiIj/FgEzUzLHKerx8eqTbM1M40o2IiMgfMSATnWY0W/H4V7vQaLLgsj7ReOAyjnQjIiLyRwzIRKf9e3Uu9pfoEKFW4N+3XcKRbkRERH6KAZkIwOajVfi42Ui3uDCOdCMiIvJXDMjk9041GDH7m90AgLtGJWEyR7oRERH5NQZk8muiKOLZFbaRbr1jgvHCtQOlLomIiIgkxoBMfu3L7UVYfdA20m3BHUMRpJRLXRIRERFJjAGZ/NbRinq8vOoAAOCvU/pzpBsREREBYEAmP2UwW/D4V7vQZLJibN9o3H9ZT6lLIiIiIg/BgEx+6d+r83Cg1DbS7c1bOdKNiIiIzmBAJr+z6UgVPjk90u31Wy7hSDciIiJqgQGZ/EpNs5Fud49KwqSBcdIWRERERB6HAZn8hn2kW0WdAX1iQ/B3jnQjIiKi82BAJr+xfHsh1hwsh1Iuw4I7hnCkGxEREZ0XAzL5haMVdXhl1UEAwF+v6odB3TnSjYiIiM6PAZl8nsFswV++3O0Y6XbfGI50IyIiotYxIJPPe/O3XBw8qUNksBL/5kg3IiIiagMDMvm0jUcq8enGEwCAf00djFiOdCMiIqI2MCCTz6ppMOKpb/YAAKZdypFuRERE5BwGZPJJoijir9+dGen2t2s40o2IiIicw4BMPumLbYVYe8g20u2dO4ZypBsRERE5jQGZfM7Rijq8+vOZkW4Du4dJXBERERF5EwZk8ikGswWPcaQbERERdQIDMvmUN37NxSGOdCMiIqJOYEAmn7EhrxL/2WQb6fbGLRzpRkRERB3DgEw+obregKe+tY10u+fSZEwYwJFuRERE1DEMyOT1RFHEsyv2orLOgL6xIfjbtQOkLomIiIi8GAMyeb1l2wqx9lCFbaTbnUMRqOBINyIiIuo4BmTyakfK6/DqKttIt2ev7o8B3TjSjYiIiDqHAZm8lsFswV++2g2D2YpxqTHIGJ0idUlERETkAxiQyWu9fnqkW1SwEm/eOpgj3YiIiMglGJDJK23Iq8Rnp0e6vX7LYMSGcqQbERERuQYDMnmd5iPd7k3nSDciIiJyLQZk8iqiKOKv39lGuqXGheD5azjSjYiIiFyLAZm8yrKtBVh3uALKABkW3MGRbkREROR6DMjkNfLK6/Dqz4cAAM9dxZFuRERE5B4MyOQVmkwW/OXLXTCYrRifGoOMMSlSl0REREQ+igGZvMK/fj2Mw2V1p0e6XQJB4Eg3IiIicg8GZPJ4f+RWYNHmfADAG7cORkyoStqCiIiIyKcxIJNHq6o34Olv9wIApqcn48r+HOlGRERE7sWATB5LFEU88+0eVNUb0C8uFHM40o2IiIi6AAMyeaylWQVYn1tpG+l25xCOdCMiIqIuwYBMHim3rA7//J9tpNucq/ujfzxHuhEREVHXCJC6APJvoijilN6EBoMZwaoARKgVMJit+MuXu2A0W3F5vxjMGJ0idZlERETkRxiQSRLaRhNWZBdjyZZ8FNToHdcnR6oRFxaI3PI6RIco8cYtHOlGREREXYsBmbpcZl4lZi7LRqPRcs5thTV6R2C+Nz2FI92IiIioy7EHmbpUZl4lMhZtR6PJAhGAeNbtzb+fvzYPmXmVXVgdEREREQMydSFtowkzl2XbgvHZyfg8RAAzl2VD22hyd2lEREREDgzI1GVWZBej0WhxKhwDthDdaLRgZU6xewsjIiIiaoYBmbqEKIpYsiW/Q8cu3pwP0dlUTURERNRJDMjUJU7pTSio0Z/Tc9wWEUBBjR61erZZEBERUddgQKYu0WAwd+r4+k4eT0REROQsBmTqEsGqzk0UDOnk8URERETO8puAbDAYMGTIEAiCgN27d7e4be/evRg7diwCAwORmJiI119/XZoifViEWoHkSDXau+WHANvmIeFqhTvKIiIiIjqH3wTkv/71r+jevfs51+t0OkyePBnJycnIzs7GG2+8gX/84x/45JNPJKjSdwmCgOkd3DJ6xpgU7qZHREREXcYvAvIvv/yC1atX48033zznti+++AJGoxELFy7EoEGDcMcdd+Avf/kL3nrrLQkq9W3j+0W36/4yAQhSynHzsAQ3VURERER0Lp8PyOXl5XjwwQfx+eefQ61Wn3N7VlYWxo0bB6VS6bhuypQpyM3NxalTp1p9XIPBAJ1O1+JCravQNeGhpdmOKRZtrQfbF4w/mpYGTRDbK4iIiKjr+HRAFkURM2bMwJ///GcMHz78vPcpKytDXFxci+vs35eVlbX62PPmzYNGo3FcEhMTXVe4jynTNuGOT7biWGUDumsC8cYtgxGklEPAuUHZfl2QQo7FGSMxLjWm6wsmIiIiv+aVAfm5556DIAgXvBw+fBjvvvsu6urqMGfOHJfXMGfOHGi1WselqKjI5c/hC05qG3HHJ1k4XtWAHuFB+PrhdNw6PBFZcyZg7vUDkRTZclU/KVKNudcPxNbnJzAcExERkSS8cnbWU089hRkzZlzwPr169cLvv/+OrKwsqFSqFrcNHz4cd999N5YsWYL4+HiUl5e3uN3+fXx8fKuPr1KpznlcaqmkthF3frIVhTV6JEYGYfkDlyLxdCDWBCmQMaYnZoxOQa3ehHqDGSGqAISrFTwhj4iIiCTllQE5JiYGMTFtry6+8847ePXVVx3fl5aWYsqUKfj6668xatQoAEB6ejr+9re/wWQyQaGw9bquWbMG/fr1Q0REhHtegB8oqtHjzk+3ovhUI5Ii1fjyoUvRIzzonPsJgoCIYCUigpXneRQiIiKirueVAdlZSUlJLb4PCQkBAPTu3RsJCbbJCHfddRdeeukl3H///Xj22Wexf/9+LFiwAG+//XaX1+srCqtt4bikthEpUbZw3E1zbjgmIiIi8kQ+HZCdodFosHr1ajz66KNIS0tDdHQ05s6di4ceekjq0rxSQXUD7vxkK0q1TegVHYwvH7oUcWGBUpdFRERE5DRBFEWx7btRW3Q6HTQaDbRaLcLCwqQuRxInqmzhuEzXhN4xwfjywUsRy3BMREREHsLZvOb3K8jkGscq63HnJ1tRUWdA39gQLH/wUsSE8iRGIiIi8j4MyNRpRyvqcOen21BZZ0C/uFB88eAoRIcwHBMREZF3YkCmTskrr8Ndn25FVb0R/eNDsfzBSxHJiRRERETkxRiQqcMOl+lw96fbUN1gxMBuYfjigVEc10ZERERejwGZOuRgqQ53/2crTulNuLiHBp/fPxLhaoZjIiIi8n4MyNRu+0u0mPbZNtTqTbgkQYOl942CRq2QuiwiIiIil2BApnbZV6zF3f/ZCl2TGUMSw7H0/pEIC2Q4JiIiIt/BgExO211Ui3s+24a6JjPSkiOwOGMEQhmOiYiIyMcwIJNTcgpPYfpn21FnMGNESgQWZYxEiIr/+RAREZHvYcKhNmUX1GD6wh2oN5gxsmckFs0YgWCGYyIiIvJRTDl0QTvyazBj4XY0GC1I7xWFz2YMh1rJ/2yIiIjIdzHpUKu2Hq/GfYt3QG+04LI+0fj03uEIUsqlLouIiIjIrRiQ6by2HKvC/Yt3otFkwdi+tnAcqGA4JiIiIt/HgEzn2HSkCg8s3YEmkxXjU2Pw8T1pDMdERETkNxiQqYUNeZV4cOlOGMxWXNk/Fh9OGwZVAMMxERER+Q+Z1AWQ51ifW4EHTofjiQPiGI6JiIjIL3EFmQAA6w6VY+ayHBgtVkwZFId37xwGZQD/fiIiIiL/w4BMWHOwHI98kQ2TRcTVF8XjnTuHQiFnOCYiIiL/xIDs537dX4ZZy3Ngtoq4dnA3zL99CMMxERER+TUmIT/2v30nHeH4hku6YwHDMRERERFXkP3VT3tK8cTXu2GxirhpaA+8cctgBDAcExERETEg+6Mfdpfgya93wyoCU4cl4PVbBkMuE6Qui4iIiMgjcMnQz/x3V7EjHN82nOGYiIiI6GxcQfYj32UX45nv9kAUgTtHJuKfN14MGcMxERERUQsMyH7imx1FeHblXogicPeoJLzyp4sYjomIiIjOgwHZDyzfVojn/7sPAHBvejJeumEQBIHhmIiIiOh8GJB93OdbC/DC9/sBABljUjD3uoEMx0REREQXwIDsw5ZsyceLPx4AADxwWU/87doBDMdEREREbWBA9lELN53Ay6sOAgAeHtcLz13dn+GYiIiIyAkMyD7oPxuP49WfDwEAHrm8N56Z0o/hmIiIiMhJDMheSBRFnNKb0GAwI1gVgAi1whGAP8o8htd+OQwAeOzKPpg9KZXhmIiIiKgdGJC9iLbRhBXZxViyJR8FNXrH9cmRakwfnYJTeiPe/f0oAOCJiX3xxMRUqUolIiIi8loMyF4iM68SM5dlo9FoOee2whq9o98YAGZPSsVfJvTtyvKIiIiIfAYDshfIzKtExqLtEAGI57m9+XUCgEsSw7ukLiIiIiJfJJO6ALowbaMJM5dl28Lx+dLx2QRg5rJsaBtN7i6NiIiIyCcxIHu4FdnFaDRanAvHsIXoRqMFK3OK3VsYERERkY9iQPZgoihiyZb8Dh27eHM+RGdTNRERERE5MCB7sFN6Ewpq9OftO74QEUBBjR61erZZEBEREbUXA7IHazCYO3V8fSePJyIiIvJHDMgeLFjVuSEjIZ08noiIiMgfMSB7sAi1AsmRarR3HzwBts1DwtUKd5RFRERE5NMYkD2YIAiYPjqlQ8fOGJPCLaaJiIiIOoAB2cNNTUtAkFIOZ7OuTACClHLcPCzBvYURERER+SgGZA+nCVLgw2lpEIA2Q7L99o+mpUETxPYKIiIioo5gQPYC41NjsChjJIIUcltQPut2+3VBCjkWZ4zEuNSYri+SiIiIyEdwzIGXGJ8ag6w5E7AypxiLN+ejoEbvuC0pUo0ZY1IwNS0BYYFcOSYiIiLqDEHkdmsuodPpoNFooNVqERYW5tbnEkURtXoT6g1mhKgCEK5W8IQ8IiIiojY4m9e4guyFBEFARLASEcFKqUshIiIi8jnsQSYiIiIiaoYBmYiIiIioGQZkIiIiIqJmGJCJiIiIiJphQCYiIiIiaoYBmYiIiIioGQZkIiIiIqJmGJCJiIiIiJphQCYiIiIiaoYBmYiIiIioGQZkIiIiIqJmGJCJiIiIiJphQCYiIiIiaoYBmYiIiIioGQZkIiIiIqJmGJCJiIiIiJphQCYiIiIiaiZA6gJ8hSiKAACdTidxJURERER0PvacZs9trWFAdpG6ujoAQGJiosSVEBEREdGF1NXVQaPRtHq7ILYVockpVqsVpaWlCA0NhSAIbn8+nU6HxMREFBUVISwszO3P5058LZ7Jl14L4Fuvh6/FM/nSawF86/XwtXgmKV6LKIqoq6tD9+7dIZO13mnMFWQXkclkSEhI6PLnDQsL8/o3iB1fi2fypdcC+Nbr4WvxTL70WgDfej18LZ6pq1/LhVaO7XiSHhERERFRMwzIRERERETNMCB7KZVKhRdffBEqlUrqUjqNr8Uz+dJrAXzr9fC1eCZfei2Ab70evhbP5MmvhSfpERERERE1wxVkIiIiIqJmGJCJiIiIiJphQCYiIiIiaoYBmYiIiIioGQZkL/THH39AEAQIgoB//OMfUpfTbs3rd+ayePFiqUt2OLv20NBQ6PX6No9rbGyERqNpcewff/zh/oI7KDMzs0WtW7ZskbqkC/KXnwvg/e//s/nK6/G290xrGhoa8NFHH+Gaa65Bjx49EBgYCJVKhZiYGIwYMQL33XcfPv30UxQVFUld6gVptVq8//77uOaaa5CSkgK1Wg2NRoPU1FTcfffd+Prrr2GxWKQus1Vn/5t2++23t3nMjBkzHPf3JK39zg8ICEBkZCR69uyJcePG4cknn8SKFStgNBqlLhkAAzJRp9TX1+P7779v834//PADdDqd+wtykSVLlrT4funSpRJV0jG++nMhz+Xt7xkAyMrKwsCBAzFz5kz88ssvKC0thcFggNFoRFVVFXbu3IlFixbhoYcewogRI6Qut1WffvopevfujVmzZuGXX35BQUEBGhsbodPpcOTIESxfvhx33HEHBg8ejE2bNkldrlO+/fZb7Nu3T+oyXMpiseDUqVPIz8/Hxo0bMX/+fNxyyy1ISEjAq6++CrPZLGl9DMgkqZkzZ2Lfvn0XvNx4441Sl3legYGBAIDPP/+8zfva72M/xpM1Njbiu+++AwCEhIQAAL755hsYDAYpy3Kar/5cyHN5+3sGAPLy8jBlyhQUFhYCAG644QYsXboUW7duRU5ODlavXo033ngDkydPhkKhkLja1j399NN46KGHUF1djYCAAEybNg3ffPMNtm3bho0bN+I///kPrrzySgDAwYMHMXHiRMfPzpOJoogXX3xR6jI67ezf+VlZWfjf//6H1157DZMmTYIgCKisrMQLL7yAMWPGoLKyUrpiRfI669evFwGIAMQXX3xR6nLazZvrb177bbfdJgIQ5XK5ePLkyVaPKS8vFwMCAkQA4u233+44fv369V1XeDt88cUXjhoXLlzo+Prbb7+VurRW+cPPxc6b3z/n4wuvxxvfM2e75ZZbHHUvWrTogvetqKgQ33vvva4prB3ef/99x2tISEgQd+3a1ep9v/jiC1GpVIoARJVKdcH7SqH5+yI6OtrxdU5OTqvHTJ8+3XE/T9Ke9/iBAwfEoUOHOu4/ZswY0WAwdE2hZ+EKMlEHTZ48GfHx8bBYLPjyyy9bvd+XX34Js9mM+Ph4TJo0qQsr7Bj7R8ODBw9GRkYG+vXr1+J6T+erPxfyXN7+nrFYLPj5558BAMOHD8eMGTMueP+YmBg8+uijXVCZ8woKCvDUU08BAIKDg7Fu3ToMGTKk1fvfddddWLhwIQDAYDDgnnvugeih+6b95S9/cew0N3fuXImrca+BAwdi8+bNGDp0KABg8+bNeP/99yWphQGZqIPkcjnuvPNOABf+ON/+S/Kuu+6CXC7vkto66uTJk1i7di0AYNq0aS3+99dff5X24y4n+eLPhTyXL7xnKisr0djYCADo06ePxNV0zPz589HU1ATAFiJTU1PbPObuu+/GVVddBQDYv38/Vq1a5dYaOyoxMREPPfQQAGDVqlXYvn27xBW5V1BQED7//HPHyYZvvvkmTCZTl9fBgEzUCffccw8AYNeuXThw4MA5tx88eBA5OTkt7uvJvvjiC1gsFshkMtx1110AbL9EBEGAyWS64IqsJ/G1nwt5Ll94zyiVSsfXhw4dkrCSjhFF0fEHb1BQEB5++GGnj33iiSccXy9atMjVpbnMnDlzEBQUBAB44YUXJK7G/QYNGuT4ZK+0tBQ7duzo8hoYkIk6YejQoRg0aBCA869W2q+76KKLLvhxn6ew13v55ZejR48eAICePXti9OjRALznI2Nf+7mQ5/KF90xkZCSSk5MBAHv27MG//vUvWK1Wiaty3oEDB1BTUwMAGDt2LDQajdPHTpw40RE8PXmiRbdu3TBz5kwAwOrVqz26VleZOHGi4+uNGzd2+fMzIJOkKioqsH///lYvFRUVUpfYpnvvvRcAsHz58hY9bKIo4osvvmhxH0+2e/du7N27F8CZj4jt7N9nZ2fj4MGDXV5bR/jKz4U8ly+9Zx577DHH18899xx69+6Nxx9/HF9//TVOnDghYWVt27Nnj+PrYcOGtetYuVyOSy65BICt1aS0tNSltbnSs88+i+DgYAC+34sMtPxZ5uXldfnzMyCTpD788ENcfPHFrV4++OADqUts09133w2ZTIaioqIWm0z88ccfKCoqavHRqydr/hHl1KlTW9x22223OT6G9YYVMcB3fi7kuXzpPfPkk0/ivvvuc3yfn5+Pd955B3fccQd69eqF+Ph43HHHHfjpp5887mS2qqoqx9fx8fHtPj4uLs7xdXV1tUtqcofY2FjMmjULALB+/XqsX79e4orcKyoqyvH1qVOnuvz5GZCJOqlHjx644oorALT8ON/+9ZVXXun46NVTmc1mLF++HABw/fXXIywsrMXtkZGRuOaaawDYei694eNXX/i5kOfytfeMTCbDZ599htWrV+Oqq65CQEBAi9vLy8vx9ddf44YbbsDIkSNx7NgxiSo9V11dneNr+xzq9mh+jKdvHPTMM88gNDQUgO/3Ijf/uTT/GXcVBmSS1IsvvghRFFu9eMvWs/aP6lesWIHGxsYWGwd4w8f4v/32G8rLywGc+1Gxnf364uJir1m58PafC3kuX33PTJo0Cb/88guqq6vxv//9Dy+99BKuv/76Fn29O3fuxNixY3Hy5EkJKz3DHhgB2y6a7dX8mLP/0PE0UVFRjhMLN2/ejN9++03agtyoeSiW4ufCgEzkAjfffDPUajV0Oh1++OEHfP/996irq0NwcDBuvvlmqctrk/0j4KioKMfYo7Ndd911CA8Pb3F/T+ftPxfyXL76nrELCwvD1Vdfjblz5+LHH39EeXk5Fi5ciIiICAC28XaesoIZHR3t+LqsrKzdx9v/0AFafqzvqWbPnu3478oXdtdrTfPWmcjIyC5/fgZkIhcICQnBTTfdBMD2Eb79Y/ybbrrJcVKFp9Jqtfjxxx8B2PrvlEolBEE45xIYGIja2loAwMqVK9HQ0CBh1c7x5p8LeS5ffs+0RqVSISMjo8XYupUrV3pE68jgwYMdX+/atatdx1osFseJljExMejevbtLa3OH8PBwzJ49GwCwbds2j53f3FnNf5b2zXe6EgMykYvYP7JfvXo11qxZ0+I6T/bNN984Buw7q76+HitXrnRTRa7lrT8X8ly+/p65kClTpiAxMRGA7cQpTzip7aKLLnKsMG7YsAFardbpY9euXQu9Xg/ANiLOWzzxxBOO1W5fXUW2/3sNAJdddlmXP39A23chImdMmDAB3bp1c/Tlde/eHRMmTJC4qrbZP/rt1q0b3nrrrTbv/8wzz6C4uBhLly71ik02vPXnQp7L198zbenevTuKiooAwLHbmZQEQcC9996L+fPno7GxEZ9++imefvppp4599913HV+3tcW2JwkNDcUzzzyD5557Djk5Ofjvf/8rdUkutX//fqxbtw6AbSfB4cOHd3kNDMhELiKXy3HPPfdgwYIFAGw7tMlknv0hzYkTJ7B582YAwNSpU3HHHXe0eczWrVuxYMEC/P777ygpKfH4SRDe+HMhz+UP75kL0ev1jrnOYWFhHtOz+/jjj+PDDz+EwWDASy+9hBtvvLHNbbO/+uor/PzzzwBsq9DXXXddV5TqMrNmzcJbb72FiooKvPjiixg6dKjUJblEY2Mj7r33Xsc4waeffvqcqSpdgb8liFzoX//6F5qamtDU1ITXXntN6nLatHTpUsc/QrfccotTx9jvZ7VasWzZMrfV5kre9nMhz+WL75n6+nqMGjUKq1atumBPsdVqxWOPPeaYLnDDDTd4xAoyAKSkpOCNN94AYHs9EyZMaLGByNm++eYbTJ8+HYBtq+3PP//cY16Ls4KDg/Hss88CAPbt24f//e9/ElfUeQcPHsRll13m6D8eP368YwfBrsYVZCI/Zj9pLTY21un+u9GjRztaFj7//HPHP9BE/sBX3zPbt2/H9ddfjx49euDGG29Eeno6kpOTERoaitraWuzatQsLFy7Evn37AAAajQavvPKKxFW39Nhjj+HYsWNYsGABCgsLMXz4cNx555244YYbkJycDJPJhMOHD2P58uWOj+9VKhWWLVvmtVvOz5z5/+3df0xV9R/H8de5aPcGCJZpF4kik4XBpsYfWa1QWc5amDhmJZGusMn0D9tcm+acjX+w+YebgjXaKElZJZs2W8ub8y7zjwxpBqgtLJowpRR/YKCEnP5g93zPFbiQX+Dee3w+Nraze96fX3c7430/+5zPp1hbt27VuXPngnZ9iFSB03MD/v77b126dEk///yzDh06JJ/PZ/0AnTNnjvbu3avx48eHpa8kyFEu2n7xInIcPXrU2uw/Ly9v2MsOXC6X8vLyVF5ersbGRh0/flxZWVmj2VUMwmnPf6SPx6nPzLhx4+T1enX+/Hm1traqrKxMZWVlg8anpaWpurpaqampY9fJYdq2bZvS09P17rvvqr29PWj3mlvNmDFDH374YVS9nHeru+++Wxs2bAg6KjyS7dy5Uzt37gwZM3nyZK1du1bvvPNOWJZWBLDEIgp1d3db17GxsWHsCaKZfV/WW4/JHYo9Ptr2d412Tnv+o2k8Tn1mPB6PWltbdfToUb333nt6/vnnNW3aNMXFxSkmJkYJCQlKT0/Xyy+/rD179qihoSGiEvxbrVq1SmfOnNH27du1cOFCpaSkyOPxKD4+Xo888oheeeUVVVdXq76+PqqT44CVK1daO4tEE5fLpcTERD344IN65plntHbtWtXU1KilpUUbNmwIa3IsSYYZaYeqY0ifffaZ9WJIRUWFioqKwtwjAGPFac+/08YDwBmYQY5CjY2N1nV6enoYewJgrDnt+XfaeAA4AzPIUebGjRvKzMxUU1OTEhIS1NbWJo/HE+5uARgDTnv+nTYeAM7BS3pR4OLFi2ppaVFzc7O2bt2qpqYmSVJRURH/TACHc9rz77TxAHAmZpCjwLZt2/T2228HfTZnzhz5fD7Fx8eHqVcAxoLTnn+njQeAMzGDHCUMw1BiYqJmzJihpUuXqri4WG63O9zdAjAGnPb8O208AJyHGWQAAADAhl0sAAAAABsSZAAAAMCGBBkAAACwIUEGAAAAbEiQAQARobm5WYZhyDAMffzxx+HuDoA7GAkyAISJ3++3EkLDMDRhwgR1dnYOWa6rq0uJiYlBZf1+/+h3GADuECTIABAhrl27pn379g0Zt3//fl29enX0OzRCUlNTZRiGVqxYEe6uAMCwkCADQAQIHLNcVVU1ZGwghqOZAWB0kCADQARYtGiRJMnn8+n8+fODxv355586ePCgJOmll14ak74BwJ2GBBkAIsCCBQvk9Xp18+ZNVVdXDxpXXV2tnp4eeb1ePffcc2PYQwC4c5AgA0AEiImJ0auvviop9DKLXbt2SZKWLVummJiYIevt7u5WeXm55s2bp8mTJ+uuu+6S1+vVCy+8oE8//VS9vb2Dll2xYoUMw1Bqaqok6fLly9q0aZMyMjIUFxeniRMn6tlnn9Xu3bsHLD937lwZhqE//vhDkvTJJ58EvVhoGIbmzp0bsv8+n0+5ubnyer1yu916+OGHVVxcrJaWliHHDgC3iwQZACJEYWGhJOmnn35SY2Njv/snT55UXV1dUGwozc3NmjlzplavXi2/368LFy7on3/+UVtbm77++msVFhYqOztb7e3tQ9b1yy+/aPbs2SopKdHJkyfV2dmpK1eu6MiRI3rttde0Zs2a/zjaoa1fv14LFizQgQMH1NbWpu7ubjU3N+uDDz7Q448/rlOnTo14mwAgkSADQMSYPXu2MjIyJA08ixz4LDMzU7NmzQpZ17Vr15STk6PTp09LkhYvXqwvv/xStbW1+uKLL5SdnS1J+v7775Wbm6ubN28OWldnZ6dyc3N18eJFbdy4UX6/X7W1taqoqNADDzwgSSorK9M333wTVK6yslL19fWaOnWqpL410/X19UF/lZWVA7ZZUVGh0tJSZWdna8+ePaqtrdW3336r119/XZL0119/6Y033gj5HQDAbTMBAGFx+PBhU5IpyaysrDRN0zS3bNliSjJTUlLM3t5eK7a3t9dMSUkxJZnvv/++aZqmWVlZaZU/fPhwUN3r1q2z7m3cuLFf2729vWZBQYEVU15e3i9m+fLl1v3ExESzoaGhX8yvv/5qejweU5K5aNGiAcf50EMPmZLM5cuXh/w+fv/9d6s9SebKlSuDvoOAoqIiK6auri5knQBwO5hBBoAIUlBQIJfLpbNnzwYd/uH3+3X27Fm5XC4tW7YsZB03btzQRx99JEnKyMjQ5s2b+8UYhqHy8nJNmjRJkrRjx46QdZaUlFiz23bTp0/X4sWLJfXNRo+UpKQkbd++XYZh9Lu3bt066/rIkSMj1iYABJAgA0AESU5O1rx58yQFL7MIXM+fP1/Jyckh6zh+/LguX74sqe9Fu8Fe5ktISNDSpUsl9a1vPnfu3IBxhmGETMqzsrIkSe3t7Va7/6/8/Hy53e4B7z366KOKj4+XJP32228j0h4A2JEgA0CECayzrampUVdXl7q6urR3796ge6E0NDRY10888UTIWPt9ezm7++67z5ppHsi9995rXXd0dAzZv+FIT08Pef+ee+4Z0fYAwI4EGQAizJIlSxQbG6urV69q//792rdvnzo6OhQXF6clS5YMWd6+K8WUKVNCxnq93gHL2cXGxoasw+X637+SUC/7/RfDbXOk2gMAu3Hh7gAAIFh8fLzy8vK0e/duVVVVyTRNSVJeXp7i4uL+U10DreEFAITGDDIARKDAUoqDBw/K5/MFfTYU+5KHtra2kLH2Y63t5QDgTkaCDAARKCcnR0lJSerp6VFPT4+mTp2qnJycYZXNzMy0rn/44YeQsceOHRuw3EhiFhtAtCFBBoAIFBMTo8LCQrndbrndbhUWFgat9Q0lKytLEydOlNR3vPNgx0l3dHTo888/lyQ99thjSkpKGpG+38rj8Ujq234OAKIBCTIARKgtW7bo+vXrun79ukpLS4ddzu12q6ioSFLfzhQlJSX9YkzT1Jo1a3ThwgVJGpWjogMCifeZM2dGrQ0AGEkkyADgQJs2bdK0adMkSZs3b1Z+fr6++uor1dXVqaamRvPnz9euXbskSU8++aTeeuutUevLU089JUn68ccfVVpaqhMnTqipqUlNTU1qbW0dtXYB4HaRIAOAA02YMEGHDh2y9hOuqanRiy++qKysLOXn51un9D399NM6cODAoIeJjITi4mLrBcD169dr1qxZSktLU1pamgoKCkatXQC4XSTIAOBQqampOnHihHbs2KHs7GxNmjRJ48eP1/3336+FCxeqqqpK33333ajvXpGcnKxjx47pzTff1PTp0601yQAQqQwzsMEmAAAAAGaQAQAAADsSZAAAAMCGBBkAAACwIUEGAAAAbEiQAQAAABsSZAAAAMCGBBkAAACwIUEGAAAAbEiQAQAAABsSZAAAAMCGBBkAAACwIUEGAAAAbEiQAQAAABsSZAAAAMCGBBkAAACw+RfQ6M9NJ0nS7AAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig_month, ax_month = subplots(figsize=(8,8))\n", - "x_month = np.arange(coef_month.shape[0])\n", - "ax_month.plot(x_month, coef_month, marker='o', ms=10)\n", - "ax_month.set_xticks(x_month)\n", - "ax_month.set_xticklabels([l[5] for l in coef_month.index], fontsize=20)\n", - "ax_month.set_xlabel('Month', fontsize=20)\n", - "ax_month.set_ylabel('Coefficient', fontsize=20);\n" - ] - }, - { - "cell_type": "markdown", - "id": "fe1d213c", - "metadata": {}, - "source": [ - "Reproducing the right-hand plot in Figure 4.13 follows a similar process." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "d636746e", - "metadata": {}, - "outputs": [], - "source": [ - "coef_hr = S2[S2.index.str.contains('hr')]['coef']\n", - "coef_hr = coef_hr.reindex(['hr[{0}]'.format(h) for h in range(23)])\n", - "coef_hr = pd.concat([coef_hr,\n", - " pd.Series([-coef_hr.sum()], index=['hr[23]'])\n", - " ])\n" - ] - }, - { - "cell_type": "markdown", - "id": "c181f106", - "metadata": {}, - "source": [ - "We now make the hour plot." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "ce6a1623", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig_hr, ax_hr = subplots(figsize=(8,8))\n", - "x_hr = np.arange(coef_hr.shape[0])\n", - "ax_hr.plot(x_hr, coef_hr, marker='o', ms=10)\n", - "ax_hr.set_xticks(x_hr[::2])\n", - "ax_hr.set_xticklabels(range(24)[::2], fontsize=20)\n", - "ax_hr.set_xlabel('Hour', fontsize=20)\n", - "ax_hr.set_ylabel('Coefficient', fontsize=20);\n" - ] - }, - { - "cell_type": "markdown", - "id": "8cafb6b7", - "metadata": {}, - "source": [ - "### Poisson Regression\n", - "\n", - "Now we fit instead a Poisson regression model to the\n", - "`Bikeshare` data. Very little changes, except that we now use the\n", - "function `sm.GLM()` with the Poisson family specified:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "9fb8b759", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
GLM(family=<statsmodels.genmod.families.family.Poisson object at 0x17dde7010>, fit_intercept=True, control=GLMControl(mxitnr=25, epsnr=1e-06, big=9.9e+35, logging=False), summarize=True)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "GLM(family=, fit_intercept=True, control=GLMControl(mxitnr=25, epsnr=1e-06, big=9.9e+35, logging=False), summarize=True)" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Poisson_GLM = GLM(family=sm.families.Poisson(),\n", - " summarize=True)\n", - "Poisson_GLM.fit(X2, Y)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "e6732bb4-7145-4a5c-8c0b-1e119b3553e7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-114020.49333264619" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Poisson_GLM.score(X2, Y)" - ] - }, - { - "cell_type": "markdown", - "id": "9cc0ea6d", - "metadata": {}, - "source": [ - "We can plot the coefficients associated with `mnth` and `hr`, in order to reproduce Figure 4.15. We first complete these coefficients as before." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "ee272341", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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coefstd errzP>|z|
intercept4.1182450.5527117.4509859.264567e-14
mnth[Jan]-0.6701700.549266-1.2201182.224200e-01
mnth[Feb]-0.4441240.451900-0.9827923.257096e-01
mnth[March]-0.2937330.385280-0.7623884.458284e-01
mnth[April]0.0215230.2905390.0740809.409465e-01
mnth[May]0.2404710.2711370.8868983.751339e-01
mnth[June]0.2232350.3304170.6756164.992844e-01
mnth[July]0.1036170.3835040.2701847.870190e-01
mnth[Aug]0.1511710.3404910.4439816.570567e-01
mnth[Sept]0.2334930.2883830.8096644.181332e-01
mnth[Oct]0.2675730.2589071.0334733.013828e-01
mnth[Nov]0.1502640.2957040.5081566.113440e-01
hr[0]-0.7543860.732596-1.0297433.031305e-01
hr[1]-1.2259790.925442-1.3247501.852542e-01
hr[2]-1.5631471.103545-1.4164781.566355e-01
hr[3]-2.1983041.527095-1.4395341.499994e-01
hr[4]-2.8304842.095401-1.3508081.767569e-01
hr[5]-1.8146571.251892-1.4495311.471893e-01
hr[6]-0.4298880.641160-0.6704855.025486e-01
hr[7]0.5751810.4096641.4040311.603095e-01
hr[8]1.0769270.3313163.2504491.152230e-03
hr[9]0.5817690.3985331.4597761.443516e-01
hr[10]0.3368520.4388270.7676194.427138e-01
hr[11]0.4941210.4084001.2098952.263191e-01
hr[12]0.6796420.3783041.7965497.240731e-02
hr[13]0.6735650.3801971.7716197.645783e-02
hr[14]0.6249100.3884671.6086541.076921e-01
hr[15]0.6537630.3842201.7015338.884303e-02
hr[16]0.8743010.3518472.4848891.295917e-02
hr[17]1.2946350.3025594.2789511.877764e-05
hr[18]1.2122810.3087383.9265668.616743e-05
hr[19]0.9140220.3439742.6572407.878339e-03
hr[20]0.6162010.3896311.5815021.137634e-01
hr[21]0.3641810.4331510.8407714.004760e-01
hr[22]0.1174930.4857760.2418678.088834e-01
workingday0.0146650.1817530.0806889.356900e-01
temp0.7852921.0669400.7360224.617171e-01
weathersit[cloudy/misty]-0.0752310.202584-0.3713597.103702e-01
weathersit[heavy rain/snow]-0.92628715.507098-0.0597339.523682e-01
weathersit[light rain/snow]-0.5758000.377274-1.5262111.269574e-01
\n", - "
" - ], - "text/plain": [ - " coef std err z P>|z|\n", - "intercept 4.118245 0.552711 7.450985 9.264567e-14\n", - "mnth[Jan] -0.670170 0.549266 -1.220118 2.224200e-01\n", - "mnth[Feb] -0.444124 0.451900 -0.982792 3.257096e-01\n", - "mnth[March] -0.293733 0.385280 -0.762388 4.458284e-01\n", - "mnth[April] 0.021523 0.290539 0.074080 9.409465e-01\n", - "mnth[May] 0.240471 0.271137 0.886898 3.751339e-01\n", - "mnth[June] 0.223235 0.330417 0.675616 4.992844e-01\n", - "mnth[July] 0.103617 0.383504 0.270184 7.870190e-01\n", - "mnth[Aug] 0.151171 0.340491 0.443981 6.570567e-01\n", - "mnth[Sept] 0.233493 0.288383 0.809664 4.181332e-01\n", - "mnth[Oct] 0.267573 0.258907 1.033473 3.013828e-01\n", - "mnth[Nov] 0.150264 0.295704 0.508156 6.113440e-01\n", - "hr[0] -0.754386 0.732596 -1.029743 3.031305e-01\n", - "hr[1] -1.225979 0.925442 -1.324750 1.852542e-01\n", - "hr[2] -1.563147 1.103545 -1.416478 1.566355e-01\n", - "hr[3] -2.198304 1.527095 -1.439534 1.499994e-01\n", - "hr[4] -2.830484 2.095401 -1.350808 1.767569e-01\n", - "hr[5] -1.814657 1.251892 -1.449531 1.471893e-01\n", - "hr[6] -0.429888 0.641160 -0.670485 5.025486e-01\n", - "hr[7] 0.575181 0.409664 1.404031 1.603095e-01\n", - "hr[8] 1.076927 0.331316 3.250449 1.152230e-03\n", - "hr[9] 0.581769 0.398533 1.459776 1.443516e-01\n", - "hr[10] 0.336852 0.438827 0.767619 4.427138e-01\n", - "hr[11] 0.494121 0.408400 1.209895 2.263191e-01\n", - "hr[12] 0.679642 0.378304 1.796549 7.240731e-02\n", - "hr[13] 0.673565 0.380197 1.771619 7.645783e-02\n", - "hr[14] 0.624910 0.388467 1.608654 1.076921e-01\n", - "hr[15] 0.653763 0.384220 1.701533 8.884303e-02\n", - "hr[16] 0.874301 0.351847 2.484889 1.295917e-02\n", - "hr[17] 1.294635 0.302559 4.278951 1.877764e-05\n", - "hr[18] 1.212281 0.308738 3.926566 8.616743e-05\n", - "hr[19] 0.914022 0.343974 2.657240 7.878339e-03\n", - "hr[20] 0.616201 0.389631 1.581502 1.137634e-01\n", - "hr[21] 0.364181 0.433151 0.840771 4.004760e-01\n", - "hr[22] 0.117493 0.485776 0.241867 8.088834e-01\n", - "workingday 0.014665 0.181753 0.080688 9.356900e-01\n", - "temp 0.785292 1.066940 0.736022 4.617171e-01\n", - "weathersit[cloudy/misty] -0.075231 0.202584 -0.371359 7.103702e-01\n", - "weathersit[heavy rain/snow] -0.926287 15.507098 -0.059733 9.523682e-01\n", - "weathersit[light rain/snow] -0.575800 0.377274 -1.526211 1.269574e-01" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "S_pois = Poisson_GLM.summary_\n", - "coef_month = S_pois[S_pois.index.str.contains('mnth')]['coef']\n", - "coef_month = pd.concat([coef_month,\n", - " pd.Series([-coef_month.sum()],\n", - " index=['mnth[Dec]'])])\n", - "coef_hr = S_pois[S_pois.index.str.contains('hr')]['coef']\n", - "coef_hr = pd.concat([coef_hr,\n", - " pd.Series([-coef_hr.sum()],\n", - " index=['hr[23]'])])\n", - "S_pois" - ] - }, - { - "cell_type": "markdown", - "id": "4a86df8c", - "metadata": {}, - "source": [ - "The plotting is as before." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "1f5bde07", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/16/8y65_zv174qgdp4ktlmpv12h0000gq/T/ipykernel_79711/1472285317.py:8: UserWarning: FixedFormatter should only be used together with FixedLocator\n", - " ax_hr.set_xticklabels(range(24)[::2], fontsize=20)\n" - ] - }, - { - "data": { - "text/plain": [ - "array([3.05489909e-01, 3.01693505e-01, 2.04213547e-01, 1.48440894e-01,\n", - " 8.44126288e-02, 7.35152968e-02, 1.09175558e-01, 1.47075630e-01,\n", - " 1.15934130e-01, 8.31644870e-02, 6.70327818e-02, 8.74406416e-02,\n", - " 5.36697470e-01, 8.56443292e-01, 1.21781051e+00, 2.33201909e+00,\n", - " 4.39070351e+00, 1.56723410e+00, 4.11086038e-01, 1.67824357e-01,\n", - " 1.09770569e-01, 1.58828548e-01, 1.92569042e-01, 1.66790505e-01,\n", - " 1.43114097e-01, 1.44550040e-01, 1.50906957e-01, 1.47625106e-01,\n", - " 1.23796431e-01, 9.15419360e-02, 9.53193535e-02, 1.18318379e-01,\n", - " 1.51811978e-01, 1.87620142e-01, 2.35978716e-01, 3.30342876e-02,\n", - " 1.13836169e+00, 4.10402210e-02, 2.40470096e+02, 1.42335871e-01])" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig_pois, (ax_month, ax_hr) = subplots(1, 2, figsize=(16,8))\n", - "ax_month.plot(x_month, coef_month, marker='o', ms=10)\n", - "ax_month.set_xticks(x_month)\n", - "ax_month.set_xticklabels([l[5] for l in coef_month.index], fontsize=20)\n", - "ax_month.set_xlabel('Month', fontsize=20)\n", - "ax_month.set_ylabel('Coefficient', fontsize=20)\n", - "ax_hr.plot(x_hr, coef_hr, marker='o', ms=10)\n", - "ax_hr.set_xticklabels(range(24)[::2], fontsize=20)\n", - "ax_hr.set_xlabel('Hour', fontsize=20)\n", - "ax_hr.set_ylabel('Coefficient', fontsize=20);\n", - "np.diag(Poisson_GLM.covariance_)" - ] - }, - { - "cell_type": "markdown", - "id": "ff00b931", - "metadata": {}, - "source": [ - "We compare the fitted values of the two models.\n", - "The fitted values are stored in the `fittedvalues` attribute\n", - "returned by the `fit()` method for both the linear regression and the Poisson\n", - "fits. The linear predictors are stored as the attribute `lin_pred`." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "b0bd66a1", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = subplots(figsize=(8, 8))\n", - "fit_pois = Poisson_GLM.predict(X2)\n", - "ax.scatter(fit2,\n", - " fit_pois,\n", - " s=20)\n", - "ax.set_xlabel('Linear Regression Fit', fontsize=20)\n", - "ax.set_ylabel('Poisson Regression Fit', fontsize=20)\n", - "ax.axline([0,0], c='black', linewidth=3,\n", - " linestyle='--', slope=1);\n" - ] - }, - { - "cell_type": "markdown", - "id": "a3b4b06e", - "metadata": {}, - "source": [ - "The predictions from the Poisson regression model are correlated with\n", - "those from the linear model; however, the former are non-negative. As\n", - "a result the Poisson regression predictions tend to be larger than\n", - "those from the linear model for either very low or very high levels of\n", - "ridership.\n", - "\n", - "In this section, we fit Poisson regression models using the `sm.GLM()` function with the argument\n", - "`family=sm.families.Poisson()`. Earlier in this lab we used the `sm.GLM()` function\n", - "with `family=sm.families.Binomial()` to perform logistic regression. Other\n", - "choices for the `family` argument can be used to fit other types\n", - "of GLMs. For instance, `family=sm.families.Gamma()` fits a Gamma regression\n", - "model.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "08610802-162a-480c-920b-081d386022ba", - "metadata": {}, - "source": [ - "## Cross-validating a GLM" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "bc558e88-59bf-427e-a30c-9967e13c84c9", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jonathantaylor/anaconda3/envs/pyglmnet/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:425: FitFailedWarning: \n", - "1 fits failed out of a total of 5.\n", - "The score on these train-test partitions for these parameters will be set to nan.\n", - "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n", - "\n", - "Below are more details about the failures:\n", - "--------------------------------------------------------------------------------\n", - "1 fits failed with the following error:\n", - "Traceback (most recent call last):\n", - " File \"/Users/jonathantaylor/git-repos/pyglmnet/glmnet/glm.py\", line 151, in newton_step\n", - " beta = np.linalg.solve(Q, V)\n", - " File \"<__array_function__ internals>\", line 200, in solve\n", - " File \"/Users/jonathantaylor/anaconda3/envs/pyglmnet/lib/python3.10/site-packages/numpy/linalg/linalg.py\", line 386, in solve\n", - " r = gufunc(a, b, signature=signature, extobj=extobj)\n", - " File \"/Users/jonathantaylor/anaconda3/envs/pyglmnet/lib/python3.10/site-packages/numpy/linalg/linalg.py\", line 89, in _raise_linalgerror_singular\n", - " raise LinAlgError(\"Singular matrix\")\n", - "numpy.linalg.LinAlgError: Singular matrix\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/Users/jonathantaylor/anaconda3/envs/pyglmnet/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 732, in _fit_and_score\n", - " estimator.fit(X_train, y_train, **fit_params)\n", - " File \"/Users/jonathantaylor/git-repos/pyglmnet/glmnet/glm.py\", line 407, in fit\n", - " super().fit(X,\n", - " File \"/Users/jonathantaylor/git-repos/pyglmnet/glmnet/glm.py\", line 268, in fit\n", - " self._final_weights) = IRLS(regularizer,\n", - " File \"/Users/jonathantaylor/git-repos/pyglmnet/glmnet/irls.py\", line 140, in IRLS\n", - " newton_weights) = quasi_newton_step(regularizer,\n", - " File \"/Users/jonathantaylor/git-repos/pyglmnet/glmnet/irls.py\", line 45, in quasi_newton_step\n", - " state = regularizer.newton_step(design,\n", - " File \"/Users/jonathantaylor/git-repos/pyglmnet/glmnet/glm.py\", line 153, in newton_step\n", - " if self.control.logging: logging.debug(\"Error in solve: possible singular matrix, trying pseudo-inverse\")\n", - "AttributeError: 'GLMRegularizer' object has no attribute 'control'\n", - "\n", - " warnings.warn(some_fits_failed_message, FitFailedWarning)\n" - ] - }, - { - "data": { - "text/plain": [ - "{'fit_time': array([0.05036902, 0.01543188, 0.01371789, 0.00338721, 0.01344681]),\n", - " 'score_time': array([0.00119591, 0.00107312, 0.00102305, 0. , 0.00105119]),\n", - " 'test_score': array([-22194.9497411 , -22955.43107546, -23490.73107438, nan,\n", - " -23060.30191966])}" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.model_selection import cross_validate, KFold\n", - "cv = KFold(5, shuffle=True, random_state=0)\n", - "cross_validate(Poisson_GLM, X2, Y, cv=cv)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "20fd8f23-e557-43e1-af09-e817c815c045", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "cell_metadata_filter": "-all", - "formats": "ipynb,Rmd" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/examples/cross_validation.ipynb b/docs/examples/cross_validation.ipynb deleted file mode 100644 index e10aab7..0000000 --- a/docs/examples/cross_validation.ipynb +++ /dev/null @@ -1,1777 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "2e0922d5-8e49-4067-ab3a-574632608c0d", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "from glmnet import GLMNet, RegGLM, GLM\n", - "from sklearn.base import clone\n", - "import statsmodels.api as sm\n", - "from ISLP.models import summarize\n", - "import logging\n", - "logging.basicConfig(filename='log.txt', level=logging.DEBUG)\n", - "from sklearn.metrics import accuracy_score\n", - "\n", - "import rpy2\n", - "%load_ext rpy2.ipython" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "86a3c000-69df-41bd-92e9-0d799885e46f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " user system elapsed \n", - " 1.025 0.012 1.037 \n" - ] - }, - { - "data": { - "text/plain": [ - "Loaded lars 1.3\n", - "\n", - "Loading required package: Matrix\n", - "Loaded glmnet 4.1-4\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%%R -o X,Y,N\n", - "#install.packages('lars', repo='http://cloud.r-project.org')\n", - "library(lars)\n", - "library(glmnet)\n", - "data(diabetes)\n", - "X = model.matrix(lm(y ~ x, data=diabetes))[,-1]\n", - "N = colnames(diabetes$x)\n", - "Y = diabetes$y\n", - "plot(glmnet(X, Y, family=gaussian(), alpha=0.4), xvar='dev')\n", - "system.time(cv.glmnet(X, Y, family=gaussian(), \n", - " alpha=0.4, nfolds=10))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "22f23ead-7a79-496c-afc2-d4f166d57cf1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
GLMNet(lambda_values=array([1.00000000e+00, 9.11162756e-01, 8.30217568e-01, 7.56463328e-01,\n",
-       "       6.89261210e-01, 6.28029144e-01, 5.72236766e-01, 5.21400829e-01,\n",
-       "       4.75081016e-01, 4.32876128e-01, 3.94420606e-01, 3.59381366e-01,\n",
-       "       3.27454916e-01, 2.98364724e-01, 2.71858824e-01, 2.47707636e-01,\n",
-       "       2.25701972e-01, 2.05651231e-01, 1.87381742e-01, 1.70735265e-01,\n",
-       "       1.55567614e-01, 1.41747416e-01, 1.29154967e-01, 1.17681195e-01,\n",
-       "       1.07226722e-01, 9.77009957e-02, 8.90215085e-02, 8.11130831e-02,\n",
-       "       7.39072203e-02, 6.73415066e-02, 6.13590727e-02, 5.59081018e-02,\n",
-       "       5.09413801e-02, 4.64158883e-02, 4.22924287e-02, 3.85352859e-02,\n",
-       "       3.51119173e-02, 3.19926714e-02, 2.91505306e-02, 2.65608778e-02,\n",
-       "       2.42012826e-02, 2.20513074e-02, 2.00923300e-02, 1.83073828e-02,\n",
-       "       1.66810054e-02, 1.51991108e-02, 1.38488637e-02, 1.26185688e-02,\n",
-       "       1.14975700e-02, 1.04761575e-02, 9.54548457e-03, 8.69749003e-03,\n",
-       "       7.92482898e-03, 7.22080902e-03, 6.57933225e-03, 5.99484250e-03,\n",
-       "       5.46227722e-03, 4.97702356e-03, 4.53487851e-03, 4.13201240e-03,\n",
-       "       3.76493581e-03, 3.43046929e-03, 3.12571585e-03, 2.84803587e-03,\n",
-       "       2.59502421e-03, 2.36448941e-03, 2.15443469e-03, 1.96304065e-03,\n",
-       "       1.78864953e-03, 1.62975083e-03, 1.48496826e-03, 1.35304777e-03,\n",
-       "       1.23284674e-03, 1.12332403e-03, 1.02353102e-03, 9.32603347e-04,\n",
-       "       8.49753436e-04, 7.74263683e-04, 7.05480231e-04, 6.42807312e-04,\n",
-       "       5.85702082e-04, 5.33669923e-04, 4.86260158e-04, 4.43062146e-04,\n",
-       "       4.03701726e-04, 3.67837977e-04, 3.35160265e-04, 3.05385551e-04,\n",
-       "       2.78255940e-04, 2.53536449e-04, 2.31012970e-04, 2.10490414e-04,\n",
-       "       1.91791026e-04, 1.74752840e-04, 1.59228279e-04, 1.45082878e-04,\n",
-       "       1.32194115e-04, 1.20450354e-04, 1.09749877e-04, 1.00000000e-04]), lambda_fractional=True, alpha=0.4, lower_limits=-inf, upper_limits=inf, penalty_factor=None, fit_intercept=True, standardize=True, family=<statsmodels.genmod.families.family.Gaussian object at 0x16c3f5e10>, control=GLMNetControl(thresh=1e-07, maxit=100000, big=9.9e+35, logging=False, mxitnr=25, epsnr=1e-06, fdev=1e-05))
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" - ], - "text/plain": [ - "GLMNet(lambda_values=array([1.00000000e+00, 9.11162756e-01, 8.30217568e-01, 7.56463328e-01,\n", - " 6.89261210e-01, 6.28029144e-01, 5.72236766e-01, 5.21400829e-01,\n", - " 4.75081016e-01, 4.32876128e-01, 3.94420606e-01, 3.59381366e-01,\n", - " 3.27454916e-01, 2.98364724e-01, 2.71858824e-01, 2.47707636e-01,\n", - " 2.25701972e-01, 2.05651231e-01, 1.87381742e-01, 1.70735265e-01,\n", - " 1.55567614e-01, 1.41747416e-01, 1.29154967e-01, 1.17681195e-01,\n", - " 1.07226722e-01, 9.77009957e-02, 8.90215085e-02, 8.11130831e-02,\n", - " 7.39072203e-02, 6.73415066e-02, 6.13590727e-02, 5.59081018e-02,\n", - " 5.09413801e-02, 4.64158883e-02, 4.22924287e-02, 3.85352859e-02,\n", - " 3.51119173e-02, 3.19926714e-02, 2.91505306e-02, 2.65608778e-02,\n", - " 2.42012826e-02, 2.20513074e-02, 2.00923300e-02, 1.83073828e-02,\n", - " 1.66810054e-02, 1.51991108e-02, 1.38488637e-02, 1.26185688e-02,\n", - " 1.14975700e-02, 1.04761575e-02, 9.54548457e-03, 8.69749003e-03,\n", - " 7.92482898e-03, 7.22080902e-03, 6.57933225e-03, 5.99484250e-03,\n", - " 5.46227722e-03, 4.97702356e-03, 4.53487851e-03, 4.13201240e-03,\n", - " 3.76493581e-03, 3.43046929e-03, 3.12571585e-03, 2.84803587e-03,\n", - " 2.59502421e-03, 2.36448941e-03, 2.15443469e-03, 1.96304065e-03,\n", - " 1.78864953e-03, 1.62975083e-03, 1.48496826e-03, 1.35304777e-03,\n", - " 1.23284674e-03, 1.12332403e-03, 1.02353102e-03, 9.32603347e-04,\n", - " 8.49753436e-04, 7.74263683e-04, 7.05480231e-04, 6.42807312e-04,\n", - " 5.85702082e-04, 5.33669923e-04, 4.86260158e-04, 4.43062146e-04,\n", - " 4.03701726e-04, 3.67837977e-04, 3.35160265e-04, 3.05385551e-04,\n", - " 2.78255940e-04, 2.53536449e-04, 2.31012970e-04, 2.10490414e-04,\n", - " 1.91791026e-04, 1.74752840e-04, 1.59228279e-04, 1.45082878e-04,\n", - " 1.32194115e-04, 1.20450354e-04, 1.09749877e-04, 1.00000000e-04]), lambda_fractional=True, alpha=0.4, lower_limits=-inf, upper_limits=inf, penalty_factor=None, fit_intercept=True, standardize=True, family=, control=GLMNetControl(thresh=1e-07, maxit=100000, big=9.9e+35, logging=False, mxitnr=25, epsnr=1e-06, fdev=1e-05))" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X = pd.DataFrame(X, columns=N)\n", - "G = GLMNet(alpha=.4)\n", - "G.fit(X, Y)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "e63da343-94ad-4186-843d-73160c2dd50e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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QC2HrW9hOZTNmzMA0TebMmQNAbm4uTz31FPfeey9XXHEFmzZtoqKigpEjRxIXF8fGjRu57777GDduHLm5uW265il7D2GPHj0YMmRIk32DBw+muLgYiDSjAlRUVDQpU1FRETuWlZVFZWVlk+PhcJiDBw/GyhzJbreTkJDQ5CGEEEKI9ncmzkO4ZMkS5s6dy8svv4zrsBbNW2+9lbFjxzJt2jScTid//OMfueCCCxg8eDA/+clP+MY3vsH//ve/Nl/3lG0hHDduHFu3bm2yb9u2bfTp0weAvLw8srKy+OCDDxg+fDgQac378ssvY82tY8aMoaamhpUrV3LuuZGRRx9++CGGYTBq1KjO+zBCCCGEOIreOO2M1roRuqey8ePHEw6Hmz323nvvxbaXLl3artc9ZQPhT37yE8aOHcvjjz/Od77zHZYvX84LL7zACy+8AETuFbj77ruZPXs2AwYMiE07k52dzTXXXANEWhQvv/xybr75ZubNm0coFOLHP/4x3/ve98jOzu7CTyeEEEIIQ492GWtnVpdxVzhlA+HIkSN54403mDFjBo8++ih5eXn85je/4frrr4+Vuf/++2loaOCWW26hpqaGCy64gIULF8bmIAR49dVX+fGPf8wll1yCqqpce+21PPfcc13xkYQQQghxGD0aCNtjUIk4NsVsy/TZIqa2tpbExEQ8Ho/cTyiEEEK0o08/G0UwuJ/zR/6P+PjBrXqv3++nqKiIvLy8Jg1BjXRdZ/Xq1YwYMaLDp53pDo739ThlB5UIIYQQ4vSmS5dxp5FAKIQQQohuxzRNdN0LnFmDSrqKBEIhhBBCdDuRdYwjd7VJC2HHk0AohBBCiG6nsXUQpIWwM0ggFEIIIUS3ExthrNpQlNN/0EdXk0AohBBCiG5H1xsA0LS4Lq7JmUECoRBCCCG6HRlQ0rkkEAohhBCi2znUQnhmBUJd1xk7dixTpkxpst/j8ZCTk8ODDz7YZP+BAwfo1asXiqJQU1PT5utKIBRCCCFEt9PYQmjR3F1ck86laRrz589n4cKFvPrqq7H9hYWFpKSkMGvWrCblp02bxrBhw076uhIIhRBCCNHthGMthGdWIAQYOHAgc+bMobCwkLKyMt58800WLFjAK6+8gs1mi5V7/vnnqamp4d577z3pa56yaxkLIYQQ4vSlh+sB0CztEwhN0yQcNA6dX9fRQyahgI7RwYOYLTYVRVFa9Z7CwkLeeOMNpk6dyvr165k5cyYFBQWx45s2beLRRx/lyy+/ZNeuXSdfx5M+gxBCCCFEOwu38z2E4aDBC3ctOWr/V3zWLuc/llueHY/V3rrUqSgKzz//PIMHDyY/P58HHnggdiwQCHDdddfx5JNP0rt373YJhNJlLIQQQohup7GF0GI5c6edeemll3C5XBQVFVFSUhLbP2PGDAYPHsz3v//9druWtBAKIYQQotsJt/M8hBabyi3Pjo+91nWdtWvXUlBQgKZ1bJ+xxdb69relS5fyzDPPsGjRImbPns20adNYvHgxiqLw4Ycfsn79ev71r38Bke5wgLS0NB588EEeeeSR1tex1e8QQgghhOhgsRbCdgqEiqI06bZVddCskX0dHQhby+v1ctNNNzF9+nQmTJhAXl4e+fn5zJs3j+nTp/Pvf/8bn88XK79ixQp++MMf8umnn9KvX782XVMCoRBCCCG6nbB+5nYZz5gxA9M0mTNnDgC5ubk89dRT3HvvvVxxxRVHhb79+/cDMHjwYJKSktp0TbmHUAghhBDdTjhcB8D+gJc9tXu6uDadZ8mSJcydO5eXX34Zl+vQgJpbb72VsWPHMm3atFgXcXuSFkIhhBBCdDuhcC0Ac756hqF9Knlo9ENdXKPOMX78eMLhcLPH3nvvvWb3X3zxxScdEqWFUAghhBDdzsGGfQCEsKKbehfX5vQnLYRCCCGE6FYM0yAY8mDT4IcFd3DVoFu7ukqnPWkhFEIIIUS3sq5qHXY1sqrIRb0v7+LanBkkEAohhBCiW/mo+D2s0ZXenLaUrq3MGUICoRBCCCG6lZWlnwBgorbbxNTi2CQQCiGEEKLbKKsvo7J+NwBWayKKonRthc4QEgiFEEII0W18uu9T3GpkChWbNbmLa3PmkEAohBBCiG7j470f44oGQqs1qUvrciaRQCiEEEKIbqEh1MAXZV/gjqYTq1UGlHQWCYRCCCGE6BY+3vsxISNEL1ciIC2EnUkCoRBCCCG6hXeK3gFgUGJPAGy2tK6sTpfQdZ2xY8cyZcqUJvs9Hg85OTk8+OCDACiKctRjwYIFbb6uBEIhhBBCdLkDvgN8vu9zAHq7kgCwWVO7sEZdQ9M05s+fz8KFC3n11Vdj+wsLC0lJSWHWrFmxfS+//DJlZWWxxzXXXNPm68rSdUIIIYTocm/tfAvd1MlPy8dq+oAzs4UQYODAgcyZM4fCwkImTpzI8uXLWbBgAStWrMBms8XKJSUlkZWV1S7XlEAohBBCiC5lmAb/2v4vAKYMmELw4B8AsNsz2u0apmkSDgRir3VdRw8GCfn9GJrWbtdpjsVub/V8ioWFhbzxxhtMnTqV9evXM3PmTAoKCpqUueOOO/jRj35E3759ue222/jBD37Q5nkbJRAKIYQQokstLV3Knto9xFvjuSL3ClaUPQqAzdZ+gTAcCPDcjd86av9n7XaFlt35539hdTha9R5FUXj++ecZPHgw+fn5PPDAA02OP/roo0ycOBGXy8WiRYu4/fbbqa+v584772xTHSUQCiGEEKJL/XnjnwG4ZsA12BQdXW8AwOHo0ZXV6nIvvfQSLpeLoqIiSkpKyM3NjR17+OGHY9sjRoygoaGBJ598UgKhEEIIIU49G/dv5IuyL9AUje8P/j6BQBkAFksSmuZst+tY7Hbu/PO/Yq91XWft2rUUFBSgdUKXcWstXbqUZ555hkWLFjF79mymTZvG4sWLW+wSHjVqFL/85S8JBALY23A9CYRCCCGE6DJ/WBe5X/DKvCvJjsumav8HADgdPdv1OoqiNOm2VXUdzWbD6nB0eCBsLa/Xy0033cT06dOZMGECeXl55OfnM2/ePKZPn97se9asWUNycnKbwiBIIBRCCCFEF9m4fyMf7f0IVVH5Uf6PAPD5igFwOnt3ZdW61IwZMzBNkzlz5gCQm5vLU089xb333ssVV1zB+vXrqaioYPTo0TgcDt5//30ef/xx7r333jZfU+YhFEIIIUSnM02T36z6DQBX5V1F36S+AHi9u4EzNxAuWbKEuXPn8vLLL+NyuWL7b731VsaOHcu0adOwWCzMnTuXMWPGMHz4cP7whz/w9NNPN5mjsLWkhVAIIYQQne7z0s/5ouwLrKqV24ffHtvv9e4CwOXu21VV61Ljx48nHA43e+y9996LbV9xxRXtel1pIRRCCCFEpwrpIX694tcAXDfoOnrF94oda2jYDoDb1a9L6namkkAohBBCiE712pbXKPIUkeJI4daCW2P7g8GDBINVALjdA7qqemckCYRCCCGE6DT76vcxd81cAO4+524SbAmxY3V1GwFwOvtgscR1Sf3OVBIIhRBCCNEpTNNk9hez8YV9nJt5Llf3v7rJ8bq69QDExw/tiuqd0SQQCiGEEKJT/GfHf/hs32dYVSszx8xEVZrGEE/tGgASE4Z3fuXOcBIIhRBCCNHhyurL+NWKXwHw4xE/pm9i01HEpmlQU7MSgMTEczq9fmc6CYRCCCGE6FBhI8wDnz5AQ6iBgvQCbhxy41Fl6uu3Eg7XoGku4uPP7oJantkkEAohhBCiQ72w7gVWVa7CbXXzxAVPoKlHLxV38OCnACQljURVrZ1dxTOeBEIhhBBCdJgvyr6IrVf88OiHyUnIabbcgQNLAEhJubDT6iYOkUAohBBCiA5RVl/G/UvuxzANvtn/m1zV96pmy4VC1dR4VgCQljqxM6sooiQQCiGEEKLd+cN+7vn4HqoD1QxOGczPR/28xbJVVYsxTZ0491m4XH06sZbdj67rjB07lilTpjTZ7/F4yMnJ4cEHH4ztmz9/PsOGDcPhcJCRkcEdd9zR5uvKWsZCCCGEaFemaTJz6Uw2HNhAoj2RZyY8g8PiaLF8RcVbAGRkNt+CeCbRNI358+czfPhwXn31Va6//noACgsLSUlJYdasWQA8/fTT/L//9/948sknGTVqFA0NDezevbvN15VAKIQQQoh29Yd1f+DdonexKBaeufgZesb1bLGs31/KweqlAGRlfr2zqtitDRw4kDlz5lBYWMjEiRNZvnw5CxYsYMWKFdhsNqqrq3nooYd46623uOSSS2LvGzZsWJuvedp0Gc+ZMwdFUbj77rtj+/x+P3fccQepqanExcVx7bXXUlFR0eR9xcXFXHXVVbhcLjIyMrjvvvsIh8OdXHshhBDi9PDWzrdiS9M9NPohRmaNPGb50rJ/ASbJSaNxOnt3WL1M08QI6k0eSvjofR3xME2z1fUtLCykoKCAqVOncssttzBz5kwKCgoAeP/99zEMg3379jF48GB69erFd77zHfbu3dvmr89p0UK4YsUK/vCHPxyVjH/yk5/w9ttv889//pPExER+/OMfM2XKFD7//HMg0k9/1VVXkZWVxdKlSykrK+OGG27AarXy+OOPd8VHEUIIIU5ZS/ctZebnMwG4cciNXDvw2mOWN4wg+/b9DYDs7O92aN3MkEHpzKVN9mUCFf/5skOvC5D96FgU29FT7RyLoig8//zzDB48mPz8fB544IHYsV27dmEYBo8//jjPPvssiYmJPPTQQ1x66aWsW7cOm83W6jqe8i2E9fX1XH/99fzxj38kOTk5tt/j8fDiiy/y9NNPM3HiRM4991xefvllli5dyhdffAHAokWL2LRpE3/9618ZPnw4V1xxBb/85S+ZO3cuwWCwqz6SEEIIccrZuH8jd398N2EzzJV5V3LPefcc9z0Vle8QDFZis2WQkXF5J9Ty1PLSSy/hcrkoKiqipKQktt8wDEKhEM899xyTJ09m9OjR/O1vf2P79u189NFHbbrWKd9CeMcdd3DVVVcxadIkZs+eHdu/cuVKQqEQkyZNiu0bNGgQvXv3ZtmyZYwePZply5aRn59PZmZmrMzkyZOZPn06GzduZMSIEUddLxAIEAgEYq9ra2s76JMJIYQQp4bt1du5dfGt+MI+RvcYzexxs49ap/hIpmmwZ09kfsJevb6Pqra+Vas1FKtK9qNjY691XWfd2rUMKyhA01rXeteWa7fW0qVLeeaZZ1i0aBGzZ89m2rRpLF68GEVR6NGjBwBDhgyJlU9PTyctLY3i4uI21fGUDoQLFixg1apVrFix4qhj5eXl2Gw2kpKSmuzPzMykvLw8VubwMNh4vPFYc5544gkeeeSRdqi9EEIIceor8hRx86Kb8QQ8DEsbxm8m/AardvyVRqr2v09DwzY0LY5ePad2eD0VRWnSbWvqYFoUVJuG2sGBsLW8Xi833XQT06dPZ8KECeTl5ZGfn8+8efOYPn0648aNA2Dr1q306tULgIMHD7J//3769GnbtD2nbJfx3r17ueuuu3j11VdxOFoeyt7eZsyYgcfjiT1O5gZOIYQQ4lRWXFvMjxb9iAP+AwxKGcTvJ/0et9V93PeZps6uXc8AkNPrBqzWhI6u6illxowZmKbJnDlzAMjNzeWpp57i/vvvZ/fu3QwcOJCrr76au+66i6VLl7JhwwZuvPFGBg0axIQJE9p0zVM2EK5cuZLKykrOOeccLBYLFouFJUuW8Nxzz2GxWMjMzCQYDFJTU9PkfRUVFWRlZQGQlZV11KjjxteNZY5kt9tJSEho8hBCCCHONLs8u/jBwh9Q6a2kb2Jf/nDpH0i0J57Qe8vKXqehYTsWSyK9e9/cwTU9tSxZsoS5c+fy8ssv43K5YvtvvfVWxo4dy7Rp0zBNk1deeYVRo0Zx1VVXMX78eKxWKwsXLsRqbds60Kdsl/Ell1zC+vXrm+z7wQ9+wKBBg/jZz35GTk4OVquVDz74gGuvjYxy2rp1K8XFxYwZMwaAMWPG8Nhjj1FZWUlGRgYQGcqdkJDQpF9eCCGEEIfsqN4Raxnsn9SfP172R1IcKSf03nC4jp27ngIgN/d2aR08wvjx41uc/u69996LbSckJPDiiy/y4osvtst1T9lAGB8fz9ChQ5vsc7vdpKamxvZPmzaNe+65h5SUFBISEigsLGTMmDGMHj0agMsuu4whQ4YwdepUfv3rX1NeXs5DDz3EHXfcgd1u7/TPJIQQQnR3mw5s4rb3b6M6UM1ZyWfxwmUvnHAYBNhV9CzB4H5crjxyenX8vYPixJyygfBEPPPMM6iqyrXXXksgEGDy5Mn8/ve/jx3XNI3//e9/TJ8+nTFjxuB2u7nxxht59NFHu7DWQgghRPf0ZdmX3PXRXTSEGhiSOoQXLn3hhLuJAWpr11FS8goAAwfMQlWl8aW7UMy2TJ8tYmpra0lMTMTj8cj9hEIIIU5b7+95n5998jNCRojzs87n2QnPEmeLO+H3G0aA5SuuoaFhG5kZX2Po0Gc7sLaR1cqKiorIy8trdvCpruusXr2aESNGdPi0M93B8b4ep+ygEiGEEEJ0jgVbFvDTj39KyAhxaZ9L+f2k37cqDALsKnqOhoZtWK0pDBw4q4NqKtrqtO4yFkIIIUTb6YbOU189xV83/xWAbw38Fg+NeghNbV2L2sGDn8cmoR501mxsthO/51B0DgmEQgghhDiKN+TlZ5/8jI9LPgbgrnPuYtrQaSiK0qrzBAJVbNz0U8CkZ/Z1ZGRMbv/KipMmgVAIIYQQTZQ3lHPnh3ey+eBmbKqNxy58jMtzW7/WsGEEWb/hDoLBKtzuAQwY8FAH1Fa0BwmEQgghhIj5qvwrfrrkpxz0HyTFkcJzE5+jIL2gTefatv2XeDwrsVjiGZb/PJrWeSuLidaRQCiEEEIITNPk71v/zq+W/4qwGWZQyiB+M+E39Izr2abzFe99mX37XgMUzh7yDC5XXvtWWLQrCYRCCCHEGS6gB3j8y8d5ffvrAFyRdwWPjH0Ep8XZpvNVVi5k+/bHAOjf7z7S0tq2vq7oPDLtjBBCCHEG21u7l6nvTOX17a+jKio/Pfen/OrCX7U5DB6sXsbGTT8BTHr2/D69e9/SvhU+zem6ztixY5kyZUqT/R6Ph5ycHB588EHmz5+PoijNPiorK9t0XWkhFEIIIc5QHxR/wMOfPUxdqI5kezJzLprD2OyxbT6fx7OKdetuwTCCpKVN4qyBM1s9KvlMp2ka8+fPZ/jw4bz66qtcf/31ABQWFpKSksKsWbPQdZ3LL286yOemm27C7/eTkZHRputKIBRCCCHOMCEjxHOrnmP+xvkADE8fzpPjnyTLndXmc9bWbWDN2h+i615Ski9g6NnPoSin/wogHWHgwIHMmTOHwsJCJk6cyPLly1mwYAErVqzAZrMB4HQeasGtqqriww8/5MUXX2zzNSUQCiGEEGeQvbV7eeDTB1i3fx0ANwy5gbvPvRuram3zOT2e1axZ+wPC4ToSE89j2LDn0bTutU6xaZqEQqHYa13XCYfDBIPBDl+6zmq1trqltLCwkDfeeIOpU6eyfv16Zs6cSUFB86O9X3nlFVwuF9/61rfaXEcJhEIIIcQZwDRN/rfrf8z+YjbesJd4azyPjnuUSX0mndR5q6u/ZO26m9H1BhITz2N4wZ/QNFc71br9hEIhHn/88aP2L1y4sMOv/fOf/zzWsneiFEXh+eefZ/DgweTn5/PAAw+0WPbFF1/k//7v/5q0GraWBEIhhBDiNFcXrOOXX/ySd4veBeCcjHOYc+EcesT1OKnzVlUtZsPGOzGMACnJ4xg2bF63DIOnqpdeegmXy0VRURElJSXk5uYeVWbZsmVs3ryZv/zlLyd1LcU0TfOkznCGq62tJTExEY/HQ0JCQldXRwghhGhiRfkKHv78YfbV70NTNKYXTOdH+T9q9XrERyopeZWt234BGKSlTmTo0N91q25iv99PUVEReXl5OByOZruM165dS0FBQbfsMl66dCnjx49n0aJFzJ49G4DFixcfdZ5p06axatUqVq9efczzHfn1OJK0EAohhBCnIW/Iy7OrnuW1La8B0DOuJ3MunMPwjOEndV7TNNi56/+xZ888ALJ7fIezzvolqtq9I4WiKE26bXVdx2KxYLPZOjwQtpbX6+Wmm25i+vTpTJgwgby8PPLz85k3bx7Tp0+Plauvr+cf//gHTzzxxElfs3v/6wkhhBCi1VZXruahzx6iuK4YgG8N/BY/PfenxNniTuq84XA9mzbfR1XVIgD65t1Nbu6PZWqZdjZjxgxM02TOnDkA5Obm8tRTT3HvvfdyxRVXxLqO//73vxMOh/n+979/0teUiamFEEKI04Qv7OOpFU9x47s3UlxXTIYrg3mT5jFrzKyTDoM+XzFfrfw2VVWLUBQbQwb/mry8wg4Ng2Y4TOWzzxKuru6wa3Q3S5YsYe7cubz88su4XIfux7z11lsZO3Ys06ZNo/FuvxdffJEpU6aQlJR00teVFkIhhBDiNLC0dCm/XPZLSupLALim/zXcN/I+Emwnf3/7gQOfsmHj3YTDNdhs6QzLf57ExBEnfd5jMQ2D0p89QO3bb9OwdCm5f/sbinr6t2ONHz+ecDjc7LH33nuvyeulS5e223UlEAohhBCnsGp/NU+ueJK3dr0FQKYrk5ljZnJRr4tO+tyGEaao6Fl273keMElIKCA///c47G2fwPpEHXjxRWrffhusVtJuueWMCINdSQKhEEIIcQpqnFfw1yt+TU2gBgWF6wZdx53n3Inb6j7p8wcCFWzY+BNqar4EoGfP/2NA/4c6ZSRxuLqa/b9/HoCsmQ8Tf8klHX7NM50EQiGEEOIUs6tmF48vf5wvyyJhbUDyAH4x5hcMSx/WLuevqnqfzVt+Tih0EE1zM2jQY2Rlfr1dzn0iDr48H9Pnwz5kMEknsfqGOHESCIUQQohTREOogXlr5/HXTX8lbIaxqTamD5/OjWffeFJLzzUKh+vYtn02ZWX/AiAubjD5Q3+Ly5V30uc+UXpNDdWvvgpA+u23ywjmTiKBUAghhOjmTNPknaJ3+H9f/T+qfFUAXNzrYu4//35y4nPa5RrV1V+yafN9+P37AIU+vW+mb9+7UdXOnWy66ve/x2howH7WWcRNnNip1z6TSSAUQgghurFt1dt44ssn+KriKwBy4nN44PwH2mXQCEAoVM2OHb+mtOwfADgcOQwZ8iTJSSPb5fyt4d+0iepXIxNpZ9x/nwwk6UQSCIUQQohuaL9vP79b/Tve2PEGhmng0BzcPOxmbjz7RuztMLDDNE3Kyv/Njh2/IhQ6CEB29ncZ0P/nWCwnN2dhm+oTClH60EOg68RfcTlx48Z1eh3OZBIIhRBCiG7EF/bxysZXeHHDi/jCPgAu63MZ9553Lz3ierTLNerrt7J12y+oqVkOgNs9kEFn/ZKkpPPa5fxtUfXccwQ2bUZLTCTr5z/vsnqcqSQQCiGEEN2AYRq8vettnl31LBXeCgCGpQ3jvpH3nfT6w42CwYPsKnqGffsWAAaq6qRv3p3k5PwAtR0GpbRV7fvvc+CPfwIg69FHsaSnd1ldzlQSCIUQQoguZJomy8qW8eyqZ9l0YBMA2e5s7j73bi7PvbxdRtkaRoiSfX+lqOg5wuFaADLSr6B//xk4nT1P+vwnw7d+A6X3/wyA5BumkjD5si6tz5lK7tYUQgghusjaqrVMWzSNW9+/lU0HNuG2urn7nLv57zf/yxV5V5x0GDRNk6qq9/ly+VVs3z6bcLiWuLghnDPiNfLzf9flYTCwYwd7b7kF0+fDPW4cmfff36X16Q50XWfs2LFMmTKlyX6Px0NOTg4PPvggACtWrOCSSy4hKSmJ5ORkJk+ezNq1a9t8XWkhFEIIITrZ9urtPLf6OT7e+zEAVtXKd8/6Lj/K/xGpztR2uUZNzVfs2PkrPJ5VkWtYU+jX96dkZ38bRdHa5Ronw79tG8U/+CF6dTWOoUPp+exvUCwSSzRNY/78+QwfPpxXX32V66+/HoDCwkJSUlKYNWsW9fX1XH755XzjG9/g97//PeFwmFmzZjF58mT27t2L1dr67n/5ygshhBCdZG/dXn6/5ve8vettTExUReXqfldzW8FtZMdlt8s16hu2s3PnU+zfvxgAVXXQO+eH9OlzCxZLfLtc42T51qyh+NbbMDwe7IMHk/PHF9DiOn9kc3c1cOBA5syZQ2FhIRMnTmT58uUsWLCAFStWYLPZWLduHQcPHuTRRx8lJycyD+WsWbMYNmwYe/bsoX///q2+pgRCIYQQooPtrd3LH9f/kbd2vkXYDANwaZ9L+fGIH9M3sW+7XMPr3U3R7t9SXv5fwEBRNHr0+DZ98+7Ebs9sl2u0h9r3FlF6//2YgQCOgmH0/sMf0JKSOvy6pmliGL7Ya13XMU0/uu4FOrbFVFWdre7+Lyws5I033mDq1KmsX7+emTNnUlBQAMBZZ51FamoqL774Ij//+c/RdZ0XX3yRwYMHk5ub26Y6KqZpmm16pwCgtraWxMREPB4PCQkJXV0dIYQQ3cie2j28sO4F3t71NrqpAzAuexyF5xRydurZ7XINn6+Yot1zKS9/AzN6jfT0y+jX917c7n7tco32YBoG++f+nv1z5wLgHn8RvZ5+GtXt7pDr+f1+ioqKyMvLw+FwoOtePl6S3yHXOp6Lx69H01ytft+WLVsYPHgw+fn5rFq1CsthXeobNmzgmmuuoaioCIABAwbw3nvv0adPn2bPdeTX40jSQiiEEEK0syJPEX9c90feLnobwzQAGNdzHLcNu63dppDx+UrYvXsuZeWvY0ZbHVNTJ9A3704SEoa1yzXaS7i6mtKf/YyGTz4FIHnqVDJ/dr/cM3gcL730Ei6Xi6KiIkpKSmKtfz6fj2nTpjFu3Dj+9re/oes6Tz31FFdddRUrVqzA6XS2+lryLyGEEEK0kx3VO/jj+j+ycPfCWBC8qNdF3DbsNvLT26d1qqFhJ3v2zKO84r+xIJiSciF98+4mMXF4u1yjPTV88QWlP3uAcEUFit1O1i9+QdI3r+n0eqiqk4vHr4+91nWdtWvXUlBQgKZ1fJdxay1dupRnnnmGRYsWMXv2bKZNm8bixYtRFIXXXnuN3bt3s2zZMtTo8n6vvfYaycnJvPnmm3zve99r9fUkEAohhBAnaV3VOv60/k98tPej2L6Le13MbQW3cXZa+3QN19ZtYPfu56mqeg+I3O2VkjyOvLw7u3SFkZYYfj9Vv3mWg/PnA2DLy6Pnb36D46yBXVIfRVGO6LbVURQHmubq8EDYWl6vl5tuuonp06czYcIE8vLyyM/PZ968eUyfPh2v14uqqk3uS2x8bRhGm64pgVAIIYRog8YJpV9c/yLLyyNLwCkoTOoziR/l/4ghqUPa5Ro1NcvZs2ceBw5+EtufnnYpfXKnk5hQcNLX6AjelSsp+/mDBPfsASDpe98l8/77UV2tv4/uTDRjxgxM02TOnDkA5Obm8tRTT3HvvfdyxRVXcOmll3Lfffdxxx13UFhYiGEYzJkzB4vFwoQJE9p0TQmEQgghRCsYpsEHxR/wp/V/iq0sYlEsfK3f1/jB0B+0y6hhwwhRWfkuxXtfpK5uQ3SvSlbm1+nT51bi4s466Wt0hHB1NVVPP03NP/8FgCUjg6xHHyH+4ou7tmKnkCVLljB37lw+/vhjXIcF6FtvvZXXX3891nX81ltv8cgjjzBmzBhUVWXEiBEsXLiQHj3att61BEIhhBDiBPjCPt7a+RZ/2fQXdtfuBsChOfjWwG9xw5Ab6BHXtl/EhwuH69hXuoC9e/9MIFAGgKra6ZE1hd69b8blan4EaVczdR3PG29Q+f+eRq+uBiDxW9eSef/9aDIDR6uMHz+ecDjc7LH33nsvtn3ppZdy6aWXttt1JRAKIYQQx7Dft5+/bfkb/9j6D2oCNQDE2+K5btB1XD/4elIcKSd9DZ9vLyUlf2Ff6d/R9XoArNZUcnpNpWfP/8Nma5/VSzqCd+VKKh57HP+mSGupfUB/sn7xC1znntvFNROtIYFQCCGEaMa26m38ZdNfeHvX24SMEAA943ry/cHf55sDvonbenLz55mmSXX1MvaW/Jn9+z+gcaCI2z2A3jk/JDPzajTNfrIfo8MEdu2i6plnqHs/uiJKfDxpt99OyvevR2nD0mmia51UIAyFQpSXl+P1eklPTycl5eT/ShJCCCG6imEaLC1dyl82/YWlpUtj+wvSC7hhyA1M7D0Ri3pybSm67qW8/E32lvyZhobtsf0pKReSk3MTqSnjW72qRWcKlZWx//l51Pz736DroKokXXst6XffhSW1+7ZkimNr9Xd1XV0df/3rX1mwYAHLly8nGAximiaKotCrVy8uu+wybrnlFkaOHNkR9RVCCCHaXW2wlv/u+C8Lti5gT21kZKyqqFzS+xJuGHJDu0wm7fXuZl/p3ygt/SfhsAcATXPRI+taevWa2q1WFWlOqLKSAy/8kZq//x0zFGkxjZs4kYx7foK9DWvniu6lVYHw6aef5rHHHqNfv358/etf5+c//znZ2dk4nU4OHjzIhg0b+PTTT7nssssYNWoUv/3tbxkwYEBH1V0IIYQ4Kduqt7FgywL+t+t/+MKRdW7jrHFc0/8arh98Pb3ie53U+Q0jzP4DH7Bv3984ePDT2H6noze9cm4gu8e3sFjiT+oaHS1UXs6BF1+i5h//wAwEAHCddx7pP7lb7hM8jbQqEK5YsYJPPvmEs89ufpLN888/nx/+8IfMmzePl19+mU8//VQCoRBCiG4lZIT4sPhD/rblb6ysWBnb3z+pP9cNuo6v9f0aLuvJzZfnD5RTWvoPSkv/TiBQHt2rkJp6ET17Xk9a6sUoSveaDPlIweJiDvzxT9T85z8QbRF0Dh9O+l134ho9ult3a4vWa1Ug/Nvf/nZC5ex2O7fddlubKiSEEEJ0hLL6Ml7f8Tqvb3udSl8lAJqiMbH3RK4bdB3nZZ53UiHHNHUOHvycfaUL2L9/MaapA2C1ppDd49v07Pk9nM7e7fJZOpJv3ToOvPQydYsWQXTVC9fIkaTedivusWMlCJ6mZJSxEEKI01bICPFJySf8a9u/+Hzf55iNS745UvjWwG/x7YHfJsuddVLX8PtLKS37N2Wl/8AfKI3tT0ocSc+e/0dGxmRUtfuOFobIPIL1S5Zw4KWX8H11qNXUfdGFpN12G65zzunC2onO0KpA+M477zB9+nTq6uoYMWIE9913H5dffjmPPvooX375JRdccAHTpk0jIyOjo+orhBBCHFdJXQmvb3+d/+z4D1W+qtj+87PO59oB1zKpzyRsmq3N5zeMEAcOfMS+0r9z4MAnQKQlzWJJJCvranpmf6/briZyOL22lpp/v071q68SKimJ7LRaSfza10i56aYuW3dYdL5WBcJ7772XKVOmcNVVV7Fw4UKuueYavv71r/Puu+8ydepU3nrrLX73u9/x0UcfMXCgfBMJIYToPCE9xEd7P+Jf2/7FsrJlsf0pjhSu7n811w64lj4JJ7fSR0PDTsrKX6es7N8Eg4eCZlLSKHpmf5f09MlomuOkrtEZ/Fu3Uv23v+F587+YvshgGjUxkeTvfJvk738fa2ZmF9dQdLZWBcI9e/Zw1113kZuby6RJkxg0aBC33norTz/9NHfddRcAd999Nw8++CD//Oc/O6TCQgghRCPTNNl0cBNv7niTd4rewRPwxI6NzR7LtQOuZULOBKxa2ydKDoVqqKj4H2Xlr1Nbuza232pNJbvHtWRnfweXK++kPkdnMPx+ahcupGbB3/GtWRPbbx84kOSp3yfxa19DdTq7roICAF3XufDCC8nKyuL111+P7fd4PAwdOpQbbriBxx57jA8++ICHH36Y9evX43a7ufHGG3nsscewWNp2N2Cr3pWbm8vy5cvJzc0F4Prrr+eWW25h3LhxsTK3334748ePb1NlhBBCiBNR5a3i7V1v8+bON9lRsyO2P92ZzjX9r+GbA75JTnxOm89vGCEOHPyEsrLX2b//Q0wzCICiaKSmjKdHj2tJS5uIqra927mz+Lduw/P6v6n5z5sYnmhgtliIv+QSkq//P1wjR8pAkW5E0zTmz5/P8OHDefXVV7n++usBKCwsJCUlhVmzZrF27VquvPJKHnzwQV555RX27dvHbbfdhq7rPPXUU226bqsC4X333ce0adPYsmULV1xxBQUFBXz22WcMGjQoVsbr9dLQ0NCmygghhBAtCegBPt77MW/ueJPPSz/HMCP37dlUG5f0voSr+1/N6B6j0dS2Tedimia1tWupqHiL8or/EgodjB2LixtCj6xvkpn1Dey2tPb4OB1Kr62l9u23qfn36/g3bIjtt2Znk/Sd75B07RQs6eldWENxLAMHDmTOnDkUFhYyceJEli9fzoIFC1ixYgU2m42///3vDBs2jJkzZwLQv39/fv3rX/Od73yHWbNmER/f+rktWxUIb7rpJuLj43nmmWd49NFH0TSNQYMGcc4553DOOecwePBgHn30UcaMGdPqirTWE088weuvv86WLVtwOp2MHTuWX/3qV5x11qGbeP1+Pz/96U9ZsGABgUCAyZMn8/vf/57Mw+6NKC4uZvr06Xz00UfExcVx44038sQTT7S5yVUIIUT7MUyDVRWreKfoHRbuXkhdsC52rCC9gKv7X83k3Mkk2BLafI36+m1UVLxFRcX/8PmLY/tttjSyMq8mq8cU4uMGHeMM3YMZDtOw7As8b75J3fvvxyaRxmol/uKLSfrWtbgvuABF697zH3YU0zTxRqfRATB0A78JXt1A7eBru1S11a2whYWFvPHGG0ydOpX169czc+ZMCgoKAAgEAjgcTe9VdTqd+P1+Vq5cycUXX9zqOiqmaZqtfhdQX1/P2rVrWbNmTeyxceNG/H4/2dnZjBo1imHDhjFs2DC++c1vtuUSx3T55Zfzve99j5EjRxIOh/n5z3/Ohg0b2LRpE253ZMHx6dOn8/bbbzN//nwSExP58Y9/jKqqfP7550Ckn3748OFkZWXx5JNPUlZWxg033MDNN9/M448/fkL1qK2tJTExEY/HQ0JC238gCSGEiDBNk23V23i76G3eLXqX8oby2LFMVybf6PcNvt7v6+Qltv2+PZ9vLxUV/6Oi4i3qG7bG9muai7S0SWRlfoOUlAtRT3Ld4o5mmiaBLVvwvPlfPG//D71qf+yYfcAAkr51LQlf/zqWlJQurGXblWzawI6vlpGZ15/BF05o1Xv9fj9FRUXk5eXhcDho0HX6fbK+g2p6bDsvysfdhiC+ZcsWBg8eTH5+PqtWrYo1Vi1atIgrrriCv/71r3znO9+hvLyc6667jk8//ZTXXnuN66677qhzHfn1OFKbA2FzdF1ny5YtTULiunXrqKioaK9LtKiqqoqMjAyWLFnCRRddhMfjIT09nddee41vfetbwKEv7LJlyxg9ejTvvvsuX/va1ygtLY21Gs6bN4+f/exnVFVVYbMd/94QCYRCCNE+SupKeLfoXd7e9TY7PTtj++OscUzqM4kr867k/Kzz29wl7PeXUlm5kIrKd6itXR3bryhWUlPHk5X5ddLSJqJpJ7dKSWcIluyj9t13qP3vWwS2b4/t15KSSLjyShK/eQ2OoUNP+XsDV779Jh+/8kcGjRvPVXfe16r3ng6B8P7772fu3Lmoqsr69etjYzggspzwI488QkNDA3a7nYcffpgZM2awYMECvvvd7x51ruMFwlb96VNcXEzv3i3Psq5pGmeffTZnn302119/PSUlJfTqdXLrQJ4oT/RG2ZToX0ErV64kFAoxadKkWJlBgwbRu3fvWCBctmwZ+fn5TbqQJ0+ezPTp09m4cSMjRow46jqBQIBAYzM8kUAohBCibfb79vP+nvd5e9fbrK06bASvamV8r/Fc2fdKLux5IQ5L26Zy8fmKqaxcSGXVwiYjhEElOXk0WZlfJz19MlZr4kl+ko4XqqikbuG71L7zLr61hz6LYrUSN3EiiVd/g7gLLkA5gcaMU4VhRFZ7Uduhm9ulquy8KP/QuXWDtWvXUlBQgKp1bKexS239+ZcuXcozzzzDokWLmD17NtOmTWPx4sWxkH/PPffwk5/8hLKyMpKTk9m9ezczZsygb9++bapjqwLhyJEjueaaa/jRj37EyJEjmy3j8Xj4xz/+wbPPPsstt9zCnXfe2aaKtYZhGNx9992MGzeOoUOHAlBeXo7NZiMpKalJ2czMTMrLy2NlMo+Ya6nxdWOZIz3xxBM88sgj7fwJhBDizNEYAhftXsTKipWx1UNUReX8rPO5Mu9KLulzSZvvC/R6i6isfJfKyoXU1W887IhCUtL5ZKRPJiPjSuz27j+oIlxVRd3ixdS+uxDvihXQ2KmnKLhGjSLhyitIuPxytNO0h8rQ2y8QKorSpJVOBxwKuDQVrZvdV+n1ernpppuYPn06EyZMIC8vj/z8fObNm8f06dNj5RRFITs7G4gsL5yTk8M5bVxVplWBcNOmTTz22GNceumlOBwOzj33XLKzs3E4HFRXV7Np0yY2btzIOeecw69//WuuvPLKNlWqte644w42bNjAZ5991uHXmjFjBvfcc0/sdW1tLTk5bZ/aQAghzgRV3qpICNyziFUVq2IhECA/LZ8r8q7g8tzLSXe1PqSZpkld3Xqq9n/A/qr3m9wTqCgaSUmjyMi4gvS0S0+JEBgqL6du0fvULnoP38pVh0Ig4BwxgoQrryR+8mVYz4BVwQw9DIDaxtsETlUzZszANE3mzJkDRKb9e+qpp7j33nu54ooryM3N5cknn+Tyyy9HVVVef/115syZwz/+8Y82h9tWBcLU1FSefvppHnvsMd5++20+++wz9uzZg8/nIy0tjeuvv57JkyfHWuk6w49//GP+97//8cknnzTpns7KyiIYDFJTU9OklbCiooKsrKxYmeXLlzc5X+P9jo1ljmS327Hbu/ealEII0R1UeitjLYGrK1c3CYHD0oZxWe5lXNrnUrLjslt9bsMIUF39JVX7F7N//wcEAod6dRTFQkryWDIyriAtbRI2W/cfUBHcs4e6xR9Qt2hRk+5gAMewYSRMvoyEyy/H2rNnF9Wwaxh6ZFTwmTQyesmSJcydO5ePP/4Yl+vQ/ay33norr7/+eqzr+N133+Wxxx4jEAhQUFDAm2++yRVXXNHm67Zp+JTT6eRb3/pWbLBGVzBNMzYk++OPPyYvr+los3PPPRer1coHH3zAtddeC8DWrVspLi6OTYszZswYHnvsMSorK2PrL7///vskJCQwZMiQzv1AQghxGiiuLebD4g/5cO+HrKlc0zQEpg/jsj6XcVmfy+gR16PV5w6FPBw48DFV+xdz4MAn6Hp97JimuUhJuYj0tEmkpU3s9vcEmoaBf8MG6j74kPoPPyCw/dDk2igKznPOIeGyS4m/9FKs2a0PzKcLM3oPYXfr0u1I48ePJxwON3vsvffei21/+OGH7Xrd7j2e/hjuuOMOXnvtNd58803i4+Nj9/wlJibidDpJTExk2rRp3HPPPaSkpJCQkEBhYSFjxoxh9OjRAFx22WUMGTKEqVOn8utf/5ry8nIeeugh7rjjDmkFFEKIE9C4dNwHez7go70fNVk1BCJzBV7W5zIuy72MLHfzPS/HOneDdwcHDnzM/v0f4fGsxDQP/aK02dJJS7uE9LRJJCePRdO6989tIxDAu3w5dR98QP2HHxGurDx00GLBNfI84idNIn7SpVgzT//u4BOhR4PRmdRC2FVO2UD4/PPPAxw1+eLLL7/MTTfdBMAzzzyDqqpce+21TSambqRpGv/73/+YPn06Y8aMia0F+Oijj3bWxxBCiFNOyAixsmJlpCWw+EMqvIemFrMoFkZmjWRi74lcnHNxq0Ogrvuprl7GgQNL2H/gI/z+kibH3e4BpKVNIj1tEgkJw1CUjp5S+OSEKiqpX/Ix9R8voWHZMkyfL3ZMdblwX3QR8ZdcQtxFF6Ildu9Wza5wJrYQdpVTNhCeyPSJDoeDuXPnMnfu3BbL9OnTh3feeac9qyaEEKed2mAtS0uXsmTvEj4p+YTa4KEpt5wWJxf0vICJvSdyYc8LSbS3Ltj4fCWRVsADH1FdvQzDODS1l6LYSE4eRVrqxaSmTsDl6tNun6kjNHYF138cCYH+TZuaHLdkZBA3YQLxky7BNWoU6mk0RUxH0MPtN8pYHNspGwiFEEJ0HNM02V27m09KPmFJyRJWV6wmfFh3bYojhYtzLmZizkRGZ4/G3oruWl33Ul39JQcOfsrBg5/i9e5qctxuzyItdQKpqReTkjK2208UHa6upuGzz6n/9BMaPv0Mvbr60EFFwTlsGHEXjyfu4ouxDxp0yk8W3ZkaWwiVM2yUcVeQQCiEEAKAkB7iq4qv+KTkEz4p+YTiuuImx/sm9mV8r/GMzxnP8PThJ7xiiGka1Ndv5sDBzzh44BNqPCsxzdBhJVQSE88hLXUCaWkTcLsHduvQFGsF/ORT6j/9BP+69U2mhlHj4nCPG0fcxRcTd9GFWFJTu7C2p7bGFkLN0va40o4Lsp3Sjvd1aPNX2OfzYZpmbEj0nj17eOONNxgyZAiXXXZZW08rhBCiE1V5q/hs32d8uu9TlpYupSHUEDtmVa2MzBrJRb0u4qKeF5GTcOJzrgYCFRw8uJSDBz/lwMHPCIUONDnucPQkJeVCUlMuIjl5DFZr955YOVReTsPnS2n4/HMali1r2goI2M86i7iLLsR94YW4RoxAsVq7qKanl9g8hG3oMrZG/w28Xi9Op7Nd63Uq8nq9wKGvy5HaHAivvvpqpkyZwm233UZNTQ2jRo3CarWyf/9+nn766SYzaQshhOgeQnqI1ZWr+az0Mz7f9znbqrc1OZ7qSOWiXhcxvtd4RmePxm11n9h5Qx6qa76g+uAyDlYvw+ttOtpY01wkJ48hJeUCUlMuxOnM7datgIbXi3fFChqWLqX+888J7tjZ5LjqduMeOxb3RRcSd+GFWFuYu1acnJNZqUTTNJKSkqiMjuZ2uVxNvuf06Ln9fv9pPWjFNE28Xi+VlZUkJSW1+FnbHAhXrVrFM888A8C//vUvMjMzWb16Nf/+97+ZOXOmBEIhhOgm9tbt5fN9n/N56ed8WfYlvvChka4KCmenns0FvS7g4l4XMzh1MOoJjNzVdR81NV9RXb2Mg9VLqavbCBiHlVCIjz872gp4IYmJI1DV7juAwgyF8K3fgPfLL2hY9gW+1asxQ4d1a6sqjvyhxI0bh3vsWJwFBdIK2AlOdum6xkUmKg+f4qfx3IbB/v372b17N2ob1ho+1SQlJbW46AacRCD0er3Ex8cDsGjRIqZMmYKqqowePZo9e/a09bRCCCFOkjfk5auKr2IhcE9t05/JqY5UxvUcx7jscYzJHkOyI/m459T1ALW1a6mu+ZLq6mV4PKsxzWCTMi5XP1KSx5KcMobkpFFYrUnt+bHalWkYBLZsoWHZFzR8+QXer1ZiRrvUGlmzs3FfcEGkJXD0KLTDVr0SneNQl3Hb4oqiKPTo0YOMjAxChwd8oL6+nquuuoqvvvqKuLi4k65rd2a1Wo/bCtrmQNi/f3/+85//8M1vfpP33nuPn/zkJ0AkhSecpotsCyFEdxQ2wmzYv4Evyr5gWeky1lWtazIi2KJYGJ4xnHE9x3FBzwsYmDzwuK2Auu7HU7uamurlVNd8SW3tagyjaQC023tEAmDyGJJTxuCwd99uU9MwCGzfjnf5CrzLl+Ndvhzd42lSRktKwjV6NO7Ro3CNGoUtt3t3a58JYi2ElpPr0tU07ahAFAwG2bNnDzabDYfDcVLnPx20ORDOnDmT//u//+MnP/kJl1xySWw5uEWLFjFixIh2q6AQQoimTNOkyFPEsrJlfFH2BSvKVzQZDALQM64n47LHMbbnWEZljSLOduwWEF334fGsorrmS2qql+OpXXtUC6DNlkZS0vmRewGTx3Tr+wBNXce/eQveFSsij5UrMY4IgKrbjWvkSFyjR+EePRr7wIEoZ0DX4amkcaUSVaad6XBtDoTnn38+u3fvpqKigoKCgtj+Sy65hClTprRL5YQQQkRUNFSwvHw5X5R9wRelX1Dpa3pPVKI9kVFZoxidPZrRPUaTE3/sEcGhUA0ezypqalZQ4/mK2tr1R0wFA3ZbJknJ55OcNIqkpFG4XHndNgAawSD+jRvxrVxJw4oV+Fauwqivb1JGcblwjRiBa+RI3KNH4Rg6FOUkpjMRHa9xHkJV/p06XJu/wnl5eZSVlR3VGtivXz8yMjJio3eEEEK03n7fflaUr2B5+XJWlK846j5Am2rjnMxzGN1jNGOyxzAoZdAxu4F9vn14PF/FAmBDw/ajytjtPUhOGkVy8iiSks7H6ezTbQOgXluLb/VqvCtX4Vu1Ct/69ZiBQJMyalwcznPPwT1yJK6RI3EMGSIDQU4xsXkIT+NRwN1FmwNhSxMc1tfXS1+8EEK00kH/Qb4q/yoWAHd5mq7eoaAwOHUwY3qMYXT2aIanD8dhaf5nrWnq1Ddsx1PzFTWer6ip+YpAoOyoci5XX5ISzyMp6TySkkbicOR0ywBomiahfaWRALhqJb6Vqwhs395kMmgALTkZ5znn4DrvvEgAHDwIRYLEKc0In9ygEnHiWv0Vvueee4DIyJ2ZM2fGJqaGyJw+X375JcOHD2+3CgohxOmoxl/DyoqVLC9fzvLy5eyoaTpvn4LCWSlncV7meZyfdT7nZp1Lgq35AXuhUC21tWvweFbh8azGU7sGXT+iu1SxEB9/diwAJiaei83WPVfQMPx+/Bs24FuzBu+aNfjWrkWv2n9UOVufPpEAeO45OM85F1te972nUbSN1zAJpGVTuv8A/bu6Mqe5VgfC1atXA5G/2NavX4/tsIW5bTYbBQUF3Hvvve1XQyGEOA3s9+3nq4qvWFm+kq8qvjoqAAL0T+rP+Vnnc37W+ZyXdR6J9sSjykQmmS2KhL/aVXg8q2ho2AEc0VqmuUhMGEFi0kiSEs8lMXF4t1wTONL6tw/fmrX41qzBt2YN/i1bIBxuWtBiwTF4MK5zzsF57jm4zjkHS1pa11RadBovKsH0bHaV7OOirq7Maa7VgfCjjz4C4Ac/+AHPPvusTDEjhBDNKKsviwTAipWsrFjJ7trdR5Xpm9iXkVkjYwEwxZFyVJlwuA5P7VpqPWvw1K7B41lNOFxzVDmnszeJCeeQmHgOiYkjcLsHoqrdr5tNr6nBt34DvvXr8K9bj2/9evQDB44qZ0lPxzl8ePRRgOPss1HldqQzTohIi2983ImtmCPars0/LV5++eX2rIcQQpyyTNOkqLaINZVrWFmxkq/Kv6K0obRJmcYu4HMzz+W8zPM4J/OcowKgYYRpaNiGp3ZNNACuxevdyZGtf6pqJyF+GImJI0hMPIeExBHYbd2vtcwIBPBv2oR//Xp869bjW7+O0J7iowtGW/8aw59r+HAs2dnS/SsIKZF7QBPipfGpo53Un48ffPABH3zwAZWVlRiG0eTYSy+9dFIVE0KI7iqoB9l4YCOrK1ezunI1ayrXUBOoaVJGUzSGpA7hvMzzODfzXIZnDG/SBWyaJn5/GbW1a/HUrqbWs5baug0Yho8jORw5JCYUkJA4nKTEc4mLG9TtloEzgkECW7fh37gR/8YN+DZuJLBt+9Fdv0Tu/XMMG4YzPx/nsHzsgwej2u1dUGvR3YWig4JSkpO6tiJngDYHwkceeYRHH32U8847jx49eshfckKI01a1v5o1lWtYXRUJfxv2byBkHDFnn2bn7NSzYy2AwzOG47IeumcvFKrhwIFPqa1dS23dempr1xEMHr2+qqbFxcJfYsJwEhKGYetmrX9mMIh/+3b8GzZGAuCGDfi3b4cjlgYD0FJTcQ4bhnNYPo78YTiHni1LwIkT4vf70bXINEFpqd1zANTppM2BcN68ecyfP5+pU6e2Z32EEKJLGabBzpqdrKtax9qqtaypWkORp+iocimOFEZkjGBExgiGZwxnSMoQrNFfXrruo65uI/tr11Fbt47a2rX4fEd3lSqKRpx7EAmJBSQkFJCYMByXqy/KcZaV60yG14t/61b8mzcT2LwZ/6bNBLZtw2wu/CUm4hg6FMfZZ+MYejbOs8+Wrl/RZqWlkdsulFCAuLj4Lq7N6a/NgTAYDDJ27Nj2rIsQQnS6Gn8N6/ZHwt+6qnVs2L+B+lD9UeXyEvM4J+MchmcMZ0TGCHrH90ZRFHQ9QH3DFirK/kFd3QZq69bR0LAd0zx6cn6nsw8JCQUkJAwjIT6f+Piz0TRnZ3zME6LX1ODfvDly39+mzfg3bya4ezcccUsQgJqYiPPss6PhLxICrT0l/J2KDMOIPcLhMKFQ6Kjnxkc4HI49dF2PPR9+DtM0m8xVrKpq7GGxWLDZbLH1g91uN/Hx8SQkJGA9YtLwHTsiI/E1b/1Jr2Usjq/NgfBHP/oRr732Gg8//HB71kcIITpM2AizvXp7rPVv3f51R60AAuC0OBmaNpRhacMoSC9gRMYIkhxJ6Lqf+vot1NV+yuZ9G6ir2xANf83cJ2fLiAS/hGEkxA8jISEfqzWpEz7l8ZmGQWjvXvxbthLYugX/1m34N28iXHr05NUAWnoajsGDcQweEnkeejbWnj0l/HUwwzAIBoMEAgECgUBsOxgMNtkOhUKx58bt5oJdY3g78tFdJCcn06NHD/r06UNubi5r164FwFJXjSZL13W4Nn+F/X4/L7zwAosXL2bYsGFHJfunn376pCsnhBBtZZomJXUlbDiwgfX717Nh/wY2H9iMX/cfVTY3IZdh6ZHwV5BeQL+kfihmkPr6LdTWbaB017tsrdtAg3dHsy1/VmsK8fFnkxA/lPiEfBISCnDYszrjYx6XXt9AYNu2SPDbspXA1q0Etm3D8HqbLW/t3Tsa/gbjGBJ5tqSnd3KtTy/BYBCv1xt7+Hy+Fh9+vz8WAAOBACYQVjVCmoWwFn1WNUKaRji6HVY1dFVFjz6HVQ1DtWCoNgy7guFQMRUFIxbgldi4dQUTxTRRzUPPGiZWBSyKgl1RsCvgUBUcioJbgTgV4hRIUBUSVAWrpqFpWqwV8Mg/FEzTxDCMWItiKBQiEAjg8/loaGigrq6OUChEdXU11dXVbNq06dCbDR1LnQfNIksOdrQ2B8J169bFViTZsGFDk2PyV6MQorPt9+1n4/6NsfC34cAGPAHPUeXirfHkp+dTkF7AsPRh5Kfl41R06uo3UV+3kbrS37Ni22a83l0cOd0LRMJfQvxQ4uOHEp8wlIT4fOz2rh9YZ+o6wT3FBLZvjwTAbdvwb91KqLiZaV4AxWbDPmAA9kFn4ThrEI7Bg7APGoQWL/dqHck0TUKhEH6/PxbaGh+BQKDJ68ZHY9jxer34DQO/xYbfaiNgtRGw2AhYrJGH1UpAsxK0JhBISiWoWQhpFoIWa2TbYsHsRveUHkkBkq0aWTYr2Q4b2XYruU47eU4b/V0O8px2LOqx/9swTZOGhgYqKyvZt28fRUVF7N27N7J+8c5NKJio0kLY4RSzpUWJxQmpra0lMTERj8cjk3QL0Uk8AQ+bD25m04FNkfC3fwNlDUd3d1pVK4NSBjE0bSj5afkMSR1Cls1KQ8Nm6uo2UV+3ibr6TQQC5c1ex2ZLJz5+CPHxQ2MhsKvDn2mahCurmgS/wLZtBHbuxAwEmn2PJSMjFvzsZ52FY9BZ2HJzUc6wX7KGYeD3+1tspTt8/5Hhr3FqtbCq4rPa8Vtt0cehbZ8lEvgO3+e32NDbaT1ll6biUlWcmtpk26EqOFQVu6pgjz7bVAVNUbAqCpboQ1Mi82EqCjR+Bxsm6JiETRPDhLBpEjJMgtHngGng0w18ukmDrlOvG3jCOrVhHU/4+N3NdlVhkNvBiAQ3IxNcjE2Oo4f9+FMmmaZJKBjgtzd8C4DC+f/A5mzflXbk93dTZ9ZPAyHEKacx/G3cv5FNBzax6cAmSupLjiqnoNA3sS9D04ZGHikDybaCz7ed+vot1Hteo2TfFnaHa5u9jtPZh/j4s4mPG0xc/BDi487Gbu/artJwdTXBHTsI7NhBYPuOWAjUPUe3fAIoDgf2/v2xDxyIfeAAHGedhX3QICzJyZ1c845nmiaBQACv1xtriWt8bgx2h3fTNu47kq4okYBns+Oz2vFGn332JPxxGbHw57Pa8dnshLW2/dq0KJBstZBk0Ui2Wki0aIce1kPbCRaNeE0jzqIRp6nEWzTitUjwU7tZ71vIMKkOhdkfClMeCFEaCFHiD7LbF2CXL8D2hgA+w2BtnY+1dT7m74u8b5DbweS0RK7JSGJwXPODqhRFwTzs/kZVuow73EkFwk8//ZQ//OEP7Ny5k3/961/07NmTv/zlL+Tl5XHBBRe0Vx2FEGeIEw1/AD3jejIkdQhnpwxhaHIOPa0GYf9u6uo3U1/zEhWlRVRw9OhYRbHidg+ItPzFDSYu/mzi4wZhsXRdV6nu8RwKfTsOPfT9+5t/g6pi69MnFvzsAwbgGDgQa04OSju1RnWFYDBIQ0PDMR+HB7+WBkToiorXFg13Ngc+axze9FR8Ngdeqx2/w4nf5sBnteFrQ8CzKgrJVo1Uq4WU2EMjxWoh1WYh2RLZTrZaSI7uj9OOvrfuVGdVFTLsVjLsVoY0E+wM02SPL8i6ei+rPF6+8NSzvs7HlgY/Wxr8PLunghHxLm7OSecb6UlHdS3rh01qrp3C39enijYHwn//+99MnTqV66+/ntWrVxOIdlV4PB4ef/xx3nnnnXarpBDi9GKaJpXeSrYc3MKmg5vYcmALWw5uOWq5t0aN4W9oygAGuuPIsujogWIa6rdS7/mE+v3VbG3mfVZrCnFxgyLBL24QcXGDcLv7oaqdvyqGaZroBw8S2LmT4K5dBHbsJLhrJ4HtOwhXVbX4Pmt2NrYB/SMtf/0HRAJgv36nxLq+kdVY/NTX19PQ0HDU85HboWbmNmxkAH6rHa/NgdfmxJuWjM9mx+9wEXS68NmdeK026jQrPrV14UFTINVqId1mIc1qJd1mITUa7lKtR25HWvFOt3DXEVRFIc9lJ89l5+qMSCt1dSjMhwdqeauqhg8O1LG6zsvtm/bwtKucR/r35JLUQ123RjQQqpqGonbf+yhPF20OhLNnz2bevHnccMMNLFiwILZ/3LhxzJ49u10qJ4Q49RmmQXFtMVsObmHzwc1sORgJfwf9B5st3zOuJ2enDKIgqQd5Thupqp+QfzcN9avx7X+TwH44cqIYRdFwufpGQ99g4qPhz2bL6PRf3KZpEi4rI7BzVyTw7dhJYNcugjt3otfUtPg+S3aPSOjrFw1/A/pj69sPLc7deZU/QaFQiPr6eurr66mrq4ttN/c4clnTI4UVFa/dgdcZjzfRgd/hJOSOJ+B047M7qLfaqFUt1CkqBif+b2lVFNJtlugjEvLSrRYy7FbSrBbSovvToq143a079nSVbLVwbVYK12alUBUM8ZfSA/yppIod3gDXr9vFd7NSeGJgL1yaGmshVNvYTS9ap81f5a1bt3LRRRcdtT8xMZGaY/zQE0KcvrwhLztqdrC1eitbD25lW/U2th7cijd89BQnmqKRl5jL8OTeDIqLJ9umEG/WEvAV4fW+h1EdJFgNRw4VsdnSiXOfRVzcWbjjBhLnPgu3eyCa1rmtfkYgQHDPHoK7iggW7SJQVBTZ3rWrxSldUBSsPXti69c3Evz69cXevz+2fv27RfALBAKxgFdXV3fUduPrQAuDVxqZQEiz4LW7aLA5CLnjCMclREKew0l9tCWvBpWGVoQ8BUizWciwWciIhrwMm7XJ6/To60Rpxev20m1W7snN4uZe6fy/3eW8sLeKv5cfZGuDn9cK+kI40mKsWSUQdoY2f5WzsrLYsWMHubm5TfZ/9tln9O3b92TrJYToxkzTpLyhPBb8tlZHwl9xbTFmM1O12DUbw5NzGZaYRq7DRqoWwBKuxOfdhR5eDzUQBA4c9h5Nc+F2DyTOPTAa/s4izn0WNltKZ33MSDfvgQORFr6i3ZGu3t2R4BcqKYGWJmmwWCL3+PXti61/P+x9+2Hv1xdbXh6qs/NXJmkMei2FvMZ9wWDwmOcxgaBmpcEVT8DpQo9PIhQXj9/hwmt3UGex4VE0qlHwt2L+CoeqkGGzkmmzkmG3RLejYc9+aDvVajnuFCbi1BNv0fhF/55cmprAzRt3s6bOyw3rdvGHtMhAEpmDsHO0ORDefPPN3HXXXbz00ksoikJpaSnLli3j3nvvldVLhDiNNLb6ba/ezvaa7bGWv9pgc6N1TXJdSYxIyqK/y02mVcdtegj796LrayKpLwiHTw2tKFbcrr643QNwuwcQFxdp/XM4enXamr5GQ0OktW/3bgK7dxPcvZvg7shro7b5UckAanw8tr552HPzsPXtiy0vF3u/fth690axduwvscZRts214B0Z9o51bx40Bj0LXlccQVc8ekIiQXc8fqcbr81BncVKjaJx0ITA8YLeYcfjNJVMm5XMxlBnj4S+TJuFTLs1FvzknjwBMC45njdGDOAbq7bzVa2XZ00LmSBzEHaSNn+VH3jgAQzD4JJLLsHr9XLRRRdht9u59957KSwsbM86CiE6gW7oFNcVs616WyT8RQNgSV3JUa1+CiYZFo38xDTOiksg2wbxNKCEyjD0MqAMAkAg8gSR+/yczjzi3ANwxw2MhD/3AJzOPqhqx7cAmMEgwZJ90bDX9BGurGz5jYqCtVevSNjL63so+PXti5aa2u5BpqWg11zYO5GgF9IsNDjjCLrjMBKSCMZFg561adA7ZoueSZOgl2jRyLBZyLRZyYoGu6zGlj27laxoS59bRoaKVjrL7eC5wb25cX0Rr9SF+V6PXPKNY9+iINrHSU9MHQwG2bFjB/X19QwZMoS4uLj2qtspQSa2FKca0zSp8Fawo2YHO2t2xgLgLs8uAnrTH7waJukWk34uN2fFJdDLbiFR8WEJV2Gazf+QjgS/XNzu/rhd/WKtfi5XboeP7o2Fvj27CRUXR1v99hAsLiZUWgrHGOCgpaRgy8097NEn8tynD6r95OvdGPSOd4/eiQQ9iLbo2ewE3fHoCY1dt268dge1WiToVZvga8VP+ASLGgt5mUeEu6zDwp9TkxGfomPdu2Uvfy07QKLnID/5/A1ue/K37X4N+f3dlKxUcpLkG0p0V6ZpcsB/IBb8tldvZ2fNTnbW7KQuVNekrEMxybQa9LJbOMudQLZdI0nxYdGroZm5/AAUxYbblYfL3T/a3dsft7s/LmcfVPX4KxG0leH3EyopIVi8l2DxnkjwO8HQp7hckXv78nKbhr8+fdASE9tUH9M08fl8LQa9w/eFD5tXrSWNQS8Ul4AeH+m6DTjdNNgd1GtWalSNgwZ4W/GTO15TD2vJs0ZDn6XJ60y7FZcEPdFN1IV1xn62jipTof+BUt6++jIS23lwifz+bqpVX9177rmHX/7yl7jdbu65555jln366adPqmJCiBNjmiYH/QfZ5dnFzpqdsQC4o2YHNYGaWDkFk2TNpJfVJMsBfV1usm1qpMXPbBwVGwAammRATYvD7eqLy90v2uIXafVzOHJQ1Y65t0evrSVYvJdQ8Z5I8NtbTKh4L8HiYsIVFcd8b2Pos/Xpg6137+h25FlLSzvhLl7DMJoNes0Fv5YmSG4UG3XrjCMYF4+RkETIHY+vcTCGZsVzIkHPBA67VFw06B1+n17j6wyblR526boVp6Z4i8YvnSEKa0x2pGbz7bU7WXjuQJkeqAO16qf56tWrY10Zq1ev7pAKCSGaZ5omVb4qdtbsjIW/xu3Dg59dMcmwGvS3mGQkmOS6HGRZIQ4v6uFpovHuvmgAsdsycbn74nb1jz73w+Xuh92W2f73yek64fJygntLCJXsjTzv3UuwpITQnj0tLs3WSI2Lw9a7N9bevVsd+gzDoKGh4bgteicyh54JBC1WGlwu9PgEwvGJhNyRUbcNNnt01K3KAeME7tFrJuhlxLppI122mbHWvUj4c1sk6InT1xAzyHVv/pm3vn4j9wzNkzDYwVoVCD/66KNmt4UQ7Uc3dErrSymqLWJXzS52eXZR5Clip2cndcFIV6+GSZrFJN1iMMJmkuEy6eWwkq7pOJQjpw45dD+aothwufrgcvWLdPe6+uJy9cXt7tfuS7fpHg+hffsOC317Ce0tIViyl1BpGRznPjktLQ1bTg623jmHgl80BGpJSUeFPl3XI2Fu374WA15dXR0NDQ0c704ZE/BbbTQ4HejxiegJiYRc8fgczkjQi86hV328UbdH5Mkju24zDmvRyzxsBK4EPSFAD4XIPFDGw6sWcfkVshxuR2tzf88TTzxBZmYmP/zhD5vsf+mll6iqquJnP/vZSVdOiNOZP+xnT+0eijxFsdC3y7OLPbV7COiBWBdvusUk3WowyWWSkWjSw6qSqIWamc730P1pNlt6JOhFA5/LlYfb3Q+HoyeK0j5hw/D5IoGvpIRQyT5CJSWE9pUQjG4bdXXHPoHViq1nT6w5OdhyemHtlYO1V89I6OuVE5uoORQKUVdXh6cx2G3b1myrnrelyaAPoytKZD1buwMjPolwfAIBlxu/3UWDLbIiRg0q1UaTBrtmPnzTl0kWLTrC1tKk+/bwFr0Mm9yjJ0RrhKMTUzvlD6RO0eZA+Ic//IHXXnvtqP1nn3023/ve9yQQCkGkm3e/bz+7a3dT5CmiyFMU2y6tLwUMEhtDn8VgkMXkwmSTDEukBVBTmmt+ikQVTXPjcuVFHs7osysXlyuvXVr7DL+fUGkZoX37mjyC+yIBUD9w4Ljn0FJTsfXqhTUnB2tOL2y9Is/WXr3QExOp93qpr6+n8vBwt3EjdV98EXt9IqtiBDULPmccPocTIz6RcFwCfqcb/2FLn9Wg4Dl2D/BRQS/VamkyZ15jq16TufVsVhwS9IRod3oo8keuTEzdOdocCMvLy+nRo8dR+9PT0ykrO3KxKSFOb/6wn+K6YnZ7dscCX+N2Q6iORC0S8Bq7eQfYTdLdBmlWE9sxbotRFBtOZ04s6B0Kfn2x2U58gERzDJ+PUGlp5BELfJHtYOk+9Kr9xz2HGh8fmaOvV0+s2T2x9OxJKDODYHIyfpeLhlCI8sPWuq2rqaa+ZO8JjbjVFQWf1Y43LomA04URn0jIHYff4YqscavZ8Kgq1aZy/MmSDwt6FoXocmeHWvQO306Pdtum26xYZVUMIbqMHmpcuk4CYWdocyDMycnh888/Jy8vr8n+zz//nOzs7JOumBDdjW7olDWUsad2D7trd7Pbszu2Xd5QSnIs9BmkWUxGWUyuSo1sW48Z+iw4HL0ioc+ZizP67HLl4nBkt6mL1zRN9JqaWOALNwa/0tJIq19pKXp19XHPo7pcWHv1QsnOJpSdTTA9nUBiIn63C5/NhjfandsY+OqLdmHu2tlyvYCAxYrX5sQbZyfkiiccF0/IFYfP4cBrjdyf50HFcyLTqhwW9OI1NbrUmeXQMmiHrY6REQ15KVZNbk4X4hQQDkXuh5YWws5xUkvX3X333YRCISZOnAjABx98wP33389Pf/rTdqugEJ3p8C7e4tpi9tTtYY9nD8V1xZTW7iFeDZAabeVLtZjkWwwmxJukJJtYTij09cHp7BMJfs7euFx5OBw9W71ShxkMEqqsjIS9sjJCZWWRoFcWCXuhsjLM49xTZwLh5GTCOTmEemQRSE0jkJCA3+nAa7HgM03q/X7q6+vx+/1g6FBRHnkccZ5QdO48X0IKXqsdPS6BkDuOgNOFz+agwWLDo2p4TIXjzsR3WBC0KJBus5LeuK6trbEV77DX9kirntyfJ8TppbGF0GKTQNgZ2hwI77vvPg4cOMDtt98eWxDd4XDws5/9jBkzZrRbBYVob6ZpUh2ojgS+2j3sqY0EvuLaPVTW7calNJCmmaRGW/uGWEwudJokxpkcqwdRUayRkOfsg/Ow4Ody9cFuzz7hOftM00Q/eDAS8MrLCJeVEyovJ1RWSjga+sJVVdDCSFldVfE7HPhTUghmZhDMyCCQnEwgLg6f3Y5PVfHqOvV+f9NpVQwdaqqhJvIypGqRkGdz4Y1LJmB3Eo6LJ+h047c7abDaqNOs1KIQaGaIy9Ef7NBmskUj/bAWvMaAl26zSGueEAIAXVoIO1WbA6GiKPzqV7/i4YcfZvPmzTidTgYMGIC9HZZ4EuJkHR76iusiwW9v7V6K64qoa9iDw6wnLdrKl2oxGWoxuNhp4nQf+7ya5o617jmdvaPBLxIC7fbM43bvmqaJUVdHqKyccEV509BXVkaovJxweTlmsOnUMSYQtNnwOZ2RsNenN353HMHUlEgXrtOJz2rFCwRamCTZDIcJmUTuy7M58KUk4rPaCbriCLvc+B0uvFY7DRYrHkU9sZB3mDhNjQW7tCMCXno04GXYLKTZLNhVac0TQhybHpZ7CDvTSS8zEBcXx8iRI9ujLkK0SuPSbI2hr7g20spXUVeEz1eMkwbSLCap0eB3nsXkUqeJ6jr2eW22jGjI630o+DlzcDr7YLWmtDiQwzTNyNx75RWRsNcY+hqfyysIlZc36coNWSyRgNf4cDrwnzUQn9NJICGBgNuNz27Hr6rNLiDXeE+ez2bHa3Xgs9nwWR347Y5Il63Dic/qoMFipVbVaG6ymmNxqo0hzxLruj083DWGvjSbrIYhhGhf4egoY4ut45bCFIfI0nWiW9MNnQpvBXvr9lJcV8zeur3s8+zG07CLQKCEOMVPqsUkRYt07/azmDjcwDFa+hTFhsPZK9K16+yF09nnUPBz9ELTnEe9xzQM9Opq/OWbCFdEgl24opJweTmhiorYs+nzHeqybQx40W1fcjKBHln4HE4Cbhd+u51wMyFKVxT8Vjs+mx2f1R5t0Yu8DjndkZBni4S8ekVDb2WXqktTSbceCnaNga4x3EWORQKfTJAshOgq0mXcudq8dN2qVatabClp72WuxOktpIfYV78vFvj21u5hf/0O6ht2Y4QqSVRDsa7dPppJgcWMBL5jhD7NmoLL2Ru3Kxeno7GFL/Jss6WjKIe6LI1gkHBlJeE9FdSXfxgJehUVhCorYtvBqip8mnZ0a549+tw3D/+QwfgdDkJH/DUbmyevMeAd8RxwOAnYnfijIc+rtj6EJVq0SLCzRsJdk9Y866HQJy15QohTRTh664y0EHaOVgXCZ599loSEBAA+/vjjjqiPOE35wr5o2NtLce0eyup2Ul2/g4C/BDV8kBSLQapmkGIxGWGJjtiNO8YJFQc2Rzbxrr64XNHA58jB6czB4eiFpjkirXo1NZFwV1xBuHIXtRVfEK6sIFRRQbCyivqaGrx+f9OAd3jLXmYG/j69CR5xb2xYVWOtdz6bHb/V1iTk+W0OAg4Hvug9ebrSunvmNCUyKXJatLWuMcylHxb4Dn9tk3vyRAcxTZOQbhLSDcK6SVA3COkGuhHdZxw6FjYan81YmbBhxp6N2GvjiNeRh2GCYTZuR47rZnR/4z4zMp7KME1M08SkcZvosaMHW7W0UqGiRBowFAWUxtcoqNH9qqKgqaCqClr0deO2RVPQVAWLeujZoqlN9lk1FUv0WVMj77FqKlZNxaap2Cwqdkvk+fDXFhkxDxw2D6G0EHaKVgXCESNGUFZWRkZGBn379mXFihWkpqZ2VN3EKaY2WBsJfXV72evZTVXdFuoaiggFSrEZtdFWPoN0i0lvlch3Xwuhz0RFtabhdPYm0d0vej9fL5zO3pHAF7QRrqyKteyFKysJVS7jQMWb1B84QIPHQ73Pi89ijbXoBRz2Q617KSkEevSI/Abg0CTIka5aWyzs+aPbjV24AZsDr9VGUGv97bdxmhoJdtEQl3p42Ds86FktJMvo2jOOaUbCVjBsEAgfeg6E9Sb7Ytv6odfBsH7ouN70ORR7NpvZZxDUTYJhPRb6Dj0bsdeic1k1BYdVw2nVDj3bNNw2DZfNgtsefbZpuOwWEhwW4uwW4qLP8Q4rCQ4LiU4rCU4rDuup2SvQOA+htBB2jlb9VktKSqKoqIiMjAx2797ddMoKcdozTZMqXxV76/ZSUruX0tqtVNdvx+srxgxW4sIb69rN1Uz6KoA1+miGocZjtfcg3pVLUtwAnM4c7JYeWBscaAdN9KoDhIsqCVVUUFe1i9LqVdTX1tLg8+FTlKPu0fM7HAQTEzCTkw7dh2e1RVvwotuHhTuf1YbfasdvsxNow1+gNkWJBbvG59TDWu3SouGu8ZhT/urv1kzTjASwUCSE+aPPjaEs9jp0KKgFwgb+0BH7QjqBoEEoGtSCwWjwChqEwjqhcORYOGxGXusGobBJWDdQzGhLVeyhHNo+4lgjpZnn4/0p0VzEsxL5heA47LiJghk9mwmYSuRZUxUUVUHRQNVUFFVB01RUCxDd1iwKqhbd1tQmrWqaosRazDRVRVOaaYVTiWxHy6pK5HWk5S7yt5yqRL8+0Va+Q6+P/nxH3spkRlsVD29hNCMHIi2NmOhG8y2WukGT1s6QYaLrTVs/Yy2m0e3DW0yDRwb66LFGkVAeps5/3Fk7T4jNopLotJLktJLsspHsjjwnuWykxdlIjbOR6raTGmcjLc5OituGtRv8vJIu487VqkB47bXXMn78eHr06IGiKJx33nloLdyPtGvXrnapoOhcQT3Ivvp9lNSVUOLZzv66LdR5iwj5y1DDB0nUQqREB3EMbPx5YYs+jmBgAWs6Dkcvktz9iFd6YPXGYfFoqBUGvvKD1O3fT32Nh931a2nwL8drmkeFvIDdTtjtxp+UHA120TBnO7R9ZOgLWlv/A0RTICXaTZtqPXTPXaq1sTWvcX+kVS9eU+V+2Q4SDhv4/GG8/jBefwifX8cXCOP3h/EHDPzBMMGgTiCgEwxGH2GDUMggHDIIh3RCYRM9pKPrBnrYxAgb6LqJqZsYuolpRLZNI/JQTFBN0ACVyLaKEnkdDWMa0WdTQY1tR56tRMKU0qqR3Fr0cToxaVxvGyLhTLNpWKxq5GHT0KwqFquCxapEtm0qVrt2/IfDgt1pwea0YHNqWE7Rlq/m6NGg6A/p+MM6vqCOLxT5Q8Qf0vEGdbzBMN6gTkMg+hwMU+8P0xAIUx+IBMg6f5i6QIg6f5haXwjDhGDYoKouQFXdsdcFP1yi0xoLiOnxdjLiI89ZCY7II9FBj0QnTlvH/RvEViqRaWc6RasC4QsvvMCUKVPYsWMHd955JzfffDPx8fEdVTfRAUzTpCZQQ0ldCXs9O6mo3YSnfie+QAlmsAqHWU+KxSBFM8nQIKPxjY5mzgXoagIWLR0XGSSEM7DVOQnvB3+5Tv3+AA31XioDfnYbxfjtFbF59LxOJ167E19SKv70Hk0HWxwR8PxWO4E2BDyVSMBLjXbJpjYGuiP2NT4nWc7sblrTjAQlPRqq9LAR2w6HIuHL6wsT8IfxB3T8gTCBgE4gGshCQZ1Q0CAU0gkHDcLR9xthA0M3MMLRABZ9YJgoholiRFq/VMM8LJC17d/BQmvn0jqR9rR2pBBpWVOj960dtq1GtxtbyBQ12vJ1WFmItnQph1rBGv8oUaJNY4f2N1+Flu6nM83I/zUeN00T0wCItp4ddtzQDYxosI48Iq/1aMA+8rzhgE440Pz8mCdDs6jYnBo256GgaHdZsLutONxWnHGRZ4fbiiPu0LPdaYl9PbsLTVVw2rR2DViGYVIfjARDjy+ExxvioDdItTdEdUOQg9HHgYYAB+qD7K8PUu0NohtmpLwvxK6qhmNeI8Vto2eSk17JTnJSXOSkuMhLdZOX7qZHggP1JL7Oh1oIZX7jztCqn53r1q3jsssu4/LLL2flypXcddddEgi7IX/YT2l9KXtri6io3cTBuki3rh6sQNNrSFTDpFgMEjTo3fgmDTh6thXCph0lnIA1mIDVG49S6yRYreE9oFJfo+ENm5F77hwOauPi8MTFR0Jdgh1f6mGDLKIhL3ZvntXW8m+sFhwe8Jp007awnWTV0E7BgGeaJnrYIBxsDGR6JKCFjt4XC2f+MAF/NJwFwoSCka7KcPDQe/VwNJyFTcywiakboEcDWWMwa+fP0rr2r5avrmNiKGAoCoYCpgqRPsTos9bYhamgaiqqpqBaGrsuVSwWBYslum1VsVo0rDYVq0XFZtOwWVWsVg27TcVu02JlY+dSlcOeI/uUJvuUJvsU5bDXhwfAU/D7sbUa/7AwdPOoPyqa+x6OvNYJBQxCgTChgN7iI+gPE/RFnjFBDxv46gx8daFW1VFRwBlvw5lgw51gw5Vgw5VowxkfeXYl2HEn2ohLdmC1n7qtkKqqkOCwkuCw0iv5xN5jRMPggYYAVXVB9tcHqIy2LlbW+amo9VPuiTwagnosVK7f5znqXE6rRt90NwMz4xmUFc/gHgkM7ZlIivvE/sCXLuPO1eZBJUuWLIktWXc6mDt3Lk8++STl5eUUFBTw29/+lvPPP7+rq9WikBGiqKaIPdXrKPesp7Z+B8HoiF2X4idFM0mymKQCsWE/zfw3pesaht8FXhdmvYtwnQN/rYOGBjsH9RT2u9PwuOPxOpyHWvGy7PhzbEdNn2K0cqSrAiRbNdKsVlJtjc8WUq1a7P671MNa8boq4DX+gmsMV+EmQSvSKqYHo7/gggahoE4gEMbvizwHAjrBQGMLWrT1LPrL0QibGCEj0mIWjgQ0OiCYHe5E/5VCmOiArkAYk7AS6QgMRwOZqUaHZmoGqKBqRO8pizwsFgUt+myxqFitoFkUrBYVq1XBalGwWRVsVi36rGKzqtitCnabhiMWzqI3rpkmJtGmqug9Xke2Rp0oRdFBMSLdu0ooMg1RrHVNid6TpkT3KSiqGnlEt1VVRVGi+9TIvki7pgpoKIqGoqioqoaqat2uNaojKYqCZlHQLHRYmDINk2BAJ+gLE/CGCfoij0D0dcAbwlcfwl8fwt/Q9DkU0DFN8NYG8dYGOXCca9ldFuKS7biTHMSl2IlLshOXHNlOSHUQl+xAs3T9/XbtRVUVkt02kt02+me0XM40TWr9YfZV+9hX46Ok2kvxQS/FB7wUHWig+IAXX0hnY2ktG0trm7w3J8XJOb2TOS83hTF9U+mX7m72j6VwMNLFLYGwc8igEuDvf/8799xzD/PmzWPUqFH85je/YfLkyWzdupWMjGP8F9GJyupL+Wrvfymp+oiwrwiXWUOWRcehQjaRR3P9ZUZYJex1oDe4aPDFcyCYQZWZzn41k2o1lQbNdWj6lEQ7/rRD06eE2ziSNr2ZEbSpR4yuTbNZSLFaTjrgRVrSDgWxptuNxyLbwYCO3xeOhDV/pCWtMag1lo11cYZMzHBjUDPbHNAiU2DogAGmHtk2jWjUatxnADqmacS2I311OoapY2CgE3k+9L/IazP6ykRHwYyEHMVAUaLDARQDVTFRFBMVA1UxUDAj+zCiQwYM1MZt0wQMFNPAYhpYDCNSL8PANHRMI9L9azbuP0EGcOJ3L52GFCUSDjUt0sKoaiha42sNTbOgWixomoZqsaJajt4XebagWa1YrFY0ixXNakWz2qKvLdHXkWMWmy1yzBZ5WG326HbTZ81y0gtWdTpFVbBHu4njU1r3Xj1k4G8IxQJh5BHAVxvCWxvAWxfE6wnSUBMg6NejATPMgX0tdJ0q4E6MhMP4VAfxKZFnd5I90vKYEGl5PJ1CI0SCf6LTSqLTypDshKOOh3WD4oNetlfWs72ijs1ldWws9bD7gJe9B33sPejjzTWlAPRMcnLJ4AyuzO/B+bkpsW5m6TLuXIrZ3KRNLbjlllt45ZVX6NGjB8XFxfTq1eu0GFQyatQoRo4cye9+9zsADMMgJyeHwsJCHnjggWO+t7a2lsTEREpLS2NzNB5O0zQcjkM34DU0tHw/hqqqOJ2Rftv3//tfPtm9GsNRjcVVgz2+Fs1yqFtEURWsdgthU8MfjKO2zkFDKA6PkUydmUidEkeDGofX4sBvdRCITyAUHUlr+n3NDzOEyL1JjkN9x5rPizsUJD4cIkEPERcOExcOkxAO4dZDpGkaCeEQ8XoIm7cByzH+SHBE5/IzgWAwRDhkREbz6Qq6EXk2DDDCClbNjq6DEQZ/IISumxg6GIaCqYNpKJimAqaCzWKL/XUZ1kPosTpEwg2xoGVi0ywoiomJESmrB4ndBG/qkaAWDW5WTUVVTEwzHClrhjCjxw4vBwYWFVQlcq2wHsIwwrFrHsmqaod+4OkG+jGC1eFlI6MXWy5rUVW0aCttdyhrGCZh04h2lx72iLbAWTQVq8WCoiiYmNHzNrbMRQdnRLtZLaqCxWJBIfJVDYX1FrtgNVXFEv25ZJgmofDRIzUbf+wpioJV06L3y0VGCTe2PhKd066xJVKJfj7TNDAME38oFB2pGn0YZuy8avS8jQLN1KFRq8qiYLW0rWwwrEc+1xEUVcVqs+N2u7E6HFjtDgxVxWKzR17bHFjtNiwOJ1abA5sjUla1WFA1C8FwCBQ1Fm4jj0iQVVQVl+vQWpF+vx/jGK26Ltehnz2BQABdb/l7rTVlnU5H7HslEAyih1u+n/Hwsg11Puo9fgL1QXzRFkZffQhfQ4hAXYiQTwMjcmNE43yKkfXMNVAcTb4/4xPjiEty4EqwoTlAtZqHBsg4ovc/OjU0TcVud8R+twaDQcLhlrvEDy8bCoUIhVruubPZ7FiifwC0VNaVbCcpw4XT6YiVDYfDBAIt/0lns9mwRgd+HK+sT1fYXNHAyj3VLNtRxcpdlQTDh/7teqU4+N55vTk7O4E1v/oJhINMe+5PxKel4/f7Wzyv1WrFFm1JNAwDn8933LKNv789Hk+zv7/PNGf8oJJgMMjKlSuZMWNGbJ+qqkyaNIlly5YdVT4QCDT5Zq+tjTSFZ2dnN3v+K6+8krfffjv2OiMjA+9ha9kebvz48bEJvz+oWcOvZ/4G01PTbFnLWUNIff7VSJedA6p+cCVGRVmzZbU+fUl7+d+Rz2YY7L/t/wgV7262bFxCArfccB1Ovxe3r57fv/Mh26uPvjcEwG238cjVlxIg0vrz+4+WsavqYLNlrZrGE9deHnv9p0+Xs6WsqtmyAE9956rY9itLV7KupLzFso9NmYw9+oNrwfK1fLW7pMWyv/jGJOIckWD6+soNLN25p8WyP79qAinuyC+zt9duZsnWlv/IuXfyRWQlRv5b+HDTNt7ftL3FsvdMGkNuWgKqYvLBtl28sWZHi2VnTT6b/OxI2YWby3hhWcv1/dWVfRidG4+mmLyzuZpffljaYtnfXJXBlWe5UTFZuK2eH/9vf4tln7sygeuHOVEUk/d3+Pnu67Utln32cid3nG9DwWTJ7jATX2n+ex3g15Ps3DUu8m+xYp/O+S+3/MfSrPE2fnFx5A+rjZU6Q//Yctl7x9h48rJI2d01Bnlz61sse/t5VuZeFQkWVQ0GGU+1XPbGAivzr4mUbQiaxD1R12LZawdb+Nu34jBMBcNUcD3W/H8XAJf0tfPna9MwTBXdVBjy7F584eZD0zl9M/n97VPQVQe6auPrD/4WT0PzX+O+2Vk8etN1hINBwsEA97/0Nw7WN/91y0yI477Lx0P0P/UnFy6horb5r0Wyy8mDX5sYe/2b9z+j5Dg/Ixqdzj8j3jrOz4gHvvkIPdMKOKDE8c7KV3h35Sstlr3vm3PpkzEIgMVr/s5/vnyhxbJ3fv3/MTB7OABLNvyHf37+2xbL3nb5YwztMxqAL7Yu5K8fP9li2XunzuGab0yhR/8kvtz4Af/3/etaLPvyyy9z0003AfDee+/xta99rcWyv/vd77jjjju4cEA6wy2lTJg+pcnxvUDjb96rhg1iwqB+WO12Vq1adczbuGbNmsUvfvELADZv3szQoUNb/mz33suTT7b82c9Ure4ruPzyyH+wp8ugkv3796PrOpmZmU32Z2ZmsmXLlqPKP/HEEzzyyCMdXi9XgwsNg5b+/rfqBj3q6rEaYDcUPIZJS387ZfsMFnxaT3LQJC4MkxpgWwtl3aaFS0MD8Cs+AnYfVnUpsd8SR1HQLEmHXiktfzsp0KSsqhx7GgGH1R7tAjVjLU4tSXaEsVtNFAXs2rG7MXslhUlyqagKJDqPPerx3F5+eiUraAqsKz72TetXDm5gSA/QFJPK6gDvb2q57PfOaWBkbuQzef0h3ljTctmLBytcHJ3fp7RBO/STshln93ExbmgiAFtqj91Bm90jmQH90wDIqN0PtBwI41MzSOkd+e/DXXMQaDkQaok90LJ6AKDUeoANLVciPgsye0W2fXXAupbLujMhMzoESvcCq49RNg0y8yLbFj+wsuWyrlTI7BfZrgsBy1su60yGzAGR7YAOfNFiUcWRiLXHoMP2fN5iWbvdSs8UDQK1YEa6+1sSr+9nVN2rsddWs+VWkBS7n+vPbYC4dIjL5Jf//E+LgTApK5sbfv1bQgE/IX+AeSumtBgIrQ4Hgy+cgBEOYxg6jqWroYVAqKoq6X3yYq9tjpb/3RRVaVLW/tUxvnegadk1R/+sblI2Jxe7LfIzx7Fx5zHLpub0ISHa+ujcVnzMsokZWSS4HOjh8HHvcwt7PyDgWY7F5sCi7T3OeV2k9orM2u/afezzJqY7Y2Xj9h27azU+zXGobGUzU0cc5mCZlzWL90YeRZuPWbaj7amXydE7Q6u6jI/06aef8oc//IFdu3bxz3/+k549e/KXv/yFvLw8LrjggvasZ4cpLS2lZ8+eLF26lDFjxsT233///SxZsoQvv/yySfnmWghzcnLavcvYNE3q6uvQWljX9vCyjec1/GGMhhB6QwijPoTuDWHUhTB9YexhC0ZdEL0hRMPBWnRvqNluY0VRcFoP1dcXCsTuFVNsKmqcDdVtRY2zormtxKclosXZUOOsBC06ODW0OCuK03JUd57bfWjxYb/fj663HMhaU9blch3qDgoECB+jG601ZZ1OJ2o0jAaDwdg63idb1uFo2h10omVDodAxB3LZ7U27g060bHt2Bx1eVtf1E+7iaU3ZE+0OOpGyFosFe+OtDKbZYut9a8u25r/7WFnThJCXhoMV4K+FYH0kJAbqIFgHgXrUgAenXgveA9Cwn4bqKvAdBO9BMJp+H6kKOK2H/hv0hg5NKQOAooIrBVzpKHFpuFJ7RUJ6fA+8lmTMuEyIy4T4TLAcChqKojTpBvb5fMe8n/zw/5ZbU/ZU/hmhh0PUVJRzoGQvB/cVc3DfPuoqy/CUl2Lo+lG3ilhsdtJ755LRtx89+g2k3/BzSUxLP+q8zWnvnxGGblBd4cVTFuDAXi97Nx+kurKBsH6obFKWm7xhqfQdkU5qdhx2+/9v777DoroSNoC/904FpAkIqAiCYgMLVkxsiYqpumaTaKqJKWvWTUxi2po1mnyrbqJJjLub4qa4JlnTk93EWIKaWDCxoVhAQbHQVXqZer4/7jAwAgrIDAO8v+e5z7Q7M+cex5mXc+45R9fi3xEZJ8/ix8VPQai1+CDqUfxpQiQeGNWtwZH67DK+es0+m/irr77Cvffei7vvvhv79++3fwCKi4uxZMkSrF+/vsUK6UyBgYFQqVTIy8tzuD8vLw8hISF19tfpdPYfhdq8vLwcvqAa0ph9AOVL18e78R9QLy8vwAu1hhRfnrAKWMtNSoAsMyoBstQEa7kRljJboCwzQlWmg6XMCFR3YZUBKDMDeWYAlShtqLVIJUHVSQO5k9Z+afaufVsDrS1Iyp6ay47CrP3DeiUN/ftc7b5ardb+ZdNa+2o0GvsXaUvuq1ar7T8SLbmvSqVq9Oe9KfvKsuyUfSVJcsq+QCP/30sSoPWCV0hk41+3+ooQSnisuKCEw/LzQMV5oLwAKCsAyvPhWZZvu52v7AcBmC4AxReUToCsmtf1vPSNPPwB71B7YIRPV8C3O+DTHR6+3QHfboDuyr1Ftf+IvZKm/L93x+8IH18/9Iju67CPxWxCYU428jNPIu9kOvJOpiP/VAZMhipczMzAxcwMpG7ZhK0A/EO7oXv/GIT1j0X3/jHw7hx4xTK01HeEt483YGsMF0KgOL8SZ45eQGbKBWSlFqLyghVHtxbg6NYC+Id6IXpEMPqMDIF3Z32LfUd09dVDrZJhVmthtFhxoqACnTpdboH7Gk35f081mt1COGTIEDz55JO477774O3tjYMHDyIyMhIHDhzADTfcgNzchs/ncDcjR47EiBEjsGqVcu6F1WpFjx49MHfu3EYPKmnPf2EIISAMFntIVC5NsJYZL7lUHhdVTZyAVgJkL43S0uittDzKnbRQeWsge9W6z1sLlZcGUjsbrUfkchazrZUxXwmIZflAWR5QmguU5jheWho5PlzvC/h0V4KibzfbZRjgFw74RwCdujR57tGOwGq1oDA7G3mn0pGbcRxZx44i//TJOjOI+4d2RfjAIYgYFIewAQOh1Tc+XLckQ4UJmSkXcDK5AKdTLsBiGxAiSUCPmAAMGNMN4TEBVzUhNQBkH0/Ff/4yHz5BXdDloVcwtncQ/Bs5f2FjdYTf76ZodgthWloaxo4dW+d+X19fFBUVXU2ZXO6pp57C/fffj2HDhmHEiBF48803UV5ejgceeKC1i+YWJEmCpFdD1quhDrzyl5AwWWEpN8JaWjtAXnpbCZLWCmWCWastUKIRf0dIerUtLGqg8tbau69lb22dVklJy+XliOpQqZWuYO/gy+8nBFBV5BgQS7KVrfgcUJIFFJ8Fqoprtvwj9b+W2gPwD68JiP62S79w5XojWhjbI1lWIaB7GAK6h6H/mAkAgKqyMmSlHcHZIyk4d+ww8k+dRGFONgpzspG88QfIKjW69umLiIFxiBg8FF0iIl32Pafz1KDPyBD0GRkCQ4UJGQcKcPzXXGQdL8LplAs4nXIBvkEeGDwxDH3iQ6Fp5sorxiqly1fr4Ympg7u15CFQA5odCENCQpCeno6IiAiH+3fs2IHIyMZ3ebiDO++8EwUFBVi4cCFyc3MxePBgbNiwoc5AE2ocSSND7acH/K7cjSMsVljLzY7BscGWSBNgFRBVZpirzEBBw+eI1C6L3EljP8/RHiCrb3eqDpJaSHoVwyNRbZKkdBd7+ANd+jW8n6EUKM6yhcRzymXxOaDoLFB0WgmO5kqgIFXZ6tMpGAjoBXSOVC4DopRL/56ApvFdwu2BvlMnRA0diaihIwEAhopynD2SgsyD+5F5aD+K83Jx7uhhnDt6GDvW/RudAgIRNXQkeg0bibABsVCpXbP2r85Tg/7XdEX/a7qiKK8CR7Zn4diuHBQXVOLn/xzHbz9kYtgN4RhwbTeoNE3r2THZzgFsrZbQjqjZXcZLly7Fxx9/jA8++ACTJk3C+vXrcfr0aTz55JP4y1/+gj/96U8tXVa3xCZn1xG2MGgpM8FSalTOgbQNlLHWus9SptwvTE2cOF0tQVXdRV1PgFR5MzwSNYvZqLQkFp0GCjOBQttl0WnlemXD0/IAktL1HBAJBPQGgvoAQX2VrVOQiw7AvRTmZuP0wQPIPLQfp1OSYa41iEPr4YGIwcPQe0Q8ouJGQNOE8ytbgslgwbFd2Uj+6SxKLygDRrwD9Lj29t7oOSiw0d+bh7duxsZ3VqLn4KGY/oJzZvbg77ejZgdCIQSWLFmCpUuX2kfb6XQ6zJ8/H6+88kqLFtKd8QPlvqwGS03rYukl5zteclsYmnjeo1qynfOodE/bg6O3Vrle3X3trYXcgovVE7VLVcXAhQxlu5gBXEivuW1oaNorKNMGBfWtFRL7AEH9OtT5iiajAWcPH0L63t04ue83lBcV2h9T63SIjBuBPvHXoueQYdC4cMUPi9mKY7tysPeHUygvVkYoh8cEYMI9feHld+Vy7PvhO2z792r0GT0WNz/xrFPKyN9vR1c17QygDIlPT09HWVkZ+vfv3+hRQO0FP1DtgzBZYCmt71xHY03ro+2yqeFR0qnqCYyamkBZfV+ny4+2JupwhFBGTFeHxPPHgYLjQMExpWWxoSWXPAOA4BggJNZ2GQME9gHU7XtNXGG1IjfjBNL3JCFt9w4U59WclK3Re6D38FEYMH4iwvrH2tbfdj6TwYJ9GzJxYPMZWM0COk81rruvHyIHX751d9cXnyDpy/9g4MQpmPTwXKeUjb/fjq46EHZ0/EB1PI7h0bHF0VJidGiFbFK3dfVoa2/HoGjvqvapaX1kqyN1eMYK4MIJoCDNdm5iGpB/DCg8ZVuu8hKyRmlBrA6KXQcDoYPa7WAWIQTyTqYjLWk70pK2o/R8zaov3oFBGDD2OvQfdz38Q+pfZaulXcwux08fHUXBGWWFn2E3RmDELT0b7ELe8tG7OPDj/zBi6u8x5q5ZTikTf78dXVUgLCoqwvvvv49jx5RZzPv374/Zs2fD19e3xQro7viBooZUT9djKa3V6lgdHEtt122btaz+icIbIulUDgFRdUlgVPkom6TjuY7UwZgqlWCYdxjIPVxzWW/XswQERgPd4oCuQ4CucUproqZ9DWQQQiD7eCqO/pKItF3bYaiomSg9bMBADJp0I3oNHwVVI+cPbC6LxYqkrzJwcIuyUku/0aEYf0/feqeo+fHvK3B0+1aMvfsBDL/1NqeUh7/fjpodCPfu3YuEhAR4eHjY1xfcs2cPKisrsWnTJsTFxbVoQd0VP1DUEqonCrcHxOqwWGLrqi6pub8prY6SRobs4xgYVT5ayD66mvsYHKm9EwIoOlMTDnMPAdkHlNHPl5LVyojqbkOBsFFA2Ahl5HM7+f9hNhqRvnc3jv6ciMyDB+wrUXn5+SP2usmIvS4BPkFdnFqGozuzse3jVAgB9L9GCYWXfv98vfQlnEreh4Q58xAzfqJTysHfb0fNDoRjxoxBr169sHr1aoclsB566CGcPHkSv/zyS4sW1F3xA0WuZG91rBUQq69bSo2wltQEyaac6yhpZFtQ1ELlo7MHRSVE6uyPsaua2pXSPCAnGcjarwTE7P3Kai6X8goCwkYq4TBsJBA6uF1MhVNyvgApiRuQsmWTw2CUrtH90PeasYgedS28/Pyd8t7p+/Kx6V+HIQQw8tZIDLsxwuHxj1+Yh7yT6Zj27EJEDR3hlDLw99tRswOhh4cHDhw4gL59HZfmOXr0KIYNG3bZdT7bE36gyF1ZjRaHgGhvcSw1wlJiqAmOTVhZRtKrlZDoa2tt9K0dHnVQ+WqVCcE5OIbaIiGUVsOs/cC5PcDZ35SQaLlkzV+VVulijrhW2cJGAtq2u1SaxWxG+p7dOPTTepw5kmJfJUWSZPSIHYQ+8WPQe8Ro6Ft40OjhX7Lw86dpgARMfWIwuvftbH/s3cdmoezCedy95A2ERPVu0fetxt9vR80OhMHBwVi7di0mT57scP/GjRtx33331VkbuL3iB4raOntwLDHCUmpQLmtt1lIjLMWGxndVS6h1HqMSEmuCY81ttjZSm2A2ADkHgbO/KtuZX5Ul/2qT1UoXczsIiGUXLyAtaQdSd/2M3PTj9vtllRoRg4ag77Xj0WvYSGh0LdNCunXtMRzdmQPvAD1m/GUEtHo1rFYLVt4zHVaLBbMeXwnz/hLoo/3hmxDRIu9Zjb/fjpp9Bumdd96J2bNnY/ny5Rg9ejQAYOfOnXjmmWcwc+bMFisgETmXrFVBDvS47LKEDl3VJQZYio2XXDfYWyMhAGuJ0n1tQlmDrynp1Uo49KkVGH119k3tq4Xkoea5jdS61DpbV/EIAH9SWs8KM4HTu4DMHUDmdmXS7erAuH2FMqK5+3AgcjwQNUEZrKJy7oCNltKpcwCG3jQVQ2+aiqLcHGWU8q5fUHAmEyf378HJ/Xug0XsgeuRo9B973VVPYXPN7b1xNrUQpReqcGDTGYy8NRLlRYWwWiyQZBkaixaVWWXQBLWvgT7uqNkthEajEc888wzeeecdmM1mAIBGo8GcOXOwbNky6HSumwCzNfEvDKIawiqUaXdsIdFScul1JUAKY+O6qSWNXBMW/arDYnVLo62L2kvD0EitRwhlxZXMHcp2aruyfF9tOh+l5TByPBA5AQjs3eYGqVw4dwapO3/GsR3bUJxf0wPoExSMAeOuw4BxE+HbpXnLvWYcyMeGdw9DrZVx319H48K54/hs0fPw7RKMO6a/hJKNp+E5LBidfx/dUocDgL/fl7rqeQgrKiqQkZEBAIiKioKnp2eLFKyt4AeKqOmsVWYlHNYKibVbGy3FyjKEjaKWbC2KtpDoV6ub2lcHtZ+OLY3kOkIocyGe/Bk4uQ049TNQWei4j093IHoy0DsB6DkW0Lad300hBLLTjuHoL1uQuusXGCtt4wUkCeGxgxF7XQJ6DR/ZpPWUhRD4ctle5J8uxfCbIqD3PIHN761C+MAhmDR8Nkq3nIXXqFD4T+vVosfC329HTQ6EW7Zswdy5c7F79+46FVhcXIzRo0fjnXfewZgxY1q0oO6KHygi5xAmqy0cKgHRXFxz3WK7bi1rXGi0tzT61YRGtZ/jbZ7TSE5htSjT3GRsVQLimd2ApWbtYaj1QMQYIDoB6D0Z8A9vtaI2lcloQPpvSTi8dTPOHD5ov9/T1w+x103GwOunNHoKmxN787DpX0fg5atFWJ/DSN74PYbeNBWDAyei7Jdz6DSmG/xuimzR8vP321GTA+Gtt96KCRMm4Mknn6z38bfeegtbt27FN9980yIFdHf8QBG1HmG2Kq2MRUpAtIfGIkNNaCw3N+q1ZE91TWisHRj99MqlN0dPUwswVijnHR7fCJzYpJx/WFuXAUC/W5QteECb6VouysvF4a2bcXhrrSlsJAmRQ4Zh8OSbEDEo7rLnGlrMVnz03E5UlZvgof8ahTmZuPHxZxB6oTvKdmXDe0IYB5U4WZMDYXh4ODZs2IB+/frV+3hqaiomT56MM2fOtEgB3R0/UETuTZgsNS2MRY6B0VykXG/UnI2ypHRF++mgrg6J/rWus5WRmkoIZVWVExuB45uUQSmi1mexc6QtHN6qDExx0frDV8NiNuPkvt+QvHk9zqQk2+/3DQ7BoIk3YMD4ifD0qX81s22fpuHwtjQYilcDEHj07TUwbb2I8j258JkUDp/re7RoWfn77ajJgVCv1+Pw4cPo1av+vvz09HTExsaisrKyRQro7viBImr7rFVmWIpqAqI9MBZW2bupYb3yV6XsqYbKX1/Twuivh9pfaWVU+/NcRrqCiotKy+Gx/wLpiY5dyz7dgJjpQOztQMjANtFyeDH7HA5uWo8jPyfal8tTaTToPWI0YiZMQtiAWMhyzR9Rp49cwDev/hOWql8R2rsv7vq/5bj4WRoqDuTD98ae8B7bvUXLx99vR00eB9+tW7fLBsJDhw4hNDT0qgtGROQqsl4NOUQNTUj9c8cJq1Am8i4ywFJY5Rgci5TbosoCa4UZ1ooymLLqn25H0qqUVsXq0Oivr7ntr+OI6Y7OszMweKayGcqA9M3A0f8qXcslWcCuVcoWGK0Ew5jbgICo1i51gzp37Y4Jsx7BtTPuQ+quX3Bw83rknUxH6s6fkbrzZ3Ty74yoYSPRrV8MvDsHIPd4KixVewAA/cfeBEBp4QeU84DJuZrcQvinP/0J27Ztw549e6DXO05MWVlZiREjRmDChAl46623WrSg7op/YRARoLQymguVgGgpNMBsv1RCZGMGwEha2daqWBMU1Z319pZGtjB2UKYqJRymfAGkbXBsOew2DBhytxIO9fV3xbqTvJPpSNmyEam7foGhvLzefWRtf0yZMw/9RnfF+Q8PoyqtEP639YbX8JAWLQt/vx01ORDm5eUhLi4OKpUKc+fORZ8+fQAo5w7+4x//gMViwf79+xEc3Lz5iNoafqCIqDGEyWILh7XCYmHNpbXEeMXXkHQqJSB2VoKiw3U/PVtROoKqEiD1eyUcntwGCNsKQmoPoP+twJB7gPBr3f58Q7PJhDOHk5GZvB95J9NRWVoMT19/aD37IftkGGLHd8e4mX1Q8N4hGE4Wo/OMPvAc3LgRy43F329HzZqH8PTp05gzZw42btwIYV/zUEJCQgL+8Y9/oGfPni1eUHfFDxQRtQRhsl4SFKuUFseLVUpgbEQLo8pHaw+IKn891AG2sBjgAbkTu6PbndI8JRgeWAsUpNbc7x8BxN0HxN0PeAW2WvGa4/hvudj8wVGE9vLF9PlDkf/PZBjPlCLg3v7wGBDQou/F329HzVpLJzw8HOvXr0dhYSHS09MhhEDv3r3h7+/f0uUjIuoQJI0MTZAnNEH1T1JsNVqUgS8Xq2C5UAlzoe36xSqYL1ZBGC329aeNmSX1vr69NTHAQwmN1YHRXw9J7d4tSlQP72Bg9Fwg/o9A1j4lGKZ8pSytl/gysG0ZMGA6MOJhZa3lNvAHQeeunQAAF7LKlSUzjUoLqKTl59PZrmpxRX9/fwwfPrylykJERA2QtSrIXTyh6VI3MAohYC03Ka2LFythvmiA+UKlPSxaig1KC2ReBcx5FXVfXIKyqkuALSzaLlW265xOx81JEtB9mLIlLAWOfAPs+ReQvR84tE7ZQgcDI/+gnGuo1rZ2iRvkF+wBSICx0ozKUhMHlbhQ21htm4iIGiRJElSdtFB10kIb5l3ncWG2KucvXqiE+WIVzBdsQfFiJcwXqpRVYWyjpg0ZxXWeL3trbEFRaVlUB9qCY6AHZD1/RtyK1lMZZDLkbuDcPmDPauDw10BOMvDtH5SWw1F/AIbOcstBKGqNCp38dSi7aEBxfgWEydZCqOEfJc7G/8lERO2cpJahCfSAJtCjzmNCCFjLTEpQPF8J84XKmsB4oVKZSqfUBGOpqd6uaLmTpqZVMVAJiUpY1EPW8SemVXUfqmyT/wrs/wj49T2gNBvYvBD4+TVg6P1Kd7NP19YuqQOfAA+UXTSg9GIVvKq7jNlC6HT830pE1IFJkgSVtxYqby104XVPrLdW2MJidVA8b7u8UAlrmQnWMhOMZSYYTzcQFm0hURPk4RAYec6iC3kFAGOeBuLnKoNQdq1SBqEk/R34bbUyAOXaJwHfbq1dUgCAd4AeOAGUXqyCZ3WXMU9bcDoGQiIiapDsqYHWUwNt97pd0dYqc01IrN26eL4S1vJaYfHSlkUJyihoW6ulOtAD6iBlU/nouGa0s6h1yrQ0g+4C0n8Ctq8Azu5WupX3r1EeG/M04NuyK4I0VSd/HQCg/GIVYFFmMpE5qMTpGAiJiKhZZL0a2m6doO3Wqc5j1iozzAW2kHi+Eqbq0FhQCWGwwGIbIW04XujwPEkj1wTEQA9ounjab7MLuoXIMhA9Geg9CcjcDmz7G3B6B7D3A+DAJ8qo5DFPKyuntAIvXyUQVhbVTMDNFkLn4/8uIiJqcbJeDW2Yd51BLvZzFs/XCooFlTAXVCjT55isMOWUw5RTdxUL2UerBMQgD4dL2VvLORabQ5KAnmOVLXMHsHWpEgyT/g7s/zdwzRPAqDmAtv4lHZ3F01cZBV1VbJusXQag4r+vszEQEhGRyzics9jTcZSrsAiYC6uUcFigBEVTQYXSBV1mgrXECEOJEYb0IsfX1KlqhURlah51sCfUnfXsfm6siGuBWd8D6YnAT4uAvBRgyytKq+Gkl5XpalwUuj28lUBoKlcmY5c0KgZ+F2AgJCIityCppJrR0P0cH7NWmpVwmG8LifkV9i5pYbDAdK4MpnNljk9SS9AEekLdxcMeEjVdPDmopSGSBPSeCERdBxz+UgmERWeAr2Yrg09u+BvQdbDTi+FpC4TmchOgkyHr2F3sCgyERETk9mQPNXQ9fKDr4TgSWpitMF+ohCm/Eub8CntYNOVXAmYrTLnlMOWWo9LhxSSoA/XQBHtB3cUTmhBP5XqAHpKKQRGyDAy8A+h3K5C0Ctj+ujL45L3xwPCHgOsXAnrnLfWm99IoV4xWQCdDYiB0CQZCIiJqsyS1DE2wFzTBjue5CauApbAKpoJKmPMqYMpXNnNeBYTRAnN+Jcz5lY4vppKUgSzBSkDUhHhCE+IFlX8H7XrW6IGxzyijkjcvVFoN96wGUn8AbnwN6HezU95W66kGJEBtq3IGQtdgICQionZHkiX76iroWzNaVggBS7ER5rxymPIqlK12ULQt71eJ8zWvpZGV7uZaIVET7AXZW9Mxzm3z7Qb8/n0g7l7g+yeBiyeBz+5WWhBvfgPwCmzRt5NlCToPNdS2OQi5dKJrMBASEVGHIUkS1H46qP100Pe5JCgWGWCyBUJTrcAoTNZ6z1GUvdRKOKzeQpUu6HYbYCLHA3N2Ab+8BuxcCRz7L3AmCbjlLaDvjS36VjpPNdQltkmp2ULoEgyERETU4UmSBLW/Hmp/vWOLolUo5yjmViitirnlyvULlbCWm2HIKHZc/1kC1AEe0ITWhERNVy+ofHXtozVR46GcQ9h/KvD1o0DBMWDdTGW1kyl/U9ZSbgFaDzXUpcq0MxxU4hoMhERERA2QZAmaIE9ogjyB2JquUWGyKC2ItoBoyi2HKacM1nKzfY7FypRa3c4eSmui1hYQNV07QRPs2XYHsYQOAh7ZBmz9q7IU3v5/A2f3ALd/BHTpe9Uvr/NQ15xDqGdUcQXWMhERURNJGhW03b0dlvSrnnTblGNrScxRQqIpvxKi0gzjqWIYT9VqTVRJ0IR6Qdu1EzTdOimXIV6QNG0kJGr0wORXgF4Tga8fVloL3xsP3PqWMkr5ql5aDdtYY8h6thC6AgMhERFRC6g96bY+2t9+vzBblVHOthVYTNllMGaXQVTVM39idUgM84a2hw+0Yd7KdDju3N0cOQ74ww7g60eAk1uVcJhzEJi4GFA1L2ZoPVTQ2I6ZLYSuwVomIiJyIkktQ9tVaQGsJoSA5WIVjNllMGWV2y5LYS0320NieVIOAED2VEPT3Ru6cB9ow5XlAN1uXedOXYB7vlK6kLevUJa/yz8K3L6mWXMWanRqaGwZmC2EruFmnygiIqL2T5JqTYsTGwTAFhILDTCeLYHxTCmMZ0thzC6DtcIMw/FCGI4X2p4M5XzEcB8lJPb0gdpP34pHYyOrlAEnIbHAt48BGVuAD28A7vpcmbqmCbQ6lf0cQtmDUcUVWMtERERuQJIkqDvroe6sh+egLgBs3c255TCeKYXhdAmMp0uU6XFs3c/lu5VWRJWfDroIH2h7+kLX0xfqII/W62Ye8DvALxz49E4g7zDw/iTgvu+AwN6NfgmNXgWtbTJwmV3GLsFaJiIiclOSWrYPXuk0uisAwFJsgOFMCYynS2HILIYpuwyWIgMqkgtQkVwAAJA7aaCL9LVtfq4PiN3igId+Aj75PXD+OPDBFOC+b5XWw0bQ6FTQVrcQemouvzO1CAZCIiKiNkTlq4NnbBA8bV3NVoMFxjMlMGSWwHiqGIYzpbCWmVB56DwqDylT38jeGuii/KDv5Q9dbz+ofXXOL6h/OPDAj8Da3wG5h4A1twD3/69RoVCtVdWcQ+jFQOgKDIRERERtmKxTQd/bH/reyshmYbbCeLYUhpPFMGQUwXCmBNZSEyqTC1Bpa0FUB3lA39sfumh/6CJ9nbe6ilcgMOt74OPbgHN7gH9PBWb9AHTpd9mnaWRAbWvRlL0YVVyBtUxERNSOSGoZOtu5hLi+B4TJCsOZEhjSi1CVXgTTuVKYCypRVlCJsl3ZgFqCrqcv9H06Qx/t3/Ldy3pf4O4vlTCYkwysnQ7M3gT4hTX4FLUQAAALAKm9LgXoZhgIiYiI2jFJI0Mf5Qd9lB98EwBrhQmGk8WoOlGIqrRCWIoMMJwoguFEEYph616O9FPOP4zya5l5ED38gHu/UUYdF6QqLYazNyn310NtUgKhSZLcew7GdoSBkIiIqAORPTXwiAmER0wghBAw51egKq0QVccLYci0dS8fLEDlQaV7WeWjtQ9O0UX5KlPlNIdnZ2WuwvcnA+fTgC8fVKakqWfyapXRAgAwNPsoqakYCImIiDooSZKgCfaCJtgL3mO7K+cfnilBVUYxDCeLYDxTCkuJ0WEEs8pfZxug4gddlB9U3trGv6Fvd2Dmf5RRxxmJwJZXgEmL6+wmV5phBVBh6zom52MgJCIiIgC28w8j/aCL9AMQDmGywHCmZoCK8WwpLIUGVOzNQ8XePADKJNm63n7KIJWePpA0VzjnL3QQMO2fwBezgJ1vAj1GAX1ucCxHqREAUGFt8UOkBjAQEhERUb0kjcp+/iEmhcNqtMCYWYKq9CIY0gthyi6HKVfZyrZnAWoZukhf6KP9Lz9AZcDvgLO/Abv/qaxqMmcX4BNqf1gUVgEASsxMhK7CQEhERESNImtV9rAH9ISlzAhDRhGqThTBcLwQlhKjfZm9YgCqznro+/grI5ijfB1bDycuAjK3A7kpwPfzgJnrAEmCMFthLagAABQaGQhdhYGQiIiImkXVSQvPQV3gOahLzQCV48roZcOpYlguVqE8KQflSTmAWoY+yhf6vrbpbQI8gOmrgXfHAsc3AIe/AmJ/j6r0IsAsUGUVKDULWC1WyCq5tQ+13WMgJCIioqvmMEBlTHdYDRal9TDtIqpSC2EpNiijmdMKAQDqQA/oevlB13sZtEf/BtWGF2ENvQ4lGzMBAFm21kGT0QqdBwOhs7XJGs7MzMTs2bPRs2dPeHh4ICoqCi+99BKMRqPDfocOHcKYMWOg1+sRFhaGV199tc5rffHFF+jbty/0ej1iY2Oxfv16Vx0GERFRuyXrVPDoHwD/3/VGyPPDETwvDj5TIqDt6QPIEsznK1G+OwcXk6ORa3wfWRdWI2f5EZhyyiF5qHHCFgjNtiloyLnaZAthamoqrFYr3n33XfTq1QuHDx/Gww8/jPLycixfvhwAUFJSgsmTJ2PixIl45513kJKSggcffBB+fn545JFHAAC7du3CzJkzsXTpUtx888349NNPMW3aNOzfvx8xMTGteYhERETthiRJ0IR4QRPiBZ/xYbBWmZVl9TKKYThdAlNOKWBVBp9ou+nhd1s/WJbuBaxWWEw8j9AVJCHaxyQ/r732Gt5++22cPHkSAPD2229jwYIFyM3NhVarzJH0/PPP49tvv0VqaioA4M4770R5eTm+//57++uMGjUKgwcPxjvvvNOo9y0pKYGvry+Ki4vh4+PTwkdFRETU/gmLFdb3fwcpazfkUbOAG/6Gfz31CwwVZty1aCT8Q7xa/D35++2oTXYZ16e4uBidO3e2305KSsLYsWPtYRAAEhISkJaWhsLCQvs+EydOdHidhIQEJCUlNfg+BoMBJSUlDhsRERE1n6SSobp+HmSpCti3Bii/AJVGiShmthC6RLsIhOnp6Vi1ahUeffRR+325ubkIDg522K/6dm5u7mX3qX68PkuXLoWvr699CwtreHFuIiIiaqTI8cqk1eZKYN8HUKmViGLhXIQu4VaB8Pnnn4dkW8i6oa26u7daVlYWpkyZgttvvx0PP/yw08v4wgsvoLi42L6dPXvW6e9JRETU7kkSMHKOcn3fv6FSKecUWhkIXcKtBpU8/fTTmDVr1mX3iYyMtF/Pzs7GhAkTMHr0aLz33nsO+4WEhCAvL8/hvurbISEhl92n+vH66HQ66HS6Kx4LERERNdGAacCPzwHFZ6ASlQAAi7ldDHVwe24VCIOCghAUFNSofbOysjBhwgQMHToUH374IWTZsbEzPj4eCxYsgMlkgkajAQBs3rwZffr0gb+/v32fxMREzJs3z/68zZs3Iz4+vmUOiIiIiBpP46GEwv1roDIWAvBnl7GLuFWXcWNlZWVh/Pjx6NGjB5YvX46CggLk5uY6nPt31113QavVYvbs2Thy5Ag+++wzrFy5Ek899ZR9nyeeeAIbNmzAihUrkJqaikWLFmHv3r2YO3duaxwWERERxf4eAKCqKgDAcwhdxa1aCBtr8+bNSE9PR3p6Orp37+7wWPUsOr6+vti0aRP++Mc/YujQoQgMDMTChQvtcxACwOjRo/Hpp5/ixRdfxJ///Gf07t0b3377LecgJCIiai3h1wBeXSBfNAAArOwydol2Mw9ha+E8RkRERC3sh/n438YQnDHG4fr7+6FvfGiLvwV/vx21yS5jIiIiasdipkOWzAAAi9HUyoXpGBgIiYiIyL2EjYJKowIAWM4lt25ZOggGQiIiInIvsgxVYAQAwJr2E2CsaN3ydAAMhEREROR25OA+AABLRSmw4flWLk37x0BIREREbkel1QIArNAAeh/AyulnnKlNTjtDRERE7ZtcvZbx0EeAycNauTTtH1sIiYiIyO3Y1zLW+rVuQToIBkIiIiJyO/YWQgu7il2BgZCIiIjcjlzdQmjh+hmuwEBIREREbkelUiKKlWsZuwQDIREREbkdWa20EFrYQugSDIRERETkduwthAyELsFASERERG7Hfg4hu4xdgoGQiIiI3I7KPsqYLYSuwEBIREREbqdmlDFbCF2BgZCIiIjcjn1QiZkthK7AQEhERERuR5arB5WwhdAVGAiJiIjI7XBiatdiICQiIiK3w0DoWgyERERE5HZq5iFkl7ErMBASERGR22ELoWsxEBIREZHbqQ6EnIfQNRgIiYiIyO3I7DJ2KQZCIiIicjvV8xCyy9g1GAiJiIjI7fAcQtdiICQiIiK3UzPKmIHQFRgIiYiIyO1wLWPXYiAkIiIityPJtkBoZQuhKzAQEhERkdvhOYSuxUBIREREbqf6HEIIthK6AgMhERERuR3J1kIIAIKthE7HQEhERERuR64VCC0cWOJ0DIRERETkdmoHQsEuY6djICQiIiK3I8s1gZADS5yPgZCIiIjcjiRJnHrGhRgIiYiIyC1VtxKyhdD5GAiJiIjILXEuQtdhICQiIiK3xOXrXIeBkIiIiNySPRDyHEKnYyAkIiIit1Q9qITTzjgfAyERERG5JQ4qcR0GQiIiInJLHFTiOgyERERE5JZklRJTGAidj4GQiIiI3BLPIXQdBkIiIiJySzJXKnEZBkIiIiJyS5x2xnUYCImIiMgt2buMeQ6h0zEQEhERkVvitDOuw0BIREREboldxq7DQEhERERuiaOMXYeBkIiIiNwSWwhdh4GQiIiI3BLPIXSdNh8IDQYDBg8eDEmSkJyc7PDYoUOHMGbMGOj1eoSFheHVV1+t8/wvvvgCffv2hV6vR2xsLNavX++ikhMREdHlsMvYddp8IHz22WfRtWvXOveXlJRg8uTJCA8Px759+/Daa69h0aJFeO+99+z77Nq1CzNnzsTs2bNx4MABTJs2DdOmTcPhw4ddeQhERERUj5q1jK2tXJL2r00Hwh9//BGbNm3C8uXL6zz2ySefwGg04oMPPsCAAQMwY8YMPP7443j99dft+6xcuRJTpkzBM888g379+uGVV15BXFwc/v73v7vyMIiIiKgeklR9DmErF6QDaLOBMC8vDw8//DDWrl0LT0/POo8nJSVh7Nix0Gq19vsSEhKQlpaGwsJC+z4TJ050eF5CQgKSkpIafF+DwYCSkhKHjYiIiFpedQshu4ydr00GQiEEZs2ahT/84Q8YNmxYvfvk5uYiODjY4b7q27m5uZfdp/rx+ixduhS+vr72LSws7GoOhYiIiBrAtYxdx60C4fPPPw9Jki67paamYtWqVSgtLcULL7zg8jK+8MILKC4utm9nz551eRmIiIg6AsmWUthC6Hzq1i5AbU8//TRmzZp12X0iIyOxZcsWJCUlQafTOTw2bNgw3H333VizZg1CQkKQl5fn8Hj17ZCQEPtlfftUP14fnU5X532JiIio5XHaGddxq0AYFBSEoKCgK+731ltv4f/+7//st7Ozs5GQkIDPPvsMI0eOBADEx8djwYIFMJlM0Gg0AIDNmzejT58+8Pf3t++TmJiIefPm2V9r8+bNiI+Pb8GjIiIiouaQVEoTIbuMnc+tAmFj9ejRw+F2p06dAABRUVHo3r07AOCuu+7C4sWLMXv2bDz33HM4fPgwVq5ciTfeeMP+vCeeeALjxo3DihUrcNNNN2HdunXYu3evw9Q0RERE1DpsDYTsMnYBtzqHsCX5+vpi06ZNOHXqFIYOHYqnn34aCxcuxCOPPGLfZ/To0fj000/x3nvvYdCgQfjyyy/x7bffIiYmphVLTkRERAAgcZSxy7TJFsJLRUREQIi6H5aBAwdi+/btl33u7bffjttvv91ZRSMiIqJmkjjK2GXabQshERERtW0yl65zGQZCIiIicktsIXQdBkIiIiJyS/YWQk4743QMhEREROSW7C2EzINOx0BIREREbknmSiUuw0BIREREbonnELoOAyERERG5JYmjjF2GgZCIiIjcEtcydh0GQiIiInJLKrUMlUaGbFuxhJynXaxUQkRERO1P7PjuiB3fvbWL0SGwhZCIiIiog2MgJCIiIurgGAiJiIiIOjgGQiIiIqIOjoGQiIiIqINjICQiIiLq4BgIiYiIiDo4BkIiIiKiDo6BkIiIiKiDYyAkIiIi6uAYCImIiIg6OAZCIiIiog6OgZCIiIiog2MgJCIiIurg1K1dgLZOCAEAKCkpaeWSEBERUWNV/25X/453dAyEV6m0tBQAEBYW1solISIioqYqLS2Fr69vaxej1UmC0fiqWK1WZGdnw9vbG5Iktehrl5SUICwsDGfPnoWPj0+LvnZHx7p1Htat87BunYv16zzuWLdCCJSWlqJr166QZZ5BxxbCqyTLMrp37+7U9/Dx8XGb/0DtDevWeVi3zsO6dS7Wr/O4W92yZbAGIzERERFRB8dASERERNTBMRC6MZ1Oh5deegk6na61i9LusG6dh3XrPKxb52L9Og/r1v1xUAkRERFRB8cWQiIiIqIOjoGQiIiIqINjICQiIiLq4BgIiYiIiDo4BsJW9o9//AMRERHQ6/UYOXIkfvvtt8vu/8UXX6Bv377Q6/WIjY3F+vXrXVTStqcpdXvkyBHcdtttiIiIgCRJePPNN11X0DaoKXW7evVqjBkzBv7+/vD398fEiROv+DnvyJpSt19//TWGDRsGPz8/eHl5YfDgwVi7dq0LS9v2NPU7t9q6desgSRKmTZvm3AK2YU2p248++giSJDlser3ehaWlSzEQtqLPPvsMTz31FF566SXs378fgwYNQkJCAvLz8+vdf9euXZg5cyZmz56NAwcOYNq0aZg2bRoOHz7s4pK7v6bWbUVFBSIjI7Fs2TKEhIS4uLRtS1Prdtu2bZg5cya2bt2KpKQkhIWFYfLkycjKynJxyd1fU+u2c+fOWLBgAZKSknDo0CE88MADeOCBB7Bx40YXl7xtaGr9VsvMzMT8+fMxZswYF5W07WlO3fr4+CAnJ8e+nT592oUlpjoEtZoRI0aIP/7xj/bbFotFdO3aVSxdurTe/e+44w5x0003Odw3cuRI8eijjzq1nG1RU+u2tvDwcPHGG284sXRt29XUrRBCmM1m4e3tLdasWeOsIrZZV1u3QggxZMgQ8eKLLzqjeG1ec+rXbDaL0aNHi3/961/i/vvvF1OnTnVBSdueptbthx9+KHx9fV1UOmoMthC2EqPRiH379mHixIn2+2RZxsSJE5GUlFTvc5KSkhz2B4CEhIQG9++omlO31DgtUbcVFRUwmUzo3Lmzs4rZJl1t3QohkJiYiLS0NIwdO9aZRW2Tmlu/L7/8Mrp06YLZs2e7ophtUnPrtqysDOHh4QgLC8PUqVNx5MgRVxSXGsBA2ErOnz8Pi8WC4OBgh/uDg4ORm5tb73Nyc3ObtH9H1Zy6pcZpibp97rnn0LVr1zp/3HR0za3b4uJidOrUCVqtFjfddBNWrVqFSZMmObu4bU5z6nfHjh14//33sXr1alcUsc1qTt326dMHH3zwAb777jt8/PHHsFqtGD16NM6dO+eKIlM91K1dACLqOJYtW4Z169Zh27ZtPIG8hXh7eyM5ORllZWVITEzEU089hcjISIwfP761i9amlZaW4t5778Xq1asRGBjY2sVpd+Lj4xEfH2+/PXr0aPTr1w/vvvsuXnnllVYsWcfFQNhKAgMDoVKpkJeX53B/Xl5eg4MaQkJCmrR/R9WcuqXGuZq6Xb58OZYtW4affvoJAwcOdGYx26Tm1q0sy+jVqxcAYPDgwTh27BiWLl3KQHiJptZvRkYGMjMzccstt9jvs1qtAAC1Wo20tDRERUU5t9BtREt852o0GgwZMgTp6enOKCI1AruMW4lWq8XQoUORmJhov89qtSIxMdHhr6ba4uPjHfYHgM2bNze4f0fVnLqlxmlu3b766qt45ZVXsGHDBgwbNswVRW1zWupza7VaYTAYnFHENq2p9du3b1+kpKQgOTnZvt16662YMGECkpOTERYW5sriu7WW+OxaLBakpKQgNDTUWcWkK2ntUS0d2bp164ROpxMfffSROHr0qHjkkUeEn5+fyM3NFUIIce+994rnn3/evv/OnTuFWq0Wy5cvF8eOHRMvvfSS0Gg0IiUlpbUOwW01tW4NBoM4cOCAOHDggAgNDRXz588XBw4cECdOnGitQ3BbTa3bZcuWCa1WK7788kuRk5Nj30pLS1vrENxWU+t2yZIlYtOmTSIjI0McPXpULF++XKjVarF69erWOgS31tT6vRRHGTesqXW7ePFisXHjRpGRkSH27dsnZsyYIfR6vThy5EhrHUKHx0DYylatWiV69OghtFqtGDFihNi9e7f9sXHjxon777/fYf/PP/9cREdHC61WKwYMGCB++OEHF5e47WhK3Z46dUoAqLONGzfO9QVvA5pSt+Hh4fXW7UsvveT6grcBTanbBQsWiF69egm9Xi/8/f1FfHy8WLduXSuUuu1o6ndubQyEl9eUup03b5593+DgYHHjjTeK/fv3t0KpqZokhBCt1TpJRERERK2P5xASERERdXAMhEREREQdHAMhERERUQfHQEhERETUwTEQEhEREXVwDIREREREHRwDIREREVEHx0BIRERE1MExEBLRZW3btg2SJKGoqKi1i9Ji2uMxXY1Zs2Zh2rRpjd4/MzMTkiQhOTnZaWWqJkkSvv32W6e/D1FHx0BI5KZmzZoFSZLqbOnp6U57z/Hjx2PevHkO940ePRo5OTnw9fV12vsCjser0WgQHByMSZMm4YMPPoDVam3R93LVMV2tjz76qN7PgF6vb9VyhYWFIScnBzExMa1aDiJqOerWLgARNWzKlCn48MMPHe4LCgqqs5/RaIRWq3VKGbRaLUJCQpzy2peqPl6LxYK8vDxs2LABTzzxBL788kv897//hVrdMl9Zrjymq+Xj44O0tDSH+yRJaqXSKFQqVZupPyJqHLYQErkxnU6HkJAQh02lUmH8+PGYO3cu5s2bh8DAQCQkJAAAXn/9dcTGxsLLywthYWF47LHHUFZW5vCaO3fuxPjx4+Hp6Ql/f38kJCSgsLAQs2bNws8//4yVK1faW6IyMzPr7V796quvMGDAAOh0OkRERGDFihUO7xEREYElS5bgwQcfhLe3N3r06IH33nuv0cfbrVs3xMXF4c9//jO+++47/Pjjj/joo4/s+xUVFeGhhx5CUFAQfHx8cN111+HgwYMAgOPHj0OSJKSmpjq89htvvIGoqCgAdbuML1y4gJkzZ6Jbt27w9PREbGws/vOf/zg8f/z48Xj88cfx7LPPonPnzggJCcGiRYsc9ikqKsKjjz6K4OBg6PV6xMTE4Pvvv7c/vmPHDowZMwYeHh4ICwvD448/jvLy8svWiSRJdT4DwcHBAICCggKEhIRgyZIl9v137doFrVaLxMREAMCiRYswePBgvPvuuwgLC4OnpyfuuOMOFBcXN/ieGzZswLXXXgs/Pz8EBATg5ptvRkZGhv3xS7uMq+szMTERw4YNg6enJ0aPHl0nyH733XeIi4uDXq9HZGQkFi9eDLPZbH/8xIkTGDt2LPR6Pfr374/Nmzdftm6IqOUwEBK1UWvWrIFWq8XOnTvxzjvvAABkWcZbb72FI0eOYM2aNdiyZQueffZZ+3OSk5Nx/fXXo3///khKSsKOHTtwyy23wGKxYOXKlYiPj8fDDz+MnJwc5OTkICwsrM777tu3D3fccQdmzJiBlJQULFq0CH/5y18cAhsArFixAsOGDcOBAwfw2GOPYc6cOXUCQmNcd911GDRoEL7++mv7fbfffjvy8/Px448/Yt++fYiLi8P111+PixcvIjo6GsOGDcMnn3zi8DqffPIJ7rrrrnrfo6qqCkOHDsUPP/yAw4cP45FHHsG9996L3377zWG/NWvWwMvLC7/++iteffVVvPzyy/bQYrVaccMNN2Dnzp34+OOPcfToUSxbtgwqlQoAkJGRgSlTpuC2227DoUOH8Nlnn2HHjh2YO3duk+ukWlBQED744AMsWrQIe/fuRWlpKe69917MnTsX119/vX2/9PR0fP755/jf//6HDRs22P9NGlJeXo6nnnoKe/fuRWJiImRZxu9+97srdt0vWLAAK1aswN69e6FWq/Hggw/aH9u+fTvuu+8+PPHEEzh69CjeffddfPTRR/jrX/9qr7/p06dDq9Xi119/xTvvvIPnnnuu2XVDRE0kiMgt3X///UKlUgkvLy/79vvf/14IIcS4cePEkCFDrvgaX3zxhQgICLDfnjlzprjmmmsa3H/cuHHiiSeecLhv69atAoAoLCwUQghx1113iUmTJjns88wzz4j+/fvbb4eHh4t77rnHfttqtYouXbqIt99++7LHO3Xq1Hofu/POO0W/fv2EEEJs375d+Pj4iKqqKod9oqKixLvvviuEEOKNN94QUVFR9sfS0tIEAHHs2LF6j6k+N910k3j66aftt8eNGyeuvfZah32GDx8unnvuOSGEEBs3bhSyLIu0tLR6X2/27NnikUcecbhv+/btQpZlUVlZWe9zPvzwQwHA4TPg5eUlpkyZ4rDfY489JqKjo8Vdd90lYmNjHermpZdeEiqVSpw7d85+348//ihkWRY5OTlCiMvXvRBCFBQUCAAiJSVFCCHEqVOnBABx4MABIURNff7000/25/zwww8CgP3Yrr/+erFkyRKH1127dq0IDQ0VQij1p1arRVZWlkM5AYhvvvmmwbIRUcvgOYREbmzChAl4++237be9vLzs14cOHVpn/59++glLly5FamoqSkpKYDabUVVVhYqKCnh6eiI5ORm33377VZXp2LFjmDp1qsN911xzDd58801YLBZ7i9jAgQPtj1d3e+bn5zfrPYUQ9vPmDh48iLKyMgQEBDjsU1lZae/WnDFjBubPn4/du3dj1KhR+OSTTxAXF4e+ffvW+/oWiwVLlizB559/jqysLBiNRhgMBnh6ejrsV/uYACA0NNR+TMnJyejevTuio6PrfY+DBw/i0KFDDi2XQghYrVacOnUK/fr1q/d53t7e2L9/v8N9Hh4eDreXL1+OmJgYfPHFF9i3bx90Op3D4z169EC3bt3st+Pj42G1WpGWllbvuYAnTpzAwoUL8euvv+L8+fP2lsEzZ85cdiBJ7foJDQ0FAOTn56NHjx44ePAgdu7caW8RBJR6r/58Hjt2DGFhYejatatDOYnINRgIidyYl5cXevXq1eBjtWVmZuLmm2/GnDlz8Ne//hWdO3fGjh07MHv2bBiNRnh6etYJEs6k0WgcbkuS1OzRwseOHUPPnj0BAGVlZQgNDcW2bdvq7Ofn5wcACAkJwXXXXYdPP/0Uo0aNwqeffoo5c+Y0+PqvvfYaVq5ciTfffNN+Dua8efNgNBobfUxXqtuysjI8+uijePzxx+s81qNHjwafJ8tyg5+BahkZGcjOzobVakVmZiZiY2Mvu/+V3HLLLQgPD8fq1avRtWtXWK1WxMTE1KmPS9Wun+oAX10/ZWVlWLx4MaZPn17nea09apqIGAiJ2o19+/bBarVixYoVkGXl9ODPP//cYZ+BAwciMTERixcvrvc1tFotLBbLZd+nX79+2Llzp8N9O3fuRHR0tL11sCVt2bIFKSkpePLJJwEAcXFxyM3NhVqtRkRERIPPu/vuu/Hss89i5syZOHnyJGbMmNHgvjt37sTUqVNxzz33AFBCzPHjx9G/f/9Gl3PgwIE4d+4cjh8/Xm8rYVxcHI4ePXrFcNdURqMR99xzD+6880706dMHDz30EFJSUtClSxf7PmfOnEF2dra99W337t2QZRl9+vSp83oXLlxAWloaVq9ejTFjxgBQBsNcrbi4OKSlpTV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Gaussian DevianceMean Squared ErrorMean Absolute ErrorSD(Gaussian Deviance)SD(Mean Squared Error)SD(Mean Absolute Error)
lambda
112.9000755963.5593215963.55932165.930331388.619692388.6196922.530825
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Gaussian DevianceMean Squared ErrorMean Absolute ErrorSD(Gaussian Deviance)SD(Mean Squared Error)SD(Mean Absolute Error)
lambda
112.9000755959.5905375959.59053765.964632339.937340339.9373402.256583
102.8703445954.9779115954.97791165.939026340.046742340.0467422.257719
93.7316265944.1571145944.15711465.882148339.461720339.4617202.255976
85.4047665929.4432265929.44322665.803881338.788360338.7883602.253929
77.8176425911.0159395911.01593965.704940338.092004338.0920042.252222
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190.602131 190.602131 \n", - "0.013599 190.617312 190.617312 \n", - "0.012391 190.626815 190.626815 \n", - "0.011290 190.572242 190.572242 \n", - "\n", - " SD(Mean Absolute Error) \n", - "lambda \n", - "112.900075 2.256583 \n", - "102.870344 2.257719 \n", - "93.731626 2.255976 \n", - "85.404766 2.253929 \n", - "77.817642 2.252222 \n", - "... ... \n", - "0.016380 1.619584 \n", - "0.014925 1.619698 \n", - "0.013599 1.619810 \n", - "0.012391 1.619923 \n", - "0.011290 1.619166 \n", - "\n", - "[100 rows x 6 columns]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "G.cv_scores_" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "d0e09bd4-0875-4701-a16f-19db95b5de0f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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Z5ZzP56OoqCh3/rrrruO2226jsLCQvLw8br75ZpYuXcqSJUsAWL58OdOnT+fqq6/mO9/5DnV1dXzpS1/ik5/8JC6X66S/JyGEEGKo29q0lY8/83Faki1MKZjCA8sf6HNQAti77x5SqcN4veOoHDW0xyq1G7ZhqS9++MMfoqoqV1xxBclkkhUrVvDTn/4097ymaTz66KPceOONLF26FJ/Px7XXXstdd901iLUWQgghhqbXa1/n089/mmg6yvSi6Txw4QN97noDCIc3cvDgbwGYPOn/UNXh0TChWJZlDXYlhrNwOEwwGCQUCsn4JSGEEKesZw48w+de+hxpM82i8kXcc949+J3+Pn+9aSZ5Y/W7iUZ3Ulb6DmbOvGcAa/vWjuX397AdsySEEEKIk+Ph7Q/zmRc+Q9pMc+GYC/npsp8eU1AC2LvvXqLRneh6IZMn/98A1XRgnNLdcEIIIYQ4foZp8L03v8fvt/0egCsnX8mXFn8JTdWO6TrNza/mJqCcOuXrOJ19H+M0FEhYEkIIIUQ3sXSMz730OV44+AIAn57/aa6beR2KohzTdZLJRrZs/QxgMbLiA5SWruj/yg4wCUtCCCGE6KIuWsennvsU25q34VSdfOOsb3DR2GNfu800U2za/ElSqUZ8vklMmvSlAajtwJOwJIQQQoicN+ve5DMvfobmRDOF7kLuPf9e5pTMOa5r7dz1NUKhNTgcAWbP+hmadvJW3OhPEpaEEEIIgWVZ/HnHn/n2G98mY2WYWjiVH533I0b6Rx7X9aqqH+LQoT8CCjOm/xCvd1z/VvgkkrAkhBBCvM0ljSTffP2bPLLrEQAuHncxXz39q3gcnuO6XkPDk+za9Q0AJk64neLi8/qtroNBwpIQQgjxNlYdruYzL36Gbc3bUBWVW+ffyrUzrj3mgdztmltWsWXrrYDFyJEfYvToG/q3woNAwpIQQgjxNvVs1bN8+ZUvE0lHKHAV8K2zv8XpFacf9/VCobVs3HgDppmiuHgZUybfedyhayiRsCSEEEK8zaTNNPeuvZdfb/k1AHNL5vLdc75Lua/8uK8Zjmxm/Yb/xTBiFBacycwZ96IoxzYf01AlYUkIIYR4G6kOV/P5lz/PxsMbAbhm+jXcctot6Kp+3NcMhdaxfsNHyGQiBIMLmD37Z2ja8Fj3rS8kLAkhhBBvA5Zl8ejeR/n6a18nlokR0APcdcZdLBuz7ISu29LyOhs2Xo9hRAkGFzB3zi/QNG8/1XpokLAkhBBCnOIiqQhfe+1rPLHvCQDml87nW2d9ixH+ESd03cbG/7J5y6cwzSSFBWcwe/b9p1xQAglLQgghxCltdd1qvvzqlznUdghN0bhxzo18dNZHj3l9tyMdPPgHduz8CmBSXHQ+M2f+5JTqeutMwpIQQghxCoqlY9yz9h7+uP2PAIz0j+RbZ32LuaVzT+i6lmWyZ+/3OXDgfgAqRvwPU6Z8DVU9dSPFqfvOhBBCiLepdQ3r+NIrX6IqUgXAlZOv5DOnfQa/039C181k2ti67XYaG58GYPy4Wxg79qZTYnqAo5GwJIQQQpwi4pk49627j99u/S0WFqXeUu46/S7OGHnGiV87XsWGjR8jGt2JojiZNvXrjBhxRT/UundWJkPjffdReM01OAoKBvS1jkbCkhBCCHEKWFmzkq+t+hoH2w4C8O6J7+b2hbeT58w74Ws3Nb3M5i23kMm04nSWMHvWzwgG553wdY/GMk1qPvd5wo89RnTlSsb+6U8oqjqgr9kbCUtCCCHEMNaSaOG7q7/Lf/b+B4Aybxl3Lr2Ts0edfcLXNs0M+/bdw/4DPwMs8vLmMGvWT3G7jn/yyr5q+uUvCT/2GOg6xTfcMGhBCSQsCSGEEMNS+7xJ31n9HVqTrSgofGDqB/jU/E/h030nfP1ksp7NW26ltfV1AEaO/CCTJn7ppNzxlmlp4fBPfwZA+Z1fJnDBBQP+mkcjYUkIIYQYZva27uWbb3yT12vtIDOpYBJfWfoVZpfM7pfrNzY+w7btXySdbkbTfEyd+g3Ky97ZL9fui+aHfo0Vj+OaPo38K688aa/bGwlLQgghxDARTUe5f8P9/H7r78lYGZyqkxvn3si1M649oeVK2mUyEXbu+jq1tX8DwO+fxqyZP8brHXfC1+4ro7WVlj/8AYCST3xiSNxpJ2FJCCGEGOIsy+LxfY/z/Te/T2O8EYBzR53LHYvuoDJQ2S+v0dLyOlu33U4icQhQGDP6esaPvwVVPbkTTTb+9KeY0SiuKVPwn3/+SX3t3khYEkIIIYawnS07ufv1u3mz/k0AKgOVfH7R5/tlADdAOt3C7t3foab2LwC43ZVMn/5dCvIX9sv1j0Vi61Za/mBPoll6x+2DOqi7MwlLQgghxBB0OH6Yn6z7Cf/Y/Q9My8Stubl+9vVcO+NaXP0wyNqyLGrr/s7u3d8mnW4GoKLifUya+EUcjhObvPK46pNOU/OlL4FhELj4IvxnnPjcUP1FwpIQQggxhMQzcX675bf8cvMviWfiACwfs5zPLvjsCS98266tbQc7dn6F1tY3APD5JjN1ytfIz1/QL9c/Ho333kty6za0YJDyL35x0OrREwlLQgghxBBgWiaP7X2Me9beQ32sHoDZxbO5feHtJ7yeW7tUqpm9+37IoUMPAyaq6mH8uE9RWfkR1H4YIH68ws88Q9ODvwCg/K67cJSUDFpdeiJhSQghhBhElmWxqnYV96y9h61NWwGo8FVwy2m3cNHYi/rlbjDTTHPw0O/Zt+9eMpkwAKUlFzNx4hfweEae8PVPRHzTZmru+BwABddcTd6K5YNan55IWBJCCCEGyYbGDdyz9h5W160GwKf7uH7W9Xxo+of6bVzS4cP/Zfee7xKL7QHA75/O5ElfoqBg8Qlf/0Qld++m+oYbsOJxfGecQdkddwx2lXokYUkIIYQ4yXa17OLedffyQvULAOiqzvumvI+PzvooRZ6ifnmN1tY32b3n24RCa+3X0AuZMP4zVFS8F0XR+uU1TkRi506qPvK/GC0tuGfOZOQ9P0JxDM1YMjRrJYQQQpyCqiPV/HT9T3ls72NYWKiKymUTLuPjcz5Ohb+iX16jLbqLPXu+x+HD/wVAVd2Mrvxfxoy5AYcj0C+vcaLi69dT9bGPY4ZCuKZNo/LBB9D8J/8OvL6SsCSEEEIMsOpwNQ9uepD/7PkPGSsDwIVjLuSmeTcxPji+X14jFtvPvv0/pq7u34CJomiMGPFexo/7FC5XWb+8Rn8IP/U0NXfcgZVM4p4zm9E//zlafv5gV+uoJCwJIYQQA+RA+AAPbHyAx/Y+hmEZAJxRcQY3z7+ZGUUz+uU14vEq9u2/j7q6f2BlX6OkZDkTxn8Wn29Cv7xGf7BMk8P3/ZTD990HgO+csxn1gx+g+k580d+BJmFJCCGE6Gf7Qvt4cOODPLbvMUzLBOCMkWfw8dkf77dpAOLxg+zffx+1dY9gZVuriorOY/y4T5GX1z8L6vaXTEsLNZ/7HNGXXgag4OqrKfvcHUN2jNKRhkcthRBCiGFgd8tuHtz0IE/ufzIXks4edTYfn/1xZpXM6pfXiEb3cODA/dTV/zsXkgoLz2L8uFsIBuf2y2v0p+hrr1Hzuc+Tqa9Hcbko/8pXyH/Puwe7WsdEwpIQQghxgjY2buQXm37B89XP586dO+pcPj7n48wo7p/utnBkM/v3/4zGxqcAC4DCgjMYN+5Tgzrzdm/MRILGH91D869/DYBz3DhG/uhHuKdMHtyKHQcJS0IIIcRxaJ9M8pebfskbdfayIQoKy8Ys46OzPsr0oun98hqtrW9w4MD9NDW/lDtfUnwhY8beSDBvzgm/xkCIrVlD7Rf/H6kDBwDIf//7KLvjDlSvd5BrdnwkLAkhhBDHwLRMnq16ll9s+kVuxm2H4uAdE97BR2Z+pF/ubjPNNA0NT1BV/Usikc3ZsyrlZe9kzJiP4fdPOeHXGAiZlhYaf/ADWv/6NwAcpaWU3/VVAueeO7gVO0ESloQQQog+iGfi/GfPf/jd1t+xP7wfALfm5srJV3LN9Gv6ZZHbTCbCoZqHqa7+DclkLQCq6mJE+eWMHn09Xu+YE36NgWAZBqF//IOG7/8Ao6UFgOCVV1B2xx1oeXmDXLsTJ2FJCCGEOIrD8cP8afuf+MuOv9CabAUg4Azwgakf4KppV1HoLjzh14jHqzl48HccqvkzhtEGgK4XUTnqakaO/CBOZ//M6j0QYmvWUP+Nb5LYareyuSZNpPwrX8F72mmDXLP+I2FJCCGE6MHOlp38buvveGzvY6TNNAAj/SP50LQP8Z5J78Gnn9j8QJZl0dKyiuqDv+Hw4WdpH7Tt801idOX/UlZ2GVo/rA83UJJ799L4wx8SeSY7U3ggQPEnPkHhh65C0fVBrl3/OqGwlE6nqaurIxaLUVJSQmHhiadrIYQQYrCYlsnKmpX8buvvWFmzMnd+Tskcrpl+DeePPh+HemLtDIYRo67uX1Qf/A3R6K7c+cLCs6is/DBFheegKMoJvcZAStfWcvhn99P697+DYYCqkn/FFZTc8mkcRUO3BexEHPN/8Ugkwu9//3sefvhh3njjDVKpFJZloSgKo0aNYvny5dxwww0sXLhwIOorhBBC9LtwKsy/d/+bh3c8zIGwfQeXqqhcMPoCrpl+Tb9MJBmL7edQzZ+oqfkrmUwIAE3zMqL8CkaNunpIzbbdk3RDA00PPEjrn/+MlbZb2vznn0/pbbfimjhxkGs3sI4pLP3gBz/gG9/4BhMmTOCd73wnX/ziF6moqMDj8dDc3MzmzZt5+eWXWb58OYsXL+bHP/4xkyZNGqi6CyGEECdkZ8tOHt7+MI/ufZR4Jg6AX/fz7onv5qppVzEqMOqErm+aGQ43PcuhQ3+iufnl3HmPezSjKq+hYsSVQ2Zx296k6+po+uWvaP3LX7CSSQC8CxZQcustp9S4pKNRLMuy+lr4Ax/4AF/60peYMePoE2wlk0keeughnE4n//u//3vClRzKwuEwwWCQUChE3ikw4l8IIU51aTPNc1XP8aftf2JN/Zrc+Yn5E/nA1A/wjvHvwKuf2HxAiWQdNTV/oabmzySTddmzCkVFZzNy5FUUF52Lomgn9BoDLVVVRdODv6D1n/+EbEuSZ+5cSj79KbxLlgzprsK+OJbf38cUlkR3EpaEEGJ4qG2r5ZHdj/DIzkdoiDcAoCka548+nw9M/QALyhacUACwLIPm5lc5VPMwhw//N7eora4XUjHivYwc+X48ntH98l4GUnzjRpp+9RCRp58G016yxbtwIUUf/xi+008f9iGp3bH8/pa74YQQQpyy0maalw6+xN92/o1XD72K1b5MiLuQKydfyXsnv5dyX/kJvUYiUUNN7d+prfkLiWRN7nx+cCEjR36Q0tIVqOrQvasN7HmS2l58kaZf/Yr4mx2tbb6zz6L44x/HO3/+INZu8B1TWHr88ce58cYbiUQizJs3j9tvv52LLrqIu+66i9dff50zzzyT6667jtLS0oGqrxBCCPGWDkYO8siuR/jn7n/SGG/MnV9UvogrJl3BsjHLcGrO476+aaZpanqeQzV/pqnpJcBugXE4gpSXX8bIivcP2Vm2OzPCYVr//ggtf/gD6YMH7ZO6TvAd76Dwwx8eluu4DYRj6oabPn06K1as4NJLL+XJJ5/kJz/5Ce985zt54oknuPrqq9mwYQMHDhzg+eefZ/Lkt8c/sHTDCSHE0JA20jxf/Tx/2/k3VtWuyp0vdBdy2cTLuGLSFYzJO7EZsKPRPdTWPUJt7d9JpTpCWH7+YkZWvI+SkhVomvuEXuNkSOzYQcuf/kToX//GitsD29VgkIL/eS8FH/oQelnZINdw4A3YmCWfz8eWLVsYO3YsAL/4xS/42Mc+xg9+8AM+/elPA3DLLbdw6NAh/vrXvx7/OxhGJCwJIcTgsSyLrc1b+dfuf/H4vscJJUO5506vOJ0rJl3BeZXnoWvHP0liOt1Kff2j1NY9Qji8IXde14uoGHEFFRX/g9c77oTex8lgJhKEn3yS1of/THz9+tx51+TJFFz9IYLveAeqxzN4FTzJBmzM0tixY3njjTdyYemqq67ihhtu4IwzzsiV+cQnPsE555xz7LUWQggh+qgx1shjex/jX3v+xe7W3bnzJZ4S3j3x3bxn0nuoDFQe9/VNM01T80vU1j7C4cPPYVkpABRFo6jwHEaMuILi4vNR1ePvyjtZEjt2Enrk77T+81+YoWyYdDgIXHABBVd9EO/ChafMoO2Bckxh6fbbb+e6665j+/btXHzxxcyZM4dXXnmFqVOn5srEYjGi0Wi/V1QIIcTbW9JI8kL1C/xr9794teZVTMseJ+RUnVww+gIum3gZS0YsQVOP75Z8y7IIhzdQX/8f6ur/TTrdnHvO75/OiPL3UFb+LlzO4v54OwPKCIcJP/YYrX9/hMTmzbnzekUF+f/zP+RfcTmOkpJBrOHwckxh6cMf/jCBQIAf/vCH3HXXXWiaxtSpU5k/fz7z589n2rRp3HXXXSxdunSg6ptz991388gjj7B9+3Y8Hg+nn3463/72t5kypWNAXSKR4DOf+QwPP/wwyWSSFStW8NOf/pSyTn2xVVVV3HjjjTz//PP4/X6uvfZa7r77bhwOuVFQCCEGm2mZrK1fy+P7HufJ/U8SSUVyz80pmcNlEy9jxdgV5DmPfxhEW9tO6uv/Q339o8QTVbnzTmcx5WWXUT7icgL+qUe5wtBgZTJEV71G6F//IvLMM7kJJNF1AueeS/6VV+A780wUbWjP7zQUHfc8S21tbWzYsIH169fnti1btpBIJKioqGDx4sXMnj2b2bNn8573vKe/681FF13E+9//fhYuXEgmk+GLX/wimzdvZuvWrfh89uKGN954I4899hi//vWvCQaD3HTTTaiqyquvvgqAYRjMnTuX8vJyvvvd71JbW8s111zD9ddfzze/+c0+1UPGLAkhRP+yLIudLTt5bN9jPLHvCeqidbnnyrxlvGvCu3jnhHcyLnj844Ti8Wrq6x+lvv4/tEV35M5rmpfi4mWUl72LwsKzUE9wHbiBZlkWye3bCf3r34QeexSj8XDuOdekSeRfeQV573wnjmG6duvBrZvZ/eYqysZNZNpZ5/XrtQdtUkrDMNi+fXuXALVx40bq6+v76yV61djYSGlpKS+++CJnn302oVCIkpIS/vjHP3LllVcCsH37dqZNm8aqVatYsmQJTzzxBO94xzuoqanJtTbdf//9fO5zn6OxsRGn8637oiUsCSFE/zgYOcgT+57gsb2PsSe0J3fer/tZNmYZl4y7hEXli467my2RqKGh4UnqGx4nHF6XO68oOkVF51Be9k6Ki89H005s9u6TIXXwEOEnHif87/+Q3NWxGK+Wn0/eJZcQfM+7cc+cOezHIq157F+88NsHmXrGOVz6qdv79doDNsC7qqqK0aN7n31U0zRmzJjBjBkzuOqqqzh48CCjRp3Yujp9FcoOWivMpuc1a9aQTqdZtmxZrszUqVMZPXp0LiytWrWKWbNmdemWW7FiBTfeeCNbtmxh3rx53V4nmUySbG/axP7HFkIIcXwOxw/zzIFneGzvY2xo7HSnmapzzqhzuGT8JZw18izcjuO7HT8er6Kh4UkaGp/scicbqBQULKG87J2UlKxA14Mn+E4GXrq+gciTTxB+/AniGzrei6Lr+M8/n+Bl78J/5pkoffhDf7gwTXsWdHWQuw6PKSwtXLiQd7/73Xz0ox9l4cKFPZYJhUL85S9/4Z577uGGG27gU5/6VL9U9GhM0+SWW27hjDPOYObMmQDU1dXhdDrJz8/vUrasrIy6urpcmbIj5pJof9xe5kh33303X/3qV/v5HQghxNtHe0B6ev/TrKlfk5tVW1VUFpUv4pJxl3DBmAuOexxSLLaPhoYnaGh4kkjblk7PKOTnL6K0ZAWlpZfgcg39Ac6ZxkYi//0v4SeeJLZ6NbR3BikK3sWLybvkYvIuugjtFO3ZMI1hGJa2bt3KN77xDS688ELcbjennXYaFRUVuN1uWlpa2Lp1K1u2bGH+/Pl85zvf4ZJLLhmoenfxyU9+ks2bN/PKK68M+Gt94Qtf4Lbbbss9DofDVFYe/+2pQgjxdtAYa7QD0oGnWVu/NheQAGYVz+LicRdz0diLKPEee4CxLItIZBONh5/lcOMzXcYgKYpGfv5iSksvpqT4wmERkNJ1dUSefobw008RX7O2IyABnnnzyLvkEgIrlqO/DVbLMI0MAOpxdr32l2MKS0VFRfzgBz/gG9/4Bo899hivvPIKBw4cIB6PU1xczFVXXcWKFStyrTsnw0033cSjjz7KSy+91KXLr7y8nFQqRWtra5fWpfr6esrLy3Nl3njjjS7Xax9f1V7mSC6XC5draK/xI4QQQ0FDrCHXgrSuYV2XgDS7eDbLxy7nwjEXUuGvOOZrm2aSlpbXaTz8Xw4ffpZksqM3QFEcFBacTmnpxRQXL8PpHPqDm1MHDhD577NEnn66SxcbgHv2bPJWLCfvoovQR44cpBoODtOwp4cY7Dv4jmuYv8fj4corr8wNnB4MlmVx8803849//IMXXniBceO63hVx2mmnoes6zz77LFdccQUAO3bsoKqqKje1wdKlS/nGN75BQ0NDbj27Z555hry8PKZPn35y35AQQpwCqsJVPFf1HM9VP8f6hvVdA1LJbJaPWc7yMcsZ4R9xzNdOp0M0Nb1A4+H/0tT0EobRlntO07wUFp5NSfEyiovPH/JjkCzTJLF5M5Fnn6PtuWdJ7uqYWBNFwTN/PnnLLyRw4YXoFcceJk8VVnbMkjYcw9JQ8MlPfpI//vGP/Otf/yIQCOTGGAWDQTweD8FgkOuuu47bbruNwsJC8vLyuPnmm1m6dClLliwBYPny5UyfPp2rr76a73znO9TV1fGlL32JT37yk9J6JIQQfdC+3MizB57l+ernu8ymDfZcSMvHLGf52OWU+3pusT/ataOx3TQ1vcDhw88TCq3BsjK5553OEoqLL6CkeBkFBaejaUP7c9tMJom98QaRZ5+l7bnnyTQ0dDzpcOBduIDAsmUEll2IXnbqd7H1hZGx/3sPy5aloeBnP/sZAOeee26X8w899BAf/vCHAfjhD3+IqqpcccUVXSalbKdpGo8++ig33ngjS5cuxefzce2113LXXXedrLchhBDDTtpMs6Z+jd2CVPUc9bGO6WEcioOF5Qs5f/T5nFt57jEHJMNI0NKyiqamFznc9DyJxMEuz/t8kyguXkZJ8TLy8majKGq/vKeBkq5voO3FF2h74UWiq1blFq0FUL1efGefTeCCC/CffRZacGi3hg0GaVk6QX2ZHsrtdnPfffdx33339VpmzJgxPP744/1ZNSGEOOWEU2FW1qzkxeoXeengS4RTHdOmeBwezhx5JuePPp+zRp5F0HVsv/Tj8YN261HT87S0rMI0O6ZnURQnBQWLKS46l6Ki8/B6x/TbexoI7d1rbS/YASmxdWuX5x2lpfjPO4/AsgvwLl6Megrd5j8QjMwwvBtOCCHE24NlWewP7+elgy/x4sEXWVe/jkynLrBCdyHnVp7L+ZXns6RiCa5j6AIzjBgtLa/T1Pwyzc0vE4vt7fK8y1VOcdF5FBWdS2Hh6UN+kshMSwvRV16l7eWXiL78CkZLS8eTioJn9mz8556D/9xzcU2dOuwnijyZ2luWlOF0N5wQQohTV9pI82b9m7x08CVeOvgSVZGqLs+PD47nnFHncE7lOcwtmdvnmbQty6StbRtNza/Q3PQSraE1WFa6UwmVYHA+xUXnUVx8Hj7f5CEdKHKtRy+9TNvLL5HYuKnL7f2q34/vjDPwn3su/rPPwlFUNIi1Hd7aW5a0QV6v9bhfPR6PY1kWXq+d+A8cOMA//vEPpk+fzvLly/utgkIIIQZOY6yRVw69wsuHXmZlzUqi6WjuOV3VWVi+kLNHnc3ZI8+mMq/vc8olk/U0N6+kufllmppfIZ1u6vK82z2SwsKzKCo8m4KCpej60J5UMV1XR/TVlURffZXoqlVdW48A15Qp+M8+C99ZZ+GdNw9F1weppqeW3DxLw7Ub7rLLLuPyyy/n4x//OK2trSxevBhd1zl8+DA/+MEPuPHGG/uznkIIIfpB2kizrmEdr9S8wquHXmVny84uzxe5izh71NmcM+ocllQswaf7+nbddIiW1tdoaV5Fc8sqYrGud8VpmpeCgqUUFp5JUeFZeDxjh3TrkRmLEVu9mujKlbS9+iqp3Xu6PK/6fPhOPx3f2WfhP+ss9F7m5hMnZljO4N3Z2rVr+eEPfwjA3/72N8rKyli3bh1///vfufPOOyUsCSHEEFEdqebVQ6/yas2rvF77OvFMxx1ZCgozimZw5qgzOXfUuUwrmobahzvMDCNOa+ubtLSsorllJZHIFsDsVEIhEJiRbT06i2BwHqo6dAczW+k08U2bib3+GtFVrxFftw4r3amrUFVxz5qJ/4wz8J1+Op45c6T16CQY9mEpFosRCAQAePrpp7n88stRVZUlS5Zw4MCBfqugEEKIYxNLx3iz/s1cQDoQ7vqZXOQu4oyRZ3BGxRksrVhKgbvgLa9pGEnC4Q20tL5OS8sqQqF1WFaqSxmvdwKFBadTULiUgvzF6Hp+f76tfmWZJsnt24mueo3o668Re3MNVizWpYxeUYHvzDPtFqQli9GOWGtUDLyObrhhOmZp4sSJ/POf/+Q973kPTz31FLfeeisADQ0N5J2iC/oJIcRQlDEzbD68mddqX2NVzSo2Nm7scueaQ3Ewt3QuZ4w8gzNHnsnkgslv2XpkGAlC4XW0trxBS+vrhMPrMM2u4cjlGmGHo4KlFBQuxe0aul1RlmmS3LWL2Burib3xBrE33sAIhbqU0fLz8S5Zgm/JYryLF+McO7S7Ct8Oci1LjmHasnTnnXfywQ9+kFtvvZULLrggt4TI008/zbx58/qtgkIIIbqyLIt9oX2sql3Fa7WvsbpudZeB2QAj/SM5o+IMTh95OovLF+N3+o96TcOIEwqtpaX1dVpb3iAU3tCt5cjpLCY/f5E99qhg6ZAed2QZBolt24mtXm1va9ZgHhGOVJ8P78KFeJcsxrdkCa7Jk1HUoT3J5dtN+wzew2oh3c4WLVrE/v37qa+vZ86cObnzF1xwAZdffnm/VE4IIYStPlrPG3Vv8Frta7xW8xoN8YYuzwddQRaXL2ZJxRKWjFhCZeDod66l062EQmtpbV1Na+hNwuFNR9zODy5nGfkFiyjIX0x+/mK83nFDNhyZqRSJLVuIr1lDdPVq4mvWYra1dSmjeL14583Du3AhviWLcc+ciTLIt6SLo2ufZ0kdrlMHjBs3jtra2m6tSBMmTKC0tBQj23QmhBDi2B2OH2Z13WreqHuD1XWru407cqpO5pfNZ8mIJSytWMrUwqlH7VqLxw8RCr2ZC0fR6K5uZVyuERTkL6agYDH5+YvweMYM2XBkhMPE160jtmYt8bVriW/ahJVMdimj+v14TpuPb+FCvAsX4p4+XQZlDzO5eZaG6wDv3pYbaWtrw+12H3eFhBDi7ag50cybdW/mwtHeUNdZrRUUphVNY+mIpSypWMLckrm4HT1/1lqWQVt0F6HWN2kNvUlr65skk7Xdynm948kPLiA/fwH5+QtxuyuHZDiyLIv0oRo7HK1dQ3zNWpK7dnWZCBJAKyjAM38+3gUL7HA0beqgL8AqToyZGaYDvG+77TYAFEXhzjvvzE1KCWAYBq+//jpz587ttwoKIcSpqDXRypr6NbxR9wZv1L3B7tau8xIpKEwpnMKCsgUsKl/EaeWnkefs+eaZdDpMOLyeUGgtodA6QuH1GMYRXVCKg0BgRi4cBYOn4XQOzZmlzUSCxObNxNevJ7Z+PfENGzAaD3cr5xwzxg5Hp83HM/80nOOG7hgqcXxipkWyuIKaw01MHMR6HHNYWrduHWAn/U2bNuHstAig0+lkzpw5fPazn+2/GgohxCngcPwwb9a/yZq6NbxZ/2a3cAQwMX8ii8oXsah8EQvKF/S4IK1lWcRi++xgFF5LKLSWaHQ3cEQri+YlmDePYP5C8oOnEQzOHZJrrNmtRoeIr99AfP164uvXk9i+HTKZrgUdDtzTpuGdPx/PafPxzp+Po7h4cCotTpoYKqmSCvYePMTZg1iPYw5Lzz//PAAf+chHuOeee2SaACGE6EFtW60djurXsKZ+DfvD+7uVGR8cz8LyhblwVOgu7FYmk4kQCm8gHFpPKLyeUGgdmUxrt3Iez2iCefMJBucTDM7D55uMqg69wctGayvxTZuJb9pIYuMm4ps2YTQ1dSvnKCnBM3dudpuDe8YMVBni8baTxm4pDPj7NpP8QDnun6SHHnqoP+shhBDDlmVZ7AvvY33DetbUr+HNujepidZ0KdPerXZa2WksKFvA/LL53cKRaWaIRncSCq/PhqMNxGJ7OLLVSFVd5AVmEwzOIxicT15wHi7n0GtlMZNJElu3kti0ifjGTcQ3bSR9oKp7wWyrUXsw8s6di6OiQrrUBGnFHnOWFxjchpkT+rPj2Wef5dlnn6WhoQHTNLs896tf/eqEKiaEEENVykixpWkL6xrWsa5hHesb1tOabO1SRlM0phdNZ0HZAk4rO425pXO7dKtZlkUiUUs4vIFQeB3h0AbCkc2YZpwjud2VBPPmkBecS37wNPz+qUNu6RAzlSK5YyeJLVtIbNlMfMsWkjt3de9Owx5r5J49G8+sWXhmz8I1bRqqyzUItRZDXTo7QL+wIH9Q63HcYemrX/0qd911FwsWLGDEiBHyF4AQ4pTVkmhhfcN61jXawWjz4c2kzSPmJNJczCiakWs5mls6F6/eMUYonW6lqellwuENhCObCIc3kko1HPlSaJo/F4yCeXPJy5uNc4i1GlmpFIldu0hs3mKHo82bSezaBel0t7JaURGe2bPxzJ6Fe9ZsPDNnyLIhok8SiQSGZk/1UFw0uDcjHHdYuv/++/n1r3/N1Vdf3Z/1EUKIQWVaJnta97CxcSMbGjewvnE9+0L7upUrdBcyr3Qe80rnMbd0LtMLp6NnP9gNI04ksoXD4Y2EIxsJhzcQj3fvflIUDb9vKnnBOeTlzSGYNxevdzxKHxayPVnMWIzEjh0ktm0juW0bia3bSO7c2XWR2SwtGMQ9cybuGTNwz5yBZ8YM6U4Tx62mxu7KVtJJ/P7AoNbluMNSKpXi9NNP78+6CCHESdeaaGXjYTsYbWzcyObDm2lLt3UrNy44jvml85lbOpd5pfMYHRiNoigYRpK26Hbqa/9CJLKZcGQj0eguLKv7xLwezxjy8uaQlzebvMAsAoEZaJrnZLzNPjFaW0ls22aPM9q6jcS2baT274cjhlkAqMEgnhkzssHIDkj6SAlGw5Fpmrktk8mQTqe77du3TCaT2wzDyO07X8OyrC5zMaqqmtscDgdOpxOn04nb7cbn8xEIBMjLy0M/YsLQ3bvtO0a1WNvwXRvuox/9KH/84x/58pe/3J/1EUKIAZMxM+xq2ZVrNdp4eGO3mbEBPA4PM4tnMrt4NnNK5jCvdB757nwMI0Fb23Yi4ZfZdmgzkcjmbDDqYVyOs9QORXmzyQvMJi9vFrqefxLe5VuzTJN0dTWJ7TtI7thOYsdOEtu2kqnpPnElgFZSjHvaNNzTptv7mTPQR46UYDTATNMklUqRTCZJJpO541Qq1eU4nU7n9u3HPYWe9mBz5DZUFBQUMGLECMaMGcPYsWPZsGEDAI5IC9pwXe4kkUjwwAMP8N///pfZs2d3S4Q/+MEPTrhyQghxvCzL4mDkIJubNrPp8CY2H97MtqZtJIxEt7Jj88Yyu8QORnNK5jAhfwKKlaKtbTvhyGZq9j7BjshmorHdPbYY6XohgcAM8gIzCeTNIi9vDm5X+cl4m2/JaIuS3LnTDkXbd5DcsYPkzp2YsViP5fXRo7PBaBru6fbeUVJykmt9akmlUsRisdwWj8d73RKJRC4cJZNJLCCjaqQ1Bxktu1c10ppGJnucUTUMVcXI7jOqhqk6MFUnpkvBdKtYioKZC7dK7v5KBQvFslCtjr2Gha6AQ1FwKQouBdyqgltR8CngV8GvQJ6qkKcq6JqGpmm51qMjQ7RlWZimmWuJSqfTJJNJ4vE40WiUSCRCOp2mpaWFlpYWtm7d2vHFpoEjEkJzDO4yNccdljZu3JibqXvz5s1dnpO/NoQQJ9vh+GG2HN6SC0abmzYTSoa6lQvoAWaVzGJOyRxml8xmVvEsPIpBpG0rbZEtRGp+yuqd24jF9nLkLftgB6O8wEwCgZkE8maSF5iFyzX4N7lYhkHqQBXJXbvscLRzJ4kdO0hX9XCrPqA4nbgmTcI1dQruKVNxT5uKa+pUtMDgjg0ZiizLIp1Ok0gkcoGmfUsmk10et2/tQSAWi5EwTRIOJwndSVJ3knQ4STp0e9N1kppOSs8jmV9ESnOQ1hykHLp97HBgDaExbEdSgAJdo9ypU+F2UuHSGetxMc7jZKLXzTiPC4d69J8Ny7KIRqM0NDRw6NAh9u3bR3V1tb0e3J6tKFiDvpCuYvW2yJvok3A4TDAYJBQKyQSdQpwkoWSIbc3b2Nq01Q5GhzdTG+3ehaSrOlMLpzKzeCazimcxvWg65U6daHQbkchW2iJbibRtJZms6/F1nM4SAoHpBAIzcwFpsIORZVlkGhq7hKLkzp0k9+zptpBsO0dpaS4UuaZMwT11Cs6xY1EG+RfQyWaaJolEotfWnc7njwxG7dPjZFSVuO4ioTuzW8dx3GGHoc7nEg4nRj+tT+fVVLyqikdTuxy7VQW3quJSFVzZvVNV0BQFXVFwZDdNsef7UhRo/w42LTCwyFgWpgUZyyJtWqSy+6RlEjdM4oZF1DBoM0xCGYNwxiCUeesuPJeqMNXnZl6ej4V5Xk4v8DPC9dbTXliWRTqV5MfXXAnAzb/+C05P/85Afyy/v99ePylCiGGnPRhtObyFrU1b2dq0lYNtB7uVU1AYHxzPzOKZ9lY4mQod4vFdtLVtpy30Rw4e2s7+TLjH1/F4xhAIzCDgn4Y/MJ2AfwYu1+B2P2VaWkjt3k1y926Su3bnApIR6t5iBqC43bgmTsQ1eTKuyZNwT5mCa+pUHAUFJ7nmA8+yLJLJJLFYLNeC075vDz2du77azx3JUBQ7/DhdxHUXsew+7son4S/NBaO47iLudJE5zgVdHQoU6A7yHRoFuoOgQ+vY9I7jPIdGQNPwOzT8mkrAoRHQ7FCkDrFem7Rp0ZLOcDidoS6ZpiaZ5mAixf54kr3xJLuiSeKmyYZInA2ROL8+ZH/dVJ+bFcVB3l2azzR/zzc4KIqC1Wk8lTpcu+EAXn75ZX7+85+zZ88e/va3vzFy5Eh+97vfMW7cOM4888z+qqMQ4m2ir8EIYKR/JNOLpjOjcDozCyoZqZtkEvuJtG2jrfVX1Nfso57ud3Epio7PN8luMfJPwx+YQcA/FYdj8LqfjFCoIxDt7tiMw90XjwVAVXGOGZMLRa5Jk3BPnoxeWYnST60YgyGVShGNRo+6dQ5FvQ1ONhSVmDMbfJxu4rqfWEkRcaebmO4i4faQcLqJ607ixxF+dEWhQNco0h0U5jaNQt1BkdNBgcM+LtAdFGTP+7XuY3mGO11VKHXplLp0pvcQekzL4kA8xca2GGtDMV4LtbEpEmd7NMH2aIJ7DtQzL+Dl+soS3lWS3627zug0oak2yN/Xxx2W/v73v3P11Vdz1VVXsW7dOpLZ5t9QKMQ3v/lNHn/88X6rpBDi1GJZFg2xBrY3b2dr81a2N21ne/P2bkuEtGsPRjMLJzHZ56fcYWAkq4i27aAt9BJth1vY0cPX6Xohfv9UOxT5p+L3T8Xnm4CqnvzZoi3LwmhuJrlnD6m9e0nu3kNq7x6Su3aTaWzs9ev0igqckybaLUYTJ9nhaMKEYbFOmj1LeYK2tjai0Wi3/ZHH6R7mbmpnAgndRczpJub0ECsuIO50kXB7SXm8xF0eYrqTiKYTV4/tF6umQJHuoMTpoFjXKXE6KMoGnyL9yGO79edUCz4DQVUUxnldjPO6uKzUbt1sSWd4rinMfxpbebYpwrpIjE9sPcAPvHV8deJILijq6A4zs2FJ1TQUdXDHbR13WPr617/O/fffzzXXXMPDDz+cO3/GGWfw9a9/vV8qJ4QY/kzLpCpcxfbm7Wxr3sb2ZjsYNSeaeyw/0j+SGYVTmZM/gnEeJ0VqgnRiP9G2dcQP/4vkYTjyZn9F0fB6x2cD0TQC2WDkdJae9F9qlmWRqa0luWevHYZ27yG5dy+pPXswWlt7/TpHxQg7EE3IBqNJE3GOn4A2yAuI9iSdTtPW1kZbWxuRSCR33NN25FJYR8ooKjGXm5gnQCzoJuH2kPYFSHp8xF1u2nQnYdVBRFEx6ft/S11RKHE6spsdgEp0B6UunWLdQXH2fHG29WeodXGdqgp0B1eUF3JFeSGNqTS/q2niFwcb2R1LctXGvbyvvJC7J4/Cq6m5liX1OLs++9Nx12DHjh2cffbZ3c4Hg0Faj/KBIIQ4dcXSMXa37mZHyw52NO9gZ8tOdjTvIJbpfpu6pmiMC45lbsFopvoDVDgVAlaYZHwfsdhTmC0pUi1w5LBtp7MEv28Kfv8UfP7J+H1T8Pkmo2knt7XITCZJHThAau8+Uvv2kty3zz7eu7fX2/JRFPSRI3FOGG+HognjcU2ciHPCxCERipLJZC78RCKRbsftj5O9DCRvZwFpzUHM5SXqdJP2+cn48+wA5PbQlm0BakUlegwBSAGKnQ5KnQ5KswGo1Kl3eVySfRyU1p8hr8Spc9vYcq4fVcL399fxQHUjf65rZkc0wR/njIeM3dKo6cM4LJWXl7N7927Gjh3b5fwrr7zC+PHjT7ReQoghzLIs6qJ1uVC0o8UORlXhKqwebrd3aU7mFoxldrCYsW4nRVoSR6aBeGwvRmYTtEIKaOr0NZrmxeebjN83ORuMpuD3TcHpLDxZb9PuOmtqsluG9u23u8/226EoffAg9HYzscNhjykaPx7nxAm4xk/ANWE8znHjUD0nf8bu9hDUWwBqP5dKpY56HQtIaTpRb4Ckx4sRyCftD5Bwe4m53EQcTkKKRgsKiWO4z9qtKpQ6dcqcOqUuR/Y4G4RcHcdFuuMtb0MXw0/AofGViSO5sCiP67fsZ30kxjUb9/LzYntQ92DPsQQnEJauv/56Pv3pT/OrX/0KRVGoqalh1apVfPazn5VZvYU4hbS3Fu1q2cWu1l25FqNwqqe7yizGevOZl1/ORK+PMt3AZ4XIJKoxjPV2IkpB52khFUXH5x2PzzcJn28Sfr/dauR2jzppa6SZ0ajdSrR/P8n9+0nt309qv/3YDPd89xyAGgjgHD8O19hxOMePxzluLK4JE3COHo2iD+wHfPvdYD21/BwZhI42FgjaQ5CDmNdPyhvAyAuS8gVIeHzEnG4iDp1WRaPZguRbhaBOz/s1lTKnTll74HHZgajM6aDMpedCkYwBEgBnFAT4x7xJvGvtLt4Mx7jHclAGgz7HEpxAWPr85z+PaZpccMEFxGIxzj77bFwuF5/97Ge5+eab+7OOQoiTwDANqiJV7GzZaQejbDg6GDnYrbVIwaLUoTErWMwUfx4VTggQRUnXYhq1QC0kgaS9A3tckcczDr9vEj7/ZDsY+Sbh8YxBVQf+L0crlSJ18FA2CHXdMg0NvX+hoqCPGmUHoXHjO0LR+PFoRUX9/ku+txDUUxDqSwhKaw6iHj8pnx8zL5+UPxuC9K4h6KgtQRZdQlDQoVHqdFDm1CnPhp7y9hYhl055toXIN4zvzBODY4rPzb3TRnPtpn38NpLh/SPGMss8erfvyXDCk1KmUil2795NW1sb06dPx+/391fdhgWZlFIMN5ZlUR+rZ3frbva07smFo72hvSSNrh9KGhYlDosJXh9T/HmMcjkIKnEcmUYsq+cPMDsUjcXnm4jPOyHXWuT1jh3wu9BygejAftJVVdnWogOkqqpI19T0uCBsO62wEOfYsZ22MfZ+zBhU14nXuz0EvdWYoL6EIMi2BDldpHwBjLz27jAfMZebsGaHoBYL4sfwCZ/nUHMBqOyI4FPeKRh5tKE7o7Q4NXx2ezW/r20iGGrm1lf/wce/++N+f41j+f0tM3ifIAlLYqiyLIumRFMuFO1q2cWe1j3sad1DJB3pUtatWJTpJqNcDqb48qhwaeQrcRxGC/QwVxGAojjxecfh9U3MdqFNxOebiNczBlV96xl6j5eZSJA+eJBUVTWpqgN2KOpjIFK8Xnss0bixXYPRmDFoweBx1ceyLOLxeK8hqPO5TKb7grtHag9BaX8eRsDuDkt6fERdbto0nVZVo9mE2DF8cgc0tVMLkJ4NRI4uj8tcOl4JQWKIiGQMTn9lI42WwsSmGh67bDnBfh7oPWAzeN9222187Wtfw+fzcdtttx21rCykK8TJYVkWzYlm9ob2sqd1Ty4c7W7dTWuyNVdOwaJAsxilW5S7YbzXR4VTtVuKrPa7t5JAtEs+0jQ/Pu94vL4J2ZYiu7XI7a5EVQdmLIERDpOqqiZddcAORdVVpKuqSVVVkamvP+rXtgci55gxOEePzh7be624uM/dZqZp9hiCegpFb7Vye+7uMI+flD+AmZdP2hcg3j4wWtMJ9SUEWUCnl/JnQ1DncUHtj0udOiNc0h0mhqeAQ+NrnjQ3t1rsLqrgvRv28ORpkwdtiodj+qRbt25drnl43bp1A1IhIUTPLMuiMd7IntY9uWDUftw5FLkUi1LdZKLDojTPYqzXTbkOfmKonX/Tto8myv5ydjnL8PrG4/NOzO4n4PVNwOUs6/9xOYZBpq6OVPVB0ger7X11NamDB0kfONDrch7tVL8f5+jR6KNHH3MgMk0zt9L50VqC+jJHkAWkHDpRrxcjkEcmECTts+8Oizpd2bvDVJrMPowJ6iEElea6vuxusLJcq5AdjHwOCUHi1DXdSvGBf/2G/7zzWm6bOW5Q58I6prD0/PPP93gshOg/hmlQ01bDvvA+9rbuZW9oL/tC+9gT2kMkZXefaVgUOyxKHCbznBalXotRbp0SzcCtHHn7d8f4F0Vx4vWOweudYHehecfj9Y7H55vQ78t9GKEQ6UOHOgWiatLVB0kdrCZdUwtvMS5HKy7GWVmJc3RlRyjKBiQtP79bIDIMww46hw71Gn4ikQjRaJS3Gn1gAQndSdTjxggEMfKCpL0B4m6PHYKycwS1vNXdYUdkrSO7w0o7tQSVdbpTTEKQEGCk05Q11fLltU9z0cWDu4Tacbeh33333ZSVlfG///u/Xc7/6le/orGxkc997nMnXDkhTmWJTIID4QPsC+3LBaK9ob0cCB8gaSRz3WYlDosS3WSZ16I0aDFCVwlq6R6m8usYD+N0ltghKBuGvN5x+HwTcLtHoij984vYjMftMHTwIOmDh0gfPEj60EFS2WMzEjn6BXQd58iR6JWVOCtHoY+qRB810g5EoypzkzSm02kikQih9tCzc2ePrUGx3iaC7MRQFHt9MJcbM5BPJpBH0usj4fISddozRbei0mJ2aejp4c13fZjv0LJ3gjm6dIl1bgkqdcqYICGORSY7KaVnCPzxcNxh6ec//zl//OMfu52fMWMG73//+yUsCYHddXY4fpj94f3sC+1jX2hf7rimrQYwCbYHIofJVIfFWQUWpQ675UhTemq2sH+Na5oPr3ecvXmye+9YvN5x/dJKZCYSpGtqSR861GVLHbLDkdHU9JbX0IqKcI4ahV5ZiV45Cucoe6+PGoURDNIWi9HW1kZD5+CzZQuR117LPe7LbNEpzUHc4yfu9mAGgmT8eSQ8PhKdlstoRSF09F61biGoSHd0mROovTWoy9xBTh23hCAh+p2Rtv8AHNaTUtbV1TFixIhu50tKSqitPXKBAiFObYlMgqpIFftD+3NhqP04mo4Q1Ozw0951NsllUeIzKdYtnEfphlcUJx5PZS4EdYSi8TidfR+s3BMzHiddU2NvuTBkH6dqDmE09rLifSdqIGDPQTRqJHrFSBwjR5IuKyVVUEDC6yWaTlPXae2wSGsLbQer+3RnmKEoxHUXMX8+SY8XMxAk7fOTcHvtNcM0JyFVpcVS3nqixE4hyKGQXSKjoyWo83FJtiusxKmjy2zRQgwaI92+3MkwDkuVlZW8+uqrjBs3rsv5V199lYqKihOumBBDjWEa1EZrORA+wP7wfvaH9ueO66I1FOQCkUmxw2Kxw+LSIvtYP2ogcuB2j7IDkWcsnuze6x2L211xXN1mlmVhtLbmwlCmPRTV1NitRTU1GC0tb3kd1etFHzUKpaKCdEUFqZISksEgCZ+XuNNJLNtFlls4dd9erL17eq8XkHTo9qrxfhdpb4CMP0Da6yfudhPT7fFAIVRCfbk1vlMICmhqdnkMR8fSGZ1mjS7NBqBCWTRViGEhk7bHXw7rlqXrr7+eW265hXQ6zfnnnw/As88+yx133MFnPvOZfqugECdT526zqnAVByIHOBA6QFWkiprwAQJqkqJs61CRw2KWw+S8gEVhgYWjT4FoDB7PGDsUeUbj9Y7D7R55zDNYW6kU6YYGOwjV1pKurbVDUK0dhNK1tVhvMYbHAjIFBWQqK0mPKCdZVEwyL4+Ex03M4SBuWbQlErS1tZFIJMA0oL7O3o64Tjo7N1A8r5CY7sLw55H2+Ul6vMSdbqIOJyFVI2QpvOVMQ51CkkMht2J8+4KpZUc+dtmtQTIeSIhTS3vLksM5jMPS7bffTlNTE5/4xCdyiy+63W4+97nP8YUvfKHfKihEf7Msi5Zkix2Gwgc4ELbDUFX4AA2R/XiVKMWaRVG2lWi6w+Isj0XQb3G0XhlF0e0A5BmDp1Mo8nrH4HJV9HlOIsuyMJqb7fBTV0umto50XR3p2hoy2UCUaWzsdRFXQ1VJuN0kCgtJlZWSKi0lWVBA0u8n7nIRV1VihkFbItH11njTgNYWaLUfplXNDkBOLzF/AUmXh4w/QMrjI+HyEM2uHB9GIdmXleM7VbfAoeVWhy91dYSfEqdDWoGEEAAYp0LLkqIofPvb3+bLX/4y27Ztw+PxMGnSJFz9sCyAECeqcyCqitihqDpcTVVkH5HoAdxWG8XZ1qEih8VMh8m5HguP7+jX1TRfrlXI4xmdDUV2QHK5yt6yy8yyLMxIhHRtHZn6uq6BqLaWdF0dmbo6rCNWf7eAlNNJ3OOxg9CY0SR8flJFhXa3mMdDXNeJAcleJki0MhnSFvY4IKebeGGQuO4i5fWT8frsleN1F1GHTkhR+xaAOvFrai70FB8Rfkqy4afU6aDY6cClSiuQEOLojMwpMGapnd/vZ+HChf1RFyGOSftyHu2BqCpstw7VR/YRj1fhIUqxw6IoG4oWOCwu9Fio3qNf1+kszQag0R2hyFOJxzMGXS/sdVC1ZVn23EJ19XYQag9E7fu6etJ1dV26x9IOhx1+2jePm8SUycQ9HpJ5eSR9PuIuFwlV7XHRkfYxQHGni5juJu50EtfdJFxuuxvM7SGuu4k6dMKqRk8TDhyNR20PQI5cd1jn4NMeiIqdMku0EKJ/ZbJ3wzmcA7d8Ul/JcidiSDNMg/pYPdWRaqoiVVRHqjkU2k8oupdk8iB+JUGRw6JQs7vMJjgs3D7gKC1EiuLE7Rlld5d5RuHxjOkIRe5RaJqn29dYponR0kKibiuZejv0ZOobyNTVka6vz+2teLyjG6w9/GSP4wUFJEeUE3d7SPq8JFwuMj0EDENRSOgu4k4Xcd2VbQmyH6c9PjsAOe0A1KZoGMfYTeXVVEr0jtDTHnbag4/9nB2GZHJEIcRgGbbdcJ2XO1m7dm2vf2H399II4tSWNtIcajuUC0PV4QMcbttNW3Q/ZrqBoJrOdZeN0SzmOCw7DB0lEGl6IV7PaHzesXjc7S1D9t7pLEFROrqBzFSKTEMDmQP1tNU9Z4eg+nrSDfW541RjI3FN694K5Mrux48jMX0aCbeb9BF/BeXmAWoPP0fsk24PSZeHRDYAxdRjDyhBh2aHHt0OPl1agfSOQCQtQEKI4SKTHY4w7FqW7rnnntzKvC+88MJA1EecouKZeDYIVVMVPkBtZA8tbbtJJg6iZpopdJgUaSaFDot5juydZf6jXFBx43RXEPCOx+vNhiF3JR5PJW73KDTNbbcGtbbawaeqnkzDXsL1r5FpqCddX0+qoZG21lZiiUTX8NO5RaislMSY0aSOGIuXUdVcq0/c6SKhO7sEoITTTdLtJp4dA2QoxzZGR1PsCRGLs6087UGnpFMY6vzYKWOAxACxLIu0YZE2TDKGRcowSRsmhpk9Z3Y8lzHb91auTMa0cnsz99g84rG9mRaYVvux/bxhZc+3n7PsextMy8KyLCzaj8k+1/3Gh95Wt1EU+497RQGl/TEKava8qihoKqiqgpZ93H7s0BQ0VcGhduwdmtrlnK6pOLJ7TbW/RtdUdE3Fqak4HSouh73v/Nghd3YCneZZGm4tS/PmzaO2tpbS0lLGjx/P6tWrKSoqGqi6iWEmnArbgShSTXVoP42R7USi+0gna3Ca4WzrkEmJw2K0iv3d10sgslBR9WI8ntEEfROy44dG4fGMtsNQykmmoTHXIpRpaCDdsIqm+n/R1tRENBSiLR4j7tBzLUFJt6ujVaiwkOSIEfanIx0TINrdX85cEEpkj9u7xZJONzHdSUo79uF+fk21Q0824BR1DkKdQ5DuoEDuAnvbsSw7iKQyJslMxz6ZMbqcyx0bHY9TGaPjeaPrPp3bWz2cM0kZFqmMkQtEHXsz91icXLqm4NY1PLrWsXdq+JwaXqcDnyu7d2p4XQ7y3A78Lgf+7D7g1slzOwh6dPI8Om59eLYmt8+zNOxalvLz89m3bx+lpaXs37//LVfkFqeW9lXvqyPVHAxXUxPeQUvbLmLxKqxUA15iue6ysZrFeAXQs1sPTDWA7hpBwDuWfP8kPJ5KXI4R6FE3WrOF0dhEZl8D6fp6Io17qWlZS1s4TDQeJ64o3cYEJdxuUsE8rIL8jnE/ujPb8pM97hR84rqThO4i4XSRPI6/XJyKkgs97fuiTq09xdng0/6cR/5aHNIsy7LDSdoOKInsvj2w5B6nO0JMMmOSSB9xLm2QTJmksyEmlcqGkpRJOmOQztjPZTKW/dgwSWcsMoaJYmVbOHKb0nF8xHPtlB72bxWze4o/OvYvBHen5y0UrOzVLMBS7L2mKiiqgqKBqqkoqoKmqagOIHusORRULXusqV1aYzRFybW0aKqKpvTQeqNiH2fLqor92G7xsf/OUZXsv0+2dajjcff3d+TwECvbGtW5Zcqyn7BbqLAwzJ5bugyTLq1kadPCMLq2muVa2rLHnVvaUkeG3exz7ezAmiGSeMtZyfrE6VAJenTyPToFXicFPnuf73VS7HdS5HdS5HNR5HdS7HdR6HOiD4HPq2HbDXfFFVdwzjnnMGLECBRFYcGCBWi9jH/Yu3dvv1RQnFwpI8WhtkMcjBzkYGgXhyPbicT2kU7UomaaCWppCrMDqie3/yw5s9sRTBygl+B2jyLfN4GAMgI95scR0lDrTeJ1zUQOH6atNcT+tg1EE28Qs6xuASjpcpHx+UjkF2RDTzboODuOjwxEKf3Yf7g0BQqzXV9FescYnyK9vRWo/bzdGhTQVBmfN0AyGZN4IkMskSGWSBNPGMSTGRKJDImkSSKVIZUySCYNUqnsljFJp00yaZNM2iCdsTDSBoZhYmQszIyJYVhYhoVpWFimfWyZ9qZYoFqgASr2sYpiP84GFY3s3lJQc8f2XscOGsox3XGoZbdTiUXnZYgVBTSnhkNX7c2poekqDl3BoSv2sVNFd2lvvbkduDwOnB4HTo+GY5i2mPTEyIaoRNogkTGIpwziaTukJ9IGsZRBLJUhljKIJrP7VIa2RIZoMkNb0g5XkUSGSDJNJJEhHE9jWpDKmDRGkjRGjr7OYmdBj54LTyUBF6UBe1+e57a3oJsRQQ8e58D9N8jN4D3cpg544IEHuPzyy9m9ezef+tSnuP766wkETnzBTnHyWJZFa7KVg5GDVIf2UB/eSqhtD/HkQaxUI26rjUKHSaFmUapBafsXunu4FmCoeTi0EryUkpcpxRnxkDkMiTqDtsNJom0xGpIJ9ptVJFz1uXmCYh4PMZeHeH4RiZIRXQc+HxF+ErqL5HGEHxU7/BRlu7mK2sPOEefa9/mOt3fXl2XZIcLIBg4jY+aOM2k7mMTiGZKJDImkQSKZIZk0SGbDSjplkE6ZpNMGmZRJJvv1ZsbENEzMTDacZDdMC8W0UEy71UQ1rU5h5fj+Ozg41vlQ+tIO048U7BYZNTtOptOxmj1ub1lR1GyLSaeykG0hUTpaT9oDu5JtUuk433MVehu/Y1n2/7U/b1kWlgmQbXXp9LxpmJjZ0Glv9mMjGz6PvG4maZBJ9jz/14nQHCpOj4bT0xGiXF4HLp+O26fj8dt7t0/H7e/YuzyO3L/nUKGpCh6n1q/hwzQt2lJ2aArF04RiaZpjKVpiaVqiKZqzW1M0SVNbisNtKVpiKQzTssvH0+xtjB71NQp9TkbmexhV4KGy0EtloZdxRT7GlfgYkedGPYF/546WpcGfv/GYPlc2btzI8uXLueiii1izZg2f/vSnJSwNQYlMgpq2GqrD+6gPb6U5YneVGal6NKOVoJqh0GGSp8Ho9i/SgO53zJOxXCiZPPRUHnosgBL2kGrRiDWptLVqxDKWPcbH7Sbs9xPyB+zAk+ciXtRpwHM2AOXGAunO3j/Ne9E5/HTp+urlOF/X0IZh+LEsCyNjkkm1hxXDDi/p7udywSWRIZnIBpdkhnTK7v7JpDq+1shkg0vGwspYWIYJRjastIeWfn4vx9Zu0vurG1iYCpiKgqmApYLdL5Pda+3dQgqqpqJqCqqjvTtIxeFQcDiyx7qK7tDQnSq6Q8Xp1HDqKrqu4XKquJxarmzuWqrSaW+fU7qcU7qcU5ROjzuHo2H4/Xis2kO3aVjdAndP38P2Y4N00iSdzJBOGr1uqUSGVNzeY4GRMYlHTOKR9DHVUVHAE3DiyXPiy3PizXPiDTrxBOy9N8+FL+jEX+BGdw3f1itVVchz6+S5dUYV9O1rzGxQaoomaYykONyWpCHbKtUQSVAfTlAXsrdoysgFrk2HQt2u5dE1xpf4mFwWYGp5gGkj8pg5Mkihr29//A7bbrjOA7xffPHF3DInp4L77ruP7373u9TV1TFnzhx+/OMfs2jRosGuVq/SZpp9rfs40LKRutAmwm27SWXvLPMqCQo1i3yHRRGQG4Lfw/ebYWiYCS/EvFhtXjIRN4mwm2jURbNRyGFfMSFfgJjb09H6U+4iUensdgu8eYx3ZClAga5RrOsUOdv3Dop0LTfep6hT689ghZ/2D//24JHpEkLs1hQjlf3wT5mkUwbJZIZE3N4nkwapZHvLS7bVJfuLw8xYmGnTbmnJ2OGFAQgtnfX1v1IaCwMwFMhgkVHszpVMNqxYavYWIs0EFVSN7BgWe3M4FLTs3uFQ0XXQHAq6Q0XXFXSHglNXcOpadq/i1FVcuoLLqeHOBZfsQBnLwiLbxJEdU3JkK0ZfKYoBiml3mSlpeyqJXKuMkh0Do2TPKSiqam/ZY1VVUZTsOdU+Z7eHqYCGomgoioqqaqiqNuRaMQaSoihoDgXNwYAFDcu0SCUNUvEMyViGVNzektnHyViaeFuaRFuaRLTrPp00sCyIhVPEwima3uK1XF4H/gIXvnw3/kIX/nwX/gL7OK/Ijb/AjeYY/PE9/UVVFQp8Tgp8TiaW9l7OsizCiQyHWuIcao1zsCVGVXOMqqYY+5qiVDXFiKcNttSE2VIT7vK1lYUe5o8uYMHYQpaOL2JCia/HPyQyKbvbcNiFpVN1gPef//xnbrvtNu6//34WL17Mj370I1asWMGOHTsoLT3Kd8tJVNtWw5vV/+Zg4/Nk4vvwWq2UOwzcKlRgbz31QZgZlUzMjRH1Eo0HaEqV0miVcFgto0UtIqp5O26BD7pIFHfcAp85zju+Snq406voiLvAip0OCnXHCYcfuwWmI6R0PW5/zj5OJQ0S8YwdZBJ2C0x7iGkvm+s2SltYmfYQYx13eLFvYzYAEyzDPrbMbAxpP2cCBpZl5o7t/g8D0zIwMTGw9x3/sx9b2UcWBgqWHQAUE0XJDs1VTFTFQlEsVExUxUTBss9hZofvmqjtx5YFmCiWicMycZimXS/TxDINLNPuUrPaz/eRCfR9tMQpSFHs4KRpdsuUqqFo7Y81NM2B6nCgaRqqQ0d1dD9n7x1ouo5D19EcOpquo+nO7GNH9rH9nMPptJ9z2pvudGWPu+41xwkv5HDSKaqCK9v1Fig8tq810iaJaDoXluwtSTycJhZOEoukiIVSRFuTpBJGNnxlaDrUS3eUAr6gHZwCRW4Chfbel++yW6zy7BarUylQgR2Kgx6doEdnekVet+czhklVc4xdDW3sqo+wrTbClpoQ+5tiVDfHqW6O86/1NQCMzPdwwbRSLpk1gkVjC3Ndd0OpG06xepqUohc33HADv/3tbxkxYgRVVVWMGjXqlBjgvXjxYhYuXMhPfvITAEzTpLKykptvvpnPf/7zR/3acDhMMBikpqYmNwdVZ5qm4XZ3DPiJRnvv/1VVFY/H7gt75t//5qX96zDdLTi8rbgCYTRHR1OzoiroLgcZSyOR8hOOuImm/YTMAiJWkIjiJ6r6iTncJHQ3yUAe6ewdX1Yi3vPtMGCPhXB39Mdp8Ri+dIpAJk2ekcafyeDPZMjLpPEZaYo1jbxMmoCRxhmL4jhKgHZn5yqygFQqTSZt2nedGAqGae9NE8yMgq65MAwwM5BIpjEMC9MA01SwDLBMBctSwFJwOpy5v0oyRhojVwf7Fz+5EGLh1BwoioWFaZc1UuQGpFqGHWKyoUbXVFTFwrIydlkrjZV9rnM5MHGooCr2a2WMNKaZyb3mkXRV6/gwMEyMo4SOzmXtu2x6L+tQVbRs695QKGuaFhnLzHZBddqyLTcOTUV3OFAUBQsre932Fp3sQOls15VDVXA4HCjY/6rpjNFrt5amqjiyn0umZZHOdL+jqP1jT1EUdE3Ljs+x72Zrb7UiO2dPewuWkn1/lmVimhaJdDp7R1V2M63cddXsddsle6hDu2Mqi4LuOL6yqYxhv68jKKqK7nTh8/nQ3W50lxtTVXE4XfZjpxvd5cTh9qA73TjddlnV4UDVHKQyaVDUXPCzNzvkKaqK19uxvlAikcA8Smug19vx2ZNMJjGM3r/XjqWsx+POfa8kUymMTO/jpzqXjUbitIUSJNtSxLMtU/G2NPFommQkTTqugWl3NrfPF2WvD6mB4u7y/RkI+vHnu/HmOdHcoOpWx2B1d3a8lUdD01RcLnfud2sqlSKT6b2bsXPZdDpNOt17j4/T6cKRDce9lfUWuMgv9eLxuHNlM5kMyWTvf+44nU707CDstyobNxS21UdZc6CFVbsbWbO3gVSm47/dqEI3718wmhkVeaz/9q2QSXHdvb8gUFxCIpHo9bq6ruPMtkCZpkk8Hn/Lsu2/v0OhUI+/vzt72w/wTqVSrFmzhi984Qu5c6qqsmzZMlatWtWtfDKZ7PKNEA7bzYsVFRU9Xv+SSy7hscceyz0uLS0l1mltsM7OOeec3GSfz7au5zt3/ggr1NpjWceU6RT97A92N4gbGj9yCWZ9bY9ltTHjKX7o7/Z7M00Of/yDpKv291jWn5fHDdd8AE8ihi/exk8ff45dLd37ogF8LidfvexCktitBj99fhV7G5t7LKtrGndfcVHu8S9efoPttY09lgX43v9cmjv+7co1bDxY12vZb1y+Alf2h/rhNzbw5v6DvZb9yruW4Xfboe2RNZtZuedAr2W/eOl5FPrsD/rHNmzjxR29/wHw2RVnUx60fxae27qTZ7bu6rXsbcuWMrY4D1WxeHbnXv6xfnevZf9vxQxmVdhln9xWywOreq/vty8Zw5KxATTF4vFtLXztuZpey/7o0lIumeJDxeLJnW3c9OjhXsvee0keV832oCgWz+xO8L5Hwr2WveciD59c5ETB4sX9Gc7/bc/f6wDfWebi02fY/y1WHzJY9FDvf0j83zlOvnKu/UfHlgaDmQ/2XvazS518d7lddn+rybj72not+4kFOvddav/SbYyalH6v97LXztH59bvtstGUhf/uSK9lr5jm4E9X+jEtBdNS8H6j558LgAvGu/jNFcWYlophKUy/p5p4pudAMX98GT/9xOUYqhtDdfLO//djQtGe/43HV5Rz14c/QCaVIpNKcsev/kRzW8//bmV5fm6/6BzI/qh/98kXqQ/3/G9R4PXw/95xfu7xj555hYNv8RnR7lT+jPjPW3xGfP49X2Vk8RyaFD+Pr/ktT6z5ba9lb3/PfYwpnQrAf9f/mX++/kCvZT/1zu8zuWIuAC9u/id/ffXHvZb9+EXfYOaYJQC8tuNJfv/Cd3st+9mrv8W733U5Iybm8/qWZ/nghz7Qa9mHHnqID3/4wwA89dRTvOMd7+i17E9+8hM++clPctakEuY6ajjvxsu7PF8NtP/mvXT2VM6bOgHd5WLt2rVHHRrzf//3f3zlK18BYNu2bcycObP39/bZz/Ld7/b+3ntyzO2vF11kfzOfKgO8Dx8+jGEYlJWVdTlfVlbG9u3bu5W/++67+epXvzrg9fJGvWiY9PZ3o26YjIi0oZvgMhVCpkVvmbsibvLwy20UpCz8GVgWhZ29lPVZDi5MTyKhxEm64ujqSnKfoN0oaI78jkdK799OCnQpqypHvxXUrbuy3UpWrqWiNwXuDC7dQlHApR29a2hUfoZ8r4qqQNBz9LtzThuVYFSBgqbAxqqjDyC9ZFqU6SNAUywaWpI8s7X3su+fH2XhWPs9xRJp/rG+97LnTlM4NztHQ01U6/gU6cGMMV7OmBkEYHv46J1eFSMKmDSxGIDS8GGg97AUKCqlcLT98+FrbQZ6D0tacARa+QgAlHAI2Nx7JQLlUDbKPo5HgI29l/WVQVn2dgQjBqw7StliKBtnHzsSwJrey3qLoGyCfRxJA2/0XtZTAGWT7OOkAbzWa1HFHUQfMbXTmVd7Lety6Yws1CAZBsvuQu1NwDjM4sgfco91q/e/ngtdCa46LQr+EvCX8bW//rPXsJRfXsE13/kx6WSCdCLJ/asv7zUs6W430846DzOTwTQN3CvXQS9hSVVVSsaMyz12unv/76aoSpeyrjeP8r0DXcuu7/5Z3aVs5VhcTvszx71lz1HLFlWOIS/bauXZWXXUssHScvK8boxM5i3H1WRiz5IMvYHD6cahVb/Fdb0UjbJn7PXuP/p1gyWeXFn/oaN3VwWK3R1lG3q4xbmT5toY6/9bbW/7th217EA70Db4E6MeUzfckV5++WV+/vOfs3fvXv76178ycuRIfve73zFu3DjOPPPM/qzngKmpqWHkyJGsXLmSpUuX5s7fcccdvPjii7z++utdyvfUslRZWdnv3XCWZRFpi6D1sk5Y57Lt1zUTGcxoGiOaxmxLY8TSmJE0VjyDK+PAjKQwommizWGMWLrHrjhFUfDoHfWNp5O5sSmKU0X1O1F9OqpfR/PpBIqDaH4nql8n5TDAo6H5dRSPo1sXic/XsZhbIpHAMHoPK8dS1uv1djSxJ5NkjtI1cSxlPR4PajaopVKp3LqIJ1rW7e7axN7Xsul0+qg3VbhcXZvY+1q2P5vYO5c1DKPPzebHUravTex9KetwOHC1dw9bVq+tvsda9lh+7nNlLQvSMaLN9ZAIQ6rNDlDJCKQikGxDTYbwGGGINUH0MNGWRog3Q6wZzK7fR6oCHr3jZzCW7pgWAABFBW8heEtQ/MV4i0bZATYwgpijAMtfBv4yCJSBo+OXsKIoXbrW4vH4Ucevdv5ZPpayw/kzwsikaa2vo+lgNc2Hqmg+dIhIQy2huhpMw+jW/e5wuigZPZbS8RMYMWEyE+aeRrC4pNt1e9LfnxGmYdJSHyNUm6SpOkb1tmZaGqJkjI6y+eU+xs0uYvy8Eooq/Lhcrn7/jNizt5onvnoblsPJryZ8jJvPG89Hlozs9Y7SIdUN19nf//53rr76aq666irWrl2b+8cJhUJ885vf5PHHHz/eS59UxcXFaJpGfX19l/P19fWUl5d3K+9yuXIfmJ35fL4uP7y96UsZsD+Q8gJH/4/X7bo+Ot36dnSWaWFG03a4akvZ4SqSxoymMNqyYasthdbmwmhLQXu3QBvQloH6DBAn0lsrg6ag+XVUvzO3zwQ6P9ZxZkOW6tWPerdQ5186b6W3/z4nWtbpdOZ+EAerrK7ruQ+Z/izrcDhyH6D9WVbTtD5/vx9LWVVVB6SsoigDUhb6+HOvKOD04Ssf3/frth9Ylh2sYk12cIoehthhiDZCWyNEG/C2NWQfN9jlsCDdBKEmu/H4UMd1vUe+kKcAAiNyYYq8CgiOgrxReIKjIDgSXG/dy9D5D7y3ciw/90PxMyIvmM/oyVO7lDEyaVpqa2jYv5f6vbup37ubhn17SCcTNO/fQ/P+PWx/7mmeBwpGjGTU9JlUTp/FqOkzCRQWv2Ud+uszIpAXgGwjqmVZhBriVG1tYv+mJg5tbyHeZLL1+Ua2Pt9IwQgfkxeVMWVxOYFCd799RlQE3Tg0lYzDScow2dUYw+8/2oKhHY7l576vjrtlad68edx6661cc801BAIBNmzYwPjx41m3bh0XX3wxdXW99x8PNYsXL2bRokX8+Md2X69pmowePZqbbrqpzwO8+5JMhyvLsrCSRi5A2fs0ZlvqiL39vJU4xsnnFFB9ut1CFbBbrFS/Ey2go/o6nQs40Xw6yil2V4kQJ52RybZONdjhqa0B2uohUgeR2q57o4/3MbqDkDfKDlHBkdl9JeSPgYKx4C895rnV3g5M06Clpob6fbup27OTQ9u20nBgb7fZQwtGVDBm9jzGzplP5YzZON19D579KRlLs39TE3vXN3JgUxNGdnC2osDomUXMOGskY2YWndBklAA1O7fzpy9/lrySUko/+jXOnlRCQR/nZ+qrk9KytGPHDs4+++xu54PBIK2trcd72UFx2223ce2117JgwQIWLVrEj370I6LRKB/5yEcGu2pDgqIoKG4HqtuBo/itf0CttIkRTWFGOoerIx/bIcuM2ZPLmdmwRR8ytuJ2ZIOUjhZw5roE1YCzW2uW4pQlSYToRnPY3WuBsqOXsyxItHYNT+EaewsdhPAhCFVDItSxNWzp+VoODxSM6QhPBdl9/hj7uA8tU6ciVdUoGlVJ0ahKpp91HgCJtjYO7dhC9ZZNHNy2mYZ9e2mpraGltob1Tz2GqjmomDKVsbPnM3buaZSOHX/SPudcXp0pi8uZsricZCzNnnWN7Hy9jkM7WzmwqYkDm5oIlniYu6ySKUtHoB/njOSphN2N5vR4uWzuyP58C8fluMNSeXk5u3fvZuzYsV3Ov/LKK4wf3/dm5KHgfe97H42Njdx5553U1dUxd+5cnnzyyW6DvkXfKLqKI98N+W/dNG4ZJmY00zVU9dqClQbTwkpkyCQy0Nh7n3Tnuqh+PTeuKheu2h/720OWE8WtSbASojNFsbvgPAVQOq33cskIhA5lA9RBex86CK3V0HrADlWZODRut7ee+MugaCIUjrf3RRPsfcE40PvezXYqcPv9TDhtMRNOWwxAMhalessm9m9Yy/6NawnV13Fw62YObt3MKw//Fn9RMRNOW8zEBYupnDEL7TgWBj8eLq/O9DMqmH5GBa31Mba8fIhtK2sJNcZ58U87eeOx/Sy4eAwzzhyJph9bj0A6O+ZosFrQjnTc3XB33303v//97/nVr37FhRdeyOOPP86BAwe49dZb+fKXv8zNN9/c33Udkt4O3XBDhZUNSkZbGiOSssdcZQetm53OGW32eSt9jJOmOhS09m6/HsKVFpBgJcRxyaTsFqjWA9CyH1qy+9YD9nG896kVQLG784rGQ9EkKJkCJVPtzV9ykt7A0NJSV8OBDevYv3EtBzatJ9NpQLXT42Hs3AVMWrSUCfMXoR/DeK7+kE4abFtZw/r/VhNpsgdvB4rcnPneSYybU9znz83Nzz/DU/ffw7i5p3H5FwbmDvRj+f193GHJsiy++c1vcvfdd+fuCnG5XHz2s5/la1/72vFccliSsDR0mUmjo1UqcsT4qiMeW8e6yKdDyY6xsrv8cqEq4LSP27sEA07UAVyVW4hTQiIETXvsrXkPNO3ueJzsbeoS7KkfSqZ2ClBToGTa22p8VDqVpHrzRna/+Rp717xBtLUl95zD5WL8/EVMWXom4+YtQD+JM2EbGZNtK2t587F9REP2nXRjZhZx3oem4st/63qseexfvPDbB5ly+tm849N3DEgdT0pYapdKpdi9ezdtbW1Mnz69z6PVTxUSlk4NVtrAiPQ0tirV0WqV3R9rsFJcWg9hSu8IW+3n/Ee/K1CItx3Lsu/saw9Qh3dC405o3Ga3SPW2FIG3CMpmQvms7H4mFE8Bx+CvMTaQLNOkbs8udq9exY7XXiFU3zEIVHd7mLRwCTPOXUbl9FnZ9QwHXjppsObJ/ax7pgozY+HyOjj/mmmMn3v0VsGVf/0Dq/72J2Yvu4gLr79pQOp2UsPS252EpbefrsGqa0uVEU51ab06pq7A9rsCA11DVK77L6+j1Upaq8TbXioGTbugcUd2LNQOaNgGLfuySxwdQdXtlqf2EFUxF0bMOWUHlluWRf3e3exY9TI7Vr1M5HDHbOiB4hJmnH0+08+5gILynlef6G/NNVH+++utNFbZM98vuGQsi945rtduued+/XPWPfEfFl12JWd98MMDUqeTFpZaW1v55S9/ybZt9uye06dP57rrriMYDB7vJYcdCUuiN+1TLhiRTq1V7aEqkj3ObmZbz5OE9kZxaV3Ck3ZEmNLy7E1xydgq8TaTjtuhqX4z1G3u2PfYnadA8WQYOR8q5kHFfLsVSh8ag4r7i2VZ1OzcztaXnmXHypdJxjomSa2cMZs5F17CxIVLBnxRZcMwWfX3PWx4zp7BfNrpIzj3Q1N7nGbgiZ98n60vP8/ZV32Ehe+6YkDqc1LC0ptvvsmKFSvweDy59VpWr15NPB7n6aefZv78+cdz2WFHwpLoD+2ThObCU3uQCme7/8Id54+ltUrRVdS8rmFKy3Oi5rk6zkmoEqc6y4LWqo7gVLcRatbZd+kdSXXYd/6NPA0ql0DlIvsOvVPk5yOTSrH7zdfY+uKz7N+wLrdCgy+/gFnnL2fW+SvIKykd0DpsfbWGF36/HcuC6WfYgenIz59H7v4/9q1fw4obb2HmucsGpB4nJSydddZZTJw4kQcffLDLsgkf/ehH2bt3Ly+99NLxXHbYkbAkTqZca1Wn8NR+bERSmOGOkHUsY6sUXc2GKCdanisXouyA5co9J91/4pQSqYfa9XBorR2eatbas5wfyVcClYvt4FS5GEbMPSWmMwgfbmTTs0+y6bmnuwwMr5g8jalnnM3kJWfiyy8YkNfevaaBp3+xGcuCxe8az4JLxnZ5/vdfuIX6vbt59x13MuG03hfQPREnJSx5PB7WrVvH1Kldp3PfunUrCxYsOOq6SacSCUtiqDJTRpfwlGupiqQwwsmOUHUMM64rbocdoILZVqpg52DlQgs67clAZaC6GI4sy25tOrQWDq6G6jfsAGUcsYaa5rS77caeaW+Vi8HZv8trnExGJsPu1a+x8b+PU7VlU272cEVRGT1rDlOWnsWkRafj7ucbuDa/dIgX/7gDFLjs03MZNbUw99zPP/Fh2poOc9U3f0j5hEn9+rrtTkpYKisr43e/+x3Lly/vcv6pp57immuu6bbW2qlKwpIY7nKhKpzCiCTtfafNjKQwQsm+d/8pdBo3ZQeojlDV8VhaqcSwkElC7Qaoft3eql63l4npTHXY3XanQHhqa25ix6pX2L7yRep278ydVzUHY+fMY+qZ5zJxwWJ0V/+0rD3/u21sfbWWQJGb9395EU63A9M0uOdDl2MaBh/+1D1k1oZxTy4guGJsv7xmu5Oy3Mn73vc+rrvuOr73ve9x+umnA/Dqq69y++2384EPfOB4LyuEOMlUp4Za7DnqUjZduv/CSYxQ6ojjZK4VCwvMsN0lmKat12sqbocdnPI6hamgK7c5gk4Uj0PGUonB5XBlu98WATfbrS4t++HAStj/Cux/2Z5wsz1Mvfx9+867UQth/Lkw4Tx74Lg2sIOn+4u/sIjTLr2M0y69jNa6WvtuupUv0Vi1n71rV7N37Wp0t4fJi09n+tnnn/A0BGe8dxLV21uINCVY93QVi981nmhrC6ZhoKgquuEkfqgNvWRwB90fd8tSKpXi9ttv5/777yeTyQD2KsY33ngj3/rWt/q8UvNwJy1LQnSwTMueOiEboIzwkcd2uLJSfev6U3S1I0jltwep9haqbLefT5dAJQaPZdkzke9/xd72vWwv+dKZK89ucRp/Low/D4onDbsB400Hq9j+6otse+UFQg0dPUd5JWXMOOd8ZpyzjGDp8S0RtmddA0/+fDMOp8o13zidpoM7+fNXPk+wtIz/ufz/CD91AO+CMgqvnNxfbwc4yfMsxWIx9uzZA8CECRPwer0ncrlhR8KSEMfOTGTs4NQpQHVupTJC9tI1feJQsi1R2QCV36nrL+jCke+SFipx8liWPdfT3hdh7wuw70WIt3QtkzcKJi+HSStg3NngHD6/Ny3LombHNra+9BzbV75EKp4dn6wojJk1l1nnr2DiwsXHtD6dZVn87Vtv0nAgwsJLx+L27uKZB37MmNnzuHDhdUSeq8a3ZAQF757Yr+9lQMPSc889x0033cRrr73W7eKhUIjTTz+d+++/n7POOuvYaz4MSVgSYmBYaTMbnOzwlAl1HBvZY7Otb4Eq10KV3xGoHPldH8sYKjEgTMOeqmDP83Z4qnoNjI613HC4YexZMHkFTFoOBWMGrarHKp1KsvuNVWx+/hmqNm/InfcG85l1/nJmX3BRn6ch2PVmPU//Ygu+oJPKKZtZ/9SjnHbpZcwtXkbbSwfxnzWS/EvH92v9BzQsvetd7+K8887j1ltv7fH5e++9l+eff55//OMfx3LZYUvCkhCDx8qYdutUqx2ecoGqNdkRqKKZPl1L9To6AlXnMJXvtvcBuctP9INUzB7ntPMp2PW0Pd6ps9IZMO2d9lY2Y9h017XW17H5+WfY/HynaQgUhfHzFjB3+aWMnTP/qGObjIzJrz/3KoloGo/7EVpq93PJp25nRNMo2lbWEDivclAHeB9zWBozZgxPPvkk06ZN6/H57du3s3z5cqqqqo7lssOWhCUhhjYrbXS0TLV2DVOZVvu4T3NSqYrdvZfvwtEeoAo6HUvrlDhWlmXPNr7rKdj5tD1A3Or0vVg4Phuc3mUPEj9J67mdCCOTYe+aN1j/zONUbVqfOx8sK2fOsouZce4yvHk9r/Lxwh93sPmFHSRDDwIWH/vZb0g/30x0dR15F44h74LR/VrXAQ1LbrebzZs3M3Fiz32Hu3fvZtasWcTj8WO57LAlYUmI4c9MZDBaO8JTLky1JHJdf5hv/VGpeh1oBe6OlqkCN44Cu3XKUSBjp8RbiDXbLU7b/g27n+3aXZc3EmZeDrPeC+Wzh0WLU3PNQTY8/ThbXnw2t8SKputMWnQ6M8+7kMoZs1DVjj8wDmxp4h/f+SlG4nVGTJrKB7/+PZr/vIPYugaCl4wjcPaofq3fgE4dMHLkyKOGpY0bNzJixIhjvawQQgwa1e1ALXegl/c8N45lWvYknq1JjJZE11DVaj+2EgZmLIMZayN9qOcpExSnZrdGtQeqAnfH4wKX3Nn3ducthLkfsLdkG+x+Brb+2+6uCx+ClT+2t+LJdmiaeQUUTRjsWveqsGIU5334Bs58/zVsX/kSG555nPq9u9n+6otsf/VF/AWFTFiwmJHTZhIoLKJu53aMxGoApp99KWC3DIM97nAwHXPL0s0338wLL7zA6tWrcbu7TkoVj8dZtGgR5513Hvfee2+/VnSokpYlIQTYrVOZFjs8GS1JMrm9HbD6MhhdcarZ1qiOEOUodOdaqKRl6m0qnbCD06a/wo4nu7Y4jVwA866yg5N76C9iX793N5uee4rtK18iGY32WEZ1TueiG29h2ukVHH5oM4kdLRRcMQnfwvJ+rcuAdsPV19czf/58NE3jpptuYsqUKYA9Vum+++7DMAzWrl1LWdnxzbcw3EhYEkL0hZU2ssGpU5Bq6dib4dRbXkNxaXZ4KrRDVJfjfPeg//UtToJEGLY/agenvS9AdiFcHB6Y/i6Y9yEYc+aQH9+USaep2rye/evXUr93N/FICG+wAKd3GjV7K5l17ijO+cAUGh/YSHJviML3T8E7t38X+B3weZYOHDjAjTfeyFNPPYWVW0NGYcWKFdx3332MGzfu+Go+DElYEkL0ByttHhGiEnZLVXPCDlN9aJnS8py58KQVuHEUZYNUkQfVL118p5xIvR2a1v0OGrd3nC8YC/OvgfnXgq940Kp3PHa+Ucczv9rKiIlBLv/saTT8dD2pqghFV0/HM6OoX19rwJc7GTNmDI8//jgtLS3s3r0by7KYNGkSBQUDszqxEEKc6hRdRS/xopf0PEGhmTLsQejNCYymOJmW7HFzgkxzAitl5NbzS+0P93j9XCtUkccOVO1hqsCN4hjaLRGiB4EyOP0mWPpJOLTGDk2b/m4vx/LsXfDCt2DG5bDoenvtumEQlgsr7MV6mw5F7WWWUnbLmeIc3O/PE1qspqCggIULF/ZXXYQQQvRCdWqopV700u5hyrIszGjabpVqjpNpTpJpiueCVPtCyJn6GJn6WPeLK9iznRdlg1R2r2WPZUqEIU5RYNQCe1txN2z5B6z+BdSshY0P29uIubD44/bYJodzsGvcq/wyDyiQimeIR9JDZoD38FjZTwghRK8URUHzO9H8TpyVgW7PWxnTHi/VFCfTnCDTlA1RzXEyTQl7tvTs3X3JPaFuX68G9GyIslukHMXZUFXsQXXLr5Ehxem1B3zPuwoOroHVD8LmR6B2Pfzz43aL05KPw2kfHpIDwh26hr/ARVtzklBDDCudbVnSBzewy3e5EEKc4hSHil7sQS/uvnK7ZVmYbWk7RB2Ok2mKd4Spprg9HUIkTSqS7rF7T/XrHa1RxXaAsoOUG9Ulv2IG1ajT7G35N2Dtr+H1ByBSA8/cCS9+F0671u7Cy6sY7Jp2kVfkoa05SaQ5ga+9G05aloQQQgwWRVHQAk60gBPXmO6DXM1YNki1h6jD2X1THLMtjdmWJtWWJnWglyCVDVB6iadLmJIxUieRrwjO+gwsvckeEL7yx/aA8FU/gTcetAeDn3krBEcOdk0BCBS5YRdEmhN427vhBrkrWMKSEEKIXqleHadXxzmqe/eemch0BKjOrVKH45jRTkHqyBYpBftuvWxrl6PYg6PE3rQ8l6zBN1AcLntqgTkfhN3/hZe/D9Wv2V11a39jP3fWZyDYvzNlHyt/gQuAaHMCDPuOe3U4D/AWQgjx9qW6HThH+nGO9Hd7zkxkyDRmA9ThOOn2QNUYx0oaGNk7+ZI7W7p8naKrHeGp2INe6s09lm69fqKqMHk5TLrQXtT3hW/DgVfgzV/Buj/Yd8+d9Rl7RvFB4AvaYSne2jH5prQsCSGEOOWobgfOykC3Aee5MVKHO4WoxjiZxpg9BULaJF0bJV3bfXZnNc9ph6cST5e9GnDKHFLHQ1Fg3Nn2tv8VeP5uOzSt+gms/S2c8WlYciM4e14GaKB4g/bdeolQdqJWFdAG97+vhCUhhBAnTZcxUuO63o1lGRaZloQdnBrtEJVujNndem1pzHCKZDhFcndr12u6tE4Byp5ewVHmxVHoli69vhp7Jnz4UXsB3/9+Beo3wXNfs1ubLrzLnnLgJAVST8AOS+moPRGromuDHoYlLAkhhBgSFE3puGtvWtfnzHjGDk4N2QDVEMt181lJg/TBNtIHj1jA2KGgF3txlHpyAUov9coA894oCkxaBhPOh81/s8NSaxX8/Tp7IPjF34aKuQNeDW82LGWiaXCpqK7Bn+dLwpIQQoghT/U4cI3OwzW66x17VsYk0xQn3RAn0xDLBal0QxwyJum6KOm6KPEuF1NwFLvRy3w4Sr3o5V77uMiNokmIQlVh9v/AtHfBqh/Dyz+wB4I/cC4s/ChccCe4B255L7dPtw9SJrhUFAlLQgghxPFTHCp6mQ+9rOu4Gsu0MFoSpBvjZOpjpBvsLVMfw0oZZBriZBriXS+mKfag8jI7POnlXvRyH1rB27Q7T3fD2bfbd889c6fd2rT6Qdj+GFzyXZj2jgF5WafXAQo4sv/kEpaEEEKIAaCoSm7WcaZ23NVlWRZGKEWmPkq6PmZvnUNUdkmYOIc7rqWrdhdepwCll/lQA2+TxYmDI+HKX8L8q+HRW6F5L/z5Krvl6R0/7PfFelVVweVx4MjOsTQUltuRsCSEEOJtQ1EUHPkuHPku3FOOCFGtSdLZsJTuFKastNnjmCjV57CDU/s2wu7WGwq/3AfE+HPhxpXw0nfh1Xtg27+hahW8816Yekm/vpTL68ARzk5IKS1LQgghxOBTFAVHgRtHgbtrS5Rp2WOi6mJ2a1Rd1D5uimNGMyT3hLqup6eAo8iDPqIjQOkVPrSg69RohdI99pil6ZfBIx+Dxm3w8AfsWcAv+ra9Nl0/cHocOCL21AEywFsIIYQYwhRVQS/xopd4YVZHd5OVNuyWp2x4StdFSde2YUYzuTmk4ps6deV57FYoZzY86RV+9DLv8B1QPmIO3PACPP8Ne/mUtb+F6tXw3l9D6dQTvrzL4+gYszQEFmse/BoIIYQQw4yiazhHBbosA9M+4Wa6NtsCVWsHqHRDHCueIbUvRGpfp1YoTUEf4cNZ4Ucf6bf35b5BXzS2z3Q3LP8aTFwGj1xvtzI9cC686177broTurSD7D1xqG5pWRJCCCFOCZ0n3HRPLsidtzKmfTdedmbydE0bqZo2rEQP80O1B6jKAM7ReTgrA/aUBkO5C2/8OfDxV+CRG2Dv83Zwqt0Ay74K2vHFDKdHQ8++Z2lZEkIIIU5xikPFWWG3HLWzLAujOUGqpo30oWh2H8GMZnIBKrqqFgDV60AfFcA1Jg/nGHsJmSG3Tp6/FD70d7tb7uXv20umNGyF9/7muOZk0l0O9Gw+lJYlIYQQ4m1IUTpNbTCrBMgGqJYkqeowqaoIqeoIqZo2zFiG5M6WjkWHFezxT2Py7AA1Lg9HvnsQ302WqtmDv8tnwT8/AXueg4cuhg/+xZ5+4Bg4XVpuzJLqGfyoMvg1EEIIIYQdoArdOArdeOeUAtkuvLooqaoIyQNhUgfC9hQH2S696Gt265OW78I1Ng/nuCCucUEcJZ7B67qb8R7IHwN/fB/Ub4ZfXgjX/AuKJ/X5Erpbw5mdCFSVbjghhBBC9EZxqLmB5P7TKwAwQkmSVWFSByIk94dI17RhtCaJrW8ktr4RANWv4xofzG75Jz88jZwPH/0v/OFKOLwTfnURXPNPu9WpD3SXhrO9ZcmrH73wSSBhSQghhBhGtKAL76wSvNnuOzNpkKoKk9wfJrUvRLIqgtmWJr7xMPGN9vQFakDHNSEf98QCXJPycQRdA1/RgjHwkSfgd++Buo3wm3fCtf/pU2ByOLWOMUs+CUtCCCGEOAGqS8M9qQD3JPsOPCtjkqqOkNwbIrmnlWRVGDOSJr6+kXi25clR4sE9qQDX5AJc44MDN+u4rxg+/Cj8/go4uBp+exl8+DEonXbUL9NVcGRbwlTf4EeVwa+BEEIIIfqN4lBxZccuccForLRJsipMcncrid2tpA9GyDTGafv/7d17eEx3/gfw98wkk5FULohMkEtF45a4RIqkDVEhtliqS9GqbFMs9aCoS2OJ2ool6tau29ZllaZoy25LmjSqPyJ0hUTkRkIWkQRt5ILcZr6/PyKnnWYy5DoT3q/nOQ/nnO8553M+8sjn+Z7v+Z7bD1B86iZgJoPFszZQdW4Flbtdwz+yU9kArx+sLJRyEoA9Y4DgKMDWqcZDzIQAAGgAyEzg8zEsloiIiJ5gMnM5VG62ULnZwiYQ0N4vR+mVApRczkdJej40d0tRevkuSi/fRQEePrLraFs53snNtmHmeWphC0z6uvLtuNtplT1NwVGV2/UwK68slsplMpOYY4rFEhER0VNEbmmOFh5t0MKjDYQQqLh1HyXp+Si5lI/SrIeP7BJv40Fi5SM7hbVSGihu4WZTOd1BXVi2qpyL6dOhwJ104OBbldMK6Jm4UlFW+RHd0jrfZcNisURERPSUkslkMHewgrmDFVoO6FA53ulaIUoyC1B65S7KrhVBU1im86adws7i4WBxW1i42ULRUvn4F7TpAEz4vPLtuMwY4NgKYMjyas3kDyqgBXD/4eM4Y2OxRERERAAejnfqaAuLjrYAXCDKNSi99utg8bLrRdDkl+L+2TzcP5sHoHKCTIvnbCsHjD9rDZn5I8YYOfYERv8DOBAExK4HnPsDnf+gG0dRGQDgvrbBb7FOWCwRERGRXjJzhTTeCUNcoC3ToCyrECUZd1GakY/ymw8/Gpx7D8UnsgEzOSw62kDlbmd4sHj3V4DrPwGn/1E52/f0U4C1o7Rb5JcAAAorTKNaYrFEREREj0WuVEiFEPAsNMVlKM28i5LLd1F6KR+awjLp0ywFABStVFB1tqt8087NRrfXKSAUyDoB5CYB38wBJkQAMhlEhRba2/cBAPllLJaIiIioGVM8o4Rlz7aw7Nn218Hilyrfsiu9WgDNLyW4F5dT+VFgMzlUbjZQdXk4RUHrFsCY7cDWAcClSODil4Dnn1CScReoECjRChRVCGg1WsgVcqPeJ4slIiIiqjedweJ+HaAt1VT2OqX/gpK0fGgKSivfukuv/CCwWZsWsOhkC4vnVkGZ8ncoIpdA6/gSCr/LAgBkP+xVKi/TwqKFcYsl4169jrKyshAcHIxnn30WLVq0gJubG5YtW4aysjKddhcuXICfnx9UKhWcnJywevXqauc6cOAAunTpApVKBU9PTxw5cqSpboOIiOiJJbdQoEW31rB75TmoFz0PhzlesB7mCuWz1oBchoo7D3DvdA5+SXBHbtmnyP55O3LCk1Gecw+yFma4/LBYqng4jYAxNcuepbS0NGi1WmzduhWdOnXCxYsXMWXKFNy7dw/h4eEAgMLCQgwdOhQBAQHYsmULkpKS8NZbb8HW1hZTp04FAJw6dQoTJkxAWFgYRowYgX379mH06NE4d+4cPDw8jHmLRERETwyZTAZztRXM1Vaw9neCtqSi8lMsmQUo/V8hynOKAG3lQHBlexVsX+0KTdhZQKuFptz445ZkQpjIJAb1tGbNGmzevBlXrlwBAGzevBkhISHIzc2FUlk5B8SiRYtw6NAhpKWlAQBee+013Lt3D9988410nv79+6NXr17YsmXLY123sLAQNjY2KCgogLW1dQPfFRER0ZNPaLTQfvoKZNmnIe8fBPzh7/jn3P9D6f0KTAztBzu1VYNfsza/v5vlYzh9CgoK0KpVK2k9Li4OAwYMkAolAAgMDER6ejry8/OlNgEBATrnCQwMRFxcXI3XKS0tRWFhoc5CREREdSdTyKEYPAdyWQkQvxu49zMU5pUlSoUJ9Cw9EcVSRkYGNm3ahGnTpknbcnNz4eDgoNOuaj03N9dgm6r9+oSFhcHGxkZanJxq/hAgERERPaaO/pUTVlY8AOJ3QGFWWaJoTGCuJZMqlhYtWgTZw4/m1bRUPUKrkp2djWHDhmHs2LGYMmVKo8e4ePFiFBQUSMv169cb/ZpERERPPJkM6De98u/x/4JCUTmGSWsCxZJJDfCeN28egoKCDLbp2LGj9PebN29i0KBB8PX1xbZt23TaqdVq5OXl6WyrWler1QbbVO3Xx8LCAhYWFo+8FyIiIqql7qOBowuBgmtQiAcAAE2F8YdWm1SxZG9vD3t7+8dqm52djUGDBqFPnz7YuXMn5HLdTjIfHx+EhISgvLwc5ubmAIDo6Gh07twZdnZ2UpuYmBjMmTNHOi46Oho+Pj4Nc0NERET0+MxbVBZM53ZDUZYPwI6P4eoqOzsb/v7+cHZ2Rnh4OG7fvo3c3FydsUYTJ06EUqlEcHAwkpOT8cUXX2DDhg2YO3eu1Gb27NmIjIzE2rVrkZaWhtDQUJw9exYzZ840xm0RERGR558AAIqS2wBMY8ySSfUsPa7o6GhkZGQgIyMDHTp00NlXNROCjY0NoqKi8M4776BPnz5o06YNli5dKs2xBAC+vr7Yt28flixZgvfffx/PPfccDh06xDmWiIiIjMXlBcCqLeS/lAIAtCbwGO6JmWfJWDjPEhERUQP7dj7+850a18q8MHhyV3TxcWzwSzyV8ywRERHRE8JjDOSyCgCApqzcyMGwWCIiIiJT49QfCnMFAEBzI8G4sYDFEhEREZkauRyKNq4AAG3690DZfeOGY9SrExEREekhd+gMANDcLwIiFxk3FqNenYiIiEgPxcNvu2phDqisAa3xphBollMHEBER0ZNNXvVtuD5TgaHexo3FqFcnIiIi0kP6NpzS1riBgMUSERERmSCpZ0lj/Bm8WSwRERGRyZFX9SxpjD93NoslIiIiMjkKRWWJojWBb8OxWCIiIiKTIzer7FnSsGeJiIiIqDqpZ4nFEhEREVF10pglPoYjIiIiqk4hvQ3HniUiIiKian59G449S0RERETVSAO8K9izRERERFSNXF41wJs9S0RERETVcFJKIiIiIgNYLBEREREZ8Os8S3wMR0RERFQNe5aIiIiIDKgqljjPEhEREZEecj6GIyIiIqpZ1TxLfAxHREREpAfHLBEREREZ8OvbcCyWiIiIiKrht+GIiIiIDJDJHxZLWvYsEREREVXDMUtEREREBlSNWYIwfu8SiyUiIiIyObKHPUsAIIzcu8RiiYiIiEyO/DfFksbIg7xZLBEREZHJ+W2xJPgYjoiIiEiXXP5rsWTsQd4sloiIiMjkyGQyk5k+gMUSERERmaSq3iX2LBERERHpYSpzLbFYIiIiIpNkKp88YbFEREREJkkqljhmiYiIiKi6qgHenDqAiIiISA8O8CYiIiIygAO8iYiIiAyQP/yYLoslIiIiIj04ZomIiIjIADln8CYiIiKqGacOICIiIjJAegzHMUtERERE1XHqACIiIiID+BiOiIiIyAC+DUdERERkAHuWiIiIiAzgmKUGUlpail69ekEmkyEhIUFn34ULF+Dn5weVSgUnJyesXr262vEHDhxAly5doFKp4OnpiSNHjjRR5ERERGQIH8M1kAULFqBdu3bVthcWFmLo0KFwcXFBfHw81qxZg9DQUGzbtk1qc+rUKUyYMAHBwcE4f/48Ro8ejdGjR+PixYtNeQtERESkx6/fhtMaNw6jXr2ejh49iqioKISHh1fbt3fvXpSVlWHHjh3o3r07xo8fj1mzZuGjjz6S2mzYsAHDhg3De++9h65du2LFihXw8vLCxx9/3JS3QURERHrIZFVjlowbR7MtlvLy8jBlyhTs2bMHlpaW1fbHxcVhwIABUCqV0rbAwECkp6cjPz9fahMQEKBzXGBgIOLi4mq8bmlpKQoLC3UWIiIianhVPUt8DFcHQggEBQXhL3/5C7y9vfW2yc3NhYODg862qvXc3FyDbar26xMWFgYbGxtpcXJyqs+tEBERUQ34bTg9Fi1aBJlMZnBJS0vDpk2bUFRUhMWLFzd5jIsXL0ZBQYG0XL9+vcljICIiehrIHlYpxu5ZMjPq1X9n3rx5CAoKMtimY8eOOHbsGOLi4mBhYaGzz9vbG6+//jp2794NtVqNvLw8nf1V62q1WvpTX5uq/fpYWFhUuy4RERE1PFOZOsCkiiV7e3vY29s/st3GjRvxt7/9TVq/efMmAgMD8cUXX6Bfv34AAB8fH4SEhKC8vBzm5uYAgOjoaHTu3Bl2dnZSm5iYGMyZM0c6V3R0NHx8fBrwroiIiKguZIrKriVjP4YzqWLpcTk7O+usP/PMMwAANzc3dOjQAQAwceJELF++HMHBwVi4cCEuXryIDRs2YN26ddJxs2fPxsCBA7F27VoMHz4cEREROHv2rM70AkRERGQcDzuWjP4YzqTGLDUkGxsbREVF4erVq+jTpw/mzZuHpUuXYurUqVIbX19f7Nu3D9u2bUPPnj1x8OBBHDp0CB4eHkaMnIiIiABAZiJvwzXLnqXfc3V1hRDVE9mjRw+cOHHC4LFjx47F2LFjGys0IiIiqiMZ34YjIiIiqpmcnzshIiIiqhl7loiIiIgMkHqWjDx1AIslIiIiMklSz5JxayUWS0RERGSa5CYygzeLJSIiIjJJHLNEREREZICMb8MRERER1cxUvg3HYomIiIhMksJMDoW5HPKHM3kbyxMxgzcRERE9eTz9O8DTv4Oxw2DPEhEREZEhLJaIiIiIDGCxRERERGQAiyUiIiIiA1gsERERERnAYomIiIjIABZLRERERAawWCIiIiIygMUSERERkQEsloiIiIgMYLFEREREZACLJSIiIiIDWCwRERERGcBiiYiIiMgAM2MH0NwJIQAAhYWFRo6EiIiIHlfV7+2q3+OGsFiqp6KiIgCAk5OTkSMhIiKi2ioqKoKNjY3BNjLxOCUV1Uir1eLmzZto2bIlZDJZg567sLAQTk5OuH79OqytrRv03E875rbxMLeNh7ltXMxv4zHF3AohUFRUhHbt2kEuNzwqiT1L9SSXy9GhQ4dGvYa1tbXJ/HA9aZjbxsPcNh7mtnExv43H1HL7qB6lKhzgTURERGQAiyUiIiIiA1gsmTALCwssW7YMFhYWxg7licPcNh7mtvEwt42L+W08zT23HOBNREREZAB7loiIiIgMYLFEREREZACLJSIiIiIDWCwRERERGcBiycg++eQTuLq6QqVSoV+/fvjpp58Mtj9w4AC6dOkClUoFT09PHDlypIkibX5qk9vk5GS8+uqrcHV1hUwmw/r165su0GaoNrndvn07/Pz8YGdnBzs7OwQEBDzy5/xpVpvcfvXVV/D29oatrS2srKzQq1cv7NmzpwmjbX5q+39ulYiICMhkMowePbpxA2zGapPbXbt2QSaT6SwqlaoJo60lQUYTEREhlEql2LFjh0hOThZTpkwRtra2Ii8vT2/72NhYoVAoxOrVq0VKSopYsmSJMDc3F0lJSU0cuemrbW5/+uknMX/+fPH5558LtVot1q1b17QBNyO1ze3EiRPFJ598Is6fPy9SU1NFUFCQsLGxETdu3GjiyE1fbXP7ww8/iK+++kqkpKSIjIwMsX79eqFQKERkZGQTR9481Da/Va5evSrat28v/Pz8xKhRo5om2GamtrnduXOnsLa2Fjk5OdKSm5vbxFE/PhZLRtS3b1/xzjvvSOsajUa0a9dOhIWF6W0/btw4MXz4cJ1t/fr1E9OmTWvUOJuj2ub2t1xcXFgsGVCf3AohREVFhWjZsqXYvXt3Y4XYbNU3t0II0bt3b7FkyZLGCK/Zq0t+KyoqhK+vr/jnP/8pJk+ezGKpBrXN7c6dO4WNjU0TRVd/fAxnJGVlZYiPj0dAQIC0TS6XIyAgAHFxcXqPiYuL02kPAIGBgTW2f1rVJbf0eBoit/fv30d5eTlatWrVWGE2S/XNrRACMTExSE9Px4ABAxoz1Gaprvn94IMP0LZtWwQHBzdFmM1SXXNbXFwMFxcXODk5YdSoUUhOTm6KcOuExZKR3LlzBxqNBg4ODjrbHRwckJubq/eY3NzcWrV/WtUlt/R4GiK3CxcuRLt27aoV/k+7uua2oKAAzzzzDJRKJYYPH45NmzZhyJAhjR1us1OX/J48eRKffvoptm/f3hQhNlt1yW3nzp2xY8cOHD58GJ999hm0Wi18fX1x48aNpgi51syMHQARPT1WrVqFiIgIHD9+3LQHczYjLVu2REJCAoqLixETE4O5c+eiY8eO8Pf3N3ZozVpRUREmTZqE7du3o02bNsYO54nj4+MDHx8fad3X1xddu3bF1q1bsWLFCiNGph+LJSNp06YNFAoF8vLydLbn5eVBrVbrPUatVteq/dOqLrmlx1Of3IaHh2PVqlX4/vvv0aNHj8YMs1mqa27lcjk6deoEAOjVqxdSU1MRFhbGYul3apvfzMxMZGVlYeTIkdI2rVYLADAzM0N6ejrc3NwaN+hmoiH+zzU3N0fv3r2RkZHRGCHWGx/DGYlSqUSfPn0QExMjbdNqtYiJidGptn/Lx8dHpz0AREdH19j+aVWX3NLjqWtuV69ejRUrViAyMhLe3t5NEWqz01A/t1qtFqWlpY0RYrNW2/x26dIFSUlJSEhIkJY//vGPGDRoEBISEuDk5NSU4Zu0hvjZ1Wg0SEpKgqOjY2OFWT/GHmH+NIuIiBAWFhZi165dIiUlRUydOlXY2tpKr09OmjRJLFq0SGofGxsrzMzMRHh4uEhNTRXLli3j1AE1qG1uS0tLxfnz58X58+eFo6OjmD9/vjh//ry4fPmysW7BZNU2t6tWrRJKpVIcPHhQ5zXhoqIiY92CyaptbleuXCmioqJEZmamSElJEeHh4cLMzExs377dWLdg0mqb39/j23A1q21uly9fLr777juRmZkp4uPjxfjx44VKpRLJycnGugWDWCwZ2aZNm4Szs7NQKpWib9++4vTp09K+gQMHismTJ+u0379/v3B3dxdKpVJ0795dfPvtt00ccfNRm9xevXpVAKi2DBw4sOkDbwZqk1sXFxe9uV22bFnTB94M1Ca3ISEholOnTkKlUgk7Ozvh4+MjIiIijBB181Hb/3N/i8WSYbXJ7Zw5c6S2Dg4O4uWXXxbnzp0zQtSPRyaEEMbq1SIiIiIydRyzRERERGQAiyUiIiIiA1gsERERERnAYomIiIjIABZLRERERAawWCIiIiIygMUSERERkQEsloiIiIgMYLFERAYdP34cMpkMd+/eNXYoDeZJvKf6CAoKwujRox+7fVZWFmQyGRISEhotpioymQyHDh1q9OsQGcJiichEBQUFQSaTVVsa86vc/v7+mDNnjs42X19f5OTkwMbGptGuC+jer7m5ORwcHDBkyBDs2LFD+tp7Q2mqe6qvXbt26f0ZUKlURo3LyckJOTk58PDwMGocRE3FzNgBEFHNhg0bhp07d+pss7e3r9aurKwMSqWyUWJQKpVQq9WNcu7fq7pfjUaDvLw8REZGYvbs2Th48CD+/e9/w8ysYf7Lasp7qi9ra2ukp6frbJPJZEaKppJCoWg2+SNqCOxZIjJhFhYWUKvVOotCoYC/vz9mzpyJOXPmoE2bNggMDAQAfPTRR/D09ISVlRWcnJwwY8YMFBcX65wzNjYW/v7+sLS0hJ2dHQIDA5Gfn4+goCD8+OOP2LBhg9SDkZWVpfeR1Zdffonu3bvDwsICrq6uWLt2rc41XF1dsXLlSrz11lto2bIlnJ2dsW3btse+3/bt28PLywvvv/8+Dh8+jKNHj2LXrl1Su7t37+Ltt9+Gvb09rK2t8dJLLyExMREAcOnSJchkMqSlpemce926dXBzcwNQ/THczz//jAkTJqB9+/awtLSEp6cnPv/8c53j/f39MWvWLCxYsACtWrWCWq1GaGioTpu7d+9i2rRpcHBwgEqlgoeHB7755htp/8mTJ+Hn54cWLVrAyckJs2bNwr179wzmRCaTVfsZcHBwAADcvn0barUaK1eulNqfOnUKSqUSMTExAIDQ0FD06tULW7duhZOTEywtLTFu3DgUFBTUeM3IyEi8+OKLsLW1RevWrTFixAhkZmZK+3//GK4qnzExMfD29oalpSV8fX2rFXmHDx+Gl5cXVCoVOnbsiOXLl6OiokLaf/nyZQwYMAAqlQrdunVDdHS0wdwQNRUWS0TN1O7du6FUKhEbG4stW7YAAORyOTZu3Ijk5GTs3r0bx44dw4IFC6RjEhISMHjwYHTr1g1xcXE4efIkRo4cCY1Ggw0bNsDHxwdTpkxBTk4OcnJy4OTkVO268fHxGDduHMaPH4+kpCSEhobir3/9q04xAwBr166Ft7c3zp8/jxkzZmD69OnVfnk+jpdeegk9e/bEV199JW0bO3Ysbt26haNHjyI+Ph5eXl4YPHgwfvnlF7i7u8Pb2xt79+7VOc/evXsxceJEvdcoKSlBnz598O233+LixYuYOnUqJk2ahJ9++kmn3e7du2FlZYUzZ85g9erV+OCDD6Rf6FqtFn/4wx8QGxuLzz77DCkpKVi1ahUUCgUAIDMzE8OGDcOrr76KCxcu4IsvvsDJkycxc+bMWuekir29PXbs2IHQ0FCcPXsWRUVFmDRpEmbOnInBgwdL7TIyMrB//3785z//QWRkpPRvUpN79+5h7ty5OHv2LGJiYiCXy/HKK6888nFoSEgI1q5di7Nnz8LMzAxvvfWWtO/EiRN48803MXv2bKSkpGDr1q3YtWsXPvzwQyl/Y8aMgVKpxJkzZ7BlyxYsXLiwzrkhalCCiEzS5MmThUKhEFZWVtLypz/9SQghxMCBA0Xv3r0feY4DBw6I1q1bS+sTJkwQL7zwQo3tBw4cKGbPnq2z7YcffhAARH5+vhBCiIkTJ4ohQ4botHnvvfdEt27dpHUXFxfxxhtvSOtarVa0bdtWbN682eD9jho1Su++1157TXTt2lUIIcSJEyeEtbW1KCkp0Wnj5uYmtm7dKoQQYt26dcLNzU3al56eLgCI1NRUvfekz/Dhw8W8efOk9YEDB4oXX3xRp83zzz8vFi5cKIQQ4rvvvhNyuVykp6frPV9wcLCYOnWqzrYTJ04IuVwuHjx4oPeYnTt3CgA6PwNWVlZi2LBhOu1mzJgh3N3dxcSJE4Wnp6dObpYtWyYUCoW4ceOGtO3o0aNCLpeLnJwcIYTh3AshxO3btwUAkZSUJIQQ4urVqwKAOH/+vBDi13x+//330jHffvutACDd2+DBg8XKlSt1zrtnzx7h6OgohKjMn5mZmcjOztaJE4D4+uuva4yNqClwzBKRCRs0aBA2b94srVtZWUl/79OnT7X233//PcLCwpCWlobCwkJUVFSgpKQE9+/fh6WlJRISEjB27Nh6xZSamopRo0bpbHvhhRewfv16aDQaqSelR48e0v6qR0m3bt2q0zWFENI4ncTERBQXF6N169Y6bR48eCA9Kho/fjzmz5+P06dPo3///ti7dy+8vLzQpUsXvefXaDRYuXIl9u/fj+zsbJSVlaG0tBSWlpY67X57TwDg6Ogo3VNCQgI6dOgAd3d3vddITEzEhQsXdHq8hBDQarW4evUqunbtqve4li1b4ty5czrbWrRoobMeHh4ODw8PHDhwAPHx8bCwsNDZ7+zsjPbt20vrPj4+0Gq1SE9P1zv26PLly1i6dCnOnDmDO3fuSD1K165dMzio+7f5cXR0BADcunULzs7OSExMRGxsrNSTBFTmvernMzU1FU5OTmjXrp1OnESmgMUSkQmzsrJCp06datz3W1lZWRgxYgSmT5+ODz/8EK1atcLJkycRHByMsrIyWFpaVvsl25jMzc111mUyWZ3faktNTcWzzz4LACguLoajoyOOHz9erZ2trS0AQK1W46WXXsK+ffvQv39/7Nu3D9OnT6/x/GvWrMGGDRuwfv16aczXnDlzUFZW9tj39KjcFhcXY9q0aZg1a1a1fc7OzjUeJ5fLa/wZqJKZmYmbN29Cq9UiKysLnp6eBts/ysiRI+Hi4oLt27ejXbt20Gq18PDwqJaP3/ttfqqK26r8FBcXY/ny5RgzZky144z9dh/Ro7BYInpCxMfHQ6vVYu3atZDLK4cj7t+/X6dNjx49EBMTg+XLl+s9h1KphEajMXidrl27IjY2VmdbbGws3N3dpV6lhnTs2DEkJSXh3XffBQB4eXkhNzcXZmZmcHV1rfG4119/HQsWLMCECRNw5coVjB8/vsa2sbGxGDVqFN544w0Alb/gL126hG7duj12nD169MCNGzdw6dIlvb1LXl5eSElJeWThU1tlZWV444038Nprr6Fz5854++23kZSUhLZt20ptrl27hps3b0q9NqdPn4ZcLkfnzp2rne/nn39Geno6tm/fDj8/PwCVA9Pry8vLC+np6TXef9euXXH9+nXk5ORIvVKnT5+u93WJGgIHeBM9ITp16oTy8nJs2rQJV65cwZ49e6SB31UWL16M//73v5gxYwYuXLiAtLQ0bN68GXfu3AFQ+RbbmTNnkJWVpfP45bfmzZuHmJgYrFixApcuXcLu3bvx8ccfY/78+fW+h9LSUuTm5iI7Oxvnzp3DypUrMWrUKIwYMQJvvvkmACAgIAA+Pj4YPXo0oqKikJWVhVOnTiEkJARnz56VzjVmzBgUFRVh+vTpGDRokM7jnd977rnnEB0djVOnTiE1NRXTpk1DXl5erWIfOHAgBgwYgFdffRXR0dG4evUqjh49isjISADAwoULcerUKcycORMJCQm4fPkyDh8+/MgB3kII5ObmVluq/m1CQkJQUFCAjRs3YuHChXB3d9cZWA1U9txMnjwZiYmJOHHiBGbNmoVx48bpfQRnZ2eH1q1bY9u2bcjIyMCxY8cwd+7cWuVCn6VLl+Jf//oXli9fjuTkZKSmpiIiIgJLliwBUPnv6u7urhNnSEhIva9L1BBYLBE9IXr27ImPPvoIf//73+Hh4YG9e/ciLCxMp427uzuioqKQmJiIvn37wsfHB4cPH5bmL5o/fz4UCgW6desGe3t7XLt2rdp1vLy8sH//fkRERMDDwwNLly7FBx98gKCgoHrfQ2RkJBwdHeHq6ophw4bhhx9+wMaNG3H48GGp10omk+HIkSMYMGAA/vznP8Pd3R3jx4/H//73P+mVeqByrM/IkSORmJiI119/3eB1lyxZAi8vLwQGBsLf3x9qtbpWM1pX+fLLL/H8889jwoQJ6NatGxYsWCD11PXo0QM//vgjLl26BD8/P/Tu3RtLly41WMQBQGFhIRwdHastt27dwvHjx7F+/Xrs2bMH1tbWkMvl2LNnD06cOKEz1q1Tp04YM2YMXn75ZQwdOhQ9evTAP/7xD73Xk8vliIiIQHx8PDw8PPDuu+9izZo1tc7F7wUGBuKbb75BVFQUnn/+efTv3x/r1q2Di4uLdN2vv/4aDx48QN++ffH222/rjG8iMiaZEEIYOwgiImocoaGhOHToUJN8moToScWeJSIiIiIDWCwRERERGcDHcEREREQGsGeJiIiIyAAWS0REREQGsFgiIiIiMoDFEhEREZEBLJaIiIiIDGCxRERERGQAiyUiIiIiA1gsERERERnw/5UN8HTkQXkAAAAAAElFTkSuQmCC", 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", 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ldl22.821719.8201.1510.250
hdl-0.477511.311-0.0420.966
tch0.84748.0790.1050.916
ltg29.57549.7393.0370.002
glu0.06493.2030.0200.984
\n", - "
" - ], - "text/plain": [ - " coef std err z P>|z|\n", - "intercept 0.0043 0.126 0.034 0.973\n", - "age 1.0364 2.792 0.371 0.711\n", - "sex -11.7477 3.078 -3.816 0.000\n", - "bmi 13.8258 3.323 4.160 0.000\n", - "map 11.5431 3.120 3.700 0.000\n", - "tc -32.9068 23.367 -1.408 0.159\n", - "ldl 22.8217 19.820 1.151 0.250\n", - "hdl -0.4775 11.311 -0.042 0.966\n", - "tch 0.8474 8.079 0.105 0.916\n", - "ltg 29.5754 9.739 3.037 0.002\n", - "glu 0.0649 3.203 0.020 0.984" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "yb = Y > 140\n", - "X.insert(0, 'intercept', np.ones(Y.shape[0]))\n", - "glm = sm.GLM(yb,X,family=sm.families.Binomial())\n", - "results=glm.fit()\n", - "summarize(results)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "43cad669-69de-49e4-b699-47d4909cd76e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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coefstd errzP>|z|
intercept0.0043242.6514390.0016310.998699
age1.03636358.7080200.0176530.985916
sex-11.74769864.717706-0.1815220.855958
bmi13.82577869.8648880.1978930.843129
map11.54305565.5848420.1760020.860292
tc-32.906817491.257990-0.0669850.946594
ldl22.821665416.6903660.0547690.956323
hdl-0.477463237.806917-0.0020080.998398
tch0.847433169.8473280.0049890.996019
ltg29.575377204.7410970.1444530.885143
glu0.06485567.3351350.0009630.999231
\n", - "
" - ], - "text/plain": [ - " coef std err z P>|z|\n", - "intercept 0.004324 2.651439 0.001631 0.998699\n", - "age 1.036363 58.708020 0.017653 0.985916\n", - "sex -11.747698 64.717706 -0.181522 0.855958\n", - "bmi 13.825778 69.864888 0.197893 0.843129\n", - "map 11.543055 65.584842 0.176002 0.860292\n", - "tc -32.906817 491.257990 -0.066985 0.946594\n", - "ldl 22.821665 416.690366 0.054769 0.956323\n", - "hdl -0.477463 237.806917 -0.002008 0.998398\n", - "tch 0.847433 169.847328 0.004989 0.996019\n", - "ltg 29.575377 204.741097 0.144453 0.885143\n", - "glu 0.064855 67.335135 0.000963 0.999231" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from glmnet import GLM, RegGLM\n", - "\n", - "#G3 = GLMNetPath(alpha=.4,family=sm.families.Binomial())\n", - "G3 = GLM(family=sm.families.Binomial(), summarize=True)\n", - "G3.fit(X.drop(columns=['intercept']), yb)\n", - "G3.summary_" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "c3283c62-2cc5-4515-8141-0151d6910f79", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.isnan(yb).sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "c36399b6-e1f1-487c-b138-a5ce0e8b6e80", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "612.7421076149917" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "G3 = RegGLM(family=sm.families.Binomial(), alpha=0.4, lambda_val=0.02081227758950013)\n", - "G3.fit(X.drop(columns=['intercept']), yb)\n", - "G3.coef_\n", - "G3.null_deviance_" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "83269ad5-0d49-4419-8a7d-71ace74818cf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "( Binomial Deviance Mean Squared Error Mean Absolute Error \\\n", - " lambda \n", - " 0.592814 1.382279 0.248996 0.498957 \n", - " 0.540150 1.368135 0.245464 0.495283 \n", - " 0.492164 1.342688 0.239118 0.488526 \n", - " 0.448442 1.317420 0.232844 0.481513 \n", - " 0.408603 1.293317 0.226903 0.474506 \n", - " ... ... ... ... \n", - " 0.000199 1.000221 0.166481 0.320840 \n", - " 0.000181 1.000308 0.166494 0.320816 \n", - " 0.000165 1.000443 0.166516 0.320814 \n", - " 0.000150 1.000558 0.166534 0.320807 \n", - " 0.000137 1.000531 0.166530 0.320810 \n", - " \n", - " Accuracy AUC SD(Binomial Deviance) SD(Mean Squared Error) \\\n", - " lambda \n", - " 0.592814 0.563232 0.656623 0.002578 0.000644 \n", - " 0.540150 0.688131 0.807424 0.004540 0.001134 \n", - " 0.492164 0.726313 0.813442 0.004864 0.001212 \n", - " 0.448442 0.737576 0.816132 0.006385 0.001589 \n", - " 0.408603 0.739747 0.817764 0.008584 0.002134 \n", - " ... ... ... ... ... \n", - " 0.000199 0.741919 0.839326 0.075760 0.014581 \n", - " 0.000181 0.741919 0.839528 0.075822 0.014591 \n", - " 0.000165 0.741919 0.839528 0.075863 0.014597 \n", - " 0.000150 0.741919 0.839528 0.075905 0.014603 \n", - " 0.000137 0.741919 0.839528 0.075891 0.014601 \n", - " \n", - " SD(Mean Absolute Error) SD(Accuracy) SD(AUC) \n", - " lambda \n", - " 0.592814 0.000651 0.042771 0.038739 \n", - " 0.540150 0.001187 0.028288 0.030698 \n", - " 0.492164 0.001300 0.027277 0.029218 \n", - " 0.448442 0.001675 0.029467 0.028982 \n", - " 0.408603 0.002219 0.028976 0.028708 \n", - " ... ... ... ... \n", - " 0.000199 0.014517 0.020239 0.026988 \n", - " 0.000181 0.014522 0.020239 0.027061 \n", - " 0.000165 0.014521 0.020239 0.027061 \n", - " 0.000150 0.014521 0.020239 0.027061 \n", - " 0.000137 0.014521 0.020239 0.027061 \n", - " \n", - " [91 rows x 10 columns],\n", - " Binomial Deviance 0.010853\n", - " Mean Squared Error 0.015746\n", - " Mean Absolute Error 0.000150\n", - " Accuracy 0.084030\n", - " AUC 0.017281\n", - " Name: lambda_best, dtype: float64,\n", - " Binomial Deviance 0.101214\n", - " Mean Squared Error 0.121913\n", - " Mean Absolute Error 0.018966\n", - " Accuracy 0.492164\n", - " AUC 0.492164\n", - " Name: lambda_1se, dtype: float64)" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "G4 = GLMNet(family=sm.families.Binomial(), alpha=0.4 )\n", - "G4.fit(X.drop(columns=['intercept']), yb)\n", - "G4.cross_validation_path(X.drop(columns=['intercept']), yb, cv=10, alignment='lambda')" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "86ac4b05-359a-490d-bd97-f25ba7181f01", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Degrees of FreedomFraction Deviance Explained
lambda
0.5928140-2.220446e-16
0.54015021.708898e-02
0.49216423.629912e-02
0.44844225.444699e-02
0.40860327.152318e-02
.........
0.000199103.161120e-01
0.000181103.161269e-01
0.000165103.161402e-01
0.000150103.161514e-01
0.000137103.161613e-01
\n", - "

91 rows × 2 columns

\n", - "
" - ], - "text/plain": [ - " Degrees of Freedom Fraction Deviance Explained\n", - "lambda \n", - "0.592814 0 -2.220446e-16\n", - "0.540150 2 1.708898e-02\n", - "0.492164 2 3.629912e-02\n", - "0.448442 2 5.444699e-02\n", - "0.408603 2 7.152318e-02\n", - "... ... ...\n", - "0.000199 10 3.161120e-01\n", - "0.000181 10 3.161269e-01\n", - "0.000165 10 3.161402e-01\n", - "0.000150 10 3.161514e-01\n", - "0.000137 10 3.161613e-01\n", - "\n", - "[91 rows x 2 columns]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "G4.summary_" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "698579c2-cb1c-4356-a963-9bad604464b4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Call: glmnet(x = X, y = Y > 140, family = \"binomial\", alpha = 0.4) \n", - "\n", - " Df %Dev Lambda\n", - "1 0 0.00 0.59280\n", - "2 2 1.71 0.54010\n", - "3 2 3.63 0.49220\n", - "4 2 5.44 0.44840\n", - "5 2 7.15 0.40860\n", - "6 3 8.92 0.37230\n", - "7 4 10.77 0.33920\n", - "8 4 12.54 0.30910\n", - "9 4 14.15 0.28160\n", - "10 5 15.62 0.25660\n", - "11 5 16.96 0.23380\n", - "12 5 18.16 0.21300\n", - "13 5 19.25 0.19410\n", - "14 5 20.24 0.17690\n", - "15 5 21.13 0.16120\n", - "16 5 21.93 0.14680\n", - "17 5 22.65 0.13380\n", - "18 4 23.30 0.12190\n", - "19 5 23.91 0.11110\n", - "20 5 24.65 0.10120\n", - "21 5 25.33 0.09222\n", - "22 5 25.93 0.08403\n", - "23 5 26.48 0.07656\n", - "24 5 26.98 0.06976\n", - "25 5 27.42 0.06357\n", - "26 5 27.82 0.05792\n", - "27 5 28.18 0.05277\n", - "28 5 28.49 0.04808\n", - "29 6 28.82 0.04381\n", - "30 6 29.13 0.03992\n", - "31 6 29.40 0.03637\n", - "32 8 29.66 0.03314\n", - "33 8 29.88 0.03020\n", - "34 8 30.08 0.02752\n", - "35 8 30.26 0.02507\n", - "36 8 30.41 0.02284\n", - "37 8 30.54 0.02081\n", - "38 9 30.66 0.01897\n", - "39 9 30.77 0.01728\n", - "40 9 30.86 0.01575\n", - "41 9 30.94 0.01435\n", - "42 9 31.01 0.01307\n", - "43 9 31.07 0.01191\n", - "44 9 31.12 0.01085\n", - "45 9 31.17 0.00989\n", - "46 9 31.21 0.00901\n", - "47 8 31.24 0.00821\n", - "48 8 31.27 0.00748\n", - "49 8 31.30 0.00682\n", - "50 8 31.32 0.00621\n", - "51 8 31.33 0.00566\n", - "52 8 31.35 0.00516\n", - "53 8 31.36 0.00470\n", - "54 8 31.37 0.00428\n", - "55 8 31.38 0.00390\n", - "56 8 31.38 0.00355\n", - "57 8 31.39 0.00324\n", - "58 8 31.40 0.00295\n", - "59 9 31.40 0.00269\n", - "60 9 31.40 0.00245\n", - "61 9 31.41 0.00223\n", - "62 10 31.41 0.00203\n", - "63 10 31.43 0.00185\n", - "64 10 31.44 0.00169\n", - "65 10 31.46 0.00154\n", - "66 10 31.47 0.00140\n", - "67 10 31.49 0.00128\n", - "68 10 31.50 0.00116\n", - "69 10 31.51 0.00106\n", - "70 10 31.52 0.00097\n", - "71 10 31.53 0.00088\n", - "72 10 31.54 0.00080\n", - "73 10 31.55 0.00073\n", - "74 10 31.56 0.00067\n", - "75 10 31.57 0.00061\n", - "76 10 31.57 0.00055\n", - "77 10 31.58 0.00050\n", - "78 10 31.58 0.00046\n", - "79 10 31.59 0.00042\n", - "80 10 31.59 0.00038\n", - "81 10 31.60 0.00035\n", - "82 10 31.60 0.00032\n", - "83 10 31.60 0.00029\n", - "84 10 31.61 0.00026\n", - "85 10 31.61 0.00024\n", - "86 10 31.61 0.00022\n", - "87 10 31.61 0.00020\n", - "88 10 31.61 0.00018\n", - "89 10 31.61 0.00016\n", - "90 10 31.62 0.00015\n", - "91 10 31.62 0.00014\n" - ] - } - ], - "source": [ - "%%R\n", - "print(glmnet(X, Y>140, family='binomial', alpha=0.4))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "7dc4693c-9c16-4f35-96ac-a29a059ec845", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", 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", 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", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def misclass_score(y, yhat, sample_weight):\n", - " # y is assumed binary\n", - " # yhat will be on response scale\n", - " label = yhat > 0.5\n", - " return 1 - accuracy_score(y, \n", - " label, sample_weight=sample_weight, normalize=True)\n", - " \n", - "G5 = GLMNet(family=sm.families.Binomial(), alpha=0.4 )\n", - "G5.fit(X.drop(columns=['intercept']), yb)\n", - "G5.cross_validation_path(X.drop(columns=['intercept']), yb, cv=10, alignment='lambda',\n", - " scorers=[('Misclassification Error', misclass_score, 'min')])\n", - "G5.plot_cross_validation(score='Misclassification Error')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "c64161f5-314c-4c79-a0c0-3646ee027cd7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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My ErrorSD(My Error)
lambda
0.5928140.5002530.013309
0.5401500.5002530.013309
0.4921640.5002530.013309
0.4484420.4957070.012334
0.4086030.4821210.013408
.........
0.0001990.2535860.027995
0.0001810.2535860.027995
0.0001650.2535860.027995
0.0001500.2535860.027995
0.0001370.2535860.027995
\n", - "

91 rows × 2 columns

\n", - "
" - ], - "text/plain": [ - " My Error SD(My Error)\n", - "lambda \n", - "0.592814 0.500253 0.013309\n", - "0.540150 0.500253 0.013309\n", - "0.492164 0.500253 0.013309\n", - "0.448442 0.495707 0.012334\n", - "0.408603 0.482121 0.013408\n", - "... ... ...\n", - "0.000199 0.253586 0.027995\n", - "0.000181 0.253586 0.027995\n", - "0.000165 0.253586 0.027995\n", - "0.000150 0.253586 0.027995\n", - "0.000137 0.253586 0.027995\n", - "\n", - "[91 rows x 2 columns]" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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FNTU1g/tQSrrvPqC83LFt1y5g1ixl4iEiIgpQQbPaq62tDXPmzMFLL72EuLi4fs+bOXOm/evc3FyMHTsWWVlZ2L17N6ZMmeL0GoPBENwrpaqqgB07oOndLgjAjh1AdTWQna1EZERERAFHseQnLi4OOp0OjY2NDu2NjY1ITEzsc/7Jkydx5swZ3HnnnfY226TeIUOG4Pjx48jKyupz3ciRIxEXF4cTJ070m/wEvZMnXR8/cUI1yQ/3LyOiUMBnnbwUS370ej0mTJiA8vJy+3J1URRRXl6OBQsW9Dk/JyfHXvfA5qmnnkJbWxv+8z//s995OufOncPFixeRlJTk888QMJwkfQ5GjfJPHDLT6XT9rvAjIlILPuvkp+iwV2lpKebOnYv8/HwUFBRg7dq16OjowLx58wAAJSUlSElJQVlZGcLDw3Hdddc5XB8TEwMA9vb29nasXLkS9957LxITE3Hy5EksXrwYo0aNQnFxsV8/m19dfTVQXAxp1y5oesxtknQ6aIqKVNPrQ0RE5AuKJj8zZszA+fPnsWLFCjQ0NCAvLw/bt2+3T4L+4osvPOr60+l0+Pe//42NGzeiubkZycnJmDp1Kp555pngntPjjs2bgZkzgffftzeJt90GXYjUOCIiInKXRpIkSekgAk1rayuio6PR0tKCqKgopcNxmyAIqNyyBcbaWnSlpGDU9OkYNmyY0mH5jCiKOHr0KABgzJgxHBMnIlXis27w3P37HTSrvcg9Xamp6EpN7f66q0vhaHxLkiQ0NTXZvyYiUiM+6+THdFLF1Jb8EBER+QKTHxVj8kNERNQXkx8VY/JDRETUF5MfFevq6uLu7kRERL0w+VEp2+qAr776SuFIiIiIAguTH5UyGo0AOPRFRETUG5e6q5TRaERHRwc6OzsxfPhwpcPxCZ1Oh29+85tKh0FEJCs+6+THnh+VYs8PERGRc0x+VCo8PBwAkx8iIqLeOOylUmrs+RFFEZ9//jkA4Nprr2XJdyJSJT7r5MfkR6VsyY/ZbIYgCNDpdApH5D1JknDhwgX710REasRnnfyY/KiA2WyGxWJxqOnz1VdfQafTQRAENDQ0ON3gTa/Xq3+3eyIiol6Y/KhAfX09zp4969B26NAh+9cnTpxwel16ejoyMjLkDI2IiCjgMPlRgaSkJKfL2U+dOoXm5mbEx8fjyy+/BADk5eXZx4/1er1f4yQiIgoETH5UwGAwOB2+iomJQXNzs0NbZGSkKub/EBERDRankKuYbdIzt7ggIiL6GpMfFVPjcnciIiJvcdhLxWzJj9VqVTgS39BqtZg8ebL9ayIiNeKzTn5MflRsyJAh0Ov1sFgsSofiExqNhvOViEj1+KyTH1NKlbP1/hAREVE3Jj8qp6bkRxRFHDt2DMeOHXMo6EhEpCZ81smPyY/KqSn5kSQJjY2NaGxsZMl3IlItPuvkx+RH5YYOHap0CERERAGFyY/Kqannh4iIyBe42kvlbMmPsaYGxro6ICEByMlROCoiIiLlMPlROW1zM8YtWYKYvXu7G5YsAYqLgc2bgdhYZYMjIiJSAIe91O6++xD9ySeObbt2AbNmKRMPERGRwpj8qFlVFbBjBzSC4NguCMCOHUB1tTJxERERKYjDXmp28qTr4ydOANnZ/onFB7RaLW688Ub710REasRnnfyY/KhZVpbr46NG+ScOH9FoNNDr9UqHQUQkKz7r5MeUUs2uvhooLobUe48Yna570nMQ9foQERH5CpMftdu8GeKttzq2FRV1r/YKMqIoorq6GtXV1Sz5TkSqxWed/DjspXaxsZD+93+xb+tWGGtrkVVcjKHjxikd1aBIkoS6ujoAwMiRIxWOhohIHnzWyY/JTwjQaDToSk1FV2oqklNTwQ0viIgolHHYK8SYzWalQyAiIlIUk58Qw+SHiIhCHZOfEMPkh4iIQh2TnxDD5IeIiEIdk58Q89VXXykdAhERkaK42ivEmM1mSJIEjUajdCge02q1KCwstH9NRKRGfNbJj8lPiJEkCVarNShLp2s0GoSHhysdBhGRrPiskx9TyhDEoS8iIgpl7PkJQcE66VkURZw+fRoAkJmZye5gIlIlPuvkx+RHxcxmMywWS5+9YVpaWhAeHg69Xg+DwaBQdJ6TJAnnzp0DAGRkZCgbDBGRTPiskx+THxWrr6/H2bNn+7TX1taitrYW6enp/B+LiIhCDpMfFUtKSsLw4cPt358/fx41NTWIjo5GVlZWUE56JiIi8pbiA4kvvvgiMjIyEB4ejsLCQuzbt8+t67Zs2QKNRoN77rnHoV2SJKxYsQJJSUkwGo0oKipCdXW1DJEHPoPBAJPJZH9FR0cDAARBgMlkCqohLyIiIl9RNPnZunUrSktL8fTTT+PAgQMYN24ciouL8eWXX7q87syZM/jpT3+Km2++uc+xF154Ab/97W+xfv167N27FxERESguLuYKJ8Ce7PBnQUREoUzR5GfNmjWYP38+5s2bh9GjR2P9+vUYOnQoNmzY0O81giBg9uzZWLlyJUaOHOlwTJIkrF27Fk899RTuvvtujB07Fq+++irq6urwzjvv9HtPs9mM1tZWh5ca2epGXL58Gc3NzWhra+vzCtaVYERERO5SLPmxWCyorKxEUVHR18FotSgqKsKePXv6vW7VqlWIj4/Hgw8+2OfY6dOn0dDQ4HDP6OhoFBYWurxnWVkZoqOj7a+0tLRBfqrANmTIEPuSyUOHDuHAgQN9XvX19QpHSUREJC/FJjxfuHABgiAgISHBoT0hIQHHjh1zek1FRQVefvllHDx40OnxhoYG+z1639N2zJlly5ahtLTU/n1ra6tqE6Dw8HB0dnZi5MiROHXqFAAgLy/PnhQF8iRorVaL/Px8+9dERGrEZ538gma1V1tbG+bMmYOXXnoJcXFxPr23wWAImcm/RqMRnZ2dDnt7RUZGQqfTKRiVezQaDSIiIpQOg4hIVnzWyU+x5CcuLg46nQ6NjY0O7Y2NjUhMTOxz/smTJ3HmzBnceeed9jZb8b4hQ4bg+PHj9usaGxuRlJTkcM+8vDwZPkXwsSV5nNtDREShSrH+NL1ejwkTJqC8vNzeJooiysvLceONN/Y5PycnB4cPH8bBgwftr7vuugu33norDh48iLS0NGRmZiIxMdHhnq2trdi7d6/Te4Yi26TnYEx+RFHEmTNncObMmT5Vq4mI1ILPOvkpOuxVWlqKuXPnIj8/HwUFBVi7di06Ojowb948AEBJSQlSUlJQVlaG8PBwXHfddQ7Xx8TEAIBD+8KFC7F69WpkZ2cjMzMTy5cvR3Jycp96QKEqmHt+JEmyV6xW65wsIiI+6+SnaPIzY8YMnD9/HitWrEBDQwPy8vKwfft2+4TlL774wuPJXosXL0ZHRwceeughNDc3Y/Lkydi+fbu9xyPUBXPPDxERkS9oJEmSlA4i0LS2tiI6OhotLS2IiopSOhyfMpvN+L//+z+HtsmTJwfFhGdBEFBRUQEgeGImIvIUn3WD5+7fb66hCzF6vd5hpRcREVGo8Sj5sVqteOCBB3D69Gm54iGZaTSakFnWT0RE5IxHyU9YWBjefPNNuWIhP+H8JyIiCmUeD3vdc889LvfJosDH5IeIiEKZx6u9srOzsWrVKnz88ceYMGFCnyqUjz/+uM+CI3kE67CXVqvF9ddfb/+aiEiN+KyTn8ervTIzM/u/mUZj3y8qmKl5tRfQvQfa8ePH7d9zNQEREamBu3+/Pe754WTn4BesPT9ERES+4FWRQ1unEZdOB5dgnfMjiiJqa2sBACkpKewOJiJV4rNOfoP6ib766qvIzc2F0WiE0WjE2LFj8dprr/k6NpJJsPb8SJKEU6dO4dSpU2BtTiJSKz7r5Odxz8+aNWuwfPlyLFiwADfddBMAoKKiAj/60Y9w4cIFPPHEEz4PknxLq9UiLCwMQ06dgrGuDkhIAHJylA6LiIjILzxOfn73u99h3bp1KCkpsbfdddddGDNmDH7+858z+QkGTU0Y89OfInrPnu7vlywBiouBzZuB2FhlYyMiIpKZx8Ne9fX1mDRpUp/2SZMmob6+3idBkczuuw9R+/Y5tu3aBcyapUw8REREfuRx8jNq1Ci88cYbfdq3bt2K7OxsnwRFMqqqAnbsgEYQHNsFAdixA6iuViYuIiIiP/F42GvlypWYMWMGPvzwQ/ucn48//hjl5eVOkyIKMCdPuj5+4gTAJJaIiFTM456fe++9F/v27UNcXBzeeecdvPPOO4iLi8O+ffvwH//xH3LESL6UleX6+KhR/omDiIhIIR71/FitVjz88MNYvnw5Nm3aJFdMJKerrwaKiyHt2uU49KXTAUVFAd3ro9VqMW7cOPvXRERqxGed/LireyjavBmYMsWxraiouz2AaTQaxMTEICYmhoU1iUi1+KyTH3d1D0WxsRC3bcO+TZtw+PnnUf/BB8D27VzmTkREIYG7uoewrtRUdKWmIiw+HklKB+MGURTt5RSSkpLYHUxEqsRnnfw8Tn5efvllxMTEoLKyEpWVlQ7HNBoNk58g1N7ejra2tn6P6/X6gNgSQ5IknDhxAgCQmJiocDRERPLgs05+HiU/kiRh9+7diI+Ph9FolCsm8rPOzk4cOHCg3+Pp6enIyMjwX0BEREQy8jj5yc7OxtGjR1nQUEUkScKYMWMQFhaGgwcPAgDy8vLsXa16vV7B6IiIiHzLo4FErVaL7OxsXLx4Ua54SEGRkZEOX5tMJphMpoAY8iIiIvIVj2dRPffcc1i0aBGOHDkiRzykkM7OTqVDICIi8guPJzyXlJSgs7MT48aNg16v7zP3p6mpyWfBkf90dHQoHQIREZFfeJz8rF27VoYwSGlMfoiIKFR4nPzMnTtXjjhIYZ2dnRBFUekwXNJqtbjuuuvsXxMRqRGfdfJz+6f6xhtvwGKx2L8/d+6cwx/Lzs5OvPDCC76NjvxCp9NBkiR0dXUpHYpLGo0Gw4cPx/Dhw1nynYhUi886+bmd/MyaNQvNzc3270ePHo0zZ87Yv29ra8OyZct8GRv5ydChQwFw0jMREYUGt5MfSZJcfk/By7ZFSaAnP6IooqGhAQ0NDQE/REdENFh81snP4zk/pD62np9An/QsSRKOHz8OABgxYoTC0RARyYPPOvlxJhVx2IuIiEKKRz0/O3bsQHR0NIDubrny8nJ7scOe84EocJnNZlgsFqddqV999ZUCEREREfmXR8lP72XuDz/8sMP3nJUe+Orr63H27FmHNlbrJiKiUOJ28sNJV+qQlJSE4cOH92mvrq5GW1ubAhERERH5Fyc8hxiDweB0o9KoqCgmP0REFBI44ZkAfL3cnYiISO3Y80MAvk5+jDU1MNbVAQkJQE6OwlE50mq1GD16tP1rIiI14rNOfhqJ1Qr7aG1tRXR0NFpaWhAVFaV0OH4hnD+PljvuwLD9+79uLC4GNm8GYmOVC4yIiMhN7v79Zs8PAQB0c+YgtrLSoU3atQvC976Hrrff7nO+Xq93OneIiIgo0A1qV/cHH3wQ3/jGN+SIh5RQVQXs2IHehQo0goAh5eX4/G9/Q1dqqsOx9PR0ZGRk+C1EoLvq6YULFwAAcXFxLK1ARKrEZ538PB5MbGlpQVFREbKzs/GLX/wCtbW1csRF/nTypMvDxiu/47y8PIwfPx7jx49HUlKSPyJzIIoiPvvsM3z22WcsvUBEqsVnnfw8Tn7eeecd1NbW4pFHHsHWrVuRkZGB6dOn43/+539gtVrliJHklpXl8nBXSgoAIDIyEiaTCSaTiUNeREQUtAY1jXzEiBEoLS3FoUOHsHfvXowaNQpz5sxBcnIynnjiCVRXV/s6TpLT1VcDxcWQdDrHdp0O0tSpfYa8iIiIgplXa+jq6+uxc+dO7Ny5EzqdDt/61rdw+PBhjB49Gr/5zW98FSP5w+bNwJQpjm1FRRBff12ZeIiIiGTi8YRnq9WKv/3tb3jllVfw/vvvY+zYsVi4cCHuu+8++7Kyt99+Gw888ACeeOIJnwdMMomNhbhtGyq3bIGxthbDCgqQcsstgCAoHRkREZFPedzzk5SUhPnz5yM9PR379u3DJ598gh/96EcO6+lvvfVWxMTEuHW/F198ERkZGQgPD0dhYSH27dvX77lvvfUW8vPzERMTg4iICOTl5eG1115zOOf++++HRqNxeE2bNs3TjxmyulJT0VRYiEtxcUqHQkREJAuPe35+85vf4Hvf+x7Cw8P7PScmJganT58e8F5bt25FaWkp1q9fj8LCQqxduxbFxcU4fvw44uPj+5w/bNgw/OxnP0NOTg70ej3effddzJs3D/Hx8SguLrafN23aNLzyyiv27zk513Pc54uIiNTK4+Rnzpw5PnvzNWvWYP78+Zg3bx4AYP369XjvvfewYcMGLF26tM/5t9xyi8P3P/7xj7Fx40ZUVFQ4JD8GgwGJiYk+izMUWSwWmM1mDBkSOHUwNRoNrrnmGvvXRERqxGed/Nz+y/bAAw+4dd6GDRvcOs9isaCyshLLli2zt2m1WhQVFWHPnj0DXi9JEv7xj3/g+PHjeP755x2O7d69G/Hx8YiNjcVtt92G1atXY/jw4f3ey2w2w2w2279vbW116zOoXVtbG2IDaGsLrVbLpJaIVI/POvm5nfz8+c9/Rnp6Oq6//nr4YjuwCxcuQBAEJCQkOLQnJCTg2LFj/V7X0tKClJQUmM1m6HQ6/OEPf8Dtt99uPz5t2jR85zvfQWZmJk6ePIknn3wS06dPx549e6DrvZT7irKyMqxcudLrz6Q2ra2tAZX8EBER+YLbyc8jjzyCzZs34/Tp05g3bx5+8IMfYNiwYXLG5pTJZMLBgwfR3t6O8vJylJaWYuTIkfYhsZkzZ9rPzc3NxdixY5GVlYXdu3djSu+l3FcsW7YMpaWl9u9bW1uRlpYm6+cIBoE270eSJDQ1NQHonv/F7mAiUiM+6+Tn9mqvF198EfX19Vi8eDH+/ve/Iy0tDd///vexY8eOQfUExcXFQafTobGx0aG9sbHRZXefVqvFqFGjkJeXh5/85Cf47ne/i7Kysn7PHzlyJOLi4nDixIl+zzEYDIiKinJ4UXfy44tePl8RRRFHjhzBkSNHWPKdiFSLzzr5ebTU3WAwYNasWdi5cyc+++wzjBkzBo8++igyMjLQ3t7u0Rvr9XpMmDAB5eXl9jZRFFFeXo4bb7zR7fuIougwX6e3c+fO4eLFi4rsRRXMtFotBEFAV1eX0qEQERH51KCX8mi1Wmg0GkiSBGGQhfBKS0sxd+5c5Ofno6CgAGvXrkVHR4d99VdJSQlSUlLsPTtlZWXIz89HVlYWzGYztm3bhtdeew3r1q0DALS3t2PlypW49957kZiYiJMnT2Lx4sUYNWqUw2owGlhkZCRaW1sDbuiLiIjIWx4lP2azGW+99RY2bNiAiooKfPvb38bvf/97TJs2DVqt5ztlzJgxA+fPn8eKFSvQ0NCAvLw8bN++3T4J+osvvnC4b0dHBx599FGcO3cORqMROTk52LRpE2bMmAEA0Ol0+Pe//42NGzeiubkZycnJmDp1Kp555hnW+vGQyWRi8kNERKqkkdyc1PHoo49iy5YtSEtLwwMPPIDZs2cjTqVVgFtbWxEdHY2WlpaQmv8jCAIqKioAADk5OTh27BgiIiLQ0dEBACgoKMDly5f7vV6v18uaZPaMb/Lkyf2u3iMiCmZ81g2eu3+/3e75Wb9+Pa666iqMHDkSH3zwAT744AOn57311lueR0sBx2QyAYA98QG6N7Ktqanp95r09HRkZGTIHRoREZFX3E5+SkpKuNwuhOj1euj1elgsFntbUlISRowYAVEUcfDgQQBAXl6efWhSr9crESoREZFHPCpySKFDo9HAZDLh4sWL9ja9Xg+j0egwwT0yMtJvXbIajQajRo2yf01EpEZ81skvcDZuooATFRXlkPw4Y7FY/DYPSKvVIiUlxSf3IiIKVHzWyY/JD8FsNsNisTgU02pvb7f36BhramCsqwMSEoCcHIdrOQ+IiIiCDZMfQn19Pc6ePevQdvDgQQxpbUXuM89g2P793Y1LlgDFxcCmTfbz/DkPSJIktLS0AACio6PZHUxEqsRnnfyY/BCSkpKc7npv/I//gK6y0rFx1y5oZ88GnnwSgH/nAYmiiEOHDgHg8k8iUi8+6+TneWVCUh2DwQCTyeT4qq/HkPJyaHrvKyMI0Lz/PoznzikTLBERkZeY/JBzJ0+6PGysrfVTIERERL7F5Iecy8pyebiLKxGIiChIMfkh566+GiguhtR7rFmngzR1KrpSU5WJi4iIyEuc8Ez927wZmDkTeP/9r9uKiiBu2gQcPerRrWzL6XuyWq2wWq0AgLCwMISFhTkcl3uvMCIiCk1Mfqh/sbEQt21D5ZYtMNbWQsjMRN73vgf0WNnljLPCh3V1dWhoaPDo7VkjiIiI5MDkhwbUlZpqH+bq6uoasHbPQIUPbcaMGQNBECCKIqqqqgC4rhGk0WgwcuRI+9dERGrEZ538mPyQRy5duoSEhASX57gqfNizLTY2FjqdDoIg2JMfVzWCtFot0tLSfPZZiIgCEZ918mPyQx5pamoaMPlxVfhQGGDIjIiISG5Mfsgjzc3NDnuA+ZMkSWhrawMAmEwmdgcTkSrxWSc/LnUntw0ZMgSCIKC9vV2R9xdFEZ9++ik+/fRTxRIwIiK58VknPyY/5LaYmBgA3fN+iIiIghWTH3KbLflpbm5WNA4iIiJvMPkht8XGxgKAfSyaiIgoGDH5IbcZDAYMHTpU6TCIiIi8wuSHPGLr/THW1GDY3r1AdbXCEREREXmGS93JI8MADFu0CMP27+9uWLIEKC4GNm3yyf2dbY1hwxpBRETkC0x+yCOxjz0GVFY6Nu7aBe3s2cCTT3p9/4G2xoiOjkZMTAzrXhCRamk0GqSnp9u/Jt9j8kPuq6qCpucO7zaCAM3778NYUmLfA2ywXG2NAXCndyJSP61Wy02dZcbkh9x38qTLw8baWq+TH1dbYxAREfkCkx9yX1aWy8NdKSmyvr0kSejs7AQADB06lN3BRKRKfNbJj6u9yH1XXw0UF0Pq1Qsj6XSQpk71utdnIKIo4pNPPsEnn3zCku9EpFp81smPyQ95ZvNmYMoUh6bWiRNx+bXXFAqIiIjIMxz2Is/ExkLctg2VW7bAWFsLa3o62hITcVVHB4w1NTDW1QEJCUBOjs/e0rb8vec8oLa2Nvs8IE6CJiIiTzD5IafMZjMsFotDl2t7ezu0Wi1EUURXaiq6UlNxzTXX4OT+/YieOROZ+/Z1n+jj2j/Olr8fOnTI/nV6ejpXRhARkduY/JBT9fX1OHv2rEObbel5T3FxcYh47jlEfvKJ4wEf1v6xLX+3Wq04fPgwACA3NxdhYWEAunt+iIiI3MXkh5xKSkrC8OHDnR7rWYNHU10N07/+1fckH9b+sS1/t1gs9rbIyEgmPURENChMfsgpg8HQ7zwah20m/FD7h4iIyJeY/JB3/Fj7p2etC1/XvbDNceoPJ1UTkb9oNBqkXvlHI2v8yIPJD3nHVvtn1y5oevQISTodMGWKT3t9bFtc9P7aF5zNceqJk6qJyF+0Wi2yBviHJXmHyQ95b/NmYOZMoMe+X5ZvfANDXn8dOHpUwcDcZ5vj5GpPMSIiUgcmP+S9XrV/ulJSoL3mGuTFxPj0bSRJcvq1L9jmOHFPMSJSmiRJMJvNALqfTRz68j0mP+Qztto/Op0OQkcHmpqafHr/njWHWPKdiNRKFEXs3bsXADB58mT+I0wG3N6CfC4pKQkA+hQmJCIiCgTs+SGfS05ORl1dHdrb22Xd8sLWLewMV2cREVF/mPyQz+n1eqQYjYh59FEM27+/u9HHW140NDSgtra23+O21Vlcwk5ERL0x+SFZpD/5JLSVlY6NV7a8MM6Z43VvUGJiIhISEgZcncUl7ERE1BuTH/K9qirodu7s235ly4sC25J4L3qD9Ho99Hr9gKuzuISdiIh6Y/JDvjfAlhcOfLgBqjPeLGG3WCy4fPlyv8c5ZEZEFJyY/JDveVKZ9EpvUOztt0Mjii6Hwny1vYWrpKbnEvr6+nqXK9Y4ZEZEctBoNEhOTrZ/Tb6n+FL3F198ERkZGQgPD0dhYSH27dvX77lvvfUW8vPzERMTg4iICOTl5eG1115zOEeSJKxYsQJJSUkwGo0oKipCdXW13B+DerJteeFBbYqxixYhd8kS6K69Fpg2Dbh0qc85A21vYbFY0NbW5vTV3t5uP6++vh4HDhxw+rINjQHdQ2bjx49HXl6evS0vLw/jx4/H+PHj7Uv6iYh8SavVIjs7G9nZ2T7fyoe6Kdrzs3XrVpSWlmL9+vUoLCzE2rVrUVxcjOPHjyM+Pr7P+cOGDcPPfvYz5OTkQK/X491338W8efMQHx+P4uJiAMALL7yA3/72t9i4cSMyMzOxfPlyFBcX47PPPkN4eLi/P2LocrLlhdsGORQ2UE+NTVJSEkaMGOF0HlDPNr1eD6PR6LOqz1x5RkQUGBRNftasWYP58+dj3rx5AID169fjvffew4YNG7B06dI+599yyy0O3//4xz/Gxo0bUVFRgeLiYkiShLVr1+Kpp57C3XffDQB49dVXkZCQgHfeeQczZ86U/TPRFb22vBh9113QLVzYZwNUp64MhRlLShw2Rh1oewtfJDXCQLG5yVmiU1dXh4aGhn6v8cUwGhMsouAnSRKsVisAICwsjENfMlAs+bFYLKisrMSyZcvsbVqtFkVFRdizZ8+A10uShH/84x84fvw4nn/+eQDA6dOn0dDQgKKiIvt50dHRKCwsxJ49e/pNfsxms0PBvNbW1sF+LOrFtuUFsrM97g0y1tY6JD8DbW/hj6TGXQMtsbfx9cozLu0nCn6iKNr/DnJ7C3kolvxcuHABgiAgISHBoT0hIQHHjh3r97qWlhakpKTAbDZDp9PhD3/4A26//XYAsP+r2tk9Xf2Lu6ysDCtXrhzsRyF39e4Nys2F7lvf6vf0rpQUPwbnW66W2Pds8/XmqVzaT0Q0sKBb7WUymXDw4EG0t7ejvLwcpaWlGDlyZJ8hMU8sW7YMpaWl9u9bW1uRlpbmg2jVxTak0rPXpb293f4H3V323qDJk7snRvcaCpN0OmDKFIden0A20FCTjbNeKGcrz6xWq0OXd1hYmMNxV0NXzpb26/V6+3tYLJY+sXIojIhCjWLJT1xcHHQ6HRobGx3aGxsbkZiY2O91Wq0Wo0aNAtD9L9rPP/8cZWVluOWWW+zXNTY2OqzEaWxsdFix05vtDwa55mxIpefqqEFxMhTWOWkSwl9/HTh61Lt7+4m7Q1z9XevpBrDubN3BJftERP1TLPnR6/WYMGECysvLcc899wDofmCXl5djwYIFbt9HFEX7fJ3MzEwkJiaivLzcnuy0trZi7969eOSRR3z9EUKObUjFmZ7DLB7pNRTWlZIC81VXYWJkpHfBysRZT01UVBRycnIgiiKqqqoAOB/icsbZJO0xY8ZAEASn9wPc37rD1XtwKIyIQpmiw16lpaWYO3cu8vPzUVBQgLVr16Kjo8O++qukpAQpKSkoKysD0D03Jz8/H1lZWTCbzdi2bRtee+01rFu3DkB3MaiFCxdi9erVyM7Oti91T05OtidYNHiuesi8nVBsGwqLjIyE2N6Ouro6r+4nF3d7atydaO1sknZsbKz9Wlvy4+nWHXIu2SciCnaKJj8zZszA+fPnsWLFCjQ0NCAvLw/bt2+3T1j+4osvHAo8dXR04NFHH8W5c+dgNBqRk5ODTZs2YcaMGfZzFi9ejI6ODjz00ENobm7G5MmTsX37dtb4CRKpqak4duwY6urqYKyp8XoDVF9zdzm9rw201YaNUqvbiIiCieITnhcsWNDvMNfu3bsdvl+9ejVWr17t8n4ajQarVq3CqlWrfBUi+dHw4cNhslqRsWgRhu3f3914ZQNUzauv2s9Tqu6FUsvpBzM3KBB5U4eINYwoVGg0GnsnAGv8yEPx5IeoJ41GgzHPPgt9ZaXjgV27MGTOHHvV51Ar+a5Uj5OveVOHiDWMKFRotVrkBEhvt1ox+aHAUlUFQ68ePwD9Vn0OFb7ucfL1Ent3eVOHyNm1tsnhtpjb2tqcfg6l2nz98yMi32DyQ4Hl5EmXh21Vn51tb0Hu82aJvTfcrUM0UCJh09raGjTDgeyZIndJkmQvV6HVajn0JQMmPxRYsrJcHrZVffakqCL15c0Se1/zZj6Tu59DqTaWFKDBEEURFRUVALi9hVyY/FBgufrqfqs+S7fdFpJDXnJwd4m9u70yng4D9UxePU3EBlrG7+xzKNXGkgJEgYnJD3nFV1teOHBS9bk5Px+GV14BTpzwNmTygD9WmXla6yiYlvEPVKKA84CIlMHkh7wiy5YXvao+C5mZaImPR0Jb29e1f+LigDFjvHsfGpCvh5WCcYWaN7i1CFFgYvJDXpFly4srbFWfc3Nz8fnHHyN+0SLk9Kr9g02bBn1/Gpivh5WCsffGG66SR0D5FWo9ydUL5aw+E1fGkdKY/JBX5NzywiY6Ohp5L7yAoU5q/2hnz7bX/iEKNM6Sx0BdoSZXL9RgNv51tXlvIJUykCsZDZV/HCiJyQ8FvqoqRFxZ+eAgxGv/UHAKpBVq/liN5qo+k6827yXyFJMfCnxu1v4hCgaBtELN16v53K3P5G4sUVFRyMnJCYiyBf5MRkVRtO9tyRo/8mDyQ4HPzdo/vhKIG6oSySGQ9oxzN5ZAKmUgZ2mE3NxcL3+i5AqTHwp8Lmr/YMoU3/X6NDVBO2sWCmxL7DmpmlTOH0Nw7q7wU8v+dRQcmPxQcHBW+2fCBBg2bPBd7Z/77gPKyx3bOKmaVMwfQ3DurvDz9f51wcjdPffU0taTv1f4Mfmh4BAbC+vf/46D//3fMNbWQnv11bgQG4thTU2+GaaqqgJ27ECf0XVOqiYiPwmkYUh/83fNKyY/FFRstX/GjRuHlooKpCxahGG+qP3DSdVEpDDb0J/VasXhw4cBANdee619o1OlJ2QH42rD/mj9+m5EPjJ06FDk/fKXiO2v9o+n/DypmoioN71eD5PJhMjISHtbTEwMEhISEB8fb2+LjY1VRVtkZCRMJhNMJpPfi1oy+SGfM5vNaGtrQ3t7u72tvb29T5tXqqow9MMPoem9f5htmOrcuT6XGGtqMGzvXqC6uu/9rr4aHTffDEnr+L+EpNNBmjqVvT5ERCrCYS/yOVn2++pFc+qUy+MOw1RurOKyWq3499KluObnP/96GA3dG6oO3bgROH7cp/ETEZFymPyQz8m535eNNHKky+MOw1RurOKqq6uDJSICp9etw4ljx2CsrYU4ciSaR4xAQnOzz2v/sJYQEZFyOOxFPmcwGOzjuL1fPceyvWKr/XOlIJhNn2Eq2yqu3ktlew2P1dXVAQBSr0yobiosRNptt2FIayviS0pQMGcOcpcsge7aa4Fp04BLlwYXd1MTtN/6lu/uR0REHmPyQ0GjZ5l3jUbTXftnyhSHcy5NmIC2P/3J/r00QA0gY20tgO6NBIcOHerQY2XbUNVnk6oB171QRERw8qwjn+OwFwUNbY/JyFqtFoiNhbhtGyq3bIGxthb60aPRYDLBdPGifVipJiYGmS7uKWm1GLZ3L7pSUnBVUZHjg8ZHG6rah7g6O/1aS4hDa0TBqc+zjnyOyQ/5hdlshsVigdhjdVZ7e7u9dL03bLV/Jk6ciEu7dyOjZ+0fAJaoKIR1dDhujaHR4HJUFMYuWvR129SpEF9//esbe1v7p/dE6wH4rJYQt+kgInKJyQ/5hT9WgBkMBuS98AIMvYapwtrbgdhY4OJFe9vlqCgMaWtzOE9TXu64lcUgav849LYsXNh3iMsFWy9Uz56aQfXeuBhaM86Zw94gIgp5TH7IL3yxAqzn/j6CINh3P7arqkL4Bx/0uU4jisDFizj0q19BKwgYnZuLsG99y9kbOA4/ebKhqrPeFqDvEJcTkkYDMSbm616oJUuAW2+FFkDBP//5dZs7vTcDbNPB3iCiwDfgs468xsFE8gu/rAAbYJhKKwhoKiwEBtgk0TYJGkC/k6o7X37Z8SJnvS1usppM0La0ODb+85/dr57cmRg9wM/A4/sREakQe35IPdwdpvJkOKvXpGrdNdfgfEwMYntuqNrfRGYXbL1Q1+bmQu+sFwpOeo1cTIy2xdKUmooR7gbBTVuJKEQx+SHF+HwSdD/DVNDpIPUcpnL3vB5sk6rHjx+P5g8+QGqvSdVuu/Iezfn5AABNr3lH7nBZvRpXJni3t/fd+sOd+xERhQAOe5Fi6uvrceDAAYf5PgcPHuzT5hEnw1QoKnJcxeXJeb30u6Gqu3q/xwC9UM4MVL3aPsHbTfaJ1j32PHO5D5qPePMe/oiPiNSLPT+kGFm2weg1TDX6rrugy8npO8/H3fN6u7Khqluu9PLsLynp/z3664UCIMFx6EvSamG95RZAkrr/8Pcz3NZngvddd0G3cGHfidu+nGjtCW+W4nMZPxH5AHt+SDFyToK2bVGB7GyfnGfnyYTiK708A76Hs16o224Dbr3Voal53Dh0NDd/vTVGP3OFbOwTvLOznb6HTydae8KbKteskE1EPsCeHyJPDDBM5dDb4k5PEtBvL5QoCPa2a+64A7qHHoLp//7P7VBdTdwezETr2Ntv7+5VGqAOkcu2Aapcu3wPb64NgDYiChxMfiho9Kx1oVjdiwEmS9smMrvdk9SDbVJ1z2ttbTqtFlH/+pd7N3Jj4vZgJloPODzmTps37+GP+ORo47AceSggnnUqx+SHyFObNwMzZwI9t60oKoK4aRNw9Kg87+npcNtAsQxiorUDZwmJu23evIebJCfXKtVmH5azVQ4nIsUx+SHy1GAnS3vD18NtHky07o+zc9xtc9dgr/UmNl+3sZ4SUeDhhGcKGr1LvivN48nS3rAlK727wHU6SFOnojk/3/NY3JxoTb7hUDmcyIVAe9apEXt+iIKFr4fb3JhoPTo3d8BVZYPVc681T9/Dm2uV4mzjWiJSBpMfCiiuqj5brVYFIwsAMg23uZpojcmTPRoec6ut9+RwT97Dm2sVapM0GlyOinKckM1J0ESK4rAXBRRXVZ8PHz6sXGABxK/DbYD7w2PutrlbcdvX1yrUdtlkwpDeq+tYm4hIUez5oYDiquqz1WplAqQEd4bHPGxzt+K2r6/1e1tuLsKcDcsFYG0if/AmlkCq2aSW30coY/JDAcVgMMBgMDg9ZrFY/BwN9eRyeGwQbd68hz/i80nbAPWUAqI2kT+G4JxtS+JuLO5eq5a24mJg40bPf8bkESY/RERy8aCektJ1iIxz5sjXm7FwYZ9tSdyOxc1r1dKGXbswZM4cGEtKun8GcXHAmDEAAqtnKth7upj8EBHJxUU9pd6UrkMka8+Kk/d2OxY3r1VLGwQB2p07UbBzZ/f3gdYzJUdP1+bNQGyss5+GbJj8UNBgyXcKSs5KFAQ6byp4e1PVm5zz9e8jkNp27QJmzQK2b+97TEZMfoiI5NR7QnaQ1CbyeY8GeSWQeqd82iYIwI4dQHW1/1awgkvdKQiYzWa0tbWhvb3d3tbe3t6njSiQ2UsUTJ3qvFo3umsEKdGmFG9iUepnpebfh6JOnPDr2yme/Lz44ovIyMhAeHg4CgsLsW/fvn7Pfemll3DzzTcjNjYWsbGxKCoq6nP+/fffD41G4/CaNm2a3B+DZOSq9k/PNqKgEUi1iZTiTSwBVMdJNb8PpY0a5de3U3TYa+vWrSgtLcX69etRWFiItWvXori4GMePH0d8fHyf83fv3o1Zs2Zh0qRJCA8Px/PPP4+pU6fi6NGjSElJsZ83bdo0vPLKK/bv+1s6TcHBVvtHEAQcOnQIADBu3DjodDqIosgEiIJPINUmWrhQ/grZVypz7y8p8TwWd69VS5s/fh+B1KbTAUVFfh3yAhROftasWYP58+dj3rx5AID169fjvffew4YNG7B06dI+57/eq7Lrf/3Xf+HNN99EeXk5SkpK7O0GgwGJiYnyBk9+Y6v903ODP5PJBJ1Ox03/KKgpXocoO9v5hOzbbgMkyXGCqjdtV/ag6zp61PNY3L1WLW2bN0OaMQMa22ovb3/2gd5WVNT9e/czxZIfi8WCyspKLFu2zN6m1WpRVFSEPXv2uHWPzs5OWK1WDBs2zKF99+7diI+PR2xsLG677TasXr2636rBQPecErPZbP++tbXVw09DRBSk/NQL5dYedDLtXxdUYmNx+d13cfC//xvG2lpcc8cd0I8ZEzg9U3L8t6EAxZKfCxcuQBAEJCQkOLQnJCTg2LFjbt1jyZIlSE5ORlFRkb1t2rRp+M53voPMzEycPHkSTz75JKZPn449e/b0uzy6rKwMK1euHPyHIb8zm80QBMHpBqg929xlsVhw+fJln92PKNj4o4fDm1hCTUD0Qvmxzd+Cdqn7c889hy1btmD37t0IDw+3t8+cOdP+dW5uLsaOHYusrCzs3r0bU3pPMLxi2bJlKC0ttX/f2tqKtLQ0+YInrzU0NKCmpsahzZu5P/X19T69HxERBS7Fkp+4uDjodDo0NjY6tDc2Ng44X+dXv/oVnnvuOezatQtjx451ee7IkSMRFxeHEydO9Jv8uNpPigJTYmIiRowY4fTYYCZBJyUl+fR+REQUuBRLfvR6PSZMmIDy8nLcc889ALr/yJSXl2PBggX9XvfCCy/g2WefxY4dO5Cfnz/g+5w7dw4XL15EUlKSr0InBQ0dOhRAd8La3zDmYCZB6/V6GI1Gn92PiIgCl6LDXqWlpZg7dy7y8/NRUFCAtWvXoqOjw776q6SkBCkpKSgrKwMAPP/881ixYgX+8pe/ICMjAw0NDQCAyMhIREZGor29HStXrsS9996LxMREnDx5EosXL8aoUaNQXFys2Ock39DpdJg4caJH1wTSXJ5AioWIAhe38pGfosnPjBkzcP78eaxYsQINDQ3Iy8vD9u3b7ZOgv/jiC2i1X9dhXLduHSwWC7773e863Ofpp5/Gz3/+c+h0Ovz73//Gxo0b0dzcjOTkZEydOhXPPPMMh7VCVCDN5QmkWIiIQpniE54XLFjQ7zDX7t27Hb4/c+aMy3sZjUbs2LHDR5GRGngzl8dsNsNisfisp4bzioiIAoPiyQ+RuwRBwIEDBwAA48ePd6s72Ju5PPX19Th79qxDm7sJirMhLovFYu/J1Ov1Dr2RvppXxKE1ouDX83kgCAKHvmTA5IeCSmdnp9/ey7athjMD9dQMNMSVnp6OjIwMH0Tp2fsSERGTH6J+uSqBMFBPjashLqC750cOHFojIhoYkx9SDVdzdICvh5rcPc9dng5xDRTzpUuXBl292tkwn+09euJQGBGFMiY/pBoDzdGxDTW5e54n7zvYIS5nsRw9erTPed5Wrx7s3CUiIjVi8kOq4WqODvD1UJO753nyvoMd4nIWi9VqhdVqBQCEhYUhLCzMfmyw1asHO3eJiEiNmPyQari7TYk325k4G6Zyd4jLF7H0nGvkamVXz1icvYezoTBvhtuIiIIJkx8KKkoXq/T1kJm3sQTycJuvcRk/EfkKkx8KGjqdDjfccIOiMfh6yMzbWOQebrO1iaKIqqoqAM57iLxpczeB4TJ+ChXc3kJ+TH6IPODNkJk3lBpuO3PmjFs9RN60uZvAcBk/EfkKkx+iIKDUcJunPUTetA3Uu+SPCtlEFBqY/FDQEAQBhw4dAgCMGzcupLqDlRpu80dPl7u9S86SPXfrJCnVxjlJNBjc3kJ+TH4oqLS1tSkdgiKUGm7zB0+X+wNfJ3vuTtxWqo1DcUSBickPESnKm8TOn8Nyg2mz4ZwkosDC5IeIglaw9IhxThJRYNEqHQARERGRPzH5ISIiopDCYS8iIpk4W43GFWBEymPyQ0Gl96ofokA2UH0mIlIGkx8KGjqdDpMmTVI6DCK3uarPxBVgA3O2n1sg1XHyR10o1viRB5MfIiKZOFuNZhsK6ymQ/tj6mrsJjLNYnO3nFkh1nPxRFyqQkjO5/vsDPN+ix1saSZIkv71bkGhtbUV0dDRaWloQFRWldDhEpCLOKloHksmTJ0On00EQBFRUVHjdlpaW1ieBcVdBQQEuX77s0BZIdZx83VZXV4eGhoZB/ayCna+26HH37zd7fihoCIKAw4cPAwByc3PZHUxBKZAKM9rIOQTnbENad2PR6/UwGo2yxBWIMjIykJycDFEUceLECQDdSYGtTpTSyZlc//0B8m3R0x8mPxRUWlpalA6ByCuBVJjRH0NwrjakHSgWJYdFlGD7b0MQBLS3twMAYmNj+Q89GTD5ISIKUf7YG83ZhrTuxuLutUSeYvJDRBSi/DEE15OroQ1XK+MGupbIU0x+iIhCVCANwQVSLKR+3N6CiIiIQgqTHyIiIgopHPaioGJb+UFEpGZ81smLRQ6dYJFDIiKi4OPu32+mlkRERBRSmPwQERFRSOGcHwoaoijai6iNGTOGY+JEpEp81smPyQ8FDUmS0NTUZP+aiEiN+KyTH9NJIiIiCilMfoiIiCikMPkhIiKikMLkh4iIiEIKkx8iIiIKKVzt5YRtdn1ra6vCkVBPgiCgo6MDQPfvRqfTKRwREZHv8Vk3eLa/2wOtkmPy40RbWxsAIC0tTeFIiIiIyFNtbW2Ijo7u9zj39nJCFEXU1dXBZDJBo9EoHQ5d0drairS0NNTU1HDPtSDA31fw4O8quPD31T9JktDW1obk5GSXxSHZ8+OEVqtFamqq0mFQP6Kiovg/fBDh7yt48HcVXPj7cs5Vj48NJzwTERFRSGHyQ0RERCGFyQ8FDYPBgKeffhoGg0HpUMgN/H0FD/6uggt/X97jhGciIiIKKez5ISIiopDC5IeIiIhCCpMfIiIiCilMfoiIiCikMPmhgFdWVoaJEyfCZDIhPj4e99xzD44fP650WOSG5557DhqNBgsXLlQ6FOpHbW0tfvCDH2D48OEwGo3Izc3FJ598onRY5IQgCFi+fDkyMzNhNBqRlZWFZ555ZsB9rKgvVnimgPfBBx/gsccew8SJE3H58mU8+eSTmDp1Kj777DNEREQoHR71Y//+/fjjH/+IsWPHKh0K9ePSpUu46aabcOutt+J///d/MWLECFRXVyM2Nlbp0MiJ559/HuvWrcPGjRsxZswYfPLJJ5g3bx6io6Px+OOPKx1eUOFSdwo658+fR3x8PD744AN84xvfUDoccqK9vR3jx4/HH/7wB6xevRp5eXlYu3at0mFRL0uXLsXHH3+Mjz76SOlQyA3f/va3kZCQgJdfftnedu+998JoNGLTpk0KRhZ8OOxFQaelpQUAMGzYMIUjof489thjuOOOO1BUVKR0KOTC3/72N+Tn5+N73/se4uPjcf311+Oll15SOizqx6RJk1BeXo6qqioAwKFDh1BRUYHp06crHFnw4bAXBRVRFLFw4ULcdNNNuO6665QOh5zYsmULDhw4gP379ysdCg3g1KlTWLduHUpLS/Hkk09i//79ePzxx6HX6zF37lylw6Neli5ditbWVuTk5ECn00EQBDz77LOYPXu20qEFHSY/FFQee+wxHDlyBBUVFUqHQk7U1NTgxz/+MXbu3Inw8HClw6EBiKKI/Px8/OIXvwAAXH/99Thy5AjWr1/P5CcAvfHGG3j99dfxl7/8BWPGjMHBgwexcOFCJCcn8/flISY/FDQWLFiAd999Fx9++CFSU1OVDoecqKysxJdffonx48fb2wRBwIcffojf//73MJvN0Ol0CkZIPSUlJWH06NEObddeey3efPNNhSIiVxYtWoSlS5di5syZAIDc3FycPXsWZWVlTH48xOSHAp4kSfh//+//4e2338bu3buRmZmpdEjUjylTpuDw4cMObfPmzUNOTg6WLFnCxCfA3HTTTX3KRlRVVSE9PV2hiMiVzs5OaLWOU3V1Oh1EUVQoouDF5IcC3mOPPYa//OUv+Otf/wqTyYSGhgYAQHR0NIxGo8LRUU8mk6nPXKyIiAgMHz6cc7QC0BNPPIFJkybhF7/4Bb7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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def my_score(y, yhat, sample_weight):\n", - " # y is assumed binary\n", - " # yhat will be on response scale\n", - " label = yhat > 0.6\n", - " return 1 - accuracy_score(y, \n", - " label, sample_weight=sample_weight, normalize=True)\n", - " \n", - "G6 = GLMNet(family=sm.families.Binomial(), alpha=0.4 )\n", - "G6.fit(X.drop(columns=['intercept']), yb)\n", - "G6.cross_validation_path(X.drop(columns=['intercept']), yb, cv=10, alignment='lambda',\n", - " scorers=[('My Error', my_score, 'min')])\n", - "G6.plot_cross_validation(score='My Error')\n", - "G6.cv_scores_" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0c154606-6cfa-4c5e-8759-34bc03982ddb", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/examples/cross_validation.md b/docs/examples/cross_validation.md new file mode 100644 index 0000000..e68989a --- /dev/null +++ b/docs/examples/cross_validation.md @@ -0,0 +1,181 @@ +--- +jupytext: + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.17.2 +kernelspec: + display_name: Python 3 (ipykernel) + language: python + name: python3 +--- + +```{code-cell} ipython3 +import numpy as np +import pandas as pd +from glmnet import GLMNet, RegGLM, GLM +from sklearn.base import clone +import statsmodels.api as sm +from ISLP.models import summarize +import logging +logging.basicConfig(filename='log.txt', level=logging.DEBUG) +from sklearn.metrics import accuracy_score + +import rpy2 +%load_ext rpy2.ipython +``` + +```{code-cell} ipython3 +%%R -o X,Y,N +#install.packages('lars', repo='http://cloud.r-project.org') +library(lars) +library(glmnet) +data(diabetes) +X = model.matrix(lm(y ~ x, data=diabetes))[,-1] +N = colnames(diabetes$x) +Y = diabetes$y +plot(glmnet(X, Y, family=gaussian(), alpha=0.4), xvar='dev') +system.time(cv.glmnet(X, Y, family=gaussian(), + alpha=0.4, nfolds=10)) +``` + +```{code-cell} ipython3 +X = pd.DataFrame(X, columns=N) +G = GLMNet(alpha=.4) +G.fit(X, Y) +``` + +```{code-cell} ipython3 +G.plot_coefficients(legend=True, xvar='dev') +``` + +```{code-cell} ipython3 +#%%prun +cv_scores, lambda_best, lambda_1se = G.cross_validation_path(X, Y, cv=10, alignment='lambda') +cv_scores +``` + +```{code-cell} ipython3 +#%%timeit +G.control.logging = True +G.cross_validation_path(X, Y, cv=50, alignment='lambda') +``` + +```{code-cell} ipython3 +G.cv_scores_ +``` + +```{code-cell} ipython3 +G.plot_coefficients(xvar='norm'); +``` + +```{code-cell} ipython3 +G.plot_coefficients(xvar='dev'); +``` + +```{code-cell} ipython3 +G.plot_cross_validation() +``` + +```{code-cell} ipython3 +G2 = clone(G) +G2.fit(X, Y) +G2.cross_validation_path(X, Y, cv=10, alignment='fraction') +G2.plot_cross_validation(c='green', label='My label', legend=True, col_1se='blue', score='Mean Absolute Error', xvar='dev') +``` + +```{code-cell} ipython3 +yb = Y > 140 +X.insert(0, 'intercept', np.ones(Y.shape[0])) +glm = sm.GLM(yb,X,family=sm.families.Binomial()) +results=glm.fit() +summarize(results) +``` + +```{code-cell} ipython3 +from glmnet import GLM, RegGLM + +#G3 = GLMNetPath(alpha=.4,family=sm.families.Binomial()) +G3 = GLM(family=sm.families.Binomial(), summarize=True) +G3.fit(X.drop(columns=['intercept']), yb) +G3.summary_ +``` + +```{code-cell} ipython3 +np.isnan(yb).sum() +``` + +```{code-cell} ipython3 +G3 = RegGLM(family=sm.families.Binomial(), alpha=0.4, lambda_val=0.02081227758950013) +G3.fit(X.drop(columns=['intercept']), yb) +G3.coef_ +G3.null_deviance_ +``` + +```{code-cell} ipython3 +G4 = GLMNet(family=sm.families.Binomial(), alpha=0.4 ) +G4.fit(X.drop(columns=['intercept']), yb) +G4.cross_validation_path(X.drop(columns=['intercept']), yb, cv=10, alignment='lambda') +``` + +```{code-cell} ipython3 +G4.summary_ +``` + +```{code-cell} ipython3 +%%R +print(glmnet(X, Y>140, family='binomial', alpha=0.4)) +``` + +```{code-cell} ipython3 +G4.plot_cross_validation() +``` + +```{code-cell} ipython3 +%%R +plot(cv.glmnet(X, Y>140, family='binomial', alpha=0.4, type.measure='auc')) +``` + +```{code-cell} ipython3 +G4.plot_cross_validation(score='Accuracy') +``` + +```{code-cell} ipython3 +G4.plot_cross_validation(score='AUC') +``` + +```{code-cell} ipython3 +def misclass_score(y, yhat, sample_weight): + # y is assumed binary + # yhat will be on response scale + label = yhat > 0.5 + return 1 - accuracy_score(y, + label, sample_weight=sample_weight, normalize=True) + +G5 = GLMNet(family=sm.families.Binomial(), alpha=0.4 ) +G5.fit(X.drop(columns=['intercept']), yb) +G5.cross_validation_path(X.drop(columns=['intercept']), yb, cv=10, alignment='lambda', + scorers=[('Misclassification Error', misclass_score, 'min')]) +G5.plot_cross_validation(score='Misclassification Error') +``` + +```{code-cell} ipython3 +def my_score(y, yhat, sample_weight): + # y is assumed binary + # yhat will be on response scale + label = yhat > 0.6 + return 1 - accuracy_score(y, + label, sample_weight=sample_weight, normalize=True) + +G6 = GLMNet(family=sm.families.Binomial(), alpha=0.4 ) +G6.fit(X.drop(columns=['intercept']), yb) +G6.cross_validation_path(X.drop(columns=['intercept']), yb, cv=10, alignment='lambda', + scorers=[('My Error', my_score, 'min')]) +G6.plot_cross_validation(score='My Error') +G6.cv_scores_ +``` + +```{code-cell} ipython3 + +``` diff --git a/docs/examples/diabetes_logistic.ipynb b/docs/examples/diabetes_logistic.ipynb deleted file mode 100644 index bec110d..0000000 --- a/docs/examples/diabetes_logistic.ipynb +++ /dev/null @@ -1,535 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "2e0922d5-8e49-4067-ab3a-574632608c0d", - "metadata": { - "execution": { - "iopub.execute_input": "2023-09-11T23:38:20.474994Z", - "iopub.status.busy": "2023-09-11T23:38:20.474777Z", - "iopub.status.idle": "2023-09-11T23:38:21.558999Z", - "shell.execute_reply": "2023-09-11T23:38:21.558662Z" - } - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import statsmodels.api as sm\n", - "\n", - "from glmnet import GLMNet, GLM\n", - "from sklearn.base import clone\n", - "import statsmodels.api as sm\n", - "from ISLP.models import summarize\n", - "import logging\n", - "logging.basicConfig(filename='log.txt', level=logging.INFO)\n", - "import rpy2\n", - "%load_ext rpy2.ipython" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "86a3c000-69df-41bd-92e9-0d799885e46f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-09-11T23:38:21.560803Z", - "iopub.status.busy": "2023-09-11T23:38:21.560679Z", - "iopub.status.idle": "2023-09-11T23:38:22.214818Z", - "shell.execute_reply": "2023-09-11T23:38:22.214537Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " user system elapsed \n", - " 0.104 0.005 0.110 \n" - ] - }, - { - "data": { - "text/plain": [ - "Loaded lars 1.3\n", - "\n", - "Loading required package: Matrix\n", - "Loaded glmnet 4.1-7\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%%R -o X,Y,N\n", - "#install.packages('lars', repo='http://cloud.r-project.org')\n", - "library(lars)\n", - "library(glmnet)\n", - "data(diabetes)\n", - "X = model.matrix(lm(y ~ x, data=diabetes))[,-1]\n", - "N = colnames(diabetes$x)\n", - "Y = diabetes$y\n", - "system.time(glmnet:::glmnet.path(X, Y>140, family=binomial,alpha=0.4))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "1e2ec28c-4408-4612-9563-90cc6e120f36", - "metadata": { - "execution": { - "iopub.execute_input": "2023-09-11T23:38:22.216330Z", - "iopub.status.busy": "2023-09-11T23:38:22.216231Z", - "iopub.status.idle": "2023-09-11T23:38:22.218303Z", - "shell.execute_reply": "2023-09-11T23:38:22.218052Z" - } - }, - "outputs": [], - "source": [ - "X = pd.DataFrame(X, columns=N)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "22ba134a-a6df-48c5-a631-1cdd4c9c8428", - "metadata": { - "execution": { - "iopub.execute_input": "2023-09-11T23:38:22.219662Z", - "iopub.status.busy": "2023-09-11T23:38:22.219564Z", - "iopub.status.idle": "2023-09-11T23:38:22.221168Z", - "shell.execute_reply": "2023-09-11T23:38:22.220920Z" - } - }, - "outputs": [], - "source": [ - "yb = Y > 140\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "619ad185-fa4f-4e7b-aacc-718a46c3e17c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-09-11T23:38:22.222385Z", - "iopub.status.busy": "2023-09-11T23:38:22.222315Z", - "iopub.status.idle": "2023-09-11T23:38:25.149918Z", - "shell.execute_reply": "2023-09-11T23:38:25.149571Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "36.1 ms ± 82.4 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%%timeit \n", - "G4 = GLMNet(family=sm.families.Binomial(), alpha=0.4 )\n", - "G4.fit(X, yb)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "bd842d06-c4c3-49f4-84ad-d7d600668a15", - "metadata": { - "execution": { - "iopub.execute_input": "2023-09-11T23:38:25.151583Z", - "iopub.status.busy": "2023-09-11T23:38:25.151484Z", - "iopub.status.idle": "2023-09-11T23:38:25.506236Z", - "shell.execute_reply": "2023-09-11T23:38:25.504774Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(91, 10)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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zQsXvfOc7/Pe//yUajbJw4UL+9Kc/HVbX2KHefieEEH2FrseIRmuJxWqJRmuJtu5j0VpisTpisQazVSfegGEkDvv5FcWOw56L3ZFHTvaJlJZ+Bbs9pxd+EiGO3KF+fvfbROhokURICNGX6HqCWKyWSKTS3KJVRCKVRKPVRKNVRKM1xGL1h/WcFosXuy0bmz0bmy3LPLZlYkvus7DZMrHbc7Hbc7Fa02Xgs/hCdF2jcusWtq9Yiq++lgu/e3ePv8agm0dICCEGAsMwiMcbCIfLCIf3m1uknEh4P+FIBdFoZfKOrINRFDsORz4Oex52RwEORx4Oez52e15rQpOD3Z6LzZaDxSJ3yorep2sa+zdvYMeKpexYuYxQS3PyWkttDRn5BSmJSxIhIYQ4ysxkp4lQeA+h0B5Cob2EQnsIh/cRDpehacGDPl5RrDgchTidJTidRTgdRTicxTgdhTgcBTgchdhs2dJqI1LO0HUqtm1m69IP2b58CWG/L3nN4fEw+th5jJ27AE9WdspilERICCF6iWHoRCLlBIM7CYZ2EQzuIhTcSTC0m0Si5SCPVHA4CnG5hpqbswSnawguZylOZwkOR76sySX6tNq9u9my5H22ffwR/oa6ZLkrLZ3Rs+cxdvZ8hkyagsVqS2GUJkmEhBCiB0SjdQQCWwgEtxMMbDf3wZ3JtbW64nQU43aPwOUegds9HLdrOC7XUJzOUumuEv1OJBBgy9L32fjuW9Tu3ZUst7vcjJk9j3HzT2Do5Gmolr6VxEsiJIQQh8EwNEKhPfj9m/AHNhPwb8Uf2EI83tBlfVW143aPxO0ehcc9Co9nFG7PaNyu4VgszqMcvRA9yzAMyjdvYP07i9m5chmJuLmkisVqZeTM2YxfcBIjph+L1W5PcaTdk0RICCG6YRg6odAefL51+Pwb8fs3EghsQdNCXdRWcLtH4PWOw+MZi9czFq93LE7nEFRV3mrFwBIJBNj0wTuse/t1mirLk+W5Q4cz+eTTGX/8ybjS+sed1vLXKYQQrRKJIM3NK2hpWdOa/KzvZgJBF2lp4/F6J5DmHY83bTxez1gsFlcKohbi6KnZvZM1i19l28cfkYhFAbA5nIw/7iQmn3IGBaPG9LtB+pIICSEGtUi0mvr6d6mvf5vGxmXJ1dLbqKqTtLRJpKdPJs07kbT0SXjcI2Wwshg0DF1n16erWP3qC5Rv2Zgszx06nKmnn834407C4XanMMIvRhIhIcSgEwrtobb2dWrrFuP3b+x0zeUaSmbmbNLTp5KRPg2PZ6x0bYlBKR6LsvmDd1n92os0VVUAoFosjJ17HNPOOIfiY8b3u9afrshftxBiwDMMg2BwB7V1b1Bb+zrB4PYOVxUyMqaTm3saebmn4naPGhBv7kIcqUgwwLo3F7F60UuEfeY0Dw63hymnncn0M88jLSc3xRH2LEmEhBADkmEY+P0bqK17k7q6xYRCu5PXFMVKdtZ88vLPJC/3VOz2gfXGLsSRCDY38emil1j75iJiYfOGgPS8fGacdQGTTzkdu6v/dn8djCRCQogBwzB0Wlo+TXZ7RaNVyWuKYicn53jy884kN/dUbLaMFEYqRN8RaGpk5YvPsOGdxcnb33NKhzLnwss4Zv4JfW7en54miZAQol8zDJ3mltXU1i6irnYx0VhN8prF4iYn5yTy8s4gN+ckrNa0FEYqRN8S9vtY+dKzrF38WvIOsMLRY5lz4ZcYNXM2iqqmOMKjQxIhIUS/09byU1P7GrW1bxCL1SavWSxe8vJOIz/vTLKzj5dJC4X4jGgoxOrXXmD1ay8SC5sznxeNOYYFX7qaoZOnDroxcpIICSH6BcMw8PnWUlO7iNraRUSj1clrVmsaubmnUZB/NtnZC1BVWZ5CiM/SEnHWvbmIZc8/RaR18dO8YSNYcPnVjJwxa9AlQG0kERJC9Flm8rOO2tpF1Na+TiRambzW1vJTkH+OJD9CHISh62xbvoQlTz5KS435BSKruJQFX7qKsXMWDJousO5IIiSE6FPa7vaqqXm1i+THQ27uqa3Jz/GyMKkQn2P/5g18+Pg/qN61AwBPZhbzL7uKSSefPuAHQR8qSYSEEClnzvOznZqaV6ipeY1wpCx5zUx+TiE//yxysk+UMT9CHAJffR0fPPZ3ti9fAoDN6WLW+Rdz7DkXYXPK31BHkggJIVImFNpnJj+1rxIM7kiWq6qL3NxTKCg4R5IfIQ5DIhbjk1dfYMWLT5OIRlEUlSmnLWTepVfiycxKdXh9kiRCQoijKhqtpab2NWqqX8bnX58sVxQ7uTknUlBwLrm5p2CxDMzJ24ToLbs/XcV7/3qE5hpz/qyScRM45avfIH/4yBRH1rdJIiSE6HWJhJ/ausVUV79EU9MywGi9opKdNZ+CgvPIyzsDmy09lWEK0S/5G+p5959/YeeqZQB4srI58SvXM27BiYP2TrDDIYmQEKJX6HqchsYPqa5+ifr6t9H1aPJaRvp0CgrPJz//bByyvIUQR0TXNNa88SpLn36ceCSMarEw4+wLmHfJFQN2OYzeIImQEKJHBQLbqKx6lurqF4nHG5PlbvdICgsvpLDgfFyuISmMUIj+r3rndt766x+p3bsLgOKx4znta7eQN3R4agPrhyQREkJ8YfF4E9XVL1NV/Rx+/6Zkuc2WQ2Hh+RQWXEBa2iRpphfiC0rEYnz8zH/45JUXMAwdh8fDCVd9lcknnzHo5wM6UpIICSGOiGHoNDYupbLqaerq3sYwzMUaFcVGbu4pFBddSnb28aiqLcWRCjEwVO3cxht/+h2NFfsBGLfgRE6+9mu4MzJTG1g/J4mQEOKwhMMVVFU9Q1XVc50mO/R6x1NcdCkFBedjt2enMEIhBpZEPM6yZ/7DqpefxzB0PJlZnPa1bzL62DmpDm1AkERICPG5dD1OfcO7VFY+RUPDh7Td9WW1plNYcAHFxZeRljYxtUEKMQDV7NnF6w/9moZyc5LR8cedxMlf/Toub1qKIxs4JBESQnQrHC6nsvJJKqueJRarS5ZnZc2juOhL5OWdIZMdCtELdF1j1cvP8/HT/0HXErgzMjnta7cwZta8VIc24EgiJIToxDA0Gho+oLziPzQ0fEBb64/NlkNx0aUUF38Jt3t4SmMU/YNhGGiGZm66udcNvX2vf+a8dd+2aYaWfA7d0DEw0HQNAyNZxzAMdDocGzo6nY8x6FRuYCSvJ49br7XF3bFusqy1ftt58n+fqddVnbaps7p7TPJ3hoHRHEZ7bSOUN5uFY/IILTyGV9Rl8MmyZL2Oj02+Tofffbf/Xbqo93lln/ff+WCvcbCyNj+Y8wOsampSEkmEhBAAxGL1VFY+Q0Xlf4lEKpLlWVnzKSm5krzcU1FVewojFJ/HMAziepxwIkw4ESaqRYkkIkS0CNFE1NxrUaJalJgW67SPalHiWrz9WI8nr8f0mLnXYsT1eHIf1+Ik9AQJI4FmaCT0BJqukTASyURFHAYDRlV4mLM5G3tCJWbVWTmhkZ0l+2D3J6mOrlf97+z/TdlrSyIkxCDn821gf/m/qal5LXnnl9WaQXHRpZSUfBm3e0SKIxyYDMMgnAgTjAcJxAME48FOWygeIpgw96FEKLkPx8PJRCeUCCWP2xKe/pJ8KChYFAuqomJRzb2qqMmyjptFsZj1VXP/2esKCopiPp+iKKiY5UB7HUWh7X+dzjvsVbou73Lfetz2s3z2evLn7KJ+x99B8jliOtZ3dqFuMbugjeJ0LGcfw7xMF/M58DnbHt/hyQ4oazvu+LqdHnOw5/vMtc/GfcBxFzNjdPl8XVWE5H+vVJBESIhBSNfj1Na9Qfn+f9PiW5MsT0+bQmnpV8jPP0fG/nyOtkSmJdqCL+Yzt6i598f8yX1yi5v7QCyAP+4nGA/2atJiVaw4rU4cFgdOqxOnxYnD6sBpcWK32HFYHDgsjuRx296m2jqV2S127Gr73mYxr9tUW3KzqBasqhWrYsWiWrAo5nlbEmNRLMnyZGIjc0ol1ZXt5dXf/oLGyjoUVWX+ZVcx+8JLUVVLqkMbFCQREmIQSST8VFQ+xf79/yQarQbMeX8K8s+mtPQaMjKmpTbAFIpqUZoiTTREGmiKNLVvUXPfHG2mOdpMS7SFpkgTLbEWEnriC7+uqqh4bB48Ng9emxe3zY3Hap67bW5zb3XjtrlxW924rK72zebqfG51JRMem8zf1OcZhsHG997i3X88TCIew5udwznf+i6l4yelOrRBRRIhIQaBSKSK/eX/pqLiv2haAAC7PZeSkqsoKf4yDkdeiiPsHeFEmPpwfXJrCDfQEGkw9+EGGiONyS0QDxzRa1hVK+n2dDIcGaTZ00i3p5NuT08ep9nTSLOn4bV7SbO1Htu8eO1evDYvLqtLWkcGoXgkwtt/+yObP3oPgOHTZnLWLbfjTs9IcWSDjyRCQgxQhmHQ3LyKior/UFv3BoZhtl643aMYNvRGCgouwGJxpDjKI6PpGg2RBmqCNVSHqqkN1R6w1YfrDzu5sSpWspxZyS3bkU2WM4tMZyZZjiwyHZlkOjPNvSOTdHu6JDLisDVVV/Lyr39OfdleFFVlweVXM/v8S2SJjBSRREiIASaR8FNV/QIVFU8QDO5IlmdmzmHY0BvJyTkJJYUDEw9FKB6iKlhFRaCC6mA1lYFKKoOVVAerqQ5WUxeqI2EcWreUw+Ig15VLniuPHFcOOc6c5D7blU2OM8dMepzZpNvTJakRvWrX6hW8/tBviIaCuDMyOe/bd1I6QbrCUkkSISEGAMPQaW5eSVX1C9TWLkLTQgCoqovCwvMpKbmS9LS+82arGzq1oVr2+/ez37+fcn855f5yKgIVlAfKaYw0fu5zWBQLee488t35FLgLyHfnt2+ufHLdZvLjtXkluREpp+say555guXPPwWYq8Wfd9udeLNzUhyZkERIiH4sGNxNdfULVFe/2GndL49nDCUlV1JUeBFWa2qm4jcMg/pwPft8+5LbXt9eynxllAfKiWrRgz4+zZZGsbeYIk8RRd4iij3FFHoLKfIUUeAuINeVm7IJ2IQ4HJFAgNf+8AB7164GYPqZ53Hi1ddjscqA9r5A3kWE6GcSCT81tYuoqnym063vFouXgoJzKCq8mIyMmUetFUQ3dCoCFexu3s2ull3sbt7NnpY97GnZgz/u7/ZxFsVCsbeYIWlDGJI2hFJvKSVpJZR4zS3DIYNGRf/XUL6fl371E5qqKrHaHZx+0zeZcPzJqQ5LdCCJkBD9gDnweSWVVc9QW/sGuh4GQFEsZGcfT1HhReTmntarc/8YhkFDpIHtjdvZ3rSdnc072dm8k90tuwknwl0+RlVUij3FDMsYxvD04QxLH8awtGEMSR9CkadIWnTEgLb701W89uADxMIh0nLzuPC7d5M/fGSqwxKfIe9CQvRhgeAOaqpforrm5U7LXrjdIykuupTCwotwOPJ7/HUTeoLdLbvZ2riVrY1b2d60nR1NO7odu2NTbYzIGMGojFGMyBzByIyRjMwYybD0YdgtsiyHGFwMw2DVy8/x0X//DYZB6fhJnHf7XXJrfB8liZAQfUwkUklN7SKqq18iENicLLdYPBTkn0Nx8WWkp0/vsa6vuBZnR/MONjVsYnPDZrY0bGFH0w5ieuyAuqqiMjRtKGOzxjI6azSjM81tSNoQad0RAkjEYix++PdsXfoBAFNPP4uTr7tJxgP1YfLOJUQfEAzuoq7uTWrrFuP3b0iWK4qVnJwTKSw4n9zcU7FYXF/odXRDZ2/LXjbUb2BD/QY2N2xmW+O2LpMej83DMVnHMC57HOOyxzE2aywjM0fisn6xGIToCwxdB00z94lE5/PP7nUdQ9Pa94bRes0AvbVc14kEA7z+/H+prtyPqqosOOF0JhwzhdCSpaAbYOitz2cem49rPTaMTtcM3SzrfA7ounmevGYARufrGO3P16HMMFrrf7Zex7K252srSz7GOPA5OpQDB16DAx8LB14Hin/5CxSrrD4vxKBhGAbB4HZqahdRW/sGodDODlcVMjOOpaDgPPLzz8Juzz7i12mJtrCubh1ra9eyoX4Dm+o3dTmAOd2ezsSciUzImcD4nPGMzx5PaVppShdCFH2XYRgY8ThGLIYRjZr71k2PxjDiMYxYvL1Ox/0BWwwjkYDkeQIj0bbFzSQlnsDQtAPPtQQktA7XtM7lbUmOZpbTYZ/8IO4hQbuVT0YUEXTasWoaM3dWkLbmT+zv0VcZuIrv+3nKXlsSISGOks7JzyJCod3Ja4piIztrHnl5C8nNOw2HPfeInr88UM6nNZ+ypnYNa2vXsqtl1wH1nBYnE3ImMDl3MpNyJzExZyKlaaUy184AYBiGmYyEQujBEHooiBEOo4fD6KEwejiEEYmghyPmcTiCHolgRMLo4QhGNNJpr0cjGJGo+Zi2pKc18RnQFAUsFnOm57a9qibPsajmpKQWc/HYRruVFWlWYqqCS4f5MQvppcPMxygKdHy8qoKqmI9XlNbnUkBpvaaAolrMa6qKoiqA0uFxrXUV5cDzLstonbFaaa+jKO3P+9ky5bPl5u9D+Ww5dL6WfP/4zGOhw/WOj1U6VDd/36kiiZAQvSwU2kN1zavU1LzaqeVHUezk5JxAfv5Z5Oacgs2WfljPaxgG+3z7+KTmE3Or/oSaUM0B9YanD2dq3lSm5E1hSt4URmWOkgU5+xjDMMzExe9D8/vR/f7kXg8E0AIB9EDQPA8G0IJB9GDQLGs7DoXQQyGzteMoU2w2FLv9wK2t3GZDsds6n1tby6yt5VYris2KYrOB1WqWW63JciyW1rpWFIulQx2Lec1iMbtWWveKxZI8RlU7l6mq+fiOiU7rc7QlN4dq56rlLHvwARKxKPnDR3HRnffgzTryVlxx9EkiJEQviESrqal5lZqaV/D7NybLVdVOdvYJFOSfTW7uKYc92WFloJIVVStYWb2SlVUrqQ3XdrpuVa1MypnE9ILpTM+bztT8qWQ75U35aDEMw0ximpvRmppINDWhNTWjtTSjtbSgt7SgNbegtbSg+Xxovhb0FjP56ekERnE4UN1uVJcLxe1CdZnHqsuF4nKhOp2obheK0zxWXE5UR+ve6TQf73SiOJyoTgeK04lit6M6HGYy07a32wdta+Laxa/x7j//gmHojJg2k3NvuxO7U8bQ9TeSCAnRQxIJP7V1i6mufommpmW0DSBUFAtZWfMpLDiPvLwzDiv5qQ5WJ1t7VlavZL+/84gDu2pnSt4Uji08lmMLjmVK3hQZzNzDjFiMRH09iYYGEvX1aA0NJOobSDQ2oDU0ojU1kmhoNM+bmiFxaGugdclmw+L1oqanYfGmoaalYUnzonrTUL1eVK/HvO7xono87Zvb3eG8NflJYVfDQGcYBkuefJSVLz4DwORTF3LaDf+DKr/zfkkSISG+AF1P0Ni0hOqqF6irfxtdjySvZWTMpLDg/NYBz5+/nlDbGJ81tWv4tOZTVlWvosxf1qmORbEwKXcSswtnM6doDlPzpuK09t4kigOZkUiQqKsjUVNDvKaWRG0tidoac19XZ261dWgtLYf93KrbjSUzE0tWlrnPzMSSkYElMwNLRgZqRgaW9AwsGelY0tNR0zOwpKeZrS6DtHWlv9ASCd78y4Ns/vBdAOZ/6SrmXnyF/Hfrx/p1IvThhx/ywAMPsHr1aqqqqnjhhRe48MILk9cNw+Cee+7hr3/9K83NzSxYsIA///nPjBkzJnVBiwEhENxBVdVzVFe/RCzW3j3ldo+gsOBCCgsvwOUactDn0HSN7U3bWV2zmk9rP2Vt7VrqwnWd6qiKyvjs8cwqnMWswlnMyJ+B1+7tlZ9poNF8PuKVleZW0bqvriJRVU28uppEbW3rLcSHwGbDmpODNScHS24O1pxcrDnZWHJysGZnY8nOwZqdhSU7G0tWFqrD0bs/nEiJWDjEK7/9BXvXfYqiqpz+tW8y+ZQzUh2W+IL6dSIUDAaZOnUq119/PRdffPEB1++//34efPBB/v3vfzNixAjuvvtuFi5cyObNm3E65Vu0ODyJhJ/qmleoqnwGn399stxmy6Kg4DyKCi8iLW1yt98MNV1jW9M2VlWv4pPqT1hduxp/rPOt7FbVyoScCczIn8GswllMz59Omj01i6b2dUYsRqyigvj+/cT27ydeXkG8fD+x8gri5eXo/u7XOUuyWrHm5WHLz8daUIC1oABbQT7WvLzkZsnNxZKZKd/4B7lQSzPP3XcPtXt2YXU4OO+2Oxk5fVaqwxI9oF8nQmeddRZnnXVWl9cMw+B3v/sdP/zhD7ngggsAePTRRykoKODFF1/kiiuuOJqhin7KXONrFZVVT1Nb+3qy60tRLOTknExR0cXk5pyMqh64jETbXV3Lq5YnBzj7Yr5OdTw2D9PzpzMjfwbT86czKXeSdHV1YMTjxMrLie3dS2zvPmL79hIvKyO2r4x4VdXntuhYsrKwFRe3bkVYi4qwFRZhKyrEWliENTdHxtKIz9VSW8NzP7+bpqpKXGnpXHTnPRSNPibVYYke0q8ToYPZs2cP1dXVnHbaacmyjIwM5syZw7Jly7pNhKLRKNFoNHnu8/m6rCcGtlisnqqq56mseppQaE+y3OMZQ3HRZRQUXtDlXD8t0RaWVy1nacVSllUtozpY3em61+ZlRsEMZhXM4tjCYxmXPU6WpgA0v5/Y7t1Ed+0mtnsX0d17iO3eTay8/KCDjxW3G/uQIdiGlGIvKcU2ZAi20hLspaXYiotR3e6j+FOIgaihvIxnf3Y3gcYG0nLzuPQHPyW7uCTVYYkeNGDfgaurzQ+ggoKCTuUFBQXJa1257777+PGPf9yrsYm+yTAMmpqXU1HxX+rq3sQw4gBYLG4K8s+luPhLpKdP69RFohs6mxs281HFRyytWMqG+g3oRnsrhU21MT1/OnOK5jC3aC4TciYM6sRHDwaJ7txJdMcOojt2msc7d5KoOXD+ozaK04l9+HBzGzasdRuKfehQLLm50mUlek3Vjm08/4v/IxLwk1M6lEv+917Scg5/slPRtw3ed+Ru3HXXXdx+++3Jc5/Px5AhBx/0Kvq3eLyZqqrnKK94gnB4b7I8PW0KxSVXUJB/LlarJ1kejAdZXrmcD8o/4MPyD2mINHR6vlEZo5hfMp8FxQuYUTBjUN7ObhgG8YpKIps3Ed22nej2bUS2bSdeVtbtY6x5edhHjcIxciT2kSNxjByBfcQIrAUFrTPjCnH07Fu/lpd+9VPi0QiFo8dy8Z3/hyvt8CY9Ff3DgE2ECgsLAaipqaGoqChZXlNTw7Rp07p9nMPhwCF3fAwKPt8Gyiv+Q03Ny+i62R1qsXgoLLyAkuIrSEubmKxbF6rjvf3v8e7+d1lZtZK4Hk9e89g8zC82E58FJQso9BQe9Z8l1eI1NYTXrSOycRORTeamNTd3WdeSm4tjzGgcY8bgGD0ax+gxOEaPwpIuHzKib9ixahmv/e6XaIkEQydP44I7fiATJQ5gAzYRGjFiBIWFhbzzzjvJxMfn87FixQpuvvnm1AYnUkbXo9TUvk55+WP4fGuT5V7vOEpLvkJBwfnJ1p+9LXt5p+wd3t3/Luvr1nd6niFpQzix9EROHHIiM/NnYrMMniUrjFiMyObNhNauJbx2HeF160hUVR1Y0WbDMWY0znHjcR4zFscxx+AYOxZrtsx0LfqurUs/YNFDv8bQdcbMmc/Zt34Xq23w/H0PRv06EQoEAuzc2b520549e1i7di3Z2dkMHTqUb3/72/z0pz9lzJgxydvni4uLO801JAaHaLSWioonKK94gnjc7MpSFBv5+WdRWvoVMtJnoCgKu1t28+beN3lr31tsb9re6Tmm5E7hlKGncPLQkxmRPmLQjE3RfD7Ca9cSWv0p4dWrCW/YgNHhhgIAVBXH2LG4Jk/COXESzokTcRwzFtV+4N10QvRVG997i8V/eRAMgwnHn8zCm78ts0UPAv06Efrkk084+eSTk+dtY3uuvfZa/vWvf/G9732PYDDITTfdRHNzM8cddxxvvPGGzCE0iLT41rF//7+orV2EYZh3HzkchZSUXElx8eU47Lns8+3jv+sf5s29b7KzuT2xtigWZhfO5rRhp3HSkJPId+en6sc4qhJNTYQ++YTQqlWEVn1CdOtWMIxOdSxZWbimTcM1daq5nzwJ1ePp5hmF6PvWLH6Vd//xMABTTj2T0278HxmbNkgohvGZdzjRic/nIyMjg5aWFtJlDEO/oOsJ6uoWs3//P2nxrUmWZ2TMZEjpteTlnUFtuIHFexezaM8iNjdsTtaxqlbmFs3ljGFncPKQk8l0ZqbgJzi6NJ+P0CefEFy+nNDyFUS3bz+gjm3YUNwzZuKeOQPXjJnYRwwfNC1iYuBb9crzfPj4PwCYcfYFnHTNjfLvewA41M/vft0iJERH8XgLlZVPsb/8UaJRc8yKotgpKDiHIaXXojpH8Na+t3hl7df5pPoTjNZFUS2KhblFczlzxJmcPORkMhwZqfwxep0ejRL+9FOCHy8juHw5kU2bDpiY0D5qFO7Zs/DMmoXr2GOx5Q+O1jAx+Kx44WmWPPkoAHMuupwFl39FkqBBRhIh0e+Fw+Xs3/9PKqueRtNCANhs2ZSWfIXCoi/xScMO/r3mMd7d/y5RrX1sy4z8GZw14ixOH3Y6Oa7PXxS1vzIMg+j2HQSXLiX48ceEPvkEIxLpVMc+fDjuuXPwzJ2Le9YsrDkD9/chRJvlzz/F0qceA2DB5Vcz9+LLUxyRSAVJhES/5fOtp6zs79TWvY5haAB4PccwZMj12DMX8MLOV3nq1aupCbVP1jciYwTnjTyPc0aeQ7G3OFWh9zrN7ye49GMCH31I8KMl5gKjHVjz8vDMn4d73jw8c+Zg6zDFhBCDQcck6LgrrmHORV9KcUQiVb5QIhSPx6muriYUCpGXl0e23BYrjoKmphXs2fsQTU0fJ8uys45j6NAbqdKz+OO2//L6np8n5/rJdGRy9oizOX/U+UzImTAgm70NwyC2Zw+Bd98l8P4HhNasAU1LXlecTtyzZuFZMB/P/Pk4xowZkL8HIQ7F8ueeZOnTjwOSBIkjSIT8fj+PP/44Tz75JCtXriQWi2EYBoqiUFpayhlnnMFNN93ErFmyKq/oOYZh0Ni0lL17HqK5ZRUAimKloOBcSkq/ysrGSn6z4hHW1q1NPmZizkSuHH8lC4cvxGEZeJNkGokEodWfEnjvPfzvvUt8X+dZm+0jRuA94Xg8x5+Ae9axqDJRqBCdk6AvX8ucCy9LcUQi1Q4rEfrNb37Dz372M0aNGsV5553H//7v/1JcXIzL5aKxsZGNGzfy0UcfccYZZzBnzhz+8Ic/MGbMmN6KXQwChmHQ0PA+e/Y+lJwAUVHsFBdfRl7RV1hUvoLvvHUHFYEKwLzr64xhZ3Dl+CuZkjtlwLV66OEwwaVL8b/9DoH33kNraUleU2w23HPm4D35JLwnnoi9tDR1gQrRB6144WlJgsQBDuv2+S9/+cv88Ic/ZOLEiQetF41G+ec//4ndbuf666//wkGmktw+nxqGYVDf8C579vwBv38DAKrqoKT4y7jzLuKpXYt5dvuzBOIBwOz++tIxX+KKY64gz52XytB7nObzEXj/ffxvvUXgoyWdBjpbMjPxnngi3lNOwbNgARavzOUjRFdWvvQsHz3xL0CSoMHiUD+/ZR6hzyGJ0NFlGAb19W+zZ+8f8Ps3AaCqLkpLrsTIOoPHt73MK7tfIaGbkyMOTx/O1ROu5rxR5w2oxU0T9fX433kX/1tvEVy+HBKJ5DVbcTHe004l7bTTcM+YgWKVex6EOJhPXn2BDx77OyB3hw0mMo+Q6FfaEqDdex4kEDAnOLRY3JSWfIWAZx4PbXmO9z6+Pjn3z4z8GVw/6XqOLz0eVRkYs7/Gq6vxv/kW/jffJLR6dafZnB1jRuM97TTSTz8dx/jxA67LT4je8unrLyeToHmXXilJkDjAYSVCixYt4uabb8bv9zN9+nS++93vcuaZZ3LvvfeyYsUKjjvuOG644QbyZfI1cYiSLUB7/oA/YLYAWSweSku+Qq19Gr/Y8jQrqh9P1j95yMlcP+l6puVPS1HEPStWXo5/8WJ8b75JZF3nhV2dkyeTdvrppJ12Go6RI1IUoRD915rFr/Levx4BYO7FlzPv0i+nOCLRFx1W19iECRNYuHAh55xzDm+88QYPPfQQ5513Hq+//jpXX30169atY9++fbz33nuMHTu2N+M+aqRrrHckxwDt/n2nBKik5GrKLGP426an2NiwEQCrYuWckedw/aTrGZk5MpVh94h4RQW+Nxbje+MNIhs2tF9QFFwzZpB+xumknX46tuKBO8+REL1t/Ttv8NYjDwEw+4JLOe7L10pL6iDTK2OEPB4PmzZtYvjw4QD87W9/4+tf/zq/+c1v+H//7/8B8O1vf5uKigqeeeaZL/YT9BGSCPWstrvAdu/5fXIQtMXiobjkK+wwhvG3zU8mFz51WpxcMvYSrp1wLUXe/j3hX7ymFt/ri/C9/nrnlh9VxT1rFmkLzyDttNNkKQshesCmD97hjT//DgyDmedexIlfuV6SoEGoV8YIDR8+nJUrVyYToauuuoqbbrqJBQsWJOv8z//8DyeeeOKRRS0GLMMwaGxcwu49v0veBm+xuCkuvorNWikPrP8vZX5zHhyvzcuXx32Zq8Zf1a+Xvkg0NeF/8y18r71GaNWq9jE/ioJ71izSzzqTtNNPx5qbm9pAhRhAtn78IYv//HswDKYtPFeSIPG5DisR+u53v8sNN9zA1q1bOeuss5g6dSpLlixh3LhxyTqhUIhgMNjjgYr+q6lpObt2/5aWlk8AUFUnRcVXsj5eyM/XPUVV0FwgNdORydUTruaKcVeQbu+frW96OEzgvfdoefkVAkuWdLrbyzV9Oulnn03awjOk5UeIXrBj5ccs+sOvMAydyaecwSnX3SRJkPhch5UIXXfddaSlpfHb3/6We++9F4vFwrhx45gxYwYzZsxg/Pjx3HvvvcybN6+34hX9SHPLanbv/i1NTcsAcx6gwqIrWBcv4KfrnqY2ZK5/levK5bqJ13HZ2Mtw29ypDPmIGJpGaMUKWl5+Bf+bb6KHQslrjvHjST/7LDLOPhtbSUkKoxRiYNu9ZhWv/u5+DF1nwvEnc9rXbkFRB8YdpaJ3HfE8QoFAgHXr1rF27drktmnTJiKRCMXFxcyZM4cpU6YwZcoULrroop6O+6iRMUKHz+fbwO49v6Wh4QMAFMVGQdGlrIsX87ctz1IXrgMg353PDZNu4OIxF+O0OlMZ8hGJ7thBy0sv0fLyK50WNbWVlJB+3rlknHcejlGjUhihEIPDvg1reeGXP0aLxxk79zjO+dZ3US2WVIclUiwlEypqmsbWrVs7JUfr16+npqbm8x/cR0kidOgCge3s3vNb6ureBEBRLOQXXMgGbSiPbHmO+nA9AIWeQm6cdCMXjbkIu8WeypAPW6KxEd+rr9Ly4ktENm9OllsyMkg760wyzj8f1/Tp0hwvxFFSsXUzz/78bhLRKKOOncN5t92FRSYZFfRSIlRWVsbQoUMPOYjy8nJK+/l6R5IIfb5QaC979jxIdc3LgAEo5OWfy2ZjJI9seTHZAlTsKebGKTdywagL+lUCZMTjBD78kObnXyDwwQft435sNrwnnkDGBReQduKJKPb+8zMJMRDU7N7J0/f+L7FwiGFTpnPh936E1WZLdViij+iVu8ZmzZrFhRdeyI033tjt6vItLS08/fTT/P73v+emm27iW9/61uFFLvqNSKSKPXsfoqrqGQxDAyAn9wx2KOP5+ZaXqA29A5gtQDdNuYkLR12IzdJ/3qQi27fT8tzztLzyClpjY7LcOWkSGRddSPrZZ2PNykphhEIMXvVle3n2Z3cTC4coHT+JC+74gSRB4ogcViK0efNmfvazn3H66afjdDqZOXMmxcXFOJ1Ompqa2Lx5M5s2bWLGjBncf//9nH322b0Vt0ihWKyRffseprziMXQ9BkBW9gmU2aZz/5bXqAwuAaDAXcDXJn+tX3WBaYEAvtcW0fzcc0TWt8/3Y8nNJeP888m48AKcA2SyUCH6q6aqCp756Q+JBPwUjh7LRd//ETZH/xtnKPqGIxojFA6Hee2111iyZAn79u0jHA6Tm5vL9OnTWbhwIZMmTeqNWFNCusbaJRIByvb/g7Kyv6Np5qrvGRnHUuOcz5+2vZmcByjPlceNk2/k0rGX9osEyDAMwp9+SvMzz+JbvBgjHDYvWK2knXwSGRdfjPe441Dk26YQKeerq+XJe76Pv6GOvKHDueye+3B501IdluiDZPX5HiKJEOh6nMrKp9i95/fE42YXkdc7noD3NP6w/T12tewGINuZzfWTrufyYy7vF3eBJZqaaHnpJZqfeZbYrl3JcvuoUWRecgkZF5yPNaf/TugoxEATaGrkqXu+T3NNFdnFpVz+f7/AnZGZ6rBEHyWrz4svrG1B1J27fkkotAcAl2s4iaxz+e3O5WzaYq7onGZP4/pJ13PluCv7/DxAhmEQWrmK5qefxv/mmxjxOACKy0X62WeReemluKZNk7u+hOhjwn4fz/3sbpprqsjIL+DSu38qSZDoEZIIiS75fOvZsePnNLesAsBmy8aedwl/3beVldv/AYDL6uLqCVdz7cRr+/xM0FpzM80vvkjzU08T27MnWe6cMIHML11G+rnnYvF6UxihEKI70VCI5++7h/r9+/BkZXPpD39GWrYsTSN6hiRCopNItJpdu35FdfULgDkbdFr+xTxZ3cTiFf8BwK7auXzc5dww6YY+vRaYoWmEVn1Cywsv4Hv9dYyYObBbcbvJOPdcMr/0JVyTJqY4SiHEwcSjEV68/16qd+3AmZbOZT/8KZkFhakOSwwgkggJADQtQlnZX9m77y/oujlYODP3LBb7XTz9yatohoaqqFw4+kJunnozhZ6++UZkJBKEPvkE3xtv4H/rbbSGhuQ1x7hxZF1xubT+CNFPaIk4r/zmPsq3bMTucnPp/95LTumhz2UnxKGQRGiQMwyDmtpX2bnzl0Sj5uKn3rRprDOO4a/r3yKiRQA4ecjJfHvGtxmZOTKV4XbJSCQIrVqFb/HiA5IfS0YG3tNPI+uyy3BOmSJjf4ToJ3RdY9FDv2HP2tVY7Q4uuvMeCkaOTnVYYgA64kQoHA5jGAZutzk4dt++fbzwwgtMmDCBM844o8cCFL3H79/M9u33JscBORxF1LlP4N7tS2iMbAdgWt40bj/2dqbnT09lqAcw4nGCK1fif2Mx/rffRmtqSl5rS37SF56JZ+4cue1diH7GMAze/tuf2L7sI1SLlQu+87+UjpNubNE7jjgRuuCCC7j44ov5xje+QXNzM3PmzMFms1FfX89vfvMbbr755p6MU/SgeLyJXbt/S0XFfwEdVXViZC7k13u3s7PlFQCGpw/n2zO/zSlDTukzrSiGrpvdXosW4V/8ZufkJzOTtNNPI23hmXjmzJbkR4h+7KMn/sWGdxajKCpn33oHw6fNTHVIYgA74kTo008/5be//S0Azz77LAUFBaxZs4bnnnuOH/3oR5II9UGGoVNR8V927f41iUQLAM6M4/lvfYwP1r0FQJYji5un3cylYy/FpqY+mTAMg8jGTfhefRXfG2+Q6LCAryUri7TTTyf9zIW4Z89GkYUWhej3Vrz4DKtefg6A0752C8fMOy7FEYmB7og/OUKhEGlp5myeb775JhdffDGqqjJ37lz27dvXYwGKnhEIbGPL1h/g860BwOkezbL4MB7duBwDA4fFwdUTrub6SdeTZk/9LK2x/ftpeeUVfC+/Qmzv3mS5mpZmJj9nn212e0nyI8SAse6tRSz5778BOOEr1zPl1IUpjkgMBkf8KTJ69GhefPFFLrroIhYvXsxtt90GQG1t7aCdgbkv0rQIe/Y+RFnZXzGMBBaLh2rnPH6381OCiUoAzhpxFrfNuI0ib1FqY21uxvf667S8/ArhNWuS5YrDgfeUk8k491w8xx+PKqu8CzHgbF36AW///c8AzLnoS8w67+IURyQGiyNOhH70ox9x5ZVXctttt3Hqqacyb948wGwdmj69bw2sHawaGz9m67YfEA6ba4DhmcFDFY1s838MwJTcKXxv9veYmjc1ZTEa8TiBJUtoefElAu++m5zpGVXFM3cO6eedT9rpp8nt7kIMYLtWr+T1P/4GDIOpp5/NgsuvTnVIYhA54kRo9uzZ7N27l5qaGqZObf8gPfXUU7n4YsnkU0nXo+za9WvK9ptLYFhteSyJD+XJrVsAc1X422bextkjzk7ZQOjojh00P/c8La+80nmun7FjybjwQtLPOQdbQX5KYhNCHD1lG9fzym/vQ9c0xh9/Mqde/40+c4OGGByOOBEaMWIEVVVVB7T+jBo1ivz8fDRN+8LBicMXDO5k46bbCAQ2A+BzTufXe/bSktiCVbFyzcRr+PqUr6dkTTDN78f32iKan3+eyPr1yXJLdjYZ551LxoUX4hw//qjHJYRIjaod23jxgZ+gxeOMOnYuZ978bRRVTXVYYpA54kSou0XrA4EATmffX3l8oDEMg4rK/7Jjx8/Q9QiqJZ2X/Vm8vX8bALMKZ/GDOT9gVOaoox5XePVqmp95Bt/iNzEi5gSNWK2knXwSGRddjPf44+R2dyEGmbqyvTx/3z3EI2GGTprKuf/ve6gWS6rDEoPQYSdCt99+OwCKovCjH/0oOaEigKZprFixgmnTpvVYgOLzJRJ+Nm/5HnV1bwIQtA3jV/saaNLqyHZmc8exd3DuyHOPanNzorGRlhdfovnZZ4nt3p0st48eReYll5Jx/nlYc/ruOmVCiN7TXF3Fcz+7m0gwQNGYY7jguz/EKjdBiBQ57ERoTevdPIZhsGHDBuwd/vHa7XamTp3KHXfc0XMRioPyB7ayYcP/EA7vA8XGilgRT+6vxUDh9GGnc/fcu8lyZh2VWAzDILRyFc1PPYnvrbehdeCz4naTfvZZZF16Kc6pU6X/X4hBzN9YzzM//SHB5ibyhg7n4jt/jN3pSnVYYhA77ETovffeA+CrX/0qv//97+VW+RSqqnqerdvuRtcjaJZM/litsTtST5o9nR/M+cFRGwyttbTQ8tJLND35VKfWH+ekSWRedhnp55yDxevp9TiEEH1b2O/juZ/9CF9dDZmFRVzyg5/glDtCRYod8Rihf/7znz0ZhzgMuh5l+46fUlHxBAC15PG7sgAhXWFe0TzuXXDvUVkdPrxxE01PPIHvtdcwolHAbP3JOO88si7/Es4JE3o9BiFE/xCLhHnhFz+mobwMb1Y2l/7gp3gyj05rtRAH84Wm5X3nnXd45513qK2tRdf1Ttf+8Y9/fKHARNcikUo2bLgFn389oLA0nMWz9QEsqo07Z9/BleOu7NVWID0axf/GGzQ+8QSRde13fjmOOYasL19B+rnnypw/QohOEvE4L/3qZ1Tt3IbTm8YlP/gJGfkFqQ5LCOALJEI//vGPuffeezn22GMpKiqScR9HQWPjUjZu+n/E403oiou/1ylsCkco8Zby6xN/zcTc3ludOV5dTdMT/6X5mWfaFzu12UhfuJCsK6/ENX2a/BsQQhxA1zUW/eEByjasxeZwcvFd/0fukGGpDkuIpCNOhB5++GH+9a9/cfXVMgNobzMMnX37/sKu3b8BdJrJ4MGKKI2aOSD6/+b/H+n23hmrFV67lsZHH8P35puQSABgLSwk64rLybz0Uqy5ub3yukKI/s/Qdd58+EF2rPgYi9XKBd/9IUWjj0l1WEJ0csSJUCwWY/78+T0Zi+hCIuFn0+Y7qK9/G4D10XQerY2hqA7umn0HXx735R5viTESCXyLF9P46KOdur/cxx5L1tVXk3bqKbLYqRDioAzD4J1//JlNH7yDoqqc8/++x7DJ01IdlhAHOOJPsxtvvJEnnniCu+++uyfjER0EAttYv+F/CIf3YmDlmSYrHwcSvdYVpvn9ND/zLI2PP0aisgoAxWYj/ZxzyL7mahn8LIQ4JIZh8MFjf2fdW6+DonDWLbczZrZ8cRZ90xEnQpFIhEceeYS3336bKVOmYPvMzMC/+c1vvnBwg1l1zSts2XIXuh4mhIs/V+vsj6ucOvRU7l1wb492hcUrKmh89DGan30WPRgEwJKTQ9aXv0zWFZdL95cQA5hhGMQ0nYRmENd04q37hNZarrdf03SDuGaYe11H0wx0w0A3zOcxAN0wqHv/JRqXvgZA7plfYbNrNJtWlyfrYJj1DMAwwMBo3SeDorVa22FyNYOOZZ1+jl743XRsa29reO+q/b1jq3yX9VoLu36+gz+2q3p0We8gr9HpocoBZV39HJ95qcOK+bPXunvGjtdPH1+AqqZmnOkRJ0Lr169PziC9cePGTtdk0OyR0/U4O3f9kv37zekJ9sXdPFJrEMXB92d9h6vGX9Vjv9/I1q00/O3v+F5/HVrXhrOPHkXOddeRft55qA5Hj7yOEKJrCU0nHNeIxHUica31uP08EteIJvQD9tGETiyhE01orfvO5zFNb90bxBI68dZzM9FpO25NePSeTSFmNn/K/KYVAHyQfRzrt3ph67oefQ0x8Gz/6VnY+1si1Daxoug50WgdGzd9i+bmlQC863fwSrNBoaeYX534K6bkTfnCr2EYBqEVK2j4298JLlmSLHfPm0vOV7+K5/jjJZEV4jMSmk4wquGPxglGNQLRBMFoglAsQTCqmfuYRiimEYqax+FY2968HoqZiU0o1p7wxLXeaMf44uwWFatFwaoq2K0qVtU8t1lULKpZ3rZXVQVVUVAVKNq/gmGtSVDF6FNIH7GA4xXzy7GC2QKgAKqitLYGKMkyc68c0IqhoNBFY0jnVhg6P+ZwHWoLU1drbBpdnBgdSttbtDpWM7oo6yqWg9Uzuig7eL3PxtRtLF38HF0/9rMHXT9fF9UOkMqPHRnx2kf4/BtZv/7rRKPVJLDy73qVDWELJ5SewM+P+zkZjowv9PyGrhN4913q//IIkQ0bzEJVJf3MM8m+4XpcE3vv1nshUskwDALRBL5IAl84bm5tx5E4vnACfySOP5LA17r3R+L4own8kQSBSIJwXOv1OJ02FZfNgrPD5rCqOG1q8thhtWBvLXNYzTJ769Z2zWFpL7NbVGyte7vVTGTsVtXcW8y91WImOzZVxWYxE5wj+TK0/LknWfreOwDMu/TLzL/sqp7+FYmjRNd1NE3rtH22rO284767ss9e62qz9NdE6KOPPuIvf/kLu3bt4tlnn6WkpITHHnuMESNGcNxxx/VUjANebe1iNm3+DroeplFz8HCtQoNm49szbuWrk76KqqhH/NxGIoHv9TdoeOQRojt2AKA4nWRefDHZX70O+5AhPfVjCNHrInGNplCMxmCMpmCcxlCMpmCMplCM5lCc5lCM5nCcppCZ8LS0bloPdf/YrSpehxWPw4LHbsXrsOJ2WHHbLLgdFtx2Cx6HFbfNrOOym2UumxV367HT1rG8PeHpry2xhmHw8TP/YflzTwKw4PKrmXvx5SmOqn/QdZ1EIoGmaSQSiU5bW1nHa58t62r/2eOuzj+vPBVOOeUUVPXIP+u+iCNOhJ577jmuvvpqrrrqKtasWUO0dYmFlpYWfv7zn7No0aIeC/KL+uMf/8gDDzxAdXU1U6dO5Q9/+AOzZ89OdVgYhtE6P9ADAGyNWPhXvUqaM5+/nXo/xxYee8TPHa+pwb/4TRoff5x4WRkAqtdL1pVXkn3tNbLyuzhiCd0goutEWvfR1uOophMzDBKGQUw3iBsGcd1AwxxMqxsGGqAZ5sBYHdB0g2AsQUskTiCqEYiaLTGBqEawtWsp2KFrKa61zmCvJP+vy1Gahh2wq5DhAMyxbhZVMVtRbGpr60pbK4qSbE2xWzu2oLS3ntha95YOYxi67F4Aoq1bU6eINHNLAAkwwl/wP8IX0OWg1k7HBw6mVTrsO3VNGQblm9ZRtbcGZcZJjJw6g1XjJ7K6rBYLoCpmN5iKubcooGJ2o1layy2t3WaWz1xXO1xXOzyXpe0xrXUtn3mN5GMUOpW1vT66gaFr6JqGoesYmoahJTA0HU1LtJ53ThY+LzHpbvvs9c+ef3ZFhr5KURQsFguqqmKxWDodf7bss+VtZZ+99tktlV8EFKOrDsRDMH36dG677TauueYa0tLSWLduHSNHjmTNmjWcddZZVFdX93SsR+Spp57immuu4eGHH2bOnDn87ne/45lnnmHbtm3k5+d/7uN9Ph8ZGRlUVlZ2ucCsxWLB6XQmz4Otd111RVVVXC5zlWVNi/DWshtItHwMwNKAlVdbbCwoOYG75txFvic/WRcgFAp12dcL5j9St9tNrLwc/+I3qV20iFBb9xdgycgg66orybr8cqwZGbjd7uS1cDh80D9Gj8eTbNIMBAJEIhHC4TChUIhQKEQ4HCYcDhOLxVBVlWg0SjQaJRgMJv/wOzaJtmn7namqimEYqKqK1WrFYrFgs9lwOBzJLSMjA5fLhdvtxmq14nA4cLvdB9ypCOB2u5N/UNFolETrJJBdcblcyW8gsViMeDzeI3WdTicWi+Ww68bjcWKxWLd1HQ4H1tb5mz6vrs1uJ66qhDQdXzRGcyhMUNPxJzSCmk5A0whpOkFNJ6paiKgWQpqOPxojGIkQ0nXCmk64dR9t21usJCxmDIamwUFiwGpFaf1vdFh1dR1av1h94boWC4rdbtY1DIhEeqiuimJvv5nACB8kszmcuqqC4nAeWd1IuPtBGAooHVZ4P6y60QgcpEVNcR1h3VgUtO7few6nLk5n8u/eiMWSN3980bqK3Y6qKICBEothJBIohtGaEBoomLe6KRioNgeKqqAYBsTjkDD/7hUMFKNtb/5uVKsN1WIBw8CIx1G0OBhmskdrIqi0JYx2BxaLxYxZ01C0OIqitCaM7UkeioLNYcdqtSXrkki0jt9SUFQzEVQUBYuiYHU4sdpsqIpi/n1qcVRFNZ83Oe5LMZMXmx2r3YYC6IkEWizWPu6rbWvNY6w2G1ab3ayraySi0c8k0u2Jtmq1YrObde8Ymk8i1v3fss1mw97696nrOuGD/G201W37/G5paTnoAvFHnAi53W42b97M8OHDOyVCu3fvZsKECUQO9iZyFM2ZM4dZs2bx0EMPAeYvcMiQIdx6663ceeedB9Rv+yBv4/P5GHKQ7qOzzz6b1157LXnu8XgIhUJd1j3xxBN5//33KW/ewkmr69lzyfnoLc1d1rWNHU/BQ/9OnlddfQFaTVWXda3DRlD416eS59Vfu5zEvj1d1rUUFFH02EvJ85pvXkt8+5Yu66oZmRQ/82byvPaObxBb/2mXdRWHk5JXPkye1//wNiIrl3ZZF6D0zZXJ44af3En4o3e7rVv80georW+KjQ/8mNBbr3Vbt+jpxVhaF3Js+sP9BF95ttu6hY++iLWwGIDmRx4k8Ozj3dYteOS/2IaPAqDl0UfwP/63buvm/+Ff2I8x51zyP/0YLX/7Q7d1cx/4M86pMwEIvPwMzQ890G3dnJ/8Btccs8s5+OarNP3q3m7rZvzofpwnnQ5A5P23aLn3e93WTf/ej3GdeT4A0eUf0fy/3+q2btq37sR9odntEVv7CU23f63bugXX3kTBBV9CwSC8Yyvbv39rt3VLLr2SIZdeiQqE9u9j7Xdv6bbukHMvYsxV1wEQrqth2bdu6rZu6elnMe6rXzfj9bXw4Teu7bZu0XEnMfGmb4JhoEXDvHfTNd3/bMfOYeo3b0+ev3ld911BuVOmM/O27yfP3/76tWjdvOFnHTOeOXfekzx/59avEQ/4u6ybPnwk8+/5efL8/Tu+SaShvsu6nuJSjvvZr5LnS35wB8HK8i7rOnPzOP7Xf0qer/i/O/Ht2dVlXVtaOsf/6Z8YioIBrPnZ3bRs2dRlXdXhYNZjL2AoCjqw/b4f4ft0VZd1ASa99C46CoaiUP7Le/Av/aDbusNeeBdc5he8+l//hOBB3iPynn8HNTMbAN/v7yP80tPd1s194jUsre8R/od/S+jpR7utm/P3Z7GOMN8jAv96mOCjf+m2bvafHsc2zhyXGXzy3wQe+V23dbN+81fs08zegdCLT+F/8Bfd1s38+YM45h4PQPiNl/Hdf0+3dfvCe4T3pm/jucL8m3zeHWXB3Lnd1r3nnnv4v//7PwA2bdrEpEmTuq17xx138MADDxxyInTEXWOFhYXs3LmT4cOHdypfsmQJI0eOPNKn7VGxWIzVq1dz1113JctUVeW0005j2bJlXT7mvvvu48c//nGvxpXlzCai+A86gt5QFOLW9haPg9bls3W7b2I0oHPdgzRHHlZdpXNd/XOaOTvXPXi/cMJqQ2mtr39OH3LCakU/5Lo2jEOta7FBsq7lc+pak/Fqn1NXs1iTv4uerNuRRT94n39pUw3D9m3DpiWoq9xD138Zpll7NjNr6WtYdJ3y3bvo/mMBJlfsZsG6jwCoqKhg+0Hqjq6r4KQt5odibW0taw8Wb0M1x281E/Lm5uaDxlvYVMeCbWsAs7X2w4PUzfc3sWCX2ZIai8U42H2xOSE/8/ZsTp6/eZC6WeEAc/e2f9l419Dp7r9IeiTEnA51P9Q1umtP9MYineouS8Tp7uunOx7tFMOn8SjdtV074jEWbF2NYuiga2wKBfB1U9euxbnZ8JNdbH5ZvM1mYX03dW3Aj9Pbu0Dutiqs7KYuwB8zzVYQRVH4kV096H+P54ak4/V6sVgsfD/NwfMHqbti2ggKiopQVAvffjqHRw5Sd9GMMZQOH4ZuwE9ezqH71Ab+Pnk4o8aNRQf++ObBn/cXY0s4ZuooNMPgyQ+yD1r3O8MLmTjBXJtt0fJs/nqQujeU5DLtmCEYGHy4Novuv67BlwqzmDGqGANYvTWLvx+k7ll5GcwaXogBbN6dyT8PUvekrDTmDM3HAPZUZnKw5dfnZnqYV5oHgNpUcZCaveuIW4Tuu+8+Hn/8cf7xj39w+umns2jRIvbt28dtt93G3Xffza23dv/t72iprKykpKSEjz/+mHnz5iXLv/e97/HBBx+wYsWKAx7TXYtQT3eN/f3RX6PEFBxGYZd1FUXB0aE5PRqNdnkrI5hNjQ6HA+w2sNmIRiPJ8QtqVMEWULAEwBICBRWn3Yw3nmHgywyTcOvdDhxwu9woqtlcGo/HzKbdbpIcZ4em7Fgshn6QJufDqevo0JQdj8XQEonWSd7Mx3T8vThsVizxEErcTyLQjBFtwVa/GUvU/FYdy5tAaMTp6DYPdocj2d2ViMcP2o12WHXtdrPZ+3DrJhIkWrvR9FiUYEM1wdoqAvWVRH3NWFv71gFcuYVkDhtL1rBjsDpcnZ5Tj2ng19DrE0SqQ/hrgsQ7dEs5020UjMuiYFwWzkw7Npst2c2YSCQO3uXWoa6maZ3+VhQUXHYLrrY7luz2ZFO2pmkHbSU+kmbvz6ubiMXw11bjq6mifv8+6vbtobZ8/wH1XBkZZBWVkls6hMJhI8gqKsZitR00BovFYv7NtequFfhw66qq2un95HDqhsPhg3aft733qKpKNBJFae3+QFWxto7xAPOt4IDu89bndbjdODwe1A6JuMfj6Vz3c7ra20QikYMOzD2cuofTJS7d5wfWTSQSnf6WP8tut3d6jzjUuofzd98b7xG93iJ05513ous6p556KqFQiBNOOAGHw8Edd9zRJ5KgI9U2LuWzPB5Ppz/M7hxKHYAbrvnOYcf2RenhBNFdzQQ/qSGytRFCQJUTW6EH74Ji3NPyUWypGbXfqyI+ePensOqv0LIctjwHp90DM78KKbpL4XCF/T72rl3N5o/eY9/6tRiN1US3r6XOZmPk9FmMO+5ERk6fhbX1jaIjTdOp2tnCthXV7FpdSzygUfdJnLpPaskbmsaoGXkMmZ5NZoG7i1fu+wzDINjcRN2+PZ22xopyDKPzh7ITyCkdSumEyQyZMInS8ZPwtHaliq5lHkbdjuMaP0/HJK4n63b3Hv5F63ZM7FNVt+MXkZ6sa7Vak0lRT9a1WCyH/Jl4OHVVVT3kuofiiFuE2sRiMXbu3EkgEGDChAl4vd6eiu0Li8ViuN1unn32WS688MJk+bXXXktzczMvvfRS9w9udagZZX8TrwsRWFpJaHUNRtz8sFDdVjyzi/DMLcKaOQBnla74FF69DarWmuclM+Hc30LR1JSGdbgCjQ1sWfI+mz54h4bysmS53eVmzJz5jJt3PEVjx+FwH/hGEY9p7Flbx7bl1ezf0tjpzqfsYg+jpucxYloeuaXePnc7t65p+Bvqaaosp6GinMbK/TRWltNQvp+wr6XLx3iysikYMYr81q3kmAm407/YnFxCiP6h1wdL9xdz5sxh9uzZ/OEP5oBVXdcZOnQo3/zmN7scLP1ZAzURaqOH4gRXVRNYVoXW3NrcqYJrQg7e+cXYR2T0uQ/EL0TXYOVfzRaimB8UFWbfBCf/Lzj71wekYRjU7dvD1qUfsHXph/gb6jpdT88rIG/YCPKHjyCndCgOlxurw4HN4cRqd5CIK1TuDLF/k5+K7T70Dnf7pGU7GTE1lxHT8igenYFq6Z2WM13XiIXDRINBQi3NBFuaCbU0EWpuJtjSRKCxgUBjA/7GBkLNzQe08LRRFJWsomLyho1IbvkjRuHNyu6VuIUQfV+vJEK33347P/nJT/B4PNx+++0HrdtXFl196qmnuPbaa/nLX/7C7Nmz+d3vfsfTTz/N1q1bKSgo+NzHD/REqI2hGUS2NBD4uJLo7vZv19Z8N945hbhnFKC6BtBE5L4qePMHsPE589xbCAt/BpMuSe1c70fI0HUqtm5my9L32bN2Nf76us9/UAcWqw2LzYlh2NDiKgZWFMUGihWL1Y4ny016jof0XC82pw3VYk0OZEVpX4JRS8RJxBNo8RhaPE6ibR+LEo/FSMRiJKJRYpEwsVCIePTw7i5VLVayiorJLi4lu6SU7JIhZBeXklM6BJvj0LtPhBADX68kQieffDIvvPACmZmZnHzyyQet25fWInvooYeSEypOmzaNBx98kDlz5hzSYwdLItRRvDpI4ONKQmtqk91mik3FNTUP75wibH2w2+SI7XoXXrsDGltvDx5xApz9K8g7JrVxfUGRQIC6fbvNAcJ799BUXUk8GiERjRKPRc19NIJ2kEGaR5PFZsOdnok7IxNPprl3Z2SSlp2LNzuHtBxz707PQOkn47qEEKklXWM9ZDAmQm30SILQmloCy6tI1LTfvWLNd+GeUYB7Wv7AGEsUj8DS38OS30AiAqoN5t0CJ3wXHH1nzFtv0BIJYpEw8XCYWDhkHkejZgtOJEIsEqGxsoW6/S00VLQQDUQx0MAw7+BxZ9jJyHOSnuvC6bZisduxWG1YbTYsrZvVbsdqd5h7mx2r3Y7d5cbh9mB3ubC73FgPcVCnEEIcql5PhO677z4KCgq4/vrrO5X/4x//oK6uju9///vdPLJ/GcyJUBvDMIjt8xFYXkV4YwMk2pc5cIzMwD29ANfkHFRHP+86a9wDb9wJ298wz9NLYOHPYcIF/bK7rKcZhkFTVYg96+vYs66emj2dZ5fJyHcxakY+o2fm98nB1kKIwaXXE6Hhw4fzxBNPMH/+/E7lK1as4IorrmDPnq5nNu5vJBHqTI8kCG+oJ/hpLbE97WOJFJuKa2IO7hkFOEZnoqj9+ENw2+vw+vegufWOrJEnwVn39/vusp4WbI6yZ309e9bVU76tET3R/laSke9i9Mx8Rs8sIKfEI0mREOKo6/VEyOl0smXLFkaMGNGpvK8tsfFFSSLUvURjhNCaWkJraknUt09upabZcU/Lwz09H3txP+1aiofho9+YXWZaFFQrzPkGnPh9cMq/g8+KRRLs29jAztW17NvYgBZvv7srs8DNqOl5jJqRT+4QaSkSQhwdvZ4IjRkzhnvuuYevfOUrncofe+wx7rnnHnbv3n0kT9vnSCL0+QzDILbfT2hNLeF1deih9plabYVu3NMLcE/Lw5LRD8cTNe6Bxf8L2xaZ594COP1emHK5dJd1IxZJsHdDPbtW15lJUaI9KUrPdSa7z/KGpklSJIToNb2eCN1///3cf//9PPDAA5xyyikAvPPOO3zve9/jO9/5Tqf1vfozSYQOj5HQiWxrIrSmhvCWRtBa/3kp4BiViXtaPq5JOajOfjaeaMdb8Pr32+8uK50NZ/0SSmakNq4+LhZJsG9DA7s+NVuKEvHOSVFb95m0FAkhelqvJ0KGYXDnnXfy4IMPJtczcTqdfP/73+dHP/rRkUXdB0kidOT0UJzQhnpCa2qJ7e0wsNaq4pqQjXt6Ps6xWSi9NFlfj0tEYdkf4cNfQTwIKDD9Kjj1HvDmpzq6Pi8WSVC2qbG1+6yeRKxDUpTnYuysAsbOLiCrsOemzhdCDF5H7fb5QCDAli1bcLlcjBkz5pDXbekvJBHqGYnGCKG1reOJ6jqMJ/JYcU1pHU80pJ90lfgq4e3/g/VPmeeOdDjxezD762A9tDWDBrt4VGsdU1TDvg2dW4ryhqYxdnYBY44twDMQpmcQQgBmj0GiMUKiLkS8LkyiLkyiLoQejFNwx7E9/v4v8wj1EEmEepZhGMQrAuYg63V16IH2Cf2sua7kIGtrzqEv3pgy+1ead5dVrjHPs0fBGT+FY86S8UOHoW1M0faVNezf1Jhc6kNRoOSYLI6ZU8jI6XnY+1t3qhCDlJHQzUSnJki8NkSiJmTuG8LQ9So5FP1wDhZvz36RlCU2eogkQr3H0AyiO5vMQdabGpKzWAPYh6bhnp6Pa3Juj/9x9Chdh7X/gXfuhWCtWTbiRHP+ocJJqY2tHwoHYuz8pJbtK2uo7rjUi01lxLQ8xs4uYOiE7F5b+0wIcXi0QIx4ZZB4VZB4dZB4VYB4bRj0rlMLxW7BmufCmufCluvCmuc2jws8KJZ+0CLUcYmNk046qdtmLEVRePfddw8/6j5IEqGjQ49qhDeZ44miO5uh7V+lquAcm4V7Wh7OCTmodksqw+xe1G/ebr/sj+bt9ooK06+GU34o44eOUEtdmB2rqtm2oobmDjObezIdjF9QxIQFxaRly/piQhwtWkuU2H4/scoA8cogscoAui/WZV3FacVW6MaW78aa37ovcGNJtx+1IRC9kgitX7+eSZMmoQ6itX4kETr6NF+M0Lo6QmtriVcEkuWK3WJO2jg9H8eozB7/9tAjmvbB2/fAphfMc7sXFnzbXLLD7k5paP2VYRjUlfnZtqKa7StriLR2pyoKDJuUw4TjSxg2KQe1P0/iKUQfo0cTZtKzP2Duy/1dJz0KWHNc2Io8nTZLhiPlYz57JRGyWCxUVVWRn5/PyJEjWbVqFTk5OT0ScF8liVBqxWtD5iDrtXVoje2TdKpeG+4peTgn5uAYntH3kqJ9y8zV7StWm+dpxXDq3TDlChhEXyR6mhbX2b2ujk0fVVCxrTlZ7s12MOmEEsbPL8ad3oe7UoXogwzDIFEfJrbPT6zMR6zMT7wm2N4y30YBW4EHW6kXe7EXW7GZ9PTV5ZV6JRHKyclh0aJFzJkzB1VVqampIS8vr0cC7qskEeobDMMgVtY6aeP6zpM2qm4rznHZuCbk4Bib1Xe6z3QdNj0Pb/8YWlqX6yicDKf9GEafmtrYBoDmmhCbPqpgy7IqokHz34NqURg1I59JJ5ZQNCoj5d9IheiLDN0gXh0ktqeF6F4f0T0tnW5caWPJdGAfkpbcbCXevvP+egh6JRG66aabePTRRykqKqKsrIzS0lIslq5/KTKztOgthqYT2dFMeEM9kS0NnZIirCrOsVm4JuXgGp+D6uoD31TiEVj5F/jw1xBtHQA88iQzISqelsrIBoREXGPn6lo2flDRaSHYnBIvk04sYezsArnjTAxqbYlPdHcL0V3NRPf4MCKJzpWsCvbSNOxD03EMTcM+NA1Lev+evqLXbp9/44032LlzJ9/61re49957SUtL67Le//t//+/wIu6jJBHq2wzNILbPR3hzA+HNDZ26z7AoOEdn4pqci3tqPootxV1SwQb48AFY9TfQW799TbrU7DLLGp7S0AaK2n0+Nn5YwY6VNcm5iWxOC+PmFjHpxBKyi2SyRjE4JBojRHY0Ed3RRHR3S+cvjJhjLu3D03GMSMcxIgN7aRqKdWB12/f6YOmvfvWrPPjgg90mQgOFJEL9h2EYxKuChDfWE97YQKK2/U4j1WvDe1wJ3rlFqV/eo3EPvPcz2PBMa3A2OParcPwdkFaQ2tgGiEgwzrbl1Wz4oJyW2vYJPIdOyGb6GUMpOSZLus3EgKJHNaK7mluTn+ZOC2EDKHYV+/AMHCMzcI7KxFbs7XtjK3uYDJbuIZII9V/x2hDhDfUEV1WjNUcBUJwWvPOK8S4oTv38RJVrzTvMdr9vntvc5gr3C/4fuDJTGNjAYegG5Vub2PBBOXvX19P2bpc3NI3pZwxl1PQ8mZNI9EuGYZCoCxPZ1khkWxPRPS3tazsCqGAfmo5zTBaO0ZnYS739ZzmjHiKDpXuIJEL9n6HphNbW4f9gP4m21gGriufYAtKOL0n9LNa73zcnZGy7w8yZYd5yP/smcHhTGdmA0lIXZt3bZWz5uCrZbZae62TSiaWMm1eIK9WJsRCfw9B0ont8RLY0EN7S2HkoAGDJduIcm2UmP6MyUt/6nWIyWLqHSCI0cBi6QWRLA773y4nv95uFCrim5JF2Yin24hQmHYYBW1+Dd38KdVvMMncOzPsmzP4aOAZ2F/TRFA7E2PB+BRveKycSNMdqqVaFUdPzmXh8McVjMqXbTPQZelQzW302NxDe2tR5kLNVwTEiA+cx2TiPycKa65J/ux3IYOkeIonQwGMYBtHdLfg/KCe6vSlZ7hibRdoJpThSedu1rpljhz74JTS2fplwZcHcW2DOTWZrkegR8ZjG9hXVbPqokroyf7I8s8BN8ZhMcko85BR7ySnx4vTaUhipGGz0UJzwlkbCG+uJ7GiCRPvHtOqx4hyXY04XMiazX93OfrT1+qKrMlhaDASxigD+D8sJr69LTh5mK/WSdkIprkm5KKmarVhLwMbnzLvMGnaYZY4MmH2jOY5Ilu3oUbX7fGxaUsmOlTXEo9oB193pdrKKPGQXuskq8pDVuncfxeUCxMCmh+KENzUQWl9HdFdzp8VJLTlOXBPN5Mc+ND1170v9zFFZff6jjz7iL3/5C7t37+aZZ56hpKSExx57jBEjRnDccccd6dP2KZIIDQ6JhjD+JRWEPqlJLv5qyXaSdnwJ7pkFqfvWpWvmch0fPgB1W80yqxOmXQXzb4XsEamJa4CKRRKUbWqkvtxPQ0WQxsoAvvpIt/VtTguZ+W4y8l3JfUaem4w8F640myRJ4qD0aILw5kbC6+rMlp8Og51thR5zPrRJuVgL3PJv6Qj0eiL03HPPcfXVV3PVVVfx2GOPsXnzZkaOHMlDDz3EokWLWLRo0REH35dIIjS4aIEYgWVVBJdVJufdUN1WPPOK8c4rSt2dZroO216DJb9tH1StqDDhQjMhKpmRmrgGgVgkQWNVkKaqEE3VQZqqgjRWh/DXhznYu6fNYSE9z0VGnov0XBfpOU7ScpzJY6t0aQxKRkInsr2J0NpawpsbIdHe9GMr9OCamod7ci7W3BTfxDEA9HoiNH36dG677TauueYa0tLSWLduHSNHjmTNmjWcddZZVFdXH3HwfYkkQoOTHtMIrarGv6QCrcm89R6rimdmPt7jSrDlpWgBVcOAvUtg6e9g59vt5cMWmAOrx54pa5kdJYm4hq8uQnNtiJbaMM11IVpqQ7TUhQk0RQ9cp+kzXGk20rKdpGU78bbtsxx4shx4M524M+yykOwAYRjmxK+hNbWEN9R3mtzQmusyk5+pedjyZWHmntTriZDb7Wbz5s0MHz68UyK0e/duJkyYQCTSfXNyfyKJ0OBmaAbhTfX4PywnXh4wCxVwjssm7fgS7CNSOLC6aj0se8gcS6S3vrFmj4J5/wNTvwx2mUU5VbS4jq8hTEtdmJbaML6GML76CP7WfVfjkD5LURU8GXY8mQ7c6ebek+HAk2nHnW6WuTPsuLw2mQupj0o0Rgh9WkNwTS1aQ4dFo9NsuKfm456ej63YI91eveRQP7+PeJKBwsJCdu7cyfDhwzuVL1myhJEjRx7p0wrRpygWBfeUPFyTc4nt8eH/sJzI1kYiW8zNVuIl7fgSXJNzj/5kZUVT4OJH4NR7YOUjsPqf0LgLXvsOvPMTmHmdeet9RunRjUtgsalkFXrIKjwwGTUMg2gwgb8xktwCrftgc5RAU5RgSwxDNwg0mecHpYDLa8OVZseVZsed1nZsw+k1EyWn14bLa8fpteH0WCVx6kV6TCO8vo7g6hpiHda+U+wWXJNycE/PxzEqUwY89yFH3CJ033338fjjj/OPf/yD008/nUWLFrFv3z5uu+027r77bm699daejjUlpEVIfFa8LkRgSQXB1bXJ/n1Lht0cRzS7ENWdolutowFY8zis+DM07TXLFAtMOB/m3AxDZoN88+wXdN0g7Iu1JkVRQi1mchRsjhJsjhLyxwi1xAj7Ywcdp9Qdh9vamhy1J0vJxCnNTJpcbQmV1ybjmQ5BrNxPcFU1obV1GG0tfgo4RmXinlmAa2KO3Op+lPV615hhGPz85z/nvvvuIxQy13RyOBzccccd/OQnPzmyqPsgSYREd7RgnODyKgLLKtED5sR8ik3FPbMA74Li1I0j0jXYvhiW/wn2ftReXjTNnK160iVgc6YmNtGjdN0gEogT8kUJ++KE/GZyFA7ECfvMfSQQJxKMEw7EiAYTn/+kXbA5LOaYphwXmW13xuW7yCxwk1U4eO9o0iMJQmtrCa6sJl4ZTJZbc5y4jy3EPT0fa2b/XsG9Pzsqt88DxGIxdu7cSSAQYMKECXi9A2tJAEmExOcxEjqhdXUEllQQr2p/M3Qek4V3QQmO0SlsBq/eaLYQrX8GtNYuFlc2zLwWjr0BMoekJi6RErqmEw0l2hOkgJkghf3t+0igNZHyxwn7Y+jawT8iRk3P44wbJw6a7jbDMIjt9xNcWU14XV1yug0sCq5JuXhmF+IYkSFdX33AUUuEBjpJhMShapuxOrC0ksiWhuRdQ9Y8F975xbhn5KM6UrT2T7AB1jwKq/4OLfvNMkWFMQvh2K/C6NNAlWZ70ZlhGMQiGmFfjJA/hq/eHPzddqdcQ0UAXTOYdEIJJ3x57IBuGdIjCUJrWlt/Onzhsea78Mwqwj0jH4tHZiDvS45KItTc3Mzf//53tmwx10aaMGECN9xwAxkZA2cZAEmExJFI1IcJLKsk+ElNcryA4rDgObYAz7xibKmaI0RLwPY3YOVfYM+H7eXppTDjGphxNaQXpyY20e/sWlPLG49sBAPmXjiSmWcOT3VIPS5WFSS4vJLQmlqMWGvrj1XBPTkPz5xC7MPSB3QC2J/1eiL0ySefsHDhQlwuF7NnzwZg1apVhMNh3nzzTWbMGBgTvEkiJL4IPZogtLqWwMeVJOrDyXLH2Cy884pwHpOduib0uu3w6b9h7X8g3LrmmmKBMafD9Kth7EKwyDdccXDr39vPR0+Zy8Cc9tUJHDOnMMURfXFGQie8sZ7Asipi+9rv/LLmufDMKcIzIz91N0WIQ9bridDxxx/P6NGj+etf/4rVajb3JxIJbrzxRnbv3s2HH374Oc/QP0giJHqCoRtEdzQRWFZFZFtjstvMku3EO6cI97EFqWtWj0dgy8vwyT+h7OP2ck8eTLncTIryx6UmNtEvLH1uJ2vfKkO1KJx761SGjMtOdUhHRPNFCayoJriiKnkDBKqCa2IOnrlFOEamcN4wcdh6PRFyuVysWbOGceM6v0Fu3ryZY489NnknWX8niZDoaYmGMIHlVWa3Wbj1Lp62pva5RdiHpqXuzbZ+B6x5DNb+F4K17eXFM2DqFeYdZ57c1MQm+ixDN3jzH5vY+UktdqeFi+6YSW5p/7hxpm3W58DHlYQ3NoBufiSq6Xa8swvxzC7Ckp6ipXXEF9LriVBBQQGPPfYYZ5xxRqfyxYsXc80111BTU3MkT9vnSCIkeose0wivqyOwvIp4RSBZbivy4JlThHt6XuoGV2txcwmPTx+DHYvbZ65WrTD6dJh6ubmch03WQxImLa7z8oNrqdzRjDfLwWV3zcLdhxMII6ET3lCPf2lF+6zxgH14Ot75xbgm5hz9SVJFj+r1ROhb3/oWL7zwAr/61a+YP38+AEuXLuW73/0ul1xyCb/73e+OKPC+RhIh0dsMwyBeHiCwvIrQurrkJI2K3YJ7eh6eOUXYi1P47TpYby7jse6/ULmmvdyeBuPOgcmXwsiTZDyRIBKM89z9q2muCVE4MoMLb5uOxda3kgktGCe4sorAsip0X8wstCq4p+XjnV+c2r810aN6PRGKxWJ897vf5eGHHyaRML8t2mw2br75Zn7xi1/gcAyMSaQkERJHkx6KE1xdS3BlFYm69sHV9qFpZivRlFwUWwpvc6/bBuuehA3PtN+GD+DOgQkXwMSLzAVg5Vb8QaupOsizv1xNLJxg3PwiTrl6XJ8YVxOvDRFY2nlGeDXNhnduMZ45hVi8fbf1ShyZozaPUCgUYteuXQCMGjUKt3tgrZ4riZBIhbY5iYIrqjqNW1CcVjwz8vHMKcRWkMJFVXUdylfChmdh84sQrGu/5smH8ee1JkXzJSkahMo2NfDqQ+swDFhw6WimnTY0JXEYhkF0VzOBjyqIbGtKlttKvHiPK8E9ORfF2rdarETP6bVE6N133+Wb3/wmy5cvP+CJW1pamD9/Pg8//DDHH3/8kUXex0giJFJN88cIflJDcFU1WmP7Ctb2Yel4Zhfimpyb2jWMtATs/dDsPtvyKkSa26958mH8uWZr0bDjwJKiMU/iqFv7dhlLn92JosC535zK0Ik5R+21k7O9f1RBvLp18kMFnONzSDuuBPsImftnMOi1ROj888/n5JNP5rbbbuvy+oMPPsh7773HCy+8cHgR91GSCIm+wtANojubzVaiLQ3QOreb4rDgnpaHZ1YhthJvat/gtTjs/gA2v3BgUuTKNscUTbgARpwA1oHRfS66ZhgG7z62la0fV2F3Wbn0+zPJKuzdVkwtGCe4onX9P3+H9f+OLSBtQQnWVE1kKlKi1xKhYcOG8cYbbzB+/Pgur2/dupUzzjiDsrKyw4u4j5JESPRFmi9G8JNqgp/UdGolshV6cB9bgGtyLtaMFCcaWhz2fACbX4atr0Koof2aPQ1Gn2omRmNOB1dW6uIUvUaL67z42zVU724hs8DNpXcei8PV862C8boQgaWVhFbXJNf+UtPteOcX451dKJMfDlK9lgg5nU42btzI6NGju7y+c+dOJk+eTDgc7vJ6fyOJkOjLDL11LNEn1YQ31kOi/c/ZVurFNT4H18QcrAUpXiFcS8C+pebEjVtehUB1+zXFYo4lGnsmjDkDcseAdFsMGCFfjGfuW0WgKcrwKbmc/Y3JPTKbumEYxPa04P+ogsjW9klKbcUe0o4vxSXjfwa9XkuERo0axa9//WsuvPDCLq8///zz3HHHHezevfuwAu6rJBES/YUeihNaV0dobR2xMl/ygwHMGawdIzJat3Qs2c7UJUa6bt6Gv+012LoI6rZ0vp413EyIxpxh3oFmH1g3YAxGNXt9vPCrT9ESOrPPG8Gsc0Yc8XMZWuv8Px9VdJp/yzk+G+9xJTL7s0jqtUTo1ltv5f3332fVqlU4nc5O18LhMLNnz+bkk0/mwQcfPLLI+xhJhER/pPljRLY0Et7cQGRnU6eWIgA1zY5jRDqOYenYR2RgK/Skbs2zxt2w7Q3Y+RbsXQJarP2axQ5D58GoU8ytYBKo8i2/P9rycSXvProVgHP+ZwrDpxzeDOV6OEFwZTWBjyvQWtrm/1HxzMzHe1wJtjxJmEVnvZYI1dTUMGPGDCwWC9/85jc55phjAHNs0B//+Ec0TePTTz+loKDgi/0EfYQkQqK/06Ma0T0txPa0EN3rI1buB63zn73isGAf1poYDUvDPiQtNbNaRwOw50PY8SbseAt85Z2ve/Jg+PHmYOsRJ0D2SOlG60c++O82Nn5Qgd1p4bK7ZpFZ8PnJS6IxYs7/s6oGI6YBoHpteOcV45lblLo1+kSf16vzCO3bt4+bb76ZxYsX0/ZwRVFYuHAhf/zjHxkx4sibPfsaSYTEQGPENWL7/UT3+Iju8xHb58OIap0rKebAa/vQNOxD07EPScOa6zq6rUaGAQ07Yde7sOs92PsRxAKd66SXmAnRsPlmN5okRn2altB56bdrqNrVQlahOXja7uw64Y6W+Qh8VGGOfWv9lLIWuEk7vgT31HyUPjZjteh7jsqEik1NTezcuRPDMBgzZgxZWQPvzg9JhMRAZ+gG8eogsb0+ontbiJX50ZqjB9RTHBbsJV5sQ9Kwl3qxF3uP7lijRAzKV5kJ0Z6PzAkdO3ajAXgLzK60YfNhyByzK03mLupTgi1Rnvn5KoItMUZOy+PMmyYlE2xDNwhvaiCwpILYPl/yMY4xmaQdX4pjTKaM/xGH7KjNLD3QSSIkBiPNFyW6z0+szEeszE+8MpC8LbkjxWHBVuTBXuzFVuzBVuDBWuA+OhM8xkKwf4WZGO1bBhWfHJgY2dxQMhOGzIbS2VB6LHgOb2yK6HnVu1t44TefoicMZp83gpmnDiH4SQ2Bjyvbp4OwmOt/pR1fgq2X5x8SA5MkQj1EEiEhwNAM4rUh4uV+Yvv9xCoC5oy9WtdvH5ZsJ7Z8N7YCN9Z8N7Z8N9Z8V++OO4pHoGI1lH0M+z6G8tUQbTmwXuZQMzlq2wongyOt9+ISXdq8tJIV/9nKSIfKSI8NpW39L7cVz5wivPOKsfTh1etF3zfgE6Gf/exnvPbaa6xduxa73U5zc/MBdcrKyrj55pt577338Hq9XHvttdx3331YrYf+ZiyJkBBdMzSdRF3YTIqqgsQrA8RrQ+iBeLePsWQ4sOa7sOZ22HJcWLMcKJYeHvOh61C/zWw12r/K7Eqr395FRQVyRkPR1PatYBJ4jt6SEINJ2zp6gaWVhDc30NbRpWQ5yDhxCO4Z+aldMkYMGIf6+d1vO89jsRiXXXYZ8+bN4+9///sB1zVN45xzzqGwsJCPP/6YqqoqrrnmGmw2Gz//+c9TELEQA4tiUbEVeg7ottCCcRI1QeI1IeI1IRK1oWSCpLVE0VqiRHc0d34yFSzpDiyZDqxZTiyZ5rElw4ElzY4lw47qth3eYG1Vhfzx5jbzOrMs0mLOYVTxqdl6VPEp+CuhYYe5bXy2/fFpRZA/AQommolR/jjIHQs2WabhSBhxndC6WgJLKpPrfylAs83C5qYoMZuFy6akeN08MSj12xahNv/617/49re/fUCL0Ouvv865555LZWVl8lb+hx9+mO9///vU1dVhtx9ak6u0CAnRM/RQnHhtiERdmER969YQJtEQ6XL80QFUBUuaDdVtQ/XYUN1WVJfVPHdaUBzW1r0F1WFBsVtQbCqKzYJiV81jVTHvKlNI7pVgHVSth6q1ULXO3Jr3dROEYk74mD8e8loTo9yxkDsanBk998saQLSWKIGV1QSXV6EHO6z/NSMf74ISEi4rT9+3ikBjlKETsznnlqmoqZrTSgwoA75F6PMsW7aMyZMnd5rPaOHChdx8881s2rSJ6dOnd/m4aDRKNNp+x4zP5+uynhDi8KhuG47hGTiGd04YDMNA98dINEXRmiOt+9bNF0XzxcwPUN1Aa4m1T6bXY4EBqgvFMh/FsgAsCmq6BYstiqr6sGi1qNH9qOHdWOJVqA0tqA3rUbd+hEoARWn9LuktNJcHyRll3safPco8zho+6FqRDMMguquF4HKz+6ttgWBLhrn+l2dW+/pfNuDsb0zh+QdWU7apkRUv7WLeRV0v4SREbxiwiVB1dfUBkzq2nVdXV3f1EADuu+8+fvzjH/dqbEKIdoqimN1i6Q4Y1vW3NkPT0fxxNF8UPZxADyXQg/HW4zhGVEOPaBgxDT2SwIhqGHEdI65hxPSDtzjpgK5jJNpXJdH9cRIApLVuo4CTunywqgRQaUaN+lAb/aiKD5U9qMoGVPzmdbcDNSMDNSsHJbsANa8UJXsoZA6BtOIBc4u/FowTWlNLcEUVibr29Sbtw9PxzivGNSmny7FgeUPTOPmacbz19818uriMvKHpjJ6ZfzRDF4NYn/rru/POO/nlL3950Dpbtmxh3LhxvRbDXXfdxe2335489/l8DBkypNdeTwjx+RSLijXTgTXTcUSPN3QDI6GbmY5hJPeGboBuYGgGaOa5EdfRQ3H0YBwtEDfHNgXMVik9GEcLmmXmJJQqupGOTmsC191Ag5bWrazDz0QQhU9RlSWo1hiKDVSHBdVlQ3G7UL0elLR01PQslLR0FKcN1W52/SkOi3lsV80uwBR2JRmaQWRHE6HVNWbrT+udhIrdYnZ/zS06pNvfx84qpK4swNq3ynjn35vJKnSTU+Lt7fCF6FuJ0He+8x2uu+66g9YZOXLkIT1XYWEhK1eu7FRWU1OTvNYdh8OBw3Fkb7ZCiL5JURWUHh6EayR0s2Uq1JocBeNmAhVKtLdahePogQhGMGyWRcHQzDgMXBi40I0ciGNuIaCp46tEgerW7SAsoNpVFIfVTIzsFhSbxSzrOE7Kap5jUVGsCorFLEMFFKV1DBWdZ+c2Wv/PMJMeI6GbSWXrzx/aWI/ua++utBV78MwqNO/+OszpEuZdOJL6/X7Ktzax6OENXHbnsThlCQ3Ry/pUIpSXl0deXl6PPNe8efP42c9+Rm1tLfn5ZhPrW2+9RXp6OhMmTOiR1xBCDF6KVcWSbseSbudwPqoNTUcPJzAimpkoNdVhNNaitzRg+FrQgyH0UBgjHEOP6hgJtTVhcrbu3Rg4MXACrcmdBnpYh3APj586RKrbintaPu5jC7AXH3krjmpRWXjjJJ6+bxW+ujBv/WOTDJ4Wva5PJUKHo6ysjMbGRsrKytA0jbVr1wIwevRovF4vZ5xxBhMmTODqq6/m/vvvp7q6mh/+8Ifccsst0uIjhEgZxaJi8drBC+CCIemYY5C6kYhBoAb81eat/r4qCNRg+GvBX4fub8bw+zDCIQzDgW44komSYdgxcLRvhgMDW+tmBcOGobrA4sBQHWCxg2rHUO2g2sBiRVGtYLGBagWbDcVuRbHbUewOFKcd+/BMXOOzzZalHuD02jjrG5N5/n5z8PTKl3cz98KD/H6E+IL67e3z1113Hf/+978PKH/vvfc46aSTgPbFYd9//308Hg/XXnstv/jFL2RCRSHEwKNrEG6CQC0E61q3egg1QKi+w3EjhBvNYz3xxV/XngbOdHCkt+7TzGNH2meOvWD3msd2b/u53Qt2j3lnXYcuue2rqnnr75sBOPOmSYyaIYOnxeEZ8DNLHy2SCAkhBiTDgKjfTIrCTeYWajtuhkjrFm42J6KMNJv1o36I+EDvfgbxI6O0JkVuc404u5el1eewtmYOVjXOpbPfJCc73p402VxmPavT3NucYHV1v7c6zLoWW+cxUGLAGvTzCAkhhDgIRTFbcJzp5lxHhysegaivNTFqMY8jvvayaKDDsc88jwXM81ig9TwI8WDrExoQ85tbq3lsot5+N+WxaSxaNZPLcr6HU/V3Hc8h/9yqmRC1JUbJBMluHlscrWWtXYUWO1jt7eVtZclyu5lcWTocq23n1vby1q5Gc9/a1Zis23resRtSkrWjRhIhIYQQh8/mNDfvF+yy0nWIh8ykKNaWHJnnaizIQn+YZ56N4/MXslh5iPPmr0PVQhAPt24djhNhM0HruE9EIRFpfz2j9fXioS8Wd29TLJ0TpI6bpcOxYgHV0uF667Gidjhuq2NpPbZ2OFbbryuW9scl963HyXOli7LW51CU9vJO1zpscGCZosDYM836KSCJkBBCiNRRVXO8kMMLFBxw2QmcXRrg2ftXU16TzsctV3LcZWMO7zV0HbTWhCgeaT1uPU/uY+11ksetey3W4ThuPkaLmcdax+NY+3U9DlqidR/r5jjefRejoUFCO+xfZ7/1wzpJhIQQQoiu5JR4Oe3a8bzxyEbWvbOf3CFexs0tOvQnUFVQW8cV9bXVTgzDHOiux83B61rrvm1Lnmsdytvqa+3nhmbWNdrK9Pb6htZaR29/TLJMM+t2rNPpWO+i3Gg/73S9bTPar3XcdK39Z/7sNaVn7jo8EpIICSGE6PNGzcjn2LOH88mivbz/+DayCj0UDB8AN7AoSutYIvk4TpXUpWBCCCHEYZh97giGT8lFS+i8/vAGgi3Rz3+QEJ9DEiEhhBD9gqIqnP7VCWQVugk2R3n94Q0k4oNoHI3oFZIICSGE6DfsLitn3zwFh9tKzR4fHzyxDZkOT3wRkggJIYToVzIL3Jxx40QUBbYuq2b9u+WpDkn0Y5IICSGE6HeGTshh/iWjAVj67A72b25McUSiv5JESAghRL809dQhjJtbiGHA4r9tpLm2j0+SKPokSYSEEEL0S4qicOJVx1AwIp1oKMGiP60nGu6BhWTFoCKJkBBCiH7LarNw1jcm48l00FQd4s2/bULXZfC0OHSSCAkhhOjXPBkOzr55MlabStmmBpa9sCvVIYl+RBIhIYQQ/V7+sHROuXY8AGvfKmPrsqoURyT6C0mEhBBCDAhjji3g2LOHA/Def7ZStasltQGJfkESISGEEAPG7HNHMHJaHnrC4PWH1+NvjKQ6JNHHSSIkhBBiwFBUhVOvG09OqZewP86iP68nFpE7yUT3JBESQggxoNidVs6+eTKuNBv1+wO8/c/NGHInmeiGJEJCCCEGnPQcF2ffPAXVqrBnXT3LX9qd6pBEHyWJkBBCiAGpcGQGp1xt3kn26eJ9cieZ6JIkQkIIIQasY+YUMvOsYQC89/hWKnc2pzYg0edIIiSEEGJAm3PeSEZNz0PXDF5/eAO++nCqQxJ9iCRCQgghBjTzTrIJ5A1NIxKI8+ofZU0y0U4SISGEEAOezWHh7Jun4Mmw01QVZPEjG9A0PdVhiT5AEiEhhBCDgjfLwTm3TMVqV9m/pYmPntyOYcht9YOdJEJCCCEGjbyhaZxxw0RQYNNHlax9e3+qQxIpJomQEEKIQWXE1DyOu3QMAB8/v5Pda+tSHJFIJUmEhBBCDDpTTill0oklYMBb/9hE7T5fqkMSKSKJkBBCiEFHURSO/9IYhk7MJhHTee2P6+W2+kFKEiEhhBCDkmpRWXjjJHJKvIR8MV75wzoigXiqwxJHmSRCQgghBi27y8p5t07Fm+WguSbEa3/6/+3de1RU98Hu8WeA4SICotwVFNHiDe9axcRo02iMMXqa2pOammipy+QQleoiosYmK40xjZc0tW9Qc3yN5+RifI/RGJMYrU2MWq0oKIKCF16VQCeikgFRRmDm/GGkQUWJjmyG+X7W2msxezZ7nln7j3nW3r/924dUfaXG6FhoRBQhAIBb82/lozHTesunhZcsBWXauipXdp5W7zYoQgAAt9c6yl+PPNtTnl4e+u9D57TzQ+YYchcUIQAAJEV1bqWfT+4mmaScHUXK/OK00ZHQCChCAAB8r1O/sNo5hvZuLNCRXcUGJ8K9RhECAOAHej0Yrb4jYyRJX72Xp5NZZw1OhHuJIgQAwHUGjYtT1yGRcjikraty9U1+qdGRcI9QhAAAuI7JZNKwCfHq2DtU9mqHPnsrm9mnmymKEAAAN+Hh6aGHkrqpbXwrVdlq9MmyQyq1VBgdC05GEQIAoB5eZk898kxPhcYEqPJilTa9eVBl53kUR3NCEQIA4BauzT7dKryFLpbatOnPB1VhtRkdC05CEQIA4Db8Arw1NqW3Atr4ylpyWZvePKjLF68YHQtOQBECAKABWgb7amxKH/kHeetCcYU++csh2S5XGx0Ld4kiBABAAwWF+mns7/vIL8CskjPl2rzskKpsPKTVlVGEAAD4EYIj/DVm+rWHtFr16VvZPLHehVGEAAD4kUKjA/TotF4y+3iqKL9Un684rJoqu9GxcAcoQgAA3IGI2CA9+lwveXl76EzuBW15O0c11ZQhV0MRAgDgDkV1bqVH/ldPeZo9dCr7nLb9Z67sNZQhV0IRAgDgLkR3aa1RzyTIw8ukk5kl+ts7R2W3O4yOhQZyySJ06tQpJSUlKTY2Vn5+foqLi9OLL76oK1fqzumQnZ2t+++/X76+voqOjtbrr79uUGIAQHPWvnsbPTylhzw8TDqe8a2+/D+UIVfhkkUoLy9PdrtdK1asUG5urt544w0tX75cc+fOrd2mrKxMI0aMUPv27XXgwAEtWrRIL730klauXGlgcgBAcxXbK1QPJXWXycOkvL0W/e0/c1XDZbImz+RwOJpFZV20aJHS09NVUFAgSUpPT9e8efNksVjk7e0tSUpLS9PGjRuVl5dX735sNptstn9PnV5WVqbo6GhZrVYFBgbe2y8BAHB5JzPPauv/zpXd7lDH3qEakdRdnmaXPO/g0srKyhQUFHTb3+9mc2SsVqtat25d+3rPnj0aOnRobQmSpJEjRyo/P1+lpaX17mfhwoUKCgqqXaKjo+9pbgBA8xLXN0yjnk2Qp5eHCg6W6LPlh5lnqAlrFkXoxIkTWrZsmaZOnVq7zmKxKDw8vM52115bLJZ69zVnzhxZrdbapbCw8N6EBgA0Wx0SQjQ6uae8zB46k3tem/8jmxmom6gmVYTS0tJkMpluuVx/WauoqEgPP/ywxo8frylTptx1Bh8fHwUGBtZZAAD4saK7ttaY6f+edHHTmwdVWVFldCxcx8voAD80a9YsTZo06ZbbdOzYsfbv4uJiDR8+XImJiTcMgo6IiNC3335bZ9211xEREc4JDADALUR1DtZjKb21edkhWQqs2rg0S2Om95J/kI/R0fC9JlWEQkNDFRoa2qBti4qKNHz4cPXr10+rV6+Wh0fdk1uDBw/WvHnzVFVVJbPZLEnatm2b4uPjFRwc7PTsAADcTERskMbN7KtP/nJQ54su6qNFB/TYjD4KCvUzOhrUxC6NNVRRUZGGDRummJgYLV68WCUlJbJYLHXG/kyYMEHe3t5KSkpSbm6uPvzwQ7355puaOXOmgckBAO4opF1L/SK1rwJDfFV2rlIfLT6g80UXjY4Fuejt8++8844mT5580/d++HWys7OVnJysjIwMhYSEaNq0aZo9e/aP+qyG3n4HAMDtVFht358ZqpBPCy+NTu6lyLggo2M1Sw39/XbJItSYKEIAAGeqrKjSp/+RLUuBVV5mD434XXfF9mrYsBA0nNvNIwQAgCvw9TfrsRm91b5HG1VX2fX58sPK2fGN0bHcFkUIAIBGZvbx1CPPJqjbkEg5HNKOD45pz4aT4iJN46MIAQBgAA9PDw37TRcNHBMrScr84rT+9s4R1VTzfLLGRBECAMAgJpNJA0bH6mdPdZHJw6Rj//xWnyw7xMSLjYgiBACAwbomRl19JMf3s1D/v9f268K/KoyO5RYoQgAANAHtu7fR46n9FNDaV9aSy1r/p/06dfic0bGaPYoQAABNREi7lho/p78iOwXpSmWNPn0rW1lbzzCI+h6iCAEA0IT4BXhrbEofdRsSKTmkf3x0QtvXHFX1FZ5efy9QhAAAaGI8va7eUXbfrzrLZJLy91r00eJMlZ2/bHS0ZociBABAE2QymdTrZ9EaM6O3fP3NKjlTrv96db8Kj14wOlqzQhECAKAJi+7SWuPn9ldoTIAqK6r0yV8OKvOL04wbchKKEAAATVxgGz/9IrWvuiRenYl6z4aT2rIyR7ZLzDd0tyhCAAC4AC+zp342sYsemBAvD0+TCrJK9OErGbIUWI2O5tIoQgAAuAiTyaQeQ9vqF6n9FBjiq/ILlfpocaYObDklh51LZXeCIgQAgIsJ7xCo/zlvoDoPCJfD7tDejQXa9JeDqrDajI7mcihCAAC4IG8/Lz30224aPrGLvMwe+iavVB++sk//fajE6GguhSIEAICLMplM6jYkSuPnDlCbti11ubxKn6Uf1pf/96iuVFYbHc8lUIQAAHBxrSP9NT6tv3o/FCOZpCO7/6UPFzCQuiEoQgAANAOeZg8NebyTxqb0UctgH5WVXNZHizP1z00FqqmyGx2vyaIIAQDQjLSLD9YT8/89kHr/Z6f04aucHaoPRQgAgGbGp4VZI5K6a+SUHvILMKv0XxVav+iAdq47xtih61CEAABopjr1C9OEFwcpflCE5JCy//6N1r68T2dyzxsdrckwOXhYyS2VlZUpKChIVqtVgYGBRscBAOCOnMk9ry/fy9PFC1fnGurUP0z3je8s/yAfg5PdGw39/aYI3QZFCADQXFyprNY/NxXo8JffyOGQvH099dOxcerxQFt5eJiMjudUFCEnoQgBAJqbkjPl+ur9fJ09VSZJCo0J0AMT4hXeofn8zlGEnIQiBABojux2h47sKtaeDSd15fLVAdTxP43QoHEd1TLY1+B0d48i5CQUIQBAc3ap7Ir+8dEJ5e+1SJK8zB7q/VCM+oyIkbevl8Hp7hxFyEkoQgAAd3D2dJl2/ddx/evE1fmGWgR666djO6rLoAh5eLreTeYUISehCAEA3IXD4VBBVon+8dEJlZ2rlCQFR7TQoLFxiu0dIpPJdQZUU4SchCIEAHA3NVV2Hd7xjfZ/fkq2iqvjh8JjAzX4f8Sp7U+CDU7XMBQhJ6EIAQDcle1ytbK+OK1D2wtV/f3zymK6tVb/RzooslMrY8PdBkXISShCAAB3V2G1KePTUzqyq1gO+9Xa0Da+lfqP6qC28cFN8pIZRchJKEIAAFxlLbmkzC2nlbfXInvN1foQ0TFQ/UZ1UPvubWRqQpMyUoSchCIEAEBd5RcqlbX1jI7sKlZN9dVLZq2j/NXnoRh1HhAuTy/j7zKjCDkJRQgAgJursNp0cNsZ5e4qVlVljSTJv5WPev0sWt3vj5K3n3HzEFGEnIQiBADArdkuVSl3Z7EObS/UpbIrkiSzj6fifxqhHg+0VZu2LRs9E0XISShCAAA0TE2VXfn7LDq47YxKLZdq10d2ClKPB9oqrneYPM2Nc9mMIuQkFCEAAH4ch8OhovxS5XxdpIKD52rvNPNtaVb8oAh1S4xS6yj/e5qBIuQkFCEAAO5cxXc2HdldrNydxar4zla7PqJjoLoOiVKnfmH35JlmFCEnoQgBAHD37DV2ncm9oCO7i3Xq8Pnas0RmH0+Nn9NfwRHOPUPU0N9v132sLAAAcBkenh7q0DNEHXqGqMJqU/5ei47svjpBY6uwFobloggBAIBG5R/ko74j26vPiBhVfHfF0IkYjZ/xCAAAuCWTyaSWwT6GZqAIAQAAt0URAgAAbosiBAAA3BZFCAAAuC2KEAAAcFsUIQAA4LZctgg99thjiomJka+vryIjIzVx4kQVFxfX2SY7O1v333+/fH19FR0drddff92gtAAAoCly2SI0fPhwrVu3Tvn5+Vq/fr1OnjypX/7yl7Xvl5WVacSIEWrfvr0OHDigRYsW6aWXXtLKlSsNTA0AAJqSZvOssU2bNmncuHGy2Wwym81KT0/XvHnzZLFY5O3tLUlKS0vTxo0blZeX1+D98qwxAABcT0N/v132jNAPXbhwQe+9954SExNlNpslSXv27NHQoUNrS5AkjRw5Uvn5+SotLa13XzabTWVlZXUWAADQPLl0EZo9e7b8/f3Vpk0bnTlzRh9//HHtexaLReHh4XW2v/baYrHUu8+FCxcqKCiodomOjr434QEAgOGaVBFKS0uTyWS65fLDy1qpqanKysrS1q1b5enpqaeeekp3e6Vvzpw5slqttUthYeHdfi0AANBENamnz8+aNUuTJk265TYdO3as/TskJEQhISH6yU9+oq5duyo6Olp79+7V4MGDFRERoW+//bbO/157HRERUe/+fXx85ONj7APgAABA42hSRSg0NFShoaF39L92u13S1TE+kjR48GDNmzdPVVVVteOGtm3bpvj4eAUHBzd4v9fOMDFWCAAA13Htd/u2V4ocLmjv3r2OZcuWObKyshynTp1ybN++3ZGYmOiIi4tzVFZWOhwOh+O7775zhIeHOyZOnOjIyclxrF271tGiRQvHihUrftRnFRYWOiSxsLCwsLCwuOBSWFh4y995l7x9/vDhw5oxY4YOHTqkiooKRUZG6uGHH9YLL7ygtm3b1m6XnZ2t5ORkZWRkKCQkRNOmTdPs2bN/1GfZ7XYVFxcrICBAJpPJ2V8Fd6isrEzR0dEqLCxkWoMmjmPlWjheroXjVT+Hw6Hy8nJFRUXJw6P+IdEuWYQA5ndyHRwr18Lxci0cr7vXpO4aAwAAaEwUIQAA4LYoQnBJPj4+evHFF5nqwAVwrFwLx8u1cLzuHmOEAACA2+KMEAAAcFsUIQAA4LYoQgAAwG1RhAAAgNuiCMFlLFy4UAMGDFBAQIDCwsI0btw45efnGx0LDfTaa6/JZDIpJSXF6CioR1FRkX7zm9+oTZs28vPzU0JCgvbv3290LFynpqZG8+fPV2xsrPz8/BQXF6c//vGPt3+mFm6qST10FbiVHTt2KDk5WQMGDFB1dbXmzp2rESNG6MiRI/L39zc6Hm4hIyNDK1asUM+ePY2OgnqUlpZqyJAhGj58uD7//HOFhobq+PHjP+oh1Wgcf/rTn5Senq41a9aoe/fu2r9/vyZPnqygoCBNnz7d6Hguh9vn4bJKSkoUFhamHTt2aOjQoUbHQT0uXryovn376q233tIrr7yi3r17689//rPRsXCdtLQ07d69Wzt37jQ6Cm7j0UcfVXh4uFatWlW77vHHH5efn5/effddA5O5Ji6NwWVZrVZJUuvWrQ1OgltJTk7W6NGj9fOf/9zoKLiFTZs2qX///ho/frzCwsLUp08fvf3220bHwk0kJiZq+/btOnbsmCTp0KFD2rVrl0aNGmVwMtfEpTG4JLvdrpSUFA0ZMkQ9evQwOg7qsXbtWmVmZiojI8PoKLiNgoICpaena+bMmZo7d64yMjI0ffp0eXt76+mnnzY6Hn4gLS1NZWVl6tKlizw9PVVTU6MFCxboySefNDqaS6IIwSUlJycrJyd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coefstd errtP>|t|
intercept0.0043240.1261160.0342830.972651
age1.0363632.7924560.3711300.710541
sex-11.7476983.078307-3.8162850.000135
bmi13.8257783.3231344.1604640.000032
map11.5430553.1195533.7002270.000215
tc-32.90681723.366760-1.4082750.159050
ldl22.82166519.8199401.1514500.249547
hdl-0.47746311.311322-0.0422110.966330
tch0.8474338.0788140.1048960.916459
ltg29.5753779.7385413.0369410.002390
glu0.0648553.2028060.0202500.983844
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" - ], - "text/plain": [ - " coef std err t P>|t|\n", - "intercept 0.004324 0.126116 0.034283 0.972651\n", - "age 1.036363 2.792456 0.371130 0.710541\n", - "sex -11.747698 3.078307 -3.816285 0.000135\n", - "bmi 13.825778 3.323134 4.160464 0.000032\n", - "map 11.543055 3.119553 3.700227 0.000215\n", - "tc -32.906817 23.366760 -1.408275 0.159050\n", - "ldl 22.821665 19.819940 1.151450 0.249547\n", - "hdl -0.477463 11.311322 -0.042211 0.966330\n", - "tch 0.847433 8.078814 0.104896 0.916459\n", - "ltg 29.575377 9.738541 3.036941 0.002390\n", - "glu 0.064855 3.202806 0.020250 0.983844" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "G = GLM(family=sm.families.Binomial(), summarize=True)\n", - "G.fit(X, yb)\n", - "G.summary_" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "aecc8577-336c-4877-b060-e03b95bc2f56", - "metadata": { - "execution": { - "iopub.execute_input": "2023-09-11T23:38:25.530593Z", - "iopub.status.busy": "2023-09-11T23:38:25.530334Z", - "iopub.status.idle": "2023-09-11T23:38:25.645225Z", - "shell.execute_reply": "2023-09-11T23:38:25.644626Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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coefstd errzP>|z|
intercept0.00430.1260.0340.973
age1.03642.7920.3710.711
sex-11.74773.078-3.8160.000
bmi13.82583.3234.1600.000
map11.54313.1203.7000.000
tc-32.906823.367-1.4080.159
ldl22.821719.8201.1510.250
hdl-0.477511.311-0.0420.966
tch0.84748.0790.1050.916
ltg29.57549.7393.0370.002
glu0.06493.2030.0200.984
\n", - "
" - ], - "text/plain": [ - " coef std err z P>|z|\n", - "intercept 0.0043 0.126 0.034 0.973\n", - "age 1.0364 2.792 0.371 0.711\n", - "sex -11.7477 3.078 -3.816 0.000\n", - "bmi 13.8258 3.323 4.160 0.000\n", - "map 11.5431 3.120 3.700 0.000\n", - "tc -32.9068 23.367 -1.408 0.159\n", - "ldl 22.8217 19.820 1.151 0.250\n", - "hdl -0.4775 11.311 -0.042 0.966\n", - "tch 0.8474 8.079 0.105 0.916\n", - "ltg 29.5754 9.739 3.037 0.002\n", - "glu 0.0649 3.203 0.020 0.984" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_ = G.design_ @ np.identity(G.design_.shape[1])\n", - "X_ = pd.DataFrame(X_, columns=G.summary_.index)\n", - "from ISLP.models import summarize\n", - "summarize(sm.GLM(yb, X_, \n", - " family=sm.families.Binomial()).fit())" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "4cb50329-79b2-4066-8699-194287374d1f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-09-11T23:38:25.649259Z", - "iopub.status.busy": "2023-09-11T23:38:25.648711Z", - "iopub.status.idle": "2023-09-11T23:38:25.654807Z", - "shell.execute_reply": "2023-09-11T23:38:25.654261Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "((442, 11), 11)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_.shape, len(G.summary_.index)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "236c34f7-bd42-4aa5-b9cb-1d30e97fcd12", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/examples/diabetes_logistic.md b/docs/examples/diabetes_logistic.md new file mode 100644 index 0000000..e0741a4 --- /dev/null +++ b/docs/examples/diabetes_logistic.md @@ -0,0 +1,82 @@ +--- +jupytext: + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.17.2 +kernelspec: + display_name: Python 3 (ipykernel) + language: python + name: python3 +--- + +```{code-cell} ipython3 +import numpy as np +import pandas as pd +import statsmodels.api as sm + +from glmnet import GLMNet, GLM +from sklearn.base import clone +import statsmodels.api as sm +from ISLP.models import summarize +import logging +logging.basicConfig(filename='log.txt', level=logging.INFO) +import rpy2 +%load_ext rpy2.ipython +``` + +```{code-cell} ipython3 +%%R -o X,Y,N +#install.packages('lars', repo='http://cloud.r-project.org') +library(lars) +library(glmnet) +data(diabetes) +X = model.matrix(lm(y ~ x, data=diabetes))[,-1] +N = colnames(diabetes$x) +Y = diabetes$y +system.time(glmnet:::glmnet.path(X, Y>140, family=binomial,alpha=0.4)) +``` + +```{code-cell} ipython3 +X = pd.DataFrame(X, columns=N) +``` + +```{code-cell} ipython3 +yb = Y > 140 +``` + +```{code-cell} ipython3 +%%timeit +G4 = GLMNet(family=sm.families.Binomial(), alpha=0.4 ) +G4.fit(X, yb) +``` + +```{code-cell} ipython3 +G4 = GLMNet(family=sm.families.Binomial(), alpha=0.4 ) +G4.fit(X, yb) +G4.plot_coefficients() +G4.coefs_.shape +``` + +```{code-cell} ipython3 +G = GLM(family=sm.families.Binomial(), summarize=True) +G.fit(X, yb) +G.summary_ +``` + +```{code-cell} ipython3 +X_ = G.design_ @ np.identity(G.design_.shape[1]) +X_ = pd.DataFrame(X_, columns=G.summary_.index) +from ISLP.models import summarize +summarize(sm.GLM(yb, X_, + family=sm.families.Binomial()).fit()) +``` + +```{code-cell} ipython3 +X_.shape, len(G.summary_.index) +``` + +```{code-cell} ipython3 + +``` diff --git a/docs/examples/elnet_compare.ipynb b/docs/examples/elnet_compare.ipynb deleted file mode 100644 index df6a02d..0000000 --- a/docs/examples/elnet_compare.ipynb +++ /dev/null @@ -1,203 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "dc49a0eb-eb0b-4752-ab2d-f0b9a025851e", - "metadata": {}, - "outputs": [], - "source": [ - "import rpy2\n", - "%load_ext rpy2.ipython\n", - "\n", - "import numpy as np\n", - "from glmnet.glmnet import RegGLM\n", - "rng = np.random.default_rng(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "bb1965ac-8dbf-478e-8782-c88683a87f4d", - "metadata": {}, - "outputs": [], - "source": [ - "n, p = 1000, 50\n", - "X = rng.standard_normal((n, p))\n", - "beta = np.zeros(p)\n", - "beta[:2] = [1,2]\n", - "y = rng.standard_normal(n) + X @ beta\n", - "W = np.ones(n) # rng.uniform(1, 2, size=(n,))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "d7c30c2d-86b0-4c28-85d5-08ce912a255a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Loading required package: Matrix\n", - "Loaded glmnet 4.1-7\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%%R -i X,y,W,n,p -o coef_,intercept_\n", - "library(glmnet)\n", - "G = glmnet(X, y, standardize=FALSE, intercept=TRUE, weights=W)\n", - "B = predict(G, s=2 / sqrt(n), type='coef', exact=TRUE, x=X, y=y, weights=W)\n", - "coef_ = B[2:(p+1)]\n", - "intercept_ = B[1]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f71dfa1b-b897-41bf-a9df-428e715ff087", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 8.96664353e-01, 1.90821670e+00, 0.00000000e+00, 0.00000000e+00,\n", - " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", - " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", - " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", - " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", - " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", - " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", - " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", - " -8.75543063e-04, 0.00000000e+00, 0.00000000e+00, 1.84666823e-02,\n", - " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", - " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", - " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", - " 0.00000000e+00, 0.00000000e+00])" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "coef_" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "48a24021-8ee3-4749-8495-87f481eb2232", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 8.96664354e-01, 1.90821670e+00, 0.00000000e+00, 0.00000000e+00,\n", - " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", - " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", - " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", - " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", - " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", - " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", - " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", - " -8.75543163e-04, 0.00000000e+00, 0.00000000e+00, 1.84666824e-02,\n", - " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", - " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", - " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", - " 0.00000000e+00, 0.00000000e+00])" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "G = RegGLM(lambda_val=2 / np.sqrt(n), standardize=False, fit_intercept=True)\n", - "G.fit(X, y, sample_weight=W)\n", - "G.coef_" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "867df277-0ffe-4825-a454-058bc6a98088", - "metadata": {}, - "outputs": [], - "source": [ - "np.testing.assert_allclose(G.intercept_, intercept_)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "e5915534-7a86-4036-8b89-dd97d09e326d", - "metadata": {}, - "outputs": [], - "source": [ - "np.testing.assert_allclose(G.coef_, coef_, rtol=1e-5, atol=1e-5)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "de66e622-832d-47a4-b5bf-3b6024391e87", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'fit_time': array([0.01464605, 0.00067592, 0.00067091, 0.0005672 , 0.00055385]),\n", - " 'score_time': array([2.57492065e-05, 2.09808350e-05, 2.02655792e-05, 1.97887421e-05,\n", - " 1.90734863e-05]),\n", - " 'test_score': array([-114.34899496, -102.4599904 , -94.61822674, -84.92798059,\n", - " -115.13325371])}" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.model_selection import cross_validate\n", - "cross_validate(G, X, y, cv=5)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b2fe3c55-3c0e-4a44-ae67-932794fb60a1", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/examples/elnet_compare.md b/docs/examples/elnet_compare.md new file mode 100644 index 0000000..72c9197 --- /dev/null +++ b/docs/examples/elnet_compare.md @@ -0,0 +1,66 @@ +--- +jupytext: + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.17.2 +kernelspec: + display_name: Python 3 (ipykernel) + language: python + name: python3 +--- + +```{code-cell} ipython3 +import rpy2 +%load_ext rpy2.ipython + +import numpy as np +from glmnet.glmnet import RegGLM +rng = np.random.default_rng(0) +``` + +```{code-cell} ipython3 +n, p = 1000, 50 +X = rng.standard_normal((n, p)) +beta = np.zeros(p) +beta[:2] = [1,2] +y = rng.standard_normal(n) + X @ beta +W = np.ones(n) # rng.uniform(1, 2, size=(n,)) +``` + +```{code-cell} ipython3 +%%R -i X,y,W,n,p -o coef_,intercept_ +library(glmnet) +G = glmnet(X, y, standardize=FALSE, intercept=TRUE, weights=W) +B = predict(G, s=2 / sqrt(n), type='coef', exact=TRUE, x=X, y=y, weights=W) +coef_ = B[2:(p+1)] +intercept_ = B[1] +``` + +```{code-cell} ipython3 +coef_ +``` + +```{code-cell} ipython3 +G = RegGLM(lambda_val=2 / np.sqrt(n), standardize=False, fit_intercept=True) +G.fit(X, y, sample_weight=W) +G.coef_ +``` + +```{code-cell} ipython3 +np.testing.assert_allclose(G.intercept_, intercept_) +``` + +```{code-cell} ipython3 +np.testing.assert_allclose(G.coef_, coef_, rtol=1e-5, atol=1e-5) +``` + +```{code-cell} ipython3 +from sklearn.model_selection import cross_validate +cross_validate(G, X, y, cv=5) +``` + +```{code-cell} ipython3 + +``` diff --git a/docs/examples/glmnet_path.ipynb b/docs/examples/glmnet_path.ipynb deleted file mode 100644 index 31714a9..0000000 --- a/docs/examples/glmnet_path.ipynb +++ /dev/null @@ -1,281 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "id": "dc49a0eb-eb0b-4752-ab2d-f0b9a025851e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The rpy2.ipython extension is already loaded. To reload it, use:\n", - " %reload_ext rpy2.ipython\n" - ] - } - ], - "source": [ - "import rpy2\n", - "%load_ext rpy2.ipython\n", - "\n", - "import numpy as np\n", - "from glmnet import GLMNet\n", - "from glmnet import RegGLM\n", - "rng = np.random.default_rng(0)\n", - "import statsmodels.api as sm" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "bb1965ac-8dbf-478e-8782-c88683a87f4d", - "metadata": {}, - "outputs": [], - "source": [ - "i_, s_ = True, False\n", - "n, p = 100, 50\n", - "X = rng.standard_normal((n, p))\n", - "beta = np.zeros(p)\n", - "beta[:2] = [1,2]\n", - "Rfam, fam = 'binomial', sm.families.Binomial()\n", - "y = (rng.standard_normal(n) + X @ beta > 0).astype(int)\n", - "#y = rng.standard_normal(n) + X @ beta\n", - "\n", - "W = rng.uniform(1, 2, size=(n,))\n", - "W *= n / W.sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "d7c30c2d-86b0-4c28-85d5-08ce912a255a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1] 73.84822\n" - ] - }, - { - "data": { - "text/plain": [ - "Loading required package: Matrix\n", - "Loaded glmnet 4.1-7\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%%R -i X,W,y,n,p,s_,i_,Rfam -o coef_,intercept_,L\n", - "y = as.numeric(y)\n", - "library(glmnet)\n", - "G = glmnet(X, y, standardize=s_, intercept=i_, family=Rfam, weights=as.numeric(W), alpha=0.5)\n", - "B = predict(G, type='coef')\n", - "L = G$lambda \n", - "print(max(G$lambda)*n)\n", - "coef_ = as.matrix(B[2:(p+1),])\n", - "intercept_ = as.numeric(B[1,])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "48a24021-8ee3-4749-8495-87f481eb2232", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.7384822402986349, 0.7384822402986349)" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "G = GLMNet(lambda_values=L,\n", - " lambda_fractional=False,\n", - " standardize=s_, \n", - " fit_intercept=i_, \n", - " family=fam, \n", - " alpha=0.5)\n", - "G.fit(X, y, sample_weight=W)\n", - "G.lambda_max_, L.max()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "867df277-0ffe-4825-a454-058bc6a98088", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.003313554032755549" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.linalg.norm(coef_.T - G.coefs_) / np.linalg.norm(coef_)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "6cb9f4e4-1b7d-4e97-8334-9cde221c82ea", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((50, 100), (100, 50))" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "coef_.shape, G.coefs_.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "9b75c571-1806-4eaa-b4b1-5906f1614f0a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.08115015, 0.83036112, 0. , 0. , 0.00463349,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ])" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "idx = 15\n", - "G.coefs_[idx]" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "7d4945c3-0bce-4de6-8e9f-0fab114c1940", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.08115111, 0.83036152, 0. , 0. , 0.0046335 ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ])" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "G_ = RegGLM(lambda_val=L[idx],\n", - " alpha=0.5,\n", - " standardize=s_, \n", - " fit_intercept=i_, \n", - " family=fam).fit(X, y, sample_weight=W)\n", - "G_.coef_" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "6388453d-0817-423c-9647-d8cf7c77fa75", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.08115134, 0.83036158, 0. , 0. , 0.0046335 ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ])" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "coef_[:,idx]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7900a1b9-9019-4963-aada-f0dd9eba0c1e", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/requirements.txt b/docs/requirements.txt new file mode 100644 index 0000000..10bce0e --- /dev/null +++ b/docs/requirements.txt @@ -0,0 +1,7 @@ +texext +numpydoc +myst_nb +sphinx-book-theme +rpy2 +sphinx_rtd_theme +jupytext From 7f1f5b2fc67ecc5965661d0367bfa735c22b4110 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Wed, 2 Jul 2025 14:59:03 -0700 Subject: [PATCH 2/7] working doc build --- docs/_config.yml | 28 ++++++++++++++++++++++++++++ docs/_toc.yml | 12 ++++++++++++ docs/api_reference.md | 30 ++++++++++++++++++++++++++++++ docs/index.md | 8 ++++++++ docs/requirements.txt | 3 +++ docs/vignettes/Coxnet.md | 2 +- docs/vignettes/glmnet.md | 7 ++++--- docs/vignettes/relax.md | 2 +- 8 files changed, 87 insertions(+), 5 deletions(-) create mode 100644 docs/_config.yml create mode 100644 docs/_toc.yml create mode 100644 docs/api_reference.md create mode 100644 docs/index.md diff --git a/docs/_config.yml b/docs/_config.yml new file mode 100644 index 0000000..17bce33 --- /dev/null +++ b/docs/_config.yml @@ -0,0 +1,28 @@ +# Jupyter Book configuration +# See https://jupyterbook.org/en/stable/customize/config.html + +title: GLMStar Documentation +author: GLMStar Team +logo: null +execute: + execute_notebooks: force +html: + number_sections: true + use_issues_button: true + use_repository_button: true + use_edit_page_button: true +repository: + url: https://github.com/your-org/glmstar + path_to_book: docs + branch: main + +# Sphinx configuration for autodoc +sphinx: + extra_extensions: + - sphinx.ext.autodoc + - sphinx.ext.napoleon + config: + autodoc_default_options: + members: true + undoc-members: true + show-inheritance: true \ No newline at end of file diff --git a/docs/_toc.yml b/docs/_toc.yml new file mode 100644 index 0000000..52c431f --- /dev/null +++ b/docs/_toc.yml @@ -0,0 +1,12 @@ +# Table of contents for GLMStar documentation +# See https://jupyterbook.org/en/stable/customize/toc.html + +format: jb-book +root: index +chapters: + - file: vignettes/glmnet.md + - file: examples/Test_design.md + - file: examples/cross_validation.md + - file: examples/diabetes_logistic.md + - file: examples/elnet_compare.md + - file: api_reference.md \ No newline at end of file diff --git a/docs/api_reference.md b/docs/api_reference.md new file mode 100644 index 0000000..7e59fce --- /dev/null +++ b/docs/api_reference.md @@ -0,0 +1,30 @@ +# API Reference + +This section provides the API reference for the GLMStar Python package. + +## Main Classes and Functions + +```{eval-rst} +.. automodule:: glmnet + :members: + :undoc-members: + :show-inheritance: +``` + +## Paths Module + +```{eval-rst} +.. automodule:: glmnet.paths.lognet + :members: + :undoc-members: + :show-inheritance: +``` + +## Inference Module + +```{eval-rst} +.. automodule:: glmnet.inference + :members: + :undoc-members: + :show-inheritance: +``` diff --git a/docs/index.md b/docs/index.md new file mode 100644 index 0000000..819a82d --- /dev/null +++ b/docs/index.md @@ -0,0 +1,8 @@ +# GLMStar Documentation + +Welcome to the documentation for the GLMStar project! + +- Explore the examples and vignettes for practical usage. +- API reference is available in the API section (see navigation). + +For more information, visit the [GitHub repository](https://github.com/your-org/glmstar). \ No newline at end of file diff --git a/docs/requirements.txt b/docs/requirements.txt index 10bce0e..476ce09 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -5,3 +5,6 @@ sphinx-book-theme rpy2 sphinx_rtd_theme jupytext +jupyter-book +sphinx +myst-parser diff --git a/docs/vignettes/Coxnet.md b/docs/vignettes/Coxnet.md index 73892d1..9994cf6 100644 --- a/docs/vignettes/Coxnet.md +++ b/docs/vignettes/Coxnet.md @@ -1,11 +1,11 @@ --- jupytext: + formats: ipynb,md:myst text_representation: extension: .md format_name: myst format_version: 0.13 jupytext_version: 1.17.2 - formats: ipynb,md:myst --- # Introduction diff --git a/docs/vignettes/glmnet.md b/docs/vignettes/glmnet.md index e662070..ce68b74 100644 --- a/docs/vignettes/glmnet.md +++ b/docs/vignettes/glmnet.md @@ -7,9 +7,9 @@ jupytext: format_version: 0.13 jupytext_version: 1.17.2 kernelspec: - name: python3 display_name: Python 3 (ipykernel) language: python + name: python3 --- # Introduction @@ -23,7 +23,7 @@ The original authors of glmnet are Jerome Friedman, Trevor Hastie, Rob Tibshiran You can install the Python `glmnet` package using pip: ```{code-cell} ipython3 -#!pip install glmnetpy +%pip install glmstar ``` --- @@ -36,7 +36,8 @@ Below is a quick demonstration of the main functions and outputs using the Pytho import numpy as np import pandas as pd import matplotlib.pyplot as plt -from sklearn.datasets import make_regression, make_classification +from sklearn.datasets import (make_regression, + make_classification) from sklearn.model_selection import train_test_split from sklearn.metrics import roc_curve, auc ``` diff --git a/docs/vignettes/relax.md b/docs/vignettes/relax.md index 7440903..200b73f 100644 --- a/docs/vignettes/relax.md +++ b/docs/vignettes/relax.md @@ -1,11 +1,11 @@ --- jupytext: + formats: ipynb,md:myst text_representation: extension: .md format_name: myst format_version: 0.13 jupytext_version: 1.17.2 - formats: ipynb,md:myst --- # Introduction From 601f855a32982031a76d78d3a30166ad614f7367 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Wed, 2 Jul 2025 15:00:14 -0700 Subject: [PATCH 3/7] doc workflow --- .github/workflows/build_docs.yml | 72 ++++++++++++++++++++++++++++++++ 1 file changed, 72 insertions(+) create mode 100644 .github/workflows/build_docs.yml diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml new file mode 100644 index 0000000..241c519 --- /dev/null +++ b/.github/workflows/build_docs.yml @@ -0,0 +1,72 @@ +# This builds and deploys glmnet docs + +name: Build docs + +# Controls when the workflow will run +on: + workflow_dispatch: + inputs: null + +# A workflow run is made up of one or more jobs that can run +# sequentially or in parallel + +jobs: # This workflow contains a single + # job called "build" + + build: + # The type of runner that the job will run on + runs-on: ubuntu-latest + + # Steps represent a sequence of tasks that will be executed as part of the job + steps: + # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it + - uses: actions/checkout@v3 + - uses: actions/setup-python@v4 + with: + python-version: '3.10' + cache: 'pip' + + # Install + - name: Install dependencies + run: | + sudo apt-get install r-base + pip install -r docs/requirements.txt + pip install . + + - name: Make docs + run: | + cd docs + jupyter-book build . + + # Store the output + - name: Upload docs + uses: actions/upload-artifact@v3 + with: + name: ISLP_docs + path: docs/_build/html + retention-days: 5 + + deploy: + runs-on: ubuntu-latest + needs: build + + # Grant GITHUB_TOKEN the permissions required to make a Pages deployment + permissions: + pages: write # to deploy to Pages + id-token: write # to verify the deployment originates from an appropriate source + + environment: + name: github-pages + url: ${{steps.deployment.outputs.page_url}} + + steps: + - uses: actions/download-artifact@master + with: + name: ISLP_docs + path: . + - uses: actions/configure-pages@v3 + - uses: actions/upload-pages-artifact@v2 + with: + path: . + - id: deployment + uses: actions/deploy-pages@main \ No newline at end of file From 02bef7e1eb3325792475d77ec9a20d30624287b7 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Wed, 28 Jan 2026 08:46:19 -0800 Subject: [PATCH 4/7] update build_and_test workflow --- .github/workflows/build_and_test.yml | 40 ++++++---------------------- 1 file changed, 8 insertions(+), 32 deletions(-) diff --git a/.github/workflows/build_and_test.yml b/.github/workflows/build_and_test.yml index bb1efec..957054f 100644 --- a/.github/workflows/build_and_test.yml +++ b/.github/workflows/build_and_test.yml @@ -4,44 +4,22 @@ name: Build and run python tests on: [push] jobs: - - build-mac: - runs-on: macos-latest + build: + runs-on: ${{ matrix.os }} strategy: - max-parallel: 5 + matrix: + os: [ubuntu-latest, macos-latest] + python-version: ['3.9', '3.10', '3.11', '3.12'] steps: - uses: actions/checkout@v4 with: submodules: recursive - - name: Set up Python 3.10 - uses: actions/setup-python@v4 - with: - python-version: '3.10' - - name: Install dependencies - run: | - pip install -r requirements.txt - pip install . - - - name: Test pyglmnet - run: | - pip install pytest - pytest tests/test*py - - build-linux: - runs-on: ubuntu-latest - strategy: - max-parallel: 5 - - steps: - - uses: actions/checkout@v4 - with: - submodules: recursive - - name: Set up Python 3.10 + - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v4 with: - python-version: '3.10' + python-version: ${{ matrix.python-version }} - name: Install dependencies run: | pip install -r requirements.txt @@ -49,7 +27,5 @@ jobs: - name: Test pyglmnet run: | - pip install pytest + pip install pytest pytest tests/test*py - - From 2ae34cdf39acc36c543c8f422eca2e5854ed1fd6 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Wed, 28 Jan 2026 08:46:45 -0800 Subject: [PATCH 5/7] update build_docs workflow --- .github/workflows/build_docs.yml | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml index 241c519..ab72aa7 100644 --- a/.github/workflows/build_docs.yml +++ b/.github/workflows/build_docs.yml @@ -20,7 +20,7 @@ jobs: # This workflow contains a single # Steps represent a sequence of tasks that will be executed as part of the job steps: # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: actions/setup-python@v4 with: python-version: '3.10' @@ -40,7 +40,7 @@ jobs: # This workflow contains a single # Store the output - name: Upload docs - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 with: name: ISLP_docs path: docs/_build/html @@ -60,13 +60,13 @@ jobs: # This workflow contains a single url: ${{steps.deployment.outputs.page_url}} steps: - - uses: actions/download-artifact@master + - uses: actions/download-artifact@v4 with: name: ISLP_docs path: . - uses: actions/configure-pages@v3 - - uses: actions/upload-pages-artifact@v2 + - uses: actions/upload-pages-artifact@v3 with: path: . - id: deployment - uses: actions/deploy-pages@main \ No newline at end of file + uses: actions/deploy-pages@v4 \ No newline at end of file From a8c063b2a28dd778227635f5ecd5d986205e1945 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Wed, 28 Jan 2026 08:48:38 -0800 Subject: [PATCH 6/7] update build_wheels workflow --- .github/workflows/build_wheels.yml | 55 +----------------------------- 1 file changed, 1 insertion(+), 54 deletions(-) diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml index 18d852d..732da36 100644 --- a/.github/workflows/build_wheels.yml +++ b/.github/workflows/build_wheels.yml @@ -23,7 +23,7 @@ jobs: submodules: recursive - name: Build wheels - uses: pypa/cibuildwheel@v3.0.0 # Use the latest version + uses: pypa/cibuildwheel@v3.3.1 # Use the latest version - name: Upload wheel artifacts uses: actions/upload-artifact@v4 @@ -31,60 +31,7 @@ jobs: name: wheels-${{ matrix.os }} path: ./wheelhouse/*.whl # cibuildwheel puts wheels in this directory - # build-wheels: - # name: Build wheels on ${{ matrix.os }} - # runs-on: ${{ matrix.os }} - # strategy: - # fail-fast: false - # matrix: - # os: [ubuntu-latest, macos-latest, windows-latest] - # python-version: ['3.9', '3.10', '3.11', '3.12'] - - # steps: - # - name: Checkout code - # uses: actions/checkout@v4 - # with: - # submodules: recursive - - # - name: Set up Python ${{ matrix.python-version }} - # uses: actions/setup-python@v4 - # with: - # python-version: ${{ matrix.python-version }} - - # - name: Install build dependencies - # run: | - # python -m pip install --upgrade pip - # pip install build wheel setuptools pybind11 numpy - - # - name: Install system dependencies (Linux) - # if: runner.os == 'Linux' - # run: | - # sudo apt-get update - # sudo apt-get install -y build-essential - - # - name: Install system dependencies (macOS) - # if: runner.os == 'macOS' - # run: | - # # macOS dependencies are handled by the build tools - - # - name: Install system dependencies (Windows) - # if: runner.os == 'Windows' - # run: | - # # Windows dependencies are handled by the build tools - - # - name: Build wheel - # run: | - # python -m build --wheel - # - name: List built artifacts - # run: | - # python -c "import os; import glob; files = glob.glob('dist/*'); print('Files in dist/:'); [print(f) for f in files]" - - # - name: Upload wheel artifacts - # uses: actions/upload-artifact@v4 - # with: - # name: coxdev-${{ runner.os }}-py${{ matrix.python-version }} - # path: dist/*.whl build-sdist: name: Build source distribution From c66df8222b9405390d8dd156a0c136688be79412 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Wed, 28 Jan 2026 08:49:19 -0800 Subject: [PATCH 7/7] update compare_R workflow --- .github/workflows/compare_R.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/compare_R.yml b/.github/workflows/compare_R.yml index f1f6a8e..4b93ba1 100644 --- a/.github/workflows/compare_R.yml +++ b/.github/workflows/compare_R.yml @@ -20,7 +20,7 @@ jobs: submodules: recursive - name: Set up Python 3.12 - uses: actions/setup-python@v3 + uses: actions/setup-python@v4 with: python-version: '3.12'