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completed task1 and task2 finally #5

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b2cab9a
completed task1 and task2 finally
haritk May 25, 2018
c710f75
did changes on task-1 and task_2
haritk Jun 6, 2018
6d7831f
made a folder
haritk Jun 6, 2018
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262 changes: 262 additions & 0 deletions .ipynb_checkpoints/task 1(updated)-checkpoint.ipynb
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164 changes: 164 additions & 0 deletions .ipynb_checkpoints/task 2(updated)-checkpoint.ipynb
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Original file line number Diff line number Diff line change
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{
"cells": [
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import time"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"x = np.random.rand(10,1)\n",
"y = np.random.rand(10,1)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"y = y.T"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.00028896331787109375\n"
]
}
],
"source": [
"start = time.time()\n",
"z = np.dot(y,x)\n",
"end = time.time()\n",
"print (end - start)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [],
"source": [
"sum =0"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.0004093647003173828\n"
]
}
],
"source": [
"start = time.time()\n",
"for i in range(10):\n",
" sum = sum + (y[0][i]*x[i][0])\n",
"end = time.time()\n",
"print (end - start)"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [],
"source": [
"sum1 = 0"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.0007643699645996094\n"
]
}
],
"source": [
"start = time.time()\n",
"for i in range(10):\n",
" sum1 = sum1 + (x[i][0]*y[0][i])\n",
"end = time.time()\n",
"print (end - start)"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum == sum1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#np.dot takes less time compared to loop"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
172 changes: 172 additions & 0 deletions .ipynb_checkpoints/task1-checkpoint.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 10, 110, 210, 310, 410, 510, 611, 711, 811,\n",
" 911, 1011, 1111, 1212, 1312, 1412, 1512, 1612, 1713,\n",
" 1813, 1913, 2013, 2113, 2213, 2314, 2414, 2514, 2614,\n",
" 2714, 2815, 2915, 3015, 3115, 3215, 3315, 3416, 3516,\n",
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" 16239, 16339, 16439, 16539, 16639, 16740, 16840, 16940, 17040,\n",
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" 39681, 39781, 39881, 39981, 40082, 40182, 40282, 40382, 40482,\n",
" 40583, 40683, 40783, 40883, 40983, 41083, 41184, 41284, 41384,\n",
" 41484, 41584, 41685, 41785, 41885, 41985, 42085, 42185, 42286,\n",
" 42386, 42486, 42586, 42686, 42787, 42887, 42987, 43087, 43187,\n",
" 43287, 43388, 43488, 43588, 43688, 43788, 43888, 43989, 44089,\n",
" 44189, 44289, 44389, 44490, 44590, 44690, 44790, 44890, 44990,\n",
" 45091, 45191, 45291, 45391, 45491, 45592, 45692, 45792, 45892,\n",
" 45992, 46092, 46193, 46293, 46393, 46493, 46593, 46694, 46794,\n",
" 46894, 46994, 47094, 47194, 47295, 47395, 47495, 47595, 47695,\n",
" 47796, 47896, 47996, 48096, 48196, 48296, 48397, 48497, 48597,\n",
" 48697, 48797, 48898, 48998, 49098, 49198, 49298, 49398, 49499,\n",
" 49599, 49699, 49799, 49899, 50000])"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import random\n",
"x = np.linspace(10,50000,500)\n",
"x = x.astype(int)\n",
"x\n"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([-0.02266808, -0.08934166, -0.02462187, -0.41728575, -0.45418811,\n",
" -0.13013111, -0.71341838, -0.04787425, -0.16391377, -0.46504352,\n",
" -0.53243525, -0.49251806, -0.23805297, -0.65576994, -0.86601395,\n",
" -0.17225647, -0.29502642, -0.01952544, -0.41306327, -0.01171141,\n",
" -0.04473519, -0.140517 , -0.24972813, -0.04535645, -0.08644707,\n",
" -0.14986849, -0.20482458, -0.09479918, -0.29093532, -0.13910937,\n",
" -0.18052547, -0.06193589, -0.00959261, -0.16663713, -0.24758673,\n",
" -0.08250443, -0.15339983, -0.08080936, -0.04947501, -0.06434988,\n",
" -0.07135629, -0.65977086, -0.03092095, -0.36986666, -0.35934959,\n",
" -0.08372994, -0.10610554, -0.28453658, -0.41513806, -0.0734825 ])"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y = (5**(-1))*np.log(x)\n",
"y"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"import matplotlib.pyplot as plt\n",
"plt.scatter(x,y)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Loading