diff --git a/.ipynb_checkpoints/task 1(updated)-checkpoint.ipynb b/.ipynb_checkpoints/task 1(updated)-checkpoint.ipynb new file mode 100644 index 0000000..3733d26 --- /dev/null +++ b/.ipynb_checkpoints/task 1(updated)-checkpoint.ipynb @@ -0,0 +1,262 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "%matplotlib inline\n", + "import random" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([420, 173, 310, 332, 348, 28, 189, 263, 441, 84, 260, 169, 322,\n", + " 94, 10, 319, 194, 98, 351, 178, 74, 397, 438, 35, 221, 338,\n", + " 381, 195, 499, 344, 352, 60, 6, 387, 300, 278, 467, 451, 186,\n", + " 424, 42, 238, 204, 402, 327, 51, 334, 56, 75, 156, 69, 273,\n", + " 450, 145, 356, 293, 498, 305, 250, 419, 454, 206, 462, 493, 372,\n", + " 79, 415, 186, 230, 360, 388, 405, 138, 210, 328, 274, 95, 491,\n", + " 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0.55683445, 0.51119878, 0.50937502,\n", + " 0.58522025, 0.37376696, 0.42195077, 0.58550719, 0.60282785,\n", + " 0.5986452 , 0.61377271, 0.54806389, 0.46821312, 0.58081425,\n", + " 0.53518581, 0.60935698, 0.55294291, 0.61820849, 0.49558271,\n", + " 0.48978398, 0.47273878, 0.32580965, 0.55134287, 0.55645204,\n", + " 0.54971682, 0.56903595, 0.55529596, 0.58171112, 0.20794415,\n", + " 0.58141305, 0.5198497 , 0.43944492, 0.60614569, 0.47184989,\n", + " 0.5811141 , 0.47449321, 0.59814142, 0.49836066, 0.56970935,\n", + " 0.55947114, 0.49052748, 0.42195077, 0.57776523, 0.5880533 ])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y = calx(x)\n", + "y" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(x,y)\n", + "plt.show()" + ] + }, + { + "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 +} diff --git a/.ipynb_checkpoints/task 2(updated)-checkpoint.ipynb b/.ipynb_checkpoints/task 2(updated)-checkpoint.ipynb new file mode 100644 index 0000000..9d6cfb4 --- /dev/null +++ b/.ipynb_checkpoints/task 2(updated)-checkpoint.ipynb @@ -0,0 +1,164 @@ +{ + "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 +} diff --git a/.ipynb_checkpoints/task1-checkpoint.ipynb b/.ipynb_checkpoints/task1-checkpoint.ipynb new file mode 100644 index 0000000..092ea58 --- /dev/null +++ b/.ipynb_checkpoints/task1-checkpoint.ipynb @@ -0,0 +1,172 @@ +{ + "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", + " 3616, 3716, 3816, 3917, 4017, 4117, 4217, 4317, 4417,\n", + " 4518, 4618, 4718, 4818, 4918, 5019, 5119, 5219, 5319,\n", + " 5419, 5519, 5620, 5720, 5820, 5920, 6020, 6121, 6221,\n", + " 6321, 6421, 6521, 6621, 6722, 6822, 6922, 7022, 7122,\n", + " 7222, 7323, 7423, 7523, 7623, 7723, 7824, 7924, 8024,\n", + " 8124, 8224, 8324, 8425, 8525, 8625, 8725, 8825, 8926,\n", + " 9026, 9126, 9226, 9326, 9426, 9527, 9627, 9727, 9827,\n", + " 9927, 10028, 10128, 10228, 10328, 10428, 10528, 10629, 10729,\n", + " 10829, 10929, 11029, 11130, 11230, 11330, 11430, 11530, 11630,\n", + " 11731, 11831, 11931, 12031, 12131, 12232, 12332, 12432, 12532,\n", + " 12632, 12732, 12833, 12933, 13033, 13133, 13233, 13333, 13434,\n", + " 13534, 13634, 13734, 13834, 13935, 14035, 14135, 14235, 14335,\n", + " 14435, 14536, 14636, 14736, 14836, 14936, 15037, 15137, 15237,\n", + " 15337, 15437, 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28260, 28361, 28461, 28561, 28661, 28761,\n", + " 28861, 28962, 29062, 29162, 29262, 29362, 29463, 29563, 29663,\n", + " 29763, 29863, 29963, 30064, 30164, 30264, 30364, 30464, 30565,\n", + " 30665, 30765, 30865, 30965, 31065, 31166, 31266, 31366, 31466,\n", + " 31566, 31666, 31767, 31867, 31967, 32067, 32167, 32268, 32368,\n", + " 32468, 32568, 32668, 32768, 32869, 32969, 33069, 33169, 33269,\n", + " 33370, 33470, 33570, 33670, 33770, 33870, 33971, 34071, 34171,\n", + " 34271, 34371, 34472, 34572, 34672, 34772, 34872, 34972, 35073,\n", + " 35173, 35273, 35373, 35473, 35574, 35674, 35774, 35874, 35974,\n", + " 36074, 36175, 36275, 36375, 36475, 36575, 36676, 36776, 36876,\n", + " 36976, 37076, 37176, 37277, 37377, 37477, 37577, 37677, 37777,\n", + " 37878, 37978, 38078, 38178, 38278, 38379, 38479, 38579, 38679,\n", + " 38779, 38879, 38980, 39080, 39180, 39280, 39380, 39481, 39581,\n", + " 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": [ + "
" + ] + }, + "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 +} diff --git a/.ipynb_checkpoints/task2-checkpoint.ipynb b/.ipynb_checkpoints/task2-checkpoint.ipynb new file mode 100644 index 0000000..24eedcb --- /dev/null +++ b/.ipynb_checkpoints/task2-checkpoint.ipynb @@ -0,0 +1,98 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import random\n", + "import time" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "x = np.random.random((10,1))\n", + "y = np.random.random((10,1))\n", + "y = y.T\n" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0001575946807861328\n" + ] + } + ], + "source": [ + "start = time.time()\n", + "np.dot(y,x)\n", + "end = time.time()\n", + "print (end - start)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0003380775451660156\n" + ] + } + ], + "source": [ + "start = time.time()\n", + "for i in range(10):\n", + " c = c + x[i][0] * y[0][i]\n", + "end = time.time()\n", + "print (end - start)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# np.dot() takes less time as comapred to for 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 +} diff --git a/README.md b/Week_1/Harit/README.md similarity index 100% rename from README.md rename to Week_1/Harit/README.md diff --git a/Task 1.txt b/Week_1/Harit/Task 1.txt similarity index 100% rename from Task 1.txt rename to Week_1/Harit/Task 1.txt diff --git a/Week_1/Harit/task 1(updated).ipynb b/Week_1/Harit/task 1(updated).ipynb new file mode 100644 index 0000000..3733d26 --- /dev/null +++ b/Week_1/Harit/task 1(updated).ipynb @@ -0,0 +1,262 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "%matplotlib inline\n", + "import random" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([420, 173, 310, 332, 348, 28, 189, 263, 441, 84, 260, 169, 322,\n", + " 94, 10, 319, 194, 98, 351, 178, 74, 397, 438, 35, 221, 338,\n", + " 381, 195, 499, 344, 352, 60, 6, 387, 300, 278, 467, 451, 186,\n", + " 424, 42, 238, 204, 402, 327, 51, 334, 56, 75, 156, 69, 273,\n", + " 450, 145, 356, 293, 498, 305, 250, 419, 454, 206, 462, 493, 372,\n", + " 79, 415, 186, 230, 360, 388, 405, 138, 210, 328, 274, 95, 491,\n", + " 437, 150, 147, 131, 284, 246, 221, 189, 135, 160, 444, 183, 83,\n", + " 274, 346, 270, 16, 380, 319, 473, 363, 364, 301, 465, 225, 403,\n", + " 392, 369, 329, 81, 283, 495, 208, 40, 302, 490, 483, 38, 70,\n", + " 19, 329, 15, 191, 451, 427, 206, 198, 89, 319, 267, 484, 171,\n", + " 168, 268, 395, 466, 189, 46, 171, 312, 353, 426, 489, 291, 144,\n", + " 107, 90, 391, 455, 432, 335, 385, 140, 369, 233, 284, 94, 214,\n", + " 57, 126, 315, 56, 405, 195, 354, 420, 285, 20, 390, 273, 198,\n", + " 117, 234, 272, 204, 183, 13, 121, 423, 319, 52, 119, 40, 497,\n", + " 287, 15, 125, 146, 390, 324, 108, 183, 379, 302, 424, 192, 255,\n", + " 337, 200, 451, 184, 239, 327, 402, 384, 294, 377, 368, 167, 464,\n", + " 108, 48, 298, 405, 494, 115, 226, 386, 120, 343, 211, 206, 201,\n", + " 293, 308, 324, 413, 436, 268, 86, 216, 220, 45, 161, 274, 492,\n", + " 122, 444, 440, 338, 484, 383, 380, 174, 39, 243, 102, 106, 174,\n", + " 119, 413, 73, 240, 459, 489, 141, 215, 350, 390, 363, 494, 36,\n", + " 285, 201, 105, 233, 298, 71, 146, 153, 52, 60, 74, 9, 366,\n", + " 380, 204, 48, 152, 74, 77, 229, 35, 279, 139, 422, 177, 272,\n", + " 36, 384, 433, 394, 211, 328, 104, 63, 192, 104, 385, 210, 402,\n", + " 10, 371, 233, 173, 16, 50, 178, 22, 85, 204, 219, 215, 62,\n", + " 408, 87, 34, 122, 260, 451, 106, 415, 311, 302, 47, 379, 340,\n", + " 498, 196, 31, 78, 157, 309, 173, 322, 58, 366, 234, 166, 223,\n", + " 449, 483, 18, 103, 324, 422, 414, 300, 204, 222, 493, 164, 385,\n", + " 193, 255, 208, 389, 89, 267, 257, 451, 38, 353, 336, 50, 456,\n", + " 144, 353, 280, 299, 366, 140, 61, 272, 333, 477, 418, 100, 51,\n", + " 10, 124, 245, 184, 359, 301, 219, 264, 347, 101, 416, 316, 232,\n", + " 266, 135, 474, 111, 242, 153, 439, 38, 7, 355, 365, 100, 13,\n", + " 267, 499, 193, 167, 310, 281, 430, 469, 36, 213, 258, 33, 491,\n", + " 48, 222, 428, 81, 201, 221, 76, 401, 421, 313, 426, 365, 282,\n", + " 371, 193, 341, 384, 398, 196, 457, 342, 289, 95, 36, 15, 471,\n", + " 344, 378, 294, 323, 473, 350, 295, 298, 458, 256, 112, 115, 162,\n", + " 260, 97, 262, 166, 163, 348, 42, 68, 349, 415, 398, 463, 240,\n", + " 108, 333, 211, 443, 252, 484, 142, 134, 113, 26, 248, 261, 244,\n", + " 296, 258, 336, 8, 335, 181, 81, 429, 112, 334, 115, 396, 146,\n", + " 298, 269, 135, 68, 323, 358])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = np.random.randint(5,500,size=(500))\n", + "x" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def calx(x):\n", + " return (10**(-1))*np.log(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.60402547, 0.51532916, 0.57365723, 0.5805135 , 0.58522025,\n", + " 0.33322045, 0.5241747 , 0.5572154 , 0.60890449, 0.44308168,\n", + " 0.55606816, 0.51298987, 0.57745515, 0.45432948, 0.23025851,\n", + " 0.57651911, 0.52678582, 0.45849675, 0.58607862, 0.51817836,\n", + " 0.43040651, 0.59839363, 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0.54467374,\n", + " 0.55834963, 0.49052748, 0.61612073, 0.47095302, 0.54889377,\n", + " 0.50304379, 0.60844994, 0.36375862, 0.19459101, 0.58721178,\n", + " 0.58998974, 0.46051702, 0.25649494, 0.55872487, 0.62126061,\n", + " 0.52626902, 0.51179938, 0.57365723, 0.56383547, 0.60637852,\n", + " 0.61506028, 0.35835189, 0.53612922, 0.55529596, 0.34965076,\n", + " 0.61964441, 0.3871201 , 0.54026774, 0.60591232, 0.43944492,\n", + " 0.53033049, 0.53981627, 0.43307333, 0.59939614, 0.60426328,\n", + " 0.57462032, 0.60544393, 0.58998974, 0.56419071, 0.59162021,\n", + " 0.52626902, 0.58318825, 0.59506426, 0.5986452 , 0.52781147,\n", + " 0.61246834, 0.58348107, 0.56664267, 0.45538769, 0.35835189,\n", + " 0.27080502, 0.61548581, 0.58406417, 0.59348942, 0.56835798,\n", + " 0.57776523, 0.61590954, 0.58579332, 0.56869754, 0.56970935,\n", + " 0.61268692, 0.55451774, 0.47184989, 0.47449321, 0.50875963,\n", + " 0.55606816, 0.4574711 , 0.55683445, 0.51119878, 0.50937502,\n", + " 0.58522025, 0.37376696, 0.42195077, 0.58550719, 0.60282785,\n", + " 0.5986452 , 0.61377271, 0.54806389, 0.46821312, 0.58081425,\n", + " 0.53518581, 0.60935698, 0.55294291, 0.61820849, 0.49558271,\n", + " 0.48978398, 0.47273878, 0.32580965, 0.55134287, 0.55645204,\n", + " 0.54971682, 0.56903595, 0.55529596, 0.58171112, 0.20794415,\n", + " 0.58141305, 0.5198497 , 0.43944492, 0.60614569, 0.47184989,\n", + " 0.5811141 , 0.47449321, 0.59814142, 0.49836066, 0.56970935,\n", + " 0.55947114, 0.49052748, 0.42195077, 0.57776523, 0.5880533 ])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y = calx(x)\n", + "y" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(x,y)\n", + "plt.show()" + ] + }, + { + "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 +} diff --git a/Week_1/Harit/task 2(updated).ipynb b/Week_1/Harit/task 2(updated).ipynb new file mode 100644 index 0000000..9d6cfb4 --- /dev/null +++ b/Week_1/Harit/task 2(updated).ipynb @@ -0,0 +1,164 @@ +{ + "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 +}