From 7421055d05f558181ca74802ed629be266c69c83 Mon Sep 17 00:00:00 2001 From: CampusX <53361867+campusx-official@users.noreply.github.com> Date: Mon, 21 Jun 2021 15:37:57 +0530 Subject: [PATCH] Add files via upload --- .../gradient-descent.ipynb | 422 ++++++++++++++++++ 1 file changed, 422 insertions(+) create mode 100644 day58-logistic-regression/gradient-descent.ipynb diff --git a/day58-logistic-regression/gradient-descent.ipynb b/day58-logistic-regression/gradient-descent.ipynb new file mode 100644 index 0000000..b4f8622 --- /dev/null +++ b/day58-logistic-regression/gradient-descent.ipynb @@ -0,0 +1,422 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 307, + "id": "948c58bd", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.datasets import make_classification\n", + "import numpy as np\n", + "X, y = make_classification(n_samples=100, n_features=2, n_informative=1,n_redundant=0,\n", + " n_classes=2, n_clusters_per_class=1, random_state=41,hypercube=False,class_sep=20)" + ] + }, + { + "cell_type": "code", + "execution_count": 308, + "id": "17570fee", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 309, + "id": "aa34bc63", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 309, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,6))\n", + "plt.scatter(X[:,0],X[:,1],c=y,cmap='winter',s=100)" + ] + }, + { + "cell_type": "code", + "execution_count": 310, + "id": "4d161699", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\91842\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\"The max_iter was reached which means \"\n" + ] + }, + { + "data": { + "text/plain": [ + "LogisticRegression(penalty='none', solver='sag')" + ] + }, + "execution_count": 310, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "lor = LogisticRegression(penalty='none',solver='sag')\n", + "lor.fit(X,y)" + ] + }, + { + "cell_type": "code", + "execution_count": 311, + "id": "0e21fffe", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[4.7808362 0.2062583]]\n", + "[5.7492783]\n" + ] + } + ], + "source": [ + "print(lor.coef_)\n", + "print(lor.intercept_)" + ] + }, + { + "cell_type": "code", + "execution_count": 312, + "id": "3c501550", + "metadata": {}, + "outputs": [], + "source": [ + "m1 = -(lor.coef_[0][0]/lor.coef_[0][1])\n", + "b1 = -(lor.intercept_/lor.coef_[0][1])" + ] + }, + { + "cell_type": "code", + "execution_count": 313, + "id": "13da1c4f", + "metadata": {}, + "outputs": [], + "source": [ + "x_input = np.linspace(-3,3,100)\n", + "y_input = m1*x_input + b1" + ] + }, + { + "cell_type": "code", + "execution_count": 329, + "id": "bb96c8fe", + "metadata": {}, + "outputs": [], + "source": [ + "def gd(X,y):\n", + " \n", + " X = np.insert(X,0,1,axis=1)\n", + " weights = np.ones(X.shape[1])\n", + " lr = 0.5\n", + " \n", + " for i in range(5000):\n", + " y_hat = sigmoid(np.dot(X,weights))\n", + " weights = weights + lr*(np.dot((y-y_hat),X)/X.shape[0])\n", + " \n", + " return weights[1:],weights[0]\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 330, + "id": "a95c96aa", + "metadata": {}, + "outputs": [], + "source": [ + "def sigmoid(z):\n", + " return 1/(1 + np.exp(-z))" + ] + }, + { + "cell_type": "code", + "execution_count": 331, + "id": "2345d4f7", + "metadata": {}, + "outputs": [], + "source": [ + "coef_,intercept_ = gd(X,y)" + ] + }, + { + "cell_type": "code", + "execution_count": 332, + "id": "c2c3582d", + "metadata": {}, + "outputs": [], + "source": [ + "m = -(coef_[0]/coef_[1])\n", + "b = -(intercept_/coef_[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 333, + "id": "b32753f8", + "metadata": {}, + "outputs": [], + "source": [ + "x_input1 = np.linspace(-3,3,100)\n", + "y_input1 = m*x_input1 + b" + ] + }, + { + "cell_type": "code", + "execution_count": 334, + "id": "5d44c5c3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(-3.0, 2.0)" + ] + }, + "execution_count": 334, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,6))\n", + "plt.plot(x_input,y_input,color='red',linewidth=3)\n", + "plt.plot(x_input1,y_input1,color='black',linewidth=3)\n", + "plt.scatter(X[:,0],X[:,1],c=y,cmap='winter',s=100)\n", + "plt.ylim(-3,2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12eedd4f", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "114b0427", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 315, + "id": "e885a117", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1. , 0.51123145, -0.11697552],\n", + " [ 1. , 0.06316371, -0.73115232],\n", + " [ 1. , -0.0425064 , -0.7081059 ],\n", + " [ 1. , -3.2891569 , -2.01199214],\n", + " [ 1. , 0.1111445 , 1.63493163],\n", + " [ 1. , -2.53070306, 0.15599044],\n", + " [ 1. , -3.49036198, 1.07782053],\n", + " [ 1. , 0.3976447 , 0.80626713],\n", + " [ 1. , -0.24666899, 0.74859527],\n", + " [ 1. , -3.65803446, 0.75152794],\n", + " [ 1. , -3.47658131, -0.90114581],\n", + " [ 1. , -3.47815037, -0.1815243 ],\n", + " [ 1. , 0.29004249, -2.98092432],\n", + " [ 1. , 1.11761831, 1.20500136],\n", + " [ 1. , -3.52530398, 0.78302407],\n", + " [ 1. , 0.69929128, 0.42968688],\n", + " [ 1. , 0.17089733, -0.73229726],\n", + " [ 1. , -3.57785124, -0.83930476],\n", + " [ 1. , 0.12965489, 0.83727062],\n", + " [ 1. , -3.46888717, -0.10255323],\n", + " [ 1. , -3.97487212, 0.65867001],\n", + " [ 1. , -3.76348686, 0.92649819],\n", + " [ 1. , -3.01519735, 0.10216193],\n", + " [ 1. , 1.92241659, 0.46886454],\n", + " [ 1. , -2.91479578, 0.45432938],\n", + " [ 1. , 0.9259563 , 1.8613386 ],\n", + " [ 1. , -3.4859014 , -0.79255991],\n", + " [ 1. , -2.73978345, -1.0004391 ],\n", + " [ 1. , -4.09896768, -0.53814137],\n", + " [ 1. , -3.50212636, 0.44027716],\n", + " [ 1. , -3.13904797, 0.27047889],\n", + " [ 1. , 1.66188378, -0.75869267],\n", + " [ 1. , 0.53718567, 0.6802322 ],\n", + " [ 1. , 0.84886927, 0.17018845],\n", + " [ 1. , 0.86681859, -1.01121977],\n", + " [ 1. , -0.00979785, -0.8394709 ],\n", + " [ 1. , -2.68650432, 0.90327412],\n", + " [ 1. , 0.98593205, 1.16981747],\n", + " [ 1. , -3.30742775, 0.53461406],\n", + " [ 1. , 1.06925594, -0.22100631],\n", + " [ 1. , -3.0242056 , -0.64584571],\n", + " [ 1. , -2.61612476, 0.21243302],\n", + " [ 1. , -3.05407331, 0.98654083],\n", + " [ 1. , 1.78068059, 0.67382928],\n", + " [ 1. , -2.50369897, -0.11323563],\n", + " [ 1. , 1.39258216, 0.91694693],\n", + " [ 1. , 0.45865727, 0.3645213 ],\n", + " [ 1. , -3.07153732, -0.78470874],\n", + " [ 1. , -2.79571273, -1.48004137],\n", + " [ 1. , -2.6184666 , 0.72931763],\n", + " [ 1. , 0.53218913, -0.60000139],\n", + " [ 1. , -0.0395452 , 0.14720034],\n", + " [ 1. , -3.11964851, 0.35215601],\n", + " [ 1. , 1.2561371 , 0.17388705],\n", + " [ 1. , 2.45686591, -0.17162755],\n", + " [ 1. , -3.2252872 , 0.03763193],\n", + " [ 1. , 1.3467772 , 0.48360467],\n", + " [ 1. , -3.75380319, -0.07909256],\n", + " [ 1. , -3.03070579, 0.06158897],\n", + " [ 1. , 0.20583358, -1.20413846],\n", + " [ 1. , 1.21108994, 0.77324391],\n", + " [ 1. , -3.01981877, 0.7640092 ],\n", + " [ 1. , -3.66318854, 1.57200032],\n", + " [ 1. , 1.28298186, 0.47296516],\n", + " [ 1. , 0.3640611 , 0.0642074 ],\n", + " [ 1. , -0.35195584, 1.52469135],\n", + " [ 1. , -3.59205904, -2.1450828 ],\n", + " [ 1. , 0.9061286 , -0.7701428 ],\n", + " [ 1. , -3.25090707, 0.84455766],\n", + " [ 1. , -2.69817214, 1.29753064],\n", + " [ 1. , -4.09493435, -0.84276652],\n", + " [ 1. , 0.58735838, 2.0615874 ],\n", + " [ 1. , 1.3843216 , -0.3036533 ],\n", + " [ 1. , -3.15603064, -0.1656085 ],\n", + " [ 1. , 1.37865857, 1.16969713],\n", + " [ 1. , 0.98696744, -0.3038555 ],\n", + " [ 1. , -3.88083743, -0.4788978 ],\n", + " [ 1. , 1.27593515, 0.28128262],\n", + " [ 1. , -3.01387754, 0.70412341],\n", + " [ 1. , 1.0278443 , 0.3757601 ],\n", + " [ 1. , 0.15011746, 0.97247545],\n", + " [ 1. , 1.653853 , 0.56868381],\n", + " [ 1. , -3.15852226, -0.39767934],\n", + " [ 1. , 1.68843893, -1.08933729],\n", + " [ 1. , -0.04572327, -1.81663939],\n", + " [ 1. , -3.56234524, 1.41775924],\n", + " [ 1. , -3.48093864, 0.27098296],\n", + " [ 1. , -2.88757001, -0.56186639],\n", + " [ 1. , 0.57853245, 0.42168159],\n", + " [ 1. , -2.98638242, -2.95373116],\n", + " [ 1. , 1.06361882, 0.19492133],\n", + " [ 1. , 1.47452652, -0.04578681],\n", + " [ 1. , -2.8571924 , -0.18860967],\n", + " [ 1. , -3.18522871, 0.44635682],\n", + " [ 1. , 1.06491218, -1.49814775],\n", + " [ 1. , -3.82161901, 0.66198774],\n", + " [ 1. , -3.23429001, 0.07153265],\n", + " [ 1. , -3.15863898, 0.32323638],\n", + " [ 1. , 1.14824982, -0.34574201],\n", + " [ 1. , 0.04627774, 0.06499922]])" + ] + }, + "execution_count": 315, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.insert(X,0,1,axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 316, + "id": "a61c1845", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1., 1., 1.])" + ] + }, + "execution_count": 316, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X1 = np.insert(X,0,1,axis=1)\n", + "np.ones(X1.shape[1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d241fde0", + "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": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}