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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "\n", | |
| "<font size=4> Implement the <b>least squares classifier</b>. Train the classifier using the dataset in file <b>“Data1.txt”</b>. Each row in the file represents one input vector. The first 2 columns correspond to the <b>input features (x1 and x2)</b> while the third column corresponds to the<b> target values (t = -1 or t = 1)</b>. Using the classifier trained on “Data1.txt”, classify the test data provided in the file “Test1.txt”. The 2 columns of the file<b> “Test1.txt”</b> correspond to the input features (x1 and x2).</font>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "<font color=blue><b>1- Import Needed Packages</b></font> " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import numpy as np\n", | |
| "import matplotlib.pylab as plt\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Training Phase" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "<font color=blue> <b>2- Load Training Data </b></font>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "<class 'numpy.ndarray'>\n", | |
| "(560, 3)\n", | |
| "[[ 3.2596 1.4977 -1. ]\n", | |
| " [ 3.9733 1.2995 -1. ]\n", | |
| " [ 2.0559 1.5692 -1. ]\n", | |
| " ..., \n", | |
| " [ 47.559 2.7349 1. ]\n", | |
| " [ 53.128 0.45482 1. ]\n", | |
| " [ 47.088 3.3481 1. ]]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "Data=np.loadtxt('Data1.txt') \n", | |
| "#Some Information about it\n", | |
| "print(type(Data))\n", | |
| "print(Data.shape)\n", | |
| "print(Data)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "(560, 2)\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "X=Data[:,0:2] # To get Only your Input Feature\n", | |
| "print(X.shape)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "(560,)\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "t=Data[:,2] # To get only your Labels\n", | |
| "print(t.shape)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "[[ 3.2596 1.4977 1. ]\n", | |
| " [ 3.9733 1.2995 1. ]\n", | |
| " [ 2.0559 1.5692 1. ]\n", | |
| " ..., \n", | |
| " [ 47.559 2.7349 1. ]\n", | |
| " [ 53.128 0.45482 1. ]\n", | |
| " [ 47.088 3.3481 1. ]]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "Xtilda=np.hstack((X,np.ones((560,1))))# To Add Ones Column\n", | |
| "print(Xtilda)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "[[ 3.2596 1.4977 1. ]\n", | |
| " [ 3.9733 1.2995 1. ]\n", | |
| " [ 2.0559 1.5692 1. ]\n", | |
| " ..., \n", | |
| " [ 47.559 2.7349 1. ]\n", | |
| " [ 53.128 0.45482 1. ]\n", | |
| " [ 47.088 3.3481 1. ]]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "#Another Way to add Ones Column\n", | |
| "Ones_V=np.ones((len(X),1)) # create array of ones (nx1)\n", | |
| "xtilda=np.append(X,Ones_V,axis=1)\n", | |
| "print(xtilda)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "<font color=blue><b> 3- Getting Weigth Vector </b></font>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 19, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "[ 0.06097988 0.45821013 -1.86029182]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "W=np.linalg.inv(xtilda.T@xtilda)@xtilda.T@t # We get W\n", | |
| "print(W)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 20, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "-1.86029181553\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "W_bias=W[2]\n", | |
| "print(W_bias)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 21, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "W1=W[0]\n", | |
| "W2=W[1]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "<font color=blue><b> 4- Decision Boundary </b></font>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 22, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "-4.5622 53.128\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "X1_max=np.max(Data[:,0])\n", | |
| "X1_min=np.min(Data[:,0])\n", | |
| "X1_Points=np.array([X1_min,X1_max])\n", | |
| "print(X1_min,X1_max)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 23, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "[ 4.6670601 -3.01051249]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "X2_Points=(-W1/W2)*X1_Points-(W_bias/W2)\n", | |
| "print(X2_Points)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "<font color=blue><b> 5- Plotting Decision Boundary and Data Points </b><font>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 14, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
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BIpLrIOk/61KICK+f/zovDnmR01ufHvL2kuOS6XJkl5C3U5y/2u55xDOTS7mm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\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x1bd0bc27160>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "color_cond=['blue' if i==1 else 'green' for i in t ]\n", | |
| "plt.scatter(Data[:,0],Data[:,1],color=color_cond)\n", | |
| "plt.plot(X1_Points,X2_Points,color='r')\n", | |
| "plt.xlabel('X1')\n", | |
| "plt.ylabel('X2')\n", | |
| "plt.title('Data (X1,X2)')\n", | |
| "plt.show()\n", | |
| "plt.savefig('Least Squrae.jpeg')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Testing Phase" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 15, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "[[ 5.9114 0.31865]\n", | |
| " [ 1.4706 2.6502 ]\n", | |
| " [ -0.93066 3.5615 ]\n", | |
| " [ -2.4272 3.8171 ]\n", | |
| " [ 2.9993 1.4877 ]\n", | |
| " [ 7.3897 0.12388]\n", | |
| " [ 0.97881 1.9301 ]\n", | |
| " [ 2.2363 2.5409 ]\n", | |
| " [ 3.3272 1.5701 ]\n", | |
| " [ 2.0631 0.38394]\n", | |
| " [ 2.0709 2.0016 ]\n", | |
| " [ 2.082 2.5281 ]\n", | |
| " [ 2.0026 2.406 ]\n", | |
| " [ 0.72158 2.462 ]\n", | |
| " [ 3.4445 1.594 ]\n", | |
| " [ 0.50737 2.8901 ]\n", | |
| " [ 3.3417 1.1764 ]\n", | |
| " [ 5.5754 1.1987 ]\n", | |
| " [ 4.6996 2.1413 ]\n", | |
| " [ 2.4212 1.3824 ]\n", | |
| " [ 49.656 2.5533 ]\n", | |
| " [ 49.637 1.3862 ]\n", | |
| " [ 50.026 2.5191 ]\n", | |
| " [ 48.686 2.5927 ]\n", | |
| " [ 52.521 0.93793]\n", | |
| " [ 53.887 -0.15672]\n", | |
| " [ 54.098 0.73924]\n", | |
| " [ 48.398 2.4613 ]\n", | |
| " [ 49.098 2.0515 ]\n", | |
| " [ 48.994 2.1361 ]\n", | |
| " [ 47.502 3.2169 ]\n", | |
| " [ 46.779 3.1687 ]\n", | |
| " [ 47.403 3.2001 ]\n", | |
| " [ 46.373 3.6654 ]\n", | |
| " [ 49.69 2.1267 ]\n", | |
| " [ 48.226 2.1527 ]\n", | |
| " [ 47.733 2.6811 ]\n", | |
| " [ 51.132 1.4697 ]\n", | |
| " [ 52.126 1.7297 ]\n", | |
| " [ 49.462 1.8757 ]]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "TestData=np.loadtxt('Test1.txt')\n", | |
| "print(TestData)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 16, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "[[ 5.9114 0.31865 1. ]\n", | |
| " [ 1.4706 2.6502 1. ]\n", | |
| " [ -0.93066 3.5615 1. ]\n", | |
| " [ -2.4272 3.8171 1. ]\n", | |
| " [ 2.9993 1.4877 1. ]\n", | |
| " [ 7.3897 0.12388 1. ]\n", | |
| " [ 0.97881 1.9301 1. ]\n", | |
| " [ 2.2363 2.5409 1. ]\n", | |
| " [ 3.3272 1.5701 1. ]\n", | |
| " [ 2.0631 0.38394 1. ]\n", | |
| " [ 2.0709 2.0016 1. ]\n", | |
| " [ 2.082 2.5281 1. ]\n", | |
| " [ 2.0026 2.406 1. ]\n", | |
| " [ 0.72158 2.462 1. ]\n", | |
| " [ 3.4445 1.594 1. ]\n", | |
| " [ 0.50737 2.8901 1. ]\n", | |
| " [ 3.3417 1.1764 1. ]\n", | |
| " [ 5.5754 1.1987 1. ]\n", | |
| " [ 4.6996 2.1413 1. ]\n", | |
| " [ 2.4212 1.3824 1. ]\n", | |
| " [ 49.656 2.5533 1. ]\n", | |
| " [ 49.637 1.3862 1. ]\n", | |
| " [ 50.026 2.5191 1. ]\n", | |
| " [ 48.686 2.5927 1. ]\n", | |
| " [ 52.521 0.93793 1. ]\n", | |
| " [ 53.887 -0.15672 1. ]\n", | |
| " [ 54.098 0.73924 1. ]\n", | |
| " [ 48.398 2.4613 1. ]\n", | |
| " [ 49.098 2.0515 1. ]\n", | |
| " [ 48.994 2.1361 1. ]\n", | |
| " [ 47.502 3.2169 1. ]\n", | |
| " [ 46.779 3.1687 1. ]\n", | |
| " [ 47.403 3.2001 1. ]\n", | |
| " [ 46.373 3.6654 1. ]\n", | |
| " [ 49.69 2.1267 1. ]\n", | |
| " [ 48.226 2.1527 1. ]\n", | |
| " [ 47.733 2.6811 1. ]\n", | |
| " [ 51.132 1.4697 1. ]\n", | |
| " [ 52.126 1.7297 1. ]\n", | |
| " [ 49.462 1.8757 1. ]]\n", | |
| "40\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "Ones_V=np.ones((len(TestData),1)) # create array of ones (nx1)\n", | |
| "xtilda_test=np.append(TestData,Ones_V,axis=1)\n", | |
| "print(xtilda_test)\n", | |
| "print(len(TestData))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 17, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "W=W.reshape(3,1)\n", | |
| "y_pred=np.dot(W.T,xtilda_test.T)\n", | |
| "#print(W.reshape(3,1))\n", | |
| "#print(y_pred)\n", | |
| "t_test=[]\n", | |
| "for i in range(y_pred.shape[1]):\n", | |
| " if y_pred[:,i] >0:\n", | |
| " t_test.append(1)\n", | |
| " else:\n", | |
| " t_test.append(-1)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 24, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
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hjz+Cjsy5qDR+POy7rxUSSE21R6tW1uC+9FLbe6G6FMkrJTdGltp82IK6DYmhR2zNkS0i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\nwTlXstq14bHHYNo0e96rF1x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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x1bd0bf86eb8>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "color_cond=['blue' if i==1 else 'green' for i in t_test ]\n", | |
| "plt.scatter(TestData[:,0],TestData[:,1],color=color_cond)\n", | |
| "plt.plot(X1_Points,X2_Points,color='r')\n", | |
| "plt.xlabel('X1_Test')\n", | |
| "plt.ylabel('X2_Test')\n", | |
| "plt.title('Test Data (X1,X2)')\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 3", | |
| "language": "python", | |
| "name": "python3" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.6.8" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } | |
| 5.9114 0.31865 | |
| 1.4706 2.6502 | |
| -0.93066 3.5615 | |
| -2.4272 3.8171 | |
| 2.9993 1.4877 | |
| 7.3897 0.12388 | |
| 0.97881 1.9301 | |
| 2.2363 2.5409 | |
| 3.3272 1.5701 | |
| 2.0631 0.38394 | |
| 2.0709 2.0016 | |
| 2.082 2.5281 | |
| 2.0026 2.406 | |
| 0.72158 2.462 | |
| 3.4445 1.594 | |
| 0.50737 2.8901 | |
| 3.3417 1.1764 | |
| 5.5754 1.1987 | |
| 4.6996 2.1413 | |
| 2.4212 1.3824 | |
| 49.656 2.5533 | |
| 49.637 1.3862 | |
| 50.026 2.5191 | |
| 48.686 2.5927 | |
| 52.521 0.93793 | |
| 53.887 -0.15672 | |
| 54.098 0.73924 | |
| 48.398 2.4613 | |
| 49.098 2.0515 | |
| 48.994 2.1361 | |
| 47.502 3.2169 | |
| 46.779 3.1687 | |
| 47.403 3.2001 | |
| 46.373 3.6654 | |
| 49.69 2.1267 | |
| 48.226 2.1527 | |
| 47.733 2.6811 | |
| 51.132 1.4697 | |
| 52.126 1.7297 | |
| 49.462 1.8757 |
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| numpy | |
| pandas | |
| matplotlib |
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| 5.9114 0.31865 | |
| 1.4706 2.6502 | |
| -0.93066 3.5615 | |
| -2.4272 3.8171 | |
| 2.9993 1.4877 | |
| 7.3897 0.12388 | |
| 0.97881 1.9301 | |
| 2.2363 2.5409 | |
| 3.3272 1.5701 | |
| 2.0631 0.38394 | |
| 2.0709 2.0016 | |
| 2.082 2.5281 | |
| 2.0026 2.406 | |
| 0.72158 2.462 | |
| 3.4445 1.594 | |
| 0.50737 2.8901 | |
| 3.3417 1.1764 | |
| 5.5754 1.1987 | |
| 4.6996 2.1413 | |
| 2.4212 1.3824 | |
| 49.656 2.5533 | |
| 49.637 1.3862 | |
| 50.026 2.5191 | |
| 48.686 2.5927 | |
| 52.521 0.93793 | |
| 53.887 -0.15672 | |
| 54.098 0.73924 | |
| 48.398 2.4613 | |
| 49.098 2.0515 | |
| 48.994 2.1361 | |
| 47.502 3.2169 | |
| 46.779 3.1687 | |
| 47.403 3.2001 | |
| 46.373 3.6654 | |
| 49.69 2.1267 | |
| 48.226 2.1527 | |
| 47.733 2.6811 | |
| 51.132 1.4697 | |
| 52.126 1.7297 | |
| 49.462 1.8757 |
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