diff --git a/Assignment/Assignment_1/210094_Akshat_Part2.ipynb b/Assignment/Assignment_1/210094_Akshat_Part2.ipynb new file mode 100644 index 0000000..52d7bd3 --- /dev/null +++ b/Assignment/Assignment_1/210094_Akshat_Part2.ipynb @@ -0,0 +1,758 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "210094_Akshat Part2.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 281 + }, + "id": "Y7OUGPWVgrpH", + "outputId": "e9aa4296-e6ad-41b0-b6c7-a887cb82551f" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "data=pd.read_csv('House_prediction.csv')\n", + "col=['area']\n", + "#pivot_data=data.pivot(index='city',columns=col,values=)\n", + "# print(data)\n", + "mean_area=data.groupby('city')['area'].mean()\n", + "mean_rooms=data.groupby('city')['rooms'].mean()\n", + "mean_bathroom=data.groupby('city')['bathroom'].mean()\n", + "mean_parking_spaces=data.groupby('city')['parking spaces'].mean()\n", + "#mean_floor=data.groupby('city')['floor'].mean()\n", + "mean_hoa=data.groupby('city')['hoa (R$)'].mean()\n", + "mean_rent=data.groupby('city')['rent amount (R$)'].mean()\n", + "mean_property=data.groupby('city')['property tax (R$)'].mean()\n", + "mean_fire=data.groupby('city')['fire insurance (R$)'].mean()\n", + "mean_total=data.groupby('city')['total (R$)'].mean()\n", + "\n", + "fig1=plt.figure()\n", + "fig1=plt.plot(mean_area)\n", + "plt.title('AREA')\n", + "plt.show(fig1)\n" + ] + }, + { + "cell_type": "code", + "source": [ + "fig2=plt.figure()\n", + "fig2=plt.plot(mean_rooms)\n", + "plt.title('ROOMS')\n", + "plt.show(fig2)" + ], + "metadata": { + "id": "q9wN4YaJkzk7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 281 + }, + "outputId": "9b52d6e1-5919-4880-936a-bfe242106d1b" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "source": [ + "fig3=plt.figure()\n", + "fig3=plt.plot(mean_bathroom)\n", + "plt.title('Bathroom')\n", + "plt.show(fig3)" + ], + "metadata": { + "id": "vDA3h_bnk3Og", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 281 + }, + "outputId": "b53ec22d-4245-44be-d552-e40a976402f9" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "source": [ + "fig4=plt.figure()\n", + "fig4=plt.plot(mean_parking_spaces)\n", + "plt.title('PARKING SPACE')\n", + "plt.show(fig4)" + ], + "metadata": { + "id": "Ol1XI2n0k_xF", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 281 + }, + "outputId": "6cc328bd-3cd5-4d68-967a-a39e84939a13" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "source": [ + "fig5=plt.figure()\n", + "fig5=plt.plot(mean_hoa)\n", + "plt.title('HOA')\n", + "plt.show(fig5)" + ], + "metadata": { + "id": "iKzCDxVclOa3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 281 + }, + "outputId": "f8f71269-5e5f-44b5-ad71-279ee24558c2" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "source": [ + "fig6=plt.figure()\n", + "fig6=plt.plot(mean_rent)\n", + "plt.title('RENT')\n", + "plt.show(fig6)" + ], + "metadata": { + "id": "n58cVh5ElbN6", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 281 + }, + "outputId": "b253856a-2bf1-4b40-eb16-8160183dd4ab" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "source": [ + "fig7=plt.figure()\n", + "fig7=plt.plot(mean_property)\n", + "plt.title('PROPERTY TAX')\n", + "plt.show(fig7)" + ], + "metadata": { + "id": "upISY_q3lkPs", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 281 + }, + "outputId": "f957d124-9996-489e-ca77-5afd667e07b8" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "source": [ + "fig8=plt.figure()\n", + "fig8=plt.plot(mean_fire)\n", + "plt.title('FIRE INSURENCE')\n", + "plt.show(fig8)" + ], + "metadata": { + "id": "JvNRA78FmCO_", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 281 + }, + "outputId": "d89f72b8-1589-49fc-f54d-53d1f9c5f51b" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "source": [ + "fig9=plt.figure()\n", + "fig9=plt.plot(mean_total)\n", + "plt.title('TOTAL')\n", + "plt.show(fig9)" + ], + "metadata": { + "id": "GRy30DkbmM02", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 281 + }, + "outputId": "0ba0fcf5-8bdc-494b-d2c1-4b0c074d63d1" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "From the above graphs we can see Campinas has low total as well as it also have quite good area, rooms, bathrooms and parking space. Porto Alegre has lowest total but other features are very much compromised." + ], + "metadata": { + "id": "AaC5zn_s1Cpm" + } + }, + { + "cell_type": "code", + "source": [ + "\n", + "\n", + "\n", + "hoabathroom=data.groupby('bathroom')['hoa (R$)'].mean()\n", + "hoaroom=data.groupby('rooms')['hoa (R$)'].mean()\n", + "hoaps=data.groupby('parking spaces')['hoa (R$)'].mean()\n", + "hoafloor=data.groupby('floor')['hoa (R$)'].mean()\n", + "fig1=plt.plot(hoabathroom)\n", + "\n", + "\n", + "plt.show(fig1)\n" + ], + "metadata": { + "id": "yd6EKjtEk0I1", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 265 + }, + "outputId": "5c1227eb-3a2a-41df-f058-c5119e6d29d7" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "source": [ + "fig2=plt.plot(hoaroom)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 265 + }, + "id": "v3mQOb4i6kj1", + "outputId": "8f4df4a3-53ed-43f8-ee94-f7747f63b404" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" 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" + ], + "image/png": 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\n" 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "For almost all the features hoa first increases and then decreases.\n" + ], + "metadata": { + "id": "2EqBbBSt1A0I" + } + }, + { + "cell_type": "code", + "source": [ + "fur=data['furniture']=='furnished'\n", + "nfur=data['furniture']=='not furnished'\n", + "notani=data['animal']=='not acept'\n", + "ani= data['animal']=='acept'\n", + "meanhoa=np.array([data.loc[fur,'hoa (R$)'].mean(),data.loc[nfur,'hoa (R$)'].mean(),data.loc[ani,'hoa (R$)'].mean(),data.loc[notani,'hoa (R$)'].mean()])\n", + "meanhoa\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "gQEdV4QlkrlN", + "outputId": "e71ce67d-578d-4825-c790-11d56ae1836a" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([1267.76170376, 1143.8106604 , 990.60401635, 1815.98358586])" + ] + }, + "metadata": {}, + "execution_count": 30 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "From above array it is clear that house association tax increases if house is funished and animals are accepeted. " + ], + "metadata": { + "id": "2UZ_HNcEuZ12" + } + }, + { + "cell_type": "code", + "source": [ + "" + ], + "metadata": { + "id": "qQK8X4fWI0wN" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "\n", + "ptbathroom=data.groupby('bathroom')['property tax (R$)'].mean()\n", + "ptroom=data.groupby('rooms')['property tax (R$)'].mean()\n", + "ptps=data.groupby('parking spaces')['property tax (R$)'].mean()\n", + "ptfloor=data.groupby('floor')['property tax (R$)'].mean()\n", + "fig1=plt.plot(ptbathroom)\n", + "\n", + "\n", + "plt.show(fig1)\n" + ], + "metadata": { + "id": "VnRHwlivvBZJ", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 265 + }, + "outputId": "3f56a01b-c9a9-4c63-d793-b3876d83b70b" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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3Sb5JnFPWIpKwHthQxYYdDfzDx0r5SFGW3+X4SqEvInHt1apGfhB4l+vPH89nLiz2uxzfKfRFJG7VHW7nK49vZkruKL5343lx+Z23Z0qhLyJxqaunly/9ZhPHu3p48DPz47pd8pnQuyAicem+32+nvLqFH39qHlPzMvwuJ2boSF9E4s7qbQf5+St7uPWiiVw3d7zf5cQUhb6IxJU9jcf4m/94m7kTsvjba2f5XU7MUeiLSNw43tnDnb/eSHKy8cCn5zMsJb6/EOVsaE5fROLGPz6zjR2hVn5x2wUUZo3wu5yYpCN9EYkLT761j//YWMuXr5zGkhl5fpcTsxT6IjLkVRw4zD88U8ElU8fy1aXT/C4npin0RWRIO3y8izt/vYns9DTuv+V8khO0kdpgKfRFEphzjvauHr/LOGvOOf7Xf2zlwKHj/Nun55EzapjfJcU8ncgVSSDdPb0EDx6hfG8LG6tbKK9upr61gxvnFfLXy2cMuZOfP3t5N4FgiH/4aCkLJmb7Xc6QoNAXiWNH2rvYVO0F/N4WttQc4rh3ZF+YNYILJ+UwekQKT5XX8tzbB/n8xSX85ZKpZKan+lz56b2xu4n7Vu/gmvMK+LPFJX6XM2Qo9EXihHOO2pbjlFc3v3ckvyPUinOQnGTMGpfBn1wwgQUTx1BWMoZxme8f1d+5ZCo/WPsuD728myfequGuK6bwuYtKGJ4am+vc61vb+dLjmynOTue+mz+iRmpnwPq+8yQ2lZWVufLycr/LEIlJXT29VA6Yqgkd6QBg1LAU5hVnUTYxm7KSMZw/IWtQDccqDx7h3t9v5w/vNlCYNYK/WjadG+YVxtTJ0e6eXj778Jtsrmnh/9+1mJkFo/0uKeaY2UbnXNlJH1PoiwwNp5uqKSsZQ9nEMSyYmM2MgoxzCurXqhr5/u+3887+w8wsyODuq2dy+fTcmDii/ufV23lgwy7+zyfmcvOCIr/LiUkKfZEh5nRTNaXjRr83TVM2MZuCzOFhr6G31/HcOwf51zU72NfcxuKpOdy9chbnFWWGfV+D9UJliNsfLedTCyfw/Zs+4lsdsU6hLxLjunp6CR44Qnl1Cxu9oK9v7ZuqyRiWwryJfUfxZRPHMHeQUzXh0tndy2NvVPN/11fRfKyT6+aO529WzGBCdnrUagCoaW7j2h+/zITsdFbdeXHMnm+IBR8W+jqRK+KDrp5eXt/VxJt7mimvbmZrzeH3pmqKxozg4ik5LCjJpmziGKbnn9tUzblKS0ni84sn8fEFRfz0D7v5+Su7+f22g3xm0US+fOU0skdG/kvG27t6uPOxjTjgwU8vUOCfAx3pi0RRxYHDrNq4n2e37qfxaCfJScbs8d5UjXfSNX90+Kdqwil0pJ0frXuXJ9+qYWRaCl9cMoU/WzyJEWmRC+K/+907PPbGPn72uTKWleZHbD/xQtM7Ij6qb23nmc0HWLWplu11raQlJ7F0Vh43zivkkmljSU8bmn9w7wy1ct/qHayrDJE/ehh/tWw6N88vIiU5vBf6/25zLV9/citfvHwKd189M6zPHa8U+iJR1t7VQyAYYtWmWl56t4FeB+dPyOLmBUV87CPjyEqP/JRItLy1t5nvPV/J5n2HmJY3im+unMnSWXlhWemzo66VG/7tVT5SlMljf35h2D9Q4pVCXyQKnHOUV7fw9Ka+q1tb27sZnzmcG+cXctP8IqbkjvK7xIhxzrGmoo5/Xr2D3Y3HWFiSzT3XzGRe8Zizfs6jHd1c95NXaG3v5r++cgl5GbE97RVLdCJXJIJqmttYtamW323eT3VTG+lpyVw9Zxw3zy9k0eQckmLowqZIMTNWzhnH0ln5PPlWDT9at5MbH3iNq+cU8DcrZjD5DD/wnHN8c9Xb7G08xm++sEiBH0YKfZGz0NrexfPvHGTVxv28ubcZM7h4Sg5fuXIaK+cURHVJZSxJTU7iM4smcuO8Qn728m4eeqmvIdqnFhbzlaXTyM0YXBfMR1/by3+9fZBvrpzJosk5Ea46sWh6R2SQenodr1Q1smpjLWsq6ujo7mVy7khunl/EDfMKh1yHymhoaO3gxy/s5PE39zEsJYkvXDaZL1w6+UM/FDfta+FPfvo6l0/P46HPLkiIv5TCTXP6IudgR10rT3vTN/WtHWSOSOW6ueO5eUERc4syY6I1Qazb03iMf1mzneffqWPsqGF89app3HLBBFIHnJhtPtbJtT9+mZRk47kvXTokun3GIoW+yBlqOtrBs1v7lllu23+ElCRjyYw8Pr6gkCtm5jEsRRcHnY3N+1r4/u+38+aeZiaPHck3Vs5gxewCzIyeXsdtv3iTN/Y08/SdFzOn0L92D0OdTuSKDEJHdw/rK+tZtWk/G3bU093rOK8wk299rJTr5o7XtzKFwbziMTx5xyLWb6/nvtXb+eKvNzGvOIt7rp7Fa7saeXlnI9+/6TwFfgQp9CWhOefYUnOIVZtq+c+tBzl8vIu8jGHcfskkbppfxIyCDL9LjDtmxtJZ+SyZkceqjbX8IPAun/zp6wDcNL+QWy6Y4HOF8U2hLwnpwKHj/G7zflZtqmV3wzGGpyaxYnYBN80v4pKpY2Oqf3y8Sk4yPnnBBD42dzyPvLqHHXWtfPeG83SOJMIU+pIw9h86TqCijtUVdbyxpxnnYOGkbL542RSuPq+AjOE6aeiHEWnJ3HXFVL/LSBinDX0zewT4KFDvnJvjjWUDTwIlwF7gk865Fuv7iL4fuAZoA25zzm3yfudW4O+9p/2Oc+7R8L4UkQ9yzrEj1MraihBrg3Vs238EgOn5o/ja0uncNL8w6u2BRfw2mCP9fwd+Avyy39jdwAvOuXvN7G7v/jeBq4Fp3r8LgQeBC70PiW8BZYADNprZs865lnC9EBHoW0u/aV8La7bVsTYYYl9zG2Ywv3gM91w9k+WzC5g0dqTfZYr45rSh75x7ycxKBgxfDyzxbj8KbKAv9K8Hfun61oH+0cyyzGyct23AOdcMYGYBYCXw+Dm/Akl47V09vFrVyNqKEOsqQzQd6yQtOYmLp+Zw55IpLJ2Vp8v4RTxnO6ef75w76N2uA040uC4EavptV+uNnWr8fzCzO4A7AIqLi8+yPIl3h9u6eHFHPWuDdWzY0UBbZw8Zw1K4YmYey2fnc/n0XM3Ri5zEOZ/Idc45MwvbFV7OuYeAh6Dv4qxwPa8MfQcPHycQDLG2IsQfdzfR3evIyxjGjfMKWT67gIsm55CWota7Ih/mbEM/ZGbjnHMHvembem98P9B/kW2RN7af96eDToxvOMt9S4JwzlFVf5S1wRBrK+rYWnsYgMljR/Lnl05mxex85hZlqTeLyBk429B/FrgVuNf7+Uy/8S+Z2RP0ncg97H0wrAG+Z2YnmmsvB+45+7IlXvX2OjbXtHgrbkLsaTwG9H0ByTdWzmB5aQFT8+K3L71IpA1myebj9B2ljzWzWvpW4dwLPGVmtwPVwCe9zZ+nb7lmFX1LNj8P4JxrNrN/At7ytvv2iZO6Ih3dPby2q4m1FSECwRCNRztISTIumpLDn10yieWl+TH/vbEiQ4UarokvjrR3sWFHA2sq6vjDjgaOdnQzMi2ZJTPzWF7ad4l+5gidiBU5G2q4JjGh8WgHq73186/vaqSrxzF2VBofmzuO5aUFXDQlh+Gp6l4pEkkKfYm4muY2HnppN0+W19DZ3cvEnHQ+v7hv2mZe8Rj1uRGJIoW+RMy7oVb+34ZdPLP1AEkGN80r4rbFJcwsyFBTLRGfKPQl7Dbva+GBDbsIBEOMSE3mtotL+PNLJzEuU18nKOI3hb6EhXOOV6uaeGBDFa/taiJzRCpfWTqN2y4uIXtkmt/liYhHoS/npLfXsTYY4sENVWytPUxexjD+7ppZfOrCYkZ9yJdfi4g/9L9SzkpXTy/PbjnAg3/YRVX9UYqz0/nejedx0/xCrcARiWEKfTkj7V09PFVew0//sJv9h44zsyCD+285n2vPG0dKsvreiMQ6hb4MypH2Ln71ejW/eHUPjUc7WTBxDN++fjZXzszTShyRIUShLx+q8WgHj7yyh1+9Xk1rRzeXTc/lriVTWDgpW2EvMgQp9OWkalva+NlLu3nirRo6e3q5Zs447lwyhTmFmX6XJiLnQKEvH1BV38qDG3bzzJb9mMGN8wr5i8unMCVXnS1F4oFCXwB4u/YQD7y4izXBOoanJPO5i/ouqBqfpQuqROKJQj+BOed4fXcTD7y4i1eqGhk9PIUvXzGV2xZP0gVVInFKoZ+Aensd6ypDPLBhF1tqDpGbMYx7rp7Jn15YrO+VFYlzCv0E0t3Ty3++fYAHN+zi3dBRJmSP4Ds3zOHjC4p0QZVIglDoxznnHA1HO1hTEeKhl3ZR03ycGfm6oEokUSn040Bndy/7Dx1nX3Mb+5qOUd3URnVzGzXNbexrbqOtsweAecVZfOujfRdU6cvERRKTQn+IONLexb6mNqqb+oJ8X/Ox924fOHSc3n7fejk8NYni7HSKs0dy8ZSxTMxJZ05hJvOLs3RBlUiCU+jHiN5eR6i1vS/IvTCv9o7c9zW30dLW9YHtc0amUZyTTtnEMRTPK6Q4ZyQTc9Ipzk4nL2OYwl1ETkqhH0XtXT3Utrx/tP7+z2PUtByns7v3vW2Tk4zCrBFMzEnnmvPGUZyd7oX6SCZkj9AqGxE5Kwr9CDl8vIsXt9fz2q5G9npH73VH2j+wzci0ZIpzRjItL4Ols/LfC/aJ2SMZlzWcVJ1kFZEwU+iHUX1rO4FgiNXb6nh9VxPdvY7skWlMyR3J4qlj3z9az0lnYnY62SPTNA0jIlGl0D9H+5raWFNRx5qKOjbua8E5KMlJ5/ZLJrFiTgHnF2VppYyIxAyF/hlyzrEj1MqabSFWV9RRefAIAKXjRvO1pdNZMSefGfkZOoIXkZik0B+E3l7H5ppDrK2oY3VFHdVNbZjBguIx/P21s1heWkBxTrrfZYqInJZC/xS6enr54+4m1lTUsbYiRH1rB6nJxkVTxnLHZZNZVppPXsZwv8sUETkjCv1+jnf28NLOBtZsq+OF7fUcPt7FiNRklszIZcXsAq6YmUfmCC2VFJGhK+FD//DxLtZvD7FmW4gN79bT3tVL5ohUls7KY+XsAi6bnqtmZCISNxIy9Otb21lbEWJNxftLK/NHD+MTCyawYnYBF07O1hp5EYlLCRP6J5ZWrq6oY1P/pZWXTmLFbC2tFJHEELeh75xje12rt4Y+9IGllV+/ajorZhcwPX+UllaKSEKJy9B/u/YQX35883tLK8sm9i2tXDG7gAnZWlopIokr6qFvZiuB+4Fk4OfOuXvDvY+iMelMGjuSv7hsCstK88nNGBbuXYiIDElRDX0zSwb+DVgG1AJvmdmzzrlgOPeTPTKNf//8wnA+pYhIXIj2EpWFQJVzbrdzrhN4Arg+yjWIiCSsaId+IVDT736tN/YeM7vDzMrNrLyhoSGqxYmIxLuYW4zunHvIOVfmnCvLzc31uxwRkbgS7dDfD0zod7/IGxMRkSiIdui/BUwzs0lmlgbcAjwb5RpERBJWVFfvOOe6zexLwBr6lmw+4pyriGYNIiKJLOrr9J1zzwPPR3u/IiISgydyRUQkcsw553cNp2RmDUC133Wco7FAo99FxBC9Hx+k9+N9ei8+6Fzej4nOuZMuf4zp0I8HZlbunCvzu45Yoffjg/R+vE/vxQdF6v3Q9I6ISAJR6IuIJBCFfuQ95HcBMUbvxwfp/Xif3osPisj7oTl9EZEEoiN9EZEEotAXEUkgCv0IMbMJZvaimQXNrMLMvup3TX4zs2Qz22xmz/ldi9/MLMvMfmtm282s0swu8rsmP5nZ173/J9vM7HEzG+53TdFkZo+YWb2Zbes3lm1mATPb6f0cE459KfQjpxv4a+dcKbAIuMvMSn2uyW9fBSr9LiJG3A+sds7NBOaSwO+LmRUCXwHKnHNz6OvLdYu/VUXdvwMrB4zdDbzgnJsGvODdP2cK/Qhxzh10zm3ybrfS95+68MN/K36ZWRFwLfBzv2vxm5llApcBDwM45zqdc4f8rcp3KcAIM0sB0oEDPtcTVc65l4DmAcPXA496tx8FbgjHvhT6UWBmJcA84A1/K/HVj4BvAL1+FxIDJgENwNrHQVUAAAGCSURBVC+86a6fm9lIv4vyi3NuP/CvwD7gIHDYObfW36piQr5z7qB3uw7ID8eTKvQjzMxGAauArznnjvhdjx/M7KNAvXNuo9+1xIgUYD7woHNuHnCMMP3pPhR5c9XX0/dhOB4YaWaf8beq2OL61taHZX29Qj+CzCyVvsB/zDn3tN/1+GgxcJ2Z7QWeAK40s1/7W5KvaoFa59yJv/x+S9+HQKK6CtjjnGtwznUBTwMX+1xTLAiZ2TgA72d9OJ5UoR8hZmb0zdlWOud+4Hc9fnLO3eOcK3LOldB3gm69cy5hj+Scc3VAjZnN8IaWAkEfS/LbPmCRmaV7/2+WksAntvt5FrjVu30r8Ew4nlShHzmLgc/Sd1S7xft3jd9FScz4MvCYmb0NnA98z+d6fOP9xfNbYBPwDn25lFAtGczsceB1YIaZ1ZrZ7cC9wDIz20nfX0P3hmVfasMgIpI4dKQvIpJAFPoiIglEoS8ikkAU+iIiCUShLyKSQBT6IiIJRKEvIpJA/hsaknCGrYGyLgAAAABJRU5ErkJggg==\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "source": [ + "fig1=plt.plot(ptroom)\n", + "\n", + "\n", + "plt.show(fig1)\n", + "\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 265 + }, + "id": "a-t0aY1_LAda", + "outputId": "b9a17a26-fbe7-4d4d-bd8f-aa75a9f25f05" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" 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\n" 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "From above graph we can see property tax increases with number of bathrooms and parking space where as becomes maximum for room at 4 and 6 and then again decreases" + ], + "metadata": { + "id": "wqHKHprx8FOk" + } + }, + { + "cell_type": "code", + "source": [ + "fibathroom=data.groupby('bathroom')['fire insurance (R$)'].mean()\n", + "firoom=data.groupby('rooms')['fire insurance (R$)'].mean()\n", + "fips=data.groupby('parking spaces')['fire insurance (R$)'].mean()\n", + "fifloor=data.groupby('floor')['fire insurance (R$)'].mean()\n", + "fig1=plt.plot(fibathroom)\n", + "plt.plot(firoom)\n", + "plt.plot(fips)\n", + "\n", + "\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 265 + }, + "id": "NIs2xvZ78EPV", + "outputId": "33b4b518-ed82-404e-ad3c-3d28b6f28b5c" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "source": [ + "" + ], + "metadata": { + "id": "E9UR26TU8w5V" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/Assignment/Assignment_1/210094_Akshat_part1.ipynb b/Assignment/Assignment_1/210094_Akshat_part1.ipynb new file mode 100644 index 0000000..33cd229 --- /dev/null +++ b/Assignment/Assignment_1/210094_Akshat_part1.ipynb @@ -0,0 +1,972 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "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" + }, + "colab": { + "name": "210094_Akshat_part1.ipynb", + "provenance": [], + "collapsed_sections": [] + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "rvFM645NE-D2" + }, + "source": [ + "# Assignment 1 - Part 1\n", + "In this assignment, we will go through basic linear algebra, NumPy, and image manipulation using Python to get everyone on the same page.\n", + "\n", + "One of the aims of this assignment is to get you to start getting comfortable searching for useful library functions online. So in many of the functions you will implement, you will have to look up helper functions.\n", + "\n", + "\\\n", + "\n", + "## Instructions\n", + "* This notebook contain blocks of code, you are required to complete those blocks(where required)\n", + "* You are required to copy this notebook (\"copy to drive\" above) and complete the code.\n", + "* For Submission, You'll be required to submit a sharable link for your copy of this notebook. (DO NOT CHANGE THE NAME OF THE FUNCTIONS)\n", + "\n", + "\\\n", + "\\\n", + "Also, I'd like to acknowledge the Stanford CS131. This assignment is highly based on the assignments from that course." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UhSVK4RoK9q5" + }, + "source": [ + "First Let's import some dependencies" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "cCKqyfhIE-EQ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "67142b6c-0221-4f97-f49e-017d6c097984" + }, + "source": [ + "# Imports the print function from newer versions of python\n", + "from __future__ import print_function\n", + "\n", + "# Setup\n", + "\n", + "# The Random module implements pseudo-random number generators\n", + "import random \n", + "import math\n", + "# Numpy is the main package for scientific computing with Python. \n", + "# This will be one of our most used libraries in this project\n", + "import numpy as np\n", + "\n", + "# The Time library helps us time code runtimes\n", + "import time\n", + "\n", + "\n", + "# Some more magic so that the notebook will reload external python modules;\n", + "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "%reload_ext autoreload" + ], + "execution_count": 68, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "id": "QLtp15rqE-EU" + }, + "source": [ + "# Part 1: Linear Algebra and NumPy Review\n", + "In this section, we will review linear algebra and learn how to use vectors and matrices in python using numpy." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "E8HDYpc0E-EV" + }, + "source": [ + "## Part 1.1 (5 points)\n", + "First, let's test whether you can define the following matrices and vectors using numpy. Look up `np.array()` for help. In the next code block, define $M$ as a $(4, 3)$ matrix, $a$ as a $(1, 3)$ row vector and $b$ as a $(3, 1)$ column vector:\n", + "\n", + "$$M = \\begin{bmatrix}\n", + "1 & 2 & 3 \\\\\n", + "4 & 5 & 6 \\\\\n", + "7 & 8 & 9 \\\\\n", + "10 & 11 & 12 \\end{bmatrix}\n", + "$$\n", + "\n", + "$$a = \\begin{bmatrix}\n", + "1 & 1 & 0\n", + "\\end{bmatrix}\n", + "$$\n", + "\n", + "$$b = \\begin{bmatrix}\n", + "-1 \\\\ 2 \\\\ 5\n", + "\\end{bmatrix} \n", + "$$ " + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "mETk2NCME-EX", + "outputId": "476cfbd2-6c9a-483a-8569-59919cfac261", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "source": [ + "### YOUR CODE HERE\n", + "\n", + "M = np.array([[1,2,3],[4,5,6],[7,8,9],[10,11,12]])\n", + "a = np.array([[1,1,0]])\n", + "b = np.array([[-1],[2],[5]])\n", + "### END CODE HERE\n", + "print(\"M = \\n\", M)\n", + "print(\"The size of M is: \", M.shape)\n", + "print()\n", + "print(\"a = \", a)\n", + "print(\"The size of a is: \", a.shape)\n", + "print()\n", + "print(\"b = \", b)\n", + "print(\"The size of b is: \", b.shape)" + ], + "execution_count": 69, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "M = \n", + " [[ 1 2 3]\n", + " [ 4 5 6]\n", + " [ 7 8 9]\n", + " [10 11 12]]\n", + "The size of M is: (4, 3)\n", + "\n", + "a = [[1 1 0]]\n", + "The size of a is: (1, 3)\n", + "\n", + "b = [[-1]\n", + " [ 2]\n", + " [ 5]]\n", + "The size of b is: (3, 1)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rSta4NheE-EZ" + }, + "source": [ + "## Part 1.2 (5 points)\n", + "Implement the `dot_product()` method below and check that it returns the correct answer for $a^Tb$." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "C5ZRjCE2MVOU" + }, + "source": [ + "def dot_product(a, b):\n", + " \"\"\"Implement dot product between the two vectors: a and b.\n", + " (optional): While you can solve this using for loops, we recommend\n", + " that you look up `np.dot()` online and use that instead.\n", + " Args:\n", + " a: numpy array of shape (x, n)\n", + " b: numpy array of shape (n, x)\n", + " Returns:\n", + " out: numpy array of shape (x, x) (scalar if x = 1)\n", + " \"\"\"\n", + " out = np.dot(a,b)\n", + " ### YOUR CODE HERE\n", + " ### END YOUR CODE\n", + " return out" + ], + "execution_count": 70, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "pbLIS5vIE-Ea", + "outputId": "023a7006-0d2d-4128-c412-98362ce906a0", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "source": [ + "# Now, let's test out this dot product. Your answer should be [[1]].\n", + "aDotB = dot_product(a, b)\n", + "print(aDotB)\n", + "\n", + "print(\"The size is: \", aDotB.shape)" + ], + "execution_count": 71, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[1]]\n", + "The size is: (1, 1)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0rGfcRU1E-Eb" + }, + "source": [ + "## Part 1.3 (5 points)\n", + "Implement the `complicated_matrix_function()` method and use it to compute $(ab)Ma^T$\n", + "\n", + "IMPORTANT NOTE: The `complicated_matrix_function()` method expects all inputs to be two dimensional numpy arrays, as opposed to 1-D arrays. This is an important distinction, because 2-D arrays can be transposed, while 1-D arrays cannot.\n", + "\n", + "To transpose a 2-D array, you can use the syntax `array.T` " + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "dglQmbuLNOk6" + }, + "source": [ + "def complicated_matrix_function(M, a, b):\n", + " \"\"\"Implement (a * b) * (M * a.T).\n", + " (optional): Use the `dot_product(a, b)` function you wrote above\n", + " as a helper function.\n", + " Args:\n", + " M: numpy matrix of shape (x, n).\n", + " a: numpy array of shape (1, n).\n", + " b: numpy array of shape (n, 1).\n", + " Returns:\n", + " out: numpy matrix of shape (x, 1).\n", + " \"\"\"\n", + " c=a.T\n", + " d=dot_product(a,b)\n", + "\n", + " out = np.multiply(d,dot_product(M,c))\n", + " ### YOUR CODE HERE\n", + " \n", + " ### END YOUR CODE\n", + " return out" + ], + "execution_count": 72, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "da_uQQLhE-Ec", + "outputId": "0759ef97-d7b3-497f-9c85-6291c629751f", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "source": [ + "# Your answer should be $[[3], [9], [15], [21]]$ of shape(4, 1).\n", + "ans = complicated_matrix_function(M, a, b)\n", + "print(ans)\n", + "print()\n", + "print(\"The size is: \", ans.shape)" + ], + "execution_count": 73, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[ 3]\n", + " [ 9]\n", + " [15]\n", + " [21]]\n", + "\n", + "The size is: (4, 1)\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "6CWXxSSOE-Ed", + "outputId": "0f4146ca-e087-47d7-882f-af90cda58e40", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "source": [ + "M_2 = np.array(range(4)).reshape((2,2))\n", + "a_2 = np.array([[1,1]])\n", + "b_2 = np.array([[10, 10]]).T\n", + "print(M_2.shape)\n", + "print(a_2.shape)\n", + "print(b_2.shape)\n", + "print()\n", + "\n", + "# Your answer should be $[[20], [100]]$ of shape(2, 1).\n", + "ans = complicated_matrix_function(M_2, a_2, b_2)\n", + "print(ans)\n", + "print()\n", + "print(\"The size is: \", ans.shape)" + ], + "execution_count": 74, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(2, 2)\n", + "(1, 2)\n", + "(2, 1)\n", + "\n", + "[[ 20]\n", + " [100]]\n", + "\n", + "The size is: (2, 1)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4fHLxLl4E-Ee" + }, + "source": [ + "## Part 1.4 (10 points) [Optional/Bonus]\n", + "Implement `eigen_decomp()` and `get_eigen_values_and_vectors()` methods. In this method, perform eigenvalue decomposition on the following matrix and return the largest k eigen values and corresponding eigen vectors (k is specified in the method calls below).\n", + "\n", + "$$M = \\begin{bmatrix}\n", + "1 & 2 & 3 \\\\\n", + "4 & 5 & 6 \\\\\n", + "7 & 8 & 9 \\end{bmatrix}\n", + "$$\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "RfaCSoRMOIc8" + }, + "source": [ + "def eigen_decomp(M):\n", + " \"\"\"Implement eigenvalue decomposition.\n", + " (optional): You might find the `np.linalg.eig` function useful.\n", + " Args:\n", + " matrix: numpy matrix of shape (m, n)\n", + " Returns:\n", + " w: numpy array of shape (m, m) such that the column v[:,i] is the eigenvector corresponding to the eigenvalue w[i].\n", + " v: Matrix where every column is an eigenvector.\n", + " \"\"\"\n", + " w,v=np.linalg.eig(M)\n", + "\n", + " ### YOUR CODE HERE\n", + " \n", + " ### END YOUR CODE\n", + " return w, v" + ], + "execution_count": 77, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "YB120rb4ONBH" + }, + "source": [ + "def get_eigen_values_and_vectors(M, k):\n", + " \"\"\"Return top k eigenvalues and eigenvectors of matrix M. By top k\n", + " here we mean the eigenvalues with the top ABSOLUTE values (lookup\n", + " np.argsort for a hint on how to do so.)\n", + " (optional): Use the `eigen_decomp(M)` function you wrote above\n", + " as a helper function\n", + " Args:\n", + " M: numpy matrix of shape (m, m).\n", + " k: number of eigen values and respective vectors to return.\n", + " Returns:\n", + " eigenvalues: list of length k containing the top k eigenvalues\n", + " eigenvectors: list of length k containing the top k eigenvectors\n", + " of shape (m,)\n", + " \"\"\"\n", + " w,v=eigen_decomp(M)\n", + "\n", + " wsort=np.argsort(w)\n", + " eigenvalue=np.sort(w)\n", + " eigenvalues=eigenvalue[:k]\n", + " eigenvectors = v[:,wsort[:k]]\n", + " row,col=M.shape\n", + " eigenvectors=eigenvectors.reshape((k,col))\n", + " \n", + " return eigenvalues, eigenvectors" + ], + "execution_count": 80, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "t0_GkrJwE-Ee", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "cf63af5b-2b8f-45d2-cf8d-28472f7a1423" + }, + "source": [ + "# Let's define M.\n", + "M = np.array([[1,2,3],[4,5,6],[7,8,9]])\n", + "\n", + "# Now let's grab the first eigenvalue and first eigenvector.\n", + "# You should get back a single eigenvalue and a single eigenvector.\n", + "val, vec = get_eigen_values_and_vectors(M[:,:3], 1)\n", + "print(\"First eigenvalue =\", val[0])\n", + "print()\n", + "print(\"First eigenvector =\", vec[0])\n", + "print()\n", + "assert len(vec) == 1\n", + "\n", + "\n", + "\n", + "# Now, let's get the first two eigenvalues and eigenvectors.\n", + "# You should get back a list of two eigenvalues and a list of two eigenvector arrays.\n", + "val, vec = get_eigen_values_and_vectors(M[:,:3], 2)\n", + "print(\"Eigenvalues =\", val)\n", + "print()\n", + "print(\"Eigenvectors =\", vec)\n", + "assert len(vec) == 2" + ], + "execution_count": 81, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "First eigenvalue = -1.1168439698070427\n", + "\n", + "First eigenvector = [-0.78583024 -0.08675134 0.61232756]\n", + "\n", + "Eigenvalues = [-1.11684397e+00 -1.30367773e-15]\n", + "\n", + "Eigenvectors = [[-0.78583024 0.40824829 -0.08675134]\n", + " [-0.81649658 0.61232756 0.40824829]]\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Yeh-V5x1PYz5" + }, + "source": [ + "## Part 1.5 (10 points)\n", + "In this section, you'll implement a gaussian elimination.\n", + "\n", + "The algorithm to to reduce a matrix to rref using gaussian elimination contains 2 parts, First reducing the matrix to partial reduced form, then back substituting to calculate the rref. First algorithm can be summed up as:\n", + "1. Partial pivoting: Find the kth pivot by swapping rows, to move the entry with the largest absolute value to the pivot position. This imparts computational stability to the algorithm.\n", + "2. For each row below the pivot, calculate the factor f which makes the kth entry zero, and for every element in the row subtract the fth multiple of the corresponding element in the kth row.\n", + "3. Repeat above steps for each unknown. We will be left with a partial r.e.f. matrix.\n", + "\n", + "$$\\begin{bmatrix}\n", + "1 & 2 & 3 \\\\\n", + "4 & 5 & 6 \\\\\n", + "7 & 8 & 9 \\end{bmatrix}\n", + "=>\n", + "\\begin{bmatrix}\n", + "7 & 8 & 9 \\\\\n", + "4 & 5 & 6 \\\\\n", + "1 & 2 & 3 \\end{bmatrix}\n", + "=>\n", + "\\begin{bmatrix}\n", + "7 & 8 & 9 \\\\\n", + "0 & 0.42 & 0.85 \\\\\n", + "0 & 0.85 & 1.71 \\end{bmatrix}\n", + "=>\n", + "\\begin{bmatrix}\n", + "7 & 8 & 9 \\\\\n", + "0 & 0.85 & 1.71 \\\\\n", + "0 & 0.45 & 0.85 \\end{bmatrix}\n", + "=>\n", + "\\begin{bmatrix}\n", + "7 & 8 & 9 \\\\\n", + "0 & 0.42 & 0.85 \\\\\n", + "0 & 0 & -0.05 \\end{bmatrix}\n", + "$$\n", + "Second algorithm:\n", + "1. Take a pivot from the last row.\n", + "2. For each row above the pivot, calculate the factor f which makes the kth entry zero, and for every element in the row subtract the fth multiple of the corresponding element in the kth row\n", + "3. Repeat the above step untill the matrix is in rref\n", + "$$\\begin{bmatrix}\n", + "7 & 8 & 0 \\\\\n", + "0 & 0.42 & 0 \\\\\n", + "0 & 0 & -0.05 \\end{bmatrix}\n", + "=>\n", + "\\begin{bmatrix}\n", + "7 & 0 & 0 \\\\\n", + "0 & 0.42 & 0 \\\\\n", + "0 & 0 & -0.05 \\end{bmatrix}\n", + "$$\n", + "\n", + "Steps for implementation:\n", + "1. Complete the function `swap_rows()`\n", + "2. Complete the function `apply_row()`\n", + "3. Complete `forward()` and `backward()`\n", + "4. Finally implement `rref()` using the `forward()` and `backward()`\n", + "\n", + "Note: You can skip this part if you want." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "qUFujiFAPYz6" + }, + "source": [ + "def swap_rows(M):\n", + " \"\"\"Implement row swapping to make the largest element in the pivotial column to be the first row.\n", + " Args:\n", + "\n", + " matrix: numpy matrix of shape (m, n)\n", + " \n", + " Returns:\n", + " Ms: matrix with swapped row\n", + " \"\"\"\n", + " col=M[:,0]\n", + " #print(col)\n", + " pivot=np.where(col==np.amax(col))\n", + " #print(pivot[0][0])\n", + " M[[pivot[0][0],0]]=M[[0,pivot[0][0]]]\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + "\n", + " ### YOUR CODE HERE\n", + " \n", + " ### END YOUR CODE\n", + " return M" + ], + "execution_count": 82, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "S8lbAUSWWpyO" + }, + "source": [ + "def apply_rows(M):\n", + " \"\"\"For each row below the pivot, calculate the factor f which makes the kth\n", + " entry zero, and for every element in the row subtract the fth multiple of the\n", + " corresponding element in the kth row.\n", + " Args:\n", + " matrix: numpy matrix of shape (m, n)\n", + " Returns:\n", + " Ms: matrix with all other entries of the pivotal col zero\n", + " \"\"\"\n", + " out = None\n", + " row,col = M.shape\n", + " for i in range(1,row):\n", + " a=M[i][0]/M[0][0]\n", + " M[i,:]=M[i,:] - M[0,:]*a\n", + " \n", + " \n", + " ### YOUR CODE HERE\n", + " pass\n", + " ### END YOUR CODE\n", + " return M" + ], + "execution_count": 83, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "GnE_-JLxPYz7" + }, + "source": [ + "def forward(M):\n", + " \"\"\"Return a partial ref using the algo described above\n", + " Args:\n", + " M: numpy matrix of shape (m, n).\n", + " Returns:\n", + " Ms: ref of M\n", + " \"\"\"\n", + " # out = None\n", + " row,col=M.shape\n", + " for i in range(row):\n", + " swap_rows(M[i:, i:])\n", + " apply_rows(M[i:,i:])\n", + " M=np.around(M,decimals=2)\n", + " \n", + "\n", + " ### YOUR CODE HERE\n", + " # pass\n", + " ### END YOUR CODE\n", + " return M" + ], + "execution_count": 84, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Wb7pPGP4XmJu" + }, + "source": [ + "def backward(M):\n", + " \"\"\"Return a rref using the algo described above\n", + " Args:\n", + " M: numpy matrix of shape (m, n).\n", + " Returns:\n", + " Ms: rref of M\n", + " \"\"\"\n", + " out = None\n", + " row,col=M.shape\n", + " for i in range(row-1):\n", + " a=M[i][col-1]/M[row-1][col-1]\n", + " M[i,:]=M[i,:]-M[row-1,:]*a\n", + " \n", + "\n", + " ### YOUR CODE HERE\n", + " pass\n", + " ### END YOUR CODE\n", + " return M" + ], + "execution_count": 85, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "XLq81xzXYR85" + }, + "source": [ + "def rref(M):\n", + " \"\"\"Return a rref using the algo descrbed above\n", + " Args:\n", + " M: numpy matrix of shape (m, n).\n", + " Returns:\n", + " Ms: ref of M\n", + " \"\"\"\n", + " out = forward(M)\n", + " row,col=out.shape\n", + " for i in reversed(range(row+1)):\n", + " backward(out[:i, :i])\n", + " out=np.around(out,decimals=2)\n", + " \n", + " ### YOUR CODE HERE\n", + " # pass\n", + " ### END YOUR CODE\n", + " return out" + ], + "execution_count": 86, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Eiz6EbsWPYz8", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "8874da74-3407-40c9-e46f-b86615bca35d" + }, + "source": [ + "# Let's define M.\n", + "M = np.array([[1.0,2,3],[4,5,6],[7,8,9]])\n", + "\n", + "# Now let's calculate it's rref.\n", + "# Note that your code may be evaluated on other test cases as well\n", + "Mrref = rref(M)\n", + "# swap_rows(M)\n", + "# apply_rows(M)\n", + "# M=np.around(M, decimals = 2)\n", + "# swap_rows(M[1:,1:])\n", + "# apply_rows(M[1:,1:])\n", + "# M=np.around(M, decimals = 2)\n", + "print(Mrref)" + ], + "execution_count": 87, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[7. 0. 0. ]\n", + " [0. 0.86 0. ]\n", + " [0. 0. 0.01]]\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "G46pyDzAE-Ef" + }, + "source": [ + "## Part 1.6 (10 points)\n", + "\n", + "To wrap up our overview of NumPy, let's implement something fun — a helper function for computing the Euclidean distance between two $n$-dimensional points!\n", + "\n", + "In the 2-dimensional case, computing the Euclidean distance reduces to solving the Pythagorean theorem $c = \\sqrt{a^2 + b^2}$. where, given two points $(x_1, y_1)$ and $(x_2, y_2)$, $a = x_1 - x_2$ and $b = y_1 - y_2$.\n", + "\n", + "\n", + "More generally, given two $n$-dimensional vectors, the Euclidean distance can be computed by:\n", + "\n", + "1. Performing an elementwise subtraction between the two vectors, to get $n$ difference values.\n", + "2. Squaring each of the $n$ difference values, and summing the squares.\n", + "4. Taking the square root of our sum.\n", + "\n", + "Alternatively, the Euclidean distance between length-$n$ vectors $u$ and $v$ can be written as:\n", + "\n", + "$\n", + "\\quad\\textbf{distance}(u, v) = \\sqrt{\\sum_{i=1}^n (u_i - v_i)^2}\n", + "$\n", + "\n", + "\n", + "Try implementing this function: first using native Python with a `for` loop in the `euclidean_distance_native()` function, then in NumPy **without any loops** in the `euclidean_distance_numpy()` function.\n", + "We've added some `assert` statements here to help you check functionality (if it prints nothing, then your implementation is correct)!" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "5xvHopPqO29C" + }, + "source": [ + "import math\n", + "def euclidean_distance_native(u, v):\n", + " \"\"\"Computes the Euclidean distance between two vectors, represented as Python\n", + " lists.\n", + " Args:\n", + " u (List[float]): A vector, represented as a list of floats.\n", + " v (List[float]): A vector, represented as a list of floats.\n", + " Returns:\n", + " float: Euclidean distance between `u` and `v`.\n", + " \"\"\"\n", + " # First, run some checks:\n", + " assert isinstance(u, list)\n", + " assert isinstance(v, list)\n", + " assert len(u) == len(v)\n", + "\n", + " # Compute the distance!\n", + " # Notes:\n", + " # 1) Try breaking this problem down: first, we want to get\n", + " # the difference between corresponding elements in our\n", + " # input arrays. Then, we want to square these differences.\n", + " # Finally, we want to sum the squares and square root the\n", + " # sum\n", + " sum=0\n", + " for i in range(len(v)) :\n", + " sum=sum+(v[i]-u[i])*(v[i]-u[i])\n", + "\n", + " out=math.sqrt(sum)\n", + "\n", + " ### YOUR CODE HERE\n", + " # pass\n", + " ### END YOUR CODE\n", + " return out" + ], + "execution_count": 88, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "wvLuK8MuO3LH" + }, + "source": [ + "#import math\n", + "def euclidean_distance_numpy(u, v):\n", + " \"\"\"Computes the Euclidean distance between two vectors, represented as NumPy\n", + " arrays.\n", + " Args:\n", + " u (np.ndarray): A vector, represented as a NumPy array.\n", + " v (np.ndarray): A vector, represented as a NumPy array.\n", + " Returns:\n", + " float: Euclidean distance between `u` and `v`.\n", + " \"\"\"\n", + " # First, run some checks:\n", + " assert isinstance(u, np.ndarray)\n", + " assert isinstance(v, np.ndarray)\n", + " assert u.shape == v.shape\n", + "\n", + " # Compute the distance!\n", + " # Note:\n", + " # 1) You shouldn't need any loops\n", + " # 2) Some functions you can Google that might be useful:\n", + " # np.sqrt(), np.sum()\n", + " # 3) Try breaking this problem down: first, we want to get\n", + " # the difference between corresponding elements in our\n", + " # input arrays. Then, we want to square these differences.\n", + " # Finally, we want to sum the squares and square root the\n", + " # sum.\n", + "\n", + " b=v-u\n", + " d=b*b\n", + "\n", + " ans=np.sum(d)\n", + " out=math.sqrt(ans)\n", + " return out\n", + " ### YOUR CODE HERE\n", + " \n", + " ### END YOUR CODE" + ], + "execution_count": 89, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "wu9MimVJE-Eg" + }, + "source": [ + "## Testing native Python function\n", + "assert euclidean_distance_native([7.0], [6.0]) == 1.0\n", + "assert euclidean_distance_native([7.0, 0.0], [3.0, 3.0]) == 5.0\n", + "assert euclidean_distance_native([7.0, 0.0, 0.0], [3.0, 0.0, 3.0]) == 5.0" + ], + "execution_count": 90, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "kJDk88g1E-Ej" + }, + "source": [ + "## Testing NumPy function\n", + "assert euclidean_distance_numpy(\n", + " np.array([7.0]),\n", + " np.array([6.0])\n", + ") == 1.0\n", + "assert euclidean_distance_numpy(\n", + " np.array([7.0, 0.0]),\n", + " np.array([3.0, 3.0])\n", + ") == 5.0\n", + "assert euclidean_distance_numpy(\n", + " np.array([7.0, 0.0, 0.0]),\n", + " np.array([3.0, 0.0, 3.0])\n", + ") == 5.0" + ], + "execution_count": 91, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "n = 1000\n", + "\n", + "# Create some length-n lists and/or n-dimensional arrays\n", + "a = [0.0] * n\n", + "b = [10.0] * n\n", + "a_array = np.array(a)\n", + "b_array = np.array(b)\n", + "\n", + "# Compute runtime for native implementation\n", + "start_time = time.time()\n", + "for i in range(10000):\n", + " euclidean_distance_native(a, b)\n", + "print(\"Native:\", (time.time() - start_time), \"seconds\")\n", + "\n", + "# Compute runtime for numpy implementation\n", + "# Start by grabbing the current time in seconds\n", + "start_time = time.time()\n", + "for i in range(10000):\n", + " euclidean_distance_numpy(a_array, b_array)\n", + "print(\"NumPy:\", (time.time() - start_time), \"seconds\")" + ], + "metadata": { + "id": "E7Z38WwHhoNl", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "cd0a9cf1-1b0a-4cc4-e0bf-ec1fbdfacd32" + }, + "execution_count": 92, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Native: 2.0112180709838867 seconds\n", + "NumPy: 0.09465336799621582 seconds\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Mjik4mQXE-Ek" + }, + "source": [ + "Next, let's take a look at how these two implementations compare in terms of runtime:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "t4e6MfhHE-Em" + }, + "source": [ + "As you can see, doing vectorized calculations (i.e. no for loops) with NumPy results in significantly faster computations! " + ] + }, + { + "cell_type": "markdown", + "source": [ + "Congrats You've come to the end of this notebook. If you solved everything above, impressive. If not, you might need to read/think a bit more. You can always ask doubts. Also, Note that you should submit it even if you cannot solve everything. We might evaluate these using a script later." + ], + "metadata": { + "id": "XvFE0Q5bhx6-" + } + } + ] +} \ No newline at end of file diff --git a/Assignment/Assignment_2/210094_Akshat.ipynb b/Assignment/Assignment_2/210094_Akshat.ipynb new file mode 100644 index 0000000..4abd63e --- /dev/null +++ b/Assignment/Assignment_2/210094_Akshat.ipynb @@ -0,0 +1,859 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "rvFM645NE-D2" + }, + "source": [ + "# Assignment 2\n", + "In this assignment, we will go through Perceptron, Linear Classifiers, Loss Functions, Gradient Descent and Back Propagation.\n", + "\n", + "\n", + "PS. this one is not from Stanford's course.\n", + "\n", + "\n", + "\n", + "\\\n", + "\n", + "## Instructions\n", + "* This notebook contain blocks of code, you are required to complete those blocks(where required)\n", + "* You are required to copy this notebook (\"copy to drive\" above) and complete the code.(DO NOT CHANGE THE NAME OF THE FUNCTIONS)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "id": "QLtp15rqE-EU" + }, + "source": [ + "# Part 1: Perceptron\n", + "In this section, we will see how to implement a perceptron. Goal would be for you to delve into the mathematics.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Zao4e-DphaGA" + }, + "source": [ + "## Intro\n", + "What's a perceptron? It's an algorithm modelled on biological computational model to classify things into binary classes. It's a supervides learning algorithm, meaning that you need to provide labelled data containing features and the actual classifications. A perceptron would take these features as input and spit out a binary value (0 or 1). While training the model with training data, we try to minimise the error and learn the parameters involved." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wDTUoAd6ixm-" + }, + "source": [ + "**How does it work?**\\\n", + "A perceptron is modelled on a biological neuron. A neuron has input dendrites and the output is carried by axons. Similarly, a perceptron takes inputs called \"features\". After processing, a perceptron gives output. For computation, it has a \"weight\" vector which is multipled with feature vector. An activation function is added to introduce some non linearities and the output is given out.\\\n", + "It can be represented as: $$ f=\\sum_{i=1}^{m} w_ix_i +b$$\n", + "\n", + "Let's implement this simple function to give an output.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "iXezofBIgzId" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "class perceptron():\n", + " def __init__(self,num_input_features=8):\n", + " self.weights = np.random.randn(num_input_features)\n", + " self.bias = np.random.random()\n", + "\n", + " def activation(self,x):\n", + " '''\n", + " Implement heaviside step activation function here (google ;))\n", + " '''\n", + " if(x<5):\n", + " return 0\n", + " return 1\n", + " \n", + " # pass\n", + "\n", + "\n", + " def forward(self,x: np.ndarray):\n", + "\n", + " '''\n", + " you have random initialized weights and bias\n", + " you can access then using `self.weights` and `self.bias`\n", + " you should use activation function before returning\n", + " \n", + " x : input features\n", + " return : a binary value as the output of the perceptron \n", + " '''\n", + " # YOUR CODE HERE\n", + " x = x*self.weights+self.bias\n", + " # out = activation(np.sum(x))\n", + " out =np.sum(x)\n", + " return self.activation(out)\n", + " \n", + " # print(out)\n", + "\n", + " pass\n", + " # YOUR CODE HERE" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "oSKwDFAyocVo" + }, + "outputs": [], + "source": [ + "np.random.seed(0)\n", + "perc = perceptron(8)\n", + "assert perc.forward(np.arange(8))==1" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "id": "NWTTg1e9r7uM" + }, + "source": [ + "# Part 2: Linear Classifier\n", + "In this section, we will see how to implement a linear Classifier.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DYDO4GcHr7uM" + }, + "source": [ + "## Intro\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-HFvjH06r7uN" + }, + "source": [ + "**How does it work?**\n", + "\n", + "Linear Classifier uses the following function: $$Y = WX+b$$ Where, $W$ is a 2d array of weights with shape (#features, #classes).\n", + "\n", + "\n", + "Let's implement this classifier.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9A13CEkGr7uN" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "class LinearClassifier():\n", + " def __init__(self,num_input_features=32,num_classes=5):\n", + " self.weights = np.random.randn(num_classes,num_input_features)\n", + " self.bias = np.random.rand(num_classes,1)\n", + "\n", + " def forward(self,x: np.ndarray):\n", + " '''\n", + " x: input features\n", + " you have random initialized weights and bias\n", + " you can access then using `self.weights` and `self.bias`\n", + " return an output vector of num_classes size\n", + " '''\n", + " \n", + " # YOUR CODE HERE\n", + " y=np.dot(self.weights,x)+self.bias\n", + " return y\n", + " pass\n", + " # YOUR CODE HERE" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "zgzPxyTsr7uN", + "outputId": "23ed3695-0356-43f3-91cb-51aef87aec3a" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[ 7.07730669],\n", + " [-10.24067722],\n", + " [ 0.75398702],\n", + " [ 9.8019519 ],\n", + " [ 2.36684038]])" + ] + }, + "metadata": {}, + "execution_count": 4 + } + ], + "source": [ + "np.random.seed(0)\n", + "lc = LinearClassifier()\n", + "lc.forward(np.random.rand(32,1))\n", + "# Should be close to:\n", + "# array([[ 7.07730669],\n", + " # [-10.24067722],\n", + " # [ 0.75398702],\n", + " # [ 9.8019519 ],\n", + " # [ 2.36684038]])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "id": "ZVgOVzJetuqo" + }, + "source": [ + "# Part 3: Loss Functions, Gradient descent and Backpropagation\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4pXryjpctuqy" + }, + "source": [ + "## Intro\n", + "\n", + "Loss Functions tells how \"off\" the output od our model is. Based upon the application, you can use several different loss functions. Formally, A loss function is a function $L:(z,y)\\in\\mathbb{R}\\times Y\\longmapsto L(z,y)\\in\\mathbb{R}$ that takes as inputs the predicted value $z$ corresponding to the real data value yy and outputs how different they are We'll implement L1 loss, L2 loss, Logistic loss, hinge loss and cross entropy loss functions." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QGRb8BHotuqy" + }, + "source": [ + "### **L1 loss**\n", + "L1 loss is the linear loss function $L = \\dfrac{1}{2}(y−z) $\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "YxVh6IL2tuqz" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "def L1Loss(z,y):\n", + " '''\n", + " y : True output.\n", + " z : Predicted output.\n", + " return : L\n", + " '''\n", + " L=abs(y-z)/2\n", + " return L\n", + "\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2xy8ZS84cKtQ" + }, + "source": [ + "### **L2 loss**\n", + "L2 loss is the quadratic loss function or the least square error function $L = \\dfrac{1}{2}(y−z)^2 $\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "JThp5P-KcKtS" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "def L2Loss(z,y):\n", + " '''\n", + " y : True output. \n", + " z : Predicted output. \n", + " return : L\n", + " '''\n", + " L=((y-z)*(y-z))/2\n", + " return L\n", + " \n", + " pass" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Z2JNLnWYcLSC" + }, + "source": [ + "### **Hinge Loss**\n", + "Hinge loss is: $ L = max( 0, 1 - yz ) $" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "gQ1YM4J-cLSC" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "def hingeLoss(z,y):\n", + " '''\n", + " y : True output. \n", + " z : Predicted output. \n", + " return : L\n", + " '''\n", + " a=1-y*z\n", + " if(a>0):\n", + " return a\n", + " return 0\n", + " \n", + " pass" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m15_MjradMNY" + }, + "source": [ + "### **Cross Entropy Loss**\n", + "Another very famous loss function is Cross Entropy loss: $ L = −[ylog(z)+(1−y)log(1−z)] $." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "snJLqhszdMNY" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import math\n", + "def CELoss(z,y):\n", + " '''\n", + " y : True output. \n", + " z : Predicted output. \n", + " return : L\n", + " '''\n", + " a1=math.log(z)\n", + " a2=math.log(1-z)\n", + " L=-(y*a1+(1-y)*a2)\n", + " return L\n", + "\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OsRPsfzxyEVL" + }, + "source": [ + "### **0-1 Loss**\n", + "Loss Function used by perceptron is: $ \\begin{cases} \n", + " 0=z-y & z=y \\\\\n", + " 1=\\dfrac{z-y}{z-y} & z\\neq y\n", + " \\end{cases} $." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "5sA7GxLHyEVM" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "def zeroOneLoss(z,y):\n", + " '''\n", + " y : True output. \n", + " z : Predicted output. \n", + " return : L\n", + " '''\n", + " if(y==z):\n", + " return 0\n", + " return 1\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CWhbibHcgRR8" + }, + "source": [ + "## Cost Function\n", + "The cost function $J$ is commonly used to assess the performance of a model, and is defined with the loss function $L$ as follows:\n", + "$$\\boxed{J(\\theta)=\\sum_{i=1}^mL(h_\\theta(x^{(i)}), y^{(i)})}$$\n", + "where $h_\\theta$ is the hypothesis function i.e. the function used to predict the output." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "SSbmhW4og97t" + }, + "outputs": [], + "source": [ + "lossFunctions = {\n", + " \"l1\" : L1Loss,\n", + " \"l2\" : L2Loss,\n", + " \"hinge\" : hingeLoss,\n", + " \"cross-entropy\" : CELoss,\n", + " \"0-1\" : zeroOneLoss\n", + "}\n", + "\n", + "def cost(Z : np.ndarray, Y : np.ndarray, loss : str):\n", + " '''\n", + " Z : a numpy array of predictions.\n", + " Y : a numpy array of true values.\n", + " return : A numpy array of costs calculated for each example.\n", + " '''\n", + " loss_func = lossFunctions[loss]\n", + " # YOUR CODE HERE\n", + " J = np.array()\n", + " for i in range(len(Z)):\n", + " J[i]=loss_func(Z[i],Y[i])\n", + " return J\n", + "\n", + "\n", + " # YOUR CODE HERE\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "upsN7A0zjGqx" + }, + "source": [ + "## Gradient Descent and Back Propagation\n", + "Gradient Descent is an algorithm that minimizes the loss function by calculating it's gradient. By noting $\\alpha\\in\\mathbb{R}$ the learning rate, the update rule for gradient descent is expressed with the learning rate $\\alpha$ and the cost function $J$ as follows:\n", + "\n", + "$$\\boxed{ W \\longleftarrow W -\\alpha\\nabla J( W )}$$\n", + "​\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AFCN-fYCqidi" + }, + "source": [ + "But we need to find the partial derivative of Loss function wrt every parameter to know what is the slight change that we need to apply to our parameters. This becomes particularly hard if we have more than 1 layer in our algorithm. Here's where **Back Propagation** comes in. It's a way to find gradients wrt every parameter using the chain rule. Backpropagation is a method to update the weights in the neural network by taking into account the actual output and the desired output. The derivative with respect to weight ww is computed using chain rule and is of the following form:\n", + "\n", + "$$\\boxed{\\frac{\\partial L(z,y)}{\\partial w}=\\frac{\\partial L(z,y)}{\\partial a}\\times\\frac{\\partial a}{\\partial z}\\times\\frac{\\partial z}{\\partial w}}$$\n", + "​\n", + " \n", + "As a result, the weight is updated as follows:\n", + "\n", + "$$\\boxed{w\\longleftarrow w-\\alpha\\frac{\\partial L(z,y)}{\\partial w}}$$\n", + "\n", + "So, In a neural network, weights are updated as follows:\n", + "\n", + "* Step 1: Take a batch of training data.\n", + "* Step 2: Perform forward propagation to obtain the corresponding loss.\n", + "* Step 3: Backpropagate the loss to get the gradients.\n", + "* Step 4: Use the gradients to update the weights of the network.\n", + "​\n", + "\n", + "Bonus Problem\n", + " \n", + "Now, Assuming that you know Back Propagation (read a bit about it, if you don't), we'll now implement an image classification model on CIFAR-10." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sJoG5kkYopRN" + }, + "source": [ + "# **Bonus Problem**\n", + "\n", + "Now, Assuming that you know Back Propagation (read a bit about it, if you don't), we'll now implement an image classification model on CIFAR-10." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_4-4RceVsor_", + "outputId": "a4a827c3-7e4a-41b4-a5ff-21f778a18ba9" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2.8.0\n" + ] + } + ], + "source": [ + "\n", + "import tensorflow as tf \n", + " \n", + "# Display the version\n", + "print(tf.__version__) \n", + " \n", + "# other imports\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from tensorflow.keras.layers import Input, Conv2D, Dense, Flatten, Dropout\n", + "from tensorflow.keras.layers import GlobalMaxPooling2D, MaxPooling2D\n", + "from tensorflow.keras.layers import BatchNormalization\n", + "from tensorflow.keras.models import Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yyplk5PLEUsJ", + "outputId": "f2082c10-ba9e-495f-96be-37bee9435b00" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n", + "170500096/170498071 [==============================] - 4s 0us/step\n", + "170508288/170498071 [==============================] - 4s 0us/step\n", + "(50000, 32, 32, 3) (50000, 1) (10000, 32, 32, 3) (10000, 1)\n" + ] + } + ], + "source": [ + "# Load in the data\n", + "cifar10 = tf.keras.datasets.cifar10\n", + " \n", + "# Distribute it to train and test set\n", + "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n", + "print(x_train.shape, y_train.shape, x_test.shape, y_test.shape)\n", + "\n", + "# Reduce pixel values\n", + "x_train, x_test = x_train / 255.0, x_test / 255.0\n", + " \n", + "# flatten the label values\n", + "y_train, y_test = y_train.flatten(), y_test.flatten()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 269 + }, + "id": "qQhkATYhEkkC", + "outputId": "af8453db-0ee7-497a-e55e-176d7e0a0084" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "'''visualize data by plotting images'''\n", + "# YOUR CODE HERE\n", + "\n", + "for i in range(1,101):\n", + " plt.subplot(10,10,i)\n", + " plt.imshow(x_train[i])\n", + "plt.show()\n", + "pass\n", + "# YOUR CODE HERE" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yJgho2AEBFbx", + "outputId": "a3776025-bc07-4738-92b5-f953ee56e697" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "number of classes: 10\n", + "Model: \"model\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " input_1 (InputLayer) [(None, 32, 32, 3)] 0 \n", + " \n", + " conv2d (Conv2D) (None, 32, 32, 32) 896 \n", + " \n", + " batch_normalization (BatchN (None, 32, 32, 32) 128 \n", + " ormalization) \n", + " \n", + " max_pooling2d (MaxPooling2D (None, 16, 16, 32) 0 \n", + " ) \n", + " \n", + " conv2d_1 (Conv2D) (None, 16, 16, 64) 18496 \n", + " \n", + " batch_normalization_1 (Batc (None, 16, 16, 64) 256 \n", + " hNormalization) \n", + " \n", + " max_pooling2d_1 (MaxPooling (None, 8, 8, 64) 0 \n", + " 2D) \n", + " \n", + " flatten (Flatten) (None, 4096) 0 \n", + " \n", + " dense (Dense) (None, 64) 262208 \n", + " \n", + " dense_1 (Dense) (None, 10) 650 \n", + " \n", + "=================================================================\n", + "Total params: 282,634\n", + "Trainable params: 282,442\n", + "Non-trainable params: 192\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "\n", + "# number of classes\n", + "K = len(set(y_train))\n", + "'''\n", + " calculate total number of classes\n", + " for output layer\n", + "'''\n", + "print(\"number of classes:\", K)\n", + "''' \n", + " Build the model using the functional API\n", + " input layer\n", + "'''\n", + "\n", + "'''\n", + " YOUR CODE HERE\n", + "'''\n", + "inputs = Input(shape=x_train[0].shape)\n", + "layer = Conv2D(filters=32, kernel_size=(3, 3), activation='relu', padding='same')(inputs)\n", + "layer = BatchNormalization()(layer)\n", + "layer = MaxPooling2D((2, 2))(layer)\n", + "\n", + "layer = Conv2D(filters=64, kernel_size= (3, 3), activation='relu', padding='same')(layer)\n", + "layer = BatchNormalization()(layer)\n", + "layer = MaxPooling2D((2, 2))(layer)\n", + "\n", + "\n", + " \n", + "layer = Flatten()(layer)\n", + " \n", + " \n", + "'''Hidden layer'''\n", + "# YOUR CODE HERE\n", + "layer = Dense(64, activation='relu')(layer)\n", + "\n", + "\n", + "\n", + "\n", + "# YOUR CODE HERE\n", + " \n", + "\"\"\"last hidden layer i.e.. output layer\"\"\"\n", + "# YOUR CODE HERE\n", + "layer= Dense(K, activation='softmax')(layer)\n", + "model = Model(inputs, layer)\n", + "\n", + "# YOUR CODE HERE\n", + " \n", + "'''model description'''\n", + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "PLc4Bay65TyA" + }, + "outputs": [], + "source": [ + "# Compile\n", + "model.compile(optimizer='adam',\n", + " loss='sparse_categorical_crossentropy',\n", + " metrics=['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "U0fGsDCRsQrn", + "outputId": "69cee585-de10-43d4-99b5-8c5996aeed6c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/11\n", + "1563/1563 [==============================] - 103s 65ms/step - loss: 1.3383 - accuracy: 0.5272 - val_loss: 1.1211 - val_accuracy: 0.6097\n", + "Epoch 2/11\n", + "1563/1563 [==============================] - 101s 65ms/step - loss: 0.9496 - accuracy: 0.6665 - val_loss: 1.0015 - val_accuracy: 0.6550\n", + "Epoch 3/11\n", + "1563/1563 [==============================] - 100s 64ms/step - loss: 0.8051 - accuracy: 0.7196 - val_loss: 0.9191 - val_accuracy: 0.6869\n", + "Epoch 4/11\n", + "1563/1563 [==============================] - 100s 64ms/step - loss: 0.7029 - accuracy: 0.7526 - val_loss: 1.0068 - val_accuracy: 0.6750\n", + "Epoch 5/11\n", + "1563/1563 [==============================] - 101s 64ms/step - loss: 0.6059 - accuracy: 0.7887 - val_loss: 1.0326 - val_accuracy: 0.6684\n", + "Epoch 6/11\n", + "1563/1563 [==============================] - 100s 64ms/step - loss: 0.5244 - accuracy: 0.8160 - val_loss: 0.9674 - val_accuracy: 0.6929\n", + "Epoch 7/11\n", + "1563/1563 [==============================] - 100s 64ms/step - loss: 0.4531 - accuracy: 0.8414 - val_loss: 1.0604 - val_accuracy: 0.6812\n", + "Epoch 8/11\n", + "1563/1563 [==============================] - 100s 64ms/step - loss: 0.3886 - accuracy: 0.8637 - val_loss: 1.1855 - val_accuracy: 0.6820\n", + "Epoch 9/11\n", + "1563/1563 [==============================] - 100s 64ms/step - loss: 0.3353 - accuracy: 0.8820 - val_loss: 1.1396 - val_accuracy: 0.6960\n", + "Epoch 10/11\n", + "1563/1563 [==============================] - 100s 64ms/step - loss: 0.2915 - accuracy: 0.8973 - val_loss: 1.3699 - val_accuracy: 0.6760\n", + "Epoch 11/11\n", + "1563/1563 [==============================] - 100s 64ms/step - loss: 0.2507 - accuracy: 0.9105 - val_loss: 1.3681 - val_accuracy: 0.6848\n" + ] + } + ], + "source": [ + "# Fit\n", + "r = model.fit(\n", + " x_train, y_train, validation_data=(x_test, y_test), epochs=11)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "RDq_RE6osSh8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 283 + }, + "outputId": "0a6744d4-d9b8-4b1b-b02a-66aa40cfb7cd" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Original label is deerdog and predicted label is deerdog\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "# label mapping\n", + " \n", + "labels = '''airplane automobile bird cat deerdog frog horseship truck'''.split()\n", + " \n", + "# select the image from our test dataset\n", + "image_number = 8889\n", + " \n", + "# display the image\n", + "plt.imshow(x_test[image_number])\n", + " \n", + "# load the image in an array\n", + "n = np.array(x_test[image_number])\n", + " \n", + "# reshape it\n", + "p = n.reshape(1, 32, 32, 3)\n", + " \n", + "# pass in the network for prediction and\n", + "# save the predicted label\n", + "predicted_label = labels[model.predict(p).argmax()]\n", + " \n", + "# load the original label\n", + "original_label = labels[y_test[image_number]]\n", + " \n", + "# display the result\n", + "print(\"Original label is {} and predicted label is {}\".format(\n", + " original_label, predicted_label))" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "210094 Akshat part2.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/Assignment/Assignment_3/210094_Akshat.ipynb b/Assignment/Assignment_3/210094_Akshat.ipynb new file mode 100644 index 0000000..1bf055f --- /dev/null +++ b/Assignment/Assignment_3/210094_Akshat.ipynb @@ -0,0 +1,85 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "210094_Akshat.ipynb", + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "W3p1lJwDevvi", + "outputId": "e971f52c-03e3-4fc8-ad1f-4fab9f9cc072" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Model: \"model_3\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " input_9 (InputLayer) [(None, 28, 28)] 0 \n", + " \n", + " lstm (LSTM) (None, 64) 23808 \n", + " \n", + " dense_5 (Dense) (None, 10) 650 \n", + " \n", + "=================================================================\n", + "Total params: 24,458\n", + "Trainable params: 24,458\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "Epoch 1/5\n", + "1875/1875 [==============================] - 27s 14ms/step - loss: 0.5216 - acc: 0.8318\n", + "Epoch 2/5\n", + "1875/1875 [==============================] - 27s 14ms/step - loss: 0.1556 - acc: 0.9536\n", + "Epoch 3/5\n", + "1875/1875 [==============================] - 27s 14ms/step - loss: 0.1122 - acc: 0.9671\n", + "Epoch 4/5\n", + "1875/1875 [==============================] - 27s 14ms/step - loss: 0.0879 - acc: 0.9736\n", + "Epoch 5/5\n", + "1875/1875 [==============================] - 26s 14ms/step - loss: 0.0729 - acc: 0.9777\n", + "313/313 [==============================] - 2s 6ms/step - loss: 0.0744 - acc: 0.9782\n", + "Loss: 0.07439503818750381 Accuracy: 0.9782000184059143\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "import tensorflow.keras.layers as KL\n", + "\n", + "data=tf.keras.datasets.mnist\n", + "(x_train, y_train),(x_test,y_test)=data.load_data()\n", + "x_train=x_train/255.0\n", + "x_test=x_test/255.0\n", + "inputs=KL.Input(shape=(28,28))\n", + "x=KL.LSTM(64, activation=\"relu\")(inputs)\n", + "\n", + "output=KL.Dense(10,activation=\"softmax\")(x)\n", + "\n", + "model=tf.keras.models.Model(inputs,output)\n", + "model.summary()\n", + "model.compile(optimizer=\"adam\",loss=\"sparse_categorical_crossentropy\",metrics=[\"acc\"])\n", + "model.fit(x_train,y_train,epochs=5)\n", + "loss,acc=model.evaluate(x_test,y_test)\n", + "print(\"Loss: \",loss,\"Accuracy:\",acc)\n" + ] + } + ] +} \ No newline at end of file