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Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/ParthBakare_180101056/_

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Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/ParthBakare_180101056/week1_bonus.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best features : 3, 8\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "from sklearn.decomposition import PCA\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "data = np.loadtxt('data.txt', delimiter='\\t', skiprows=1)\n",
+    "\n",
+    "max1a = 0\n",
+    "max1b = 0\n",
+    "max2a = 0\n",
+    "max2b = 0\n",
+    "\n",
+    "max1 = 0\n",
+    "max2 = 0\n",
+    "\n",
+    "for a in range(1, 11):\n",
+    "    for b in range(a+1, 11):\n",
+    "        \n",
+    "        x1=[]\n",
+    "        \n",
+    "        x2=[]\n",
+    "        \n",
+    "        for k in range(0, data.shape[0]):\n",
+    "            if data[k][0] == np.float64(1):\n",
+    "                x1.append([data[k][a], data[k][b]])\n",
+    "                \n",
+    "            else:\n",
+    "                x2.append([data[k][a], data[k][b]])\n",
+    "        \n",
+    "        x1 = StandardScaler().fit_transform(x1)\n",
+    "        pca = PCA(n_components=1)\n",
+    "        principalComponents1 = pca.fit_transform(x1)\n",
+    "        #print(\"{}, {}\".format(a, b))\n",
+    "        #print(pca.explained_variance_ratio_)\n",
+    "        #print(x1)\n",
+    "        #print(principalComponents1)\n",
+    "        \n",
+    "        if(pca.explained_variance_ratio_ > max1):\n",
+    "            max1a = a\n",
+    "            max1b = b\n",
+    "            max1 = pca.explained_variance_ratio_\n",
+    "        \n",
+    "        x2 = StandardScaler().fit_transform(x2)\n",
+    "        pca = PCA(n_components=1)\n",
+    "        principalComponents1 = pca.fit_transform(x2)\n",
+    "        #print(\"{}, {}\".format(a, b))\n",
+    "        #print(pca.explained_variance_ratio_)\n",
+    "        #print(x1)\n",
+    "        #print(principalComponents1)\n",
+    "        \n",
+    "        if(pca.explained_variance_ratio_ > max2):\n",
+    "            max2a = a\n",
+    "            max2b = b\n",
+    "            max2 = pca.explained_variance_ratio_\n",
+    "            \n",
+    "print(\"Best features : {}, {}\".format(max1a, max1b))"
+   ]
+  }
+ ],
+ "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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/ParthBakare_180101056/week1_ques1.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 2.          3.201       5.77470148 ... -0.08273688 -0.31659793\n",
+      "   8.9705947 ]\n",
+      " [ 1.          1.066       0.51969346 ... -0.05726641  0.12953615\n",
+      "   6.40681803]\n",
+      " [ 2.          1.395       6.18460515 ... -0.21614335  0.6549017\n",
+      "  17.42562827]\n",
+      " ...\n",
+      " [ 1.          0.854       0.97744937 ... -0.14189873  0.35539943\n",
+      "   0.06023684]\n",
+      " [ 1.          0.03        0.09371179 ... -0.13902791  0.49674919\n",
+      "   0.07933982]\n",
+      " [ 2.          3.881       5.77286718 ...  0.84925859 -0.92115962\n",
+      "  17.13040574]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "data = np.loadtxt('data.txt', delimiter='\\t', skiprows=1)\n",
+    "\n",
+    "print(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}