Class 11.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gagan Chordia - 19BCE0788\n",
    "\n",
    "- K Means Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "colors = plt.rcParams['axes.prop_cycle'].by_key()['color']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class KMeans():\n",
    "    def __init__(self, k=5, max_iter=50, tol=0.001):\n",
    "        self.k = k\n",
    "        self.max_iter = max_iter\n",
    "        self.tol = tol\n",
    "        \n",
    "    def fit(self, x):\n",
    "        self.centroids = {}\n",
    "        for ii in range(self.k):\n",
    "            self.centroids[ii] = x[ii]\n",
    "        for ii in range(self.max_iter):\n",
    "            self.classifications = {}\n",
    "            for jj in range(self.k):\n",
    "                self.classifications[jj] = []\n",
    "                \n",
    "            for feature in x:\n",
    "                distances = [np.linalg.norm(feature - self.centroids[centroid]) for centroid in self.centroids]\n",
    "                classification = distances.index(min(distances))\n",
    "                self.classifications[classification].append(feature)\n",
    "            \n",
    "            prev_centroids = dict(self.centroids)\n",
    "            for classification in self.classifications:\n",
    "                self.centroids[classification] = np.average(self.classifications[classification], axis=0)\n",
    "                \n",
    "            optimized = True\n",
    "            \n",
    "            for centroid in self.centroids:\n",
    "                original_centroid = prev_centroids[centroid]\n",
    "                current_centroid = self.centroids[centroid]\n",
    "                \n",
    "                if (np.sum((current_centroid - original_centroid) / original_centroid * 100) > self.tol):\n",
    "                    print(np.sum((current_centroid - original_centroid) / original_centroid * 100))\n",
    "                    optimized = False\n",
    "                          \n",
    "                if optimized:\n",
    "                    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = load_iris()\n",
    "x = df.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "767.3263462688444\n",
      "666.3047619047618\n",
      "1.1010266329415046\n",
      "67.63094141610478\n",
      "8.307397650522443\n",
      "47.970061554742855\n",
      "5.173362139772175\n",
      "5.859897250426241\n",
      "3.005369492925997\n",
      "5.079414856579939\n",
      "1.804801987213922\n",
      "4.425999239999891\n",
      "3.5491575368446373\n",
      "6.0533309590486635\n",
      "2.45593052190444\n",
      "2.9375146612005247\n",
      "3.333051824214075\n",
      "4.03710974879181\n",
      "2.666264305377924\n",
      "2.689218176151795\n",
      "3.7131306225365273\n",
      "1.8394282892145153\n",
      "0.4617153882508516\n",
      "1.4177894959074588\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "km = KMeans(k = 3)\n",
    "km.fit(x)\n",
    "for classification in km.classifications:\n",
    "    color = colors[classification]\n",
    "    for featureset in km.classifications[classification]:\n",
    "        plt.scatter(featureset[0], featureset[1], marker=\"x\", color=color, s=50, linewidths=2)\n",
    "        \n",
    "for centroid in km.centroids:\n",
    "    plt.scatter(km.centroids[centroid][0], km.centroids[centroid][1], marker=\"o\", color=\"k\", s=50, linewidths=5)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}