7. Kmeans Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b8d14b9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fcd6f279",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = pd.read_csv('iris.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b4cffad7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>73</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>101</td>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
       "72    73            6.3           2.5            4.9           1.5   \n",
       "8      9            4.4           2.9            1.4           0.2   \n",
       "100  101            6.3           3.3            6.0           2.5   \n",
       "46    47            5.1           3.8            1.6           0.2   \n",
       "\n",
       "             Species  \n",
       "72   Iris-versicolor  \n",
       "8        Iris-setosa  \n",
       "100   Iris-virginica  \n",
       "46       Iris-setosa  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.sample(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fa62011c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(iris['SepalLengthCm'], iris['PetalLengthCm'])\n",
    "plt.xlabel(\"Sepal Length in cm\")\n",
    "plt.ylabel(\"Petal Length in cm\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5862eabb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[5.1, 1.4],\n",
       " [4.9, 1.4],\n",
       " [4.7, 1.3],\n",
       " [4.6, 1.5],\n",
       " [5.0, 1.4],\n",
       " [5.4, 1.7],\n",
       " [4.6, 1.4],\n",
       " [5.0, 1.5],\n",
       " [4.4, 1.4],\n",
       " [4.9, 1.5],\n",
       " [5.4, 1.5],\n",
       " [4.8, 1.6],\n",
       " [4.8, 1.4],\n",
       " [4.3, 1.1],\n",
       " [5.8, 1.2],\n",
       " [5.7, 1.5],\n",
       " [5.4, 1.3],\n",
       " [5.1, 1.4],\n",
       " [5.7, 1.7],\n",
       " [5.1, 1.5],\n",
       " [5.4, 1.7],\n",
       " [5.1, 1.5],\n",
       " [4.6, 1.0],\n",
       " [5.1, 1.7],\n",
       " [4.8, 1.9],\n",
       " [5.0, 1.6],\n",
       " [5.0, 1.6],\n",
       " [5.2, 1.5],\n",
       " [5.2, 1.4],\n",
       " [4.7, 1.6],\n",
       " [4.8, 1.6],\n",
       " [5.4, 1.5],\n",
       " [5.2, 1.5],\n",
       " [5.5, 1.4],\n",
       " [4.9, 1.5],\n",
       " [5.0, 1.2],\n",
       " [5.5, 1.3],\n",
       " [4.9, 1.5],\n",
       " [4.4, 1.3],\n",
       " [5.1, 1.5],\n",
       " [5.0, 1.3],\n",
       " [4.5, 1.3],\n",
       " [4.4, 1.3],\n",
       " [5.0, 1.6],\n",
       " [5.1, 1.9],\n",
       " [4.8, 1.4],\n",
       " [5.1, 1.6],\n",
       " [4.6, 1.4],\n",
       " [5.3, 1.5],\n",
       " [5.0, 1.4],\n",
       " [7.0, 4.7],\n",
       " [6.4, 4.5],\n",
       " [6.9, 4.9],\n",
       " [5.5, 4.0],\n",
       " [6.5, 4.6],\n",
       " [5.7, 4.5],\n",
       " [6.3, 4.7],\n",
       " [4.9, 3.3],\n",
       " [6.6, 4.6],\n",
       " [5.2, 3.9],\n",
       " [5.0, 3.5],\n",
       " [5.9, 4.2],\n",
       " [6.0, 4.0],\n",
       " [6.1, 4.7],\n",
       " [5.6, 3.6],\n",
       " [6.7, 4.4],\n",
       " [5.6, 4.5],\n",
       " [5.8, 4.1],\n",
       " [6.2, 4.5],\n",
       " [5.6, 3.9],\n",
       " [5.9, 4.8],\n",
       " [6.1, 4.0],\n",
       " [6.3, 4.9],\n",
       " [6.1, 4.7],\n",
       " [6.4, 4.3],\n",
       " [6.6, 4.4],\n",
       " [6.8, 4.8],\n",
       " [6.7, 5.0],\n",
       " [6.0, 4.5],\n",
       " [5.7, 3.5],\n",
       " [5.5, 3.8],\n",
       " [5.5, 3.7],\n",
       " [5.8, 3.9],\n",
       " [6.0, 5.1],\n",
       " [5.4, 4.5],\n",
       " [6.0, 4.5],\n",
       " [6.7, 4.7],\n",
       " [6.3, 4.4],\n",
       " [5.6, 4.1],\n",
       " [5.5, 4.0],\n",
       " [5.5, 4.4],\n",
       " [6.1, 4.6],\n",
       " [5.8, 4.0],\n",
       " [5.0, 3.3],\n",
       " [5.6, 4.2],\n",
       " [5.7, 4.2],\n",
       " [5.7, 4.2],\n",
       " [6.2, 4.3],\n",
       " [5.1, 3.0],\n",
       " [5.7, 4.1],\n",
       " [6.3, 6.0],\n",
       " [5.8, 5.1],\n",
       " [7.1, 5.9],\n",
       " [6.3, 5.6],\n",
       " [6.5, 5.8],\n",
       " [7.6, 6.6],\n",
       " [4.9, 4.5],\n",
       " [7.3, 6.3],\n",
       " [6.7, 5.8],\n",
       " [7.2, 6.1],\n",
       " [6.5, 5.1],\n",
       " [6.4, 5.3],\n",
       " [6.8, 5.5],\n",
       " [5.7, 5.0],\n",
       " [5.8, 5.1],\n",
       " [6.4, 5.3],\n",
       " [6.5, 5.5],\n",
       " [7.7, 6.7],\n",
       " [7.7, 6.9],\n",
       " [6.0, 5.0],\n",
       " [6.9, 5.7],\n",
       " [5.6, 4.9],\n",
       " [7.7, 6.7],\n",
       " [6.3, 4.9],\n",
       " [6.7, 5.7],\n",
       " [7.2, 6.0],\n",
       " [6.2, 4.8],\n",
       " [6.1, 4.9],\n",
       " [6.4, 5.6],\n",
       " [7.2, 5.8],\n",
       " [7.4, 6.1],\n",
       " [7.9, 6.4],\n",
       " [6.4, 5.6],\n",
       " [6.3, 5.1],\n",
       " [6.1, 5.6],\n",
       " [7.7, 6.1],\n",
       " [6.3, 5.6],\n",
       " [6.4, 5.5],\n",
       " [6.0, 4.8],\n",
       " [6.9, 5.4],\n",
       " [6.7, 5.6],\n",
       " [6.9, 5.1],\n",
       " [5.8, 5.1],\n",
       " [6.8, 5.9],\n",
       " [6.7, 5.7],\n",
       " [6.7, 5.2],\n",
       " [6.3, 5.0],\n",
       " [6.5, 5.2],\n",
       " [6.2, 5.4],\n",
       " [5.9, 5.1]]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = iris['SepalLengthCm'].values\n",
    "Y = iris['PetalLengthCm'].values\n",
    "\n",
    "data = []\n",
    "\n",
    "for i in range(len(X)):\n",
    "    data.append( [ X[i], Y[i] ])\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6a9d7a66",
   "metadata": {},
   "outputs": [],
   "source": [
    "# RANDOM CENTROIDS\n",
    "\n",
    "k = 5\n",
    "\n",
    "centroids = []\n",
    "\n",
    "for i in range(k):\n",
    "    rand = np.random.randint(0,len(X)-1)\n",
    "    centroids.append( [ X[rand], Y[rand] ] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b2d31a80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[5.1, 1.4], [6.4, 4.3], [4.6, 1.4], [4.9, 1.5], [6.5, 5.5]]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6b9c5842",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.1 1.4\n",
      "4.9 1.4\n",
      "4.7 1.3\n",
      "4.6 1.5\n",
      "5.0 1.4\n",
      "5.4 1.7\n",
      "4.6 1.4\n",
      "5.0 1.5\n",
      "4.4 1.4\n",
      "4.9 1.5\n",
      "5.4 1.5\n",
      "4.8 1.6\n",
      "4.8 1.4\n",
      "4.3 1.1\n",
      "5.8 1.2\n",
      "5.7 1.5\n",
      "5.4 1.3\n",
      "5.1 1.4\n",
      "5.7 1.7\n",
      "5.1 1.5\n",
      "5.4 1.7\n",
      "5.1 1.5\n",
      "4.6 1.0\n",
      "5.1 1.7\n",
      "4.8 1.9\n",
      "5.0 1.6\n",
      "5.0 1.6\n",
      "5.2 1.5\n",
      "5.2 1.4\n",
      "4.7 1.6\n",
      "4.8 1.6\n",
      "5.4 1.5\n",
      "5.2 1.5\n",
      "5.5 1.4\n",
      "4.9 1.5\n",
      "5.0 1.2\n",
      "5.5 1.3\n",
      "4.9 1.5\n",
      "4.4 1.3\n",
      "5.1 1.5\n",
      "5.0 1.3\n",
      "4.5 1.3\n",
      "4.4 1.3\n",
      "5.0 1.6\n",
      "5.1 1.9\n",
      "4.8 1.4\n",
      "5.1 1.6\n",
      "4.6 1.4\n",
      "5.3 1.5\n",
      "5.0 1.4\n",
      "7.0 4.7\n",
      "6.4 4.5\n",
      "6.9 4.9\n",
      "5.5 4.0\n",
      "6.5 4.6\n",
      "5.7 4.5\n",
      "6.3 4.7\n",
      "4.9 3.3\n",
      "6.6 4.6\n",
      "5.2 3.9\n",
      "5.0 3.5\n",
      "5.9 4.2\n",
      "6.0 4.0\n",
      "6.1 4.7\n",
      "5.6 3.6\n",
      "6.7 4.4\n",
      "5.6 4.5\n",
      "5.8 4.1\n",
      "6.2 4.5\n",
      "5.6 3.9\n",
      "5.9 4.8\n",
      "6.1 4.0\n",
      "6.3 4.9\n",
      "6.1 4.7\n",
      "6.4 4.3\n",
      "6.6 4.4\n",
      "6.8 4.8\n",
      "6.7 5.0\n",
      "6.0 4.5\n",
      "5.7 3.5\n",
      "5.5 3.8\n",
      "5.5 3.7\n",
      "5.8 3.9\n",
      "6.0 5.1\n",
      "5.4 4.5\n",
      "6.0 4.5\n",
      "6.7 4.7\n",
      "6.3 4.4\n",
      "5.6 4.1\n",
      "5.5 4.0\n",
      "5.5 4.4\n",
      "6.1 4.6\n",
      "5.8 4.0\n",
      "5.0 3.3\n",
      "5.6 4.2\n",
      "5.7 4.2\n",
      "5.7 4.2\n",
      "6.2 4.3\n",
      "5.1 3.0\n",
      "5.7 4.1\n",
      "6.3 6.0\n",
      "5.8 5.1\n",
      "7.1 5.9\n",
      "6.3 5.6\n",
      "6.5 5.8\n",
      "7.6 6.6\n",
      "4.9 4.5\n",
      "7.3 6.3\n",
      "6.7 5.8\n",
      "7.2 6.1\n",
      "6.5 5.1\n",
      "6.4 5.3\n",
      "6.8 5.5\n",
      "5.7 5.0\n",
      "5.8 5.1\n",
      "6.4 5.3\n",
      "6.5 5.5\n",
      "7.7 6.7\n",
      "7.7 6.9\n",
      "6.0 5.0\n",
      "6.9 5.7\n",
      "5.6 4.9\n",
      "7.7 6.7\n",
      "6.3 4.9\n",
      "6.7 5.7\n",
      "7.2 6.0\n",
      "6.2 4.8\n",
      "6.1 4.9\n",
      "6.4 5.6\n",
      "7.2 5.8\n",
      "7.4 6.1\n",
      "7.9 6.4\n",
      "6.4 5.6\n",
      "6.3 5.1\n",
      "6.1 5.6\n",
      "7.7 6.1\n",
      "6.3 5.6\n",
      "6.4 5.5\n",
      "6.0 4.8\n",
      "6.9 5.4\n",
      "6.7 5.6\n",
      "6.9 5.1\n",
      "5.8 5.1\n",
      "6.8 5.9\n",
      "6.7 5.7\n",
      "6.7 5.2\n",
      "6.3 5.0\n",
      "6.5 5.2\n",
      "6.2 5.4\n",
      "5.9 5.1\n"
     ]
    }
   ],
   "source": [
    "for point in data:\n",
    "    print(point[0], point[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c7ef425a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.9"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centroids[3][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3c59b3e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0,\n",
       " 3,\n",
       " 2,\n",
       " 2,\n",
       " 0,\n",
       " 0,\n",
       " 2,\n",
       " 3,\n",
       " 2,\n",
       " 3,\n",
       " 0,\n",
       " 3,\n",
       " 3,\n",
       " 2,\n",
       " 0,\n",
       " 0,\n",
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       " 0,\n",
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       " 2,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 0,\n",
       " 0,\n",
       " 3,\n",
       " 3,\n",
       " 0,\n",
       " 0,\n",
       " 0,\n",
       " 3,\n",
       " 0,\n",
       " 0,\n",
       " 3,\n",
       " 2,\n",
       " 0,\n",
       " 0,\n",
       " 2,\n",
       " 2,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 0,\n",
       " 2,\n",
       " 0,\n",
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       " 4,\n",
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       " 3,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
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       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
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       " 1,\n",
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       " 1,\n",
       " 1,\n",
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       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 3,\n",
       " 1,\n",
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       " 4,\n",
       " 4,\n",
       " 4,\n",
       " 4,\n",
       " 4,\n",
       " 1,\n",
       " 4,\n",
       " 4,\n",
       " 4,\n",
       " 4,\n",
       " 4,\n",
       " 4,\n",
       " 4,\n",
       " 4,\n",
       " 4,\n",
       " 4,\n",
       " 4,\n",
       " 4,\n",
       " 4,\n",
       " 4,\n",
       " 1,\n",
       " 4,\n",
       " 1,\n",
       " 4,\n",
       " 4,\n",
       " 1,\n",
       " 1,\n",
       " 4,\n",
       " 4,\n",
       " 4,\n",
       " 4,\n",
       " 4,\n",
       " 4,\n",
       " 4,\n",
       " 4,\n",
       " 4,\n",
       " 4,\n",
       " 1,\n",
       " 4,\n",
       " 4,\n",
       " 4,\n",
       " 4,\n",
       " 4,\n",
       " 4,\n",
       " 4,\n",
       " 4,\n",
       " 4,\n",
       " 4,\n",
       " 4]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ASSIGN LABELS TO POINTS BY EUCL. DIST\n",
    "\n",
    "#j=0\n",
    "\n",
    "labels = []\n",
    "\n",
    "for point in data:\n",
    "    min_dist = float('inf') # +infinity\n",
    "    label = None\n",
    "    \n",
    "    for i in range(k):\n",
    "        new_dist = np.sqrt( (centroids[i][0] - point[0])**2 + (centroids[i][1] - point[1])**2 )\n",
    "        # j=j+1\n",
    "        if new_dist < min_dist:\n",
    "            min_dist = new_dist\n",
    "            label = i\n",
    "        \n",
    "    labels.append(label)\n",
    "\n",
    "# print(j)  \n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5400b0d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[24, 51, 10, 18, 47]\n",
      "[[5.279166666666667, 1.4541666666666666], [5.927450980392156, 4.3235294117647065], [4.51, 1.3000000000000003], [4.916666666666666, 1.7555555555555555], [6.678723404255319, 5.612765957446807]]\n",
      "\n",
      "\n",
      "[24, 51, 10, 18, 47]\n"
     ]
    }
   ],
   "source": [
    "# UPDATE CENTROIDS\n",
    "\n",
    "new_centroids = [[0,0] for i in range(k)]\n",
    "counts = [0]*k\n",
    "# we can't directly access its location (in below function), without shaping it\n",
    "\n",
    "for point, label in zip(data, labels):\n",
    "    # point - (x,y); data = badha (x,y) - [(x1,y1), (x2,y2)]\n",
    "    # labels have 150 cluster number according to its point(x,y)\n",
    "    \n",
    "    new_centroids[label][0] += point[0]\n",
    "    new_centroids[label][1] += point[1]\n",
    "    \n",
    "    counts[label] += 1\n",
    "    \n",
    "    # count number of point in particular clusters\n",
    "print(counts)\n",
    "    \n",
    "for i in range(k):\n",
    "    if counts[i]==0:\n",
    "        continue\n",
    "    new_centroids[i][0] = new_centroids[i][0]/counts[i]\n",
    "    new_centroids[i][1] = new_centroids[i][1]/counts[i]\n",
    "    \n",
    "print(new_centroids)\n",
    "print(\"\\n\")\n",
    "print(counts)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fc2f9d68",
   "metadata": {},
   "outputs": [],
   "source": [
    "def should_stop(old_centroids, new_centroids, k, threshold=10):\n",
    "    total_movement = 0\n",
    "    \n",
    "    for i in range(k):\n",
    "        total_movement += np.sqrt( (old_centroids[i][0] - new_centroids[i][0])**2 + (old_centroids[i][1] - new_centroids[i][1])**2 )\n",
    "\n",
    "    return total_movement < threshold\n",
    "    # stop when total_movement goes below threshold(10 to pow -5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2176147f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def k_means(data, k):\n",
    "    centroids = []\n",
    "\n",
    "    for i in range(k):\n",
    "        rand = np.random.randint(0,len(X)-1)\n",
    "        centroids.append( [ X[rand], Y[rand] ] )\n",
    "        \n",
    "    while True:\n",
    "        old_centroids = centroids\n",
    "        \n",
    "        \n",
    "        # Assigns Lables\n",
    "        \n",
    "        labels = []\n",
    "\n",
    "        for point in data:\n",
    "            min_dist = float('inf') # +infinity\n",
    "            label = None\n",
    "\n",
    "            for i in range(k):\n",
    "                new_dist = np.sqrt( (centroids[i][0] - point[0])**2 + (centroids[i][1] - point[1])**2 )\n",
    "                if new_dist < min_dist:\n",
    "                    min_dist = new_dist\n",
    "                    label = i\n",
    "                    \n",
    "            labels.append(label)\n",
    "        \n",
    "        \n",
    "        # update centroids\n",
    "        \n",
    "        new_centroids = [[0,0] for i in range(k)]\n",
    "        counts = [0]*k\n",
    "\n",
    "        for point, label in zip(data, labels):\n",
    "            new_centroids[label][0] += point[0]\n",
    "            new_centroids[label][1] += point[1]\n",
    "\n",
    "            counts[label] += 1\n",
    "\n",
    "        for i in range(k):\n",
    "            new_centroids[i][0] = new_centroids[i][0]/counts[i]\n",
    "            new_centroids[i][1] = new_centroids[i][1]/counts[i]\n",
    "          \n",
    "        \n",
    "        # Should break or not\n",
    "        \n",
    "#         if should_stop(old_centroids, new_centroids, k):\n",
    "        break\n",
    "        \n",
    "    return labels, new_centroids\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "98bca1e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = []\n",
    "\n",
    "for i in range(len(X)):\n",
    "    data.append( [ X[i], Y[i] ])\n",
    "    \n",
    "k = 5\n",
    "\n",
    "final_labels = []\n",
    "final_cents = []\n",
    "final_labels, final_cents = k_means(data, k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f9b99acf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color = ['red', 'green', 'orange', 'magenta', 'cyan']\n",
    "lname = ['Cluster 1','Cluster 2','Cluster 3','Cluster 4','Cluster 5']\n",
    "\n",
    "plt.scatter(X, Y)\n",
    "\n",
    "for i in range(k):\n",
    "    plt.scatter(final_cents[i][0], final_cents[i][1], s=300, c = color[i], label = lname[i])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a198671",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1eb87e6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c77d8de",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "07227a06",
   "metadata": {},
   "outputs": [],
   "source": [
    "def should_stop(old_centroids, new_centroids, k, threshold=2):\n",
    "    total_movement = 0\n",
    "    \n",
    "    for i in range(k):\n",
    "        total_movement += np.sqrt( (old_centroids[i][0] - new_centroids[i][0])**2 + (old_centroids[i][1] - new_centroids[i][1])**2 )\n",
    "\n",
    "    return total_movement < threshold\n",
    "    # stop when total_movement goes below threshold(10 to pow -5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "63f3df06",
   "metadata": {},
   "outputs": [],
   "source": [
    "def k_means(data, k):\n",
    "    centroids = []\n",
    "\n",
    "    for i in range(k):\n",
    "        rand = np.random.randint(0,len(X)-1)\n",
    "        centroids.append( [ X[rand], Y[rand] ] )\n",
    "        \n",
    "    while True:\n",
    "            \n",
    "        old_centroids = centroids\n",
    "\n",
    "        labels = []\n",
    "\n",
    "        for point in data:\n",
    "            min_dist = float('inf') # +infinity\n",
    "            label = None\n",
    "\n",
    "            for i in range(k):\n",
    "                new_dist = np.sqrt( (centroids[i][0] - point[0])**2 + (centroids[i][1] - point[1])**2 )\n",
    "                if new_dist < min_dist:\n",
    "                    min_dist = new_dist\n",
    "                    label = i\n",
    "\n",
    "            labels.append(label)\n",
    "\n",
    "        new_centroids = [[0,0] for i in range(k)]\n",
    "        counts = [0]*k\n",
    "\n",
    "        for point, label in zip(data, labels):\n",
    "            new_centroids[label][0] += point[0]\n",
    "            new_centroids[label][1] += point[1]\n",
    "\n",
    "            counts[label] += 1\n",
    "\n",
    "        for i in range(k):\n",
    "            new_centroids[i][0] = new_centroids[i][0]/counts[i]\n",
    "            new_centroids[i][1] = new_centroids[i][1]/counts[i]\n",
    "        \n",
    "        if should_stop(old_centroids, new_centroids, k):\n",
    "            break\n",
    "\n",
    "    return labels, new_centroids\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "90d4fa3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = []\n",
    "\n",
    "for i in range(len(X)):\n",
    "    data.append( [ X[i], Y[i] ])\n",
    "    \n",
    "k = 5\n",
    "\n",
    "final_labels = []\n",
    "final_cents = []\n",
    "final_labels, final_cents = k_means(data, k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "15f68fdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color = ['red', 'green', 'orange', 'magenta', 'cyan']\n",
    "lname = ['Cluster 1','Cluster 2','Cluster 3','Cluster 4','Cluster 5']\n",
    "\n",
    "plt.scatter(X, Y)\n",
    "\n",
    "for i in range(k):\n",
    "    plt.scatter(final_cents[i][0], final_cents[i][1], s=300, c = color[i], label = lname[i])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60ad7dbe",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}