k-means_clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math as ma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.array([(-10,-5),(-6,1),(2,3),(7,8),(9,13),(21,25),(27,28),(29,35),(99,35),(45,11),(12,19),(50,60),(90,100),(99,150),(89,103),(77,107),(6,19),(9,33),(40,39),(30,41)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# view data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-10,  -5],\n",
       "       [ -6,   1],\n",
       "       [  2,   3],\n",
       "       [  7,   8],\n",
       "       [  9,  13],\n",
       "       [ 21,  25],\n",
       "       [ 27,  28],\n",
       "       [ 29,  35],\n",
       "       [ 99,  35],\n",
       "       [ 45,  11],\n",
       "       [ 12,  19],\n",
       "       [ 50,  60],\n",
       "       [ 90, 100],\n",
       "       [ 99, 150],\n",
       "       [ 89, 103],\n",
       "       [ 77, 107],\n",
       "       [  6,  19],\n",
       "       [  9,  33],\n",
       "       [ 40,  39],\n",
       "       [ 30,  41]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>19</th>\n",
       "      <td>30</td>\n",
       "      <td>41</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1\n",
       "0  -10   -5\n",
       "1   -6    1\n",
       "2    2    3\n",
       "3    7    8\n",
       "4    9   13\n",
       "5   21   25\n",
       "6   27   28\n",
       "7   29   35\n",
       "8   99   35\n",
       "9   45   11\n",
       "10  12   19\n",
       "11  50   60\n",
       "12  90  100\n",
       "13  99  150\n",
       "14  89  103\n",
       "15  77  107\n",
       "16   6   19\n",
       "17   9   33\n",
       "18  40   39\n",
       "19  30   41"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>27</td>\n",
       "      <td>28</td>\n",
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      "text/plain": [
       "    0   1\n",
       "6  27  28"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[6:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "0",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3077\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3078\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3079\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 0",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-90-914663236446>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdfx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mdfy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2686\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2687\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2688\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2689\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2690\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2693\u001b[0m         \u001b[1;31m# get column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2694\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2695\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2696\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2697\u001b[0m         \u001b[1;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[1;34m(self, item)\u001b[0m\n\u001b[0;32m   2487\u001b[0m         \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2488\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2489\u001b[1;33m             \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2490\u001b[0m             \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2491\u001b[0m             \u001b[0mcache\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, item, fastpath)\u001b[0m\n\u001b[0;32m   4113\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4114\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4115\u001b[1;33m                 \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4116\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4117\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3078\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3079\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3080\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3081\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3082\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 0"
     ]
    }
   ],
   "source": [
    "dfx = df[0]\n",
    "dfy = df[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# #visualize data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x6265130>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(dfx, dfy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the numberof clusters to form: 3\n"
     ]
    }
   ],
   "source": [
    "k = int(input(\"Enter the numberof clusters to form: \"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n",
      "20\n"
     ]
    }
   ],
   "source": [
    "a = {}\n",
    "div = round(len(df)/k)\n",
    "m={}\n",
    "print(div)\n",
    "print(len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1: [], 2: [], 3: []}\n",
      "{1: array([[21, 25]]), 2: array([[ 90, 100]]), 3: array([[30, 41]])}\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,k+1):\n",
    "    a[i] = []\n",
    "    m[i] = df.loc[div*i-2:div*i-2].values\n",
    "print(a)\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# functions to find distance and centroid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dist(med, point):\n",
    "    #print(med)\n",
    "    #print(point)\n",
    "    dist = ma.sqrt((med[0][0]-point[0][0])**2+(med[0][1]-point[0][1])**2)\n",
    "    return dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def centroid(p, key):\n",
    "    x = 0\n",
    "    y=0\n",
    "    for i in p:\n",
    "        #print (i)\n",
    "        x += i[0][0]/len(p)\n",
    "        y += i[0][1]/len(p)\n",
    "    #print(x)\n",
    "    #print(y)\n",
    "    m[key] = np.array([[x,y]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter to continue\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter to continue\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter to continue\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter to continue\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for _ in range(0,200):\n",
    "    for key in a:\n",
    "        a[key] = []\n",
    "    for i in range(0,len(df)):\n",
    "        low = None\n",
    "        for ke in m:\n",
    "            if low is None:\n",
    "                low= dist(m[ke], df.loc[i:i].values)\n",
    "                temp = ke\n",
    "            elif (dist(m[ke], df.loc[i:i].values) < low):\n",
    "                low = dist(m[ke], df.loc[i:i].values)\n",
    "                temp = ke\n",
    "        a[temp].append(df.loc[i:i].values)\n",
    "    \n",
    "    for key in a:\n",
    "        centroid(a[key], key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m[1][:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key in a:\n",
    "    a[key] = np.concatenate(a[key])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a[1][:,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "df1x = a[1][:,0]\n",
    "df1y = a[1][:,1]\n",
    "df2x = a[2][:,0]\n",
    "df2y = a[2][:,1]\n",
    "df3x = a[3][:,0]\n",
    "df3y = a[3][:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range (1,k+1):\n",
    "    plt.plot(a[i][:,0],a[i][:,1])\n",
    "    plt.scatter(m[i][:,0],m[i][:,1])\n",
    "    #plt.scatter(m[i][:,0],m[i][:,1], 'o')"
   ]
  },
  {
   "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}