abc_clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['target_names', 'data', 'target', 'DESCR', 'feature_names']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display\n",
    "data = pd.DataFrame(data=iris.get('data'), columns=iris.get('feature_names'))\n",
    "display(data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Partitioned Data')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,7))\n",
    "\n",
    "x = MinMaxScaler().fit_transform(data.values)\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.scatter(x[:,0], x[:,1])\n",
    "plt.title('Original Data')\n",
    "\n",
    "km = KMeans(n_clusters=2)\n",
    "km.fit(x)\n",
    "target = km.predict(x)\n",
    "colors = {0: 'r', 1:'g'}\n",
    "\n",
    "plt.subplot(122)\n",
    "for instance, tg in zip(x, target):\n",
    "    plt.scatter(instance[0], instance[1], color=colors[tg])\n",
    "plt.title('Partitioned Data')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OiE8iFklquYiIpE9+Ihs55+YTmzTacNkNDf78OnB6eksTyYzikcUtzvEoHlnsQzUiIrkloeAhEiZ1E0hLXyilxtUQsQjFI4s1sVRExAPtfqslU/StFhERkfBI57daREQkpGpra9m8eXP7G4qkiYKHiEgOcc7x6quv8utf/5pzzjmHnj17Mnjw4PpOrSKZpjkeIiIh5pzjzTffZOnSpZSXl/Pss8+yZ8+eRtv069ePvXv3+lSh5BoFDxGREHHOsX79epYuXcq8efNYsWIFu3fvbrRNr169OPPMM5kyZQpnnXUWn/nMZzAznyqWXKPgISKS5TZu3MiyZcuYN28ey5Yta9anpGfPnpxxxhlMmTKFs88+m6OPPlpBQ3yj4CEikmXeeecdli1bRkVFBUuWLKFp083u3btz2mmn1QeNoUOHKmhIYCh4SEaNe2AcS95eUn9/7BFjWXzpYh8rCray1WVMXzKdTbs3MbjXYGaPnc3U4VP9Lkt89t5777Fs2TLmz5/P4sWLeffddxut79q1KyeffDLnn38+Y8eOZfjw4eTl6bsDgVZWBtOnw6ZNMHgwzJ4NUzPwu+7VfpKg4CEZ0zR0ACx5ewnjHhin8NGCstVlFJcXs+/gPgA27t5IcXnsaqoKH7llx44dPPXUUyxYsIDKykq2bNnSaH2XLl0YNWoUU6ZMYezYsZxwwglEIrrkf9YoK4PiYtgX+11n48bYfUhvKPBqP0nSBcQkY2xm66d23U/VyqepotuL2Lh7Y7Plhb0K2fCDDd4XJJ7ZtWsXK1asYOHChSxcuJANGzY0Wt+pUydOOOGE+jMaI0eOpFOnTv4UKx1XVBQLAU0VFkKT1z4r9hOX6AXEdMZDJCA27d6U1HLJXv/97395+umnqaysZMGCBaxbt67R+vz8fI4//nimTJnCuHHjOOmkk+jcubNP1UrabWrld7q15UHfT5IUPEQCYnCvwS2e8Rjca7AP1Ug67d27l2effZZFixYxb9481qxZQ8OzzXl5eQwbNozzzjuPc845h1NPPZWuXbv6WHHidu/eTUlJCdOnT2fYsGF+l5MdBg9u+UzE4DT/rnu1nyQpeEjGjD1ibLM5HnXLpbnZY2c3muMBEO0UZfbY2T5WJanYv38///jHP3jyySeZN28er7/+OrW1tfXrzYxjjz2WyZMnc+6553LaaafRvXt3HytOTW1tLf/zP//Dk08+yY4dO1i0aJHfJWWH2bMbz70AiEZjy7NxP0lS8JCMWXzpYn2rJQl1E0j1rZbs89FHH/H888+zePFiysvLefXVV6murq5fb2YMGTKESZMmMX78eM444wx69uzpY8Xp8fOf/5wnn3ySbt26cffdd/tdTvaom9iZ6W+beLWfJGlyqYhIkg4ePEhVVRVLlizhiSee4OWXX+bgwYONtvnMZz7DxIkTGT9+PF/4whc49NBDfao2MxYtWsSECRNwzlFeXs55553nd0niM00uFRFJk+rqal566SWWLFlCeXk5VVVVfPzxx422GTRoEOPHj2fixImceeaZ9O3b16dqM2/jxo186UtfwjnHddddp9AhSVHwEBFpora2lpdffpmlS5cyd+5c/vnPf3LgwIFG2xQUFHDOOecwadIkxowZw6c+9SmfqvXWgQMHmDBhAnv37mX06NHM9nm+gGQfBQ8RyXnOOV577TWWLFnC3Llzee6559jXcEIecNhhhzF27FgmT57MmDFjKCgo8Klaf11++eW88cYbHHbYYfz973/XFVIlaQoeIpJzEmkV37dvX84++2zOO+88xowZw2Cfv4IYBPfccw8PPvgg+fn5LFiwgD59+vhdkmQhBQ8RCb1EWsUfeuihjVrFH3HEEWqs1sA///lPrrzySgDuvfdeTjjhBJ8rkmyl4CEiobRhw4b6VvHLly9vsVX86NGjOe+88zj77LMZMmSIgkYrtm/fzqRJk6ipqeFb3/oWl112md8lSRZT8BCRUKhrFT9v3jyWLl3aYqv4008/vb5V/LHHHqugkYDq6mrOP/983n//fYYPH67rdUiHKXhIRnnV5j3Z/aj9fPbbtm0by5cvb7VVfLdu3Rq1ij/uuOM0ETIF1157Lc899xyHHHIICxYsUM8Y6TAFD8kYr9q8J7sftZ/PTjt27GD58uUsWLCARYsWtdgq/sQTT+T888/n7LPP5vOf/7xaxXfQ3/72N26//XbMjLlz5zJw4EC/S5IQ0JVLJWO8avOe7H7Ufj477Nq1i6eeeqq+VfzGJs2uOnXqxIgRIxq1is/P1/+l0uWNN97g85//PB999BE33ngjP/zhD/0uSQJOVy4V33nV5j3Z/aj9fDAl0ir+c5/7XH2r+BNPPFGn/TNkz549jB8/no8++ojzzjuPa6+91u+SJEQUPCRjvGrznux+1H4+GPbu3cszzzzDokWLqKioaLFV/HHHHVffKv6UU07Jmlbx2cw5x8UXX8ymTZsoLCzkoYce0iRcSSsFD8kYr9q8J7sftZ/3R12r+EWLFjFv3jz+/e9/N2oVn5eX16xVfDQa9bHi3HTTTTdRUVFBly5dqKyspEePHn6XJCGj4CEZ41Wb92T3o/bz3mjaKn716tXU1NTUrzczjj766PqgccYZZ+gfOZ8tX76c66+/HoAHH3yQz372sz5XJGGkyaUikhYHDx5k1apV9f1OWmsVP2nSpPpW8b169fKpWmlqy5YtDB06lD179nDVVVdxyy23+F2SZBlNLhWRjKqurubFF1+sDxovvvhis1bxgwcPZsKECUyYMIEzzzxTvT0C6qOPPmLSpEns2bOHU089lZtuusnvkiTEFDxEJCGJtIofOHAg5557LhMnTmTMmDH079/fp2olGSUlJaxevZq+ffvyxBNP6PonklEKHiLSotraWl577bX6oNFSq/gBAwYwbtw4Jk2axJgxY/j0pz/tU7WSqj/96U/86U9/IhKJsGDBAoVFyTgFDxEBYl+jfOONNxq1iv/www8bbdOvXz/OPvtsJk+ezFlnncWgQYN8qlbS4aWXXqK4OHbV3t/+9receOKJPlckuUDBQyRHOedYt25dfav4p59+ulmr+N69ezNmzBjOO++8+lbxEg47d+5k4sSJVFdX87WvfY3vfOc7fpckOULBQySHtNcq/pBDDmnUKv6oo47SxaNCqLa2li9+8Yu89957HHvssfzhD3/wuyTJIQoeHghTJ9QwHUsu2LJlC8uWLaOioqLNVvF1jdWOOeYYBY0cMH36dJ5++mm6d+/OggUL/L0ibFkZTJ8OmzbB4MEwezZM1d8pYabgkWFh6oQapmMJq23btrFs2bL6VvHbtm1rtL5bt26ccsop9UFDreJzT3l5Ob/61a8wMx577DEKCwv9K6asDIqLoW7S8saNsfug8BFiuoBYhoWpE2qYjiUsGraKr6ys5J133mm0vkuXLpx00kn1QeNzn/ucviqZw9atW8fw4cM5cOAAM2fO5IYbbvC3oKKiWNhoqrAQNmzwuhrpIF1ALCDC1Ak1TMeSrdprFd+5c+dGreJHjBihVvECwL59+5gwYQIHDhzg3HPP5Sc/+YnfJcU+XklmuYSC/kbKsDB1Qg3TsWSL//73v6xYsaK+Vfz69esbrW/YKv6cc87hxBNPpFOnTj5VK0HlnOPrX/8669evZ+DAgTzyyCPBmMszeHDLZzwG6++UMFPwyLAwdUIN07EE1Ycffsizzz5b38F17dq1jVrFRyKRZq3iu3Tp4mPFkg1uv/12HnvsMTp37kxlZWVweuTMnt14jgdANBpbLqGl4JFhYeqEGqZjCYr9+/ezcuVKnnzyyVZbxQ8dOrQ+aJx66qlqFS9JefbZZ7nmmmsAuP/++xk2bJjPFTVQN4FU32rJKZpcKuKhjz76iOeee66+Vfyrr77aZqv4008/Xa3iJWXbtm3j2GOP5YMPPuC73/0ud955p98lSYhpcqlIANS1il+8eHF9q/jq6upG2xx55JFMnDhRreIlrQ4ePMjkyZP54IMPGDlyJLfddpvfJYkACh4iaZVIq/jCwsL6VvGjR49Wq3jJiO9///u8+OKL9O7dm4qKCk06lsBQ8BDpgJqamkat4letWtViq/jx48czceJEzjzzTHX/lIwrKyvjnnvuIS8vj4qKCg477DC/SxKpp+AhkoTa2lpeffXVRq3i9+/f32gbtYoXP61evZpvfvObANx2222ceuqpPlck0piCh0gbGraKnzt3LitXrmzWKr5///6NWsUffvjhPlUruW737t1MmDCBgwcPcuGFF/K9733P75JEmlHwEGmgYav48vJynn76af773/822qZPnz6NWsUXFRX5U6xIA7W1tVx44YVs3bqVo446ivvvvz8YFwkTaULBQ5IyrWIapS+UUuNqiFiE4pHF3DX5rrRtD953wH377bcbtYrftWtXo/WHHHJIbG7GCf1ZWL2QrZ228sKhL/ClUV9S6JDAmDVrFosXL6Zbt24sXLhQ13uRwFLwkIRNq5jG3VV319+vcTX191sKE8luD950wK1rFT9v3jyWLl3Kjh07Gq3v0aMHp59+OlOmTKlvFf/gqw/G6spTZ14JnsrKSmbOnAnAo48+ypFHHulzRSKt0wXEpJldu3axc+fOZn955c/Kp8bVNNs+YhGqb6hutjx/Vj4179dAN2K3draH1Dvg1tTU8Mgjj3DDDTfw4x//mG984xv169prFR+NRhu1ih82bFizVvHqzCtBtWHDBo477jj27t3Lj370I2brcuPiE11ATFKya9cuxo0bx7Zt21i+fDlDhgypX9dS6Ghz+Y4auA/oAVxKffhobXtIvgNudXU1ZWVlTJ8+nd27d/Phhx/yhz/8gWg0yoIFC1i0aFGLreJPPvnk+qBx/PHHt9sqXp15JYj279/PhAkT2Lt3L2eeeSY/+9nP/C5JpF0KHlKvLnS8+OKLAIwZM6ZR+IhYpNUzHk2tXbs2Fjr2xG8PUB8+Wtq+TqIdcD/++GPuu+8+brjhBvbu3dvomyYrV67k6aefrr/fuXNnRo4cyZQpU1JuFa/OvBI0zjm+9a1v8eabbzJgwAD+/ve/NztTJxJEepcK0Dx0AGzdupUxY8bEQgRQPLK4xcc2Xb527VrGjBkTCxx13iUWPva3/jwQ64Ab7dR4UlzDDrgHDhzgzjvvZODAgVx11VW89957zb7e2rlzZ4YOHcrMmTN59tln+fDDD1m5ciXXX389J510UtKhI5G6RLx2zz338PDDD5Ofn8/ChQvp3bu33yWJJMY558tt5MiRToJj3bp1rqCgwAHNbgUFBW7NmjXOOedK5pW4yMyIYwYuMjPiSuaVNHqeNWvWtPo89MR97d6vtVvLnFfmuMLbCp3NMFd4W6Gb88oct3fvXnfzzTe7Qw891HXv3r3l54/funTp4m699da0j1FLdYn44fnnn3eRSMQB7r777vO7HBHnnHNAlUvg339NLpV6dWcqtm7d2mxdQUFBszkf6X58a8rLy7n00kv54IMP6NSpE9FotP4NXFtbS3V1NdXV1dTW1tK5c2e6du3KVVddxQ033JD0vkSCbvv27Rx77LG8//77XH755fz+97/3uyQRIPHJpQoe0kiq4SFToQPgtdde4/nnn6dHjx5079692a1ueZcuXXTBJAm16upqzjjjDJ5//nmOP/54Vq1aRefOnf0uSwRIc/AwswnAHUAE+L1z7lctbPMVYAax090vO+e+1tZzKngEV7IhIpOhQ0Q+8YMf/IA77riDQw45hNdff52BAwf6XZJIvUSDR7uTS80sAvwWmAgMBS42s6FNthkCXA+c7pwbBvwgpaolEIYMGcLy5cspKChotq7phFOFDhFvPProo9xxxx2YGXPnzlXokKyVyLdaTgLWOefecs59DDwMXNBkmyuA3zrndgE45/6T3jLFa4mEj/nz5yt0iHjgjTfe4JJLLgHgxhtv5Mwzz/S5IpHUJRI8BgKbG9zfEl/W0NHA0Wb2rJk9F/9ophkzKzazKjOr2r59e2oVi2faCx+TJ09W6BDJsD179nDuuefy8ccfM2XKFK655hq/SxLpkHRdxyMfGAKMAS4G7jWzQ5tu5Jwrdc6Ncs6N6t+/f5p2LZnUVvhoiUKHSPo457jooovYvHkzRUUQHC+fAAAgAElEQVRFPPjgg5pALVkvkeDxDjCowf3D48sa2gLMdc4ddM69DawhFkSEWOOzotuLyJuZR9HtRZStLsvIfqZVTCN/Vj4208iflc+0imlped5Ew0dLoSOVY/divDI1VqFVVgZFRZCXF/tZloH3cLL78KImn/3qV79iwYIFdO3alcrKSnr06OF3SSId196FPoidzXgLOALoDLwMDGuyzQTg/vif+xH7aKZvW8+bKxcQm/PKHBedHXXMoP4WnR1N+8WnSuaVNNpH3a3pBb46YtJPJ7V54a6KiopG26dy7F6MlxdjFSpz5jgXjToHn9yi0dhyv/bhRU0+W7p0qTMzB7jHHnvM73JE2kU6LyBmZpOA24l9nfaPzrnZZjYrvpO5Fjv3d0s8gNQAs51zD7f1nLnydVqvupom2zk2WWvXruXokUc3vgx6E03PeKRy7F6MV6bHKnSKimBj89eEwkLYsMGffXhRk482b97MsGHD2LNnD1dffTU333yz3yWJtCut3Wmdc/OB+U2W3dDgzw64Kn6TBrzqapps59hktNh7pQV133apCx+pHLsX45XJsQqlTa2MfWvLvdiHFzX55KOPPmLSpEns2bOH0047jRtvvNHvkkTSSk3iMqy17qXp7mraWsfXtjrBJqKt63S0pOF1PlI5di/GK1NjFVqDWxn71pZ7sQ8vavLJ//7v//Lqq6/Sr18/nnjiCSIRvS8lXBQ8MsyrrqaJdo5NRpuhoyfwtfjPJurCx/eO+l7Sx+7FeGVirEJt9myINn5NiEZjy/3ahxc1+eCPf/wj9913H5FIhAULFtCvXz+/SxJJv0QmgmTiliuTS53zrqtpe51jk9Fel1m+F9vHxb+7uM2utjeX35z0sXsxXukcq5wwZ45zhYXOmcV+ZmISZ7L78KImD73wwgv1HWd/97vf+V2OSNJQd1pJlXq1iHjr/fffZ+jQofznP//h61//Og888IDfJYkkLW29WiS3pBIikuntIiKN1dTU8MUvfpH//Oc/HHvssZSWlvpdkkhGKXhIvY6cuVD4EEnNj370I5555hl69OjBwoUL6dq1q98liWSUgocAsH79+g5/XJJI+Fi/fn3aahbJdnPnzuWmm27CzPj73//O4BB8K0ekPQoeAkCfPn0YMGBAs+XJztFoK3wMGDCAPn36dLhWkTBYt24dF110EQCzZs1i3LhxPlck4g0FDwGgd+/eLF68mBEjRtQvS3ViaEvhY8SIESxevJjevXunrWaRbLV3717OPfdcDhw4wPjx45k+fbrfJYl4RsFD6jUMHx39NkrD8KHQIfIJ5xyXXHIJb7/9NocffjiPPPKIOs5KTknokumSO+rCx86dOznyyCM79FxDhgxhxYoV9OnTR6FDJO62227j8ccfp3PnzlRWVnLIIYf4XZKIp3TGQ5rp3bt3q6Ej2Zb1z+17jhPuPyGjLe4lR02bBvn5YBb7OW2a3xW165lnnuHaa68F4IEHHmDo0KGxFVl4LCKp0hkPSVjZ6jKKy4vZd3AfABt3b6S4PHaZ8anDp3Z4e5GETZsGd9/9yf2amk/u33WXPzW149133+W8886jtraWK6+8sn5iaTYei0hH6MqlkrBkW9Z70eJeclR+fuwf6KYiEaiu9r6edhw8eJCTTjqJf/3rX5x44omsXLmS/Pz4//uy7FhEWqMrl0raJduy3osW95KjWvqHuq3lPrvyyiv517/+RZ8+fSgvL/8kdEDWHYtIRyl4SMKSbVnvRYt7yVGttYoPYAv5OXPmUFpaSl5eHhUVFRx22GGNN8iiYxFJBwUPSViyLeu9aHEvOaq4OLnlPnnllVf45je/CcAdd9zBKaec0nyjLDkWkXRR8JCETR0+ldIppRT2KsQwCnsVUjqltNWJosluL5Kwu+6CkpJPzgpEIrH7AZqM+cEHHzBhwgSqq6v5yle+wne/+92WN8yCYxFJJ00uFRFJs9raWsaNG8eyZcsYMmQIL7/8Mt26dfO7LJGM0uRSERGfzJgxg2XLlhGNRqmsrFToEGlAwUNEJI0WLFjAz372MwAeffRRjjjiCJ8rEgkWBQ8RkTR5++23ufDCCwH48Y9/zKRJk3yuSCR4FDxERNJg//79jB8/nn379nHWWWcxc+ZMv0sSCSQFDxGRDnLOcdlll7F27Vo+/elP89hjj5GXp79eRVqi3wwRkQ666667eOSRR8jPz2fhwoUceuihfpckElg5HzyS7bbq1T6mVUwjf1Y+NtPIn5XPtIpgdKv0Yrwkw8rKoKgI8vJiP8ty6DVMtgtsAmP13HPP8f3vfx+AP/7xjxx//PHprzuFujr8GK/eJ7n8fsxVzjlfbiNHjnR+m/PKHBedHXXMoP4WnR11c16Z4+s+SuaVNNq+7lYyryRtdaXCi/GSDJszx7lo1Dn45BaNxpaHXUlJ4+Ouu5W08nuVwFi99957rk+fPg5wV1xxhTfHkcprmOxjvHqf5PL7MYSAKpfAv/85fQExL7qnprKP/Fn51LjmDaIiFqH6Bv+6VarbbAgUFcHG5q8hhYWwYYPX1Xgr2S6w7YxVdXU1p512GqtWreLzn/88zz//PJ07d0572cnWlZbHePU+yeX3YwjpAmIJ8KJ7air7aCl0tLXcK+o2GwKbWnmtWlseJsl2gW1nrK666ipWrVpFr169qKio8CZ0JFBXWh7j1fskl9+POSyng4cX3VNT2UfEWu5K2dpyr6jbbAgMbuW1am15mCTbBbaNsfrLX/7Cb37zG/Ly8igvL6egoCA9NSYildcw2cd49T7J5fdjDsvp4OFF99RU9lE8suWulK0t94q6zYbA7NkQbfwaEo3Gloddsl1gWxmrf0+bxqWXXgrATTfdxBe+8IU0FpmAVF7DZB/j1fskl9+PuSyRiSCZuAVhcqlzsQmThbcVOpthrvC2woxMlExlHyXzSlxkZsQxAxeZGfF9YmkdL8ZLMmzOHOcKC50zi/3MpYl8JSXORSKxSYyRSOsTS+s0GavdpaXu8MMPd4C74IILXG1trSdlt1dXQq9hso/x6n2Sy+/HkEGTS0VE0sc5x6RJk1i4cCFHHHEEq1evpnv37n6XJRIYmlwqIpJGv/jFL1i4cCFdu3alsrJSoUMkRQoeIiLtWLJkCT/5yU8AePjhhxkyZIjPFYlkLwUPEZE2bNq0iS9+8Ys457j22mu54IIL/C5JJKspeIiItOLAgQNMnDiRDz/8kDPOOINf/vKXfpckkvUUPEREWlFcXMzrr79O//79efzxx4m0ds0PEUmYgoeISAvuvfde/vznPxOJRFi4cCF9+/b1uySRUFDw8IBXHV2T3c+4B8ZhM63+Nu6BcRmpSyQwEuyEWlVVRUlJCQC/+93vGDFiRGbrGjcu1jG37jYuA7+LXuxDJAEKHhlWtrqM4vJiNu7eiMOxcfdGisuL0x4+kt3PuAfGseTtJY2WLXl7icKHhFdZWewqpRs3xvqgbtwYu98kfOzYsYOJEydSU1PDN77xDS6//PLM1jVuHCxp/LvIkiXpDQZe7EMkQbqAWIZ51dE12f3YTGv1udxP/XlPiGRUAp1Qa2pqGD16NCtXrmTYsGG88MILdOnSJbN1Weu/i6Tr72cv9iE5TxcQCwivOrqqc6xIOxLohHrdddexcuVKevbsyYIFCzIfOkRykIJHhnnV0VWdY0Xa0U4n1Mcff5ybb74ZM+Pxxx9n0KBBHhYnkjsUPDLMq46uye5n7BFjk1oukvXa6IS6Zs0aLr74YgB+/vOfc/bZZ3tX19hWfudaWx7UfYgkSMEjw6YOn0rplFIKexViGIW9CimdUsrU4VN93c/iSxc3CxljjxjL4ksXp7UukcCYOhVKS2NzOsxiP0tL+fCCCxg/fnz9xcKuv/56b+tavLh5ABg7NrY8m/YhkiBNLhWRnOWc44ILLqC8vJxBgwbx2muv0bNnT7/LEslKmlwqItKOW265hfLycjp37syiRYsUOkQ8oOAhIjnDOcett97Kjh07WLFiBT/84Q8BKCsr45hjjvG5OpHcoOAhIjmjvLyc6667jqFDhzJlyhScc3z/+9/nwgsv9Ls0kZyR73cBIiJeqGtrf/DgQbZv346Z8ZnPfIZbbrnF79JEcorOeIhITqioqOCdd96pv++cY9u2bXznO9/xsSqR3KPgISKhV3e2Y+/evc3WtbRMRDIndMEj2Q6tXnWO9cK0imnkz8rHZhr5s/KZVjGtze1TOfbQjFeCXUo7ZNo0yM+PXTMiPz92P1fqSmUfGbRw4UI2b97caFm3bt245ppreOihh1p/YCodXVN5DZMdLy/eJ6lIpa6gHkuykj2OsBx3KpxzvtxGjhzp0m3OK3NcdHbUMYP6W3R21M15ZU5atg+yknkljY6j7lYyr6TF7VM59tCM15w5zkWjzsXaY8Vu0WhsebqUlDR+/rpbScuvR6jqSmUfGVRbW+uGDRvmgPpbNBp1Dz30UNsPHDu25eMYO7b1x6TyGiY7Xl68T1KRSl1BPZZkJXscYTnuJoAql8C//6G6gFiyHVq96hzrhfxZ+dS4mmbLIxah+obqZstTOfbQjFcCXUo7LD8fapq/HkQiUN389QhVXansI4MWLVrEl770Jfbu3UteXh6HHHIIlZWVnHTSSW0/MJWOrqm8hsmOlxfvk1SkUldQjyVZyR5HWI67iUQvIBaqb7Uk26E1TB1dWwodbS1P5dhDM14JdCntsJb+IWlreVv7z7a6UtlHhrgGczu6dOnCoEGDWLJkCYNbaxjXUam8hsmOlxfvk1SkUldQjyVZyR5HWI47RaGa45Fsh9YwdXSNWCSp5akce2jGq50upWkRaXncW13e1v6zra5U9pEhS5cu5Y033iAajXLGGWfw0ksvZS50QGqvYbLj5cX7JBWp1BXUY0lWsscRluNOUaiCR7IdWr3qHOuF4pHFSS1P5dhDM15tdClNm+KWx73V5WGqK5V9ZIBzjmuuuYaPP/6Yb33rW1RWVtKjR4/EnyCVjq6pvIbJjpcX75NUpFJXUI8lWckeR1iOO1WJTAQBJgBvAuuA69rY7svEJm+Nau85MzG51LnYBMjC2wqdzTBXeFthuxMfk90+yErmlbjIzIhjBi4yM9LqxNI6qRx7aMZrzhznCgudM4v9zMSkrpIS5yKR2MSxSCSxyZVhqSuVfaTZM8884yKRiLvnnntSf5KmE0zbmlhaJ5XXMNnx8uJ9kopU6grqsSQr2eMIy3E3QLoml5pZBFgDnANsAVYBFzvnXm+yXU+gAugMXOmca3PmqLrTikgmbdu2jS1btjBqVLtz3UQkDdLZnfYkYJ1z7i3n3MfAw8AFLWz3M+BG4EBSlYqIZMCAAQMUOkQCKJHgMRBoeOWdLfFl9cxsBDDIOVfR1hOZWbGZVZlZ1fbt25MuVkTCa9euXaxfvz4tz7V+/Xp27dqVlucSkfTq8ORSM8sDbgWubm9b51ypc26Uc25U//79O7prEQmJXbt2MW7cOEaPHs3atWs79Fxr165l9OjRjBs3TuFDJIASCR7vAIMa3D88vqxOT+A4YLmZbQBOAeaamc5xiki76kLHiy++yNatWxkzZkzK4WPt2rWMGTOGrVu38uKLLyp8iARQIsFjFTDEzI4ws87AV4G5dSudc7udc/2cc0XOuSLgOeD89iaXiog0DB11Ug0fDUNHHYUPkeBpN3g456qBK4FK4N/AI86518xslpmdn+kCRSS8du7cybZt25otTzZ8tBQ66mzbto2dO3d2uFYRSY9Q9WoRkezTVmgoKChg+fLlDBkyJGOPF5H0SOfXaUMtNG3eJXjC1CI8gy2/hwwZwvLlyyno3bvZujbPfJSVsXbgQMYcfXTiocOL8Q3qaxgWGt/sl8hVxjJxy9SVS5MRmjbvEjxhahHuRcvvOXPcmq5dXUGD1vUNbwUFBW7NmjWpb59qXckK6msYFhrfQCNdVy7NlCB81BKaNu8SPGFqEe5Fy+/4Y9YCY4Dm5y8an8FYO3AgY7ZubXe7DteVrKC+hmGh8Q20RD9qyengkTczD0fz4zeM2p/W+lCRhEZeXuz/Y02ZQW0r761UHuOFZOvq4LG3Fz7uvfderpg8ueX1wPI1a1qe0+HF+Ab1NQwLjW+gaY5HAkLT5l2CJ0wtwr1o+d1g3RBgObEQ0dTWrVuZ3FboKChofSKpF+Mb1NcwLDS+oZDTwSM0bd4leMLUItyLlt9NHjMEWN61a4sTTltSEN9+yE03JbyPhOpKVlBfw7DQ+IZDIhNBMnELwuRS50LU5l2CJ0wtwr1o+d3CY9asWeMKCgpanEBadysAt6agIDjjG9TXMCw0voGFJpeKSBjMnz+fyZMnt7q+oqKCSZMmeViRiLREczxEJOutXbuWK664os1trrjiig43lhMR7yh4iEggtXVF0oY62lhORLyl4CEigZNo6Kij8CGSPRQ8RCRQ2uu9UlFRQUFB8y/bKnyIZAcFDxEJjEQavk2aNCnW20XhQyQrKXiISCAk02W2vrGcwodI1lHwEMmUVLpoTpsG+fmxS0Dn58fup3sfXkjyONauXcuYk09OqrX9kH/+s80rnLYYPsIyvkGtK6iCOF5BrMkriVzsIxO3oFxATCQjUumiWVLSePu6W0lJ+vbhhSSPY82aNa6gd++WLw7Wu3fzLrPONTr2NfGLiLX4+IZdasMyvkGtK6iCOF5BrCkN0AXERHyUShfN/HyoqWm+PBKB6ur07MMLSRzH+vXrGT16dMtnOoj3XnnnnebP1eTY22sst2LFCo787GfDMb5BrSuogjheQawpDXQBMRE/bdqU3HJo+R/Ftpansg8vJHEcffr0YcCAAc2WFxBrFDfk3Xdbfq4mx9hWY7kBAwbQp0+f8IxvUOsKqiCOVxBr8pCCh0gmpNJFMxJJbnlQO3UmcRy9e/dm8eLFjOjcuX5ZfeiApI5xCLEzJA0nnI4YMYLFixfTu3fv8IxvUOsKqiCOVxBr8pCCh0gmpNJFs7g4ueVB7dSZ5HH07t2bxb/5DSPMGoeOFDrgDrnppvpvuzQKHSnUFdjxDWpdQRXE8QpiTV5KZCJIJm6aXCqhl0oXzZIS5yKR2GSzSKT1iY8d2YcXkj0O59zO3/3OrSsoSEsH3HXr1rmdO3d2vK6gjm9Q6wqqII5XEGvqIDS5VERERLyiyaUiIiISOAoeIiIi4hkFDxEREfGMgoeIiIh4RsFDREREPKPgISIiIp5R8JDgCUvXRi+OY9iwWKfVutuwYenfBwTzNUmlpiAeh0iuSeRiH5m46QJi0qKwdG304jiGDm252+rQoenbh3PBfE1SqSmIxyESIugCYpKVwtK10YvjMGt9XTp/r4P4mqRSUxCPQyREEr2AmIKHBEteXsv/aJpBba339aTKi+PwKngE8TVJpaYgHodIiOjKpZKdwtK1MSzHAcE8llRqCuJxiOQgBQ8JlrB0bfTiOIYOTW55qoL4mqRSUxCPQyQHKXhIsEydCqWlsc/dzWI/S0tjy7OJF8fx2mvNQ8bQobHl6RTE1ySVmoJ4HCI5SHM8REREpMM0x0NEREQCR8FDREREPKPgISIiIp5R8BARERHPKHiIiIiIZxQ8RERExDMKHiKJ8qKzabL7CGq31aDWJRIUOfw7ku93ASJZoawMioth377Y/Y0bY/chfRegSnYfXtSUiqDWJRIUOf47oguIiSTCi86mye4jqN1Wg1qXSFCE9HdE3WlF0smLzqbJ7iOo3VaDWpdIUIT0d0RXLhVJJy86mya7j6B2Ww1qXSJBkeO/IwoeIonworNpsvsIarfVoNYlEhQ5/jui4CGSCC86mya7j6B2Ww1qXSJBkeO/I5rjISIiIh2mOR4iIiISOAoeIiIi4hkFDxEREfGMgoeIiIh4RsFDREREPKPgISIiIp5R8BARERHPKHjksiC2eQ8yL1rWh2m8Mm3aNMjPj12AKT8/dj9b6XWXXOKc8+U2cuRIJz6aM8e5aNS5WKui2C0ajS3Ppn14JdljSeXYwzRemVZS0nic6m4lJX5Xljy97hISQJVL4N//hK5camYTgDuACPB759yvmqy/Cvg2UA1sB77lnGuh5+8ndOVSnwWxzXuQedGyPkzjlWn5+VBT03x5JALV1d7X0xF63SUkEr1yabvBw8wiwBrgHGALsAq42Dn3eoNtzgKed87tM7MSYIxz7qK2nlfBw2dBbPMeZF60rA/TeGWaWevrfGoDkTK97hIS6bxk+knAOufcW865j4GHgQsabuCcW+ac2xe/+xxweLIFi8eC2OY9yLxoWR+m8cq0SCS55UGm111yTCLBYyCwucH9LfFlrbkcWNDSCjMrNrMqM6vavn174lVK+gWxzXuQedGyPkzjlWnFxcktDzK97pJr2psEAlxIbF5H3f2vA3e2su0lxM54dGnveTW5NADmzHGusNA5s9jPTExm82IfXkn2WFI59jCNV6aVlDgXicQmY0Yi2TmxtI5edwkB0jW51MxOBWY458bH718fDyy/bLLdOOA3wJnOuf+0F3g0x0NERCQ80jnHYxUwxMyOMLPOwFeBuU12dgLwO+D8REKHiIiI5KZ2g4dzrhq4EqgE/g084px7zcxmmdn58c3+L9ADeNTM/mVmc1t5OhEREclh+Yls5JybD8xvsuyGBn8el+a6REREJIR0yXQRERHxjIKHiIiIeEbBQ0RERDyj4JHL1BEzOWHqhioi4pOEJpdKCJWVxa7yuC9+pfuNGz+56uPUqf7VFVTTpsHdd39yv6bmk/t33eVPTSIiWSih7rSZoAuI+UwdMZMTpm6oIiIZkM4LiEkYbdqU3PJc11LoaGu5iIi0SMEjV6kjZnLC1A1VRMRHCh65Sh0xkxOmbqgiIj5S8MhVU6dCaWlsTodZ7GdpqSaWtuauu6Ck5JMzHJFI7L4mloqIJEWTS0VERKTDNLlUREREAkfBQ0RERDyj4CEiIiKeUfAQERERzyh4iIiIiGcUPERERMQzCh4pKFtdRtHtReTNzKPo9iLKVudQV9dc7miby8ceRHo9RLKSutMmqWx1GcXlxew7GOvqunH3RorLY1evnDo85BffyuWOtrl87EGk10Mka+kCYkkqur2Ijbubd3Ut7FXIhh9s8L4gL+VyR9tcPvYg0ushEji6gFiGbNrdcvfW1paHSi53tM3lYw8ivR4iWUvBI0mDe7XcvbW15aGSyx1tc/nYg0ivh0jWUvBI0uyxs4l2atzVNdopyuyxOdDVNZc72ubysQeRXg+RrKXgkaSpw6dSOqWUwl6FGEZhr0JKp5SGf2Ip5HZH21w+9iDS6yGStTS5VERERDpMk0tFREQkcBQ8RERExDMKHiIiIuIZBQ8RERHxjIKHiIiIeEbBQ0RERDyj4CEi7Zs2DfLzY9fMyM+P3U83dZsVyQnqTisibZs2De6++5P7NTWf3L/rrvTsQ91mRXKGLiAmIm3Lz4+FjaYiEaiuTs8+1G1WJOvpAmIikh4thY62lqdC3WZFcoaCh4i0LRJJbnkq1G1WJGcoeIhI2+rmWiS6PBXqNiuSMxQ8RKRtd90FJSWfnOGIRGL30zWxFNRtViSHaHKpiIiIdJgml4qIiEjgKHiIiIiIZxQ8RERExDMKHiIiIuIZBQ8RERHxjIKHiIiIeEbBQ0RERDyj4CEiIiKeUfAQERERzyh4iIiIiGcUPERERMQzCh4iIiLiGQUPERER8YyCh4iIiHhGwUNEREQ8o+AhIiIinlHwEBEREc8oeIiIiIhnFDxERETEMwoeIiIi4hkFDxEREfFMQsHDzCaY2Ztmts7MrmthfRcz+0t8/fNmVpTuQiUgysqgqAjy8mI/y8r8rkhERLJIu8HDzCLAb4GJwFDgYjMb2mSzy4FdzrmjgNuAG9NdqARAWRkUF8PGjeBc7GdxscKHiIgkLJEzHicB65xzbznnPgYeBi5oss0FwP3xP/8VGGtmlr4yJRCmT4d9+xov27cvtlxERCQBiQSPgcDmBve3xJe1uI1zrhrYDfRt+kRmVmxmVWZWtX379tQqFv9s2pTcchERkSY8nVzqnCt1zo1yzo3q37+/l7uWdBg8OLnlIiIiTSQSPN4BBjW4f3h8WYvbmFk+0At4Px0FSoDMng3RaONl0WhsuYiISAISCR6rgCFmdoSZdQa+Csxtss1c4BvxP18ILHXOufSVKYEwdSqUlkJhIZjFfpaWxpaLiIgkIL+9DZxz1WZ2JVAJRIA/OudeM7NZQJVzbi7wB+DPZrYO2EksnEgYTZ2qoCEiIilrN3gAOOfmA/ObLLuhwZ8PAP+T3tJEREQkbHTlUhEREfGMgoeIiIh4RsFDREREPKPgISIiIp5R8BARERHPKHiIiIiIZxQ8RERExDMKHiIiIuIZBQ8RERHxjIKHiIiIeEbBQ0RERDyj4CEiIiKeUfAQERERzyh4iIiIiGfMOefPjs22AxszuIt+wI4MPr/EaJy9o7H2hsbZOxpr73gx1oXOuf7tbeRb8Mg0M6tyzo3yu46w0zh7R2PtDY2zdzTW3gnSWOujFhEREfGMgoeIiIh4JszBo9TvAnKExtk7GmtvaJy9o7H2TmDGOrRzPERERCR4wnzGQ0RERAJGwUNEREQ8k9XBw8wmmNmbZrbOzK5rYX0XM/tLfP3zZlbkfZXhkMBYX2Vmr5vZK2a2xMwK/agzDNob6wbbfdnMnJkF4ity2SaRcTazr8Tf16+Z2YNe1xgWCfz9MdjMlpnZS/G/Qyb5UWe2M7M/mtl/zOzVVtabmf06/jq8YmYjvK4RAOdcVt6ACLAe+AzQGXgZGNpkm2nAPfE/fxX4i991Z+MtwbE+C4jG/1yisc7cWMe36wmsAJ4DRvldd7bdEnxPDwFeAnrH73/K77qz8ZbgWJcCJfE/DwU2+F13Nt6A0cAI4NVW1k8CFgAGnAI870ed2XzG4yRgnXPuLefcx8DDwAVNtrkAuD/+578CY8iG2sMAAAKeSURBVM3MPKwxLNoda+fcMufcvvjd54DDPa4xLBJ5XwP8DLgROOBlcSGSyDhfAfzWObcLwDn3H49rDItExtoBh8T/3AvY6mF9oeGcWwHsbGOTC4AHXMxzwKFm9mlvqvtENgePgcDmBve3xJe1uI1zrhrYDfT1pLpwSWSsG7qcWKqW5LU71vHTo4OccxVeFhYyibynjwaONrNnzew5M5vgWXXhkshYzwAuMbMtwHzge96UlnOS/bs8I/K93qGEm5ldAowCzvS7ljAyszzgVuAyn0vJBfnEPm4ZQ+wM3gozG+6c+8DXqsLpYuA+59wtZnYq8GczO845V+t3YZJ+2XzG4x1gUIP7h8eXtbiNmeUTO4X3vifVhUsiY42ZjQOmA+c75z7yqLawaW+sewLHAcvNbAOxz2nnaoJp0hJ5T28B5jrnDjrn3gbWEAsikpxExvpy4BEA59w/gK7EmppJeiX0d3mmZXPwWAUMMbMjzKwzscmjc5tsMxf4RvzPFwJLXXyGjSSl3bE2sxOA3xELHfosPHVtjrVzbrdzrp9zrsg5V0RsPs35zrkqf8rNWon8/fE4sbMdmFk/Yh+9vOVlkSGRyFhvAsYCmNmxxILHdk+rzA1zgUvj3245BdjtnHvX6yKy9qMW51y1mV0JVBKbNf1H59xrZjYLqHLOzQX+QOyU3TpiE26+6l/F2SvBsf6/QA/g0fj83U3OufN9KzpLJTjW0kEJjnMlcK6ZvQ7UANc653TGNEkJjvXVwL1m9v8Sm2h6mf6TmDwze4hYWO4Xny/zU6ATgHPuHmLzZyYB64B9wDd9qVOvrYiIiHglmz9qERERkSyj4CEiIiKeUfAQERERzyh4iIiIiGcUPERERMQzCh4iIiLiGQUPERER8cz/D3WMC9M9zVHPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,7))\n",
    "for instance, tg in zip(x, target):\n",
    "    plt.scatter(instance[0], instance[1], color=colors[tg])\n",
    "plt.title('Partitioned Data')\n",
    "\n",
    "centroids = km.cluster_centers_\n",
    "for centroid in centroids:\n",
    "    plt.scatter(centroid[0], centroid[1], color='k', marker='x', lw=5, s=500)\n",
    "    plt.annotate('Centroids', xytext=(0.8, 0.8), xy=(centroid[0], centroid[1]),\n",
    "                arrowprops=dict(facecolor='black', shrink=0.1, width=1, headwidth=5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from matplotlib.style import use\n",
    "from metaheuristics.algorithm.abc import ABC\n",
    "from metaheuristics.optimization.objection_function import QuantizationError\n",
    "from metaheuristics.optimization.objection_function import SumOfSquaredErrors\n",
    "\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def decode_centroids(centroids, n_clusters, data):\n",
    "    return centroids.reshape(n_clusters, data.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = MinMaxScaler().fit_transform(load_iris()['data'][:, [1,3]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Original Data')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 9))\n",
    "plt.scatter(data[:,0], data[:,1], s=50, edgecolor='w', alpha=0.5)\n",
    "plt.title('Original Data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Original Groups')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['r', 'g', 'y']\n",
    "target = load_iris()['target']\n",
    "\n",
    "plt.figure(figsize=(7,6))\n",
    "for instance, tgt in zip(data, target):\n",
    "    plt.scatter(instance[0], instance[1], s=50,\n",
    "                edgecolor='w', alpha=0.5, color=colors[tgt])\n",
    "plt.title('Original Groups')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "objective_function = SumOfSquaredErrors(dim=6, n_clusters=3, data=data)\n",
    "optimizer = ABC(obj_function=objective_function, colony_size=30, n_iter=300, max_trials=100)\n",
    "optimizer.optimize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: array([ 0.27599188,  0.47577794]), 1: array([ 0.42885731,  0.78282495]), 2: array([ 0.5859216 ,  0.07129218])}\n"
     ]
    }
   ],
   "source": [
    "centroids = dict(enumerate(decode_centroids(optimizer.optimal_solution.pos, n_clusters=3, data=data)))\n",
    "print(centroids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def assign_centroid(centroids, point):\n",
    "    distances = [np.linalg.norm(point - centroids[idx]) for idx in centroids]\n",
    "    return np.argmin(distances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "custom_tgt = []\n",
    "for instance in data:\n",
    "    custom_tgt.append(assign_centroid(centroids, instance))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Partitioned Data found by ABC')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['r', 'g', 'y']\n",
    "plt.figure(figsize=(7,6))\n",
    "for instance, tgt in zip(data, custom_tgt):\n",
    "    plt.scatter(instance[0], instance[1], s=50, edgecolor='w', alpha=0.5, color=colors[tgt])\n",
    "\n",
    "for centroid in centroids:\n",
    "    plt.scatter(centroids[centroid][0], centroids[centroid][1],\n",
    "                color='k', marker='x', lw=5, s=500)\n",
    "plt.title('Partitioned Data found by ABC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Iteration')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "itr = range(len(optimizer.optimality_tracking))\n",
    "val = optimizer.optimality_tracking\n",
    "plt.figure(figsize=(10, 9))\n",
    "plt.plot(itr, val)\n",
    "plt.title('Sum of Squared Errors')\n",
    "plt.ylabel('Fitness')\n",
    "plt.xlabel('Iteration')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}