lec/lec24/lec24.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lecture 24: Clustering – Data 100, Spring 2022\n",
    "\n",
    "**Notebook by Josh Hug**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>4.9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>5.3</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>6.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width     species\n",
       "112           6.8          3.0           5.5          2.1   virginica\n",
       "117           7.7          3.8           6.7          2.2   virginica\n",
       "106           4.9          2.5           4.5          1.7   virginica\n",
       "48            5.3          3.7           1.5          0.2      setosa\n",
       "135           7.7          3.0           6.1          2.3   virginica\n",
       "137           6.4          3.1           5.5          1.8   virginica\n",
       "108           6.7          2.5           5.8          1.8   virginica\n",
       "39            5.1          3.4           1.5          0.2      setosa\n",
       "100           6.3          3.3           6.0          2.5   virginica\n",
       "86            6.7          3.1           4.7          1.5  versicolor"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = pd.read_csv(\"iris.csv\")\n",
    "iris.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>6.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>5.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>7.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width\n",
       "92            5.8          2.6           4.0          1.2\n",
       "95            5.7          3.0           4.2          1.2\n",
       "130           7.4          2.8           6.1          1.9\n",
       "58            6.6          2.9           4.6          1.3\n",
       "139           6.9          3.1           5.4          2.1\n",
       "114           5.8          2.8           5.1          2.4\n",
       "32            5.2          4.1           1.5          0.1\n",
       "40            5.0          3.5           1.3          0.3\n",
       "122           7.7          2.8           6.7          2.0\n",
       "102           7.1          3.0           5.9          2.1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = iris.drop(\"species\", axis = 1)\n",
    "iris.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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"
    }
   ],
   "source": [
    "sns.scatterplot(data = iris, x = \"petal_length\", y= \"petal_width\", color=\"black\")\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y');\n",
    "#plt.savefig('2d_data_needing_clustering.png', dpi = 300, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Center():\n",
    "    def __init__(self, data):\n",
    "        \"\"\"generates a random center inside the region bounded by the data\"\"\"        \n",
    "        num_dimensions = data.shape[1]\n",
    "        self.coordinates = np.array([0.0] * num_dimensions)\n",
    "        for i in range(num_dimensions):\n",
    "            min_value = np.min(data[:, i])\n",
    "            max_value = np.max(data[:, i])            \n",
    "            random_value = random.uniform(min_value, max_value)            \n",
    "            self.coordinates[i] = random_value\n",
    "    \n",
    "    def __str__(self):\n",
    "        return str(self.coordinates)\n",
    "\n",
    "    def __repr__(self):\n",
    "        return repr(self.coordinates)\n",
    "\n",
    "    def dist(self, data_point):\n",
    "        return np.sqrt(np.sum((self.coordinates - data_point)**2, axis = 1))\n",
    "    \n",
    "    def dist_sq(self, data_point):\n",
    "        return np.sum((self.coordinates - data_point)**2, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "c1 = Center(iris.loc[:, ['petal_length', 'petal_width']].values)\n",
    "c2 = Center(iris.loc[:, ['petal_length', 'petal_width']].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# force coordinates from the lecture demo\n",
    "c1.coordinates = np.array([2.52364007, 2.31040024])\n",
    "c2.coordinates = np.array([6.53276402, 1.211463])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_centers_and_black_data(iris, centers):\n",
    "    sns.scatterplot(data = iris, x = \"petal_length\", y= \"petal_width\", color=\"black\")\n",
    "    for center in centers:\n",
    "        plt.plot(center.coordinates[0], center.coordinates[1], '*', markersize = 10)    \n",
    "    plt.xlabel('petal_length')\n",
    "    plt.ylabel('petal_width')\n",
    "    legend_text = ['c' + str(i) for i in range(1, len(centers) + 1)]\n",
    "    legend_text.append('data')\n",
    "    plt.legend(legend_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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z5s0T2jo7O31oaCjUvpX0PUI1xFhIVEKdQSE3JQOR0sh2MZow+1ZS7UE1xFhI0yWDOrwWEpGwsl2MJsy+lVR7UA0xloqSgYhklO1iNGH2raTag2qIsVSUDEQko0yLsixbtmxCW0dHB7FYLNS+lbSgSzXEWCoaWioi09JootqhOgMREVGdgYhMdPjwYXp6etizZw/RaJR4PM4RRxzB8PBwWT7xZzpv2PMUul8mNX0VkWmYUSVvGloqkrtDhw75+vXrJ4ytX79+vb/++uve2dlZ8vqBoaGhwPMeOnQo1HnCxpNv3LVQk4DqDERk1GOPPRY4tv7hhx8ObC92/cD27dsDj5cpzsnnCRtPvnHXQk3CdMmgRq5vRCSsPXv2ZBxbH9Re7PqBRCIReLy9e/eGOk/YePKNu9ZrEpQMROpMNBoNHFu/YMGCwPZi1w9kG8/k84SNJ9+4a74mIdMlQyVvuk0kkjt9Z6DvDII2DS0VqUOjo4n27t3LggULaGlp0WiiEKp9NJHqDERERHUGIrWoVJ9Sq/3TsISjv1GRKpRMJunu7iYWi7F69WpisRjd3d1TZhOtlvNI+ek2kUgV2r17N7FYbMJQx0gkQl9fH0uWLKm680hpTHebSFcGIlWoVGPea31svbxByUCkCpVqzHvNj62XMUoGIlWoVPPwa77/+qHvDESqlEYTSbZUZyAiIqozEKlFpaq6zefcpVJp8VSlTPNUVPKmuYmk3pVqPp58zl0qlRZPJUNzE4nUlrDj/4tRJ1BptQeVFk8lK0idgZm1mtmAmb1sZq+Y2atm9krhwhSRsEo1h38+5y6VSounWmVzU+1rwIXu/mZ3P9rd57r70cUKTEQyK9Uc/vmcu1QqLZ5qlU0yeMHdnylaJCISWtjx/8WoE6i02oNKi6dazfidgZm1pn89GzgOuBs4NPq8u3cXK7hM9J2BiEYTVXI8lSqvOgMz65zmaXf3/5FPcLlQMhARyV5edQbu/sn0Qc5y922TDnzWDCe+Bfgw8Ft3f2fA8+cA/wL8Ot3U7e5/O1NMIiJSWNkUnX0LOC1E23gbgHaga5o+j7r7h7OIQ6Qm5HtrY3BwkJ6eHvbt28eCBQuIx+PMnj17yjGB0OcJWn6yoaEhr2NKdZgxGZjZe4Azgflm9tlxTx0NNE63r7s/YmaL8opQpAaNLhrT1tbG4ODg2Jeera2tod5UBwcH2bRpE2vWrBnbv729nZNPPpnzzjtvrO3222/n0KFDoc4zPDzMxo0bufrqq8f6dnZ20tjYOGH/bI4p1SPM39wRwFGkEsfccdsrwMUFiOE9ZvaEmd1nZssKcDyRijcwMDD2ZgqpN/e2tjYGBgZC7d/T0zOWCEb3X7NmDUNDQxPaenp6Qp+nr69vLBGM9t21a9eU/bM5plSPMN8ZPAw8bGYb3P25Ap9/J3CCu79mZh8iNVIpcDyYmV0JXAmwcOHCAochUlrTFUqFqZrNtP8LL7wwoS2ZTIY+TyKRmNI3aP9sjinVI8xtou8Dnv59yvPufmGuJ3f3V8b9/gMz6zCzee7+YkDfdcA6SI0myvWcIpVgtFBq8hQKYQulFixYELj/scceO6FfY2Nj6PNEo9EpfYP2z+aYUj3C3Cb6OvB/SI34GQRuTm+vAU/lc3IzO87SGcbMTk/HcyCfY4pUg3wLpeLxOO3t7RP2b29vZ9asWRPa4vF46PPEYjE6Ojom9F26dOmU/bM5plSP0BPVmdkj7r5qprZJz28CzgHmAS8A1wKzANz9JjNbA3waGCaVaD7r7v82UyyqM5BaUMjRRM3NzbS0tGg0kUyrIIvbmNkzwAXu/qv04xOBH7j7KQWLNCQlAxGR7BVqcZs/Bx4ys1+lHy8CrsozNpGqVqppEII+sTc1Tf3vGxRPMpkM9Wm/Ej/Za5qJEsq00EHQBswGVqS32dnsW8hNi9tIJSjVoipDQ0Pe2dk54TydnZ0+NDQUKp7NmzdPaNu8eXNVLAajRWsKj2kWtwmTAN6f/tkatM20fzE2JQOpBP39/WNvVKNbJBLx/v7+gp5n+/btgefZvn17qHjWrl07oW3t2rUliTtfpfrzrSfTJYMw11tnp3/+94BN00hI3SrVoipB4/8HBwdJJBKh4kkmkxPapqsTqCRatKa0whSdXZv++cnihyNSPfKtFQgraPx/JBIhGo2GimfyPfZqqRMo1Z+vpGSz7OUvzew2M/tTM1tazKBEqkGpFlUJGv/f0dFBLBYLFc+yZctmrB2oxDoBLVpTWtkMLZ0NvBt4H3AW8A7gCXe/qHjhBdPQUqkUGk1UXBpNVFiFqjNoAlpIfYfwXuAY4BfuXvLhpUoGIiLZK1SdwSvAk8A3gJvdXdNGSNUrxifPw4cP09PTw549e4hGo8TjcRoaGqZ8OgcCP+3nUwUc1KZP0hJKpmFGkzfgI8D1wMPAA8BXgXPD7l/ITUNLpRCKMY790KFDvn79+gnH3LRpk2/YsGFC26233jpl/H9nZ6cPDg5OqSnYsGGDb926dUqck9u2bt2qcfkyLfKpM5iyQ+q7gj8HngMGs92/EJuSgRRCMcaxP/bYY1OOmWlc/+Tx/5FIxB955JHQfau1fkDKZ7pkkM1oou+Z2S+BbwJvAtqAt4a/BhGpLMUYx75nz57Q8/9PHv8/ODjI3r17Q/et1voBqUzZfGfw98BOdx8JetLMPuDuDxQmLJHiK8Y49rBrAgSN/49EIhx//PGh+1Zr/YBUptBXBu7ekykRpP1DAeIRKZlijGMPWmfglFNO4cYbb5zQdtNNN00Z/9/R0UE8Hp9SU3DjjTfS0tIyJc7JbVpnoEIdfBk2X576WcFCDy2d8UBmfe4em7ln/jS0VAqlmKOJ9u7dy4IFC2hpadFoonr2xGa46yq4aB2suKSsoRSkziDESXa6+2kFOdgMlAxEpGpsuAB+8xgseh9ccU9ZQylUnYGITBJ0ZQHhPp1nc1WSzxWMqnhL7NYL4dcPv/G48YjUz+d/Dl958xvtJ54Nn9ha2timUch/Eb8p4LFEKl4ymaS7u5tYLMbq1auJxWLce++9U9q6u7sDR/6E6Zdt30LuKzla9TmYFXnj8cjhiT8h9fyqz5c2rhnMeJvIzFqne97duwsaUQi6TSSVYPfu3cRisQmjd9auXcvXv/71KSN6+vr6WLJkybT7BvXLtm+YGMPuK3n49SPw3Y/C0ODU52ZF4PItcOL7Sh7WdLeJwlwZBK1joPUMpO4F1SmEHeufTY1DPvUQWhOgTE5cBRdvgKY5E9ub5qTay5AIZhJmPQOtYyASIKhOIexY/2xqHPKph9CaAGV08GVoaARrgMbZMHIo9bhCh5hm9Z2BmV1gZn9pZl8e3YoVmEilC6pTCDvWP5sah3zqIbQmQBn1dcHh1+HYd8Jl3039PPw69H2n3JEFymYK65uAI4HVwLeBi4Ht7v4nxQsvmL4zkEqh0USS0abL4ISz4IyroaEBkiPwsw547t/gsk1lCalQ6xn8wt2Xj/t5FNDt7ucVMtgwlAxERLKX7xfIo0ZvOr5uZguAIeDEfIMTEZHyy6bo7B4zewupNQ12kpoi99vFCErqS7XcxsjnlpBIpcsmGXzN3Q8B3zOze4A5wMHihCX1YrQoqq2tjcHBwbEvOFtbWyvqTTVTnLNnz+aSSy6p6NhFwsjmX+xPR39x90Pu/vL4NpFcDAwMjL3BQmoMfFtbGwMDA2WObKJMcfb09FR87CJhzJgMzOw4M1sJRMwsZmanpbdzSI0uEslZtRRFZYozaNGZSotdJIwwt4n+G3AFEAW+Ma79FeCLRYhJ6ki1FEVlijNo0ZlKi10kjBmvDNz9VndfDVzh7qvHbR8px7xEUluqpSgqU5xBi85UWuwiYWRTZ3AccB2wwN3PN7OlwHvcfX0xAwyiOoPaotFEIqVRqKKz+4BO4EvuvsLMmoA+dz+1cKGGo2QgIpK9Qi1uM8/d7zCzvwZw92Ezm25NZMzsFlIzm/7W3d8Z8LwB3wQ+BLxO6lbUzixiEglcJrKpaeo/7bD9oPDTP4CuIKTCuXuoDXgIOAbYmX58BvDwDPusAk4Dnsrw/IeA+wBLH+/nYWJZuXKli7i7Dw0NeWdnp0ciEQc8Eol4Z2enDw0N5dTP3X1kZMS3bNkyoe+WLVt8ZGRkxniC9t26dWvOxxMpJKDXM71fZ3piSsfUm/o24L/SP/8fsDzEfoumSQb/DFw27vFuoHmmYyoZyKjt27ePvcmObpFIxLdv355TP3f3/v7+wL79/f0zxhO079q1a3M+nkghTZcMsrlOfRq4C+gBXgBuTieEfBwPPD/ucSLdNoWZXWlmvWbWu3///jxPK7UikUgEjv9PJBI59YPCLyYTdsEbkXLKJhl0Ae8A/jfwLWAxkO/E3BbQFviNtruvc/e4u8fnz5+f52mlVkSj0bGhnaMikQjRaDSnfvBGTcHkvtksJjPe6II3uRxPpFSySQZL3P1/uvtP0tuVwMl5nj8BvH3c4yiwN89jSh2JxWJ0dHRMGOvf0dFBLBbLqR8UfjGZsAveiJRTNkNLNwA3ufvP0o/fDXzC3a+eYb9FwD0ePJroAmANqS+S3w38k7ufPlMsGloq42k0kUg4haozeAZYAvx7umkh8AyQBNzdlwfsswk4B5hH6nuGa4FZpHa4KT20tB34IKmhpZ909xnf5ZUMRESyV6g6gw9me2J3v2yG5x24JtvjiohIYYVOBu7+XDEDERGR8tFNSxERUTIQERElAxERQclARERQMhAREZQMREQEJQMREUHJQEREUDIQERGUDEREBCUDERFByUBERFAyEBERlAxERAQlAxERQclARERQMhAREZQMREQEJQMREUHJQEREUDIQERGUDEREBCUDEREBmsodQD1IJpMMDAywb98+mpubWbx4MQ0NysMiUjn0jlRkyWSS7u5uYrEYq1evJhaL0d3dTTKZLHdoIiJjlAyKbGBggLa2NgYHBwEYHBykra2NgYGBMkcmIvIGJYMi27dv31giGDU4OMi+ffvKFJGIyFRKBkXW3NxMJBKZ0BaJRGhubi5TRCIiUykZFNnixYvp6uoaSwiRSISuri4WL15c5shERN6g0URF1tDQQGtrK6eeeqpGE4lIxVIyKIGGhgaWLFnCkiVLyh2KiEigon88NbMPmtluM3vWzL4Q8Pw5ZvaymT2e3r5c7JgqQTKZZPfu3Tz00EPs3r1bQ01FpKyKmgzMrBG4ATgfWApcZmZLA7o+6u7vSm9/W8yYKoFqD0Sk0hT7yuB04Fl3/5W7HwY2Ax8p8jkrnmoPRKTSFDsZHA88P+5xIt022XvM7Akzu8/MlgUdyMyuNLNeM+vdv39/MWItGdUeiEilKXYysIA2n/R4J3CCu68AvgXcHXQgd1/n7nF3j8+fP7+wUZaYag9EpNIUOxkkgLePexwF9o7v4O6vuPtr6d9/AMwys3lFjqusVHsgIpWm2ENLe4DFZnYisAe4FLh8fAczOw54wd3dzE4nlaAOFDmuslLtgYhUmqImA3cfNrM1wA+BRuAWd99lZn+afv4m4GLg02Y2DAwCl7r75FtJNUe1ByJSSawa33fj8bj39vaW9Jxh1yQ4fPgwPT097Nmzh2g0Sjwep6mpacq+QOg1DrQegogUgpntcPd44JPuXnXbypUrvZRGRkZ8y5YtHolEHPBIJOJbtmzxkZGRCf0OHTrk69evn9Dvlltu8a1bt07ZN6ht8vGyObeIyEyAXs/wvlr2N/ZctlIng/7+/rE349EtEol4f3//hH6PPfZYYL+1a9eGapt8vGzOLSIyk+mSge41hBC2LmDPnj2B/SZXFmdqC6ozUE2CiJSCkkEIYesCotFoYL/J9/cztQXVGagmQURKQckghLB1AfF4nPb29gn9brjhBlpaWqbsG9QWVGegmgQRKQWNJgop29FEe/fuZcGCBbS0tGg0kYhUhOlGEykZiIjUiemSgT5eioiIVjoLa3h4mL6+PhKJBNFolFgsRjKZDFVgFnRLR7d+RKSSKBmEMDw8zMaNG7n66qsZHBwkEonQ2dnJwYMH+fSnPz3WdsMNNzBv3jwuueSSsbauri5aW1snvNGPLm4zuqZBpn4iIqWid54Q+vr6xhIBpMb579q1aywRjLZdc8019PT0zLhojRa3EZFKo2QQQiKRmFL4lUwmQxeYTS4QUyGZiFQaJYMQgorJGhsbQxeYTS4QUyGZiFQaJYMQYrEYHR0dEwq/li5dyo033hiqwGxygZgKyUSk0qjOIKTpRhPNVGCm0UQiUglUdCYiItMmg7oZWprNJ/Ggq4Dh4eGxq4Djjz+eeDzO8PAwO3fuZN++fSxYsIBYLEZDQwO9vb1jbStXrsTM6OnpGWuLx+PMnj1b01GISOXINLd1JW/ZrmeQzQIxQ0ND3tnZOaHvfffdN2XRmu7u7sCFbDZt2jShrbOzc0rb+vXr/dFHH9XiNiJSUtT74jbZLBCzffv2KX3vv//+KW2bN2/OayGbBx98UIvbiEhJTZcM6uJeQzbj+oNqCl588cUpba+99lpeC9m88MILoeJRTYKIlEJdJINsxvUH1RTMnz9/StvcuXPzWsjm2GOPDRWPahJEpBTqIhlkM64/qKYgmUxOWbRm1qxZgQvZLF26dEJbR0fHlLb29nZmzZqlxW1EpGLUzdDSQo0mGt2/paVlwmii5uZmTjvttAmjiZqbm4nH4xNGE43uq9FEIlJqqjMQERHVGWSS7yfuoP3HL3s5Wo8wZ86cIr4KEZH81W0yyHdNgaD97733Xn7961+zZs2asbb29nYuv/xyJQQRqWh1e+M53zUFgvY/fPjwWCIYbVuzZg09PT3FeREiIgVSt8kg3/H7QfsH1SOoJkBEqkHdJoN8x+8H7R9Uj6CaABGpBnWbDPIdvx+0f1DtQXt7Oy0tLcV5ESIiBVLXQ0uLOZpofE2BvjwWkUqgOgMREZk2GRT9NpGZfdDMdpvZs2b2hYDnzcz+Kf38L8zstGLHJCIiExU1GZhZI3ADcD6wFLjMzJZO6nY+sDi9XQncWMyYRERkqmJfGZwOPOvuv3L3w8Bm4COT+nwE6EpPt/0z4C1mpuE3IiIlVOxkcDzw/LjHiXRbtn0wsyvNrNfMevfv31/wQEVE6lmxk4EFtE3+xjpMH9x9nbvH3T0+f/78ggQnIiIpxZ6bKAG8fdzjKLA3hz4T7Nix40Uzey7HmOYBL+a4byWqpddTS68F9HoqWS29Fgj/ek7I9ESxk0EPsNjMTgT2AJcCl0/qsxVYY2abgXcDL7v7tPM3uHvOlwZm1ptpaFU1qqXXU0uvBfR6KlktvRYozOspajJw92EzWwP8EGgEbnH3XWb2p+nnbwJ+AHwIeBZ4HfhkMWMSEZGpij6Ftbv/gNQb/vi2m8b97sA1xY5DREQyq8e5idaVO4ACq6XXU0uvBfR6KlktvRYowOupyukoRESksOrxykBERCZRMhARkfpJBmZ2i5n91syeKncs+TKzt5vZT8zsGTPbZWZ/Vu6Y8mFmc8xsu5k9kX49Xy13TPkys0Yz6zOze8odS77M7Ddm9qSZPW5mVT9dsJm9xczuNLP+9P+h95Q7plyY2ZL038no9oqZfSbn49XLdwZmtgp4jdQ8SO8sdzz5SM/d1OzuO81sLrAD+EN3f7rMoeXEzAx4k7u/ZmazgMeAP0vPVVWVzOyzQBw42t0/XO548mFmvwHi7l4TRVpmdivwqLt/28yOAI509/8qc1h5SU8Kugd4t7vnVJBbN1cG7v4I8FK54ygEd9/n7jvTv78KPEPAfE7VIj1J4Wvph7PSW9V+SjGzKHAB8O1yxyITmdnRwCpgPYC7H672RJB2LvDLXBMB1FEyqFVmtgiIAT8vcyh5Sd9WeRz4LfCAu1fz6/lH4C+BZJnjKBQHfmRmO8zsynIHk6ffA/YDnenbeN82szeVO6gCuBTYlM8BlAyqmJkdBXwP+Iy7v1LuePLh7iPu/i5Sc1OdbmZVeSvPzD4M/Nbdd5Q7lgI6y91PI7X2yDXpW67Vqgk4DbjR3WPA74Api25Vk/StrguBLfkcR8mgSqXvrX8PuM3du8sdT6GkL9kfAj5Y3khydhZwYfo++2bg/Wa2sbwh5cfd96Z//ha4i9Q6JdUqASTGXXneSSo5VLPzgZ3u/kI+B1EyqELpL1zXA8+4+zfKHU++zGy+mb0l/XsE+AOgv6xB5cjd/9rdo+6+iNSl+4Pu/rEyh5UzM3tTepAC6dsp5wFVOyLP3f8DeN7MlqSbzgWqcuDFOJeR5y0iKMHcRJXCzDYB5wDzzCwBXOvu68sbVc7OAj4OPJm+zw7wxfQ8UNWoGbg1PSKiAbjD3at+SGaNOBa4K/X5gybgu+5+f3lDytv/Am5L3175FVU8OaaZHQl8ALgq72PVy9BSERHJTLeJREREyUBERJQMREQEJQMREUHJQEREUDIQERGUDEQAMLMrzGxBiH4bzOziaZ5/yMziBY7tLWZ29bjH59TC1NhSWZQMRFKuAGZMBmXyFuDqmTqJ5EPJQGqSmS1KL15yq5n9Ir2YyZFmttLMHk7PwPlDM2tOf9KPk6pKfdzMImb2ZTPrMbOnzGxdegqQbGM4z8x+amY7zWxLemLB0cVivppuf9LM3pFun29mD6Tb/9nMnjOzecDfAyelY7s+ffijxi3Qclsu8YmMp2QgtWwJsM7dlwOvANcA3wIudveVwC3Ade5+J9AL/LG7v8vdB4F2d29JL4QUAbJaoCb9Jv43wB+kZ/zsBT47rsuL6fYbgc+l264lNZfRaaQmhFuYbv8Cqbnq3+Xun0+3xYDPAEtJTct8VjbxiUxWN3MTSV163t23pX/fCHwReCfwQPqDdCOwL8O+q83sL4EjgbcBu4DvZ3HuM0i9UW9Ln+sI4Kfjnh+daXYH0Jr+/b3ARQDufr+Z/ec0x9/u7gmA9PxUi0itECeSEyUDqWWTJ956Fdjl7tOueWtmc4AOUks9Pm9mXwHmZHluI7VIz2UZnj+U/jnCG/8Ps7nVc2jc7+OPIZIT3SaSWrZw3GLnlwE/A+aPtpnZLDNbln7+VWBu+vfRN/4X0/f5M44emsbPgLPM7PfT5zrSzE6eYZ/HgI+m+58HvDUgNpGiUDKQWvYM8Akz+wWpWz3fIvXG/g9m9gTwOHBmuu8G4Kb0LZdDwM3Ak8DdQE+2J3b3/aRGKG1Kn/9nwDtm2O2rwHlmtpPUgiX7gFfd/QCp201PjfsCWaSgNIW11KT02tD3pL8ArgpmNhsYcffh9NXLjemlQEWKTvcZRSrHQuAOM2sADgOfKnM8Ukd0ZSCSAzO7CzhxUvNfufsPyxGPSL6UDERERF8gi4iIkoGIiKBkICIiKBmIiAjw/wG8+txZtR7lBQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_centers_and_black_data(iris, (c1, c2))\n",
    "#plt.savefig('2means_demo_initial_placement.png', dpi = 300, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_cluster_number(dists):\n",
    "    return np.where(dists == np.min(dists))[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "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</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>dist1</th>\n",
       "      <th>dist2</th>\n",
       "      <th>cluster</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",
       "      <td>2.390890</td>\n",
       "      <td>5.231474</td>\n",
       "      <td>0</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",
       "      <td>2.390890</td>\n",
       "      <td>5.231474</td>\n",
       "      <td>0</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",
       "      <td>2.439484</td>\n",
       "      <td>5.329623</td>\n",
       "      <td>0</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",
       "      <td>2.345555</td>\n",
       "      <td>5.133398</td>\n",
       "      <td>0</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",
       "      <td>2.390890</td>\n",
       "      <td>5.231474</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.080387</td>\n",
       "      <td>4.900416</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2.303101</td>\n",
       "      <td>5.213064</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.345555</td>\n",
       "      <td>5.133398</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.390890</td>\n",
       "      <td>5.231474</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2.435920</td>\n",
       "      <td>5.154034</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width     dist1     dist2  \\\n",
       "0           5.1          3.5           1.4          0.2  2.390890  5.231474   \n",
       "1           4.9          3.0           1.4          0.2  2.390890  5.231474   \n",
       "2           4.7          3.2           1.3          0.2  2.439484  5.329623   \n",
       "3           4.6          3.1           1.5          0.2  2.345555  5.133398   \n",
       "4           5.0          3.6           1.4          0.2  2.390890  5.231474   \n",
       "5           5.4          3.9           1.7          0.4  2.080387  4.900416   \n",
       "6           4.6          3.4           1.4          0.3  2.303101  5.213064   \n",
       "7           5.0          3.4           1.5          0.2  2.345555  5.133398   \n",
       "8           4.4          2.9           1.4          0.2  2.390890  5.231474   \n",
       "9           4.9          3.1           1.5          0.1  2.435920  5.154034   \n",
       "\n",
       "   cluster  \n",
       "0        0  \n",
       "1        0  \n",
       "2        0  \n",
       "3        0  \n",
       "4        0  \n",
       "5        0  \n",
       "6        0  \n",
       "7        0  \n",
       "8        0  \n",
       "9        0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris[\"dist1\"] = c1.dist(iris[[\"petal_length\", \"petal_width\"]])\n",
    "iris[\"dist2\"] = c2.dist(iris[[\"petal_length\", \"petal_width\"]])\n",
    "iris[\"cluster\"] = iris[[\"dist1\", \"dist2\"]].apply(get_cluster_number, axis = 1)\n",
    "iris.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    79\n",
       "1    71\n",
       "Name: cluster, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris[\"cluster\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_centers_and_colorized_data(iris, centers):\n",
    "    current_palette = sns.color_palette()[0:len(centers)]\n",
    "    sns.scatterplot(data = iris, x = \"petal_length\", y= \"petal_width\", hue=\"cluster\", palette=current_palette)  \n",
    "    for center in centers:\n",
    "        plt.plot(center.coordinates[0], center.coordinates[1], '*', markersize = 10)\n",
    "    plt.xlabel('petal_length')\n",
    "    plt.ylabel('petal_width')\n",
    "    legend_text = ['c' + str(i) for i in range(1, len(centers) + 1)]\n",
    "    plt.legend(legend_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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"
    }
   ],
   "source": [
    "plot_centers_and_colorized_data(iris, (c1, c2))\n",
    "#plt.savefig('2means_demo_initial_placement_colored.png', dpi = 300, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "average_c1_length = np.mean(iris[iris[\"cluster\"] == 0])[\"petal_length\"]\n",
    "average_c1_width = np.mean(iris[iris[\"cluster\"] == 0])[\"petal_width\"]\n",
    "c1.coordinates = (average_c1_length, average_c1_width)\n",
    "\n",
    "average_c2_length = np.mean(iris[iris[\"cluster\"] == 1])[\"petal_length\"]\n",
    "average_c2_width = np.mean(iris[iris[\"cluster\"] == 1])[\"petal_width\"]\n",
    "c2.coordinates = (average_c2_length, average_c2_width)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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"
    }
   ],
   "source": [
    "plot_centers_and_black_data(iris, (c1, c2))\n",
    "#plt.savefig('2means_demo_center_position_2.png', dpi = 300, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris[\"dist1\"] = c1.dist(iris[[\"petal_length\", \"petal_width\"]])\n",
    "iris[\"dist2\"] = c2.dist(iris[[\"petal_length\", \"petal_width\"]])\n",
    "iris[\"cluster\"] = iris[[\"dist1\", \"dist2\"]].apply(get_cluster_number, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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"
    }
   ],
   "source": [
    "plot_centers_and_colorized_data(iris, (c1, c2))\n",
    "#plt.savefig('2means_demo_center_position_2_colorized.png', dpi = 300, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "average_c1_length = np.mean(iris[iris[\"cluster\"] == 0])[\"petal_length\"]\n",
    "average_c1_width = np.mean(iris[iris[\"cluster\"] == 0])[\"petal_width\"]\n",
    "c1.coordinates = (average_c1_length, average_c1_width)\n",
    "\n",
    "average_c2_length = np.mean(iris[iris[\"cluster\"] == 1])[\"petal_length\"]\n",
    "average_c2_width = np.mean(iris[iris[\"cluster\"] == 1])[\"petal_width\"]\n",
    "c2.coordinates = (average_c2_length, average_c2_width)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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"
    }
   ],
   "source": [
    "plot_centers_and_black_data(iris, (c1, c2))\n",
    "#plt.savefig('2means_demo_center_position_3.png', dpi = 300, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris[\"dist1\"] = c1.dist(iris[[\"petal_length\", \"petal_width\"]])\n",
    "iris[\"dist2\"] = c2.dist(iris[[\"petal_length\", \"petal_width\"]])\n",
    "iris[\"cluster\"] = iris[[\"dist1\", \"dist2\"]].apply(get_cluster_number, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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"
    }
   ],
   "source": [
    "plot_centers_and_colorized_data(iris, (c1, c2))\n",
    "#plt.savefig('2means_demo_center_position_3_colorized.png', dpi = 300, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "average_c1_length = np.mean(iris[iris[\"cluster\"] == 0])[\"petal_length\"]\n",
    "average_c1_width = np.mean(iris[iris[\"cluster\"] == 0])[\"petal_width\"]\n",
    "c1.coordinates = (average_c1_length, average_c1_width)\n",
    "\n",
    "average_c2_length = np.mean(iris[iris[\"cluster\"] == 1])[\"petal_length\"]\n",
    "average_c2_width = np.mean(iris[iris[\"cluster\"] == 1])[\"petal_width\"]\n",
    "c2.coordinates = (average_c2_length, average_c2_width)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sUZalS5eOaWttbSUWi4Xat5QWdCmHGAtFQ0tFZEoaTVQ5VGcgIiKqMxCRsQ4fPkxnZye7d+8mGo3S0NDAG97wBgYHB4vyiX+y84Y9T677TaairyImG2ZUypuGlopk7tChQ7527doxY+vXrl3rr732mre1tRW8fmBgYCDwvIcOHQp1nrDxZBt3JdQkoDoDERn22GOPBY6tf/jhhwPb810/sHXr1sDjTRbn+POEjSfbuCuhJmGqZFAh1zciEtbu3bsnHVsf1J7v+oF4PB54vD179oQ6T9h4so270msSlAxEqkw0Gg0cWz9v3rzA9nzXD6Qbz/jzhI0n27grviZhskuGUt50m0gkc/rOQN8ZBG0aWipShYZHE+3Zs4d58+bR2Nio0UQhlPtoItUZiEhmDr4E91wFf7kGZr2x2NFIllRnIFKBCvIpddd90HMvfQ+1sWvWO8ry07CEo79RkTKUSCTo6OggFouxcuVKYrEYHR0dE2YTzZZv/y4Au+5cndfzSPHpNpFIGdq1axexWGzMUMdIJEJ3dzeLFi3K/MC3XwC/eXjkYaJmBjWJAQ4NOjPrbKT91WMbOfKqn2Z+HimKqW4T6cpApAzlbcz7WZ+DGa8Pn6xJDACMTQSHnd7jPpzdeaTkKBmIlKG8jXlfcBZceteYhDDaq4edpu8NEVlyXnbnkZKjZCBShvI6D/+Cs+CidVA3a0xz/4DT/IMhLr/+9qqc77/SaTSRSBmqqamhqamJZcuW5Wc00cGXoKYWrAavnQlDh5gx8w203PAFjj2vSaOJKpD+RkXKVE1NDYsWLeLss89m0aJFuX2D7m6Hw6/BsW/HLvlX7Ni3U5c4RP3vf6pEUKF0ZSBSpvJadTvzaDjva3DG1VBTAwtWwC9a4bn/yPyYeVRq8ZSlyeapKOVNcxNJtSvUfDzZnLtQSi2eUobmJhKpLGHrDPJRj5C3GocMlVo8pSwndQZm1mRmvWb2kpkdMLOXzexA7sIUkbAKNYd/NuculFKLp1ylc1Pt68AF7v5Gdz/a3We7+9H5CkxEJleoOfyzOXehlFo85SqdZLDX3XfmLRIRCS1snUE+6hHyWuNQAfGUq2m/MzCzptSvK4DjgHuAQ8PPu3tHvoKbjL4zECncHP7ZnLtQSi2eUpXVegZm1jbF0+7u/z2b4DKhZCAikr6s1jNw90+mDnKmu28Zd+AzpznxbcCHgD+4+9sDnj8b+DfgN6mmDnf/++liEhGR3Eqn6OzbwPIQbaOtA1qA9in6POruH0ojDpGKkO2tjf7+fjo7O+nr62PevHk0NDQwc+bMCccEQp8naPnJmpqarI4p5WHaZGBm7wLeDcw1s8+OeupooHaqfd39ETObn1WEIhVoeHGa5uZm+vv7R770bGoKN+9Pf38/GzZsYNWqVSP7t7S0cPLJJ3PeeeeNtN15550cOnQo1HkGBwdZv349V1999UjftrY2amtrx+yfzjGlfIT5m3sDcBTJxDF71HYAuCgHMbzLzJ4ws/vMbGkOjidS8np7e0feTCH55t7c3Exvb2+o/Ts7O0cSwfD+q1atYmBgYExbZ2dn6PN0d3ePJILhvjt27JiwfzrHlPIR5juDh4GHzWyduz+X4/NvB05091fM7AMkRyoFjgczsyuAKwBOOOGEHIchUlhTFUqFqZqdbP+9e/eOaUskEqHPE4/HJ/QN2j+dY0r5CHOb6AeAp36f8Ly7X5Dpyd39wKjff2RmrWY2x91fCOh7C3ALJEcTZXpOkVIwXCg1fgqFsIVS8+bNC9z/2GOPHdOvtrY29Hmi0eiEvkH7p3NMKR9hbhPdCHyD5IiffuDW1PYK8FQ2Jzez4yyVYczs9FQ8+7M5pkg5yLZQqqGhgZaWljH7t7S0MGPGjDFtDQ0Noc8Ti8VobW0d03fJkiUT9k/nmFI+Qk9UZ2aPuPtZ07WNe34DcDYwB9gLXAfMAHD3m81sFXAVMEgy0XzW3f9julhUZyCVIJejierr62lsbNRoIplSVkVnow6yE/iguz+berwA+JG7L85ZpCEpGYiIpC+rorNR/hZ4yMyeTT2eD1yZZWwiZa1Q0yAEfWKvq5v43zconkQiEerTfil+stc0EwU02UIHQRswEzg1tc1MZ99cblrcRkpBoRZVGRgY8La2tjHnaWtr84GBgVDxbNy4cUzbxo0by2IxGC1ak3tMsbhNmARwTupnU9A23f752JQMpBT09PSMvFENb5FIxHt6enJ6nq1btwaeZ+vWraHiWb169Zi21atXFyTubBXqz7eaTJUMwlxvrUj9/G8Bm6aRkKpVqEVVgsb/9/f3E4/HQ8WTSCTGtE1VJ1BKtGhNYYUpOrsu9fOT+Q9HpHxkWysQVtD4/0gkQjQaDRXP+Hvs5VInUKg/X0lKZ9nLX5vZHWb2N2a2JJ9BiZSDQi2qEjT+v7W1lVgsFiqepUuXTls7UIp1Alq0prDSGVo6E3gn8F7gTOBtwBPufmH+wgumoaVSKjSaKL80mii3clVnUAc0kvwO4T3AMcCv3L3gw0uVDERE0perOoMDwJPAN4Fb3V3TRkjZy8cnz8OHD9PZ2cnu3buJRqM0NDRQU1Mz4dM5EPhpP5sq4KA2fZKWUCYbZjR+Az4M3AA8DDwAfBU4N+z+udw0tFRyIR/j2A8dOuRr164dc8wNGzb4unXrxrTdfvvtE8b/t7W1eX9//4SagnXr1vnmzZsnxDm+bfPmzRqXL1MimzqDCTskvyv4W+A5oD/d/XOxKRlILuRjHPtjjz024ZiTjesfP/4/Eon4I488ErpvudYPSPFMlQzSGU30PTP7NfAt4EigGXhz+GsQkdKSj3Hsu3fvDj3///jx//39/ezZsyd033KtH5DSlM53Bv8AbHf3oaAnzex97v5AbsISyb98jGMPuyZA0Pj/SCTC8ccfH7pvudYPSGkKfWXg7p2TJYKUf8xBPCIFk49x7EHrDCxevJg1a9aMabv55psnjP9vbW2loaFhQk3BmjVraGxsnBDn+DatMyDZCD20dNoDmXW7e2z6ntnT0FLJlXyOJtqzZw/z5s2jsbFRo4mkJOSkziDESba7+/KcHGwaSgYiIunLVZ2BiIwTdGUB4T6dp3NVks0VjKp4JZTJhhmluwEduTrWdJuGlkopCKpTCDvWP50ah2zqIbQmgIzGFENLp71NZGZN0ySTjpxkpTToNpGUgl27dhGLxcaM3lm9ejU33njjhBE93d3dLFq0aMp9g/ql2zdMjGH3lcoz1W2iMNeKQesYaD0DqXpBdQphx/qnU+OQTT2E1gSQsMKsZ6B1DEQCBNUphB3rn06NQzb1EFoTQMJK61skM/ugmf2dmX15eMtXYCKlLqhOIexY/3RqHLKph9CaABJWOlNY3wwcAawEvgNcBGx190/lL7xg+s5ASoVGE0k5ydV6Br9y91NG/TyK5Aii83IZbBhKBiIi6cv2C+RhwzcdXzOzecAAsCDb4EREpPjSKTr7oZm9ieSaBttJTpH7nXwEJdWlXG5jZHNLSKTUpZMMvu7uh4DvmdkPgVnAwfyEJdUikUjQ0dFBc3Mz/f39I19wNjU1ldSb6mRxzpw5k49+9KMlHbtIGOn8i/358C/ufsjdXxrdJpKJ3t7ekTdYSI6Bb25upre3t8iRjTVZnJ2dnSUfu0gY0yYDMzvOzE4DImYWM7Plqe1skqOLRDJWLkVRk8UZtOhMqcUuEkaY20T/FbgMiALfHNV+APhiHmKSKlIuRVGTxRm06EypxS4SxrRXBu5+u7uvBC5z95Wjtg8XY14iqSzlUhQ1WZxBi86UWuwiYaRTZ3AccD0wz93PN7MlwLvcfW0+AwyiOoPKotFEIoWRq6Kz+4A24EvufqqZ1QHd7r4sd6GGo2QgIpK+XC1uM8fd7zKzLwC4+6CZTbUmMmZ2G8mZTf/g7m8PeN6AbwEfAF4jeStqexoxiQQuE1lXN/Gfdth+kPvpH0BXEFLiJlvoYPwGPAQcA2xPPT4DeHiafc4ClgNPTfL8B4D7AEsd75dhYtHiNjJsYGDA29raxize0tbW5gMDAxn1c8/9YjJhF7wRyTemWNwmnWSwHNgC/Cn18z+BU0LsN3+KZPAvwCWjHu8C6qc7ppKBDNu6devIm+zwFolEfOvWrRn1c3fv6ekJ7NvT0zNtPEH7rl69OuPjieTSVMkgnevUp4HvA53AXuDWVELIxvHA86Mex1NtE5jZFWbWZWZd+/bty/K0Uini8Xjg+P94PJ5RP8j9YjJhF7wRKaZ0kkE78Dbg/wLfBhYC383y/BbQFviNtrvf4u4N7t4wd+7cLE8rlSIajY4M7RwWiUSIRqMZ9YPXawrG901nMZnRhhe8yeR4IoWSTjJY5O7/w91/ltquAE7O8vxx4K2jHkeBPVkeU6pILBajtbV1zFj/1tZWYrFYRv0g94vJhF3wRqSY0hlaug642d1/kXr8TuAT7n71NPvNB37owaOJPgisIvlF8juBf3b306eLRUNLZTSNJhIJJ1d1BjuBRcDvUk0nADuBBODufkrAPhuAs4E5JL9nuA6YQXKHm1NDS1uA95McWvpJd5/2XV7JQEQkfbmqM3h/uid290umed6Ba9I9roiI5FboZODuz+UzEBERKR7dtBQRESUDERFRMhAREZQMREQEJQMREUHJQEREUDIQERGUDEREBCUDERFByUBERFAyEBERlAxERAQlAxERQclARERQMhAREZQMREQEJQMREUHJQEREUDIQERGUDEREBCUDERFByUBERFAyEBERoK7YAVSDRCJBb28vfX191NfXs3DhQmpqlIdFpHToHSnPEokEHR0dxGIxVq5cSSwWo6Ojg0QiUezQRERGKBnkWW9vL83NzfT39wPQ399Pc3Mzvb29RY5MROR1SgZ51tfXN5IIhvX399PX11ekiEREJlIyyLP6+noikciYtkgkQn19fZEiEhGZSMkgzxYuXEh7e/tIQohEIrS3t7Nw4cIiRyYi8jqNJsqzmpoampqaWLZsmUYTiUjJUjIogJqaGhYtWsSiRYuKHYqISKC8fzw1s/eb2S4ze8bMrg14/mwze8nMHk9tX853TKUgkUiwa9cuHnroIXbt2qWhpiJSVHlNBmZWC9wEnA8sAS4xsyUBXR9193ektr/PZ0ylQLUHIlJq8n1lcDrwjLs/6+6HgY3Ah/N8zpKn2gMRKTX5TgbHA8+PehxPtY33LjN7wszuM7OlQQcysyvMrMvMuvbt25ePWAtGtQciUmrynQwsoM3HPd4OnOjupwLfBu4JOpC73+LuDe7eMHfu3NxGWWCqPRCRUpPvZBAH3jrqcRTYM7qDux9w91dSv/8ImGFmc/IcV1Gp9kBESk2+h5Z2AgvNbAGwG7gYuHR0BzM7Dtjr7m5mp5NMUPvzHFdRqfZAREpNXpOBuw+a2Srgx0AtcJu77zCzv0k9fzNwEXCVmQ0C/cDF7j7+VlLFUe2BiJQSK8f33YaGBu/q6iroOcOuSXD48GE6OzvZvXs30WiUhoYG6urqJuwLhF7jQOshiEgumNk2d28IfNLdy2477bTTvJCGhoZ806ZNHolEHPBIJOKbNm3yoaGhMf0OHTrka9euHdPvtttu882bN0/YN6ht/PHSObeIyHSALp/kfbXob+yZbIVOBj09PSNvxsNbJBLxnp6eMf0ee+yxwH6rV68O1Tb+eOmcW0RkOlMlA91rCCFsXcDu3bsD+42vLJ6sLajOQDUJIlIISgYhhK0LiEajgf3G39+frC2ozkA1CSJSCEoGIYStC2hoaKClpWVMv5tuuonGxsYJ+wa1BdUZqCZBRApBo4lCSnc00Z49e5g3bx6NjY0aTSQiJWGq0URKBnl24OAAn7vrCW78yKkcPWtGscMRkSo2VTLQx8s8++nTe/nJ03v59517ix2KiMiktNJZSIODg3R3dxOPx4lGo8RiMRKJxLQFZnd2vgjAXZ1xLoxFR46nWz8iUkqUDEIYHBxk/fr1XH311fT39xOJRGhra+PgwYNcddVVI2033XQTG34/l/98aXiy1meoNQeMrudeZP61944cc+HsIbZcf/HIvu3t7TQ1NSkhiEhR6J0nhO7u7pFEAMlx/jt27BhJBMNt11xzDfNf7SExcHBk3yFPJoaBode/m5lZa2xd9/da3EZESoaSQQjxeHxC4VcikQgsBvsv/Ik/3P3VMQlhtMiMWj6zfCYHntk2YV8VkolIsSgZhBBUTFZbWztpgdmh3z3JC//2jyQGDo15fmZdDS2Xxjjn7cHFaSokE5FiUTIIIRaL0draOqbwa8mSJaxZs2bSArOamUeCJzCcWXU11BjU1hgHDg6okExESo7qDEKaajRRUIHZp+95ll1/TLC4/mi+8IG38Q/39fB03wHOWHAMG644Q6OJRKTgVHRWBJff3sXpf/YWPnXmAmpqjKGEs/axZ+n87R+5tTl4OnERkXyaKhlUzdDSdD6JB10FDA4OjlwFHH/88TQ0NDA4OMj27dvp6+tj3rx5xGIxampq6Orq4i9m9TGPfg4ePA4zo7Ozkzf29dG8YB79/f3MnDlT01GISOmYbG7rUt7SXc8gnQViBgYGvK2tbUzf++67b8KiNR0dHYEL2WzYsGFMW1tb24S2tWvX+qOPPqrFbUSkoKj2xW3SWSBm69atE/ref//9E9o2btyY1UI2Dz74oBa3EZGCmioZVMW9hnQWiAmqKXjhhRcmtL3yyitZLWSzd+/eCW1a3EZEiqUqkkE6C8QE1RTMnTt3Qtvs2bOzWsjm2GOPDRWPFrcRkUKoimSQzrj+oJqCRCIxYdGaGTNmBC5ks2TJkjFtra2tE9paWlqYMWOGFrcRkZJRNUNLczWaaHj/xsbGMaOJ6uvrWb58+choouG2hoaGkdFEo/fVaCIRKTTVGYiIiOoMJpPtJ+6g/UcvezlcjzBr1qw8vgoRkexVbTJIJBJ0dHTQ3Nyc0ZoCQfvfe++9/OY3v2HVqlUjbS0tLVx66aVKCCJS0qr2xnNvb+/IGzmkv6ZA0P6HDx8eSQTDbatWraKzszM/L0JEJEeqNhlkO34/aP+gegTVBIhIOajaZJDt+P2g/YPqEVQTICLloGqTQbbj94P2D6o9aGlpobGxMT8vQkQkR6p6aGk+RxONrinQl8ciUgpUZyAiIlMmg7zfJjKz95vZLjN7xsyuDXjezOyfU8//ysyW5zsmEREZK6/JwMxqgZuA84ElwCVmtmRct/OBhantCmBNPmMSEZGJ8n1lcDrwjLs/6+6HgY3Ah8f1+TDQnppu+xfAm8xMw29ERAoo38ngeOD5UY/jqbZ0+2BmV5hZl5l17du3L+eBiohUs3wnAwtoG/+NdZg+uPst7t7g7g1z587NSXAiIpKU77mJ4sBbRz2OAnsy6DPGtm3bXjCz5zKMaQ7wQob7lqJKej2V9FpAr6eUVdJrgfCv58TJnsh3MugEFprZAmA3cDFw6bg+m4FVZrYReCfwkrtPOX+Du2d8aWBmXZMNrSpHlfR6Kum1gF5PKauk1wK5eT15TQbuPmhmq4AfA7XAbe6+w8z+JvX8zcCPgA8AzwCvAZ/MZ0wiIjJR3qewdvcfkXzDH91286jfHbgm33GIiMjkqnFuoluKHUCOVdLrqaTXAno9paySXgvk4PWU5XQUIiKSW9V4ZSAiIuMoGYiISPUkAzO7zcz+YGZPFTuWbJnZW83sZ2a208x2mNmnix1TNsxslpltNbMnUq/nq8WOKVtmVmtm3Wb2w2LHki0z+62ZPWlmj5tZ2U8XbGZvMrO7zawn9X/oXcWOKRNmtij1dzK8HTCzz2R8vGr5zsDMzgJeITkP0tuLHU82UnM31bv7djObDWwD/tLdny5yaBkxMwOOdPdXzGwG8Bjw6dRcVWXJzD4LNABHu/uHih1PNszst0CDu1dEkZaZ3Q486u7fMbM3AEe4+5+KHFZWUpOC7gbe6e4ZFeRWzZWBuz8CvFjsOHLB3fvcfXvq95eBnQTM51QuUpMUvpJ6OCO1le2nFDOLAh8EvlPsWGQsMzsaOAtYC+Duh8s9EaScC/w600QAVZQMKpWZzQdiwC+LHEpWUrdVHgf+ADzg7uX8ev4J+DsgUeQ4csWBn5jZNjO7otjBZOnPgH1AW+o23nfM7MhiB5UDFwMbsjmAkkEZM7OjgO8Bn3H3A8WOJxvuPuTu7yA5N9XpZlaWt/LM7EPAH9x9W7FjyaEz3X05ybVHrkndci1XdcByYI27x4BXgQmLbpWT1K2uC4BN2RxHyaBMpe6tfw+4w907ih1PrqQu2R8C3l/cSDJ2JnBB6j77RuAcM1tf3JCy4+57Uj//AHyf5Dol5SoOxEdded5NMjmUs/OB7e6+N5uDKBmUodQXrmuBne7+zWLHky0zm2tmb0r9HgH+AugpalAZcvcvuHvU3eeTvHR/0N0/VuSwMmZmR6YGKZC6nXIeULYj8tz998DzZrYo1XQuUJYDL0a5hCxvEUEB5iYqFWa2ATgbmGNmceA6d19b3KgydibwceDJ1H12gC+m5oEqR/XA7akRETXAXe5e9kMyK8SxwPeTnz+oA/7V3e8vbkhZ+5/AHanbK89SxpNjmtkRwPuAK7M+VrUMLRURkcnpNpGIiCgZiIiIkoGIiKBkICIiKBmIiAhKBiIigpKBCABmdpmZzQvRb52ZXTTF8w+ZWUOOY3uTmV096vHZlTA1tpQWJQORpMuAaZNBkbwJuHq6TiLZUDKQimRm81OLl9xuZr9KLWZyhJmdZmYPp2bg/LGZ1ac+6TeQrEp93MwiZvZlM+s0s6fM7JbUFCDpxnCemf3czLab2abUxILDi8V8NdX+pJm9LdU+18weSLX/i5k9Z2ZzgH8ATkrFdkPq8EeNWqDljkziExlNyUAq2SLgFnc/BTgAXAN8G7jI3U8DbgOud/e7gS7gr939He7eD7S4e2NqIaQIkNYCNak38f8D/EVqxs8u4LOjuryQal8DfC7Vdh3JuYyWk5wQ7oRU+7Uk56p/h7t/PtUWAz4DLCE5LfOZ6cQnMl7VzE0kVel5d9+S+n098EXg7cADqQ/StUDfJPuuNLO/A44A3gLsAH6QxrnPIPlGvSV1rjcAPx/1/PBMs9uAptTv7wEuBHD3+83sj1Mcf6u7xwFS81PNJ7lCnEhGlAykko2feOtlYIe7T7nmrZnNAlpJLvX4vJl9BZiV5rmN5CI9l0zy/KHUzyFe/3+Yzq2eQ6N+H30MkYzoNpFUshNGLXZ+CfALYO5wm5nNMLOlqedfBmanfh9+438hdZ9/0tFDU/gFcKaZ/XnqXEeY2cnT7PMY8JFU//OANwfEJpIXSgZSyXYCnzCzX5G81fNtkm/s/2hmTwCPA+9O9V0H3Jy65XIIuBV4ErgH6Ez3xO6+j+QIpQ2p8/8CeNs0u30VOM/MtpNcsKQPeNnd95O83fTUqC+QRXJKU1hLRUqtDf3D1BfAZcHMZgJD7j6YunpZk1oKVCTvdJ9RpHScANxlZjXAYeDyIscjVURXBiIZMLPvAwvGNf9vd/9xMeIRyZaSgYiI6AtkERFRMhAREZQMREQEJQMREQH+P+UlBQLwsXuHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_centers_and_black_data(iris, (c1, c2))\n",
    "#plt.savefig('2means_demo_center_position_4.png', dpi = 300, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris[\"dist1\"] = c1.dist(iris[[\"petal_length\", \"petal_width\"]])\n",
    "iris[\"dist2\"] = c2.dist(iris[[\"petal_length\", \"petal_width\"]])\n",
    "iris[\"cluster\"] = iris[[\"dist1\", \"dist2\"]].apply(get_cluster_number, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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09NOvg+jA5CHyRoOBUqr7DJ0M171gi9mIwISL4fz7YcFP7QQx2D0Cs2+Ayx6B0Y4P9cFjbK2RkfPth6gE2UnlJf+EpJme10k+DS56wFZRCw6zOX+mXNFtb9ND0gy46mkYkGD7PmMZzL+jR62A6jP7DJRSvUh5rh3Hj06EMEf1wcoiqC62FcoiYm1bTTlU5NmVQwPibVt9jS1IExphC720piTLzjHEjugZH7xl2bbAfWxSQDaX9Yt9BkqpXiTaS22PqEH2y1V4tP1yFRIOg1N9u87AER3qnt+0FbwCyK/BQERGAs8CidjsS08YYx5qdowADwEXAZXATcaYTf7sl1LKC2Og8JD96zVmmB2aEYGSTCg+aid5h4yzk7vluXb9f2ikrTEQFmVXxhQcAAEGj4XIgYF+R92jogAKD9rhqCFjPYNXaxrqofCAnQQfmAxxKf7rZxv8fWdQD/zcGLNJRGKAjSLyoTFml8sxFwLjHF+nAX9zfFdKdRdjYO+78NqtdoVL2AC4/HH7AfX8NXaoJijEFmYfczasutHuDRCBU26FuTfDe/8Ghz61rzd2MVz8AAwaFdj35W/5++DVH8CJLfbx9KVw7u/sMFBb6qphy/Pw3q9sOoqowXDtChi1wK9dbolfJ5CNMSea/so3xpQBu4Hm922XAc8aax0QJyI+/CaVUl2m8BC89gPnUsfaCnj9Vjj8hQ0EYMfe3/83u7a/KQ+/MbDjFdjztjMQABz4wH0/QV/U2AgblzsDAcC2F+DoN76dn7cb3vmZDQRg7w5eu83u0A6AbltNJCKpwCxgfbOnRgDHXB5n4hkwlFL+VJZtA4Cruiqo9bLWvyTL/XH8eDj4medx+z/qsu71SDVlsO99z/ZMHxe3lBzz3tYUfLtZtwQDEYkGXgXuMsaUNn/ayykeS5xE5FYRSReR9Ly8wPyylOqzoofZ8X9XIeEQ5mVdfvNcQAUHIO07nsd5W+vfl4RHw+hzPNuHz/Lt/NhkL23DYcCQzvWrg/weDEQkFBsIVhpjXvNySCbgmqQ8GTje/CBjzBPGmLnGmLkJCQn+6axS/dXg0XaOIMRRVyMkHC57FEadDhFxtk2CYNFv7T6AOJe5gHHnw8TvwkiXqb5RC2DChd3W/YAICoZTboF4l01vky61vzNfDJ0E5/+3cxNeeKz9bxATmFFyv+4zcKwUegYoNMbc1cIxFwN3YlcTnQb8nzHm1NZeV/cZKOUHjY12ZUvpCbsEcshY+0FVlOFYTTTIriYKjbDHFB60dxPx4+0+gIoCKNgPCMSPsxOi/UFZrn3fIeH2d9G0R8IX9bX23Ip8u5JosH/T8Le2z8DfweAM4EtgO87CnvcCKQDGmMcdAeMR4ALs0tKbjTGtftJrMFBKqfYL2KYzY8xXeJ8TcD3GAHf4sx9KqU6or7H7CsKinZvC6mvtnUFIRNt/zZbn2hUzMcN7VP5+N1UlUFNqdz+HRQW6NwGhO5CVUi3L3w+f/Ql2vWGHiC78H5tC4tu/w5aVdijo7HvtnEHzzKO1lXbvwgf/btNMnHqr/RroZeI0kI6thzW/huytMOZcOPc+GDY50L3qdj00TCulAq62At671+4jaKy36+JXLrHVutKfsumgS4/Dm3fCsXWe52dthFdvgbITdpnq2odg60vd/z5aU3AIViyB45ugsQH2v283kXlLi93HaTBQSnlXmmU3j7lqqLWTzM1leSkwc+xbz7ZNy+1Ec09ReNAOD7nK3WFLZvYzGgyUUt6FRNoVRM2Fe9l7MMDLcu8YL7n6B6Xa1Ug9hbeVPyHh7csv1EdoMFBKeRc3Ei74H/e2sedB4nSbrK7J4NEw8hTP81PmwyCXYjTBYXDWv9m8Rz1FwkSY+X33tnN+672ITh+n9QyUUi2rq4ITW+0u46h4W0hmQLzNv5O3x+4zSJoJiVO9n1+YYc+vr7KFbRKn2eR2PUlFHhzfCuXZ9s4laYb3u58+IGD7DPxFg4FSSrVfa8FAh4mUUkrpPgOl+qUjX8Ou1ZCzw+YWGn2Wbd/7DmR8CSkLYOLFMHyG57lVpZDxOWx61iZbm/19GDHH87j2OL4Fdr8Nx76BtDNtXqOowbDvQ9j5qq19PO0qSJjgeW5dlX0/G5dDaBTMuRGST/Ve5vLENtj2EuTusnWIx5ztLKfZloo8OPSZrUEwdLKtxZw0rRNvumfRYSKl+pvjW+HFpXbpaJPZN4JphM3POdtGzIGrl3tW39r6Arz+Q+fj0Ej4lw8gaXrH+lOUAS/dYDd9NRl9Jsy+CV652dkWNwpuftdz09r+D+3+hyYSBDevgZR57sfl7YOnF0NVkbPt3N/DGXe13UdjYO3/g4/uc7ZFDoJbPrD5iHoJHSZSSjnl7XIPBABbVnguBc3aCLl73NsqC+HzP7u31VVBppc9Bb7K3e0eCAAOfW43urkqPuLZn/o6+OZR9zbTCLtXe14ne7t7IAD48gHP+gzelGbBFw+4t1UVQfaOts/tJTQYKNXveFvN08IKH4+VPy2tBPLDCiFvq448uiP2TsCDlzavXRTfu+61Pz1sZVQnaDBQqr9JmAQDR7q3zb7RJpRzlXyqXYfvKmoQnPlr97bQKBjZatb51g2dbJenuhpzDhDs3jZotO27q+AQmH+7e1tQMEy+1PM6w6Z5ptVeeA/E+lBYcWCyPdZV1GAY1sKS2l5I5wyU6o+OfAN73oHcnXYjWdpCwMC+D+wE8qjTYfwF3ucBqkvhyFqbqC52BEy/FkbM7lx/jm+FfWvg6DpbNW3c+fbD9sDHsOtNO/4/6bstTyAfWw+bnrOBadb3IPkUZ9EYV9k7bK6l3D0w/Ro7We1rZbGKfFsTetsqW5hm6lUt76/ooXSfgVJKKZ1AVkp1UHUJvLjMfld9mu4zUKo3Mgayt0HOTptYLWkmDBnT9dfZ9aYdTnr/32HihZA0C2IDU6NX+ZcGA6V6o6Pr4NlLbUppsGP3N7zR9Wvev/4/+33zs/Zr9Nlwxd+9ZyRVvZoGA6V6m7oqu+a9KRCAXQd/+KvOB4NnLoXDn7f8/KFP4a/j7cTrjV7W8qteS+cMlOpt6mugOMOzvex451974S/sjuLWBIfBwl92/lqqR9FgoFRvExkHc/7Fsz1tYedfO20hLFvVekC4+H/t8k/Vp2gwUKo3mnqlLRQTMdDOF1z1ZOeTxTVJWwhLlkNI84pkYgu/zPpe11xH9Sg6Z6BUbxSbBGf+CmbfYKuOeSs72RnVJXbTlgTZYaGGWhscBib3qRQMyknvDJTqrUQgdnjXBwKwK4dqK226haUv2O91Ve5ZTVWfoncGSvVWZdmQfwBCwuwqosg478dVl0L+fqirsLV9m6eA9iY8Fhb/EebdDkFBdvXQusds3QCwmT4LD9r0D/ETICLAZSLLc+17DAq2v4vmOYhUmzQdhVK9Ue4ueGEZFB22jydeAhf92TPpWnkufPwH51/0MYmw7OWO1x4Am9/n+WuhNNM+nn4tnPcH+9qBkL8PVt1k8yyBLdTz3YdhUEprZ/VLmo5Cqb6koR7WPe4MBAB73oYj6zyPzdrkPrRTlg2f/BFqqzp27foa+OIvzkAAtnJYZgD/ONvygjMQgK1GdvDjgHWnt/I5GIjIlSKyX0RKRKRURMpEpNSfnVNKeVFTZjOLNpe9zbOt+Ihn27F1UF3csWtXldiMpc0VHOjY63VWXbXdCNfcUS+BUbWqPXcGfwYuNcYMNMbEGmNijDGx/uqYUqoFEbEwbrFnu7c00t7yFY0+q+Nj6pFxNuV1c0MnerZ1h9AIGH+hZ/voM7u/L71ce4JBjjFmt996opTyTVAwzL3FfV/BnJth5DzPY4fPgjN+5qwGFj/BFqcJCe/YtUPCYMFPYegU+1gE5t0BI7wOQ3eP6VdDqssmuKlLumYDXj/T5gSyiFzp+PFMIBF4A6hpet4Y85q/OtcSnUBWCluPuPCQ/WAfPAbCorwfV18N+QftaqJBaRDdBUtRK/Kh8LDdqTxkrP0LPZCqSqDwAASF2N9FeHRg+9NDtTaB7MvS0u+6/FwJuN6fGqDbg4FSCjvU48twT0gEJE7p2msPiLdfPUXkwK7bgd1PtRkMjDE3A4jIAmOM28yRiCxo7VwReRq4BMg1xnjUhxORs4A3gaZlEa8ZY/7gU8+V6gsq8u1ftdEJdi6gPSoL7cRtUIhzvsAYKD4GNNo6x02lH0uy7EqggSNaHyIqPQ5lORA91B4LUFthVyGFxUDMUNtWX2MzpQaHO49TvVp7Np09DDSfofLW5mo58AjwbCvHfGmMuaQd/VCq9zPGrgha/RO7RHTkPLj4AUic5tv5xzZA+tOwfZXd+HXGz+xE6r41dumnabBj+afcAoe/hA/uhapimHa1zWk0OM3zNQ99Du/92u5hSJgIF/wJYpLg/XvtUs3YEXDJ/9pNXV/8Bba+YHMjnfefMOUKHZrp5doMBiIyHzgdSBCRu12eigW8VJx2MsZ8ISKpneqhUn1R/n54/hqb4gHscs9XfwA3veNbgfY9b8PW5+3PNaXw8e8hLsV+b/LVg3Zz2Rs/dLZtewkiB8H5/+VeMD53D7z2AyjPsY/z9tiUFOV5zmWspVnw/m9g/AWwZaVtqyqC1XfCoFE6advL+bKaKAyIxgaOGJevUmBJF/RhvohsFZE1ItLFA5tK9VBFh52BoEnebig51va5efthz1ue7cfWw5QrnY+Dw2xZzOa2vWR3Jrv155AzEDQZPMZzP8Oo+fZupLnjm9vut+rRfJkz+Bz4XESWG2O87GDplE3AKGNMuYhchF2pNM7bgSJyK3ArQEqKbjNXvZy3PEJhA2xOoLZEDYK4NCg46N4elwL7P3I+bqzznsTO22qbiEH2TqGxwdlWXWIniSvynW2lx+3qoeaBo3kaDNXrtHlnICJvichq4GERWd38qzMXN8aUGmPKHT+/C4SKiNclCsaYJ4wxc40xcxMS/JClUanulDAJTvlX97bz/+R9LL+5AfEw/w4bPJoMHg3Jp9hlpE0i4myb6x6A4DCbRyi8WWK5xOmw4GfubZGD4cK/uKesDo+Fs+51r3WQNNNeR/VqvuwzaNrKdyV2n8EKx+OlQIYx5t42zk8F3m5hNVEidjObEZFTgVewdwqtdkr3Gag+obIIsrfbv7IHp9o00W2VnHR1+Cso2G8/4BMmQPJcu/Y/ZyeYRhg6GeLH2r/ms7dDbTnET4Rhk73XJCjPtcM9JZk2NfbwWTYg5Oy0a/ijhsCwaXblU84uO68QGmUnvXVFUa/Q2j4Dn7OWisgXxpiFbbU1e/4F4CwgHsgBfgeEAhhjHheRO4EfAfVAFXC3MebrtvqiwUAppdqvs5vOmiSIyGhjzCHHi6YBrY7XGGOWtvH8I9ilp0r1Xg21QBAE+7k8SHWZvXNo6zr1dSDG3jG4tjXUuM8VNDbavgd693Bb6qrt3gitsOZX7fnX+zPgMxE55HicCtzW5T1SqreoKbNr89c9BmHRdhw/Zb7N39OV8vbB3ndh15t2TmHOzd4L0tfVwNGv4euHoaHO9mfUAsjaABuehrLjMGOpTXJXUwYbl0PmBpvbZ9JlEDeya/vdWUVHYfebsP0VGHkazLkRhumCQ39pV3EbEQkHmtIT7jHG1LR2vL/oMJHqEXavhpeudz4WgZvehVGnd901Gurhg3+H9X9ztoXHwvWveU7aHv4Snmm2f3PJP+0+gNoKZ9s1z8E7d0NFnrNt1g1201tHE9h1tboqWP1T2P6Ssy0mCW75wK6aUh3SqeI2InKO4/uVwMXAGMfXxS5J7JTqX+pr4OvH3NuMgT3vdO118vfBxqfd22pKve8f2P6yZ9umZz13NefvdQ8EAFtWQFFXrxzvhKIM2NFsP0PZCbs5TvmFL8NEZwKf4J6wrokmqlP9k4j3lT/tWQ3k03WC7Nh/fbOb8CAv/+uGeslaGhoJNSXNXtNL4oCgEFvruKeQYPtl6t3bvb1v1SXa/K9vjPmd4/vNXr7+xf9dVKoHCg6DBT9xn9QMCYcJXgqtdMbQibZ+gKvY4d5zGE29CoJDnY8lyI6zF2a4H5cwydY1cHX6TyAutSt63DUGpcFpt7u3DZsKQycFpj/9QHuWlh4E1gFfAl8YY3b5s2Ot0TkD1SPU19rJ2V2r7QawiRfD8Nldv+qlOBOOfAUHPoRBY2D8YrunoLnGRji+0Q5VNdTCxO/a4zLTYf/7UHrCBqtRp9uhpgMfwYntMO5cGHVG19Q56ErlOZCx1vYzaSaMXeS9cpvyWVftMwgHTgO+AyzATiRvNcZc0VUd9ZUGA6WUar9OTSC7aADqHN8bsZvIcls9QymlVK/QntmYUmA78CDwD2NMgX+6pFQ3Ksu2wyiFB20O/xFzfUsh3ZqMr+1wTX0tDJ9p6/Pm7bY1CCrzbdvIec5rFx+xY+EjT7N1CDK/hdy9MCjVLh8dOAKyNsOJzRCdCCNPsYVrjm+BrHQ7cTzyFNv/7B32NU29fS9JM3SzlvJJe4LBUuAM4HbgX0Xka+zcwcd+6ZlS/lZdYtfwuy7JPP2ncM5vOr7e/vBXsOr7Ns8/2And778Ob/3E1itucvnjsPst2OuyFPX8/7Z5gdY96mwbf6GdBH7hOmfbsKlw0V/hmYucWUYHxNv9A89fYzeUgU0md+PbNlAo1Qafh4mMMW8aY36J3XX8LnAT8Laf+qWU/+Xt81yb/83DtpRkRx361BkIwO4EPrHVPRAAfPKfnjWEa0rh28fd2/atsZXHXOXssHcErummQyJscGkKBGAzmKY/afc/KNUGn4OBiLzqWFH0EDAAuAEY5K+OKeV3dRWebabRs+hMezTfzAXuaaWbVBbYkpFu125w/4A/eb6Xjf71zfoYHms3ZTVXnGlXGSnVhvZMIP8JGG+MOd8Y80djzOfGmJP/ykXkvK7vnlJ+NHgsxCS6tyVOs2vcO2rsIs+2hAmem6Vm3whHvnFva2yExBnubdFDPdMvBIfZ9NSu8vbA5Ms8r33KLRDcanVapYD2DRNtMMZ4+bPlpP/pgv4o1X3ikmHZy3Y9/oB4mLEMrnyycxPII+fBZY/aovEDR8Ki++wmr2tX2D0I0cNsofo5N8N590HaWfba0661+xQuedCWrhwQD2PPhSXLIXWhLXgfPRRGzocb3oTkU+0cw8BkGxiuXQlpZ8JVT9m1+INS4dKHYfTZXfGbUv1AuxLVtfpCIpuNMbO65MXaoPsMVJeqq7aTyZGDISS07eN9UXTEzhfEj3W2lefZAjNxo5ypH6pK7Aqj2OHOVBa1VTbD6IAEiHCUwTTGnh8W5Z6GuiLfTlK7DjlVFtnhrs6uilJ9TlfVM2iLzlKp3ik0outz+g8a5dkWnYBHCZDIgfbLVVik505bEYgZ6vmazSehwdZIVqqdNOuTUp1RkgV5e23BmYSJdiinPNe2NdTaHEBxyd7Pra20Y/3luXZeIGGCLUrvTf4BuyIpMs5ep+mOwReVRXafQ22FLWbvS51l1e90ZTDI6MLXUqrny9kFz18LJUft4+RT4OIH4Z1fQOZ62xabDN9b5VmUpbYC1j8OH//BPg4OtbUHJnlJDpyxFp6/2lmTYM6/wKL/gKjBbfex9AS89ytbGAcgchB8/1UYMaf971f1ab7UM7iyta+m44wxWttA9R+NjbDpGWcgALsf4PAXzkAAUJoJ6f/0XDKat9cZCMDOL6z+sa3u5aqyEN7+mXtxmo1Pw4ltvvXz+CZnIAC7B+Lj/3R/PaXw7c7AWx2DJlrPQPVP9VVwZK17W/QwyN7ueeyRtXbvguvEb3mO53FVRVBVAINclpJWl9hiNM15O9+b4mOebcc3QXWpzbSqlEObwcAYc3N3dESpXiVsgK0b7PrhX3IMFtwF2150P3by5e6BAOwcQVAINLoUbxk4EmKGux83IAFSz4CMr9zbvU1Qe5MwwbNt/AUQpSuNlLt2lTYSkYtF5B4R+W3Tl786plSPN20JTHTUHJYgmH2zrQtw2o/sY7C5haZf43lu/ARY8rRzSejAZFjyFMQMcz8uPBou+B9IcGwyC4208xLeitt4M2I2LPqd3agGdl7jOz+HkLD2vVfV57WnnsHjQBRwNvAksAT41hhzi/+6553uM1A9Rk25rdcbFAyDR9sEd/U1duVPY73d/BUe0/L5RRl2tU9skuduaFcVBXZ+IjwGBo1uX4nKhnrbn/oqu8chMs73c1Wf0lXFbbYZY6a7fI8GXjPGLO7KzvpCg4FSSrVfV206a8qMVSkiw4ECQBcsqy6RV1ZNQUUt8QPCiY/pYPpof6sus6uDQqOcY/b1NXa3cVCQrSEcrFt3VO/Unn+5b4tIHPAXYBN2JdGT/uiU6l/WHyrg5y9vJbOoiuRBkfz1mhmcltbDJjjz9sG7v4TDn9mhmsV/tLmAvvgLbH3BTgYv+Cmcdpud9FWql2nPBPKfjTHFxphXgVHYGsh/9E+3VH9xrLCSW5/bSGaRvfHMLKrituc2kllUGeCeuaivgc//ZAMB2JoBb/0UsjbClpU2D1BDrQ0MR74OaFeV6qj2BIOT+XaNMTXGmBLXNqU6Iqu4kpKqOre24sq6nhUMyvNs4ZjmCg56th3Qwn+qd2pzmEhEEoERQKSIzAKaCqrGYlcXKdVhcVFhBAcJDY3OhQwhQcKgqB609DE8xlFfuNmuX29r9Yf5uORTqR7GlzuD84EHgGTgQeCvjq+fAff6r2uqPxgTH81vLprk1nbvRZMYHR/dwhkBEDnQrvUPcclsOv4CSJ7rnjU0YRKMPaf7+6dUF2jP0tKrHPMFAadLS/uWqtp69maXc6KkiqSBkUxIjCYyrAeuysndAwX7bYnJoZNtSurCwzbzaFAoDJsEsSMC3UulWtRVS0vXishTwHBjzIUiMhmYb4x5qkt6qfqtyLAQZqbEMZO4QHeldUMn2i9Xg9M0JbTqE9ozgfxP4H2gKXnKPuCuru6QUh1Snm936balphxKj7d9nDFQVQz1dW0e6lVtpV115Kq+xiaeU6oHak8wiDfGrAIaAYwx9UBrNZERkadFJFdEdrTwvIjI/4nIARHZJiKz29EfpaAsGzY/ByuugJVXwbZVLQeFQ5/BKzfBM9+16aNzdnk/ruCQff4fZ8PqO3xPFw1QVwP7P4TnroCnz4etL0JlMRxdBy9dD08ugq8f9S0gKdWN2jNMVCEiQ3CUtxSReUBbf+YsBx4Bnm3h+QuBcY6v04C/Ob4r5ZtDn8Gbdzofv/YDuPoZmHK5+3HHvoUXlznz+H/5V1th7KK/OGsPg71zeP9e2LfGPi48BAc/gX/92LdMoVkbYOUS5+PXb4Olq+Dl6+2dAcAH90JVIZz9m/blGFLKj9rzL/FuYDUwWkTWYj/gf9zaCcaYL4DCVg65DHjWWOuAOBFJakefVH/W2Ahbnvds3+mlxEbubs+CLttehPz97m3FR5yBoElFHuTv861P+97zbMvZ7gwETdb/DcpO+PaaSnWD9twZ7AJeByqBMuAN7LxBZ4wAXKtvZDraPP4vEZFbgVsBUlJSmj+t+qOgIGcKaFcRXgrCB3vZtxAaZctNur1mqGedgZbO9yYizrPNW7rosGjPaysVQO25M3gWm4Liv4CHsUM7z3Xy+uKlzetaV2PME8aYucaYuQkJmvtFOcy50f1DNSTcc4gIIGmGTTHt6js/h6HuexwYnGYL1LhKme95XEvGLbYf9E0kCIbPsYVrXJ37e4ge6ttrKtUN2nNnMMEYM8Pl8acisrWT188EXP8vSQZ0Zk35Lu0sWPYKHP4cJBhGL7QFZpobNtkWk8lYa4eCRp0OKad7HhccCvN+ZIvCHFtvN5KlLvD9gztpOty8xtZCrq2AMWfZYHD963D4S5v1NHWh3bCmVA/SnmCwWUTmOcb2EZHTgLVtnNOW1cCdIvIiduK4xBijA6nKd8Eh9gN3zFltHzt8lv1qy4B4mHix/eqIpOn2y1X8OPulVA/VnmBwGnCDiBx1PE4BdovIdsAYY6Y3P0FEXgDOAuJFJBP4HRCKPeFx4F3gIuAAdi5C6y0rpVQAtCcYXNDeFzfGLG3jeQPc0d7XVUop1bV8DgbGmCP+7IhSSqnA0R0vSimlNBgopZTSYKCUUgoNBkoppdBgoJRSCg0GSiml0GCglFIKDQZKKaXQYKCUUgoNBkoppdBgoJRSCg0GSiml0GCglFIKDQZKKaXQYKCUUgoNBkoppdBgoJRSCg0GSiml0GCglFIKDQZKKaXQYKCUUgoNBkoppdBg0G0qquvJK6vGGBPoriillIeQQHegrzPG8G1GIQ+8v4+MggqunpPM0lNTGDk4KtBdU0qpkzQY+Nme7DKuf/JbahsaAXjss4OUVNVx33enEBqiN2ZKqZ5BP438bF9O2clA0OSlDcfILq0KUI+UUsqTBgM/iwoL9mgbGBlKWIhnu1JKBYoGAz+bMnwgExOj3dr+/eJJDIuNCFCPlFLKk84Z+NnwuEieuOEUNh8tIre0mmnJccxIHhjobimllBsNBt0gZXAUKbp6SCnVg+kwkVJKKf8HAxG5QET2isgBEfm1l+fPEpESEdni+Pqtv/vUExwtrGTluiP88uWtvL4pk5wSXV2klAocvw4TiUgw8ChwHpAJbBCR1caYXc0O/dIYc4k/+9KTFJTXcPeqLaRnFAHw8sZMrp2bzH2XTiEyTEfulFLdz993BqcCB4wxh4wxtcCLwGV+vmaPdyC3/GQgaLJqYyYZBZUB6pFSqr/zdzAYARxzeZzpaGtuvohsFZE1IjLF2wuJyK0iki4i6Xl5ef7oa7dpaPTMT2SM93allOoO/g4G4qWt+SfeJmCUMWYG8DDwhrcXMsY8YYyZa4yZm5CQ0LW97GZjEqIZHT/Are3M8QmkDtEVR0qpwPD3AHUmMNLlcTJw3PUAY0ypy8/vishjIhJvjMn3c98CZtjACP5+/RxWpR/j64MFXDAlkUtnDic6IjTQXVNK9VP+DgYbgHEikgZkAdcBy1wPEJFEIMcYY0TkVOzdSoGf+xVw44bFcO9Fk6ipbyQiVFNTKKUCy6/BwBhTLyJ3Au8DwcDTxpidIvJDx/OPA0uAH4lIPVAFXGf6SdJ/EdFAoJTqEaQ3fu7OnTvXpKend9v1jDHszS5jf245A8KDmZw0kMSB3nMLbc8sZm9OGTX1jYwfGsMpaYPJK6th1/ESSqrqGJ0QzcTEGCpq69l9ooyc0mpGDo5iYmIMUV6WlTY0GvZml3Igr5yBEaFMSoplqOY1Ukp1gIhsNMbM9facLmr3wfrDhdzwlLMmweyUOB5eOpsRgyLdjtt0pJC7V209uUR0QFgwf79+Dk99dZhP99oVUMFBwhPXz2HTkSIe/ezgyXP/cOkUvjdvFMFB7nPuXx/I5+blG6h3rDRaMGYIf71mZovBSCmlOkLTUbShrLqOP63Z7VaTYNPRYrZlFnscuyGjyG2vQEVtA8u/zqC0uv5kW0Oj4Xerd5JXXut27v3v7iajoMKtraiilvve2nkyEACsPVjAjqySzr4tpZRyo8GgDRW19RzKr/Bozyuv8WjLLPJMKXEwr4KkZn/FZxVXkRAT7tZWU99ImUvQaLr2ES8b0Yoraz3alFKqMzQYtCF+QDhXzPTcJzdxWIxH2ympgzzavjs9ifQjhW5tiycPY9sx9x3IKYMjSY5zH3ZKiAnnu9OTPF5z9NBojzallOoMDQZtCAkO4l/OSOPymcMJEhgUFcpfr57BNC81CWalDOSX508gJjyEkCBhyZwRLJ4yjPu+O+XkncDiycO454KJ3HnOOMYk2I1ns0bG8dj35hDf7G4hPCSYn5w7ngunJiIC8dFhPLJsFlOSYv3/xpVS/YquJvJRTX0DJ4qrCQ8NImlgZKvH7sgqob6hkTEJ0cRE2o1kOaXVVNY2kBgbQaSjFGZhRQ0llXUMiQ4nNrLlDWfVdfVkl9QQGRasFdKUUh2mq4m6QHhIMKnNUki0ZOoI511DaXUdv1i1lQeumeHxQT54QDiDB4Q3P91DRGgIqfH6n0op5T/6CeOjowUVHMqrYEB4MJOSYomOCOVoYQUHcysIDhLGD40mMS6Sytp6MvIrqW1oYNSQAXy6J5cPduXw8e4crpiVfPL1jDEczq+gsKKWYbERjNRKaEqpANJg4INNR4r4xctbOZRfQZDA9fNGcfnM4fz3mj1860hFvWjiUH66aBwvbjjK89/aRK0zkuNoysu3akPmyWBQ39DImh3Z3PPKNqrqGhgYGcqjy2ZxxrjenYBPKdV7aTBoQ1lVHY98euDk8tJGA+/uyCY2MvRkIAD4eE8u88cM4cUNzozdWzOLadpDln6kkNRfv+P1GiVVdfzkxS28decZHhvZlFKqO+hqojbkllWTnuG+NHTCsBi+2u+ZVHVDRiHJzT7Mm/aL1TU4J+rDgj1/7YUVteSVVXdBj5VSqv00GLQhPjqc6SPi3NoO5ZVzatpgj2NnjIyjuLKu1deLDA3mD5dNoVnWCWIjQnyaTFZKKX/QYNCGgVFh/HjRWLcdw3NTB7F4yjDGD3Nu/pqdEsf80UOYN3rIybbmlX3CQ4J4ZNksrpw9gj9ePvVkHqLwkCD+es0MUrS4jVIqQHSfgY/2ZZdxKL+cqLAQJifGEB8bwf6cMg7klhMcJIwbGk1aQjQlVXUczC3no905/HNtBjX1DYSFBFHrqFtw/xVTuWJWMrX1jRzKLye/rIakuEjShgwgqPntglJKdSHdZ9AFxifGMD7RPQXFuGExjGuWlmJgZCizRw3iz+/vobq+gclJsfz6won8ac0edp0oPbmqKCwkiImJsZDYne9CKaW861fBoKSqloqaBuKjwwgLabmoTEVNHVnF1QyMDGFYrJ0Qrqyp53B+OeGhwYwd6gwAO44XEyzCpCTnRrM9J0oxBn61eBy3njmOoCBhaHQYL244xmGXzKTlNXWUVtUzKCrs5K5kb+obGskrqyEiLJhBUWGd+RUopZRX/SIYGGNYf6iA/3xnNwfzyrlk+nDuOHsMafGeCd+2Hivmsc8O8Pm+PMYkRPOL8ycwKDKUlzdm8sbmLOJjwrnr3PGMHzqAD3fn8szXGYQEB3Hrd0azYMwQtmQW88gnByivqWfmyDh2Hi/lWFElD364j6yiKi6YmsimI0UEBcF/vbOHrZnFnDkugZ+fP54JiZ45h44WVvLkl4d4ZWMmSQMj+I9LJnPG2HhCvKxIUkqpjuoXcwZ7s0u59JG11NQ7axIsnjyMh66b5fYXeUF5Dbev3Mj6w879A9fOGUFEWAjPfHPkZNuQAWH8+Jyx3PfWLrfr/O81M/jFK9tocKk/cM8FE1i+NoPcMmfK60umJfGdsUP41es7TrZNSIzh+X89jSHRzonquoZG7lu9k5Xrj55sCxJ44/YFTB8Z5/P7V0opaH3OoF/8eXkwt8ItEAB8sCuHEyXu9QcO51e4BQKAC6cl8ebW425tV8wazlvbTnhc56PduYxNcL/beHVjJqekui9DXbMz2yMN9d7sMo96CLml1bycnunW1mhgX26Zx7WVUqoz+kUwiI7wHA0bGBnqUYw+MjSYAc3G7kur6xnaLLX00cIqhnvJXDo8LpKiZoVnhsdFUljh3jZkQBjNb8hCgoSocM/+NC+CAxAT3nKGU6WU6oh+EQwmJcVyxth4t7bfXjKZ4c2KyUxKiuGuc8e5ta3ZcZyfLhrvtklsf24518xNJsolcMRFhXL2xATE5biw4CBuOSON8hr3jWg/XzyeDRkFbm13nTuO1CHuWVEHR4dz36VT3F5z6ohYpo3wrKWglFKd0S/mDACyS6rZnlVCQXkNYxKimZYcS0So5x1DYXkNm48Vczi/goSYcKYOH8jQ6FA2HivhYF45A8JCmJAYw6yUQXxzMJ99OeUEiTAhMZpT04aQnlHI3pwyausbGTc0mlNHDWRXdgX7csopra4jNX4A00fEEBQUzI6sEk6UVDNqSBRThw/0WtOgtr6RncdL2JdTRlxUGFOHxzJikG5OU0q1X2tzBv0mGCilVH/X7yeQlVJKta5f7DNoyZGCCj7ancuX+/I4a0ICiyYNZeRg36qZ1dQ1kH6kiFXpxwgPCWLJnJFMHx7NNxnFvLPtOMWV9VwyPYm5o+JI9vE1lVIqUPptMCiqrOWeV7ax/rBNT/3Zvjw+35fHQ9fNarUecZNvMwq5/qlvTz5+dVMW/7xpLj98buPJZawf7c7hgauns0SDgVKqh+u3w0SH8ipOBoImn+7N43B+RQtnODU2GpavzXBrm5wUw7pDhR77GZZ/ncGJ4spO91cppfyp3waDlibOfZlON0Cjl/O9ndsL5+eVUv1Qvw0GoxOimZMS59b2nXHxpMW3PaQTHCTcvCDNrW3n8VLmpQ32qGJ24/xRJMXpUlClVM/Wr5eWHsmvYM3ObD7fm8u5k4Zx3uREnwvMVNc1sCGjkJXrjxIeEsSy01KYlhjDt0eKeW1zFiVVtVw+cwRzRsWRMsQzIZ5SSnU33WeglFJK9xkopZRqnd+DgYhcICJ7ReSAiPzay/MiIv/neH6biMz2d5+UUkq582swEJFg4FHgQmAysFREJjc77EJgnOPrVuBv/uyTUkopT/6+MzgVOGCMOWSMqQVeBC5rdsxlwLPGWgfEiUiSn/ullFLKhb+DwQjgmMvjTEdbe49BRG4VkXQRSc/Ly+vyjiqlVH/m72AgXtqaL1/y5RiMMU8YY+YaY+YmJCR0SeeUUkpZ/s5NlAmMdHmcDBzvwDFuNm7cmC8iR1o7phXxQH4Hz+2J+tL76UvvBfT99GR96b2A7+9nVEtP+DsYbADGiUgakAVcByxrdsxq4E4ReRE4DSgxxngWGHZhjOnwrYGIpLe0zrY36kvvpy+9F9D305P1pfcCXfN+/BoMjDH1InIn8D4QDDxtjNkpIj90PP848C5wEXAAqARu9meflFJKefJ7CmtjzLvYD3zXtsddfjbAHf7uh1JKqZb1xx3ITwS6A12sL72fvvReQN9PT9aX3gt0wfvplbmJlFJKda3+eGeglFKqGQ0GSiml+k8wEJGnRSRXRHYEui+dJSIjReRTEdktIjtF5KeB7lNniEiEiHwrIlsd7+f3ge5TZ4lIsIhsFpG3A92XzhKRDBHZLiJbRKTX544XkTgReUVE9jj+H5of6D51hIhMcPw3afoqFZG7Ovx6/WXOQEQWAuXYPEhTA92fznDkbkoyxmwSkRhgI3C5MWZXgLvWISIiwABjTLmIhAJfAT915KrqlUTkbmAuEGuMuSTQ/ekMEckA5hpj+sQmLRF5BvjSGPOkiIQBUcaY4gB3q1McSUGzgNOMMR3akNtv7gyMMV8AhYHuR1cwxpwwxmxy/FwG7MZLPqfewpGksNzxMNTx1Wv/ShGRZOBi4MlA90W5E5FYYCHwFIAxpra3BwKHRcDBjgYC6EfBoK8SkVRgFrA+wF3pFMewyhYgF/jQGNOb38//A+4BGgPcj65igA9EZKOI3BroznTSaCAP+KdjGO9JEWm78HnPdx3wQmdeQINBLyYi0cCrwF3GmNJA96czjDENxpiZ2NxUp4pIrxzKE5FLgFxjzMZA96ULLTDGzMbWHrnDMeTaW4UAs4G/GWNmARWAR9Gt3sQx1HUp8HJnXkeDQS/lGFt/FVhpjHkt0P3pKo5b9s+ACwLbkw5bAFzqGGd/EThHRFYEtkudY4w57vieC7yOrVPSW2UCmS53nq9gg0NvdiGwyRiT05kX0WDQCzkmXJ8CdhtjHgx0fzpLRBJEJM7xcyRwLrAnoJ3qIGPMvxljko0xqdhb90+MMd8PcLc6TEQGOBYp4BhOWQz02hV5xphs4JiITHA0LQJ65cILF0vp5BARdENuop5CRF4AzgLiRSQT+J0x5qnA9qrDFgDXA9sd4+wA9zryQPVGScAzjhURQcAqY0yvX5LZRwwDXrd/fxACPG+MeS+wXeq0HwMrHcMrh+jFyTFFJAo4D7it06/VX5aWKqWUapkOEymllNJgoJRSSoOBUkopNBgopZRCg4FSSik0GCillEKDgVIAiMhNIjLch+OWi8iSVp7/TETmdnHf4kTkdpfHZ/WF1NiqZ9FgoJR1E9BmMAiQOOD2tg5SqjM0GKg+SURSHcVLnhGRbY5iJlEiMkdEPndk4HxfRJIcf+nPxe5K3SIikSLyWxHZICI7ROQJRwqQ9vZhsYh8IyKbRORlR2LBpmIxv3e0bxeRiY72BBH50NH+dxE5IiLxwJ+AMY6+/cXx8tEuBVpWdqR/SrnSYKD6sgnAE8aY6UApcAfwMLDEGDMHeBq43xjzCpAOfM8YM9MYUwU8Yow5xVEIKRJoV4Eax4f4vwPnOjJ+pgN3uxyS72j/G/ALR9vvsLmMZmMTwqU42n+NzVU/0xjzS0fbLOAuYDI2LfOC9vRPqeb6TW4i1S8dM8asdfy8ArgXmAp86PhDOhg40cK5Z4vIPUAUMBjYCbzVjmvPw35Qr3VcKwz4xuX5pkyzG4ErHT+fAVwBYIx5T0SKWnn9b40xmQCO/FSp2ApxSnWIBgPVlzVPvFUG7DTGtFrzVkQigMewpR6Pich9QEQ7ry3YIj1LW3i+xvG9Aef/h+0Z6qlx+dn1NZTqEB0mUn1Zikux86XAOiChqU1EQkVkiuP5MiDG8XPTB3++Y5y/xdVDrVgHLBCRsY5rRYnI+DbO+Qq4xnH8YmCQl74p5RcaDFRfthu4UUS2YYd6HsZ+sP+PiGwFtgCnO45dDjzuGHKpAf4BbAfeADa098LGmDzsCqUXHNdfB0xs47TfA4tFZBO2YMkJoMwYU4AdbtrhMoGsVJfSFNaqT3LUhn7bMQHcK4hIONBgjKl33L38zVEKVCm/03FGpXqOFGCViAQBtcAPAtwf1Y/onYFSHSAirwNpzZp/ZYx5PxD9UaqzNBgopZTSCWSllFIaDJRSSqHBQCmlFBoMlFJKAf8fjHoqkqzKVlsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_centers_and_colorized_data(iris, (c1, c2))\n",
    "#plt.savefig('2means_demo_center_position_4_colorized.png', dpi = 300, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "average_c1_length = np.mean(iris[iris[\"cluster\"] == 0])[\"petal_length\"]\n",
    "average_c1_width = np.mean(iris[iris[\"cluster\"] == 0])[\"petal_width\"]\n",
    "c1.coordinates = (average_c1_length, average_c1_width)\n",
    "\n",
    "average_c2_length = np.mean(iris[iris[\"cluster\"] == 1])[\"petal_length\"]\n",
    "average_c2_width = np.mean(iris[iris[\"cluster\"] == 1])[\"petal_width\"]\n",
    "c2.coordinates = (average_c2_length, average_c2_width)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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"
    }
   ],
   "source": [
    "plot_centers_and_black_data(iris, (c1, c2))\n",
    "#plt.savefig('2means_demo_center_position_5.png', dpi = 300, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris[\"dist1\"] = c1.dist(iris[[\"petal_length\", \"petal_width\"]])\n",
    "iris[\"dist2\"] = c2.dist(iris[[\"petal_length\", \"petal_width\"]])\n",
    "iris[\"cluster\"] = iris[[\"dist1\", \"dist2\"]].apply(get_cluster_number, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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"
    }
   ],
   "source": [
    "plot_centers_and_colorized_data(iris, (c1, c2))\n",
    "# plt.savefig('2means_demo_center_position_5_colorized.png', dpi = 300, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "average_c1_length = np.mean(iris[iris[\"cluster\"] == 0])[\"petal_length\"]\n",
    "average_c1_width = np.mean(iris[iris[\"cluster\"] == 0])[\"petal_width\"]\n",
    "c1.coordinates = (average_c1_length, average_c1_width)\n",
    "\n",
    "average_c2_length = np.mean(iris[iris[\"cluster\"] == 1])[\"petal_length\"]\n",
    "average_c2_width = np.mean(iris[iris[\"cluster\"] == 1])[\"petal_width\"]\n",
    "c2.coordinates = (average_c2_length, average_c2_width)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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"
    }
   ],
   "source": [
    "plot_centers_and_black_data(iris, (c1, c2))\n",
    "# plt.savefig('2means_demo_center_position_6.png', dpi = 300, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "average_c1_length = np.mean(iris[iris[\"cluster\"] == 0])[\"petal_length\"]\n",
    "average_c1_width = np.mean(iris[iris[\"cluster\"] == 0])[\"petal_width\"]\n",
    "c1.coordinates = (average_c1_length, average_c1_width)\n",
    "\n",
    "average_c2_length = np.mean(iris[iris[\"cluster\"] == 1])[\"petal_length\"]\n",
    "average_c2_width = np.mean(iris[iris[\"cluster\"] == 1])[\"petal_width\"]\n",
    "c2.coordinates = (average_c2_length, average_c2_width)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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"
    }
   ],
   "source": [
    "plot_centers_and_colorized_data(iris, (c1, c2))\n",
    "# plt.savefig('2means_demo_center_position_6_colorized.png', dpi = 300, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example for K > 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import copy\n",
    "def compute_centers_after_N_iterations(data, column_names, centers, N):\n",
    "    centers = copy.deepcopy(centers)\n",
    "    \n",
    "    for i in range(N):\n",
    "        #recompute clusters        \n",
    "        dist_names = []\n",
    "        for center_num in range(len(centers)):        \n",
    "            data[\"dist\" + str(center_num)] = centers[center_num].dist(data[column_names])\n",
    "            dist_names.append(\"dist\" + str(center_num))\n",
    "        \n",
    "        data[\"cluster\"] = data[dist_names].apply(get_cluster_number, axis = 1)    \n",
    "        \n",
    "        #update centers\n",
    "        for center_num in range(len(centers)):\n",
    "            for col_num in range(len(column_names)):\n",
    "                col_name = column_names[col_num]\n",
    "    \n",
    "                centers[center_num].coordinates[col_num] = np.mean(data[data[\"cluster\"] == center_num])[col_name]\n",
    "\n",
    "    return centers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "c1 = Center(iris.loc[:, ['petal_length', 'petal_width']].values)\n",
    "c2 = Center(iris.loc[:, ['petal_length', 'petal_width']].values)\n",
    "c1.coordinates = np.array([2.52364007, 2.31040024])\n",
    "c2.coordinates = np.array([6.53276402, 1.211463])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>dist1</th>\n",
       "      <th>dist2</th>\n",
       "      <th>cluster</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",
       "      <td>0.112040</td>\n",
       "      <td>3.824028</td>\n",
       "      <td>0</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",
       "      <td>0.112040</td>\n",
       "      <td>3.824028</td>\n",
       "      <td>0</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",
       "      <td>0.203412</td>\n",
       "      <td>3.916407</td>\n",
       "      <td>0</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",
       "      <td>0.061068</td>\n",
       "      <td>3.732042</td>\n",
       "      <td>0</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",
       "      <td>0.112040</td>\n",
       "      <td>3.824028</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.229889</td>\n",
       "      <td>0.676487</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>3.870173</td>\n",
       "      <td>0.230631</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.093707</td>\n",
       "      <td>0.420388</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.406168</td>\n",
       "      <td>0.779445</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>3.920660</td>\n",
       "      <td>0.210959</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width     dist1     dist2  \\\n",
       "0             5.1          3.5           1.4          0.2  0.112040  3.824028   \n",
       "1             4.9          3.0           1.4          0.2  0.112040  3.824028   \n",
       "2             4.7          3.2           1.3          0.2  0.203412  3.916407   \n",
       "3             4.6          3.1           1.5          0.2  0.061068  3.732042   \n",
       "4             5.0          3.6           1.4          0.2  0.112040  3.824028   \n",
       "..            ...          ...           ...          ...       ...       ...   \n",
       "145           6.7          3.0           5.2          2.3  4.229889  0.676487   \n",
       "146           6.3          2.5           5.0          1.9  3.870173  0.230631   \n",
       "147           6.5          3.0           5.2          2.0  4.093707  0.420388   \n",
       "148           6.2          3.4           5.4          2.3  4.406168  0.779445   \n",
       "149           5.9          3.0           5.1          1.8  3.920660  0.210959   \n",
       "\n",
       "     cluster  \n",
       "0          0  \n",
       "1          0  \n",
       "2          0  \n",
       "3          0  \n",
       "4          0  \n",
       "..       ...  \n",
       "145        1  \n",
       "146        1  \n",
       "147        1  \n",
       "148        1  \n",
       "149        1  \n",
       "\n",
       "[150 rows x 7 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inertia(data, centers):\n",
    "    total_inertia = 0\n",
    "    for center_num in range(len(centers)):\n",
    "        data_in_this_cluster = data[data[\"cluster\"] == center_num]        \n",
    "        total_inertia += np.sum(centers[center_num].dist(data_in_this_cluster[[\"petal_length\", \"petal_width\"]]))\n",
    "    return total_inertia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def distortion(data, centers):\n",
    "    total_distortion = 0\n",
    "    for center_num in range(len(centers)):\n",
    "        data_in_this_cluster = data[data[\"cluster\"] == center_num]        \n",
    "        total_distortion += np.sum(centers[center_num].dist(data_in_this_cluster[[\"petal_length\", \"petal_width\"]]))/len(data_in_this_cluster)\n",
    "    return total_distortion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inertia: 44.95723374524917, distortion: 1.254497095901379)\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"
    }
   ],
   "source": [
    "random.seed(25)\n",
    "c1 = Center(iris.loc[:, ['petal_length', 'petal_width']].values)\n",
    "c2 = Center(iris.loc[:, ['petal_length', 'petal_width']].values)\n",
    "c3 = Center(iris.loc[:, ['petal_length', 'petal_width']].values)\n",
    "c4 = Center(iris.loc[:, ['petal_length', 'petal_width']].values)\n",
    "new_centers = compute_centers_after_N_iterations(iris, ['petal_length', 'petal_width'], [c1, c2, c3, c4], 12)\n",
    "print(f\"inertia: {inertia(iris, new_centers)}, distortion: {distortion(iris, new_centers)})\")\n",
    "plot_centers_and_colorized_data(iris, new_centers)\n",
    "# plt.savefig(\"k4_example1.png\", bbox_inches = \"tight\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inertia: 45.94868633864745, distortion: 1.3083110705058751)\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"
    }
   ],
   "source": [
    "random.seed(29)\n",
    "c1 = Center(iris.loc[:, ['petal_length', 'petal_width']].values)\n",
    "c2 = Center(iris.loc[:, ['petal_length', 'petal_width']].values)\n",
    "c3 = Center(iris.loc[:, ['petal_length', 'petal_width']].values)\n",
    "c4 = Center(iris.loc[:, ['petal_length', 'petal_width']].values)\n",
    "new_centers = compute_centers_after_N_iterations(iris, ['petal_length', 'petal_width'], [c1, c2, c3, c4], 12)\n",
    "print(f\"inertia: {inertia(iris, new_centers)}, distortion: {distortion(iris, new_centers)})\")\n",
    "plot_centers_and_colorized_data(iris, new_centers)\n",
    "# plt.savefig(\"k4_example2.png\", bbox_inches = \"tight\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inertia: 54.34774261570242, distortion: 1.50090424867691)\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"
    }
   ],
   "source": [
    "random.seed(40)\n",
    "c1 = Center(iris.loc[:, ['petal_length', 'petal_width']].values)\n",
    "c2 = Center(iris.loc[:, ['petal_length', 'petal_width']].values)\n",
    "c3 = Center(iris.loc[:, ['petal_length', 'petal_width']].values)\n",
    "c4 = Center(iris.loc[:, ['petal_length', 'petal_width']].values)\n",
    "new_centers = compute_centers_after_N_iterations(iris, ['petal_length', 'petal_width'], [c1, c2, c3, c4], 12)\n",
    "print(f\"inertia: {inertia(iris, new_centers)}, distortion: {distortion(iris, new_centers)})\")\n",
    "plot_centers_and_colorized_data(iris, new_centers)\n",
    "# plt.savefig(\"k4_example3.png\", bbox_inches = \"tight\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inertia: 44.95723374524917, distortion: 1.2544970959013793)\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"
    }
   ],
   "source": [
    "random.seed(75)\n",
    "c1 = Center(iris.loc[:, ['petal_length', 'petal_width']].values)\n",
    "c2 = Center(iris.loc[:, ['petal_length', 'petal_width']].values)\n",
    "c3 = Center(iris.loc[:, ['petal_length', 'petal_width']].values)\n",
    "c4 = Center(iris.loc[:, ['petal_length', 'petal_width']].values)\n",
    "new_centers = compute_centers_after_N_iterations(iris, ['petal_length', 'petal_width'], [c1, c2, c3, c4], 12)\n",
    "print(f\"inertia: {inertia(iris, new_centers)}, distortion: {distortion(iris, new_centers)})\")\n",
    "plot_centers_and_colorized_data(iris, new_centers)\n",
    "# plt.savefig(\"k4_example4.png\", bbox_inches = \"tight\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "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"
    }
   ],
   "source": [
    "random.seed(20)\n",
    "np.random.seed(20)\n",
    "iris_small = iris.sample(7)\n",
    "c1 = Center(iris_small.loc[:, ['petal_length', 'petal_width']].values)\n",
    "c2 = Center(iris_small.loc[:, ['petal_length', 'petal_width']].values)\n",
    "new_centers2 = compute_centers_after_N_iterations(iris_small, ['petal_length', 'petal_width'], [c1, c2], 12)\n",
    "plot_centers_and_colorized_data(iris_small, new_centers2)\n",
    "# plt.savefig(\"distortion_computation.png\", dpi = 300, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "73     0.837778\n",
      "129    0.121111\n",
      "143    0.547778\n",
      "dtype: float64\n",
      "47    4.765\n",
      "74    0.845\n",
      "67    0.425\n",
      "89    0.425\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "def print_distances_squared(data, centers):\n",
    "    for center_num in range(len(centers)):\n",
    "        data_in_this_cluster = data[data[\"cluster\"] == center_num]        \n",
    "        print(centers[center_num].dist(data_in_this_cluster[[\"petal_length\", \"petal_width\"]])**2)\n",
    "\n",
    "print_distances_squared(iris_small, new_centers2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.409399633729917"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inertia(iris_small, new_centers2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.7693026035868602"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distortion(iris_small, new_centers2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>dist1</th>\n",
       "      <th>dist2</th>\n",
       "      <th>cluster</th>\n",
       "      <th>dist0</th>\n",
       "      <th>dist3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.182888</td>\n",
       "      <td>4.783274</td>\n",
       "      <td>1</td>\n",
       "      <td>4.334487</td>\n",
       "      <td>3.637045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.274755</td>\n",
       "      <td>1.415877</td>\n",
       "      <td>0</td>\n",
       "      <td>0.915302</td>\n",
       "      <td>0.401564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.919239</td>\n",
       "      <td>1.691724</td>\n",
       "      <td>1</td>\n",
       "      <td>1.233333</td>\n",
       "      <td>0.545981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2.438237</td>\n",
       "      <td>0.508523</td>\n",
       "      <td>0</td>\n",
       "      <td>0.348010</td>\n",
       "      <td>1.042109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.651920</td>\n",
       "      <td>2.017764</td>\n",
       "      <td>1</td>\n",
       "      <td>1.535506</td>\n",
       "      <td>0.888636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.651920</td>\n",
       "      <td>1.960509</td>\n",
       "      <td>1</td>\n",
       "      <td>1.520234</td>\n",
       "      <td>0.814145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.797320</td>\n",
       "      <td>0.222950</td>\n",
       "      <td>0</td>\n",
       "      <td>0.740120</td>\n",
       "      <td>1.340931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width     dist1     dist2  \\\n",
       "47            4.6          3.2           1.4          0.2  2.182888  4.783274   \n",
       "73            6.1          2.8           4.7          1.2  1.274755  1.415877   \n",
       "74            6.4          2.9           4.3          1.3  0.919239  1.691724   \n",
       "129           7.2          3.0           5.8          1.6  2.438237  0.508523   \n",
       "67            5.8          2.7           4.1          1.0  0.651920  2.017764   \n",
       "89            5.5          2.5           4.0          1.3  0.651920  1.960509   \n",
       "143           6.8          3.2           5.9          2.3  2.797320  0.222950   \n",
       "\n",
       "     cluster     dist0     dist3  \n",
       "47         1  4.334487  3.637045  \n",
       "73         0  0.915302  0.401564  \n",
       "74         1  1.233333  0.545981  \n",
       "129        0  0.348010  1.042109  \n",
       "67         1  1.535506  0.888636  \n",
       "89         1  1.520234  0.814145  \n",
       "143        0  0.740120  1.340931  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_small"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example of Inertia Failing to Match Intuition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "c1.coordinates = [1.2, 0.15]\n",
    "c2.coordinates = [4.906000000000001, 1.6760000000000006]\n",
    "iris[\"dist1\"] = c1.dist(iris[[\"petal_length\", \"petal_width\"]])\n",
    "iris[\"dist2\"] = c2.dist(iris[[\"petal_length\", \"petal_width\"]])\n",
    "iris[\"cluster\"] = iris[[\"dist1\", \"dist2\"]].apply(get_cluster_number, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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"
    }
   ],
   "source": [
    "plot_centers_and_colorized_data(iris, (c1, c2))\n",
    "#plt.savefig(\"intuitive_clustering.png\", dpi=300, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inertia: 94.41444765828233, distortion: 1.0979144373055645)\n"
     ]
    }
   ],
   "source": [
    "print(f\"inertia: {inertia(iris, [c1, c2])}, distortion: {distortion(iris, [c1, c2])})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "average_c1_length = np.mean(iris[iris[\"cluster\"] == 0])[\"petal_length\"]\n",
    "average_c1_width = np.mean(iris[iris[\"cluster\"] == 0])[\"petal_width\"]\n",
    "c1.coordinates = (average_c1_length, average_c1_width)\n",
    "\n",
    "average_c2_length = np.mean(iris[iris[\"cluster\"] == 1])[\"petal_length\"]\n",
    "average_c2_width = np.mean(iris[iris[\"cluster\"] == 1])[\"petal_width\"]\n",
    "c2.coordinates = (average_c2_length, average_c2_width)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FixePa2trayMWi4Xat5QWdCmHGAtFQ0tFZFoaTVQ5VGcgIiKqMxCR8Q4cOEBXVxc7duwgGo3S2NjIQQcdxNDQUFG+8U913rDnyXW/qVT0VcRUw4xKedPQUpHM7d+/31evXj1ubP3q1av99ddf9/b29oLXDwwODgaed//+/aHOEzaebOOuhJoEVGcgIiMefvjhwLH1mzZtCmzPd/3Ali1bAo83VZwTzxM2nmzjroSahOmSQYVc34hIWDt27JhybH1Qe77rB+LxeODxdu7cGeo8YePJNu5Kr0lQMhCpMtFoNHBs/bx58wLb810/kG48E88TNp5s4674moSpLhlKedNtIpHM6ZmBnhkEbRpaKlKFRkYT7dy5k3nz5tHU1KTRRCGU+2gi1RmIiMi0yaB8UpqIjJNIJOjr6+OBBx6gr69v0uRxGdv3Mqy/NPkzn+eRkqJkIFKGEokEnZ2dxGIxVqxYQSwWo7OzMzcf1H0/gd57oe+n+T2PlBQlA5Ey1N/fT0tLy+hQx4GBAVpaWujv78/+4D3fG/2Z1/NISdF0FCJlaLox7wsXLkzvYLedD3/Y9MbftQclfz77GxY+fSqvf7EeqOe+3w1x7trXMz+PlDRdGYiUoZyOeT/z81A/5ljDB8b/BF474Fz70P7sziMlTclApAzldB7+48+ES+8cnxDGGLKDaP7BMJueGa7q+f4rnYaWipSpnI957/spbPgoDO17o61uNokL2+m3E8p2bL28QVNYi1SgmpoaFi5cmLt79/tehppasBqonQXD+6Gmlpr9e1m4LIfnkZKk9C5SpsKO/w9dJ9DTAQdehyPfBpd8P/nzwOtvjC7K5JgFUmrxlKWp5qko5U1zE0m1y8t8PN+/2H3z9e4jrw0PuW/+TrI902MWQKnFU8rQ3EQilaWvr49YLDZueGkkEqGnp2fc7Zyw/fJx7kIptXhKWU6mozCzZjPrN7OXzewVM9trZq/kLkwRCatQc/hnc+5CKbV4ylU6zwy+Dpzv7n/i7oe7+2Hufni+AhORqRVqDv9szl0opRZPuUonGTzn7k/mLRIRCS1snUFO6xHyeMxslFo85WrGZwZm1pz6dTlwFHAPsH/kdXfvzFdwU9EzA5HCzeGfzbkLpdTiKVVZrWdgZu3TvOzu/vFsgsuEkoGISPqyKjpz94+lDnKGu2+ecOAzZjjxrcAHgOfd/W0Br58F/Afwh1RTp7v/00wxiYhIbqVTgXw9cHKItrHWAK1AxzR9HnL3D6QRh0hFyPbWxsDAAF1dXezatYt58+bR2NjIrFmzJh0TCH2eoOUna2pqsjqmlIcZk4GZnQ68E5hrZp8b89LhQO10+7r7g2Y2P6sIRSrQyKIxI2sFjDz0bG5uDvWhOjAwwO23387KlStH929tbeXEE0/k3HPPHW2744472L9/f6jzDA0NsXbtWq666qrRvu3t7dTW1o7bP51jSvkI82/uIOBQkonjsDHbK8CFOYjhdDN71Mx+YmaLc3A8kZKX7aIxXV1do4lgZP+VK1cyODg4rq2rqyv0eXp6ekYTwUjf7du3T9o/nWNK+QjzzGATsMnM1rj7Mzk+/zbgOHd/1czeR3KkUuB4MDO7HLgc4Nhjj81xGCKFle3iNFPt/9xzz41rSyQSoc8Tj8cn9Q3aP51jSvkIc5voh4Cnfp/0urufn+nJ3f2VMb//2MzazGyOu78Q0Pdm4GZIjibK9JwipWCkUGriFAphC6XmzZsXuP+RRx45rl9tbW3o80Sj0Ul9g/ZP55hSPsLcJvoG8E2SI34GgFtS26vA49mc3MyOslSGMbNTU/HsyeaYIuUg20KpxsZGWltbx+3f2tpKfX39uLbGxsbQ54nFYrS1tY3ru2jRokn7p3NMKR+hJ6ozswfd/cyZ2ia8fjtwFjAHeA64BqgHcPebzGwlcCUwRDLRfM7d/2umWFRnIJUgl6OJGhoaaGpq0mgimVZWRWdjDvIk8H53/33q7+OBH7v7STmLNCQlAxGR9OVqpbPPAg+Y2e9Tf88HrsgyNpGyVqhpEIK+sdfVTf7fNyieRCIR6tt+KX6z1zQTBTTVQgdBGzALWJbaZqWzby43LW4jpaBQi6oMDg56e3v7uPO0t7f74OBgqHjWr18/rm39+vVlsRiMFq3JPaZZ3CZMAjg79bM5aJtp/3xsSgZSCnp7e0c/qEa2SCTivb29OT3Pli1bAs+zZcuWUPGsWrVqXNuqVasKEne2CvXPt5pMlwzCXG8tT/38XwGbppGQqlWoRVWCxv8PDAwQj8dDxTNxPeDp6gRKiRatKawwRWfXpH5+LP/hiJSPbGsFwgoa/x+JRIhGo6HimXiPvVzqBAr1z1eS0ln28ndmts7M/tbMFuUzKJFyUKhFVYLG/7e1tRGLxULFs3jx4hlrB0qxTkCL1hRWOkNLZwHvAN4NnAG8FXjU3S/IX3jBNLRUSoVGE+WXRhPlVq7qDOqAJpLPEN4FHAH81t0LPrxUyUBEJH25qjN4BXgM+BZwi7tr2ggpe/n45nngwAG6urrYsWMH0WiUxsZGampqJn07BwK/7WdTBRzUpm/SEspUw4wmbsAHgeuATcB9wNeAc8Lun8tNQ0slF/Ixjn3//v2+evXqcce8/fbbfc2aNePabrvttknj/9vb231gYGBSTcGaNWt848aNk+Kc2LZx40aNy5dpkU2dwaQdks8KPgs8Awyku38uNiUDyYV8jGN/+OGHJx1zqnH9E8f/RyIRf/DBB0P3Ldf6ASme6ZJBOqOJfmBmvwO+DRwCtABvDn8NIlJa8jGOfceOHaHn/584/n9gYICdO3eG7luu9QNSmtJ5ZvAvwDZ3Hw560cze4+735SYskfzLxzj2sGsCBI3/j0QiHH300aH7lmv9gJSm0FcG7t41VSJI+dccxCNSMPkYxx60zsBJJ53EjTfeOK7tpptumjT+v62tjcbGxkk1BTfeeCNNTU2T4pzYpnUGJBuhh5bOeCCzHnePzdwzexpaKrmSz9FEO3fuZN68eTQ1NWk0kZSEnNQZhDjJNnc/OScHm4GSgYhI+nJVZyAiEwRdWUC4b+fpXJVkcwWjKl4JZaphRuluQGeujjXTpqGlUgqC6hTCjvVPp8Yhm3oIrQkgYzHN0NIZbxOZWfMMyaQzJ1kpDbpNJKWgr6+PWCw2bvTOqlWr+MY3vjFpRE9PTw8LFy6cdt+gfun2DRNj2H2l8kx3myjMtWLQOgZaz0CqXlCdQtix/unUOGRTD6E1ASSsMOsZaB0DkQBBdQphx/qnU+OQTT2E1gSQsNJ6imRm7zezL5rZV0a2fAUmUuqC6hTCjvVPp8Yhm3oIrQkgYaUzhfVNwMHACuC7wIXAFnf/RP7CC6ZnBlIqNJpIykmu1jP4rbsvHfPzUJIjiM7NZbBhKBmIiKQv2wfII0ZuOr5uZvOAQeD4bIMTEZHiS6fo7Edm9iaSaxpsIzlF7nfzEZRUl3K5jZHNLSGRUpdOMvi6u+8HfmBmPwJmA/vyE5ZUi0QiQWdnJy0tLQwMDIw+4Gxubi6pD9Wp4pw1axYXXXRRSccuEkY6/8X+auQXd9/v7i+PbRPJRH9//+gHLCTHwLe0tNDf31/kyMabKs6urq6Sj10kjBmTgZkdZWanABEzi5nZyantLJKji0QyVi5FUVPFGbToTKnFLhJGmNtEfwFcBkSBb41pfwX4Uh5ikipSLkVRU8UZtOhMqcUuEsaMVwbufpu7rwAuc/cVY7YPFmNeIqks5VIUNVWcQYvOlFrsImGkU2dwFHAtMM/dzzOzRcDp7r46nwEGUZ1BZdFoIpHCyFXR2U+AduDL7r7MzOqAHndfkrtQw1EyEBFJX64Wt5nj7nea2T8CuPuQmU23JjJmdivJmU2fd/e3BbxuwLeB9wGvk7wVtS2NmEQCl4msq5v8n3bYfpD76R9AVxBS4qZa6GDiBjwAHAFsS/19GrBphn3OBE4GHp/i9fcBPwEsdbzfhIlFi9vIiMHBQW9vbx+3eEt7e7sPDg5m1M8994vJhF3wRiTfmGZxm3SSwcnAZuCPqZ//D1gaYr/50ySDfwcuGfN3H9Aw0zGVDGTEli1bRj9kR7ZIJOJbtmzJqJ+7e29vb2Df3t7eGeMJ2nfVqlUZH08kl6ZLBulcpz4B3A10Ac8Bt6QSQjaOBp4d83c81TaJmV1uZt1m1r179+4sTyuVIh6PB47/j8fjGfWD3C8mE3bBG5FiSicZdABvBf4PcD2wAPhelue3gLbAJ9rufrO7N7p749y5c7M8rVSKaDQ6OrRzRCQSIRqNZtQP3qgpmNg3ncVkxhpZ8CaT44kUSjrJYKG7/427/zK1XQ6cmOX548AxY/6OAjuzPKZUkVgsRltb27ix/m1tbcRisYz6Qe4Xkwm74I1IMaUztHQNcJO7/zr19zuAj7r7VTPsNx/4kQePJno/sJLkg+R3AN9x91NnikVDS2UsjSYSCSdXdQZPAguB/041HQs8CSQAd/elAfvcDpwFzCH5nOEaoJ7kDjelhpa2Au8lObT0Y+4+46e8koGISPpyVWfw3nRP7O6XzPC6A59K97giIpJboZOBuz+Tz0BERKR4dNNSRESUDERERMlARERQMhAREZQMREQEJQMREUHJQEREUDIQERGUDEREBCUDERFByUBERFAyEBERlAxERAQlAxERQclARERQMhAREZQMREQEJQMREUHJQEREUDIQERGUDEREBCUDERFByUBERIC6YgdQDRKJBP39/ezatYuGhgYWLFhATY3ysIiUDn0i5VkikaCzs5NYLMaKFSuIxWJ0dnaSSCSKHZqIyCglgzzr7++npaWFgYEBAAYGBmhpaaG/v7/IkYmIvEHJIM927do1mghGDAwMsGvXriJFJCIymZJBnjU0NBCJRMa1RSIRGhoaihSRiMhkSgZ5tmDBAjo6OkYTQiQSoaOjgwULFhQ5MhGRN2g0UZ7V1NTQ3NzMkiVLNJpIREqWkkEB1NTUsHDhQhYuXFjsUEREAuX966mZvdfM+szsKTO7OuD1s8zsZTN7JLV9Jd8xlYJEIkFfXx8PPPAAfX19GmoqIkWV12RgZrXADcB5wCLgEjNbFND1IXd/e2r7p3zGVApUeyAipSbfVwanAk+5++/d/QCwHvhgns9Z8lR7ICKlJt/J4Gjg2TF/x1NtE51uZo+a2U/MbHHQgczscjPrNrPu3bt35yPWglHtgYiUmnwnAwto8wl/bwOOc/dlwPXAPUEHcveb3b3R3Rvnzp2b2ygLTLUHIlJq8p0M4sAxY/6OAjvHdnD3V9z91dTvPwbqzWxOnuMqKtUeiEipyffQ0i5ggZkdD+wALgYuHdvBzI4CnnN3N7NTSSaoPXmOq6hUeyAipSavycDdh8xsJfAzoBa41d23m9nfpl6/CbgQuNLMhoAB4GJ3n3grqeKo9kBESomV4+duY2Ojd3d3F/ScYdckOHDgAF1dXezYsYNoNEpjYyN1dXWT9gVCr3Gg9RBEJBfMbKu7Nwa+6O5lt51yyileSMPDw75hwwaPRCIOeCQS8Q0bNvjw8PC4fvv37/fVq1eP63frrbf6xo0bJ+0b1DbxeOmcW0RkJkC3T/G5WvQP9ky2QieD3t7e0Q/jkS0SiXhvb++4fg8//HBgv1WrVoVqm3i8dM4tIjKT6ZKB7jWEELYuYMeOHYH9JlYWT9UWVGegmgQRKQQlgxDC1gVEo9HAfhPv70/VFlRnoJoEESkEJYMQwtYFNDY20traOq7fDTfcQFNT06R9g9qC6gxUkyAihaDRRCGlO5po586dzJs3j6amJo0mEpGSMN1oIiUDEZEqMV0y0NdLERHRSmdhDQ0N0dPTQzweJxqNEovFSCQSoQrMgm7p6NaPiJQSJYMQhoaGWLt2LVdddRUDAwNEIhHa29vZt28fV1555WjbDTfcwJw5c7joootG2zo6Omhubh73QT+yuM3ImgZT9RMRKRR98oTQ09MzmgggOc5/+/bto4lgpG3lZ7/A1//rj+wbttG2oEVrtLiNiJQaJYMQ4vH4pMKvRCIxqc2iS3nW38LBf3bqaFtQgZgKyUSk1CgZhBBUTFZbWzup7fC3/wUAhyx9z2hbUIGYCslEpNTomUEIsViMtra2cc8MFi1axGlXr+P3rx802q8WZxiYHV3Ecf/wo9H2rz34Et8fM1P1SCHZxGcGKiQTkWJRMgihrq6OD3/4wyxevHjcaKKGp17gb763lQPDyX7DqVU+rbZ+dN/Z9TX83dnjP+S1uI2IlBoVnWXpv373Ap9Y083A4PCk1yL1tdx6WROnn3BEESITERlvuqKzqrkySGdcf1BNwdDQ0Og0E0cffTSNjY0MDQ0xtOMJLjrmVdY9HWFwzESkdeZcuewgljVEGBgYoKuri127djFv3jwaGxuZNWuWpqMQkZJRFckgnXH9QTUFnZ2d7Ny5k5UrV462rVu3jpdeeomVK1dSc/w7eMtffIrag2ZTZ3BgOMGBof1cc+31HP7qJcyePZuPf/zjo/u2trZy4okncu65584Yj2oSRKQQquLTJJ1x/UE1BWY2mghG2g4cODDadujSc7H6WRx4/g8sr+3lwPNPY/WzOeity7nqqqt44oknxtcjrFzJ4OBgqHhUkyAihVAVySCdcf1BNQUvvPDCpLZXX311tC2x/zVeun81O9s/zVH8kf/p+Cwv/fJWfP/rUy5k89xzz4WKRzUJIlIIVXGbaGRc/9gP1anG9Y/UFIztO3fu3Elthx122Gjb7ruvHT1mTU0NeIK9Xfewt+ueKReyOfLIIye1Tbe4TZjYRUQyVRVXBuksEDNSUzC2byKRmLRoTX19feBCNosWLRrX1tbWNqmttbWV+vp6LW4jIiWjaoaW5mo00cj+TU1NDA0NsW3bttG2k08+mZqaGrq7u0fbGhsbMbNJ+2o0kYgUmha3ERER1RlMJdtv3EH7j132cqQeYfbs2Xl8FyIi2avaZJDt+P2g/e+9917+8Ic/jKtHaG1t5dJLL1VCEJGSVrU3nrMdvx+0/9jag5G2lStX0tXVlZ83ISKSI1WbDLIdvx+0f1A9gmoCRKQcVG0yyHZNgaD9R+oRMj2miEixVG0yyHb8ftD+QbUHra2tNDU15edNiIjkSFUPLc3naKKxNQV6eCwipUB1BiIiMm0yyPttIjN7r5n1mdlTZnZ1wOtmZt9Jvf5bMzs53zGJiMh4eU0GZlYL3ACcBywCLjGzRRO6nQcsSG2XAzfmMyYREZks31cGpwJPufvv3f0AsB744IQ+HwQ6POnXwJvMTMNvREQKKN/J4Gjg2TF/x1Nt6fbBzC43s24z6969e3fOAxURqWb5TgYW0DbxiXWYPrj7ze7e6O6Nc+fOzUlwIiKSlO+5ieLAMWP+jgI7M+gzztatW18ws2cyjGkO8EKG+5aiSno/lfReQO+nlFXSe4Hw7+e4qV7IdzLoAhaY2fHADuBi4NIJfTYCK81sPfAO4GV3n3b+BnfP+NLAzLqnGlpVjirp/VTSewG9n1JWSe8FcvN+8poM3H3IzFYCPwNqgVvdfbuZ/W3q9ZuAHwPvA54CXgc+ls+YRERksrxPYe3uPyb5gT+27aYxvzvwqXzHISIiU6vGuYluLnYAOVZJ76eS3gvo/ZSySnovkIP3U5bTUYiISG5V45WBiIhMoGQgIiLVkwzM7FYze97MHi92LNkys2PM7Jdm9qSZbTezTxc7pmyY2Wwz22Jmj6bez9eKHVO2zKzWzHrM7EfFjiVbZva0mT1mZo+YWdlPF2xmbzKzu8ysN/X/0OnFjikTZrYw9e9kZHvFzD6T8fGq5ZmBmZ0JvEpyHqS3FTuebKTmbmpw921mdhiwFfhLd3+iyKFlxMwMOMTdXzWzeuBh4NOpuarKkpl9DmgEDnf3DxQ7nmyY2dNAo7tXRJGWmd0GPOTu3zWzg4CD3f2PRQ4rK6lJQXcA73D3jApyq+bKwN0fBF4sdhy54O673H1b6ve9wJMEzOdULlKTFL6a+rM+tZXttxQziwLvB75b7FhkPDM7HDgTWA3g7gfKPRGknAP8LtNEAFWUDCqVmc0HYsBvihxKVlK3VR4Bngfuc/dyfj//BnwRSBQ5jlxx4OdmttXMLi92MFn6U2A30J66jfddMzuk2EHlwMXA7dkcQMmgjJnZocAPgM+4+yvFjicb7j7s7m8nOTfVqWZWlrfyzOwDwPPuvrXYseTQGe5+Msm1Rz6VuuVaruqAk4Eb3T0GvAZMWnSrnKRudZ0PbMjmOEoGZSp1b/0HwDp37yx2PLmSumR/AHhvcSPJ2BnA+an77OuBs81sbXFDyo6770z9fB64m+Q6JeUqDsTHXHneRTI5lLPzgG3u/lw2B1EyKEOpB66rgSfd/VvFjidbZjbXzN6U+j0C/DnQW9SgMuTu/+juUXefT/LS/X53/3CRw8qYmR2SGqRA6nbKuUDZjshz9/8BnjWzhammc4CyHHgxxiVkeYsICjA3Uakws9uBs4A5ZhYHrnH31cWNKmNnAB8BHkvdZwf4UmoeqHLUANyWGhFRA9zp7mU/JLNCHAncnfz+QR3wfXf/aXFDytrfAetSt1d+TxlPjmlmBwPvAa7I+ljVMrRURESmpttEIiKiZCAiIkoGIiKCkoGIiKBkICIiKBmIiAhKBiIAmNllZjYvRL81ZnbhNK8/YGaNOY7tTWZ21Zi/z6qEqbGltCgZiCRdBsyYDIrkTcBVM3USyYaSgVQkM5ufWrzkNjP7bWoxk4PN7BQz25SagfNnZtaQ+qbfSLIq9REzi5jZV8ysy8weN7ObU1OApBvDuWb2KzPbZmYbUhMLjiwW87VU+2Nm9tZU+1wzuy/V/u9m9oyZzQH+BTghFdt1qcMfOmaBlnWZxCcylpKBVLKFwM3uvhR4BfgUcD1wobufAtwKXOvudwHdwF+7+9vdfQBodfem1EJIESCtBWpSH+L/G/jz1Iyf3cDnxnR5IdV+I/D5VNs1JOcyOpnkhHDHptqvJjlX/dvd/QupthjwGWARyWmZz0gnPpGJqmZuIqlKz7r75tTva4EvAW8D7kt9ka4Fdk2x7woz+yJwMPAWYDvwwzTOfRrJD+rNqXMdBPxqzOsjM81uBZpTv78LuADA3X9qZi9Nc/wt7h4HSM1PNZ/kCnEiGVEykEo2ceKtvcB2d592zVszmw20kVzq8Vkz+yowO81zG8lFei6Z4vX9qZ/DvPH/YTq3evaP+X3sMUQyottEUsmOHbPY+SXAr4G5I21mVm9mi1Ov7wUOS/0+8sH/Quo+/5Sjh6bxa+AMM/uz1LkONrMTZ9jnYeBDqf7nAm8OiE0kL5QMpJI9CXzUzH5L8lbP9SQ/2P/VzB4FHgHemeq7BrgpdctlP3AL8BhwD9CV7ondfTfJEUq3p87/a+CtM+z2NeBcM9tGcsGSXcBed99D8nbT42MeIIvklKawloqUWhv6R6kHwGXBzGYBw+4+lLp6uTG1FKhI3uk+o0jpOBa408xqgAPAJ4scj1QRXRmIZMDM7gaOn9D8D+7+s2LEI5ItJQMREdEDZBERUTIQERGUDEREBCUDEREB/j989fMN9XkYBQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_centers_and_black_data(iris, (c1, c2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris[\"dist1\"] = c1.dist(iris[[\"petal_length\", \"petal_width\"]])\n",
    "iris[\"dist2\"] = c2.dist(iris[[\"petal_length\", \"petal_width\"]])\n",
    "iris[\"cluster\"] = iris[[\"dist1\", \"dist2\"]].apply(get_cluster_number, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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"
    }
   ],
   "source": [
    "plot_centers_and_colorized_data(iris, (c1, c2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inertia: 87.28316581620376, distortion: 0.9789681402782842)\n"
     ]
    }
   ],
   "source": [
    "print(f\"inertia: {inertia(iris, [c1, c2])}, distortion: {distortion(iris, [c1, c2])})\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Agglomerative Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(42)\n",
    "np.random.seed(42)\n",
    "iris_small = iris.sample(13).loc[:, 'sepal_length':'petal_width'].reset_index(drop=True)\n",
    "iris_small = iris_small.drop(8).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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"
    }
   ],
   "source": [
    "sns.scatterplot(data = iris_small, x = \"petal_length\", y= \"petal_width\", color=\"black\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_small[\"cluster\"] = np.array(range(0, len(iris_small)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal_length  sepal_width  petal_length  petal_width  cluster\n",
       "0            6.1          2.8           4.7          1.2        0\n",
       "1            5.7          3.8           1.7          0.3        1\n",
       "2            7.7          2.6           6.9          2.3        2\n",
       "3            6.0          2.9           4.5          1.5        3\n",
       "4            6.8          2.8           4.8          1.4        4\n",
       "5            5.4          3.4           1.5          0.4        5\n",
       "6            5.6          2.9           3.6          1.3        6\n",
       "7            6.9          3.1           5.1          2.3        7\n",
       "8            5.8          2.7           3.9          1.2        8\n",
       "9            6.5          3.2           5.1          2.0        9\n",
       "10           4.8          3.0           1.4          0.1       10\n",
       "11           5.5          3.5           1.3          0.2       11"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_small"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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"
    }
   ],
   "source": [
    "def plot_clusters(data):\n",
    "    p1 = sns.scatterplot(data = data, x = \"petal_length\", y= \"petal_width\")\n",
    "    for line in range(0,data.shape[0]):\n",
    "         p1.text(data[\"petal_length\"][line]+0.05, data[\"petal_width\"][line] - 0.03,   \n",
    "         data[\"cluster\"][line], horizontalalignment='left', \n",
    "         size='medium', color='black', weight='semibold')\n",
    "            \n",
    "plot_clusters(iris_small)\n",
    "#plt.savefig(\"agglomerative_start.png\", dpi=300, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.spatial import distance\n",
    "\n",
    "def dist_between_clusters(data, cnum1, cnum2):\n",
    "    cluster1 = data[data[\"cluster\"] == cnum1]\n",
    "    cluster2 = data[data[\"cluster\"] == cnum2]    \n",
    "    return distance.cdist(cluster1[[\"petal_length\", \"petal_width\"]], cluster2[[\"petal_length\", \"petal_width\"]]).max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "def closest_clusters(data):\n",
    "    cluster_values = data[\"cluster\"].unique()\n",
    "    smallest_distance = float(\"inf\")\n",
    "    best_pair = [-1, -1]\n",
    "    for cnum1 in cluster_values:\n",
    "        for cnum2 in cluster_values:\n",
    "            if cnum1 == cnum2:\n",
    "                continue\n",
    "            cur_dist = dist_between_clusters(data, cnum1, cnum2)\n",
    "            if cur_dist < smallest_distance:\n",
    "                best_pair = [cnum1, cnum2]\n",
    "                smallest_distance = cur_dist\n",
    "                \n",
    "    return best_pair"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "def merge_clusters(data, cnum1, cnum2):\n",
    "    data.loc[data[\"cluster\"] == cnum2, \"cluster\"] = cnum1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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"
    }
   ],
   "source": [
    "i = 0\n",
    "while len(iris_small[\"cluster\"].unique()) != 2:\n",
    "    i += 1\n",
    "    cnum1, cnum2 = closest_clusters(iris_small)\n",
    "    merge_clusters(iris_small, cnum1, cnum2)\n",
    "    plot_clusters(iris_small)\n",
    "#     plt.savefig(f\"agglomerative_merge{i}.png\", dpi=300, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run on full dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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"
    }
   ],
   "source": [
    "iris_small = iris.copy()\n",
    "iris_small[\"cluster\"] = np.array(range(0, len(iris_small)))\n",
    "plot_clusters(iris_small)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#my code is too slow\n",
    "#i = 0\n",
    "#while len(iris_small[\"cluster\"].unique()) != 2:\n",
    "#    i += 1\n",
    "#    print(i)\n",
    "#    cnum1, cnum2 = closest_clusters(iris_small)\n",
    "#    merge_clusters(iris_small, cnum1, cnum2)\n",
    "    #plot_clusters(iris_small)\n",
    "    #plt.savefig(f\"agglomerative_merge{i}.png\", dpi=300, bbox_inches = \"tight\")\n",
    "    #plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "clustering = AgglomerativeClustering().fit(iris[[\"petal_length\", \"petal_width\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clustering.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris[\"cluster\"] = clustering.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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"
    }
   ],
   "source": [
    "sns.scatterplot(data = iris, x = \"petal_length\", y= \"petal_width\", hue =\"cluster\", legend = None);\n",
    "#plt.savefig(\"agglomerative_output.png\", dpi = 300, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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"
    }
   ],
   "source": [
    "plot_clusters(iris)\n",
    "# plt.savefig(\"agglomerative_output_numbers.png\", dpi = 300, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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"
    }
   ],
   "source": [
    "#from https://github.com/scikit-learn/scikit-learn/blob/70cf4a676caa2d2dad2e3f6e4478d64bcb0506f7/examples/cluster/plot_hierarchical_clustering_dendrogram.py\n",
    "from scipy.cluster.hierarchy import dendrogram\n",
    "\n",
    "def plot_dendrogram(model, **kwargs):\n",
    "    # Children of hierarchical clustering\n",
    "    children = model.children_\n",
    "\n",
    "    # Distances between each pair of children\n",
    "    # Since we don't have this information, we can use a uniform one for plotting\n",
    "    distance = np.arange(children.shape[0])\n",
    "\n",
    "    # The number of observations contained in each cluster level\n",
    "    no_of_observations = np.arange(2, children.shape[0]+2)\n",
    "\n",
    "    # Create linkage matrix and then plot the dendrogram\n",
    "    linkage_matrix = np.column_stack([children, distance, no_of_observations]).astype(float)\n",
    "\n",
    "    # Plot the corresponding dendrogram\n",
    "    dendrogram(linkage_matrix, **kwargs)\n",
    "\n",
    "\n",
    "\n",
    "plt.title('Hierarchical Clustering Dendrogram')\n",
    "plot_dendrogram(clustering, labels=clustering.labels_)\n",
    "# plt.savefig(\"dendrogram.png\", bbox_inches = \"tight\", dpi = 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Elbow Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans \n",
    "distortions = [] \n",
    "inertias = [] \n",
    "mapping1 = {} \n",
    "mapping2 = {} \n",
    "K = range(1,10) \n",
    "X = iris[[\"petal_length\", \"petal_width\"]]\n",
    "    \n",
    "for k in K: \n",
    "    #Building and fitting the model \n",
    "    kmeanModel = KMeans(n_clusters=k).fit(X)     \n",
    "      \n",
    "    distortions.append(sum(np.min(distance.cdist(X, kmeanModel.cluster_centers_, \n",
    "                      'euclidean'),axis=1)) / X.shape[0]) \n",
    "    inertias.append(kmeanModel.inertia_) \n",
    "  \n",
    "    mapping1[k] = sum(np.min(distance.cdist(X, kmeanModel.cluster_centers_, \n",
    "                 'euclidean'),axis=1)) / X.shape[0] \n",
    "    mapping2[k] = kmeanModel.inertia_ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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"
    }
   ],
   "source": [
    "plt.plot(K, inertias, 'bx-') \n",
    "plt.xlabel('Values of K') \n",
    "plt.ylabel('Inertia') \n",
    "plt.title('The Elbow Method using Inertia') ;\n",
    "#plt.savefig(\"elbow.png\", dpi=300, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Silhouette Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    petal_length  petal_width\n",
       "98           3.0          1.1"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.query(\"petal_length < 3.2 and petal_length > 2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 2 The average silhouette_score is : 0.7667234280554557\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"
    }
   ],
   "source": [
    "from sklearn.metrics import silhouette_samples, silhouette_score\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "fig, ax1 = plt.subplots(1, 1)\n",
    "#fig.set_size_inches(18, 7)\n",
    "\n",
    "n_clusters = 2\n",
    "\n",
    "# The 1st subplot is the silhouette plot\n",
    "# The silhouette coefficient can range from -1, 1 but in this example all\n",
    "# lie within [-0.1, 1]\n",
    "ax1.set_xlim([-0.1, 1])\n",
    "# The (n_clusters+1)*10 is for inserting blank space between silhouette\n",
    "# plots of individual clusters, to demarcate them clearly.\n",
    "ax1.set_ylim([0, len(X) + (n_clusters + 1) * 10])\n",
    "\n",
    "cluster_labels = clustering.fit_predict(X)\n",
    "\n",
    "\n",
    "# The silhouette_score gives the average value for all the samples.\n",
    "# This gives a perspective into the density and separation of the formed\n",
    "# clusters\n",
    "silhouette_avg = silhouette_score(X, cluster_labels)\n",
    "print(\"For n_clusters =\", n_clusters,\n",
    "      \"The average silhouette_score is :\", silhouette_avg)\n",
    "\n",
    "# Compute the silhouette scores for each sample\n",
    "sample_silhouette_values = silhouette_samples(X, cluster_labels)\n",
    "\n",
    "y_lower = 10\n",
    "for i in range(n_clusters):\n",
    "    # Aggregate the silhouette scores for samples belonging to\n",
    "    # cluster i, and sort them\n",
    "    ith_cluster_silhouette_values = \\\n",
    "        sample_silhouette_values[cluster_labels == i]\n",
    "\n",
    "    ith_cluster_silhouette_values.sort()\n",
    "\n",
    "    size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
    "    y_upper = y_lower + size_cluster_i\n",
    "\n",
    "    color = cm.nipy_spectral(float(i) / n_clusters)\n",
    "    ax1.fill_betweenx(np.arange(y_lower, y_upper),\n",
    "                      0, ith_cluster_silhouette_values,\n",
    "                      facecolor=color, edgecolor=color, alpha=0.7)\n",
    "\n",
    "    # Label the silhouette plots with their cluster numbers at the middle\n",
    "    ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n",
    "\n",
    "    # Compute the new y_lower for next plot\n",
    "    y_lower = y_upper + 10  # 10 for the 0 samples\n",
    "\n",
    "ax1.set_title(\"The silhouette plot for the various clusters.\")\n",
    "ax1.set_xlabel(\"The silhouette coefficient values\")\n",
    "ax1.set_ylabel(\"Cluster label\")\n",
    "\n",
    "# The vertical line for average silhouette score of all the values\n",
    "ax1.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\");\n",
    "# plt.savefig(\"silhoutte_plot.png\", dpi=300, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 3 The average silhouette_score is : 0.6571856448729174\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"
    }
   ],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "clustering = AgglomerativeClustering(n_clusters = 3).fit(iris[[\"petal_length\", \"petal_width\"]])\n",
    "\n",
    "\n",
    "fig, ax1 = plt.subplots(1, 1)\n",
    "#fig.set_size_inches(18, 7)\n",
    "\n",
    "n_clusters = 3\n",
    "\n",
    "# The 1st subplot is the silhouette plot\n",
    "# The silhouette coefficient can range from -1, 1 but in this example all\n",
    "# lie within [-0.1, 1]\n",
    "ax1.set_xlim([-0.1, 1])\n",
    "# The (n_clusters+1)*10 is for inserting blank space between silhouette\n",
    "# plots of individual clusters, to demarcate them clearly.\n",
    "ax1.set_ylim([0, len(X) + (n_clusters + 1) * 10])\n",
    "\n",
    "cluster_labels = clustering.fit_predict(X)\n",
    "\n",
    "# The silhouette_score gives the average value for all the samples.\n",
    "# This gives a perspective into the density and separation of the formed\n",
    "# clusters\n",
    "silhouette_avg = silhouette_score(X, cluster_labels)\n",
    "print(\"For n_clusters =\", n_clusters,\n",
    "      \"The average silhouette_score is :\", silhouette_avg)\n",
    "\n",
    "# Compute the silhouette scores for each sample\n",
    "sample_silhouette_values = silhouette_samples(X, cluster_labels)\n",
    "\n",
    "y_lower = 10\n",
    "for i in range(n_clusters):\n",
    "    # Aggregate the silhouette scores for samples belonging to\n",
    "    # cluster i, and sort them\n",
    "    ith_cluster_silhouette_values = \\\n",
    "        sample_silhouette_values[cluster_labels == i]\n",
    "\n",
    "    ith_cluster_silhouette_values.sort()\n",
    "\n",
    "    size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
    "    y_upper = y_lower + size_cluster_i\n",
    "\n",
    "    color = cm.nipy_spectral(float(i) / n_clusters)\n",
    "    ax1.fill_betweenx(np.arange(y_lower, y_upper),\n",
    "                      0, ith_cluster_silhouette_values,\n",
    "                      facecolor=color, edgecolor=color, alpha=0.7)\n",
    "\n",
    "    # Label the silhouette plots with their cluster numbers at the middle\n",
    "    ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n",
    "\n",
    "    # Compute the new y_lower for next plot\n",
    "    y_lower = y_upper + 10  # 10 for the 0 samples\n",
    "\n",
    "ax1.set_title(\"The silhouette plot for the various clusters.\")\n",
    "ax1.set_xlabel(\"The silhouette coefficient values\")\n",
    "ax1.set_ylabel(\"Cluster label\")\n",
    "\n",
    "# The vertical line for average silhouette score of all the values\n",
    "ax1.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\");\n",
    "# plt.savefig(\"silhoutte_plot_k3.png\", dpi=300, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.14062817006789843"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(sample_silhouette_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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"
    }
   ],
   "source": [
    "iris[\"cluster\"] = cluster_labels\n",
    "current_palette = sns.color_palette()[0:3]\n",
    "sns.scatterplot(data = iris, x = \"petal_length\", y= \"petal_width\", hue=\"cluster\", palette = current_palette);\n",
    "# plt.savefig(\"iris_3_class_agglomerative.png\", dpi = 300, bbox_inches = \"tight\")"
   ]
  }
 ],
 "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}