Group AK (3)- DBSCAN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "705d4e89",
   "metadata": {},
   "source": [
    "**Table of Contents** <br>\n",
    "* [1. Imports](#imports)\n",
    "* [2. Self Organizing Maps](#2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21bbe737",
   "metadata": {},
   "source": [
    "<hr>\n",
    "<a class=\"anchor\" id=\"Imports\">\n",
    "    \n",
    "# 1. Imports\n",
    "    \n",
    "</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3847c305",
   "metadata": {},
   "source": [
    "#### Initial Note:\n",
    "https://github.com/joaopfonseca/Data-Mining-22-23"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6bec2d5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install kmodes\n",
    "#!pip install graphviz\n",
    "import graphviz\n",
    "import pandas as pd\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from math import ceil\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler, OneHotEncoder, RobustScaler\n",
    "from sklearn.impute import KNNImputer\n",
    "sns.set()\n",
    "from sklearn.neighbors import LocalOutlierFactor\n",
    "from sklearn import preprocessing\n",
    "from kmodes.kprototypes import KPrototypes\n",
    "from sklearn.metrics import silhouette_score, silhouette_samples\n",
    "import matplotlib.cm as cm\n",
    "from sklearn.cluster import DBSCAN\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "from collections import Counter\n",
    "from sklearn.mixture import GaussianMixture\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "from scipy.cluster.hierarchy import dendrogram\n",
    "from sklearn.metrics import pairwise_distances\n",
    "from sklearn.manifold import TSNE\n",
    "import umap\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import sompy\n",
    "from sompy.visualization.mapview import View2D\n",
    "from sompy.visualization.bmuhits import BmuHitsView\n",
    "from sompy.visualization.hitmap import HitMapView\n",
    "from sklearn.metrics import calinski_harabasz_score\n",
    "import seaborn as sb\n",
    "from kmodes.kprototypes import KPrototypes\n",
    "from matplotlib.lines import Line2D\n",
    "from sklearn.tree import export_graphviz\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "\n",
    "from sklearn_extra.cluster import KMedoids\n",
    "# ! pip install scikit-learn-extra\n",
    "# ! pip install sklearn-extra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "71d4aa5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('df.csv',index_col=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0ca63c30",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_outliers_ohc = pd.read_csv('df_outliers_ohc.csv',index_col=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f200ea31",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ClaimsRate</th>\n",
       "      <th>Age</th>\n",
       "      <th>MotorPercentageSpent</th>\n",
       "      <th>HouseholdPercentageSpent</th>\n",
       "      <th>HealthPercentageSpent</th>\n",
       "      <th>LifePercentageSpent</th>\n",
       "      <th>WorkPercentageSpent</th>\n",
       "      <th>SalarySpent</th>\n",
       "      <th>EducDeg_2.0</th>\n",
       "      <th>EducDeg_3.0</th>\n",
       "      <th>EducDeg_4.0</th>\n",
       "      <th>Children_1</th>\n",
       "      <th>Cancelled_1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>-0.559322</td>\n",
       "      <td>-0.482759</td>\n",
       "      <td>0.357075</td>\n",
       "      <td>-0.257555</td>\n",
       "      <td>0.012021</td>\n",
       "      <td>0.619809</td>\n",
       "      <td>-0.177071</td>\n",
       "      <td>-0.140650</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.677966</td>\n",
       "      <td>-0.965517</td>\n",
       "      <td>-0.865761</td>\n",
       "      <td>0.978485</td>\n",
       "      <td>-0.588569</td>\n",
       "      <td>3.104708</td>\n",
       "      <td>1.449848</td>\n",
       "      <td>-1.225286</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>-0.745763</td>\n",
       "      <td>-0.103448</td>\n",
       "      <td>-0.372926</td>\n",
       "      <td>0.415420</td>\n",
       "      <td>-0.326120</td>\n",
       "      <td>1.419295</td>\n",
       "      <td>1.756908</td>\n",
       "      <td>-0.224922</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0.457627</td>\n",
       "      <td>-0.482759</td>\n",
       "      <td>-0.308089</td>\n",
       "      <td>-0.430432</td>\n",
       "      <td>1.962267</td>\n",
       "      <td>0.414575</td>\n",
       "      <td>0.212183</td>\n",
       "      <td>-0.759114</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>0.305085</td>\n",
       "      <td>-0.206897</td>\n",
       "      <td>0.289338</td>\n",
       "      <td>-0.416528</td>\n",
       "      <td>0.471710</td>\n",
       "      <td>-0.089648</td>\n",
       "      <td>0.545922</td>\n",
       "      <td>-0.341952</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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      "text/plain": [
       "        ClaimsRate       Age  MotorPercentageSpent  HouseholdPercentageSpent  \\\n",
       "CustID                                                                         \n",
       "1.0      -0.559322 -0.482759              0.357075                 -0.257555   \n",
       "2.0       0.677966 -0.965517             -0.865761                  0.978485   \n",
       "3.0      -0.745763 -0.103448             -0.372926                  0.415420   \n",
       "4.0       0.457627 -0.482759             -0.308089                 -0.430432   \n",
       "5.0       0.305085 -0.206897              0.289338                 -0.416528   \n",
       "\n",
       "        HealthPercentageSpent  LifePercentageSpent  WorkPercentageSpent  \\\n",
       "CustID                                                                    \n",
       "1.0                  0.012021             0.619809            -0.177071   \n",
       "2.0                 -0.588569             3.104708             1.449848   \n",
       "3.0                 -0.326120             1.419295             1.756908   \n",
       "4.0                  1.962267             0.414575             0.212183   \n",
       "5.0                  0.471710            -0.089648             0.545922   \n",
       "\n",
       "        SalarySpent  EducDeg_2.0  EducDeg_3.0  EducDeg_4.0  Children_1  \\\n",
       "CustID                                                                   \n",
       "1.0       -0.140650          1.0          0.0          0.0         1.0   \n",
       "2.0       -1.225286          1.0          0.0          0.0         1.0   \n",
       "3.0       -0.224922          0.0          0.0          0.0         0.0   \n",
       "4.0       -0.759114          0.0          1.0          0.0         1.0   \n",
       "5.0       -0.341952          0.0          1.0          0.0         1.0   \n",
       "\n",
       "        Cancelled_1  \n",
       "CustID               \n",
       "1.0             0.0  \n",
       "2.0             0.0  \n",
       "3.0             0.0  \n",
       "4.0             0.0  \n",
       "5.0             0.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c09f32f9",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ClaimsRate</th>\n",
       "      <th>Age</th>\n",
       "      <th>MotorPercentageSpent</th>\n",
       "      <th>HouseholdPercentageSpent</th>\n",
       "      <th>HealthPercentageSpent</th>\n",
       "      <th>LifePercentageSpent</th>\n",
       "      <th>WorkPercentageSpent</th>\n",
       "      <th>SalarySpent</th>\n",
       "      <th>EducDeg_2.0</th>\n",
       "      <th>EducDeg_3.0</th>\n",
       "      <th>EducDeg_4.0</th>\n",
       "      <th>Children_1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>52.0</th>\n",
       "      <td>6.234544</td>\n",
       "      <td>0.821705</td>\n",
       "      <td>2.026776</td>\n",
       "      <td>-0.438482</td>\n",
       "      <td>-0.261338</td>\n",
       "      <td>-0.089257</td>\n",
       "      <td>-0.800124</td>\n",
       "      <td>2.817521</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>113.0</th>\n",
       "      <td>3.759082</td>\n",
       "      <td>0.418605</td>\n",
       "      <td>1.903978</td>\n",
       "      <td>-0.490234</td>\n",
       "      <td>-0.024285</td>\n",
       "      <td>-0.095918</td>\n",
       "      <td>-0.186179</td>\n",
       "      <td>1.859967</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136.0</th>\n",
       "      <td>3.542384</td>\n",
       "      <td>0.139535</td>\n",
       "      <td>0.773943</td>\n",
       "      <td>-0.060141</td>\n",
       "      <td>0.501841</td>\n",
       "      <td>0.588865</td>\n",
       "      <td>-0.599826</td>\n",
       "      <td>1.163966</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>150.0</th>\n",
       "      <td>-0.070108</td>\n",
       "      <td>-0.046512</td>\n",
       "      <td>0.007735</td>\n",
       "      <td>0.016679</td>\n",
       "      <td>-0.314962</td>\n",
       "      <td>-0.027620</td>\n",
       "      <td>10.410992</td>\n",
       "      <td>0.025863</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172.0</th>\n",
       "      <td>65.084767</td>\n",
       "      <td>0.108527</td>\n",
       "      <td>1.203981</td>\n",
       "      <td>-0.260292</td>\n",
       "      <td>0.829942</td>\n",
       "      <td>-0.080349</td>\n",
       "      <td>-0.000334</td>\n",
       "      <td>0.287343</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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      ],
      "text/plain": [
       "        ClaimsRate       Age  MotorPercentageSpent  HouseholdPercentageSpent  \\\n",
       "CustID                                                                         \n",
       "52.0      6.234544  0.821705              2.026776                 -0.438482   \n",
       "113.0     3.759082  0.418605              1.903978                 -0.490234   \n",
       "136.0     3.542384  0.139535              0.773943                 -0.060141   \n",
       "150.0    -0.070108 -0.046512              0.007735                  0.016679   \n",
       "172.0    65.084767  0.108527              1.203981                 -0.260292   \n",
       "\n",
       "        HealthPercentageSpent  LifePercentageSpent  WorkPercentageSpent  \\\n",
       "CustID                                                                    \n",
       "52.0                -0.261338            -0.089257            -0.800124   \n",
       "113.0               -0.024285            -0.095918            -0.186179   \n",
       "136.0                0.501841             0.588865            -0.599826   \n",
       "150.0               -0.314962            -0.027620            10.410992   \n",
       "172.0                0.829942            -0.080349            -0.000334   \n",
       "\n",
       "        SalarySpent  EducDeg_2.0  EducDeg_3.0  EducDeg_4.0  Children_1  \n",
       "CustID                                                                  \n",
       "52.0       2.817521          0.0          1.0          0.0         1.0  \n",
       "113.0      1.859967          0.0          0.0          1.0         1.0  \n",
       "136.0      1.163966          0.0          1.0          0.0         1.0  \n",
       "150.0      0.025863          0.0          0.0          0.0         1.0  \n",
       "172.0      0.287343          0.0          1.0          0.0         1.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_outliers_ohc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "608bf458",
   "metadata": {},
   "source": [
    "<hr>\n",
    "<a class=\"anchor\" id=\"2\">\n",
    "    \n",
    "# 2. DBSCAN\n",
    "    \n",
    "</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c8586f35",
   "metadata": {},
   "outputs": [],
   "source": [
    "insurance_features_ohc=[\n",
    "    'MotorPercentageSpent',\n",
    "    'HouseholdPercentageSpent',\n",
    "    'HealthPercentageSpent',\n",
    "    'LifePercentageSpent',\n",
    "    'WorkPercentageSpent',\n",
    "    'Cancelled_1',\n",
    "    \"ClaimsRate\"\n",
    "]\n",
    "\n",
    "dfInsurance=df[insurance_features_ohc]\n",
    "\n",
    "demographic_features_ohc=[\n",
    "    'SalarySpent',\n",
    "    'Children_1',\n",
    "    'EducDeg_2.0',\n",
    "    'EducDeg_3.0',\n",
    "    'EducDeg_4.0',\n",
    "    'Age'\n",
    "]\n",
    "dfDemographic=df[demographic_features_ohc]\n",
    "\n",
    "demographic_metric_features_ohc=[\n",
    "    'SalarySpent',\n",
    "    'Age'\n",
    "]\n",
    "dfDemographic_metric=df[demographic_metric_features_ohc]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "ef49db94",
   "metadata": {},
   "outputs": [
    {
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     "text": [
      "Initialization method and algorithm are deterministic. Setting n_init to 1.\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 1, iteration: 1/100, moves: 2929, ncost: 38344.52450674712\n",
      "Run: 1, iteration: 2/100, moves: 505, ncost: 38297.42423095938\n",
      "Run: 1, iteration: 3/100, moves: 73, ncost: 38295.66568903512\n",
      "Run: 1, iteration: 4/100, moves: 10, ncost: 38295.565942166315\n",
      "Run: 1, iteration: 5/100, moves: 3, ncost: 38295.55371128061\n",
      "Run: 1, iteration: 6/100, moves: 2, ncost: 38295.54295432316\n",
      "Run: 1, iteration: 7/100, moves: 4, ncost: 38295.529169356116\n",
      "Run: 1, iteration: 8/100, moves: 0, ncost: 38295.529169356116\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 2, iteration: 1/100, moves: 1913, ncost: 38452.95644418713\n",
      "Run: 2, iteration: 2/100, moves: 552, ncost: 38302.54069310144\n",
      "Run: 2, iteration: 3/100, moves: 99, ncost: 38297.119637806696\n",
      "Run: 2, iteration: 4/100, moves: 40, ncost: 38296.01098562799\n",
      "Run: 2, iteration: 5/100, moves: 27, ncost: 38295.57501238975\n",
      "Run: 2, iteration: 6/100, moves: 9, ncost: 38295.52916935612\n",
      "Run: 2, iteration: 7/100, moves: 0, ncost: 38295.52916935612\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 3, iteration: 1/100, moves: 3051, ncost: 39365.33553821436\n",
      "Run: 3, iteration: 2/100, moves: 1277, ncost: 38505.603553032335\n",
      "Run: 3, iteration: 3/100, moves: 494, ncost: 38344.98028234808\n",
      "Run: 3, iteration: 4/100, moves: 228, ncost: 38313.49019025125\n",
      "Run: 3, iteration: 5/100, moves: 153, ncost: 38300.31845495135\n",
      "Run: 3, iteration: 6/100, moves: 86, ncost: 38296.47160023322\n",
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      "Run: 3, iteration: 8/100, moves: 11, ncost: 38295.55581891335\n",
      "Run: 3, iteration: 9/100, moves: 3, ncost: 38295.54295432315\n",
      "Run: 3, iteration: 10/100, moves: 4, ncost: 38295.52916935611\n",
      "Run: 3, iteration: 11/100, moves: 0, ncost: 38295.52916935611\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 4, iteration: 1/100, moves: 2377, ncost: 38397.67196034755\n",
      "Run: 4, iteration: 2/100, moves: 511, ncost: 38288.11414909138\n",
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      "Run: 4, iteration: 6/100, moves: 9, ncost: 38276.600459685375\n",
      "Run: 4, iteration: 7/100, moves: 1, ncost: 38276.598341001176\n",
      "Run: 4, iteration: 8/100, moves: 0, ncost: 38276.598341001176\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 5, iteration: 1/100, moves: 1441, ncost: 38381.193785501724\n",
      "Run: 5, iteration: 2/100, moves: 371, ncost: 38300.66649134077\n",
      "Run: 5, iteration: 3/100, moves: 78, ncost: 38296.78101194521\n",
      "Run: 5, iteration: 4/100, moves: 35, ncost: 38295.91635522472\n",
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      "Run: 5, iteration: 6/100, moves: 8, ncost: 38295.52916935611\n",
      "Run: 5, iteration: 7/100, moves: 0, ncost: 38295.52916935611\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 6, iteration: 1/100, moves: 1414, ncost: 38370.78416065229\n",
      "Run: 6, iteration: 2/100, moves: 364, ncost: 38301.23942858974\n",
      "Run: 6, iteration: 3/100, moves: 93, ncost: 38296.08963519268\n",
      "Run: 6, iteration: 4/100, moves: 26, ncost: 38295.58118477615\n",
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      "Run: 6, iteration: 6/100, moves: 2, ncost: 38295.54295432316\n",
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      "Run: 6, iteration: 8/100, moves: 0, ncost: 38295.52916935611\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 7, iteration: 1/100, moves: 2634, ncost: 38979.27597543713\n",
      "Run: 7, iteration: 2/100, moves: 851, ncost: 38480.36170064052\n",
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      "Run: 7, iteration: 8/100, moves: 11, ncost: 38295.555818913366\n",
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      "Run: 7, iteration: 10/100, moves: 4, ncost: 38295.529169356116\n",
      "Run: 7, iteration: 11/100, moves: 0, ncost: 38295.529169356116\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 8, iteration: 1/100, moves: 2224, ncost: 45405.43076394285\n",
      "Run: 8, iteration: 2/100, moves: 2124, ncost: 41201.148549543475\n",
      "Run: 8, iteration: 3/100, moves: 2292, ncost: 38472.34111930777\n",
      "Run: 8, iteration: 4/100, moves: 560, ncost: 38322.618072018115\n",
      "Run: 8, iteration: 5/100, moves: 175, ncost: 38304.2988155555\n",
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      "Run: 8, iteration: 7/100, moves: 48, ncost: 38296.02588762579\n",
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      "Run: 8, iteration: 9/100, moves: 7, ncost: 38295.553711280605\n",
      "Run: 8, iteration: 10/100, moves: 2, ncost: 38295.54295432316\n",
      "Run: 8, iteration: 11/100, moves: 4, ncost: 38295.5291693561\n",
      "Run: 8, iteration: 12/100, moves: 0, ncost: 38295.5291693561\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 9, iteration: 1/100, moves: 1573, ncost: 39116.52354905208\n",
      "Run: 9, iteration: 2/100, moves: 845, ncost: 38461.57713113868\n",
      "Run: 9, iteration: 3/100, moves: 404, ncost: 38335.5702795554\n",
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      "Run: 9, iteration: 6/100, moves: 61, ncost: 38296.84583304913\n",
      "Run: 9, iteration: 7/100, moves: 32, ncost: 38296.08029924244\n",
      "Run: 9, iteration: 8/100, moves: 31, ncost: 38295.57501238973\n",
      "Run: 9, iteration: 9/100, moves: 9, ncost: 38295.529169356116\n",
      "Run: 9, iteration: 10/100, moves: 0, ncost: 38295.529169356116\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 10, iteration: 1/100, moves: 2109, ncost: 38705.50255518701\n",
      "Run: 10, iteration: 2/100, moves: 750, ncost: 38322.0628386533\n",
      "Run: 10, iteration: 3/100, moves: 177, ncost: 38301.32526653069\n",
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      "Run: 10, iteration: 6/100, moves: 38, ncost: 38295.730145145535\n",
      "Run: 10, iteration: 7/100, moves: 15, ncost: 38295.55550097972\n",
      "Run: 10, iteration: 8/100, moves: 6, ncost: 38295.529169356116\n",
      "Run: 10, iteration: 9/100, moves: 0, ncost: 38295.529169356116\n",
      "Best run was number 4\n",
      "For n_clusters = 2, the average silhouette_score is : 0.23818584445231597\n",
      "Initialization method and algorithm are deterministic. Setting n_init to 1.\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 1, iteration: 1/100, moves: 3679, ncost: 35059.03240391238\n",
      "Run: 1, iteration: 2/100, moves: 818, ncost: 34709.12586590936\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run: 1, iteration: 23/100, moves: 11, ncost: 33821.45835664519\n",
      "Run: 1, iteration: 24/100, moves: 3, ncost: 33821.44237869358\n",
      "Run: 1, iteration: 25/100, moves: 0, ncost: 33821.44237869358\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 2, iteration: 1/100, moves: 3675, ncost: 34503.53367872215\n",
      "Run: 2, iteration: 2/100, moves: 1541, ncost: 33933.745548083636\n",
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      "Run: 2, iteration: 11/100, moves: 0, ncost: 33821.78896416178\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 3, iteration: 1/100, moves: 4323, ncost: 36690.45587425582\n",
      "Run: 3, iteration: 2/100, moves: 2334, ncost: 34940.35153064326\n",
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      "Run: 3, iteration: 23/100, moves: 0, ncost: 33846.329614604976\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 4, iteration: 1/100, moves: 3851, ncost: 34302.11111229826\n",
      "Run: 4, iteration: 2/100, moves: 1097, ncost: 33899.68643960932\n",
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      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 5, iteration: 1/100, moves: 2150, ncost: 35100.49873070798\n",
      "Run: 5, iteration: 2/100, moves: 1300, ncost: 34239.168001508886\n",
      "Run: 5, iteration: 3/100, moves: 775, ncost: 33979.005397687644\n",
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      "Run: 5, iteration: 13/100, moves: 0, ncost: 33902.86780914212\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 6, iteration: 1/100, moves: 1782, ncost: 34022.3434480144\n",
      "Run: 6, iteration: 2/100, moves: 639, ncost: 33880.35138651466\n",
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      "Run: 6, iteration: 30/100, moves: 0, ncost: 33821.78863041821\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 7, iteration: 1/100, moves: 3795, ncost: 34251.80300447445\n",
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      "Run: 7, iteration: 12/100, moves: 0, ncost: 33764.81968317721\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 8, iteration: 1/100, moves: 3433, ncost: 34624.999713215264\n",
      "Run: 8, iteration: 2/100, moves: 1313, ncost: 33984.68520667422\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run: 8, iteration: 19/100, moves: 1, ncost: 33846.32961460497\n",
      "Run: 8, iteration: 20/100, moves: 0, ncost: 33846.32961460497\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 9, iteration: 1/100, moves: 2357, ncost: 34824.75395782066\n",
      "Run: 9, iteration: 2/100, moves: 976, ncost: 34179.19866261748\n",
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      "Run: 9, iteration: 14/100, moves: 29, ncost: 33823.537272644404\n",
      "Run: 9, iteration: 15/100, moves: 38, ncost: 33822.102820696\n",
      "Run: 9, iteration: 16/100, moves: 17, ncost: 33821.844000832636\n",
      "Run: 9, iteration: 17/100, moves: 3, ncost: 33821.801085562955\n",
      "Run: 9, iteration: 18/100, moves: 1, ncost: 33821.78863041823\n",
      "Run: 9, iteration: 19/100, moves: 0, ncost: 33821.78863041823\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 10, iteration: 1/100, moves: 3111, ncost: 35061.56737384498\n",
      "Run: 10, iteration: 2/100, moves: 1067, ncost: 34658.90418409838\n",
      "Run: 10, iteration: 3/100, moves: 504, ncost: 34490.07552571419\n",
      "Run: 10, iteration: 4/100, moves: 381, ncost: 34322.46887718714\n",
      "Run: 10, iteration: 5/100, moves: 441, ncost: 34130.126173278455\n",
      "Run: 10, iteration: 6/100, moves: 353, ncost: 34004.38966720891\n",
      "Run: 10, iteration: 7/100, moves: 287, ncost: 33927.982147751456\n",
      "Run: 10, iteration: 8/100, moves: 207, ncost: 33891.00848806603\n",
      "Run: 10, iteration: 9/100, moves: 164, ncost: 33868.73573609647\n",
      "Run: 10, iteration: 10/100, moves: 156, ncost: 33851.63028951286\n",
      "Run: 10, iteration: 11/100, moves: 135, ncost: 33839.66460857898\n",
      "Run: 10, iteration: 12/100, moves: 103, ncost: 33832.95039894682\n",
      "Run: 10, iteration: 13/100, moves: 76, ncost: 33829.24428986752\n",
      "Run: 10, iteration: 14/100, moves: 56, ncost: 33826.97711205455\n",
      "Run: 10, iteration: 15/100, moves: 59, ncost: 33824.49917403686\n",
      "Run: 10, iteration: 16/100, moves: 34, ncost: 33823.42591970123\n",
      "Run: 10, iteration: 17/100, moves: 30, ncost: 33822.65510759604\n",
      "Run: 10, iteration: 18/100, moves: 29, ncost: 33821.95503417\n",
      "Run: 10, iteration: 19/100, moves: 13, ncost: 33821.80265094331\n",
      "Run: 10, iteration: 20/100, moves: 7, ncost: 33821.73104862754\n",
      "Run: 10, iteration: 21/100, moves: 11, ncost: 33821.64228213146\n",
      "Run: 10, iteration: 22/100, moves: 11, ncost: 33821.45835664519\n",
      "Run: 10, iteration: 23/100, moves: 3, ncost: 33821.44237869359\n",
      "Run: 10, iteration: 24/100, moves: 0, ncost: 33821.44237869359\n",
      "Best run was number 7\n",
      "For n_clusters = 3, the average silhouette_score is : 0.15411020362889422\n",
      "Initialization method and algorithm are deterministic. Setting n_init to 1.\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 1, iteration: 1/100, moves: 4104, ncost: 32055.1005733306\n",
      "Run: 1, iteration: 2/100, moves: 1639, ncost: 30353.1849907539\n",
      "Run: 1, iteration: 3/100, moves: 897, ncost: 29900.403859762097\n",
      "Run: 1, iteration: 4/100, moves: 565, ncost: 29791.09440933652\n",
      "Run: 1, iteration: 5/100, moves: 249, ncost: 29755.58723770265\n",
      "Run: 1, iteration: 6/100, moves: 153, ncost: 29740.368169523204\n",
      "Run: 1, iteration: 7/100, moves: 88, ncost: 29734.08406210263\n",
      "Run: 1, iteration: 8/100, moves: 79, ncost: 29729.196205986034\n",
      "Run: 1, iteration: 9/100, moves: 33, ncost: 29728.407045717322\n",
      "Run: 1, iteration: 10/100, moves: 12, ncost: 29728.258326576277\n",
      "Run: 1, iteration: 11/100, moves: 10, ncost: 29728.1269664682\n",
      "Run: 1, iteration: 12/100, moves: 5, ncost: 29728.10591673359\n",
      "Run: 1, iteration: 13/100, moves: 3, ncost: 29728.096971258292\n",
      "Run: 1, iteration: 14/100, moves: 6, ncost: 29728.06972604517\n",
      "Run: 1, iteration: 15/100, moves: 1, ncost: 29728.068226958207\n",
      "Run: 1, iteration: 16/100, moves: 0, ncost: 29728.068226958207\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 2, iteration: 1/100, moves: 3734, ncost: 30888.865831559786\n",
      "Run: 2, iteration: 2/100, moves: 1632, ncost: 30081.039865501727\n",
      "Run: 2, iteration: 3/100, moves: 606, ncost: 29939.675875259407\n",
      "Run: 2, iteration: 4/100, moves: 634, ncost: 29750.76793965624\n",
      "Run: 2, iteration: 5/100, moves: 328, ncost: 29713.726187087093\n",
      "Run: 2, iteration: 6/100, moves: 118, ncost: 29707.231617519326\n",
      "Run: 2, iteration: 7/100, moves: 68, ncost: 29704.906224962753\n",
      "Run: 2, iteration: 8/100, moves: 34, ncost: 29704.387097220086\n",
      "Run: 2, iteration: 9/100, moves: 18, ncost: 29704.045201993962\n",
      "Run: 2, iteration: 10/100, moves: 9, ncost: 29704.00834767107\n",
      "Run: 2, iteration: 11/100, moves: 1, ncost: 29704.006459492608\n",
      "Run: 2, iteration: 12/100, moves: 1, ncost: 29704.00538291969\n",
      "Run: 2, iteration: 13/100, moves: 0, ncost: 29704.00538291969\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 3, iteration: 1/100, moves: 3819, ncost: 31587.822698307893\n",
      "Run: 3, iteration: 2/100, moves: 1801, ncost: 30638.966339055147\n",
      "Run: 3, iteration: 3/100, moves: 665, ncost: 30515.79080005782\n",
      "Run: 3, iteration: 4/100, moves: 297, ncost: 30471.463414540005\n",
      "Run: 3, iteration: 5/100, moves: 215, ncost: 30445.40030288088\n",
      "Run: 3, iteration: 6/100, moves: 126, ncost: 30435.248386666713\n",
      "Run: 3, iteration: 7/100, moves: 428, ncost: 30267.100368742565\n",
      "Run: 3, iteration: 8/100, moves: 474, ncost: 30070.54238510204\n",
      "Run: 3, iteration: 9/100, moves: 608, ncost: 29913.02693161861\n",
      "Run: 3, iteration: 10/100, moves: 389, ncost: 29802.27131903397\n",
      "Run: 3, iteration: 11/100, moves: 268, ncost: 29757.184754865266\n",
      "Run: 3, iteration: 12/100, moves: 166, ncost: 29738.83379282913\n",
      "Run: 3, iteration: 13/100, moves: 96, ncost: 29731.61560650605\n",
      "Run: 3, iteration: 14/100, moves: 69, ncost: 29728.655750906433\n",
      "Run: 3, iteration: 15/100, moves: 26, ncost: 29728.020405283685\n",
      "Run: 3, iteration: 16/100, moves: 10, ncost: 29727.9418770904\n",
      "Run: 3, iteration: 17/100, moves: 2, ncost: 29727.938418065074\n",
      "Run: 3, iteration: 18/100, moves: 0, ncost: 29727.938418065074\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 4, iteration: 1/100, moves: 4405, ncost: 30655.910392470872\n",
      "Run: 4, iteration: 2/100, moves: 1269, ncost: 30136.631337378185\n",
      "Run: 4, iteration: 3/100, moves: 479, ncost: 30045.532611736584\n",
      "Run: 4, iteration: 4/100, moves: 577, ncost: 29795.375040864\n",
      "Run: 4, iteration: 5/100, moves: 530, ncost: 29717.215269133132\n",
      "Run: 4, iteration: 6/100, moves: 191, ncost: 29701.089942365754\n",
      "Run: 4, iteration: 7/100, moves: 79, ncost: 29697.636328138087\n",
      "Run: 4, iteration: 8/100, moves: 29, ncost: 29696.907523721962\n",
      "Run: 4, iteration: 9/100, moves: 17, ncost: 29696.697909154256\n",
      "Run: 4, iteration: 10/100, moves: 5, ncost: 29696.689564268978\n",
      "Run: 4, iteration: 11/100, moves: 1, ncost: 29696.688044132767\n",
      "Run: 4, iteration: 12/100, moves: 0, ncost: 29696.688044132767\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 5, iteration: 1/100, moves: 3057, ncost: 30244.33539570942\n",
      "Run: 5, iteration: 2/100, moves: 1074, ncost: 29804.307788156177\n",
      "Run: 5, iteration: 3/100, moves: 450, ncost: 29719.16232539457\n",
      "Run: 5, iteration: 4/100, moves: 181, ncost: 29701.20706517969\n",
      "Run: 5, iteration: 5/100, moves: 93, ncost: 29697.687956576596\n",
      "Run: 5, iteration: 6/100, moves: 43, ncost: 29696.69418105055\n",
      "Run: 5, iteration: 7/100, moves: 19, ncost: 29696.461065560856\n",
      "Run: 5, iteration: 8/100, moves: 5, ncost: 29696.412512830524\n",
      "Run: 5, iteration: 9/100, moves: 0, ncost: 29696.412512830524\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 6, iteration: 1/100, moves: 1982, ncost: 31510.256103399777\n",
      "Run: 6, iteration: 2/100, moves: 1066, ncost: 30696.91027199887\n",
      "Run: 6, iteration: 3/100, moves: 1041, ncost: 29984.000444941918\n",
      "Run: 6, iteration: 4/100, moves: 680, ncost: 29780.95237677131\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run: 6, iteration: 5/100, moves: 312, ncost: 29737.142604803408\n",
      "Run: 6, iteration: 6/100, moves: 122, ncost: 29730.10608773147\n",
      "Run: 6, iteration: 7/100, moves: 61, ncost: 29728.369753314644\n",
      "Run: 6, iteration: 8/100, moves: 19, ncost: 29727.959237876996\n",
      "Run: 6, iteration: 9/100, moves: 5, ncost: 29727.939712457395\n",
      "Run: 6, iteration: 10/100, moves: 1, ncost: 29727.937910564953\n",
      "Run: 6, iteration: 11/100, moves: 0, ncost: 29727.937910564953\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 7, iteration: 1/100, moves: 3498, ncost: 32008.007190963177\n",
      "Run: 7, iteration: 2/100, moves: 1192, ncost: 31502.111577367225\n",
      "Run: 7, iteration: 3/100, moves: 675, ncost: 31114.959457903242\n",
      "Run: 7, iteration: 4/100, moves: 635, ncost: 30581.139433658856\n",
      "Run: 7, iteration: 5/100, moves: 595, ncost: 30197.388708603445\n",
      "Run: 7, iteration: 6/100, moves: 425, ncost: 30081.103702093886\n",
      "Run: 7, iteration: 7/100, moves: 235, ncost: 30052.975213392034\n",
      "Run: 7, iteration: 8/100, moves: 169, ncost: 30038.33449990739\n",
      "Run: 7, iteration: 9/100, moves: 126, ncost: 30031.185795400354\n",
      "Run: 7, iteration: 10/100, moves: 86, ncost: 30028.246793233146\n",
      "Run: 7, iteration: 11/100, moves: 44, ncost: 30027.49986513815\n",
      "Run: 7, iteration: 12/100, moves: 255, ncost: 29951.365345796872\n",
      "Run: 7, iteration: 13/100, moves: 242, ncost: 29936.13554604502\n",
      "Run: 7, iteration: 14/100, moves: 102, ncost: 29932.57047143097\n",
      "Run: 7, iteration: 15/100, moves: 478, ncost: 29761.40892313938\n",
      "Run: 7, iteration: 16/100, moves: 319, ncost: 29722.621907703488\n",
      "Run: 7, iteration: 17/100, moves: 136, ncost: 29709.67320789136\n",
      "Run: 7, iteration: 18/100, moves: 89, ncost: 29704.083463526906\n",
      "Run: 7, iteration: 19/100, moves: 74, ncost: 29699.18069814121\n",
      "Run: 7, iteration: 20/100, moves: 52, ncost: 29697.71020919247\n",
      "Run: 7, iteration: 21/100, moves: 32, ncost: 29697.387818948846\n",
      "Run: 7, iteration: 22/100, moves: 11, ncost: 29697.188665875416\n",
      "Run: 7, iteration: 23/100, moves: 7, ncost: 29697.085513447\n",
      "Run: 7, iteration: 24/100, moves: 1, ncost: 29697.08415755184\n",
      "Run: 7, iteration: 25/100, moves: 0, ncost: 29697.08415755184\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 8, iteration: 1/100, moves: 3708, ncost: 32518.75557462027\n",
      "Run: 8, iteration: 2/100, moves: 1514, ncost: 31729.440293165153\n",
      "Run: 8, iteration: 3/100, moves: 1053, ncost: 30895.64451421125\n",
      "Run: 8, iteration: 4/100, moves: 1203, ncost: 30105.63252447258\n",
      "Run: 8, iteration: 5/100, moves: 662, ncost: 29843.25983108608\n",
      "Run: 8, iteration: 6/100, moves: 353, ncost: 29743.8959181449\n",
      "Run: 8, iteration: 7/100, moves: 210, ncost: 29715.22616651468\n",
      "Run: 8, iteration: 8/100, moves: 116, ncost: 29706.783677864623\n",
      "Run: 8, iteration: 9/100, moves: 51, ncost: 29704.996500641126\n",
      "Run: 8, iteration: 10/100, moves: 29, ncost: 29704.334971100445\n",
      "Run: 8, iteration: 11/100, moves: 13, ncost: 29704.161182180625\n",
      "Run: 8, iteration: 12/100, moves: 2, ncost: 29704.145930324496\n",
      "Run: 8, iteration: 13/100, moves: 3, ncost: 29704.126037647515\n",
      "Run: 8, iteration: 14/100, moves: 6, ncost: 29704.045196401778\n",
      "Run: 8, iteration: 15/100, moves: 1, ncost: 29704.026451662223\n",
      "Run: 8, iteration: 16/100, moves: 3, ncost: 29703.966367673147\n",
      "Run: 8, iteration: 17/100, moves: 3, ncost: 29703.94842374162\n",
      "Run: 8, iteration: 18/100, moves: 0, ncost: 29703.94842374162\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 9, iteration: 1/100, moves: 3348, ncost: 32364.19303670962\n",
      "Run: 9, iteration: 2/100, moves: 1808, ncost: 31221.254754964582\n",
      "Run: 9, iteration: 3/100, moves: 1208, ncost: 30443.977561529162\n",
      "Run: 9, iteration: 4/100, moves: 796, ncost: 30084.717648030404\n",
      "Run: 9, iteration: 5/100, moves: 345, ncost: 30025.46369406162\n",
      "Run: 9, iteration: 6/100, moves: 176, ncost: 30009.353943507333\n",
      "Run: 9, iteration: 7/100, moves: 97, ncost: 30004.59068163161\n",
      "Run: 9, iteration: 8/100, moves: 190, ncost: 29930.024258400685\n",
      "Run: 9, iteration: 9/100, moves: 298, ncost: 29907.17262335129\n",
      "Run: 9, iteration: 10/100, moves: 131, ncost: 29816.65850399837\n",
      "Run: 9, iteration: 11/100, moves: 549, ncost: 29732.952709130717\n",
      "Run: 9, iteration: 12/100, moves: 189, ncost: 29713.96671560928\n",
      "Run: 9, iteration: 13/100, moves: 94, ncost: 29709.460999438354\n",
      "Run: 9, iteration: 14/100, moves: 57, ncost: 29706.88872214836\n",
      "Run: 9, iteration: 15/100, moves: 35, ncost: 29705.69390292027\n",
      "Run: 9, iteration: 16/100, moves: 33, ncost: 29705.026922151308\n",
      "Run: 9, iteration: 17/100, moves: 19, ncost: 29704.72116604312\n",
      "Run: 9, iteration: 18/100, moves: 19, ncost: 29704.40345903719\n",
      "Run: 9, iteration: 19/100, moves: 18, ncost: 29704.15530543735\n",
      "Run: 9, iteration: 20/100, moves: 7, ncost: 29704.042097997\n",
      "Run: 9, iteration: 21/100, moves: 5, ncost: 29704.00908613012\n",
      "Run: 9, iteration: 22/100, moves: 1, ncost: 29704.00635784764\n",
      "Run: 9, iteration: 23/100, moves: 1, ncost: 29704.00528182526\n",
      "Run: 9, iteration: 24/100, moves: 0, ncost: 29704.00528182526\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 10, iteration: 1/100, moves: 4020, ncost: 31882.565240281758\n",
      "Run: 10, iteration: 2/100, moves: 1776, ncost: 30674.062819693572\n",
      "Run: 10, iteration: 3/100, moves: 956, ncost: 30008.96504260733\n",
      "Run: 10, iteration: 4/100, moves: 547, ncost: 29799.306860125263\n",
      "Run: 10, iteration: 5/100, moves: 319, ncost: 29744.334425589237\n",
      "Run: 10, iteration: 6/100, moves: 137, ncost: 29735.53345899777\n",
      "Run: 10, iteration: 7/100, moves: 74, ncost: 29729.688909095847\n",
      "Run: 10, iteration: 8/100, moves: 41, ncost: 29728.466915097815\n",
      "Run: 10, iteration: 9/100, moves: 21, ncost: 29728.236098399564\n",
      "Run: 10, iteration: 10/100, moves: 10, ncost: 29728.121948062082\n",
      "Run: 10, iteration: 11/100, moves: 3, ncost: 29728.11123876677\n",
      "Run: 10, iteration: 12/100, moves: 3, ncost: 29728.102576304755\n",
      "Run: 10, iteration: 13/100, moves: 0, ncost: 29728.102576304755\n",
      "Best run was number 5\n",
      "For n_clusters = 4, the average silhouette_score is : 0.1798418492220166\n",
      "Initialization method and algorithm are deterministic. Setting n_init to 1.\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 1, iteration: 1/100, moves: 4269, ncost: 28649.17690679993\n",
      "Run: 1, iteration: 2/100, moves: 1262, ncost: 28215.06956084888\n",
      "Run: 1, iteration: 3/100, moves: 593, ncost: 28092.639953055143\n",
      "Run: 1, iteration: 4/100, moves: 438, ncost: 28012.683778225928\n",
      "Run: 1, iteration: 5/100, moves: 361, ncost: 27955.599127343798\n",
      "Run: 1, iteration: 6/100, moves: 368, ncost: 27896.069597773334\n",
      "Run: 1, iteration: 7/100, moves: 378, ncost: 27816.70628375769\n",
      "Run: 1, iteration: 8/100, moves: 593, ncost: 27490.150463314698\n",
      "Run: 1, iteration: 9/100, moves: 699, ncost: 27306.85580588866\n",
      "Run: 1, iteration: 10/100, moves: 243, ncost: 27286.943309195216\n",
      "Run: 1, iteration: 11/100, moves: 94, ncost: 27282.935960942566\n",
      "Run: 1, iteration: 12/100, moves: 66, ncost: 27281.169714787793\n",
      "Run: 1, iteration: 13/100, moves: 38, ncost: 27280.586778147568\n",
      "Run: 1, iteration: 14/100, moves: 22, ncost: 27280.41899161018\n",
      "Run: 1, iteration: 15/100, moves: 5, ncost: 27280.396210185685\n",
      "Run: 1, iteration: 16/100, moves: 1, ncost: 27280.394459424955\n",
      "Run: 1, iteration: 17/100, moves: 0, ncost: 27280.394459424955\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 2, iteration: 1/100, moves: 3551, ncost: 28718.45012345903\n",
      "Run: 2, iteration: 2/100, moves: 1206, ncost: 28262.638134675326\n",
      "Run: 2, iteration: 3/100, moves: 704, ncost: 28072.66109257754\n",
      "Run: 2, iteration: 4/100, moves: 361, ncost: 28032.63804679512\n",
      "Run: 2, iteration: 5/100, moves: 172, ncost: 28020.848378821436\n",
      "Run: 2, iteration: 6/100, moves: 81, ncost: 28017.18425827783\n",
      "Run: 2, iteration: 7/100, moves: 53, ncost: 28014.966983651306\n",
      "Run: 2, iteration: 8/100, moves: 50, ncost: 28012.55404393688\n",
      "Run: 2, iteration: 9/100, moves: 40, ncost: 28011.32274976572\n",
      "Run: 2, iteration: 10/100, moves: 24, ncost: 28010.692670641198\n",
      "Run: 2, iteration: 11/100, moves: 41, ncost: 28008.52013918217\n",
      "Run: 2, iteration: 12/100, moves: 37, ncost: 28006.245459737198\n",
      "Run: 2, iteration: 13/100, moves: 47, ncost: 28004.365393614105\n",
      "Run: 2, iteration: 14/100, moves: 22, ncost: 28003.957671611653\n",
      "Run: 2, iteration: 15/100, moves: 12, ncost: 28003.77079161717\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run: 2, iteration: 16/100, moves: 9, ncost: 28003.690207737007\n",
      "Run: 2, iteration: 17/100, moves: 2, ncost: 28003.682343377975\n",
      "Run: 2, iteration: 18/100, moves: 0, ncost: 28003.682343377975\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 3, iteration: 1/100, moves: 4282, ncost: 28444.709221565216\n",
      "Run: 3, iteration: 2/100, moves: 1868, ncost: 27518.892660042875\n",
      "Run: 3, iteration: 3/100, moves: 686, ncost: 27364.56389386134\n",
      "Run: 3, iteration: 4/100, moves: 340, ncost: 27318.254357092173\n",
      "Run: 3, iteration: 5/100, moves: 173, ncost: 27308.128050466883\n",
      "Run: 3, iteration: 6/100, moves: 83, ncost: 27305.329014238843\n",
      "Run: 3, iteration: 7/100, moves: 60, ncost: 27303.768260573954\n",
      "Run: 3, iteration: 8/100, moves: 35, ncost: 27303.27814023956\n",
      "Run: 3, iteration: 9/100, moves: 24, ncost: 27302.986725843468\n",
      "Run: 3, iteration: 10/100, moves: 27, ncost: 27302.507207712384\n",
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      "Run: 3, iteration: 13/100, moves: 1, ncost: 27302.250474945897\n",
      "Run: 3, iteration: 14/100, moves: 0, ncost: 27302.250474945897\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 4, iteration: 1/100, moves: 4429, ncost: 29172.174852987402\n",
      "Run: 4, iteration: 2/100, moves: 1648, ncost: 28225.24094552712\n",
      "Run: 4, iteration: 3/100, moves: 1006, ncost: 27871.714585814072\n",
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      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 5, iteration: 1/100, moves: 3789, ncost: 29042.794327943877\n",
      "Run: 5, iteration: 2/100, moves: 1452, ncost: 28508.143369688867\n",
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      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 6, iteration: 1/100, moves: 2513, ncost: 28610.933960874634\n",
      "Run: 6, iteration: 2/100, moves: 1056, ncost: 28031.70711557504\n",
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      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 7, iteration: 1/100, moves: 4757, ncost: 28881.326189932228\n",
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      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 8, iteration: 1/100, moves: 4097, ncost: 30146.549178057063\n",
      "Run: 8, iteration: 2/100, moves: 1395, ncost: 29048.988027757576\n",
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      "Run: 8, iteration: 21/100, moves: 0, ncost: 27990.674739840822\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 9, iteration: 1/100, moves: 3934, ncost: 29874.893769836443\n",
      "Run: 9, iteration: 2/100, moves: 2394, ncost: 28401.936824810226\n",
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      "Init: initializing centroids\n",
      "Init: initializing clusters\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting iterations...\n",
      "Run: 10, iteration: 1/100, moves: 3935, ncost: 29661.536481870535\n",
      "Run: 10, iteration: 2/100, moves: 2017, ncost: 28177.93246109336\n",
      "Run: 10, iteration: 3/100, moves: 1381, ncost: 27558.86790794182\n",
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      "Run: 10, iteration: 9/100, moves: 19, ncost: 27297.93498995921\n",
      "Run: 10, iteration: 10/100, moves: 12, ncost: 27297.871484688516\n",
      "Run: 10, iteration: 11/100, moves: 5, ncost: 27297.84539947058\n",
      "Run: 10, iteration: 12/100, moves: 0, ncost: 27297.84539947058\n",
      "Best run was number 9\n",
      "For n_clusters = 5, the average silhouette_score is : 0.1583719327489885\n",
      "Initialization method and algorithm are deterministic. Setting n_init to 1.\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 1, iteration: 1/100, moves: 3779, ncost: 28463.23318566772\n",
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      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 2, iteration: 1/100, moves: 3159, ncost: 27909.90860518088\n",
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      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 3, iteration: 1/100, moves: 4545, ncost: 26854.958573381115\n",
      "Run: 3, iteration: 2/100, moves: 1941, ncost: 25766.56606072362\n",
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      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 4, iteration: 1/100, moves: 4784, ncost: 27440.78048774456\n",
      "Run: 4, iteration: 2/100, moves: 1430, ncost: 26737.11566906261\n",
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      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 5, iteration: 1/100, moves: 4378, ncost: 27753.831518898427\n",
      "Run: 5, iteration: 2/100, moves: 2124, ncost: 26479.23834118152\n",
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      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 6, iteration: 1/100, moves: 3056, ncost: 27002.3956330823\n",
      "Run: 6, iteration: 2/100, moves: 1243, ncost: 26594.345597465803\n",
      "Run: 6, iteration: 3/100, moves: 861, ncost: 26167.01885849821\n",
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      "Run: 6, iteration: 15/100, moves: 0, ncost: 25432.04003313322\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 7, iteration: 1/100, moves: 4695, ncost: 27190.58971087505\n",
      "Run: 7, iteration: 2/100, moves: 1567, ncost: 26540.4778160482\n",
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      "Run: 7, iteration: 6/100, moves: 526, ncost: 25901.379877721018\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run: 7, iteration: 7/100, moves: 269, ncost: 25847.464700328903\n",
      "Run: 7, iteration: 8/100, moves: 208, ncost: 25772.89730148656\n",
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      "Run: 7, iteration: 12/100, moves: 72, ncost: 25573.541966065022\n",
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      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 8, iteration: 1/100, moves: 3918, ncost: 29149.751479759034\n",
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      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 9, iteration: 1/100, moves: 4324, ncost: 28142.668535005643\n",
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      "Run: 9, iteration: 36/100, moves: 0, ncost: 25570.205351559853\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 10, iteration: 1/100, moves: 4727, ncost: 27440.29507038336\n",
      "Run: 10, iteration: 2/100, moves: 1680, ncost: 26214.219489133746\n",
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      "Run: 10, iteration: 18/100, moves: 0, ncost: 25447.952223754626\n",
      "Best run was number 6\n",
      "For n_clusters = 6, the average silhouette_score is : 0.16481461899954675\n",
      "Initialization method and algorithm are deterministic. Setting n_init to 1.\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 1, iteration: 1/100, moves: 4159, ncost: 26391.55408762849\n",
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      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 2, iteration: 1/100, moves: 3305, ncost: 25398.308199961382\n",
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      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 3, iteration: 1/100, moves: 4000, ncost: 26554.002220578393\n",
      "Run: 3, iteration: 2/100, moves: 2485, ncost: 25138.96382773863\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run: 3, iteration: 7/100, moves: 347, ncost: 24300.09319888066\n",
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      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 4, iteration: 1/100, moves: 4967, ncost: 26552.36486463067\n",
      "Run: 4, iteration: 2/100, moves: 1984, ncost: 25133.14152740976\n",
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      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 5, iteration: 1/100, moves: 4405, ncost: 26547.882018674103\n",
      "Run: 5, iteration: 2/100, moves: 1968, ncost: 25784.060132412378\n",
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      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 6, iteration: 1/100, moves: 3145, ncost: 25896.285350018592\n",
      "Run: 6, iteration: 2/100, moves: 1325, ncost: 25448.900468671316\n",
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      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 7, iteration: 1/100, moves: 4809, ncost: 25520.991482309153\n",
      "Run: 7, iteration: 2/100, moves: 1116, ncost: 25221.599569376176\n",
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      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 8, iteration: 1/100, moves: 3842, ncost: 27338.160277339706\n",
      "Run: 8, iteration: 2/100, moves: 1927, ncost: 26077.141677365944\n",
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      "Run: 8, iteration: 26/100, moves: 0, ncost: 23792.898504002165\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 9, iteration: 1/100, moves: 4556, ncost: 26620.417569066853\n",
      "Run: 9, iteration: 2/100, moves: 1737, ncost: 25855.03462043703\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run: 9, iteration: 3/100, moves: 1182, ncost: 25272.812081658165\n",
      "Run: 9, iteration: 4/100, moves: 1103, ncost: 24839.47866728229\n",
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      "Run: 9, iteration: 6/100, moves: 527, ncost: 24364.32778235218\n",
      "Run: 9, iteration: 7/100, moves: 175, ncost: 24350.68982716345\n",
      "Run: 9, iteration: 8/100, moves: 74, ncost: 24347.40028133691\n",
      "Run: 9, iteration: 9/100, moves: 40, ncost: 24346.294331066\n",
      "Run: 9, iteration: 10/100, moves: 35, ncost: 24345.640265909362\n",
      "Run: 9, iteration: 11/100, moves: 22, ncost: 24345.397006308325\n",
      "Run: 9, iteration: 12/100, moves: 15, ncost: 24345.137892431092\n",
      "Run: 9, iteration: 13/100, moves: 12, ncost: 24344.901932755532\n",
      "Run: 9, iteration: 14/100, moves: 2, ncost: 24344.86780187251\n",
      "Run: 9, iteration: 15/100, moves: 2, ncost: 24344.847346302668\n",
      "Run: 9, iteration: 16/100, moves: 4, ncost: 24344.808552859653\n",
      "Run: 9, iteration: 17/100, moves: 3, ncost: 24344.79019695226\n",
      "Run: 9, iteration: 18/100, moves: 5, ncost: 24344.61443213196\n",
      "Run: 9, iteration: 19/100, moves: 3, ncost: 24344.570967880125\n",
      "Run: 9, iteration: 20/100, moves: 2, ncost: 24344.55760062463\n",
      "Run: 9, iteration: 21/100, moves: 2, ncost: 24344.540738636602\n",
      "Run: 9, iteration: 22/100, moves: 1, ncost: 24344.534570925513\n",
      "Run: 9, iteration: 23/100, moves: 0, ncost: 24344.534570925513\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 10, iteration: 1/100, moves: 4615, ncost: 25442.395983499955\n",
      "Run: 10, iteration: 2/100, moves: 1488, ncost: 24697.332332964692\n",
      "Run: 10, iteration: 3/100, moves: 672, ncost: 24478.553065014985\n",
      "Run: 10, iteration: 4/100, moves: 543, ncost: 24313.989873602266\n",
      "Run: 10, iteration: 5/100, moves: 431, ncost: 24162.125036758174\n",
      "Run: 10, iteration: 6/100, moves: 510, ncost: 23943.191753330048\n",
      "Run: 10, iteration: 7/100, moves: 310, ncost: 23862.90994328229\n",
      "Run: 10, iteration: 8/100, moves: 178, ncost: 23823.43313268617\n",
      "Run: 10, iteration: 9/100, moves: 70, ncost: 23812.234559151013\n",
      "Run: 10, iteration: 10/100, moves: 42, ncost: 23806.990377107802\n",
      "Run: 10, iteration: 11/100, moves: 38, ncost: 23803.45359483052\n",
      "Run: 10, iteration: 12/100, moves: 24, ncost: 23802.09266093496\n",
      "Run: 10, iteration: 13/100, moves: 14, ncost: 23801.67290710055\n",
      "Run: 10, iteration: 14/100, moves: 9, ncost: 23801.61344372534\n",
      "Run: 10, iteration: 15/100, moves: 2, ncost: 23801.609341540596\n",
      "Run: 10, iteration: 16/100, moves: 0, ncost: 23801.609341540596\n",
      "Best run was number 6\n",
      "For n_clusters = 7, the average silhouette_score is : 0.1656567607546673\n",
      "Initialization method and algorithm are deterministic. Setting n_init to 1.\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 1, iteration: 1/100, moves: 4306, ncost: 25596.134517373084\n",
      "Run: 1, iteration: 2/100, moves: 1867, ncost: 24232.33885227879\n",
      "Run: 1, iteration: 3/100, moves: 1042, ncost: 23654.900538981063\n",
      "Run: 1, iteration: 4/100, moves: 674, ncost: 23372.91421673476\n",
      "Run: 1, iteration: 5/100, moves: 491, ncost: 23232.68083662451\n",
      "Run: 1, iteration: 6/100, moves: 366, ncost: 23157.926097767206\n",
      "Run: 1, iteration: 7/100, moves: 269, ncost: 23121.010902704867\n",
      "Run: 1, iteration: 8/100, moves: 178, ncost: 23102.424310454124\n",
      "Run: 1, iteration: 9/100, moves: 136, ncost: 23084.8218748207\n",
      "Run: 1, iteration: 10/100, moves: 257, ncost: 23014.818918369117\n",
      "Run: 1, iteration: 11/100, moves: 174, ncost: 23009.37234502725\n",
      "Run: 1, iteration: 12/100, moves: 46, ncost: 23004.513519475975\n",
      "Run: 1, iteration: 13/100, moves: 44, ncost: 23002.20565102644\n",
      "Run: 1, iteration: 14/100, moves: 25, ncost: 23001.655819094856\n",
      "Run: 1, iteration: 15/100, moves: 5, ncost: 23001.601338838303\n",
      "Run: 1, iteration: 16/100, moves: 0, ncost: 23001.601338838303\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 2, iteration: 1/100, moves: 3181, ncost: 24262.357055622753\n",
      "Run: 2, iteration: 2/100, moves: 1253, ncost: 23897.63022575375\n",
      "Run: 2, iteration: 3/100, moves: 616, ncost: 23756.389654435403\n",
      "Run: 2, iteration: 4/100, moves: 434, ncost: 23661.4844302526\n",
      "Run: 2, iteration: 5/100, moves: 324, ncost: 23598.23330012605\n",
      "Run: 2, iteration: 6/100, moves: 285, ncost: 23537.78190785643\n",
      "Run: 2, iteration: 7/100, moves: 170, ncost: 23524.163016886298\n",
      "Run: 2, iteration: 8/100, moves: 98, ncost: 23516.24138603222\n",
      "Run: 2, iteration: 9/100, moves: 94, ncost: 23506.473687351634\n",
      "Run: 2, iteration: 10/100, moves: 121, ncost: 23489.25255101964\n",
      "Run: 2, iteration: 11/100, moves: 142, ncost: 23461.644782025363\n",
      "Run: 2, iteration: 12/100, moves: 239, ncost: 23373.00633678829\n",
      "Run: 2, iteration: 13/100, moves: 336, ncost: 23195.905085398957\n",
      "Run: 2, iteration: 14/100, moves: 450, ncost: 23010.29315465248\n",
      "Run: 2, iteration: 15/100, moves: 396, ncost: 22892.027490269757\n",
      "Run: 2, iteration: 16/100, moves: 281, ncost: 22839.4345257027\n",
      "Run: 2, iteration: 17/100, moves: 168, ncost: 22819.735027760882\n",
      "Run: 2, iteration: 18/100, moves: 101, ncost: 22815.304453109962\n",
      "Run: 2, iteration: 19/100, moves: 47, ncost: 22814.054310236395\n",
      "Run: 2, iteration: 20/100, moves: 25, ncost: 22813.655317293036\n",
      "Run: 2, iteration: 21/100, moves: 12, ncost: 22813.58282510922\n",
      "Run: 2, iteration: 22/100, moves: 2, ncost: 22813.578894428018\n",
      "Run: 2, iteration: 23/100, moves: 0, ncost: 22813.578894428018\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 3, iteration: 1/100, moves: 4110, ncost: 25196.71065668693\n",
      "Run: 3, iteration: 2/100, moves: 1854, ncost: 24358.29390059863\n",
      "Run: 3, iteration: 3/100, moves: 1521, ncost: 23627.185689643284\n",
      "Run: 3, iteration: 4/100, moves: 1128, ncost: 23159.2525996918\n",
      "Run: 3, iteration: 5/100, moves: 857, ncost: 22854.14963740321\n",
      "Run: 3, iteration: 6/100, moves: 610, ncost: 22690.84221353113\n",
      "Run: 3, iteration: 7/100, moves: 319, ncost: 22642.440415260528\n",
      "Run: 3, iteration: 8/100, moves: 272, ncost: 22601.32244917431\n",
      "Run: 3, iteration: 9/100, moves: 150, ncost: 22594.788192500797\n",
      "Run: 3, iteration: 10/100, moves: 83, ncost: 22587.912970649646\n",
      "Run: 3, iteration: 11/100, moves: 24, ncost: 22586.378477815433\n",
      "Run: 3, iteration: 12/100, moves: 17, ncost: 22585.610614188696\n",
      "Run: 3, iteration: 13/100, moves: 13, ncost: 22585.22543759342\n",
      "Run: 3, iteration: 14/100, moves: 7, ncost: 22585.060778794657\n",
      "Run: 3, iteration: 15/100, moves: 1, ncost: 22585.049461552586\n",
      "Run: 3, iteration: 16/100, moves: 0, ncost: 22585.049461552586\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 4, iteration: 1/100, moves: 5247, ncost: 25089.071794683947\n",
      "Run: 4, iteration: 2/100, moves: 2184, ncost: 23548.097101219035\n",
      "Run: 4, iteration: 3/100, moves: 1246, ncost: 23004.19589376412\n",
      "Run: 4, iteration: 4/100, moves: 592, ncost: 22865.268475125304\n",
      "Run: 4, iteration: 5/100, moves: 390, ncost: 22812.533835460603\n",
      "Run: 4, iteration: 6/100, moves: 202, ncost: 22800.945186733123\n",
      "Run: 4, iteration: 7/100, moves: 97, ncost: 22798.312436417025\n",
      "Run: 4, iteration: 8/100, moves: 55, ncost: 22797.33189190125\n",
      "Run: 4, iteration: 9/100, moves: 30, ncost: 22797.01237176989\n",
      "Run: 4, iteration: 10/100, moves: 15, ncost: 22796.87829965262\n",
      "Run: 4, iteration: 11/100, moves: 19, ncost: 22796.684377956397\n",
      "Run: 4, iteration: 12/100, moves: 17, ncost: 22796.535997137955\n",
      "Run: 4, iteration: 13/100, moves: 16, ncost: 22796.438517051247\n",
      "Run: 4, iteration: 14/100, moves: 7, ncost: 22796.416132092414\n",
      "Run: 4, iteration: 15/100, moves: 2, ncost: 22796.4098009663\n",
      "Run: 4, iteration: 16/100, moves: 2, ncost: 22796.407321030347\n",
      "Run: 4, iteration: 17/100, moves: 0, ncost: 22796.407321030347\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 5, iteration: 1/100, moves: 4414, ncost: 24840.358755573314\n",
      "Run: 5, iteration: 2/100, moves: 2158, ncost: 23618.698193648142\n",
      "Run: 5, iteration: 3/100, moves: 1137, ncost: 23254.86917286365\n",
      "Run: 5, iteration: 4/100, moves: 582, ncost: 23136.577513681368\n",
      "Run: 5, iteration: 5/100, moves: 431, ncost: 23049.80345257669\n",
      "Run: 5, iteration: 6/100, moves: 398, ncost: 22968.71119319275\n",
      "Run: 5, iteration: 7/100, moves: 458, ncost: 22868.567103565074\n",
      "Run: 5, iteration: 8/100, moves: 469, ncost: 22768.45368695833\n",
      "Run: 5, iteration: 9/100, moves: 330, ncost: 22725.750254550243\n",
      "Run: 5, iteration: 10/100, moves: 314, ncost: 22618.768130585373\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run: 5, iteration: 11/100, moves: 283, ncost: 22590.026499833926\n",
      "Run: 5, iteration: 12/100, moves: 133, ncost: 22586.143367071014\n",
      "Run: 5, iteration: 13/100, moves: 31, ncost: 22585.779635715928\n",
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      "Run: 5, iteration: 16/100, moves: 1, ncost: 22585.708725488876\n",
      "Run: 5, iteration: 17/100, moves: 0, ncost: 22585.708725488876\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 6, iteration: 1/100, moves: 3636, ncost: 23728.819924155006\n",
      "Run: 6, iteration: 2/100, moves: 1248, ncost: 23297.78379561366\n",
      "Run: 6, iteration: 3/100, moves: 856, ncost: 23069.214793518586\n",
      "Run: 6, iteration: 4/100, moves: 546, ncost: 22962.694630853035\n",
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      "Run: 6, iteration: 10/100, moves: 149, ncost: 22820.412574522503\n",
      "Run: 6, iteration: 11/100, moves: 137, ncost: 22814.337823494818\n",
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      "Run: 6, iteration: 17/100, moves: 27, ncost: 22808.46758088125\n",
      "Run: 6, iteration: 18/100, moves: 24, ncost: 22808.17977753949\n",
      "Run: 6, iteration: 19/100, moves: 28, ncost: 22807.774440197336\n",
      "Run: 6, iteration: 20/100, moves: 48, ncost: 22807.217014681028\n",
      "Run: 6, iteration: 21/100, moves: 22, ncost: 22807.0593486577\n",
      "Run: 6, iteration: 22/100, moves: 17, ncost: 22806.970676940164\n",
      "Run: 6, iteration: 23/100, moves: 4, ncost: 22806.88641539003\n",
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      "Run: 6, iteration: 25/100, moves: 6, ncost: 22806.78560322115\n",
      "Run: 6, iteration: 26/100, moves: 4, ncost: 22806.76663784824\n",
      "Run: 6, iteration: 27/100, moves: 1, ncost: 22806.749072548122\n",
      "Run: 6, iteration: 28/100, moves: 0, ncost: 22806.749072548122\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 7, iteration: 1/100, moves: 4858, ncost: 23181.257171598132\n",
      "Run: 7, iteration: 2/100, moves: 1246, ncost: 22767.50405136588\n",
      "Run: 7, iteration: 3/100, moves: 253, ncost: 22752.987803823697\n",
      "Run: 7, iteration: 4/100, moves: 82, ncost: 22751.079446354204\n",
      "Run: 7, iteration: 5/100, moves: 50, ncost: 22750.235500109527\n",
      "Run: 7, iteration: 6/100, moves: 23, ncost: 22749.958964917158\n",
      "Run: 7, iteration: 7/100, moves: 5, ncost: 22749.94108289929\n",
      "Run: 7, iteration: 8/100, moves: 0, ncost: 22749.94108289929\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 8, iteration: 1/100, moves: 3691, ncost: 25295.58043103666\n",
      "Run: 8, iteration: 2/100, moves: 1662, ncost: 24501.755616421437\n",
      "Run: 8, iteration: 3/100, moves: 978, ncost: 24218.262028515062\n",
      "Run: 8, iteration: 4/100, moves: 586, ncost: 24115.37976802081\n",
      "Run: 8, iteration: 5/100, moves: 499, ncost: 24030.00088424154\n",
      "Run: 8, iteration: 6/100, moves: 567, ncost: 23900.01548946478\n",
      "Run: 8, iteration: 7/100, moves: 576, ncost: 23763.31386752693\n",
      "Run: 8, iteration: 8/100, moves: 634, ncost: 23608.40434133403\n",
      "Run: 8, iteration: 9/100, moves: 574, ncost: 23485.86989696782\n",
      "Run: 8, iteration: 10/100, moves: 466, ncost: 23377.012073877282\n",
      "Run: 8, iteration: 11/100, moves: 461, ncost: 23189.853838299725\n",
      "Run: 8, iteration: 12/100, moves: 527, ncost: 23057.035789892543\n",
      "Run: 8, iteration: 13/100, moves: 379, ncost: 22969.604474933843\n",
      "Run: 8, iteration: 14/100, moves: 287, ncost: 22921.927974890237\n",
      "Run: 8, iteration: 15/100, moves: 243, ncost: 22890.138232693815\n",
      "Run: 8, iteration: 16/100, moves: 222, ncost: 22865.679114777882\n",
      "Run: 8, iteration: 17/100, moves: 152, ncost: 22856.770245714957\n",
      "Run: 8, iteration: 18/100, moves: 124, ncost: 22809.16597015427\n",
      "Run: 8, iteration: 19/100, moves: 294, ncost: 22795.24579382869\n",
      "Run: 8, iteration: 20/100, moves: 130, ncost: 22785.702933197463\n",
      "Run: 8, iteration: 21/100, moves: 68, ncost: 22784.52357130481\n",
      "Run: 8, iteration: 22/100, moves: 34, ncost: 22783.917385676967\n",
      "Run: 8, iteration: 23/100, moves: 13, ncost: 22783.691080021188\n",
      "Run: 8, iteration: 24/100, moves: 5, ncost: 22783.677456035573\n",
      "Run: 8, iteration: 25/100, moves: 1, ncost: 22783.67426459572\n",
      "Run: 8, iteration: 26/100, moves: 2, ncost: 22783.668317848802\n",
      "Run: 8, iteration: 27/100, moves: 1, ncost: 22783.666474710695\n",
      "Run: 8, iteration: 28/100, moves: 0, ncost: 22783.666474710695\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 9, iteration: 1/100, moves: 4441, ncost: 25433.68051045378\n",
      "Run: 9, iteration: 2/100, moves: 1789, ncost: 24397.080814245117\n",
      "Run: 9, iteration: 3/100, moves: 1288, ncost: 23788.06422567095\n",
      "Run: 9, iteration: 4/100, moves: 951, ncost: 23473.617743794748\n",
      "Run: 9, iteration: 5/100, moves: 598, ncost: 23320.614286033076\n",
      "Run: 9, iteration: 6/100, moves: 444, ncost: 23212.274405753247\n",
      "Run: 9, iteration: 7/100, moves: 437, ncost: 23107.37737833308\n",
      "Run: 9, iteration: 8/100, moves: 411, ncost: 23027.555137601383\n",
      "Run: 9, iteration: 9/100, moves: 596, ncost: 22826.94835813219\n",
      "Run: 9, iteration: 10/100, moves: 423, ncost: 22754.78928974551\n",
      "Run: 9, iteration: 11/100, moves: 247, ncost: 22728.117972706077\n",
      "Run: 9, iteration: 12/100, moves: 373, ncost: 22618.399971665574\n",
      "Run: 9, iteration: 13/100, moves: 222, ncost: 22588.354809785797\n",
      "Run: 9, iteration: 14/100, moves: 82, ncost: 22586.121996116286\n",
      "Run: 9, iteration: 15/100, moves: 22, ncost: 22585.7724449282\n",
      "Run: 9, iteration: 16/100, moves: 6, ncost: 22585.520397736\n",
      "Run: 9, iteration: 17/100, moves: 4, ncost: 22585.367586951674\n",
      "Run: 9, iteration: 18/100, moves: 4, ncost: 22585.334634454965\n",
      "Run: 9, iteration: 19/100, moves: 4, ncost: 22585.31721181783\n",
      "Run: 9, iteration: 20/100, moves: 0, ncost: 22585.31721181783\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 10, iteration: 1/100, moves: 3930, ncost: 24498.501248524615\n",
      "Run: 10, iteration: 2/100, moves: 1702, ncost: 23507.855629532296\n",
      "Run: 10, iteration: 3/100, moves: 902, ncost: 23145.25312180406\n",
      "Run: 10, iteration: 4/100, moves: 669, ncost: 22867.927565952315\n",
      "Run: 10, iteration: 5/100, moves: 474, ncost: 22707.355424263656\n",
      "Run: 10, iteration: 6/100, moves: 289, ncost: 22652.737116987486\n",
      "Run: 10, iteration: 7/100, moves: 158, ncost: 22632.76175170997\n",
      "Run: 10, iteration: 8/100, moves: 133, ncost: 22602.225865644898\n",
      "Run: 10, iteration: 9/100, moves: 130, ncost: 22596.200055534824\n",
      "Run: 10, iteration: 10/100, moves: 41, ncost: 22595.21972716376\n",
      "Run: 10, iteration: 11/100, moves: 7, ncost: 22595.13400704834\n",
      "Run: 10, iteration: 12/100, moves: 15, ncost: 22595.015539372984\n",
      "Run: 10, iteration: 13/100, moves: 7, ncost: 22594.990955314595\n",
      "Run: 10, iteration: 14/100, moves: 0, ncost: 22594.990955314595\n",
      "Best run was number 3\n",
      "For n_clusters = 8, the average silhouette_score is : 0.1498400272771526\n",
      "Initialization method and algorithm are deterministic. Setting n_init to 1.\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 1, iteration: 1/100, moves: 4496, ncost: 24310.760018653167\n",
      "Run: 1, iteration: 2/100, moves: 1979, ncost: 23126.996925215313\n",
      "Run: 1, iteration: 3/100, moves: 1403, ncost: 22537.846740081455\n",
      "Run: 1, iteration: 4/100, moves: 787, ncost: 22335.518931942657\n",
      "Run: 1, iteration: 5/100, moves: 678, ncost: 22178.984441249384\n",
      "Run: 1, iteration: 6/100, moves: 502, ncost: 22102.892568140483\n",
      "Run: 1, iteration: 7/100, moves: 364, ncost: 22058.78605582234\n",
      "Run: 1, iteration: 8/100, moves: 230, ncost: 22041.215512160525\n",
      "Run: 1, iteration: 9/100, moves: 155, ncost: 22032.29051253822\n",
      "Run: 1, iteration: 10/100, moves: 112, ncost: 22026.010199980177\n",
      "Run: 1, iteration: 11/100, moves: 84, ncost: 22021.726333279727\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "Init: initializing centroids\n",
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      "Init: initializing centroids\n",
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      "Init: initializing centroids\n",
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      "Starting iterations...\n",
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      "Init: initializing centroids\n",
      "Init: initializing clusters\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting iterations...\n",
      "Run: 6, iteration: 1/100, moves: 3752, ncost: 22908.84367953852\n",
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      "Init: initializing centroids\n",
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      "Starting iterations...\n",
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      "Init: initializing centroids\n",
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      "Starting iterations...\n",
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      "Init: initializing centroids\n",
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      "Starting iterations...\n",
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      "Init: initializing centroids\n",
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      "Starting iterations...\n",
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      "Best run was number 8\n",
      "For n_clusters = 9, the average silhouette_score is : 0.15042909202956012\n",
      "Initialization method and algorithm are deterministic. Setting n_init to 1.\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
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     ]
    },
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     ]
    },
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     "text": [
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      "Run: 9, iteration: 30/100, moves: 14, ncost: 21104.313038268185\n",
      "Run: 9, iteration: 31/100, moves: 4, ncost: 21104.302077295655\n",
      "Run: 9, iteration: 32/100, moves: 1, ncost: 21104.301068361543\n",
      "Run: 9, iteration: 33/100, moves: 0, ncost: 21104.301068361543\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 10, iteration: 1/100, moves: 4817, ncost: 22811.43412260345\n",
      "Run: 10, iteration: 2/100, moves: 1531, ncost: 22086.062675800167\n",
      "Run: 10, iteration: 3/100, moves: 1167, ncost: 21784.59721921487\n",
      "Run: 10, iteration: 4/100, moves: 778, ncost: 21564.4074942215\n",
      "Run: 10, iteration: 5/100, moves: 633, ncost: 21402.388582061398\n",
      "Run: 10, iteration: 6/100, moves: 361, ncost: 21363.442502944916\n",
      "Run: 10, iteration: 7/100, moves: 347, ncost: 21308.08530502979\n",
      "Run: 10, iteration: 8/100, moves: 289, ncost: 21289.7209263839\n",
      "Run: 10, iteration: 9/100, moves: 238, ncost: 21272.0570815119\n",
      "Run: 10, iteration: 10/100, moves: 214, ncost: 21255.766517356704\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run: 10, iteration: 11/100, moves: 259, ncost: 21233.420139818525\n",
      "Run: 10, iteration: 12/100, moves: 266, ncost: 21209.49846002701\n",
      "Run: 10, iteration: 13/100, moves: 254, ncost: 21181.353723794273\n",
      "Run: 10, iteration: 14/100, moves: 243, ncost: 21154.454214300626\n",
      "Run: 10, iteration: 15/100, moves: 241, ncost: 21106.08632930019\n",
      "Run: 10, iteration: 16/100, moves: 336, ncost: 21014.53075840649\n",
      "Run: 10, iteration: 17/100, moves: 298, ncost: 20950.52337672539\n",
      "Run: 10, iteration: 18/100, moves: 210, ncost: 20915.58805405951\n",
      "Run: 10, iteration: 19/100, moves: 129, ncost: 20898.93006685416\n",
      "Run: 10, iteration: 20/100, moves: 91, ncost: 20889.06020861677\n",
      "Run: 10, iteration: 21/100, moves: 71, ncost: 20881.378370223378\n",
      "Run: 10, iteration: 22/100, moves: 62, ncost: 20878.65624059615\n",
      "Run: 10, iteration: 23/100, moves: 25, ncost: 20877.967259277826\n",
      "Run: 10, iteration: 24/100, moves: 27, ncost: 20876.87460801399\n",
      "Run: 10, iteration: 25/100, moves: 27, ncost: 20876.160380307916\n",
      "Run: 10, iteration: 26/100, moves: 15, ncost: 20875.987242910982\n",
      "Run: 10, iteration: 27/100, moves: 6, ncost: 20875.96691124392\n",
      "Run: 10, iteration: 28/100, moves: 4, ncost: 20875.95244329158\n",
      "Run: 10, iteration: 29/100, moves: 5, ncost: 20875.92048226603\n",
      "Run: 10, iteration: 30/100, moves: 6, ncost: 20875.902577741123\n",
      "Run: 10, iteration: 31/100, moves: 2, ncost: 20875.899207442133\n",
      "Run: 10, iteration: 32/100, moves: 1, ncost: 20875.89615060477\n",
      "Run: 10, iteration: 33/100, moves: 0, ncost: 20875.89615060477\n",
      "Best run was number 10\n",
      "For n_clusters = 10, the average silhouette_score is : 0.13848336988474497\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1300x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1300x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1300x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1300x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1300x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1300x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1300x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1300x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1300x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "range_clusters = range(1, 11)\n",
    "\n",
    "# Adapted from:\n",
    "# https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html#sphx-glr-auto-examples-cluster-plot-kmeans-silhouette-analysis-py\n",
    "\n",
    "# Storing average silhouette metric\n",
    "avg_silhouette = []\n",
    "for nclus in range_clusters:\n",
    "    # Skip nclus == 1\n",
    "    if nclus == 1:\n",
    "        continue\n",
    "    \n",
    "    # Create a figure\n",
    "    fig = plt.figure(figsize=(13, 7))\n",
    "\n",
    "    # Initialize the KMeans object with n_clusters value and a random generator\n",
    "    # seed of 10 for reproducibility.\n",
    "    kproto = KPrototypes(n_clusters=nclus, init='Cao',verbose=2, random_state=1)\n",
    "    cluster_labels = kproto.fit_predict(df,categorical=[8,9,10,11,12])\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 clusters\n",
    "    silhouette_avg = silhouette_score(df, cluster_labels)\n",
    "    avg_silhouette.append(silhouette_avg)\n",
    "    print(f\"For n_clusters = {nclus}, the average silhouette_score is : {silhouette_avg}\")\n",
    "\n",
    "    # Compute the silhouette scores for each sample\n",
    "    sample_silhouette_values = silhouette_samples(df[metric_features], cluster_labels)\n",
    "\n",
    "    y_lower = 10\n",
    "    for i in range(nclus):\n",
    "        # Aggregate the silhouette scores for samples belonging to cluster i, and sort them\n",
    "        ith_cluster_silhouette_values = sample_silhouette_values[cluster_labels == i]\n",
    "        ith_cluster_silhouette_values.sort()\n",
    "        \n",
    "        # Get y_upper to demarcate silhouette y range size\n",
    "        size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
    "        y_upper = y_lower + size_cluster_i\n",
    "        \n",
    "        # Filling the silhouette\n",
    "        color = cm.nipy_spectral(float(i) / nclus)\n",
    "        plt.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",
    "        plt.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",
    "    plt.title(\"The silhouette plot for the various clusters.\")\n",
    "    plt.xlabel(\"The silhouette coefficient values\")\n",
    "    plt.ylabel(\"Cluster label\")\n",
    "\n",
    "    # The vertical line for average silhouette score of all the values\n",
    "    plt.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")    # The silhouette coefficient can range from -1, 1\n",
    "    xmin, xmax = np.round(sample_silhouette_values.min() -0.1, 2), np.round(sample_silhouette_values.max() + 0.1, 2)\n",
    "    plt.xlim([xmin, xmax])\n",
    "    \n",
    "    # The (nclus+1)*10 is for inserting blank space between silhouette\n",
    "    # plots of individual clusters, to demarcate them clearly.\n",
    "    plt.ylim([0, len(df[metric_features]) + (nclus + 1) * 10])\n",
    "\n",
    "    plt.yticks([])  # Clear the yaxis labels / ticks\n",
    "    plt.xticks(np.arange(xmin, xmax, 0.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "def2f8db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,5))\n",
    "plt.plot(avg_silhouette)\n",
    "plt.ylabel(\"Average silhouette\")\n",
    "plt.xlabel(\"Number of clusters\")\n",
    "plt.title(\"Average silhouette plot over clusters\", size=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "66ead550",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10222, 13)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "a487c5cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# K-distance graph to find out the right eps value\n",
    "neigh = NearestNeighbors(n_neighbors=20)\n",
    "neigh.fit(df)\n",
    "distances,_ = neigh.kneighbors(df)\n",
    "distances = np.sort(distances[:, -1])\n",
    "plt.plot(distances)\n",
    "plt.show()\n",
    "\n",
    "# MinPts: As a rule of thumb, minPts = 2 x dim can be used, but it may be necessary to choose larger values for very large data, for noisy data or for data that contains many duplicates.\n",
    "minPts = 13*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4b869471",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of estimated clusters : 2\n"
     ]
    }
   ],
   "source": [
    "# Perform DBSCAN clustering\n",
    "dbscan = DBSCAN(eps=1.7, min_samples=14,n_jobs=-1)\n",
    "dbscan_labels = dbscan.fit_predict(df)\n",
    "\n",
    "dbscan_n_clusters = len(np.unique(dbscan_labels))\n",
    "print(\"Number of estimated clusters : %d\" % dbscan_n_clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "223ebc1d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0: 10104, -1: 118})"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter(dbscan_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "17b4763d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#With this method we are not going to achieve very good clustering results for this spcecific dataset\n",
    "#but we can use it to outlier detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "e0182e4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialization method and algorithm are deterministic. Setting n_init to 1.\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 1, iteration: 1/100, moves: 1544, ncost: 27587.23801095548\n",
      "Run: 1, iteration: 2/100, moves: 502, ncost: 27451.925700875472\n",
      "Run: 1, iteration: 3/100, moves: 320, ncost: 27387.417182602054\n",
      "Run: 1, iteration: 4/100, moves: 208, ncost: 27361.910454349905\n",
      "Run: 1, iteration: 5/100, moves: 129, ncost: 27351.630977362907\n",
      "Run: 1, iteration: 6/100, moves: 62, ncost: 27349.670293085168\n",
      "Run: 1, iteration: 7/100, moves: 24, ncost: 27349.318883803284\n",
      "Run: 1, iteration: 8/100, moves: 16, ncost: 27349.152764683557\n",
      "Run: 1, iteration: 9/100, moves: 10, ncost: 27349.075058681337\n",
      "Run: 1, iteration: 10/100, moves: 7, ncost: 27349.05036558345\n",
      "Run: 1, iteration: 11/100, moves: 1, ncost: 27349.049638017947\n",
      "Run: 1, iteration: 12/100, moves: 5, ncost: 27349.021595087514\n",
      "Run: 1, iteration: 13/100, moves: 7, ncost: 27348.98937296952\n",
      "Run: 1, iteration: 14/100, moves: 5, ncost: 27348.980204008305\n",
      "Run: 1, iteration: 15/100, moves: 0, ncost: 27348.980204008305\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 2, iteration: 1/100, moves: 2985, ncost: 28310.69012131556\n",
      "Run: 2, iteration: 2/100, moves: 1470, ncost: 27610.04308414872\n",
      "Run: 2, iteration: 3/100, moves: 538, ncost: 27463.632598282336\n",
      "Run: 2, iteration: 4/100, moves: 346, ncost: 27393.43641506548\n",
      "Run: 2, iteration: 5/100, moves: 228, ncost: 27363.185954588804\n",
      "Run: 2, iteration: 6/100, moves: 130, ncost: 27352.32188317757\n",
      "Run: 2, iteration: 7/100, moves: 70, ncost: 27349.765331911578\n",
      "Run: 2, iteration: 8/100, moves: 26, ncost: 27349.339971377816\n",
      "Run: 2, iteration: 9/100, moves: 18, ncost: 27349.15276468354\n",
      "Run: 2, iteration: 10/100, moves: 10, ncost: 27349.07505868134\n",
      "Run: 2, iteration: 11/100, moves: 7, ncost: 27349.050365583444\n",
      "Run: 2, iteration: 12/100, moves: 1, ncost: 27349.04963801794\n",
      "Run: 2, iteration: 13/100, moves: 5, ncost: 27349.02159508752\n",
      "Run: 2, iteration: 14/100, moves: 7, ncost: 27348.989372969514\n",
      "Run: 2, iteration: 15/100, moves: 5, ncost: 27348.980204008305\n",
      "Run: 2, iteration: 16/100, moves: 0, ncost: 27348.980204008305\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 3, iteration: 1/100, moves: 1760, ncost: 27479.098773739464\n",
      "Run: 3, iteration: 2/100, moves: 485, ncost: 27359.880008906017\n",
      "Run: 3, iteration: 3/100, moves: 115, ncost: 27351.771670633734\n",
      "Run: 3, iteration: 4/100, moves: 54, ncost: 27350.163278547814\n",
      "Run: 3, iteration: 5/100, moves: 30, ncost: 27349.62152756771\n",
      "Run: 3, iteration: 6/100, moves: 27, ncost: 27349.205707074736\n",
      "Run: 3, iteration: 7/100, moves: 18, ncost: 27349.048049029196\n",
      "Run: 3, iteration: 8/100, moves: 8, ncost: 27349.013383005422\n",
      "Run: 3, iteration: 9/100, moves: 2, ncost: 27349.009008661582\n",
      "Run: 3, iteration: 10/100, moves: 3, ncost: 27349.001237943663\n",
      "Run: 3, iteration: 11/100, moves: 4, ncost: 27348.987207787053\n",
      "Run: 3, iteration: 12/100, moves: 0, ncost: 27348.987207787053\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 4, iteration: 1/100, moves: 1857, ncost: 34579.697734648595\n",
      "Run: 4, iteration: 2/100, moves: 990, ncost: 34134.89528017721\n",
      "Run: 4, iteration: 3/100, moves: 1546, ncost: 31030.252825605196\n",
      "Run: 4, iteration: 4/100, moves: 2334, ncost: 27781.89079187055\n",
      "Run: 4, iteration: 5/100, moves: 970, ncost: 27374.955190019882\n",
      "Run: 4, iteration: 6/100, moves: 226, ncost: 27350.745643476574\n",
      "Run: 4, iteration: 7/100, moves: 50, ncost: 27349.430715542414\n",
      "Run: 4, iteration: 8/100, moves: 18, ncost: 27349.160627001074\n",
      "Run: 4, iteration: 9/100, moves: 11, ncost: 27349.07505868133\n",
      "Run: 4, iteration: 10/100, moves: 7, ncost: 27349.050365583447\n",
      "Run: 4, iteration: 11/100, moves: 1, ncost: 27349.049638017936\n",
      "Run: 4, iteration: 12/100, moves: 5, ncost: 27349.02159508751\n",
      "Run: 4, iteration: 13/100, moves: 7, ncost: 27348.989372969532\n",
      "Run: 4, iteration: 14/100, moves: 5, ncost: 27348.980204008294\n",
      "Run: 4, iteration: 15/100, moves: 0, ncost: 27348.980204008294\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 5, iteration: 1/100, moves: 1899, ncost: 28182.271936008645\n",
      "Run: 5, iteration: 2/100, moves: 976, ncost: 27491.28704482942\n",
      "Run: 5, iteration: 3/100, moves: 399, ncost: 27388.939496590898\n",
      "Run: 5, iteration: 4/100, moves: 203, ncost: 27361.754198605067\n",
      "Run: 5, iteration: 5/100, moves: 127, ncost: 27352.616139338155\n",
      "Run: 5, iteration: 6/100, moves: 68, ncost: 27350.275529123297\n",
      "Run: 5, iteration: 7/100, moves: 36, ncost: 27349.621527567713\n",
      "Run: 5, iteration: 8/100, moves: 27, ncost: 27349.205707074747\n",
      "Run: 5, iteration: 9/100, moves: 18, ncost: 27349.04804902919\n",
      "Run: 5, iteration: 10/100, moves: 8, ncost: 27349.01338300542\n",
      "Run: 5, iteration: 11/100, moves: 2, ncost: 27349.009008661593\n",
      "Run: 5, iteration: 12/100, moves: 3, ncost: 27349.001237943663\n",
      "Run: 5, iteration: 13/100, moves: 4, ncost: 27348.987207787046\n",
      "Run: 5, iteration: 14/100, moves: 0, ncost: 27348.987207787046\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 6, iteration: 1/100, moves: 1922, ncost: 27371.147070661027\n",
      "Run: 6, iteration: 2/100, moves: 249, ncost: 27352.1187992197\n",
      "Run: 6, iteration: 3/100, moves: 67, ncost: 27349.71718189782\n",
      "Run: 6, iteration: 4/100, moves: 24, ncost: 27349.339971377827\n",
      "Run: 6, iteration: 5/100, moves: 18, ncost: 27349.152764683535\n",
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      "Run: 6, iteration: 12/100, moves: 0, ncost: 27348.980204008312\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 7, iteration: 1/100, moves: 1160, ncost: 27759.332712282583\n",
      "Run: 7, iteration: 2/100, moves: 591, ncost: 27471.91579127149\n",
      "Run: 7, iteration: 3/100, moves: 355, ncost: 27388.652480555433\n",
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      "Run: 7, iteration: 10/100, moves: 11, ncost: 27349.01978849419\n",
      "Run: 7, iteration: 11/100, moves: 4, ncost: 27349.009008661586\n",
      "Run: 7, iteration: 12/100, moves: 3, ncost: 27349.001237943663\n",
      "Run: 7, iteration: 13/100, moves: 4, ncost: 27348.98720778705\n",
      "Run: 7, iteration: 14/100, moves: 0, ncost: 27348.98720778705\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 8, iteration: 1/100, moves: 1180, ncost: 35091.429769942326\n",
      "Run: 8, iteration: 2/100, moves: 1878, ncost: 30888.07390178386\n",
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      "Run: 8, iteration: 16/100, moves: 5, ncost: 27348.980204008298\n",
      "Run: 8, iteration: 17/100, moves: 0, ncost: 27348.980204008298\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 9, iteration: 1/100, moves: 1804, ncost: 31039.800696292066\n",
      "Run: 9, iteration: 2/100, moves: 2371, ncost: 27459.979441598236\n",
      "Run: 9, iteration: 3/100, moves: 483, ncost: 27363.92799997662\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run: 9, iteration: 4/100, moves: 134, ncost: 27352.791926930848\n",
      "Run: 9, iteration: 5/100, moves: 71, ncost: 27350.246709470437\n",
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      "Run: 9, iteration: 9/100, moves: 8, ncost: 27349.013383005433\n",
      "Run: 9, iteration: 10/100, moves: 2, ncost: 27349.009008661582\n",
      "Run: 9, iteration: 11/100, moves: 3, ncost: 27349.00123794367\n",
      "Run: 9, iteration: 12/100, moves: 4, ncost: 27348.987207787046\n",
      "Run: 9, iteration: 13/100, moves: 0, ncost: 27348.987207787046\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 10, iteration: 1/100, moves: 2188, ncost: 28593.49261591863\n",
      "Run: 10, iteration: 2/100, moves: 1347, ncost: 27468.423569287683\n",
      "Run: 10, iteration: 3/100, moves: 396, ncost: 27374.042627839368\n",
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      "Run: 10, iteration: 14/100, moves: 0, ncost: 27348.98720778705\n",
      "Best run was number 4\n",
      "For n_clusters = 2, the average silhouette_score is : 0.2839181459132749\n",
      "Initialization method and algorithm are deterministic. Setting n_init to 1.\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 1, iteration: 1/100, moves: 2133, ncost: 25341.694108846154\n",
      "Run: 1, iteration: 2/100, moves: 2097, ncost: 23734.814502782632\n",
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      "Run: 1, iteration: 27/100, moves: 0, ncost: 22748.321730161188\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 2, iteration: 1/100, moves: 3847, ncost: 25656.01688852878\n",
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      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 3, iteration: 1/100, moves: 1261, ncost: 24438.303199562717\n",
      "Run: 3, iteration: 2/100, moves: 730, ncost: 23980.49728655984\n",
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      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 4, iteration: 1/100, moves: 2581, ncost: 23852.67536248549\n",
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      "Run: 4, iteration: 26/100, moves: 0, ncost: 22748.321730161206\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 5, iteration: 1/100, moves: 1737, ncost: 23298.940553696306\n",
      "Run: 5, iteration: 2/100, moves: 595, ncost: 23024.562554005774\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run: 5, iteration: 3/100, moves: 515, ncost: 22869.540452740286\n",
      "Run: 5, iteration: 4/100, moves: 366, ncost: 22797.163935438733\n",
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      "Run: 5, iteration: 13/100, moves: 0, ncost: 22748.316550522522\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 6, iteration: 1/100, moves: 2957, ncost: 24333.41319592601\n",
      "Run: 6, iteration: 2/100, moves: 788, ncost: 24050.33628922833\n",
      "Run: 6, iteration: 3/100, moves: 663, ncost: 23712.31106590255\n",
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      "Run: 6, iteration: 17/100, moves: 0, ncost: 22748.423832561588\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 7, iteration: 1/100, moves: 2373, ncost: 24113.35762461343\n",
      "Run: 7, iteration: 2/100, moves: 1449, ncost: 23084.732238916873\n",
      "Run: 7, iteration: 3/100, moves: 673, ncost: 22912.295580898004\n",
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      "Run: 7, iteration: 21/100, moves: 1, ncost: 22748.321730161195\n",
      "Run: 7, iteration: 22/100, moves: 0, ncost: 22748.321730161195\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 8, iteration: 1/100, moves: 1805, ncost: 23549.58023032579\n",
      "Run: 8, iteration: 2/100, moves: 1011, ncost: 23120.695635506898\n",
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      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 9, iteration: 1/100, moves: 3088, ncost: 25962.29755477321\n",
      "Run: 9, iteration: 2/100, moves: 2020, ncost: 24160.604086918443\n",
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      "Run: 9, iteration: 9/100, moves: 242, ncost: 22770.76407934186\n",
      "Run: 9, iteration: 10/100, moves: 170, ncost: 22755.38869846529\n",
      "Run: 9, iteration: 11/100, moves: 88, ncost: 22751.49666644237\n",
      "Run: 9, iteration: 12/100, moves: 71, ncost: 22748.986265195097\n",
      "Run: 9, iteration: 13/100, moves: 35, ncost: 22748.44067588147\n",
      "Run: 9, iteration: 14/100, moves: 12, ncost: 22748.36172778285\n",
      "Run: 9, iteration: 15/100, moves: 8, ncost: 22748.31704155993\n",
      "Run: 9, iteration: 16/100, moves: 1, ncost: 22748.316550522526\n",
      "Run: 9, iteration: 17/100, moves: 0, ncost: 22748.316550522526\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 10, iteration: 1/100, moves: 4310, ncost: 25261.58261209952\n",
      "Run: 10, iteration: 2/100, moves: 2067, ncost: 23523.482637901612\n",
      "Run: 10, iteration: 3/100, moves: 968, ncost: 23064.625774466083\n",
      "Run: 10, iteration: 4/100, moves: 428, ncost: 22980.465830768993\n",
      "Run: 10, iteration: 5/100, moves: 250, ncost: 22949.59090484123\n",
      "Run: 10, iteration: 6/100, moves: 158, ncost: 22931.3389857487\n",
      "Run: 10, iteration: 7/100, moves: 136, ncost: 22918.72504236898\n",
      "Run: 10, iteration: 8/100, moves: 210, ncost: 22833.851803346595\n",
      "Run: 10, iteration: 9/100, moves: 362, ncost: 22803.085713387478\n",
      "Run: 10, iteration: 10/100, moves: 223, ncost: 22783.413797770358\n",
      "Run: 10, iteration: 11/100, moves: 167, ncost: 22768.773194776324\n",
      "Run: 10, iteration: 12/100, moves: 127, ncost: 22760.511783476308\n",
      "Run: 10, iteration: 13/100, moves: 96, ncost: 22753.69886096778\n",
      "Run: 10, iteration: 14/100, moves: 63, ncost: 22749.328780884836\n",
      "Run: 10, iteration: 15/100, moves: 31, ncost: 22748.76482194302\n",
      "Run: 10, iteration: 16/100, moves: 9, ncost: 22748.705834887536\n",
      "Run: 10, iteration: 17/100, moves: 12, ncost: 22748.531630142556\n",
      "Run: 10, iteration: 18/100, moves: 10, ncost: 22748.403978006587\n",
      "Run: 10, iteration: 19/100, moves: 3, ncost: 22748.379662722356\n",
      "Run: 10, iteration: 20/100, moves: 3, ncost: 22748.36628985849\n",
      "Run: 10, iteration: 21/100, moves: 2, ncost: 22748.359188384784\n",
      "Run: 10, iteration: 22/100, moves: 4, ncost: 22748.350879280686\n",
      "Run: 10, iteration: 23/100, moves: 4, ncost: 22748.342054188983\n",
      "Run: 10, iteration: 24/100, moves: 3, ncost: 22748.328589099336\n",
      "Run: 10, iteration: 25/100, moves: 1, ncost: 22748.321730161177\n",
      "Run: 10, iteration: 26/100, moves: 0, ncost: 22748.321730161177\n",
      "Best run was number 5\n",
      "For n_clusters = 3, the average silhouette_score is : 0.23207174007247175\n",
      "Initialization method and algorithm are deterministic. Setting n_init to 1.\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 1, iteration: 1/100, moves: 2771, ncost: 22559.56910385587\n",
      "Run: 1, iteration: 2/100, moves: 1390, ncost: 22094.018666758748\n",
      "Run: 1, iteration: 3/100, moves: 656, ncost: 21908.081406659952\n",
      "Run: 1, iteration: 4/100, moves: 544, ncost: 21749.295148940913\n",
      "Run: 1, iteration: 5/100, moves: 458, ncost: 21615.20996759427\n",
      "Run: 1, iteration: 6/100, moves: 383, ncost: 21522.25397244686\n",
      "Run: 1, iteration: 7/100, moves: 333, ncost: 21423.119014257176\n",
      "Run: 1, iteration: 8/100, moves: 331, ncost: 21337.8649423422\n",
      "Run: 1, iteration: 9/100, moves: 241, ncost: 21296.7398438374\n",
      "Run: 1, iteration: 10/100, moves: 196, ncost: 21271.22742365628\n",
      "Run: 1, iteration: 11/100, moves: 141, ncost: 21254.407551230383\n",
      "Run: 1, iteration: 12/100, moves: 95, ncost: 21248.720033812195\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run: 1, iteration: 13/100, moves: 63, ncost: 21246.870228130912\n",
      "Run: 1, iteration: 14/100, moves: 25, ncost: 21246.558320072632\n",
      "Run: 1, iteration: 15/100, moves: 18, ncost: 21246.331265834226\n",
      "Run: 1, iteration: 16/100, moves: 5, ncost: 21246.28573955189\n",
      "Run: 1, iteration: 17/100, moves: 7, ncost: 21246.256299025634\n",
      "Run: 1, iteration: 18/100, moves: 0, ncost: 21246.256299025634\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 2, iteration: 1/100, moves: 3702, ncost: 21962.486417330074\n",
      "Run: 2, iteration: 2/100, moves: 1519, ncost: 21190.218412108614\n",
      "Run: 2, iteration: 3/100, moves: 1548, ncost: 20273.272512935055\n",
      "Run: 2, iteration: 4/100, moves: 1297, ncost: 19692.436976894853\n",
      "Run: 2, iteration: 5/100, moves: 720, ncost: 19547.442585605957\n",
      "Run: 2, iteration: 6/100, moves: 345, ncost: 19516.008044147013\n",
      "Run: 2, iteration: 7/100, moves: 155, ncost: 19510.16071141518\n",
      "Run: 2, iteration: 8/100, moves: 62, ncost: 19509.0537017577\n",
      "Run: 2, iteration: 9/100, moves: 29, ncost: 19508.645262250393\n",
      "Run: 2, iteration: 10/100, moves: 31, ncost: 19507.730274749196\n",
      "Run: 2, iteration: 11/100, moves: 23, ncost: 19507.358169933756\n",
      "Run: 2, iteration: 12/100, moves: 17, ncost: 19507.0652217597\n",
      "Run: 2, iteration: 13/100, moves: 15, ncost: 19506.917458469034\n",
      "Run: 2, iteration: 14/100, moves: 11, ncost: 19506.873945674975\n",
      "Run: 2, iteration: 15/100, moves: 3, ncost: 19506.87072387286\n",
      "Run: 2, iteration: 16/100, moves: 1, ncost: 19506.869806298215\n",
      "Run: 2, iteration: 17/100, moves: 0, ncost: 19506.869806298215\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 3, iteration: 1/100, moves: 2952, ncost: 21239.30194344197\n",
      "Run: 3, iteration: 2/100, moves: 1068, ncost: 20818.079213108897\n",
      "Run: 3, iteration: 3/100, moves: 387, ncost: 20708.10817733506\n",
      "Run: 3, iteration: 4/100, moves: 278, ncost: 20643.17714166477\n",
      "Run: 3, iteration: 5/100, moves: 435, ncost: 20480.972264966484\n",
      "Run: 3, iteration: 6/100, moves: 712, ncost: 20064.353498769287\n",
      "Run: 3, iteration: 7/100, moves: 904, ncost: 19641.89561972859\n",
      "Run: 3, iteration: 8/100, moves: 652, ncost: 19474.297946198003\n",
      "Run: 3, iteration: 9/100, moves: 331, ncost: 19435.926931576065\n",
      "Run: 3, iteration: 10/100, moves: 175, ncost: 19425.862665998186\n",
      "Run: 3, iteration: 11/100, moves: 122, ncost: 19421.70799683695\n",
      "Run: 3, iteration: 12/100, moves: 63, ncost: 19420.33729656866\n",
      "Run: 3, iteration: 13/100, moves: 21, ncost: 19420.214702850622\n",
      "Run: 3, iteration: 14/100, moves: 6, ncost: 19420.199596709925\n",
      "Run: 3, iteration: 15/100, moves: 0, ncost: 19420.199596709925\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 4, iteration: 1/100, moves: 3271, ncost: 21629.09082613029\n",
      "Run: 4, iteration: 2/100, moves: 1785, ncost: 20304.83426306879\n",
      "Run: 4, iteration: 3/100, moves: 887, ncost: 19661.59277898904\n",
      "Run: 4, iteration: 4/100, moves: 522, ncost: 19465.95368724232\n",
      "Run: 4, iteration: 5/100, moves: 210, ncost: 19435.310711875445\n",
      "Run: 4, iteration: 6/100, moves: 113, ncost: 19424.812899778928\n",
      "Run: 4, iteration: 7/100, moves: 87, ncost: 19421.590956021595\n",
      "Run: 4, iteration: 8/100, moves: 38, ncost: 19420.656443612523\n",
      "Run: 4, iteration: 9/100, moves: 26, ncost: 19420.348575491553\n",
      "Run: 4, iteration: 10/100, moves: 16, ncost: 19420.210836943083\n",
      "Run: 4, iteration: 11/100, moves: 6, ncost: 19420.1283318161\n",
      "Run: 4, iteration: 12/100, moves: 4, ncost: 19420.098801343815\n",
      "Run: 4, iteration: 13/100, moves: 1, ncost: 19420.09727344194\n",
      "Run: 4, iteration: 14/100, moves: 0, ncost: 19420.09727344194\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 5, iteration: 1/100, moves: 2437, ncost: 19830.36401381789\n",
      "Run: 5, iteration: 2/100, moves: 731, ncost: 19527.791547706514\n",
      "Run: 5, iteration: 3/100, moves: 372, ncost: 19437.400247710677\n",
      "Run: 5, iteration: 4/100, moves: 161, ncost: 19422.147005209437\n",
      "Run: 5, iteration: 5/100, moves: 50, ncost: 19420.534070966136\n",
      "Run: 5, iteration: 6/100, moves: 33, ncost: 19420.12218336866\n",
      "Run: 5, iteration: 7/100, moves: 12, ncost: 19420.0261504545\n",
      "Run: 5, iteration: 8/100, moves: 4, ncost: 19419.996839299823\n",
      "Run: 5, iteration: 9/100, moves: 0, ncost: 19419.996839299823\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 6, iteration: 1/100, moves: 3412, ncost: 21974.473700267063\n",
      "Run: 6, iteration: 2/100, moves: 1504, ncost: 20973.641318096306\n",
      "Run: 6, iteration: 3/100, moves: 880, ncost: 20354.958738339737\n",
      "Run: 6, iteration: 4/100, moves: 644, ncost: 20085.56904235678\n",
      "Run: 6, iteration: 5/100, moves: 600, ncost: 19791.771564462913\n",
      "Run: 6, iteration: 6/100, moves: 601, ncost: 19575.00774122257\n",
      "Run: 6, iteration: 7/100, moves: 427, ncost: 19470.36940153921\n",
      "Run: 6, iteration: 8/100, moves: 252, ncost: 19438.037021923174\n",
      "Run: 6, iteration: 9/100, moves: 120, ncost: 19426.9830751517\n",
      "Run: 6, iteration: 10/100, moves: 72, ncost: 19422.375941728755\n",
      "Run: 6, iteration: 11/100, moves: 51, ncost: 19420.63746405011\n",
      "Run: 6, iteration: 12/100, moves: 28, ncost: 19420.307512487452\n",
      "Run: 6, iteration: 13/100, moves: 12, ncost: 19420.220736734198\n",
      "Run: 6, iteration: 14/100, moves: 4, ncost: 19420.1914261532\n",
      "Run: 6, iteration: 15/100, moves: 1, ncost: 19420.18969151452\n",
      "Run: 6, iteration: 16/100, moves: 0, ncost: 19420.18969151452\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 7, iteration: 1/100, moves: 3174, ncost: 20242.972653879526\n",
      "Run: 7, iteration: 2/100, moves: 1401, ncost: 19577.44803879022\n",
      "Run: 7, iteration: 3/100, moves: 599, ncost: 19464.428040348666\n",
      "Run: 7, iteration: 4/100, moves: 266, ncost: 19432.320989505082\n",
      "Run: 7, iteration: 5/100, moves: 153, ncost: 19422.860641299132\n",
      "Run: 7, iteration: 6/100, moves: 70, ncost: 19420.533896681725\n",
      "Run: 7, iteration: 7/100, moves: 16, ncost: 19420.29864666597\n",
      "Run: 7, iteration: 8/100, moves: 10, ncost: 19420.208316249013\n",
      "Run: 7, iteration: 9/100, moves: 2, ncost: 19420.19142615319\n",
      "Run: 7, iteration: 10/100, moves: 1, ncost: 19420.189691514508\n",
      "Run: 7, iteration: 11/100, moves: 0, ncost: 19420.189691514508\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 8, iteration: 1/100, moves: 3153, ncost: 19957.341653772557\n",
      "Run: 8, iteration: 2/100, moves: 1278, ncost: 19502.312959724626\n",
      "Run: 8, iteration: 3/100, moves: 431, ncost: 19450.5211163151\n",
      "Run: 8, iteration: 4/100, moves: 224, ncost: 19428.73842388217\n",
      "Run: 8, iteration: 5/100, moves: 139, ncost: 19423.083680980868\n",
      "Run: 8, iteration: 6/100, moves: 74, ncost: 19421.130585865485\n",
      "Run: 8, iteration: 7/100, moves: 31, ncost: 19420.504822473362\n",
      "Run: 8, iteration: 8/100, moves: 25, ncost: 19420.22707436877\n",
      "Run: 8, iteration: 9/100, moves: 9, ncost: 19420.13234296441\n",
      "Run: 8, iteration: 10/100, moves: 7, ncost: 19420.098561000967\n",
      "Run: 8, iteration: 11/100, moves: 1, ncost: 19420.097031895464\n",
      "Run: 8, iteration: 12/100, moves: 0, ncost: 19420.097031895464\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 9, iteration: 1/100, moves: 3658, ncost: 22083.012954988862\n",
      "Run: 9, iteration: 2/100, moves: 2757, ncost: 19682.01340886048\n",
      "Run: 9, iteration: 3/100, moves: 810, ncost: 19473.95675697781\n",
      "Run: 9, iteration: 4/100, moves: 238, ncost: 19437.074455822403\n",
      "Run: 9, iteration: 5/100, moves: 127, ncost: 19426.615740341284\n",
      "Run: 9, iteration: 6/100, moves: 76, ncost: 19422.799210886344\n",
      "Run: 9, iteration: 7/100, moves: 44, ncost: 19421.195042159157\n",
      "Run: 9, iteration: 8/100, moves: 27, ncost: 19420.546540615767\n",
      "Run: 9, iteration: 9/100, moves: 27, ncost: 19420.22537798633\n",
      "Run: 9, iteration: 10/100, moves: 12, ncost: 19420.124047290712\n",
      "Run: 9, iteration: 11/100, moves: 4, ncost: 19420.097980257895\n",
      "Run: 9, iteration: 12/100, moves: 0, ncost: 19420.097980257895\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 10, iteration: 1/100, moves: 3965, ncost: 21315.987886524406\n",
      "Run: 10, iteration: 2/100, moves: 1857, ncost: 20160.917996675864\n",
      "Run: 10, iteration: 3/100, moves: 1147, ncost: 19610.155751045535\n",
      "Run: 10, iteration: 4/100, moves: 710, ncost: 19470.73871291904\n",
      "Run: 10, iteration: 5/100, moves: 276, ncost: 19437.909532668593\n",
      "Run: 10, iteration: 6/100, moves: 128, ncost: 19426.34863605113\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run: 10, iteration: 7/100, moves: 64, ncost: 19422.9996844842\n",
      "Run: 10, iteration: 8/100, moves: 58, ncost: 19421.21244160968\n",
      "Run: 10, iteration: 9/100, moves: 28, ncost: 19420.54654061577\n",
      "Run: 10, iteration: 10/100, moves: 27, ncost: 19420.225377986335\n",
      "Run: 10, iteration: 11/100, moves: 12, ncost: 19420.124047290716\n",
      "Run: 10, iteration: 12/100, moves: 4, ncost: 19420.0979802579\n",
      "Run: 10, iteration: 13/100, moves: 0, ncost: 19420.0979802579\n",
      "Best run was number 5\n",
      "For n_clusters = 4, the average silhouette_score is : 0.22381373358025272\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1300x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1300x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zyiuvaNWqVfrRj34kSZoyZYre8573aPny5Vq/fn2/5Jo7d66SyaS+//3v73L5U089pfXr1/dMxcjlcmpqatpl94Onn376dZ/jhg0b1NDQ0PM5zp49W48//rjuv//+njdj3f/C3u21owemTJmihoYGvfzyy7s8zqhRo3Trrbe+5b9o//znP99liP5Pf/pThWH4hnPplyxZojAMe57jbt/73vckvTq95LWZ90R3OfKDH/xgl8t/8IMfKIqi101deSuvfZ56k6O3X3vdTjnlFG3cuFF33XWXfN/Xscce23Pd008/ralTp+r000/vKRc2bdqkZ599tmf0xJ6YPHmyRo4c+bqvwZdeekm//e1ve74Gu9cs2Lhx4y4ZdvaZz3xGp59+upxzymQyOvLII3vWmNjd98yiRYv00ksv6X//9397LisWi3r/+9+vb3/726+7fX/leO3X1J5+/7yRc845R5/61KckSQ0NDVq3bp3OP/98tbS0qK2tbbf3AwAMDIxgAAD0yqWXXqpzzjlH733ve3XuuecqlUrpG9/4hn7yk5/07ObwWuPGjdPo0aP1qU99Sm1tbZo4caL+8Ic/6Gc/+5ne+9739kuu2tpaXXzxxbrrrruUSCR05JFH6uWXX9Ztt92mqVOn6rTTTpMkHXnkkfrqV7+qv/3bv9UZZ5yhZ599Vg888MAub3rWrVunr33ta3rHO96hSy65RGPGjNEvf/lLfelLX9Lb3vY2JRIJSZURCn/84x/15JNPas6cOT1vTv/jP/5DNTU1mjFjhq666ipdf/318n1fRx55pFpaWnT33Xdr06ZNux1C3m3Dhg163/vepwsvvFAbNmzQ5z73Oa1cuXKXnRe6HX744Vq6dKk++tGPatOmTZoxY4aefPJJfelLX9Jpp53Ws5hidXW1tm7dqp/97Gc6+OCD1djY+JbPbffzd/vtt6uzs1OLFy/WM888ozvvvFNLly7VypUr9+wgdXmj52lP7M3XXrdp06bp4IMP1te//nWdcMIJPW+uJWnOnDm6++67dd9992nevHl68cUX9cUvflGlUkmdnZ17/Hl5nqcPfvCD+shHPqKrr75aa9euVVNTk+68807V1NT0jLxYtWqVbrrpJl1//fV617vepQ0bNuiuu+7aZXrJsmXL9MADD+jDH/6w1q5dq3K5rPvvv1+1tbW7nSqwbt06ffWrX9X73vc+XXHFFaqrq9NDDz2kcrms884773W3768c1dXV+u///m/96le/0syZM/f4++eNLF68WF/5ylc0YsQIzZ8/X5s2bdIDDzygJUuW9Izcee6551QqlTRz5sw9PjYAgP2DggEA0CszZszQww8/rM9//vO69tpr5ZzTtGnTdNddd+moo47a7f3uvPNOfe5zn9Ntt92mpqYmjRkzRpdffvkuK/T31fvf/36NGDFCX/va1/SNb3xDtbW1Ov7443XllVf2zPVesWKFrrvuOn31q1/Vj3/8Yx1yyCG68847dc455/Q8Tjab1cMPP6xbb71Vn/3sZ9Xa2qpx48bp6quv1jvf+c6e273zne/UjTfeqHe961164IEHtGDBAp100kl6+OGH9Ytf/ELf//73deaZZyqXy+n+++/XN77xDWWzWS1YsEC33HJLz7SN3VmzZo2qq6t78p922mk9OyG8ljFGX/ziF3X77bfrwQcf1Pbt2zV+/Hh98IMf3GVKwbp16/Szn/1Ml112ma644oo9fv4//elPa9KkSXrkkUf0pS99SY2Njbrwwgt16aWX9npUxEEHHfS652lP7O3XXrdTTjlFN998c8/ijt26t5N86KGHdNddd2nMmDE65ZRTep7TlpaWnukub2XdunXK5XL64he/qMsuu0z5fF4rV67UBz/4wZ41LCZPnqzPfOYzuueee3TxxRfrwAMP1Cc/+Ul98pOf7HmcVatW6ZZbbtFXvvKVngUVFy5cqIceemiXhTB3ls/n9bWvfU1///d/r09+8pOy1mrevHl66KGHNGHChNdNn+ivHOeff77+8Ic/6D3veY9uuukmnXzyyXv0/fNGPvCBDyiZTOqRRx7RXXfdpaqqKq1evVpXX311z21uuOEGvfLKK/q3f/u3PTomAID9x7ju1bEAAMCAsXr1ai1ZskQ333xz3FEAAAD2CGswAAAAAACAPqNgAAAAAAAAfcYUCQAAAAAA0GeMYAAAAAAAAH1GwQAAAAAAAPqMggEAAAAAAPQZBQMAAAAAAOizIM4P7pyTtawxOVh5nuH4DVIcu8FtUB4/V5IUxZ0idkZGxjNy1slpkB1DcPwGOY7f4MbxG/iMc6r8rrddlzj1HCojGVN5/ydn5LwqyfjxBEWveZ6RMWaPbhtrwWCMUUtLh8LQvvWNMaAEgae6uhzHbxDi2A1ug/L4uVA1G9fKizbHnWRA8HxPNrK8PB6kOH67UbTyrtkgSbKfHSOlBuYgWY7f4MbxG7icUnJeXjJJSYFkjJyMpMqbUmOMkolA5XIk56RC1Xkq5U6NMzJ6ob4+J98fBAUDAGA4cJIry5pqOa8+7jCxMp6Rn/AVlSO5wTYKBRy/NxUq+M/nJEllTZT8gfcSk+M3uHH8BhIrY1vk2S2SSct6tSpnjlI5vUzWHyXnVcmZhCRfkicZIz8IlKzNq725Q2EoycvE/DlgXxl4P/0BAEOUkczA/FfN/cZ0PQfGVf5gcOH47d7O39vGG5jf6xy/wY3jFz/n5EUbZFyrnFetUma1SrlTFabmy3nVb35fz5P8nOQZyRskIzCxVygYAAAAAAC7ZWyL/OhlWa9Rhap3qZRZrShxSKX4AXZCwQAAAAAAeD3n5EV/lZFVObVMhap3qJxZEXcqDGAUDAAAAACAXbmCgvJfZP2RKuROUWfN5ZLh7SPeHF8hAAAAAIBXuYKC8AWVU/PVUXuNotScuBNhkKBgAAAAAABUuIKC8P9UTs5Ve8ONssGEuBNhEKFgAAAAQN9kAxX/5/i4UwDoK9shP3pZYWKq2us/QbmAXhuAewgBAAAAAPYnY9sURC8qTC1R68h7ZRNT4o6EQYgRDAAAAAAwjJloqzzXqlJ6lTrqPiznN8YdCYMUBQMAAAD6phgp+Jv/kSSFN86RUn7MgQDsKS/aIuNaVcocr/b66yWTjDsSBjGmSAAAAKBvIif/XzfJ/9dNUuTiTgNgD3nhyzKuqGL2VLXX30C5gD5jBAMAAAAADDNeuF4yvjqr3qVC1UWSMXFHwhDACAYAAAAAGE5cWca1qpg7TYXqt1MuoN9QMAAAAADAcOGc/PD/FCWmqlD1trjTYIihYAAAAACA4cA5edFLcl6NOmo/xG4R6HcUDAAAAAAwDHh2q2QS6qy5RGH60LjjYAiiYAAAAACAIc7YVhnbolLmWBXz58QdB0MUu0gAAACgbzK+ir8+uuc0gAHGhfKjV1TMHKOO2qvjToMhjIIBAAAAfWOMlOVlJTAgOSc/fFFRMEGdtR+SvGzciTCEMUUCAAAAAIYoY7dJJqXOmstlgzFxx8EQR8EAAACAvilZBR/9HwUf/R+pZONOA6Cb7ZBnm1TKHqVS5vi402AYoGAAAABA34RW/vfWy//eeimkYAAGBOfkR+sVpuarvfYjlalMwD5GwQAAAAAAQ4wXvSLn1aiz+hLWXcB+Q8EAAAAAAEOJK8m4ThXy5ypML4k7DYYRCgYAAAAAGCq6do2wwSSVcmvjToNhhoIBAAAAAIYC5+RHL8j6I9Ve/3HZYHTciTDMUDAAAAAAwBBg7HY5k1Zn7YcUpubFHQfDEAUDAAAAAAx2tlOe3aZy+jCVMsfEnQbDVBB3AAAAAAxyGV/F/1jdcxrAfuac/Oglham56qhjS0rEh4IBAAAAfWOMVJ+MOwUwPDknL3pJzqtTZ80H5LyauBNhGGOKBAAAAAAMUsZuk0xChep3KkwtiDsOhjlGMAAAAKBvSlbBZ/8kSQqvmSEl+TcsYH8wtkWebVIxt06FqgvijgMwggEAAAB9FFr53/ir/G/8VQpt3GmA4cE5+dErKqdXqKPuurjTAJIoGAAAAABg0PGi9bL+KHXWXC4Z1kDBwEDBAAAAAACDiLGtMiqqkD9XUXJG3HGAHhQMAAAAADBYuFBetF5hciHrLmDAoWAAAAAAgMHAOfnhX2X9seqou4apERhwKBgAAAAAYKBzVn74vJxXpc6ayxUlpsadCHgdtqkEAAAAgIHMOfnRi7J+gzrq/k7lzOFxJwLeEAUDAAAA+ibtq/j/Du85DaAfOSfPbpJTUoWqt1MuYECjYAAAAEDfeEYal407BTD0dI1ccCatYtXbVMyfF3ci4E1RMAAAAADAQOOs/Oj/5ExeHbUfUil3UtyJgLdEwQAAAIC+KVv5tz8rSYqumCYlWEcc6BNXkB++KOuPUWftB1TKnhB3ImCPUDAAAACgb8pWwT/8nyQpet9UCgagL1yoIPw/RcEktY64XTYxOe5EwB6jYAAAAACAAcDYVnnRRkXBZLXXf5xyAYMOBQMAAAAAxMyLNsu4NpUyR6uj7sNyfkPckYBeo2AAAAAAgBiZaKuM61Axd4Y6aj8omWTckYC9QsEAAAAAAHFwTl70iozKKuZOVUfttZJhDRMMXhQMAAAAALC/uZKC8C+yXp0KuTPUWXMl5QIGPQoGAAAAANiPjG2TF72iMJiiQvU7VcqtjTsS0C8oGAAAANA3aV+l76zoOQ1g94xtlxetV5heprb6m+X8urgjAf2GggEAAAB94xm5qVVxpwAGNmflhy9KxihMzlV73Q2UCxhyKBgAAAAAYF9yBfnhi3L+SBXy56tQdT47RWBIomAAAABA35St/C89L0mK3nOglGChOqCH7VAQvagweYja625QlJwedyJgn6FgAAAAQN+UrYJ7uwqGt0+mYAC62U750V9VTs5T24jbmRKBIY+CAQAAAAD6mRdtlHHtipKHqL3+E5QLGBYoGAAAAACgvzgnL3pZMr6K2dPUUXuV5OXiTgXsFxQMAAAAANBXzsqzW2Vsi5yXU6HqXSpUvz3uVMB+RcEAAAAAAH1goib5dqOsN0LlzEoVc2eonFkZdyxgv6NgAAAAAIC94Qryok0ycipmjlNnzftkEwfGnQqIDQUDAAAAAPSGc/Ki9TKuQzYYq0L2VBWq3y0ZE3cyIFYUDAAAAOiblK/S1w/tOQ0MWa4sL1ovz7XLeiNVzJ2kzprL5LzquJMBAwIFAwAAAPrGN3KzauJOAew7XSMWPNeiKJikjty7FKYWKEwtijsZMKBQMAAAAADA7jgnL/qrZDIq5M5ToeodssHouFMBAxIFAwAAAPqmbOV/7UVJUvS2SVLCizkQ0A9cSV60RcZ1yHnV6qy5XMX8GXGnAgY0CgYAAAD0Tdkq+PyfJUnR2RMoGDC4OSfPbpFntyoKJqiYWady+jCFqSVxJwMGPAoGAAAAALCd8twOGdsq59WomFunjurL5IJRcScDBg0KBgAAAADDl22Xb7dIrizrj1Ipf5xK2TUKk3PZdhLoJQoGAAAAAMOOsS3yos2SpCgxRaXsWhVzJ8v5dTEnAwYvCgYAAAAAw4axbfKiDZJJygbjVMyfpULuTMlLxx0NGPQoGAAAAAAMfa4gL9ou4wqKEtNUqDpXpewpTIMA+hEFAwAAAIChyxXkRxskSdYfrUJ2rQpVb5O8TMzBgKGHggEAAAB9k/JV+vLintNA7JyTsU3y7FbJBLL+KBVzZ6qQP5tiAdiHKBgAAADQN76RW9wQdwpAck6e3SRjd8h5dSqnl6uUPVGlzBGSl487HTDkUTAAAAAAGPxcJD/8i5xfr2L+fBVzaxQlD4k7FTCsUDAAAACgb8pW3iMvSZLs6ROkhBdzIAwrrixjd8iz22T9Meqo+6jKmcPiTgUMSxQMAAAA6JuyVeLGZyRJxbXjKBiwf7iC/HC9ZIycqVI5vULF/PkqZ1bEnQwYtigYAAAAAAwetlN+tFFSWVHyYBWq3q4wOVM2mBB3MmDYo2AAAAAAMPDZorzyehnbqSgxWYXcWSplj5LzG+NOBqALBQMADCDWWv3TPz2s733vO9q8ebMmTJio88+/UMcee0Lc0QAAiIezMuEWKdwh549XZ9XpKubXyXk1cScD8BoUDAAwgNx//736+tcf0rvedYkOPnimfvWrx/WJT/ydjDE65pjj444HAMD+5Urywxckv0aqP1vtmfep7GrjTgVgNygYAGCAKBQK+ta3/lFnnnmuLrjg7ZKkRYuW6M9/fkbf/vY3KBgAAMOHi+RFG2Vcp6LEVJXqLlHVuNPkmjuk0MadDsBuUDAAwACRSCR0zz1fUV1d3esub29viykVAAD7kQvlRVvkuWZZf7SKmZPUWXOZ/GStZEzc6QC8BQoGABggfN/X1KkHSZKcc2pq2q4f/OCf9dRTT+qaa/4m5nQA8CaSnsp3Lug5DfSKK8qzO2TsDklG1h+hQvpoFavOU5Q4KO50AHqBggEABqCf/OTHuuGGj0qSli8/TMcdxyKPAAawwJM9nJX80QuuJC/aIuM6K2f9eoWpeSplj1cpc5ScPyLmgAD2BgUDAAxAM2fO0p133qfnn/9ffelL9+rqq6/QHXd8Me5YAADsPRfK2GZ5tlmSZP2RKqePUjk1X2HqUNlgdLz5APQZBQMADEDjxo3XuHHjNW/eAmWzOX360x/X737331q0aFHc0QDg9cpW3g/XS5LsiWOlBNMkIMk5eXZr19QHTzKSM9WyiYkqZk9RMbdWzm+IOyWAfkTBAAADRFNTk37968e1bNly1dXV91w+ffoMSdLWrVviigYAb65slfi7P0iSiseMpmAY7mxnpVhwRTkvrzA1T2FylqLkIQqTcxmpAAxhFAwAMEAUi0V9+tMf13vfe5kuuOAdPZc/+eSvJUkHHshCVwCAAco5GbtVnt0hmYSsP0rF7LEqp1coTM5jBwhgmKBgAIABYvTo0VqzZq0eeOB++X6gadOm63e/+289/PA/6KSTTtHkyVPijggAwK5cKM9ul7HNcl6tSrk1KqcWqpQ5XvLScacDsJ9RMADAAPKhD31EY8eO0/e+96g2bdqgxsZRete73qtzz70g7mgAALzKleVFG2Vch5zfqGJunYr5dYqSs+JOBiBGFAwAMIAkEglddNG7dNFF74o7CgAAr+fK8uw2eXaHIn+MirkLVMifzWKNACRRMAAAAAB4K852jVhol/VHqpA7RoXqd8sGY+JOBmAAoWAAAAAA8HrOybgWGdsiz7XJ+o0qZE9Xoertcn5d3OkADEAUDAAAAOibpKfyLfN6TmOQc6E8u03G7pDzquX8RnXk3q1S5ijZxAFxpwMwgFEwAAAAoG8CT/bY0XGnQF84K+NaZWyLjCtUdoTIHqti/lyFyTmS8eNOCGAQoGAAAAAAhisXyou2yLNbZP1RssEEldOHqZhfJ+uPk4yJOyGAQYSCAQAAAH0TWnn/tlmSZFc3SgHTJAY0216ZAuFKkvFkvXoVM6ermD+ra7QCpQKAvUPBAAAAgL4pWSU+9FtJUvHXR1MwDDQulHEdMnaHPLtDzquS9UepmF6lMDVHYWqunN8Yd0oAQwAFAwAAADDUuKK8aJuMa5eML2dyssE4lZLHq5xZpXJqseTl4k4JYIihYAAAAACGChfKs9vlRVsVJQ5QGCxUObNS5dRC2WCCZHj5D2Df4ScMAAAAMJg5J+Pa5NmtMq4k649QMXeqOmqvlPNHxJ0OwDBCwQAAAAAMRq4gL2qS51rkTFZRMEXlzBEq5k6RDcbGnQ7AMETBAAAAAAwGzkkqyriivGijZJKyXr0K6dUqpw9XOXOkZFhgE0B8KBgAAACAgaxrpIJxbZJJy5mkwtRCldMrVcifxWKNAAYMCgYAAAD0TcJT+ZOzek6jj1xZxrbIuE4ZV5SMJ+fVqpRepWLuNFl/lKw/TjIm7qQAsAsKBgAAAPRNwpM9ZXzcKQY3F8mzW2Rsh4xCWa9ezh+lYmalouQhKqcPlfOq404JAG+KggEAAACIg7MyrkWebZZxRVm/UaXsoQqTs1XOHCEbUNoAGFwoGAAAANA3oZX3y62SJLt8hBQwTeLNGNsmL1ovych5VYoS01XMHqdyeqVsYlLc8QBgr1EwAAAAoG9KVonLfyNJKv76aAqG1zC2XcZul1FY2QnCBIqSs1XKHKlS5kjZ4ADWUwAwJFAwAAAAAP3FORnXJmPbZNQpOVUKBX+0ytmjZb06RYkpClOLJJOIOy0A9CsKBgAAAKCPjG2WF22RjJEzWTm/XuXkckWJaYoSB6mcPpRCAcCQR8EAAAAA9JaLuraS7JBx7XJeXuX0oQqTcxSmFytMLmDaA4Bhh4IBAAAA2BOuKC/aJuM6K2e9atnERIXBNJVyJypML405IADEi4IBAAAA2B3nZFyrvGirZIysP1LFzOkKU3MVJabLBuPiTggAAwYFAwAAAPBariwv2ijjCnJeTmFylor5M1TOrJLzquJOBwADEgUDAAAA+ibhqfw3B/ecHrRcKGN3yHOtkivL+iNVTq1UIf82RcmDWaQRAN4CBQMAAAD6JuHJnjMp7hR7x9mubSWbukYr1CsKxquYO0Ol7HFyfkPcCQFg0KBgAAAAwPDiXGVbSdckOSvnVckGE1ROL1cxd7psMJEdIABgL1AwAAAAoG8iJ/Ob7ZIkt6Be8gfYm3NnJVuSSm3yyi2Sc3JeVuXUIpUzR6qcWkapAAD9gIIBAAAAfVOMlHzXf1VO/vpoKTsAXmI6K+M65UUbJCPJpKVElcpV56gUTFeUmKwocQilAgD0owHw0x8AAADoI+dkXHvXegrtkpxkUoqCSSpUXySTnqbqhikqtGUVhjbutAAwJFEwAAAAYHByTp7dJOPaJRfKmbycV61S7ghFyVmKEtMUJqZLXkZB4EmJnKT2uFMDwJBFwQAAAIDBwzlJZXnRFhnXKuc1qJw+TOXUYkXJgxUm57CdJADEhIIBAAAAA5ezXSMUil1TIMqSSch6tSqlj1Yxf6ai5Ky4UwIARMEAAACAgcQ5SUUZ2y7PtXRtI5mXMylFiUNUTh+mMDlbUWKKnN8Qd1oAwE4oGAAAABAfF8nYlsrijK4kyUomLedlFQYzVMqtVTm1SDaYIBleugLAQMZPaQAAAPRNwlN41fSe02/KhTK2VcZ1yLiOykVerWwwRlFiuqLkTIXJWZURCqaabSQBYBChYAAAAEDfJDxF75i8++udk3Gt8qKtMirLenVyfr3KqaNVTs1XmJwvG0ykTACAQY6CAQAAAP3PdspzrTKurbKFpFetKDlV5fRhKuZOkfXHUygAwBBDwQAAAIC+iZzMMy0ytlNmeqfklSWTkvVqVE4fqyg5Q+XUoYoSUykVAGAIo2AAAADA3nGRjG2W196s4Lz/lSQVnzhO5dojFCbnKUzNkfOqYg4JANhfKBgAAACwZ1xRxnZ0LdBYkGTlvFpFiYMUqFIwtIy4R6oaEW9OAEAsKBgAAACwe7ZDnm2ScZ2SCeRM9tUdHxJTVMqeIFus0UiNqdzey8SbFwAQGwoGAAAAvMpZGdcmY9sq20iahKJggsrpFQqTsxUlZ8r6Y3ZdS6HYHl9eAMCAQcEAAAAwnDknqShjW+XZFhmVZL1aOb9BpdRxKmWPVpicx8gEAMBbomAAAAAYTlxUGZngypWRCq4kmZScl1Ups0pRYprK6eWKkodIxo87LQBgEKFgAAAAGOpcqTJCwbVKriTn1UgmUBRMU5g+VOX0YkXBgXJ+XdxJAQCDGAUDAADAUOQK8qONMq4oZ5JyJq8omKRS9jiVM6sU+WMkk9t1LYW9lUio/UMf7jkNABieKBgAAACGAucq20faZnmuRc5kFAWTVcocKZuYpHJqkZw3on8KhddKJtVx7d/0/+MCAAYVCgYAAIDBylnJFWVcm3y7Xdbk5fwGdWbPVZicqTC1sDIdAgCA/YCCAQAAYDBwTsa1ythWGVesXGacnNKSl1Epc7QK+bMUJmdJXm7/ZrNW/rN/liRF06ZLnrd/Pz4AYECgYAAAABiInJWxTTKuKKOC5JycSStKHCAbTFSUmCobjFUUTFYUjJe8fHxZOztVf/hSSdKWFzZIuf1ccAAABgQKBgAAgIHCFbt2e2iTXFnOq5UNxihMzlSUnKkwcbCi5Ox9s44CAAB9RMEAAAAQJ9shz26rTHswvpxXq3JyscrpQ1XOrJYNxsadEACAPULBAAAAsL+4kuRCGddZ2fHBlSRjZL0RKqeXqJxapnLmMDmvOu6kAAD0GgUDAADAvuIiGdcuY9tl3A7JpOVMUlJCNjFFYfIQhcnZKmVW7/+FGQEA6GcUDAAAAP3BOUlFeVGTjOuoXGaMnMnJBiMVJo9RmF6qMDFVzquS80aylgIAYEihYAAAANhbzsq4Fnm2WXKRZAJZv0FhMEdhepkif4yixDTZYAJlAgBgyKNgAAAA6A1nZewOedFWSU7Oq1GYnKVyernC5CyFyTmSl4075f6VSKjj0it6TgMAhicKBgAAgN1xTlJZxnXKi1qlKJQflWRNjaLEVBVzp6icPlQ2MSXupPFKJtX+8U/FnQIAEDMKBgAAAElyBRnbVtkuUqGMK0jyJZOQM2nZYJxUPVcFN03FxAJFwWTJ+HGnBgBgwKBgAAAAw48rV7aJtK1dhYKRjNe1IOM4Oa9eUWKSwuQsWb9RNhgnLzVWyfoalZraFYU27s9gYLFW3ssvVU6OnyB5XsyBAABxoGAAAABDX/cOD7ZFxrZIxpczOUWJKYqSM2X9MYoSByhMzpHz6t9wQUbP8KZ5tzo71bBotiRpywsbpBxbbgLAcETBAAAAhh7nZOx2ebZFUli5zKTlvKyK+TNUTi9XlJgh649idwcAAPoJBQMAABganJVxrV3rKOyQ80aonF4oZ6oVJWcqTE5XlJgm5zfEnRQAgCGJggEAAAw+znatodBcWUPBGElOzmTl/AaVUsepmD9DUXJm3EkBABg2KBgAAMDA50J5druM65BcKMnImbScV6VS+jjZYKKs36goMU1RYirTHgAAiAEFAwAAGFhcKOM6u6Y6FCRZSUbWr1eYWqQomKAoebCiYFxlq0gvG3diAAAgCgYAABCn7jLBtVfKhO7RCV5WzssqTM5XlDhIUXCAypnD5byauBMDAIDdoGAAAAD7lytXpjvYdkmRnFcl51WpnFwi649TlJimsGvrSHnpuNNiTwSBOt/x7p7TAIDhid8AAABg33FOUijjCl2LMlZKBeuPUpiepXJ6ucrpw2T9RsnLxJ0WeyuVUttnPhd3CgBAzCgYAABA/3AlebZZxrZJRpKM5KxkfDmTkfOyKqVXKUrNUylzhJxfH3diAADQjygYAADA3nNOxrXJizZIJiHr1amcPVo2mCTr18t5tbJerWwwXtYfIZlU3ImxLzgns21b5WRDA7t4AMAwRcEAAAB6z5Xk2SYZ2yRn8gpTc1XMnapyehUjE4ajjg6NmDlFkrTlhQ1SLhdzIABAHCgYAADAnnFWxrXIi7ZJRrJeg8rZk1TKHq1yepVk/LgTAgCAGFEwAACAN+Zs1xaSbZV1FeQquz1kVqicPlyl7DFsGwkAAHpQMAAAgK61FDokV5JRUca2SArkTErOr1Upc4TC1CKFyVmywQHMsQcAAK9DwQAAwHDknIxrl7FNMipLcpJScl5aztR1FQqLFSWmKUpMlkwy7sQAAGCAo2AAAGC4cFbGtVbWUFBRzquR82tUzJ6sKBinKJgiG0ysTHtghAIAAOglCgYAAIYi5ySV5NmWyrQH11m52KuSDcaplF6pUu4ERcEUycvEmxUAAAwJFAwAAAx2zkquIOOKMq5VxpUkOcmk5LycomCyosRURclZKqcWsoYC+l8QqHD2eT2nAQDDE78BAAAYbLqmOhjbJuOKkiI5k5VMUlHyYEWJmYqC8YqS0xUFE+T8kXEnxlCXSqn1jnvjTgEAiBkFAwAAA50rydgWGddRWZDRmcpijH6dysl5ipLTFSYPVhRMlvPqGZ0AAABiQcEAAMBA4sJKkeA6K+smuFAynpxXqyiYrjA1VzaYpDA5XVHiEMoEDAzOSR0dldPZLF+XADBMUTAAABAX2ynPtUmu2DXVwUhGciYv5+UVJhZWpjokpqucXibnN8SdGHhjHR0aOXmMJGnLCxukXC7mQACAOFAwAACwPzgn2aJUbpdX2i45I5mkrFctG4xXlJwtG4xRFExSlJgi64+WDL+mAQDA4MErFwAA+pMLu9ZLKMioIMl1lQlOxgSSXy+bmqVi6giFqbkKk7Mlk4w7NQAAQJ9RMAAAsDeclVSWsa2V7SFVlJwqUxy8GtlgoqLgAEWJSbJ+o2QyMsmRqm5crPYWKQxt3J8BAABAv6JgAADgzbhIUlnGhV1lQnvlcuNJCuS8KkXB5EqhkDhIUWKSwuQ8Ob/+dQ8VBJ7k5yS178/PAAAAYL+gYAAADF/OVgoDV5BxJUlWxpVVGYqgnVbCT8oZX87PK/KnK0pMVZhaIOvVKUocJOfXxpMfAABgAKFgAAAMfS6UcQXJlStbQKooSTK2IOvXyZmsomCcnFct642Q8+skk5D1amX9MbLBGDmTkQ3GsV4CAADAblAwAAAGN+dUGXEQ9hQJRqEqoxEKlSkOxpczKckkFQUHyAYHyAbj5LwqhYlpClML2bEB6AvfV/HkU3tOAwCGJ15NAQAGLmclV6wUBq4so1LlvAsr15vuHRqMJF9OgeSl5UxOzmTlvFpFiYNUTi+rjFDwR8p51bF+SsCQlE6r5csPxZ0CABAzCgYAwH7h2e1yKlfOOKvKqIPuvyUj2zUawb669oGzciZTGV1gAjlTpygYJ+uPlfOqZP2GSmlgsnJeRs7UKArGSl4uhs8QAABgeKNgAADsU855+nPYqoxtk6I2hfLUrpRC+QqNr0hGVp6sfBXlq92k1erSKilQp0lqm6rU6ZLqcEmVFEjWSGru+vNSrJ9bb3meUWpzQsViWda6uOOglzh+gxvHb3Dj+O2Zsiur5Epqt20qu1DWRSp3j/rbDc8YGXl6Z8O7dXT1cfspKYYqCgYAwD4VGqv3lPJaX9qmGr9WZZk3uJWTFHX9KUlq2a8Z9xcjIz/wFIVWTrxAHmw4fruX6bT672OflSTN/5dp6sx4MSd6PY7f4Mbxq5QH7bZNkbOSJM94Sppk5bkxvnz58ownT558E8h0/c83ngIllPSSSighY8xO1wXyun4v1/p1cX56GCIoGAAA+5yV0ajERNUHDXFHiZUxRomkr3IpknPD8wXyYMbx271UFEqqFAwTkhNVTA68l5gcv8FtKB8/55wKrqCCLajgOhU5q0ihrLOysvKMLyurhElqTGKc0iatEYmRmpWerbqgXjkvp4zJKOfnlTRJpUxKWS+npEnKN57SJqNqv1o5Ly9j3qjkB/rPwPvpDwAAAABDVMEW1G7b1GbbFLlITk4pL620SWtCcpLyXl55P68qr1oZL6PGYJQaghGqC+o1KTlJI4NGBex8hAGKr0wAAAAA2AeKtqhO16l226aiLaqsslImrRq/VrMyczQ/u0B1fr0mJCdpRmqGagOmKWBwo2AAAAAAgH5QtEU1R81qtS3y5CkwgTJeViODRi3ILlKdX6cZ6ZlanFuqrJeNOy7Q7ygYAAAAAKCXiraoDtuhsiup03Wq4ApKmKRGBiN1aH6FpqYO0rT0DB2UmqZ6v571DzAsUDAAAAAAwB6wzmp7tE3N0Q4lTUIZL6u0l9Hc9HwdmJqqScnJOrzqCOW8XNxRgVhQMAAAAKBPrGf0xPKRPaeBocI5p5IrKVKodtuh5qhJtX6tDq9apbU1p+mg1HQ1BA0sugh04TsBAAAAfVJO+br+8wvjjgH0i5ItaYdtVpttU+hCpUxKCZNU0iS1rvZMnV13ng5ITY47JjAgUTAAAAAAGLaccyq7slpsi1ptqySpzq/TYfnDNSczTwelpqvKr9KYYAy7PABvgYIBAAAAwLCzI9qhLeFmGRn5JlCNX6MVucN0VPWxWpxdovqgIe6IwKBDwQAAAIA+SXWG+ubx/y5JOutHR6qY4SUmBp7IRdoebVdb1KpIkVJeSguzi7Qst0IHpadrZnqmqv2auGMCgxo//QEAANBn6UIUdwTgdZxz2hxuVpttk5GU8/NamFus2Zk5mpycoqOqjmX7SKAfUTAAAAAAGDI6bYd2RDtUdEV12A6NCEbqpJq1mpudr0PSszQ5NSXuiMCQRcEAAAAAYFAru7K2h9vUaluVMAnV+nWanjxYM9IH6+jq43RwembcEYFhgYIBAAAAwKDTaTu1I9qhdtsuSarxqzU/u1Bn152nZbnlSnvpmBMCww8FAwAAAIBBoezKagqbtCNqVmACjQwatbr6aM3LLNCszGxNSExkTQUgRhQMAAAAAAasoi2q2TapFBZVCItqCBq0Ir9SR+RX65jq4xmpAAwgFAwAAADoE2eMfregruc00BfWWW2LtqrkymqzbUooUEOiQbPyh2hFepVWZo/UqMSouGMCeAMUDAAAAOiTUtrXtfcsjTsGBrHQhT1rKrTZVjUmGjU1OUm1fp3mZxfqzBFnaWzDCDU1tSsMbdxxAewGBQMAAACA/a7symqOmtVqW+ScU97La2xyrFbmV2lNzVpNTE7quW3geTEmBbCnKBgAAAAA7HOhC7U92q4O2y7rrKycGoJ6HVd1ouZl52t2Zq4mJicpMLxFAQYrvnsBAADQJ6nOUA+d+jNJ0oWPrVIxw0tMVDjn1BRtV3PUrEiRGoIGLcou0SGZWRodjNGK/ErVBw1xxwTQT/jpDwAAgD6rbS7HHQEDSEvUopZohzpdp6q9ah2Sma2jqo7RqqojNSYxNu54APYRCgYAAAAAfVawndoablXBFZT2MjogNVkr80doYXaR5mbmy7DDCDDkUTAAAAAA2GsdtkPry6/IM75GBiN1dtWJmpOdp+W5wygVgGGGggEAAABArzjn1Ok6tbG8QcYYTUweoDPrztax1cerxq+NOx6AmFAwAAAAANgjZVfW5nCTOmyHUialMYmxelvDRTq+eo0yXibueABiRsEAAAAA4E112A5tDbeo5EqqD+p1dNVxOqr6WM1Kz1Ler4o7HoABgoIBAAAAfeKM0Z8Pru45jaGjPWrT+vAVpb2MJqem6ITqk7S66hg1JhrjjgZgAKJgAAAAQJ+U0r6ueHB53DHQjzpshzaU18sYTwelpuvcurfpuJoTFRjePgDYPX5CAAAAAJBzTq22VVvDLfKNr0Mys3VyzSk6uvo45bxc3PEADAIUDAAAAMAwVrQFbY+a1Bq1KONntSC3SCfXnKqjqo5hxAKAXuEnBgAAAPokVYh03zm/kCRd/E8rVUz7MSfCWynaopqjJrXaNiVMQo1Bow7PH6EV+cO0uuoYecaLOyKAQYiCAQAAAH3jnEZvKPScxsATulAbyusVKpJzVp7xVefXamXVETq+eo1mpmeyGwSAPqNgAAAAAIaogi1oa7hFHa5DI4NGjQhG6vD8EZqenqH52YWsrQCgX+1RwbB+/fpePejYsWP3KgwAAACAvrHOqjlq1rZoqwKT0LjEOJ1QvUan1p6uuqA+7ngAhrA9KhhWr14t04s9jZ955pm9DgQAAACg98qupK3hNrXZVlV5VVqaW651tWdoWW650l467ngAhoE9KhhuvPHGXhUMAAAAAPY955y2R9vUHDUrMAlV+1U6tXadjq4+TjNSB/MaHsB+tUcFw7p16/Z1DgAAAAB7oOxKKtiCmqNmFV1R1X6Njqo6RifVnqqpqYM0IhgRd0QAw9ReLfK4fft2ffnLX9Yvf/lLbdmyRffff79+8pOfaMaMGTr66KP7OyMAAAAGMmP04uR8z2n0P+usNoeb1GbblTCB0iathmCE1taepgWZhZqdmctoBQCx63XB8NJLL+ncc89VsVjUwoUL9ac//UlRFOmFF17Q3XffrbvvvltHHHHEPogKAACAgaiY9nXxPx0Wd4whqSXaoS3hVklSrV+rs+pO1qG5FZqYnKSRQaOSXjLmhADwql4XDJ/5zGfU0NCgr371q8pms5o1a5Yk6dZbb1WxWNS9995LwQAAAADsJeusWm2rNoUblTZpzUgfrJNq1mpRbokmJQ+IOx4A7FavC4Zf/epXuvHGG1VdXa0oina57uyzz9aVV17ZX9kAAACAYcE6q4IraEu4WSVXUs7L67D84Tqp5hQdnj9Cgdmrmc0AsF/t1U+qIHjju5VKJeZ+AQAADDOpQqQ73v4rSdL7HzxUxbQfc6LBwTqrrdFWbQ+3yTO+cl5WDcEIHZ4/QifUnMQuEAAGnV4XDIsWLdIXv/hFHXrooUqlUpIkY4ystfrHf/xHLViwoN9DAgAAYABzTpNeaOs5jTdXtEVtCNcrcpFq/Botzx+mZbnlmp9dqCnJA5XyUnFHBIC90uuC4eqrr9a5556rY489VkuXLpUxRl/+8pf1/PPP68UXX9TXv/71fZETAAAAGLScc2q1rWqKtqvoShqfGK8jqo7SSTUna0pqatzxAKBf9LpgmDZtmh555BHdcccdeuKJJ+T7vn75y19q8eLF+sxnPqPp06fvi5wAAADAoFOyJe2wzdoablWdX6dpqRk6pvo4HVl1lEYlRscdDwD61V6twXDAAQfo1ltv7e8sAAAAwJBQtAVtCDcocpHqgnqtyK/UOxrerTmZefINa1QAGJr2qmDo6OjQo48+qqeeekotLS2qr6/XsmXLdPLJJyuZZC9eAAAADC8lW1JT1KR21y4jyZOnkUGjzqg7WyfXnKJqvybuiACwz/W6YHjppZd00UUXaf369ZowYYIaGhr0f//3f/rnf/5nPfTQQ3rwwQdVV1e3L7ICAAAAA0rkIr1SflklV1JDMFJz0/O0KLtE45MTtCC7SFkvG3dEANhvel0w3HzzzTLG6LHHHtOMGTN6Lv/d736n97///brpppv093//9/0aEgAAAAOYMdo4Jt1zeqgr2ZK2RJtVtEVZWY0IRur0urN0Zu05yvv5uOMBQGx6XTD88pe/1Kc//eldygVJmjt3rj74wQ/qU5/6VL+FAwAAwMBXTPu66LEj4o6xz1hntTncpE5XkHVWTlYjg0bNzy7U3Mx8HZ4/QpNTU+KOCQCx63XBkM1mlUgk3vC6+vp6+T6L1gAAAGBwc86p4ApqjprUattU79dpTnquZqYP0QHJyTqy6mhGKwDAa/S6YDj//PN12223ae7cuWpsbOy5vK2tTV/84hd1zjnn9GtAAAAAYH+JXKRN4UZ12A6lvYzyXl7ras/SCTVrNCV1YNzxAGBA26OC4cILL9zl/AsvvKBjjjlGCxYs0IgRI7Rjxw49/fTTstZq7Nix+yQoAAAABqZkIdItlzwhSfrQvUtVSg++Ea0FW9C2aJvabZsag1E6s/YcLcgt1iHpQ5T3q+KOBwCDwh4VDM65Xc4vWLBAkhSGoTZu3ChJmjlzpiRp06ZN/ZkPAAAAA5xxTtOfaek5PVg457Ql3KxW26qESWpUYpROy5+hM2rP0tjkuLjjAcCgs0cFw1e/+tV9nQMAAADYL0IXakfUrC3hFtX6tTqm+nidVHOK5mbmKeWl4o4HAINWr9dgeDMdHR166qmndPjhh/fnwwIAAAB91h61a2Npk4q2qPqgXkdWHa1z6s/TwuziuKMBwJDQ64LhlVde0cc//nE9+eSTKpVKb3ibZ555ps/BAAAAgL4KXajt0XZtCTerNlmjA1KTdXjuSK2tPU0jg5FxxwOAIaXXBcNNN92k3/zmNzrzzDP1m9/8RplMRvPmzdPjjz+uZ599Vnfccce+yAkAAADssZItaXO4SZ2uoBHBCB1RfaROHbNWK4IjFdhk3PEAYEjyenuH//qv/9JVV12lj370o1q3bp1SqZSuueYaPfLII1q8eLF++tOf7oucAAAAwFsq2qI2lNfrr+UXVRfU6+3179Q/HPCPumPSPTpj1BlKe+m4IwLAkNXrEQzt7e2aPn26JGnKlCm68847JUm+7+u8887TZz7zmf5NCAAAgAGvuTYRy8d1zmlDuEFF1ynJyJOnsYlxOiW/TmfUns1uEACwH/W6YGhsbNTWrVslSZMmTdKOHTu0ZcsWjRw5UrW1tdq2bVu/hwQAAMDAVcwEOvvHR+3Xj1myJW2PtqnVtqrWr9OamrUalxinCcmJWpBdpIyX2a95AAB7UTCsWrVKX/jCFzR69GjNnz9fo0eP1le+8hVddtlleuSRRzRq1Kh9kRMAAABQ0Ra1MdygsitrVGK0jq4+TqfUrtPB6ZlxRwOAYa/XBcMVV1yhP/zhD7rtttv04IMP6qqrrtKHP/xhPfjgg5Kk66+/vr8zAgAAYBizzqrF7tC2cJs846nBH6ETqtfo/IYLVe3XxB0PANCl1wVDXV2dvvWtb2nz5s2SpLVr12rs2LH67W9/qzlz5mjJkiX9HhIAAAADV7IQ6VNXPSVJ+ujnF6mU9vv8mM45dbgObQu3qegKqvXrNC+7QMdWH6+jq45VlV/d548BAOhfvS4YujU2NvacXrRokRYtWtQvgQAAADC4GOc09zdNPaf3lnNORVfUlnCzWm2bav0ajU6M1rFVJ+iYmuM0KXlAPyUGAOwLe1QwXHjhhXv8gMYY/cM//MNeBwIAAMDw0V0qtNoWNUVNSpu06oMGXVjzTs3JzNX87EIFZq//TQwAsB/t0U9r14smuje3BQAAwPAUulAt0Q5tj5qU8pLKmKyOqjpGa2tO0+zMXOX9fNwRAQC9tEcFw1e/+tV9nQMAAADDQFvUpq3RFlk5VXtVOqrqaK2rO1OTk1NUHzTEHQ8A0AeMNwMAAMA+VbIlbYu2qsW2Ku/lNDM9S2tqTtby/EqNDEbGHQ8A0E8oGAAAANDvrLPaGm5Rq22TZzzV+DU6PLtAZ9adqyXZpfKMF3dEAEA/o2AAgAHEWqvvfe87evTRb2v9+ldUW1uvlSsP17ve9V7lcsxHBjBwFbq3pnRO28Pt2hxu1IhgpNbUnKyluWVamjtU1X5NvCEBAPsUBQMADCAPP/yQ7r//Hp177gVauHCxXnrpr7r//nv1l788r89//q644wHAG+pMezrq3w5Vc9Skdve8alyN5mUX6OpRH9bB6ZlxxwMA7Ce9Lhj+7u/+TmeccYbmzp27L/IAwLBlrdXDD/+D1q5dp0suuVyStHjxUtXU1OhjH/sb/fnPz2jWrFkxpwSAV7XbNm0Nt6rFtqjBH6GxyXFalV+tQ3PLNTszV8aYuCMCAPajXhcM3/ve93TCCSfsiywAMKy1t7fr+ONP1OrVx+xy+cSJB0iSXnnlZQoGALFyzqnNtmp7tF2hC5X0UpqQnKgl2WU6suooHZKZrcAwQBYAhqte/waYP3++nnjiCS1fvnxf5AGAYauqqkpXXnnN6y7/xS/+Q5I0efKU/ZoHALoVbVHbo21qs23KellNTk3R4fkjdEh6thbnlioohqp+59skSS1f+ZqUTsecGAAQh14XDNOnT9eXv/xl/ehHP9KMGTOUzWZ3ud4YoxtvvLHfAgLAcPb//X9/0Ne+9g9asWKlpkyZGnccAMNI2ZW1I9qhpmi7AhNoZNCoU2tP16G5FZqdmauUl3r1xlFRqZ/8S9fpKJ7AAIDY9bpg+Nd//Vc1NjaqXC7r97///euuZ64dAPSP//mf3+raa6/S2LFj9Td/87G44wAYBkIXalO4SQXbKd8EynpZzUzP0kk1a3V8zRrlvFzcEQEAA1ivC4Z/+7d/2xc5AAA7+elP/0Wf/vQNmjBhom699Q7V1NTGHQnAEFZyJW0Lt6nVtmhkMFKH5Q/XqvwROiQzW6ODMfwDEgBgj+z1KjzWWj377LPavHmzFixYoDAMVVtb24/RAGB4+vrXv6p77rld8+cv1I033qJ8Ph93JABDUMmW1Gyb1Rq1yjNGdX69Tq1dpzPrztHoxJi44wEABqG9Khi++93v6tZbb9XmzZtljNG3v/1t3XHHHUokErr11luVTCb7OycADAuPPfaI7r77Nh111DH66Ec/oUQiEXckAENIwRbUEu1Qm22TldXIoFFLqpdpddVRmpdZoPqgIe6IAIBBrNcFww9/+ENdd911Wrt2rY488khdddVVkqRjjjlGN9xwg+6++25deeWV/Z0TAIa8bdu26o47PqcxY8Zq3bqz9ec//2mX68eNG6+RI3nxD6B3Ihep4ApqCrer5EpqCEbo8KojdUhmlo6qOkbVfk3cEQEAQ0SvC4Z7771X55xzjj7+8Y8r2mmV4NNPP13bt2/XN7/5TQoGANgLv/rV4yoWi9qwYb0uu+zdr7v+b/7mY1q79pQYkgEYbJxzarNt2hhukC9faS+thmCETqw5WefXX6i0xzaSAID+1+uC4YUXXtB11133htfNnTtXd9xxR59DAcBwdNJJp+ikkygQAOy90IXaEe3Q9mibMl5GszKzdXLNqTo4fYgmJ6co6e2jaay5nLZsbtk3jw0AGDR6XTA0NDTo+eef14oVK1533fPPP6+GBobvAgAA7E9FW9SGcL1CF6rWr9NJNafo+OoTNTczf9+VCgAAvEavC4YTTzxRt99+uxobG7Vq1SpJkjFGf/jDH3T33XfrpJNO6veQAAAA2FXZldUStWiH3SEjaXRijE6tOV3HVp+gUYlRcccDAAxDvS4YrrzySj377LO68sor5XmeJOmCCy5QR0eHFi1apA984AP9HhIAAACVKRCtUYu2RlvlG195r0qTk1N0fPWJWld3pnJeLp5ghYKqL7tYktRy131SmjUeAGA46nXBkEwmdf/99+vxxx/Xr3/9azU3N6uqqkpLlizRqlWrZIzZFzkBAACGnbIrq9N2qM22qyXaoaSXUt7La3Zmro6qOkaH5Q/X+MSE+F9/RZFS//xY5fTt98QaBQAQn14XDI899phWrVqlFStWvG4dhi1btuixxx7Te97znn4LCAAAMNyUXUnbwu3aYXeo3q9T1stqUXaJVuQP06LsEo1NjIu/VAAA4DV6XTB85CMf0Te+8Q3V1dW97rpnnnlGt99+OwUDAABAL1ln1WE7tDncJGOMqr0anV57pt414r2q9qqV8lJxRwQA4E3tUcFw8cUX6/nnn5dU2Vf5sssuUzL5+hWJt23bpokTJ/ZvQgAAgCEsdKGao2Ztj7Yr5+U0LT1dZ9adq0Nzy1UfsDsXAGDw2KOC4ZJLLtG3vvUtSdKjjz6qmTNnqr6+fpfbeJ6n6upqrVu3rv9TAgAADCHOObXbdrXYHeqwHarz63VqzWlaU7NWszJz5Bs/7ogAAPTaHhUMCxYs0IIFC3rOX3rppZowYcI+CwUAADDUOOdUcJ3aGm5VyZWU9jLKehmtzB+hS0ZepjGJsXFHBACgT3q9BsNNN90kSero6FA2m5Uk/fjHP9b69eu1evVqTZo0qX8TAgAADGKhC7U92q4dUbMyXlZ5v1qn1a7TobnDNCl5gPJ+Pu6IAAD0i14XDH/5y1/03ve+V2vWrNGVV16pL3zhC7r33nslSV/4whf0la98RQsXLuz3oAAAAINJZcHGzQpdWSOCETqheo1OqV2nKakDVePXxh2vf2Wz2vLChp7TAIDhyevtHW655RYFQaCjjjpKpVJJX//613XiiSfqqaee0sqVK/WFL3xhH8QEAAAYHMqurFfKL+vF0gualZmtKxs/pK8e8A19fOynNT+7cOiVC5JkjJTLVf6wfSYADFu9HsHw1FNP6cYbb9Ts2bP1n//5n2ptbdXZZ5+tfD6vc845R+9///v3RU4AAIABK3ShOmy7toXbVHQljU+O13HVJ+rSke9XtV8TdzwAAPaLXhcM5XJZ1dXVkqSf//znymQyPVMioihSEPT6IQEAAAalgu3U5nCzSq6kKr9aMzIH66iqY3Vc9YlqGE5bTBaLqvrQByRJrbfcJqVSMQcCAMSh123AtGnT9C//8i+aPHmyfvSjH+mwww5TEAQql8t6+OGHNW3atH2REwAAYMAIXahN4UYVbEGTUwfquKrjtapqtSYlD5AZjlMEwlDpb3xdktR6860UDAAwTPW6YLjiiit02WWX6eGHH1YymdR73vMeSdJxxx2nrVu39iz4CAAAMJRYZ7W+uEk7Si3y5KnWr9O5dRfogoa3K+Nl4o4HAEDsel0wrFixQv/8z/+s3//+95o7d67GjRsnSbrooou0bNkyTZ8+vd9DAgAAxCVykTaFG7U12qIJ6QlakT9Mx1adoPnZRRoZjIw7HgAAA8ZeLZgwYcIETZgwYZfLLrroon4JBAAAMBCUXVmbw03qsB0aGTTqvJrjdM7EszQ1mqkwtHHHAwBgwOl1wfCRj3zkLW9z00037VUYAACAuLXbdm0JNytykSYkJ+n46hN0Qs1JmpAZr7rqnJqa2uOOCADAgNTrguGJJ5543WUdHR1qbm5WbW2tZs+e3S/BAAAA9peSK2lLuFkFV1CghEYGjVpbe5pOrVmnuqA+7ngAAAwKvS4Y/u3f/u0NL3/++ed1+eWX69RTT+1rJgAAgH3OOacddoeawu1yRhoVjNKx1Sdoenq6ludWKu2l444IAMCgsldrMLyRAw88UO9///t1xx13aM2aNf31sAAAAP2q3barJWrRjqhZNX6tluYP1cr8Kh1TdZzyflXc8QanbFZb//iXntMAgOGp3woGScrn83rllVf68yEBAAD6zDmnFtuijeEGZUxWtUGdjq46VsdUH6eF2cUyxsQdcXAzRm7EiLhTAABi1uuCYf369a+7LIoibdq0SbfffrsOPPDAfgkGAADQV5GLtDXcohbboqyX1ZH5o3R8zYlalluhrMe/tAMA0J96XTCsXr36DVt+55zS6bTuvPPOfgkGAACwt0IXan15vQquoMagUcfnV+r46hO1NHeoPOPFHW/oKRaVv76y01jbJ26SUqmYAwEA4tDrguHGG298XcFgjFE+n9fSpUtVVcXcRQAAEI92267N4WZZF2lEMFIX1b5Tx1YfrwnJiXFHG9rCUJkH7pcktV3/SQoGABimel0wrFu3bl/kAAAA2GtFW9BL5ZcUmIQmJyfrjLqztSy3XKMTY+KOBgDAsLFHBUNvpj0YY3TZZZftdSAAAIA90W7b1RQ2qcO1K2mSGpsYp7W1p+mC+rfLN37c8QAAGHYoGAAAwKDSFrVqU7hJCZNQfdCgIzKrtSi3RKvyR6jKr447HgAAw9YeFQx/+tOf9nUOAACAN9UWtWljuEEpL61xifF638jLtTJ/hJJeMu5oAABAe7EGQ6FQUDqd3uWyZ555RgcffHC/hQIAAOhWsAVtKK9X4AWak5mnd4+4RPMy8ykWAAAYYPZ4n6Y///nPOv300/XAAw/scnlLS4tOP/10nXLKKXrhhRf6PSAAABieCragvxSf18Zwo0YnxuhvRn9Md0/8kpbkllIuAAAwAO3RCIaXX35ZF154odLptCZPnrzLdYlEQtdee60eeOABnXfeeXrsscc0atSofRIWAAAMbdZZNUdN2hZtU2ACHZSeppNrTtXx1Scq77MV9oCVyWjbU7/vOQ0AGJ72qGC47777VFtbq3/8x39UfX39LtdlMhm9/e1v15o1a3TmmWfqi1/8oq6//vp9EhYAAAxNzjk1RU3aFm1VtV+jpblDdVz1iTqy6ihlvWzc8fBWPE924qS4UwAAYrZHBcOvfvUrXXzxxa8rF3Y2cuRIvfOd79TDDz/cb+EAAMDQ5ZzT9mi72myr2m27GoIROrr6OL29/l06KD0t7ngAAKCX9qhg2Lx5sw444IC3vN20adO0cePGvmYCAABDWOhCrS+/opIrqdqvUUMwQidkT9KampM1Nzs/7njYG6WScjd+QpLU/jfXS0nWyACA4WiPCob6+npt3rz5LW/X1NSkmpqaPocCAABDT0vUoi3hZkWKNDYxTidUr9ERVUdpenpG3NHQV+WysnffLklqv+YjFAwAMEztUcGwePFifec739GaNWve9HaPPfaYZs6c2S/BAADA4GadVYtt0Y6oWSVXUsbLakF2kVbmV2lV1ZEakxgbd0QAANCP9qhguOCCC3Tuuefq5ptv1lVXXaVUKrXL9aVSSV/4whf085//XPfdd98+CQoAAAaHki2pKWpSm21V3q/SvOwCHZSapqW55VqcXSLP7PEu2QAAYBDZo4Jh9uzZ+shHPqIbb7xR3/3ud3XooYdq/PjxiqJI69ev1xNPPKGmpiZ94AMf0MqVK/d1ZgAAMABZZytrK6ikOr9eR+SO0nn1F2h2Zk7c0QAAwH6wRwWDJJ1//vmaMWOGvvzlL+unP/2pisWiJCmXy+mwww7TO9/5Ts2dO3efBQUAAAOPc04drl1NYZM6XIdGBiN1fu2FOrfubcr7VXHHAwAA+9EeFwyStHDhQi1cuFCStH37dgVBoOrq6n0SDAAADFzWWW0JN6vVtirtZdSYGKWV+VU6ueZUHZCaHHc8AAAQg14VDDurr6/vzxwAAGCAc86pzbZpa7hFkSI1BA06Ln+iTqpZqzmZeUp5qbd+EAAAMGTtdcEAAACGj9aoVRvDDcp4WY1PTtDa2tO0PHeYJqemxB0NA0Emo+0/f6LnNABgeKJgAAAAu9UatWprtEWBSWh8YoI+0Hi1lucPU2B4CYGdeJ6iGQfHnQIAEDNeHQAAgF1ELtKG8gZ1ug5lvKwOTB2ki+rfoVVVqykWAADAbvEqAQAAyDqrNtuqVtuqdtuusYlxOjW/Tgtzi7U0e6iSXjLuiBjISiVlv3CLJKnjyg9JSb5eAGA4omAAAGAYi1ykbdE2bQk3a0QwQqMTY7Usd6hOrT1dk5IHxB0Pg0W5rNwtN0uSOi77AAUDAAxTFAwAAAxDRVvQDtui5qhJDcEInVZ7hk6pOU1zMvNkjIk7HgAAGIQoGAAAGAYqW0y2qjlqVrtrV8ZkVRfU6ajsMbqw4Z06OD0z7ogAAGCQo2AAAGCIss5qe7RNLbZVkYtU5eU1JTVVK/OrdGBqqhbllijn5eKOCQAAhggKBgAAhojKQo1tKriC2qJWlVVWnV+vpdllWphdrLnZ+TokPUue8eKOCgAAhiAKBgAABjnnnDaHm9Rm25Tzcsr71Zqemq552YU6quoYTUoewLoKAABgn6NgAABgkApdqE3hRrVGrarz63Ru/du0puZkTUlOpVAAAAD7HQUDAACDiHNOG8MN6rAd8oynWr9ei7JLdF79BZqXWUCxgHik02r68b/3nAYADE8UDAAADALd0yC2Rts0IhihRdklOqHmJC3LHar6oCHueBjufF/h/IVxpwAAxIyCAQCAAaxgC9oSblbJlVXlV+nM2rO1uvpoLcstjzsaAADALigYAAAYYJxzao6a1WS3y5On0YkxWlN9slZVrdaU1IFxxwNer1RS5r57JEmdF79PSiZjDgQAiAMFAwAAA0S7bdfWcIvKrqwqr0qH54/UsdXHaUl2mfJ+VdzxgN0rl5X/xN9Jkjrf8W4KBgAYpigYAACIkXVW26Jtao6alDIpHZCcrKOqj9XS3KE6OD0z7ngAAAB7jIIBAIAYhC7UxvIGtdt21Qf1OrrqWB1edaSOqjpGgeHXMwAAGHx4BQMAwH7inFNr2KoNxY0KXajRiTG6pO5yLcou0dT0QXHHAwAA6BMKBgAA9iHnnDpdp7aFW1VwBVUnqzQlfaCOzh+n46pP0KjE6LgjAgAA9AsKBgAA+lnRFtVid6jDdih0kVJeSiOCRh1WvVKnjz9FB4TTZKO4UwIAAPQvCgYAAPpBwRa0Ldqqoi3KyqoxaNS07MFanF2i+dkFmp46WNlkWnXVOTU1tcvKxh0ZAACgX1EwAADQB61RizaFm5QwSTUGjVpWvVzT0tO1uupoVfnVcccD9o90Ws2P/qDnNABgeKJgAACgl8qurOaoWdujbcp6OU1OHahLR75fC7KLlPNycccD9j/fV3nFyrhTAABiRsEAAMAeCF2oTeEmddpOBcZXtV+jU2rW6ejq47Qou5itJQEAwLDHqyEAAHbDOadW26KmqElFV9TE5CQtzx2mhdnFmpOZq7qgPu6IwMBQLiv90AOSpMKF75ASiZgDAQDiQMEAAMBrFG1RzVGTdtgdqvFqNCczT0tzh+r46jVqTDTGHQ8YeEolVX3kQ5KkwjnnUzAAwDBFwQAAGNacc+pwHSrYglqiHbKy8o2vOr9ey/MrdV79BZqWmi5jTNxRAQAABjQKBgDAsFN2Ze2ImtVm21R2obJeRlkvq9mZuVqcW6LJqSlalluhrJeNOyoAAMCgQcEAABgWIhep1bZqe7hNxhjV+rWan12k2ZnZWpJdpoPS09kBAgAAoA8oGAAAQ07kIhVsp1psqwq2UzKSdVY5L6+ZmVk6rfYMrcgdptqgLu6oAAAAQwYFAwBg0AtdqKaoSa1Ri5ycPOMpZdLK+3nNTB+i+dmFqgvqdEh6tg5MTZVv/LgjAwAADDkUDACAQSV0odpsqzptQQVXkHWRPOOp1q/V8vxhmp6eoampaZqQmKgDUpMVGH7VAQAA7A+86gIADFhFW1DJlVRwBbVGrT2jE6q8atUFdZqRPlhjE+M1JXmgluaWqS6ojzsyMDylUtrx8Dd7TgMAhicKBgBArJxzihSp7MrqtB0qupKKrqCSKyttUkp7GaVMSodXLdLk5GQdlJ6m2Zl5GhmMjDs6gG5BoNIxx8edAgAQMwoGAMB+010mdNoOtdt2tdt2SUa+8eSbQDkvp9HBaI1PTtDU1EGalDxAczLzVO83KOkl444PAACAN0HBAADoV845bQw3aFN5o/6v9IL+WvqrJOmV8BU1R83yjK+MSSvjZbUgu1iLcos1MTlJGZPR1PQ0tooEBqNyWalHKlMkiqefJSUSMQcCAMSBggEA8KYiF2lruFXtXQsr7oia1WZb1Rq1qt22q+zKKruSdkQ71Bw166XSX7U53KSSK6nDdsiXJ894mpdZoLPrzlN90KDRwWhNSE5kNwdgqCiVVH3F+yRJW04+lYIBAIYpCgYAGOKss2qzrdoeblebbVPRFVR2oXZEzWqJdlQWUbSdaolatC3cqjbbprIrq+RKaoqaVLCdKrmSQhfKyqrsynKyMjKSjCTX9X8n3/hKmqRyXk7VXrWSQWVaw4vl/9N7Rlyiw/KHx/pcAAAAYN+hYACAAcg5V1n00HVoW7hVO6IdKrtQ8iIlraetLc3qDAsKFao1alVL1KIO295TDlhFilwkK6v1pcrUhLIrK1KoyFk5uZ7tHSXJdX1Mzxj58mVkZIxRoIQC4yttUgq8vHx5CkyiVyMPrLP76FkCAADAQELBAAD9oGiLChUqdGW1Rq0quaI6bUHbwq3aEm5WU7RdHbZTTlbWWVlZOefU4Tq0pbxZna5ToSsrdGHPtAMnp8hFPVMQnJyccTKeZCPX9ZErIwhc1ygCz3iVckCm61qjwARKm4wyfkaBfHmmUiD48mWMie05AwAAwNBCwQBgSOvetaB7eL+Tk+t6g192odpsqwq20DUFoKyyCxW5UKFCRS5S6MrqdAW1RDvUFrUqklXYdb/WqEVtUZtabYu2hFt6SoPIhYpk5WRVciVZZ2WMVxkm0F0GSD1FQMIkuoqBSjngG09e1+nABMp4GSVUuY1nPKWSCYXlyscCAAAABgoKBgBD1s9b/0Nf3HpXZTqAnOS639pX/mtlFbpIVlFX6eC6/mdlnZO67mddpEj21VEEkqyL9jiHJ0/aqQzoHl1QSeJUcqXuSG/NSH7kKYrsnt8nZlZWns3rdxusSkEp7jix8nyjqrxRa1txp1EoGCw4frvnd5Z0Rtfpn71YUpQZeIs8cvwGpmLkFFqpbJ1KkbSt06oYVi6r/HGKnCRjdOY8T/Pq404M4M1QMAAYsuZk5uqQzGwVbVGBCeTJk2eMTOWUEiahrJdVxssqaZIKTKCESSgwCQUmkN81imAg8XxPVfm0WtsKstHgWNsgctKNvyjoH7ZMk6+2uOPEykjy/TZFkRss/RB2wvHbvUypo6dguOEX7epMDrxniOPXv3o7is4YVX6rGinwJM9U/iQ8I890Xd993pO8rvuY7vt6Rp/7z2Y9tLZ6n3w+APoHBQOAIas2qNPfjL4+7hj9Kgg81dXl1OS3KwwHR8FQjpxua2tWNi3VZ7y448TKGCmRCFQuh2KGy+DD8ds9L8roUxffI0kaPyIj6w+8LWg5fruKrFNYGdwn6yrnS12jBkqRU3eH3f1UWefkG6PO0CnhSZ4xSgeVQsCYXcuAtG9UmzZKB0ajcp6SvpRJePKNlPClXKJyXco3qum6XWCkRNf9sgmjhFd5bN+rFBKJwFN9fV5NTe1xPWUA9gAFAwAAAPrE+oF+seikuGMMW9ZVphe0lZxC6yqFgXt1dl53SRDaysiA7vMJvzJqwEgKPKOEL9WljUbnfVWnKoWAbyTf005lQeXN/8isp5E5Tw2ZSoHgGVMpAjzJ9/p/9B+LEgODAwUDAAAAMAC5rrWDIiuVu0YWFMPKugXWqWu9oMob74QvVSWNRmR9pQOpKunJ26kg8I1Uk/aUTxplAqNJNb5GZCvlQMKrjCqoTXtKBbyRB7D3KBgAAADQJ14UasV//0iS9Pj842V9XmK+lnNO5a4pCNZVFi8sRt1TFCqFQTGSJCdPlWEF1lVGHHhdIwOSvlFVymhCytPYKl8jskb1GU9j85XRBFPrA+USFAQA4sNPfwAAAPRJIizpo/e9T5K09o4/qzjEC4bIVkYWuK6pCKXo1V0PrHPqKFfWItiZc5W1BAJP8k1lbYGkLyU9o4asp5RfGVlQlTIaX+WrIWuUS1TWI0gHRjUpo8acp6qkYboAgAFraP/0BwAAAN5C9+iCcvTqaIKoe/HDrgKhHDkZY+RcZVKC53X93bU4YbJrikI6MJpSF2hslafASMnAqDZlVJX0VJepFAXpoFIcZBNGSZ+yAMDQQcEAAACAIaN73QLnJKtKaVAInUqRk+dLnaVIkXUyMnKqjDToHl3ge69um1jZMlGqThiNzPqa3uCrPuMplzCqSXmqTnWPQjCqT1dGIVAWABjuKBgAAAAwoLmdRhWUo8oih6GVOspO1rldpgx075xgdioJ0oHRmCpfNdmEsr6v8XlP6UDKJoyqkkapwKgx6ymTMKpOmZ22UWT3AgDoDQoGAAAA7HPOOUVOKoRS5JyirjULKiMNXNfCh5UdE5wqIwy6Rxc4OXnGyDeV3RJSfqUIOHiErwPrAuWTRrmkUWCkVFC5LtE1GqE2bTQ676k2G6iuLqempnaFoY376QCAIYmCAQAAAP2ms+zU4qzKtjI1oRxV1ipwqowoSAeVRQ4DT8onK6MEfK8yoqAu7WlktrJVYsKTqlOVrROzgVEmURmJUJv21JjzlA0YXQAAAw0FAwAMUJs3b9KFF56tG2+8RQsWLIo7DoBhyDmn0EnWSqGrlAehrUxPKNtXRxfkylHPfXYUrKJsZYeEg0ckNCprNLHGV13GU33G05i8p3yyMjUh4VESAMBQQsEAAAPQpk0bdfXV71dbW1vcUQAMUaF1KnZNVyjttK6BdZWdFCqLIDoFXYse+qayZkF9xusZaZAJKjsoNCYT+uVHb1cmYXT7yQ0aUZNSNmGUYNFDABhWKBgAYACx1upHP/qB7rrrCz0LlQHAnoisUyGSrK3solCOukcZVAqE0Kl73wRJlUUQM13TFZKBNCrna0zeUzZhlE9WRh00ZLzK1osJo2wgjavylQp2UxosePv++lQBAAMUBQMADCDPP/+/uuWWm3TaaWdo0aIluuaaK+OOBGAfcM6p3LXIoXVSqWv0QPeUAydJTj3bLTpVdlCwXX8qRYFkusoC1/XfTGDke5XLAk9qzBkl/cpog9E5T8nAKBMY1WeMRmQ9Taj2VZWsLIQYeIw2AAD0DQUDAAwgo0aN1j/906NqbByl3/zmqbjjAHgLtqsoiGxlqoFz6jkf2p3OdzUGlffwRtZVtk8MPMkzRglfqkpVFjz0jKfASJ5XGV2Q8Co7J2QTlXIglzRKeEZ+1/3TgVFN12KIE6s95RJGnmeUCaR80ts/T0QYKvnvP5EklY48Wgp4iQkAwxE//QFgAKmurlF1dU3cMYBhoXvbROcqBUDYVQp0X9YzcsBWFjosRVIicLKR7RlZIDkl/Mr2ib5n5EkKfCmXMqpJeUr5lTf+9RmjpG9UlTKqTlamIDTmPNWkKpfXps3+KwP2hWJRNeefJUna8sIGCgYAGKb46Q8AAIaU7uKgs+x6phU4V1mDoL3k1DWIQM5Vtk3sXsDQ9ypbJ/peZX2CpF85nw08ZRJGmYTR3PFZJV2otOeUTZiubRMrOyJkAqNkYJRPmN2vUwAAwBBGwQAAAAY065zKUWWEQeQqaxVEO402CG1lpIHtKgysqxQGmUSlIPBUmW6QDYxmjvC1YExCtSmjXMIon/KU9NSzsGF1yiibqIxIeO32iUHgqa4up6amdoWhjeW5AABgIKNgAAAA+9zO0xG6FyqslAJO1mmn7RErlxWjSonQLeFX1izwTNeogoRRdcpTdcqoLu0pFUg1KaPReV+1qUpxMK7KU13X4oUJTz2LHwIAgH2DggEAALwl11UEdE85iJxUjl69LLSVkQWlqDKKwPXss2q61itwCjwj0zUdwTOVzRI9YypTFLoWLEwFRpnAUzqQptX7mlofaETWU126sl5BypeqUp4ywetHGAAAgHhRMAAAMAz0jCBQZRRBKZKKketZyNB1jRwI7Wu2Q+wqAlzX6e5ywN9ph4OUL43O+conjSbV+KrqGlVQkzI9axx4pjLqIOlX1i1I+ZURBSm/UipkA6M0pQEAAIMaBQMAAINE9ygCq66dD7rKACe3y64HpUhqKzlJrucNe/eChqbrzX5gpHSiUgAEXmWbxKRfKQWqU0Zpv7IdYm1XUZBPVhYzzASVkqCywKFUl/aU9CkGAAAABQMADFgLFizSf/7nU3HHwD7gnOtapLBSEETOqRRKhbCyHaJ13berjCroWTvASdppeoHvSUHXEIPKdIPKzVKBNL7a1yEjA9WmPeUSRg0Zo6qUp4RXWcsgn/Q0Ju8xagD9I5lU60239JwGAAxPFAwAALyJndceKISuZ+pA1DWCwDnXM6JAqnQAka0UA8aYygVSpQQwkuc5haF9dd0BI3ldixBOawh6djIIvEphkPAqowjSgZTyjUblPWWDyoiDTFAZZdD9GIGprGOQSxp5lAbYnxIJFd51cdwpAAAxo2AAAAxLzrmdygCp3FUKlG3XSIJICl1l6kHgVaYSpAPTswZBOpByCaN04Cnpm53WJjDKJ6TReV9+V0ngqTJKIPCNGmpSyitU2nfKJyujCzJBZeHCfJJSAAAADF4UDACAQal7QcLKNIPKlINS1LWzgV4tDpycjCoFgXVdCxZ2/fGknhIg8ColQeBJE6p9jc17OrDO19gqX/WZysKEo3OeqlKVNQgSXu+nFgSBp7q6nJqa2hWG9q3vAAwWUaTEr38pSSovWy75fsyBAABxoGAAAAwIzjkVQqlsXaUo6Nr20PbsaFApCkzXFohGRoFf2dXA7yoH0oF0YF2gVGCUTxhVJaV0ojK9oCpZWaAw4VemHyQ9o6Rf2dkgFVSmJWSCymKGKRYtBHqnUFDtaWskSVte2CDlcjEHAgDEgYIBALBfRK4yuiC0UmfoVOxa0LAUOfnGqBw5Zbq2MfSMVJX0dECtr1yiUgJUJT1lAqmma9HCmpRRVcr0FAe5ZGVng8CjGAAAAIgDBQMAoFecc+oMKyVBKXTa0mHVUnTaUXQqRU4b2qyaCladZWlH0Wpjm1V10uivLVadZSffqyxWWJvxNDbvaWKNr1E5T+nAaHpDZUpC2mehQgAAgMGGggEAhjHrnDrKTsVI2tZhta3TqmylYlgpDCo7JTi93GK1sd1qe2elTCiElTUPrKQwqkxpcNpp14SuhQ09r7KmQaEsnTojpaMOSCqTMGrMeapPG6YhAAAADCEUDAAwyO28dkH3m37rJGudWkuVAqHctRji1g6r55oibWyL1NTptKndqhA5WVu5fzHq3l2hsqZB9w6LkXVKeEbJQPJNZQ2DVFDZdjGR8pT0tdvRBtY5vdhsdei4hBaMSey35wUAAAD7FwUDAMRo560S20qV6QbFqDKCoNC1RkFkKwVCU8GqaI02Fzu1o62s9pJVc8GpreRUjJys637MV4uB0FZGIVhXuTxylcu6t1tM+UYJX0oEUs7zlPIrOypILHIIAACA3qFgAIB+Zl1lHYKNbVaF0Gl7p9NfWyJtabdqLTm1l512FKwKYeXNfveWiZVtFl8tBKytTDuo7JdQmX5gJBnPKIysUr6R70mB6frbq2zB2N0LGEm+51UuN5Xzntn9SAMAAACgLygYAGAPhNapueDUUrTqDKXmglUpqvz9UotVKXLa3F5Zw2BLR+U2xbBSFnSPKPBMpQTwTGWagd9dCKhSAASelE148ntu033dq4WAMVIiEahcDuXcbsICwP6WSKjt+k/2nAYADE8UDACGnUJYGUUQdi1muLXD6pVWq/ZyZReEHUWnQtlpc9eih8VIai1aFbvWObBOKtvKSIXuUQWlyCkTVNYmCDyjTCDVp71XSwRGDQAYypJJdV7+gbhTAABiRsEAYMgIbWXdgh1Fp5ai0/bOyhSFrZ1Om9qs/nd7qK1dWyqGtjKqwLrKAoilqDIRwZjKughSd1nQNZrAM0p1rVPgGSnRdTnFAQAAAFBBwQBgwHGusptBKXLqLFd2QihbvbojQlRZ9HB7weqlHZHay05/bbFqKViFrrJbQmgrWydaV9kuUZJ8T0r6Rkm/awcESZ6prFGQ9CkLAGCvRZGC//mtJCmcM0/y/VjjAADiQcEAoN9E1nVNM+hapNBVRgp0hk7bOp3aS1ZNhcoog+auv8vWqdxdJoTS9s7KQoid5VcXOwytk3Pq2VFBXSMNunWvVxB4UjowSiakhOcxygAA9pdCQXXHHSlJ2vLCBimXizkQACAOFAwAduFcZcTAhjbbs0ih7dresKPs1F5yais7tRSsNrVbFaLKm/62ktPLLVadoetZ1NCp8p+oaxpCaLve8Es9WypKlYULva6/E15lOsLO0xMSnidj1LVjAoUBAAAAMBBRMABDnHVOTQWn1qJTa8mqpSwlNxmt39apHQWrtq7pB02dVs1Fp1daIrWVK6MLbNcOCFL3Noq7jh5wrjJyQF0jCLqnH3SXCDKSkZFvjAKfcgAAAAAYyigYgEGqEDrtKFjtKFZGHOwoOHWGldNNBau/7oj0f82RWkuuZ02CnlEEXpts1xAC1zXMwJhXRw2kfKO6dGVtgu46wHRvm0hBAAAAAOANUDAAMQptZcpBU6GydkF72am5YNVcqOyCUIxcz0KHG9tt1wKHUjHqWvgwqjxG2FUc7PzWv3tKQTYwSnhSOvCU8CuLGaaSCZXLYc/oBAAAAADoKwoGYC9ZVxk10FqqjBxoKlR2OLA7jRQoRpXLOsPKwoelyGlzu1VTZ+Wy7QUr6yqXR7ayzkFktcsUBKlyPvC6RxBIXtcWiplA8rsWMwy8PRtdwAAEAAAAAPsCBQOGLde18GD3LgXbO51CW9kesbVUGVmwqd2qpWhVCKUdxcqih6Wu6ze0RSqElft2L4RYjl59fKPKm/nuhQ6dnJx2Xbww6VXWM8gnjZKeke8xDQEAAADA4ETBgF6zrrIAYKFs1Vl2Koddc/m7ru+e06+dzu88Er9nh4GunQkiJ9muN/YdZSen7h0GXl1k0Em7rCMQdZ0uRpXrreu6bdd9u7c8DK3ruq3UVrLa0GbVUnSKnOv6+9XHL0eVx+neDjHsWqPAyMi5yhoFXvcIAiMlPKN0UJl64Hdf1g/bfleem303d6Gyg4Pr+VwxuAzG4zdYcgLog0RC7R/6cM9pAMDwZNy+fCezB5qa2hWGNs4I6KVL/1+LWktOiYSvcjnqWSxQ2rVI2IV7/XU7FxLdhUPodr3tzl2F06slQvd9rH3NbbtOWFWKhd3ddjgzRvJ9T1FkeeM3CA3a41dq11kj/qLp2ba4k8TK943y+bTa2gqKosF0ACFx/AY7jt/gNpyOn3NOURSqVCqpXC6rVCrpr399UaVSSc45OWflnJO1lb/DMNKIEQ26/PKrlM/n447/hoLAU11djvd+g1R9fU6+7+3RbRnBgF5738KsfvBcSel0QsViWW6ngqF7ZP/OOw+8duHB7vO+Z2RU+Zd/36tMF0gHUsI3PdMLurc7rOxgYJTo2urQ75pOgN7zfaOqfEatbZ1D/hf0UDQYj18URbrhY5/Q1//3cQVmcGTeVwZtQQRJHL/BjuM3uA3m4xdF4R7dzhijVCqlZDIlz/Mqo2e7/k6n0wqCQDtvF9592hijDRvW65VXXtb06TP21acB7BEKBvTa7MZA88cmaSEHqUqDnFFTk+XYDUKD8fiVy2Xltv1/ytZVq76+Ie44sTLG9Iz+inkAIfYCx2/3jHOa2F4ZofTXXF5uAK4lxPEb3Aby8SuVSuroaFd7e7uKxaKkV9fTck4KgkDpdEbGGCWTCQVBQul0WjU1taqurlZ1dbUymaySyaQaG0dp1KhRSqXSCoKEEolAiURCI0c2qq6uvqdw2Ll8YO0uDCQUDAAAAOiTZBTpvl8+Lklau/poFQNeYmLocs6pra1NO3Y0q1gsKggSymazmjz5QB144FTV1NQqkQi6CoQa5fN5jR8/XslkSrW1tcrl8pQCGLL46Q8AAAAAu1EsFtTc3Kz/v737Do+qTN8Hfs8kmSSTTHpPCEkI6T2kV4qggLBgw6WJgLCg7AqCHWHVXUXApUvRZUUEVBQLqCuoqAgiuMDPRWUVpENCkkkyvb2/P2Lmy5AESZ2U+3NdXk5OmXlOHs4kc+c979FqtbBY6kYPOju7IDKyD+LjE1BcXIqoqGj4+/szOKAejwEDERERERERAJPJhOpqJVQqFcxm82+BgQQBAQGIiIhCRkYmIiKiEBoaiuTkVAYKRNdgwEBERERERD2SEAI1NdVQqdTQ6bSQSCRQKDyQmJiMpKRk+Pn5IygoGDk5eXB1dbV3uUSdHgMGIiIiIiLq9upu/2iGRqNBTU01DAYD9Ho9fHx8EB7eG0lJyUhPz0BiYjKCg0PsXS5Rl8SAgYiIiIiIuiW9Xg+VSgWlUglAwMHBAc7OLujbNxa9eoUjPLw30tLSkZaWYe9SiboFBgxERERERNTl1Y9Q0Ol0qKmpQXW1Em5ubnBzc0deXj4KC4sREhIKX18/REf35fwJRO2AAQMRERERtYpZKsWbvSOsj4k6St0cCjWoqLgCIQAHBwfIZDL4+PigX78slJYORF5ePry9fexdKlGPwICBiIiIiFrFJJViQ2ycvcugHkIIgerqatTW1kCr1UKh8EBSUgqGDbsVgYHB8Pf3Q69evSGTyexdKlGPw4CBiIiIiIg6NbVaBa1WA6WyGgAgl7uhb99YZGXlID09E1lZ2bzkgagTYMBARERERK0iEQIBOi0AoMzFFYIf9KiVhBBQq9WoqqqEwWCEs7Mz/Px8UFRUirS0TERERCI+PoGhAlEnw4CBiIiIiFpFZjbj1S+/AACMGDAIekf+ikktI4RAZWUlqqqqIJe7IiIiCgUFhUhLS8PQoYOhVGpgMlnsXSYRNYHv/kREREREZFe2d35wx+DBN2PUqNuQlpYBiUQCR0cpRysQdQEMGIiIiIiIqEMJIaDVaqHValFdrYSjoxPc3d0wcOBNuOuuPyIlJY2BAlEXxICBiIiIiIg6hF6vx6VLF6HVauDu7gE3NzlSU9MxYsQo5OcXwNPTy94lElErMGAgIiIiIqJ2U39byepqJXQ6HcLCemH48JFITk5FQkIC3N0V9i6RiNoIAwYiIiIiImpzJpMJZWVlUKlU8PT0QEhIKAoLSzBu3AR4e/vYuzwiagcMGIiIiIiIqNXMZjO0Wi1UqlrU1NRAKpXC19cft946EsOGjUBMTKy9SySidsaAgYiIiIhaxSKV4r1e4dbH1LNoNBqUl5dBr9dDoVBAofBA//6DEBcXj8LCEgQGBtq7RCLqIAwYiIiIiKhVjFIpVsUn2LsM6kAmkwlXrpSjpqYGLi4uCA/vjQEDBqGoqBSRkVFwcnKyd4lEZAcMGIiIiIiIqElCCOj1OtTWqqDTaWE0GmCxCAQEBKK4uD9yc/MwYMBNcHTkRwuino7vAkRERETUOkLA02gEAFQ7OQESiZ0LotYSQkCtVqOqqhJ6vR7Ozs6Qy90QEhKGpKRkZGT0Q3Z2Lnx9fe1dKhF1IgwYiIiIiKhVnM1mvPH5pwCAEQMGQc+/ZHdZtbW1KC8vg8FggLu7AgEBgRgw4CZkZ+cgIiKKgQIRXRff/YmIiIiIeriKiitQKpWQSqXo1y8bxcWlCAsLR3p6Bpydne1dHhF1EQwYiIiIiIh6IL1ej8rKClRXV8PLywtDh96KgoJC5OUVwtXV1d7lEVEXxICBiIiIiKiHsFgsqKi4ApVKBZPJhKCgYKSnZ2LUqNtRUFBk7/KIqItjwEBERERE1M1ZLBZcuHAeKpUKfn5+yMrKQXp6BkaPvgMKhYe9yyOiboIBAxERERFRNySEQG1tLZTKKuj1egQEBGL06DsxaNBgJCQk2rs8IuqGGDAQEREREXUT9beXVCqroNVqIZe7ITAwCCNHjkb//gMRGhpm7xKJqBtjwEBERERErWKRSvHvkBDrY+pYQgjo9TpUVVWhuroaCoUHevUKR05OHgoLi5GYmAwnJyd7l0lEPQADBiIiIiJqFaNUiiVJKfYuo8cRQqCiogKXL1+Ch4cn/Pz8MGTIUAwYMAgZGf0gZdhDRB2MAQMRERERURchhEBNTTWqq6uh1Wrh7e2DP/zhNmRk9ENpaX9O2EhEdsWAgYiIiIhaRwg4m80AAL2DAyCR2Lmg7sVkMlnnVNBqtVAoFIiJiUN+fgGKi/sjOrqvvUskIgLAgIGIiIiIWsnZbMZ7n+4GAIwYMAh6R/6K2RZ0Oh0uX74Es9kMb28fJCWlIDq6L0pK+iM9PRMSBjlE1Mnw3Z+IiIiIqBOwWCyoqamBUlkFs9kCISzo1SscY8aMxYABg+Dt7WPvEomIrosBAxERERGRHQghYDQaoVKpoNNpUV1dDR8fXyQnpyIxMRmBgYEYNmwE5HK5vUslIrohDBiIiIiIiDpI3SiFaqjVami1Ojg6OkKhUCA4OATDh4/EkCFDkZiYZO8yiYhahAEDEREREVE7M5lMuHz5EtRqNTw9PeHj44thw0YgISEJUVF94O/vzzkViKjLY8BARERERNRO9HodysrKoNPpEBAQiIkT70VRUQmioqIZKBBRt8OAgYiIiIioDZlMJqhUKiiVVQAkCAgIwLBht+Lmm4chNDTM3uUREbUbBgxERERE1CoWiQRfBAZaH/dEJpMJ5eVlqK6uhkwmg0KhQHR0DMaMGYvCwmK4u7vbu0QionbHgIGIiIiIWsXo4IBnU9PtXYZd1NbW4vz583BwkCIoKBjDho1AZGQUMjOzEB7e297lERF1KAYMRERERETNUFVVBZWqFgaDAU5OMiQlJSM3Nw933fVHeHv72Ls8IiK7YcBARERERPQ7LBYLlEolKiquwM3NDTExccjMzEJiYhLy8wvh4OBg7xKJiOyOAQMRERERtYqzyYT3Pt0NABgxYBD0jl3/V0yLxQKVqha1tSro9ToAEri5uaFfvyzcccfdKC0dYO8SiYg6na7/7k9ERERE1AZMJhNqampQW1sDnU4HDw9P+Pr6Ii+vANHRfZGamo7evSN4e0kioiYwYCAiIiKiHksIAbVahUuXLkEqdYCHhwfS0jKQlZWDrKwc9O0bA8duMCKDiKgj8N2SiIiIiHoUi8WC2tpaVFVVwmAwwN1dgeTkVIwYMQo5OXnw9/fnKAUiohZgwEBERERE3Z7ZbEZVVQWqqpQwGo3w8vJGeHgEhg8fgdTUdMTGxnGkAhFRK/FdlIiIiIi6LbVajfLyMpjNZvj5+SIyMhI33zwc+fmFiIrqY+/yiIi6FQYMRERERNQtCCFgMOihUqmh1Wqg0+kgk8kQFRWN/Px83HvvRMjlXjCZLPYulYioW2LAQETUyRw8eADr1q3GqVO/wMfHF6NG3YG77x7H64GJqNOySCT4xs/f+rgjCSFQW1uD8vIrMJmMkMvd4OLiguDgEMTHJ6K4uBT5+YWQy13g7e2Gqip1h9ZHRNSTMGAgIupEvv/+/2HevL9g4MCbMGXKdBw7dgRr1iyH2WzG+PH32Ls8IqJGGR0cMD8js8NeT6fTQa1WQalUAgDkcjfExcUjP78AqakZ6N07AgEBAQxmiYg6GAMGIqJO5JVX1qJv31g8+eTTAIDc3HyYTCZs2vRP3HnnGDg6yu1cIRGRfZhMJlRXV6OqqhKOjk5wd3dHTEwcbrllGBITk5GUlMxAgYjIzhgwEBF1EgaDAf/5z2Hce+80m+X9+w/E66+/imPHjiAvL99O1RERdTyj0YDy8nJoNBoAgKenF4qLS5GXV4DS0gHw9PSCVCq1c5VERFSPAQMRUSdx4cJ5GI1GhIeH2ywPDe0FADhz5jQDBiLqlJxNJrzx+WcAgDtL+0Pfwts9mkwmqFS1UKlU0Gq1AIBevcJxxx1jEB4egeTkFISH926zuomIqG0xYCAi6iRUKhWAumuJryaX110WoVZzYjIi6rxcLOYW72symXDhwnmYTGYoFO6IiYlF796R6N07AsOHj4CXl3cbVkpERO2FAQMRUSchxPVvmyaRcBgwEXUPQgjodFqoVHUTNTo4OMLPzw8TJ96L/PxCBAeH2LtEIiJqAQYMRESdhJubOwBYrzWup9HUjVxwd3fv8JqIiNqKxWJBWVkZNBo1hACcnZ3h5uaOoUOHo7i4PxISEhEYGGTvMomIqBUYMBARdRKhoWFwcHDA+fNnbZafO1f3de/eEXaoioio5YQQ0Go1qKmpQW1tLby8vDFw4GCkpqYhJiYWUVHRDE+JiLoRBgxERJ2Es7MzUlPTsXfvZ7j77vHW2619/vmncHd3R0JCkp0rJCL6fWazGRXV1VCpamEymeHi4gIPDw8MHz4SI0aMQlRUH3uXSERE7YQBAxFRJzJx4mT85S8z8OSTj2DYsBH4/vtj2LJlE6ZPvx8uLi72Lo+IqAEhBKqqKq1fnz79K1x8/RAbG4/+/QciLS0DMTGxcHJysmOVRETUERgwEBF1IpmZWXjmmUV45ZW1eOyxh+DnF4AZM/6Mu+8eZ+/SiIhsaDQaKJVKqFS1CFAocMTTCwqFAjMfeBAlNw+Dv7+/vUskIqIOxoCBiKiTKSnpj5KS/vYug4ioAZPJhIsXL0Cj0UAul8PLyxu3334nBg++BSF9oiGRSHC7vYskIiK7YcBARERERI2qu52k7rdJGmtgMpkQGBiMQYOGIDs7FyUl/Xn5FhERWTFgICIiIiKYzWbU1tZCo1FDr9dDCAEhBFxcXCCXuyEysg8KC4sxevTtCA4OsXe5RETUCTFgICIiIuqhLBYLysvLoVLVwmKxwMPDE+7uCqSkpCM6OhrBwSGIiYlD374x1x+poFbDt1/dnW4qDn0PuLl10BEQEVFnwoCBiIiIqIcwGg3QaLRQq9XQ6XQQwgJvb28UF5ciOrovBg0agoiISOttcptDWlHRDhUTEVFXwoCBiIiIqJsyGg1QqeouedBqNdZRCgEBAYiJiUNqahoyMvohMjLK3qUSEVE3wICBiIiIqBsQQsBoNEKjUUOj0UCr1UIqdYBCoYC3tzeysrJRUFCMvLwC+Pj4tGiUAhER0fUwYCAiIiLqwmpqalBTUw2dTg8nJ0e4ubkhJCQUERGRyMrKQXFxKby9fexdJhER9QAMGIiIiIi6GLPZjIqKCtTW1kImc0JAQCCKi0uRlZWLvn1jOEKBiIjsggEDERERUSdnMpmg1+thNBp+u5WkBgEBgSgsLEJp6UDcfPNQODg42LtMIiLq4RgwEBEREXUyZrMZGo0GKpUKSqUSrq4ukMmc4ezsjMjIPigqKsbQobciODjE3qXWkUphTEu3PiYiop6JAQMRERFRJyCEgEajhlKphEajgYeHBwICAlBS0h/5+QWIiIhCQEAAFAqPznf5g6srlP/ea+8qiIjIzhgwEBEREdmBxWKBXq+DwWCEVquBUlkFd3cPeHt7Y+TI2zB06HD06RPNSx+IiKjLYMBARERE1EF0Op31rg8SiRQuLi6QyWTw8PBEaelA5OcXoLR0IKS8zICIiLogBgxERERE7cBsNltHKFRVVcFkMsLFxRWurq7Izc1HQUERUlPT4ePjC19f38532UNzaDTwKcoGAFR+eRCQy+1cEBER2QMDBiIiIqJWslgsqK2thV6vg0ajgclkgkQigVwuh5OTE6KiopCYmIwhQ25BaGgv+Pv7d+1A4VpCwOHsGetjIiLqmRgwEBERETWDEAJarQZ6vR4GgxEqVS1MJhO8vLyhUHggJSUNffr0RXh4b4SFhSEsLBw+Pj7dK1AgIiJqBAMGIiIiot9hsVigVCqhVtdCrdbA3d0dLi6ucHNzQ25uPpKSkpGbm4/Q0DBOykhERD0WAwYiIiKi3xiNRuslDvX/6fV6mExG+Pj4ICcnBxER0cjIyEJiYiLc3RUcmUBERPQbBgxERETUY2k0GpSXl8FkMkEIAQcHB7i7K+Do6AgvLy94enqhT59oJCQkIiUlBdnZ6aiqUsNksti7dCIiok6HAQMRERH1GGazGdXVSqjVahgMBjg5yRASEoqUlDSEhISiV69eSExMhp+fP5ycnGz2dXTkrSOJiIiuhwEDERERdVsWiwU1NTUwGPTWUMHPzx8hIWFIS0tHSckApKdnwNnZ2d6ldm0SCUyxcdbHRETUMzFgICIiom5BCAG9Xg+1WoXa2lpYLBZYLBZ4enpCofBAZGQf5Obm4+abhyI4OMTe5XYvcjmqvjxo7yqIiMjOGDAQERFRlySEgMlkglargVqtRmVlJTw8PODurkB2di7i4uIRGtoLMTGxiI7uy8kYiYiI2hkDBiIiIur0LBYL1Go1lMoqWCwWGI1GABI4OjpCLpfDw8MDN988DJmZWcjJyYObm5u9SyYiIupxGDAQERFRpyOEgFarxZUr5dDpdHB0dIKLizN8fHwRGxsHb28fhIf3RmRkFHr3jkBAQCAcHBzsXXbPpdHAe0gpAKDq488Budyu5RARkX0wYCAiIiK7qg8TtFotdDotVCoVnJycIJPJEBXVBzk5+UhOTkFAQCAiIiIhk8nsXTJdSwg4/vSj9TEREfVMDBiIiIiowxiNRmg0GtTUVEOn00EIAQcHB7i4uEAud4O/fwAKC4sRH5+I+PgEJCenwtGRv64QERF1BfyJTURERO2quroaFRUVkEgAiUQKuVyOyMgoJCYmw98/AGFhYfDy8kZKShpHJxAREXVhDBiIiIioTQghoNPVXepgNJqg1+ug1xvg4uKCtLR0FBeXonfvCERERCIoKJh3dSAiIupmGDAQERFRiwghoNfrUFurglqtgsFghFwuh7OzDK6ucqSkpCI8vDcyM7OQm5vPSx2IiIi6Of6kJyIioibV3RLSAKPRBK1WYx2dIJVKYLFY4OQkg0Lhgbi4BOTk5CIhIQlxcQnw9vaGVCq1d/lERETUgRgwEBERkY3q6mrU1tZArzdAIgFkMmc4OjpCJnOCv38gcnJy4e8fAF9fX4SH90ZsbDxcXV3tXTbZk0QCc69w62MiIuqZGDAQERH1YBaLBUplFaqqqgAAZrMZbm7uCA+PQEZGJqKjYxAZGQVPT094eXnD3d3dzhVTpySXo/Lw9/augoiI7IwBAxERUQ9hsVig1WqgVmug02lhNpthsVigUHggN7cAMTGx6NUrHF5eXsjLK4CDg4O9SyYiIqIuhAEDERFRN2UymaDRqFFTUwOtVgupVAq53A0uLi4ID++NXr3C0adPNIqKStC7d4S9yyUiIqIujgEDERFRNyGEQE1NDTQaNbRaHSwWMxQKD3h7+2DAgEwkJSUjLS0DAQEBcHdX2Ltc6k60WniNvBkAoHz3I4BzchAR9UgMGIiIiLogIQRUKhVUqlrodDoAdZdAuLrK4enpiVtuuRV9+vRBamoaIiP7QMKJ96g9WSxwOvIf62MiIuqZGDAQERF1EUajESpVLWpq6u7w4ObmBg8PD+TlFSAmJg5eXl7o06cvEhISGSgQERFRh2PAQERE1IkJIaBUKlFRcQUA4OXljaSkFGRn5yIlJRXJyamQyWR2rpKIiIiIAQMREVGnIYSAXq+H0WiEXq9DbW0tzGYz3N0VyMnJQ05OHgYOvAkBAYEcoUBERESdDgMGIiIiOzCbzTAaDTAYDNDrDdBo1DCZTJDJnCGTyeDs7IyCgiIkJCQhPT0Dqanp9i6ZiIiI6LoYMBAREbUTs9kMtVoNvV4Pk8kIvV4Pi8UMiUQKAJDJZJDJZHB0lCEjox/S0zORlpaOwMAgeHv7wMXFxc5HQERERHTjGDAQERG1EZPJhJqaGlRXV8NsNkEqlcLNzR1yuRweHgGIiIhAaGgwvL394e8fgMDAYAQEBMLT05PzKFCXZ/H1tXcJRERkZwwYiIiImkEIAbPZDIPBAJ1OB71e99u8CSY4OTnB2dkZUVFRyMnJR3R0XyQnpyA4OARSqRSOjlJ4e7uhqkoNk4m38qNuxM0NFT+csncVRERkZwwYiIiIGmGxWKyTLWo0Wuh0WlgsFpjNZjg5ySCTOcHZ2QUhIaEICwtHTEwMoqNj4Ofnj9jYOEilUnsfAhEREVGHYsBAREQ9Vn2IYDKZoNfrYTAYUFtbC0dHB1gswjpHgq+vH8LCwhAXFw9vbx+EhobBz88fQUFBUCg87H0YRERERJ0CAwYiIur2hBCoqamxTrao0+kgkUhgsVggkznDwUEKV1dX+Pv7o6ioGEFBwYiIiEJgYCD8/PwRGhrG20ISXY9WC8+7bwMAVG/ZDri62rkgIiKyBwYMRETUbQghoNPpfhuVUBckaLVaABLI5XLI5XKEhoYhMTEJgYFB1gBBofBAWFgvuPJDEVHLWCyQff2V9TEREfVMDBiIiKhLMplMMBgM0Gq10GjUMBqNsFgEXFxcIJPJ4OTkiF69whETE4fY2DgkJCQhJiYWjo780UdERETUHvhbFhERdQlCCGi1WiiVVVCpVJDJnOHk5ARXV1fExMQhMzMLERGRCA0NQ0BAILy8vODs7GzvsomIiIh6DAYMRETU6dTfCrK6WgmtVgu9Xg+LxQJXVzn8/f1x001DkJdXiJCQUAQFBcHDw9PeJRMRERH1eAwYiIjIrupHJhiNRtTW1k3EKJFIIJFIoVAoEB+fiLCwXkhOTkFoaBiSk1Mhk8nsXTYRERERXYMBAxERdTiLxQKlsgpVVVUwmUxwc3OHTCZDaGgvpKdnIDQ0DL17RyAyMgrBwSH2LpeIiIiIbgADBiIiajdarRa//PIzAODSpUuorq6GEAISiQRyuRsyMvohI6MfkpJSEBERCV9fX07CSNRFCbnc3iUQEZGd8bc4IiJqEyqVCidP/ozPP/8UJ0+ehFJZhStXyqHRqCGXu6FXr3AkJiYjKysb/v4BCAkJQVRUtL3LJqK24OaGK79esncVRERkZwwYiIjohgkhoFRW4eLFi9DptDh//hxOnvwF//nPdygvL4NarYZGo4azs/Nvt4p0goeHJ8rLy/D880tRWFhs70MgIiIionbCgIGIiJqkVqtx4sSPqKqqxHffHcaxY0dQXl4OrVYLi8UMo9EIAHBxcYGLiwt8fHwQGhoKiURifQ6LxWKv8omIiIioAzFgICIiGydP/ozPPvsUX331BcrKLkOlqoXRaITZbIarqytcXeXw8/ODg4MUjo5ONmECEfVQOh087h0HAKh55TXAxcXOBRERkT0wYCAi6oFUqlqcOnUSNTU1OHv2DE6dOonLl+smYbxw4RyUSiWcnZ3h5uYOPz8/ODu7QCqV2rtsIuqszGY47/639TEREfVMDBiIiHoArVaLsrLLOHHiRxw//l/s2fMJqqurYTIZYbFYYDQa4ebmBgcHR7i6uiIqqg8cHBzsXTYRERERdSEMGIiIuiEhBP773+9x9Oh/cPjwt/jppx9hMBigVqsgBH6bL8Ebrq5ySCQSXuZARERERK3GgIGIqIszGAyoqLiCX375GRcunMfp07/i+++P4fz586iuVsLR0RHu7grI5fVzJ3BkAhERERG1PQYMRERdhMlkglJZjcuXz+LQoSM4c+YMLl68iGPHjqCmpgY6nRZms/m3EQp18ycEBARw7gQiIiIi6hAMGIiIOokrV67gxIkfUVFxBdXV1aitrYVGo8bFixdw5Uo5lEoldLq620Oq1RoIAUgkgIuLK9zc3ODl5QWZTGbvwyAiIiKiHkoihBD2LMBs5v3RuyoHByn710V1pd6p1WpoNGqbZf/3rmXXt68Wu/ptt+6hsD4WwgIhRINt6qdIqJ8voavNmSCEgI+PL1x467oudf5RQ+xfEywWOJw9AwAw9woHOunIKfava2P/ujb2r+uSSm/8d0+7BwxERNdz6dKlJte19dtXZ3k77K6XNPj4+HCEBREREVE3xoCBiIiIiIiIiFqte/6ZjIiIiIiIiIg6FAMGIiIiIiIiImo1BgxERERERERE1GoMGIiIiIiIiIio1RgwEBEREREREVGrMWAgIiIiIiIiolZjwEBERERERERErcaAgYiIiIiIiIhajQEDEREREREREbUaAwYiIiIiIiIiajUGDERERERERETUagwYiIiIiIiIiKjV2iVg0Ov1WLhwIfLy8pCeno45c+agsrLyuvt8/fXXuOOOO5Camori4mIsXrwYBoOh0W0rKytRWFiIFStWtEf5PV5L+vfdd99h/PjxyMzMRFFRER5//HEolcpGtz116hTS09Px9ttvt0P11JL+Xb3viBEjrtsbnn/tqyX9O3fuHKZNm4aMjAwUFhbiH//4B8xmc6Pbsn9ty2KxYPny5SgqKkJaWhqmTp2Ks2fPNrl9VVUV5syZg6ysLGRnZ2PhwoXQarU22xw7dgxjx45FSkoKSkpKsHz5clgslvY+lB6puf27er8pU6Y0eh6xfx2nuf373//+h/vuuw85OTnIy8vDrFmzcOHCBZtt9u7di9GjRyM5ORmDBg3C5s2b2/sweqzm9u+///0vJk6ciPT0dOTm5mL+/Pmora212YbnX8dp6fsnALz33nuIjY3FuXPnbJazf91DuwQMCxYswFdffYUVK1bgX//6F06ePIlZs2Y1uf1PP/2EadOmIT8/H++//z7+/ve/Y8eOHVi8eHGj2z/xxBMoLy9vj9IJze/fqVOnMHnyZMTGxuKNN97Aiy++iGPHjuHPf/5zg22NRiMeeughaDSa9jyEHq25/atXW1uLGTNm4Keffrrudjz/2ldz+2c0GjF58mQAwNatW7FgwQJs2bIFq1atanR79q9trV69Gq+//jqefvppbN261frBs6mAfNasWTh9+jQ2btyIZcuWYe/evViwYIF1/alTpzBhwgT06dMH7733Hh577DFs3LgRL7/8cgcdUc/S3P4BgMFgwGOPPYYvv/yywTr2r2M1p39VVVWYNGkSXFxcsGnTJqxfvx6VlZWYMmUK9Ho9AODgwYP405/+hNLSUuzcuRPTpk3Ds88+i127dnX0ofUIzenflStXMGnSJISGhuLtt9/G6tWrcfjwYTzyyCPWbXj+dayWvH8CwPnz5/HXv/61wXL2rxsRbezSpUsiLi5OfP7559ZlJ0+eFDExMeK7775rdJ/PPvtMPPnkkzbLnn32WTF06NAG227dulXccsstoqCgQCxfvrxti6cW9W/p0qVi8ODBwmKxWJd9++23IiYmRpw5c8Zm2yVLlogJEyaImJgYsX379vY5iB6sJf0TQog9e/aI0tJSMWrUqOv2hudf+2pJ/95//32RlJQklEqlddnWrVtFRkaG0Ov1Ntuyf21Lr9eL9PR0sXnzZuuy6upqkZKSIt5///0G23/33XciJiZG/Pzzz9ZlX375pYiNjRWXLl0SQgjx8MMPi9tuu83m/XTZsmVi+vTp7XgkPVNz+yeEEIcPHxbDhg0TAwcOFP369WtwHrF/Hae5/XvjjTdEenq60Gq11mUXLlwQMTEx4uuvvxZCCDFu3DjxwAMP2Oz36KOPioULF7bTUfRcze3fkSNHxIMPPiiMRqN12caNG0Vqaqr1a55/Hacl759CCGE2m8Xdd99t/Sxw9uxZ6zr2r/to8xEMhw8fBgDk5uZal0VGRiIwMBDffvtto/uUlpZakywhBI4dO4bdu3ejoKDAZrtTp05h8eLFeOGFFyCTydq6dELL+jdixAg8//zzkEgk1mX1j6urq63Lvv32W2zbtg3PPfdce5ROaFn/AGD37t0YM2YMtm7d2uQ2PP/aX0v6d+jQISQmJsLT09O6LDc3FyqVCj/88IN1GfvX9n788Ueo1Wrk5eVZl3l4eCAhIaHRfh06dAj+/v7o06ePdVl2djYkEom191999RWGDx9u8346a9YsrFmzph2PpGdqbv+AuuHzRUVF2LFjBxQKRYP17F/HaW7/8vLysHr1ari4uFiXSaV1vwbX1NRAq9Xi0KFDuPXWW232+9vf/ob58+e301H0XM3tX2pqKpYuXQpHR0cAwC+//IJ3333X5rMCz7+O05L3TwB46aWXYDQaMW3atAbr2L/uw7Gtn/Dy5cvw9vaGs7OzzfKAgABcunTpuvuazWZkZGRAp9MhISEB999/v3Wd0WjEnDlzMHnyZCQmJrZ12fSblvTv6l+W661fvx7+/v6IjY0FUPfDe968eXjiiScQHBzc9oUTgJaff3/729+u+7w8/zpGS/p36dIlBAUFNdgeAC5evIjU1FT2r53U9+Ta97Sm+nX58uUG28pkMnh5eeHixYtQqVQoLy+HQqHAY489hi+++AIeHh74wx/+gMmTJ8PBwaH9DqYHam7/AODBBx9s8vnYv47V3P6FhYUhLCzMZtm6devg4uKCrKwsnD59GhaLBQ4ODpg1axa+/fZbBAQEYNy4cbjjjjva70B6qJacf/WGDBmCX3/9FaGhoVi5ciUAnn8drSX9O3bsGF555RW89dZbuHz5ss069q97afYIhnPnziE2NrbJ/7RabaN/HXN2drZe49YUi8WC1157DevWrYNGo8HUqVMhhAAALF++HM7Ozpg6dWpzS6artGf/6j3//PP4/PPPsWDBAjg5OQGou648PT29wV8GqHk6on+N4fnXNtqjfzqdrsE+9QFF/T7sX/uon5yxse9/Y/36vf6qVCoAde+hISEhWL9+PaZMmYK1a9dyUs520Nz+/R72r2O1tn+bNm3Ca6+9hoceegg+Pj7W/s2fPx/9+vXDK6+8glGjRmHhwoV488032/4AerjW9G/x4sXYtGkTfH19MWHCBKjVap5/Hay5/dNoNHjooYfw0EMPISIiosF69q97afYIhsDAwOtOdrN3795GJ/fQ6/VwdXW97nM7OTkhOTkZQN0wmzFjxuDQoUMQQmDLli145513mGC1Unv2z2g0Yv78+dixYweefvppDBo0CACwY8cOHDp0CO+//37riqd27V9TDh48yPOvjbRH/1xcXBrsU//DXS6Xs3/tqH6otcFgsBl23VS/GutV/fZyudw69Dc/P986gi8+Ph6VlZVYtWoV/vznP9sMHaXWaW7/fg/717Fa2j8hBJYtW4Y1a9bgT3/6E8aPHw8A1j+IjBw5EhMmTABQ17/6SVk5iqFtteb8q/+ssHLlSpSUlOCTTz5BYWEhAJ5/HaW5/XvmmWcQGRmJMWPGNPp8fP/sXpodMDg5OTU6JL7eTz/9BKVSCYPBYJNqlZWVITAwsNF9jh8/jpqaGpvrjuuH1l++fBn79u2DRqPBiBEjrOu1Wi3Wrl2Ljz76CDt37mzuYfRY7dE/oC55vP/++3Ho0CEsXboUt9xyi3Xd9u3bUVFRgdLSUpt9nnrqKezatQsbNmxo+QH1MO3Vv+t55513eP61kfboX1BQEE6cOGGzrKysDEBdoLFlyxb2r53UDw0tKytDeHi4dXlZWZn1Z9jVgoKCsHv3bptlBoMBSqUSAQEB1stjYmJibLbp27cvNBoNKisr4evr2w5H0jM1t3+/h/3rWC3pn9FoxKOPPooPPvgAjz76KO655x7ruvpLza7tX3R0NG+r3Q6a27+TJ0/izJkzNr9LBgYGwsvLy+byQp5/HaO5/du+fTtkMhnS09MBwHor7eHDh2P69OmYOnUq+9eNtPkcDJmZmbBYLDh8+LB14o9Tp07h8uXLyMrKanSf999/Hx9++CF2795tTbCOHj0KoO6NPS8vD9OnT7fZZ/z48Rg8eDAmTZrU1ofQo7WkfwaDAdOmTcMPP/yAl19+GTk5OTbrFy9eDJ1OZ7Ns8ODBmDVrls2HHmq9lvTv9zz00EM8/zpIS/qXlZWFHTt2QKVSwd3dHQBw4MABuLm5IS4ujv1rR3FxcXB3d8c333xj/QWrpqYGx48fx7hx4xpsn5WVhcWLF+P06dPo3bs3gLoRQkBd7x0cHJCRkWH9+Vfvp59+goeHB7y8vNr3gHqY5vbv97B/Hasl/Zs3bx4++eQTLFmyBMOGDbNZFxgYiPDwcBw9ehQjR460Lj9x4oTNByhqG83t39dff41Fixbhq6++goeHBwDgzJkzqKqqQp8+fXj+dbDm9u/f//63zddHjx7F3LlzsW7dOsTExLB/3U173Jpi9uzZYsCAAeLAgQPi6NGj4g9/+IMYN26cdb1erxdlZWXWW6idPn1apKWliUcffVScPHlSfPHFF6J///4NbhV0tf79+/M2a+2kuf1bvny5iI2NFR988IEoKyuz+e/a2+TV420q209z+3etG+kNz7/209z+6XQ6MWjQIDF58mTxww8/iE8++URkZ2eLFStWNPka7F/bWbp0qcjOzha7d+8WP/zwg7j33nvF4MGDhcFgECaTSZSVlVlvi2exWMSYMWPEqFGjxNGjR8X+/ftF//79xSOPPGJ9vgMHDoj4+HixfPlycfr0abFz506RmZl53X5SyzWnf9dq7Dxi/zpWc/q3fft2ERMTIzZs2NDgd5X6bXbs2CESEhLEa6+9Js6cOSO2bNkiEhMTxTvvvGPHo+y+mtO/qqoqUVRUJO677z5x4sQJ8e2334qRI0eK22+/XZhMJiEEz7+O1pr3zwMHDjS4TSX71320S8CgVqvF448/Lvr16yf69esnZs+eLSorK63r6/9RHThwwLrs6NGjYty4cSItLU0UFBSI5557rskPQELwF+T21Nz+DR48WMTExDT639U9vhoDhvbTkvPvagwY7Ksl/fv111/FpEmTRHJysigsLBT/+Mc/hNlsbvI12L+2YzKZxKJFi0Rubq5IS0sTU6dOtf7CdPbs2Qbn05UrV8QDDzwg0tLSRE5OjnjqqaeETqezec4vvvhCjBo1SiQmJorS0lKxdu3a6/aTWq65/btaU+cR+9dxmtO/SZMmNfm7ytU93rFjh7jllltEYmKiGDx4sHjjjTfscmw9QXPPv5MnT4r77rtPZGZmiuzsbPHoo4+K6upqm+fk+ddxWvP+2VjAIAT7111IhPjtNg1ERERERERERC3U7NtUEhERERERERFdiwEDEREREREREbUaAwYiIiIiIiIiajUGDERERERERETUagwYiIiIiIiIiKjVGDAQERERERERUasxYCAiIuqGGrsLNe9M3b74/SUiop6OAQMREdnFI488gtjY2Ov+N378eADA+PHjrY872ttvv43Y2FicO3eu0VpiY2OxYsUKu9TWlDfffBPPP/+89euamhrMmzcPhw4dsmNVzXfgwAEMGTIESUlJmDJlClQqFaZPn47U1FRkZWXh119/RWxsLN5+++0ber5z5841a/vm2LNnDx5++OE2f95rtecxEBERtZajvQsgIqKeacaMGRgzZoz169WrV+P48eNYuXKldZm7u7s9SrNRWlqKbdu2ISAgwN6l3LA1a9YgOzvb+vUPP/yAd999F7fddpsdq2q+RYsWwWKxYN26dfD19cWOHTvw2WefYf78+ejbty9CQkKwbds2hIeH39DzBQQENGv75ti4cWObPycREVFXw4CBiIjsIjw83OaDno+PD2QyGdLS0uxXVCN8fHzg4+Nj7zJ6JKVSiaysLOTn5wMAPvnkEwDAH//4R0gkEgBo1r+Xzvjvi4iIqDvhJRJERNQlCCGwfv16lJaWIiUlBXfddReOHTtms82JEycwbdo0ZGRkICMjAzNnzsTZs2ev+7yVlZWYM2cOCgoKkJycjJEjR2LHjh3W9ddeItEYlUqFxx9/HNnZ2UhPT8esWbNw5coVm2127dqF0aNHIz09HQUFBZg/fz6qq6ut6x955BEMGDDAZp/GhsMrlUrMnz8f+fn5SE5Oxp133on9+/db1w8YMADnz5/HO++8Y913woQJAIAJEybYXN6xe/dujB49GsnJySgoKMAzzzwDjUZz3e+XEAIbN27ELbfcgpSUFNx00014+eWXbeYf2LdvH/74xz8iMzMTOTk5mDNnDi5evGjzPBcuXMDs2bORnZ2N1NRUTJw4EcePH7c57vPnz2PHjh3Wy2XqL0WJi4vDI4880uj35+TJk7j//vuRnZ2NrKwsTJs2Db/88kuT38/r1XH1Ph9++CFmzZqF9PR0ZGdn44knnrB+r8aPH4+DBw/i4MGDiI2NxTfffNPg+/b+++8jNjYWJ06csFm+e/duxMbGWl/zxx9/xP3334/c3FwkJiaiqKgIzzzzDHQ6XaP9WLFiBWJjYxssv/bSHb1ej0WLFqGkpARJSUm49dZbsWvXLpt9vv/+e0ycOBGZmZlIT0/HPffcgyNHjjT6ukRERI1hwEBERF3C4cOH8cknn+DJJ5/ECy+8gLKyMvzpT3+CyWQCAJw6dQpjxoxBRUUFnn/+eTz77LM4e/Ys7r77blRUVDT5vHPnzsUvv/yChQsXYv369UhISMDDDz+MAwcO3HBtr776KoxGI5YtW4Y5c+bg008/xV//+lfr+tWrV2P27NlIS0vD8uXLMXPmTHz88ccYP358kx8cG6PX6zFx4kTs2bMHDz74IFauXImgoCBMmTLFGjKsXLkS/v7+KCkpwbZt23DTTTdh/vz5AID58+fjqaeeAlD3gXfmzJmIiorCqlWrcP/99+O9997DjBkzrjtZ4aJFi7Bo0SIMGDAAL730Em6//XYsXrwY69atAwDs2LED9957L4KDg7F06VI8+uij+M9//oO77rrL2ofKykqMGTMG//3vf/Hkk09iyZIlsFgsGDt2LH755RfrpQxXH8fjjz+O22+/HQCwbds2zJgxo0Ftly9fxl133YVff/0VCxYswAsvvIArV65g4sSJUCqVDbb/vTqu9tRTTyE0NBSrV6/G5MmT8dZbb2HNmjXWdQkJCUhISMC2bduQmJjY4LUGDRoEuVyOnTt32iz/4IMP0LdvXyQkJKCsrAxjx46FVqvFc889h/Xr12PYsGHYtGkTXn311SZ78nuEEJg5cya2bt2KSZMmYc2aNUhPT8eDDz5oDdNUKhWmTJkCb29vrFixAi+++CK0Wi0mT56M2traFr82ERH1LLxEgoiIugSZTIZ169bBy8sLQN3EhU888QR+/vlnxMXFYeXKlXB1dcXGjRutczfk5eVh0KBB2LBhQ5MT8B08eBAzZ87EoEGDAADZ2dnw8vKCTCa74dqSk5OxaNEi62sePXoUe/fuBQBUV1djzZo1uPPOO60f9AEgJiYGY8eOxfbt2zF27Ngbep13330XP/74I9544w2kpqYCAIqLizF+/HgsXrwY27dvR0JCAmQyGXx8fKyXA0RHR1v/Hx0dDSEEFi9ejKKiIixevNj6/BEREbjnnnuwd+9elJaWNnj9mpoavPrqqxg3bhzmzp0LAMjPz0d5eTm+/fZbTJ06FYsXL0ZhYSGWLFli3S8jIwNDhw7Fyy+/jHnz5uFf//oXlEoltmzZgtDQUOtxDB06FMuWLcPy5cuRlpbW4DiCgoIA/N9lEdeOKtm4cSMMBgP++c9/wt/fH0DdaIe7774bR48eRZ8+fWy2v5E66pWUlFj/DeXl5WHfvn34/PPPMWfOHERHR1v/zTV1CYarqyuGDBmCXbt24cEHHwQAqNVqfPbZZ5g5cyaAuhE48fHxWLZsmfX58vPzsW/fPnzzzTe47777Gn3u3/P111/jyy+/xIsvvoihQ4cCAIqKiqDVarF48WIMHz4cP//8M6qqqjBhwgRkZGQAAKKiorBt2zao1WooFIoWvTYREfUsHMFARERdQnR0tDVcAICwsDAAsP519cCBA8jOzoaLiwtMJhNMJhPc3d3Rr18/fP31100+b05ODlasWIFZs2bhzTffxJUrV/Dwww9bP2TdiMzMTJuvw8LCUFNTAwA4cuQIDAYDhg8fbrNNv379EBoaioMHD97w6+zfvx/+/v5ITEy0HqPZbEb//v3x/fff21xycT0nT57EpUuXMGDAAOvzmEwmZGVlwd3dHfv27Wt0vyNHjsBkMmHw4ME2y5944gls2LABp06dQnl5eYNjDQ8PR3p6uvVY9+/fj/j4eAQGBlpfWyqVori4+Lq9+j2HDx9GWlqaNVwA6kKJzz77DCUlJQ22b04d1wYHQUFBv3s5ybVGjhyJM2fOWC/t2bNnDwwGA0aMGAEAKCwsxGuvvQZnZ2f8/PPP2LNnD9asWYPKykoYDIZmvda1xymRSFBSUmLT7wEDBqC8vBz/+9//0LdvX/j4+GD69OmYP38+PvnkE/j5+WHu3LnWYIeIiOj3cAQDERF1CXK53OZrqbQuI7dYLADq5ibYtWtXg+vKAVx3ksYXX3wRL730Ej788EN8/PHHkEqlyM/Px1//+lfrX7VbUlv9ZQb1H/r9/Pwa7Ofn59es4edKpRLl5eWNDsEHgPLycnh6et7Q8wDAwoULsXDhwgbry8rKrrtfU9/P+vVNHWv9PANKpRKnT59u8ji0Wi1cXV2vdwhNvn598HSj2/9eHfWurefqHt+onJwcBAYGYufOnUhJScHOnTuRnZ1t/QBvsViwdOlSbN68GRqNBsHBwUhJSYGzs3OzXudaSqUSQogmQ7OysjLEx8dj8+bNWLNmDT788ENs27YNLi4uGDlyJJ544olmjeghIqKeiwEDERF1CwqFAvn5+Zg0aVKDdY6OTf+4UygUmDt3LubOnYuTJ09iz549WL16NRYuXGidV6A16j/wX7lyBVFRUTbrysvL0atXLwCARCKB2Wy2WX/tX8gVCgUiIiJsLmu42o1+uPbw8AAAzJs3z+Z2ltfW3NR+lZWVNsdy4cIFnDlzBt7e3gDQYIJLoO5Y69crFApkZ2dj3rx5jb5OSz/MKhQKVFZWNli+f/9+hIWFWe88cfX27VFHU6RSKW699VZ88MEHmD59Ovbt22czV8e6deuwceNGLFy4EIMHD7ZellA/90Rj6o/JbDbDwcEBQN2lF1dTKBSQy+VNzuPQu3dvAHWXRLzwwgswm804duwY3n33XWzZsgXh4eGYMmVKyw+ciIh6DF4iQURE3UJ2djZ+/vlnxMfHIzk5GcnJyUhKSsLGjRuttze81vnz51FSUoKPPvoIQN0HrKlTpyI/Px8XLlxok7pSU1Mhk8nwwQcf2Cw/dOgQLly4YP2rspubG6qqqqDX663bHD58uMExXrx4Eb6+vtZjTE5Oxr59+7BhwwbrB8z60R316pfXi4qKgq+vL86dO2fzPIGBgViyZInNXRSulpKSAicnJ3z22Wc2y1955RXMnj0bffv2hb+/f4NjPXv2LI4cOWI91uzsbJw6dQqRkZE2r//uu+/irbfealDvjerXrx+OHj1qEzJUVFRgypQp1jkxrtaWdVz7PW/KyJEjcenSJaxatQoODg42l5scPnwY0dHRuO2226zhwuXLl3HixAnrSJ1r1c/VcOnSJZvnufY4NRoNhBA2x3nixAmsWrUKJpMJH330EXJzc1FeXg4HBwekp6djwYIF8PDwaLNzgYiIuj8GDERE1C3MmDEDZ86cwbRp07B79258+eWXeOCBB7Bz507ExcU1uk9oaCiCgoLwzDPP4K233sLBgwfxyiuvYO/evRgyZEib1OXl5YX77rsPb7zxBp5++ml89dVX2Lp1Kx544AFER0dj1KhRAID+/ftDr9fj8ccfx4EDB/Dqq69i3bp1Nh9yR48ejZCQEEyaNAnvvPMODhw4gKVLl2LZsmUICAiAk5MTgLqRBsePH8fBgweh0+msH1Y///xz/Pjjj3BwcMCDDz6IrVu34plnnsG+ffvw4YcfYvLkyTh+/HiTlwz4+PhgwoQJ2LhxI5YtW4b9+/fjpZdewpYtWzBt2jRIpVLMnj0bX331FebMmYO9e/dix44dmDRpEjw9Pa2jS+655x5YLBbcc8892LVrF/bv348nn3wSmzZtQmRkZIu/1/fccw9kMhmmTJmCjz/+GJ9++immT5+OoKAg3HrrrY1u31Z1eHh44NSpU9i/f/9158KIiYlBfHw8Xn/9dQwaNMgaEAB1Ac5PP/2EdevW4eDBg3jzzTcxduxYGAwGm8s1rlY/t8T8+fPx9ddfY/v27ViwYAHc3NxstsnKysKMGTPw+uuv45tvvsH69euxYMECSKVS+Pj4ICMjAxaLBTNnzsTu3buxf/9+zJ8/H7W1tQ3m3CAiImoKL5EgIqJuIS4uDps3b8aLL76IefPmQQiBmJgYrFq1CgMHDmxyv5UrV1o/pFdVVSE4OBj3339/i2fsb8wDDzwAPz8/vPbaa9i2bRu8vLxw88034y9/+Yt1/oaCggI8/PDD2LRpEz7++GMkJiZi5cqVGDNmjPV55HI5Nm/ejCVLluCFF15AbW0tQkNDMWfOHNx7773W7e6991787W9/w+TJk/HPf/4TGRkZGD58ODZv3owvv/wSH3zwAe644w64ublhw4YN2LZtG+RyOTIyMrB48WLrZRuNmTt3Lnx9fbF161Zs2LABYWFhePLJJ611jh49Gm5ubli7di1mzpwJd3d3FBUVYfbs2dbJFwMDA7F161YsWbIECxYsgF6vR0REBJ599tnrXg7we4KDg/H666/jhRdewCOPPAKZTIacnBy8+OKL8PT0bDDfRVvWMXbsWHz//feYOnUq/v73vzcaaNQbOXIknnvuOevkjvWmTZuGqqoqvPrqq1i1ahWCg4MxcuRISCQSrF271jpx6NUiIyPx/PPPY82aNbjvvvvQp08fPP3003j66aet20ilUqxbtw7Lli3D2rVrUVFRgcDAQEyaNMl6B4uAgABs2LABy5Ytw+OPPw6tVou+fftixYoVyM3Nbdb3goiIei6JaO4MRURERERERERE1+AlEkRERERERETUagwYiIiIiIiIiKjVGDAQERERERERUasxYCAiIiIiIiKiVmPAQEREREREREStxoCBiIiIiIiIiFqNAQMRERERERERtRoDBiIiIiIiIiJqNQYMRERERERERNRqDBiIiIiIiIiIqNUYMBARERERERFRqzFgICIiIiIiIqJW+/8cLj9Yz1OaygAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1300x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "range_clusters = range(1, 5)\n",
    "# Adapted from:\n",
    "# https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html#sphx-glr-auto-examples-cluster-plot-kmeans-silhouette-analysis-py\n",
    "\n",
    "# Storing average silhouette metric\n",
    "avg_silhouette = []\n",
    "for nclus in range_clusters:\n",
    "    # Skip nclus == 1\n",
    "    if nclus == 1:\n",
    "        continue\n",
    "    \n",
    "    # Create a figure\n",
    "    fig = plt.figure(figsize=(13, 7))\n",
    "\n",
    "    # Initialize the KMeans object with n_clusters value and a random generator\n",
    "    # seed of 10 for reproducibility.\n",
    "    kproto = KPrototypes(n_clusters=nclus, init='Cao',verbose=2, random_state=1)\n",
    "    cluster_labels = kproto.fit_predict(dfInsurance,categorical=[5])\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 clusters\n",
    "    silhouette_avg = silhouette_score(dfInsurance, cluster_labels)\n",
    "    avg_silhouette.append(silhouette_avg)\n",
    "    print(f\"For n_clusters = {nclus}, the average silhouette_score is : {silhouette_avg}\")\n",
    "\n",
    "    # Compute the silhouette scores for each sample\n",
    "    sample_silhouette_values = silhouette_samples(dfInsurance, cluster_labels)\n",
    "\n",
    "    y_lower = 10\n",
    "    for i in range(nclus):\n",
    "        # Aggregate the silhouette scores for samples belonging to cluster i, and sort them\n",
    "        ith_cluster_silhouette_values = sample_silhouette_values[cluster_labels == i]\n",
    "        ith_cluster_silhouette_values.sort()\n",
    "        \n",
    "        # Get y_upper to demarcate silhouette y range size\n",
    "        size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
    "        y_upper = y_lower + size_cluster_i\n",
    "        \n",
    "        # Filling the silhouette\n",
    "        color = cm.nipy_spectral(float(i) / nclus)\n",
    "        plt.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",
    "        plt.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",
    "    plt.title(\"The silhouette plot for the various clusters.\")\n",
    "    plt.xlabel(\"The silhouette coefficient values\")\n",
    "    plt.ylabel(\"Cluster label\")\n",
    "\n",
    "    # The vertical line for average silhouette score of all the values\n",
    "    plt.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")    # The silhouette coefficient can range from -1, 1\n",
    "    xmin, xmax = np.round(sample_silhouette_values.min() -0.1, 2), np.round(sample_silhouette_values.max() + 0.1, 2)\n",
    "    plt.xlim([xmin, xmax])\n",
    "    \n",
    "    # The (nclus+1)*10 is for inserting blank space between silhouette\n",
    "    # plots of individual clusters, to demarcate them clearly.\n",
    "    plt.ylim([0, len(dfInsurance) + (nclus + 1) * 10])\n",
    "\n",
    "    plt.yticks([])  # Clear the yaxis labels / ticks\n",
    "    plt.xticks(np.arange(xmin, xmax, 0.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "dc13a57c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# K-distance graph to find out the right eps value\n",
    "neigh = NearestNeighbors(n_neighbors=6)\n",
    "neigh.fit(dfInsurance)\n",
    "distances,_ = neigh.kneighbors(dfInsurance)\n",
    "distances = np.sort(distances[:, -1])\n",
    "plt.plot(distances)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6ed218c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of estimated clusters : 3\n"
     ]
    }
   ],
   "source": [
    "# Perform DBSCAN clustering\n",
    "dbscan = DBSCAN(eps=0.77, min_samples=14,n_jobs=-1)\n",
    "dbscan_labels = dbscan.fit_predict(dfInsurance)\n",
    "\n",
    "dbscan_n_clusters = len(np.unique(dbscan_labels))\n",
    "print(\"Number of estimated clusters : %d\" % dbscan_n_clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0c54f752",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0: 7617, -1: 614, 1: 1991})"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter(dbscan_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "7bae0b11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialization method and algorithm are deterministic. Setting n_init to 1.\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 1, iteration: 1/100, moves: 2794, ncost: 6489.65980557965\n",
      "Run: 1, iteration: 2/100, moves: 171, ncost: 6487.0333660944325\n",
      "Run: 1, iteration: 3/100, moves: 49, ncost: 6486.533997894754\n",
      "Run: 1, iteration: 4/100, moves: 20, ncost: 6486.462572491545\n",
      "Run: 1, iteration: 5/100, moves: 5, ncost: 6486.454173142345\n",
      "Run: 1, iteration: 6/100, moves: 4, ncost: 6486.451017196836\n",
      "Run: 1, iteration: 7/100, moves: 0, ncost: 6486.451017196836\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 2, iteration: 1/100, moves: 1435, ncost: 6526.22206365021\n",
      "Run: 2, iteration: 2/100, moves: 454, ncost: 6491.199489834492\n",
      "Run: 2, iteration: 3/100, moves: 139, ncost: 6487.358558873557\n",
      "Run: 2, iteration: 4/100, moves: 56, ncost: 6486.618943372264\n",
      "Run: 2, iteration: 5/100, moves: 26, ncost: 6486.4797184159015\n",
      "Run: 2, iteration: 6/100, moves: 12, ncost: 6486.454173142348\n",
      "Run: 2, iteration: 7/100, moves: 4, ncost: 6486.451017196837\n",
      "Run: 2, iteration: 8/100, moves: 0, ncost: 6486.451017196837\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 3, iteration: 1/100, moves: 3420, ncost: 7067.168034245861\n",
      "Run: 3, iteration: 2/100, moves: 1388, ncost: 6627.508640697219\n",
      "Run: 3, iteration: 3/100, moves: 534, ncost: 6620.77939211707\n",
      "Run: 3, iteration: 4/100, moves: 52, ncost: 6620.30263096466\n",
      "Run: 3, iteration: 5/100, moves: 5, ncost: 6620.293519827656\n",
      "Run: 3, iteration: 6/100, moves: 477, ncost: 6496.630241773252\n",
      "Run: 3, iteration: 7/100, moves: 224, ncost: 6487.239926870968\n",
      "Run: 3, iteration: 8/100, moves: 62, ncost: 6486.504899726911\n",
      "Run: 3, iteration: 9/100, moves: 19, ncost: 6486.451247242862\n",
      "Run: 3, iteration: 10/100, moves: 1, ncost: 6486.451017196833\n",
      "Run: 3, iteration: 11/100, moves: 0, ncost: 6486.451017196833\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 4, iteration: 1/100, moves: 1952, ncost: 6691.655293726884\n",
      "Run: 4, iteration: 2/100, moves: 586, ncost: 6627.1195633143\n",
      "Run: 4, iteration: 3/100, moves: 176, ncost: 6620.974801018366\n",
      "Run: 4, iteration: 4/100, moves: 60, ncost: 6620.31342550492\n",
      "Run: 4, iteration: 5/100, moves: 8, ncost: 6620.295339667447\n",
      "Run: 4, iteration: 6/100, moves: 463, ncost: 6497.6942237866115\n",
      "Run: 4, iteration: 7/100, moves: 238, ncost: 6487.257208412889\n",
      "Run: 4, iteration: 8/100, moves: 63, ncost: 6486.5048997269205\n",
      "Run: 4, iteration: 9/100, moves: 19, ncost: 6486.451247242862\n",
      "Run: 4, iteration: 10/100, moves: 1, ncost: 6486.4510171968395\n",
      "Run: 4, iteration: 11/100, moves: 0, ncost: 6486.4510171968395\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 5, iteration: 1/100, moves: 1584, ncost: 6638.289605801621\n",
      "Run: 5, iteration: 2/100, moves: 580, ncost: 6621.592011654999\n",
      "Run: 5, iteration: 3/100, moves: 81, ncost: 6620.369773406279\n",
      "Run: 5, iteration: 4/100, moves: 19, ncost: 6620.295339667444\n",
      "Run: 5, iteration: 5/100, moves: 463, ncost: 6497.694223786611\n",
      "Run: 5, iteration: 6/100, moves: 238, ncost: 6487.257208412886\n",
      "Run: 5, iteration: 7/100, moves: 63, ncost: 6486.504899726917\n",
      "Run: 5, iteration: 8/100, moves: 19, ncost: 6486.451247242866\n",
      "Run: 5, iteration: 9/100, moves: 1, ncost: 6486.451017196836\n",
      "Run: 5, iteration: 10/100, moves: 0, ncost: 6486.451017196836\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 6, iteration: 1/100, moves: 2407, ncost: 6628.734621642329\n",
      "Run: 6, iteration: 2/100, moves: 750, ncost: 6487.745449314609\n",
      "Run: 6, iteration: 3/100, moves: 236, ncost: 6486.548990426769\n",
      "Run: 6, iteration: 4/100, moves: 23, ncost: 6486.453863348554\n",
      "Run: 6, iteration: 5/100, moves: 4, ncost: 6486.451017196838\n",
      "Run: 6, iteration: 6/100, moves: 0, ncost: 6486.451017196838\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 7, iteration: 1/100, moves: 1712, ncost: 6544.349154132673\n",
      "Run: 7, iteration: 2/100, moves: 501, ncost: 6492.930103540511\n",
      "Run: 7, iteration: 3/100, moves: 160, ncost: 6487.723402498768\n",
      "Run: 7, iteration: 4/100, moves: 72, ncost: 6486.662273693251\n",
      "Run: 7, iteration: 5/100, moves: 31, ncost: 6486.479718415905\n",
      "Run: 7, iteration: 6/100, moves: 12, ncost: 6486.454173142349\n",
      "Run: 7, iteration: 7/100, moves: 4, ncost: 6486.451017196836\n",
      "Run: 7, iteration: 8/100, moves: 0, ncost: 6486.451017196836\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 8, iteration: 1/100, moves: 3484, ncost: 6800.076746303033\n",
      "Run: 8, iteration: 2/100, moves: 947, ncost: 6638.821476379255\n",
      "Run: 8, iteration: 3/100, moves: 304, ncost: 6621.703303563521\n",
      "Run: 8, iteration: 4/100, moves: 86, ncost: 6620.369773406277\n",
      "Run: 8, iteration: 5/100, moves: 19, ncost: 6620.295339667453\n",
      "Run: 8, iteration: 6/100, moves: 463, ncost: 6497.694223786609\n",
      "Run: 8, iteration: 7/100, moves: 238, ncost: 6487.25720841288\n",
      "Run: 8, iteration: 8/100, moves: 63, ncost: 6486.504899726911\n",
      "Run: 8, iteration: 9/100, moves: 19, ncost: 6486.451247242862\n",
      "Run: 8, iteration: 10/100, moves: 1, ncost: 6486.451017196832\n",
      "Run: 8, iteration: 11/100, moves: 0, ncost: 6486.451017196832\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 9, iteration: 1/100, moves: 1848, ncost: 6621.179365194832\n",
      "Run: 9, iteration: 2/100, moves: 94, ncost: 6620.310060447387\n",
      "Run: 9, iteration: 3/100, moves: 8, ncost: 6620.2935198276555\n",
      "Run: 9, iteration: 4/100, moves: 477, ncost: 6496.630241773253\n",
      "Run: 9, iteration: 5/100, moves: 224, ncost: 6487.239926870972\n",
      "Run: 9, iteration: 6/100, moves: 62, ncost: 6486.504899726915\n",
      "Run: 9, iteration: 7/100, moves: 19, ncost: 6486.451247242863\n",
      "Run: 9, iteration: 8/100, moves: 1, ncost: 6486.451017196837\n",
      "Run: 9, iteration: 9/100, moves: 0, ncost: 6486.451017196837\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 10, iteration: 1/100, moves: 2292, ncost: 6579.647180201291\n",
      "Run: 10, iteration: 2/100, moves: 636, ncost: 6496.394429866062\n",
      "Run: 10, iteration: 3/100, moves: 200, ncost: 6488.312390336003\n",
      "Run: 10, iteration: 4/100, moves: 93, ncost: 6486.7250252422355\n",
      "Run: 10, iteration: 5/100, moves: 35, ncost: 6486.486801800413\n",
      "Run: 10, iteration: 6/100, moves: 12, ncost: 6486.457533529236\n",
      "Run: 10, iteration: 7/100, moves: 6, ncost: 6486.451017196838\n",
      "Run: 10, iteration: 8/100, moves: 0, ncost: 6486.451017196838\n",
      "Best run was number 8\n",
      "For n_clusters = 2, the average silhouette_score is : 0.31188481577228333\n",
      "Initialization method and algorithm are deterministic. Setting n_init to 1.\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 1, iteration: 1/100, moves: 3017, ncost: 5090.617393092446\n",
      "Run: 1, iteration: 2/100, moves: 1317, ncost: 4990.82283923131\n",
      "Run: 1, iteration: 3/100, moves: 423, ncost: 4974.161478441887\n",
      "Run: 1, iteration: 4/100, moves: 184, ncost: 4970.8280253461435\n",
      "Run: 1, iteration: 5/100, moves: 64, ncost: 4970.398590135641\n",
      "Run: 1, iteration: 6/100, moves: 24, ncost: 4970.347863110536\n",
      "Run: 1, iteration: 7/100, moves: 9, ncost: 4970.336464786562\n",
      "Run: 1, iteration: 8/100, moves: 4, ncost: 4970.334203032987\n",
      "Run: 1, iteration: 9/100, moves: 0, ncost: 4970.334203032987\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 2, iteration: 1/100, moves: 1926, ncost: 4992.146445899901\n",
      "Run: 2, iteration: 2/100, moves: 674, ncost: 4974.534785324375\n",
      "Run: 2, iteration: 3/100, moves: 195, ncost: 4970.83511057865\n",
      "Run: 2, iteration: 4/100, moves: 65, ncost: 4970.3985901356455\n",
      "Run: 2, iteration: 5/100, moves: 24, ncost: 4970.347863110542\n",
      "Run: 2, iteration: 6/100, moves: 9, ncost: 4970.336464786563\n",
      "Run: 2, iteration: 7/100, moves: 4, ncost: 4970.334203032993\n",
      "Run: 2, iteration: 8/100, moves: 0, ncost: 4970.334203032993\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 3, iteration: 1/100, moves: 3168, ncost: 5303.213661588798\n",
      "Run: 3, iteration: 2/100, moves: 1164, ncost: 5188.8832720907885\n",
      "Run: 3, iteration: 3/100, moves: 552, ncost: 5090.035675491457\n",
      "Run: 3, iteration: 4/100, moves: 164, ncost: 5088.178464354955\n",
      "Run: 3, iteration: 5/100, moves: 62, ncost: 5087.797209764066\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run: 3, iteration: 6/100, moves: 32, ncost: 5087.7091612706945\n",
      "Run: 3, iteration: 7/100, moves: 11, ncost: 5087.690514870352\n",
      "Run: 3, iteration: 8/100, moves: 10, ncost: 5087.672789734083\n",
      "Run: 3, iteration: 9/100, moves: 10, ncost: 5087.6605004150215\n",
      "Run: 3, iteration: 10/100, moves: 1, ncost: 5087.660300637892\n",
      "Run: 3, iteration: 11/100, moves: 0, ncost: 5087.660300637892\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 4, iteration: 1/100, moves: 2566, ncost: 5344.614083261591\n",
      "Run: 4, iteration: 2/100, moves: 1360, ncost: 5151.569935857615\n",
      "Run: 4, iteration: 3/100, moves: 688, ncost: 5102.14842643865\n",
      "Run: 4, iteration: 4/100, moves: 301, ncost: 5091.85192008523\n",
      "Run: 4, iteration: 5/100, moves: 162, ncost: 5088.682418267929\n",
      "Run: 4, iteration: 6/100, moves: 76, ncost: 5088.003474875057\n",
      "Run: 4, iteration: 7/100, moves: 49, ncost: 5087.7535159960435\n",
      "Run: 4, iteration: 8/100, moves: 25, ncost: 5087.698382019192\n",
      "Run: 4, iteration: 9/100, moves: 14, ncost: 5087.676924692885\n",
      "Run: 4, iteration: 10/100, moves: 10, ncost: 5087.661932271597\n",
      "Run: 4, iteration: 11/100, moves: 3, ncost: 5087.660300637889\n",
      "Run: 4, iteration: 12/100, moves: 0, ncost: 5087.660300637889\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 5, iteration: 1/100, moves: 2361, ncost: 5082.766800027503\n",
      "Run: 5, iteration: 2/100, moves: 1419, ncost: 4974.276952671182\n",
      "Run: 5, iteration: 3/100, moves: 195, ncost: 4971.052230175062\n",
      "Run: 5, iteration: 4/100, moves: 72, ncost: 4970.471265258998\n",
      "Run: 5, iteration: 5/100, moves: 34, ncost: 4970.361300728589\n",
      "Run: 5, iteration: 6/100, moves: 9, ncost: 4970.349373221337\n",
      "Run: 5, iteration: 7/100, moves: 0, ncost: 4970.349373221337\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 6, iteration: 1/100, moves: 3307, ncost: 5203.218470060512\n",
      "Run: 6, iteration: 2/100, moves: 1410, ncost: 4957.496460693424\n",
      "Run: 6, iteration: 3/100, moves: 400, ncost: 4948.833161704755\n",
      "Run: 6, iteration: 4/100, moves: 112, ncost: 4947.678852770594\n",
      "Run: 6, iteration: 5/100, moves: 47, ncost: 4947.444023392303\n",
      "Run: 6, iteration: 6/100, moves: 24, ncost: 4947.3874022971495\n",
      "Run: 6, iteration: 7/100, moves: 7, ncost: 4947.37962880228\n",
      "Run: 6, iteration: 8/100, moves: 2, ncost: 4947.378832027453\n",
      "Run: 6, iteration: 9/100, moves: 0, ncost: 4947.378832027453\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 7, iteration: 1/100, moves: 3850, ncost: 5128.954593239303\n",
      "Run: 7, iteration: 2/100, moves: 837, ncost: 5067.894876926736\n",
      "Run: 7, iteration: 3/100, moves: 604, ncost: 4952.126501972288\n",
      "Run: 7, iteration: 4/100, moves: 304, ncost: 4948.050747441531\n",
      "Run: 7, iteration: 5/100, moves: 66, ncost: 4947.541589989384\n",
      "Run: 7, iteration: 6/100, moves: 31, ncost: 4947.425930605239\n",
      "Run: 7, iteration: 7/100, moves: 18, ncost: 4947.388777574663\n",
      "Run: 7, iteration: 8/100, moves: 7, ncost: 4947.383239975186\n",
      "Run: 7, iteration: 9/100, moves: 3, ncost: 4947.3803726672795\n",
      "Run: 7, iteration: 10/100, moves: 4, ncost: 4947.37835298269\n",
      "Run: 7, iteration: 11/100, moves: 0, ncost: 4947.37835298269\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 8, iteration: 1/100, moves: 3551, ncost: 5150.598492388166\n",
      "Run: 8, iteration: 2/100, moves: 1585, ncost: 4970.701992319343\n",
      "Run: 8, iteration: 3/100, moves: 429, ncost: 4950.978888012703\n",
      "Run: 8, iteration: 4/100, moves: 169, ncost: 4947.944699762612\n",
      "Run: 8, iteration: 5/100, moves: 62, ncost: 4947.520415316589\n",
      "Run: 8, iteration: 6/100, moves: 27, ncost: 4947.426664658199\n",
      "Run: 8, iteration: 7/100, moves: 18, ncost: 4947.3887775746625\n",
      "Run: 8, iteration: 8/100, moves: 7, ncost: 4947.383239975188\n",
      "Run: 8, iteration: 9/100, moves: 3, ncost: 4947.380372667279\n",
      "Run: 8, iteration: 10/100, moves: 4, ncost: 4947.378352982691\n",
      "Run: 8, iteration: 11/100, moves: 0, ncost: 4947.378352982691\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 9, iteration: 1/100, moves: 2726, ncost: 5020.602061942193\n",
      "Run: 9, iteration: 2/100, moves: 623, ncost: 4984.030719375104\n",
      "Run: 9, iteration: 3/100, moves: 186, ncost: 4980.487310167437\n",
      "Run: 9, iteration: 4/100, moves: 65, ncost: 4980.052476713484\n",
      "Run: 9, iteration: 5/100, moves: 25, ncost: 4979.974981222128\n",
      "Run: 9, iteration: 6/100, moves: 10, ncost: 4979.96224220289\n",
      "Run: 9, iteration: 7/100, moves: 5, ncost: 4979.958484587573\n",
      "Run: 9, iteration: 8/100, moves: 10, ncost: 4979.946975881907\n",
      "Run: 9, iteration: 9/100, moves: 6, ncost: 4979.942495313593\n",
      "Run: 9, iteration: 10/100, moves: 3, ncost: 4979.941189383959\n",
      "Run: 9, iteration: 11/100, moves: 0, ncost: 4979.941189383959\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 10, iteration: 1/100, moves: 2995, ncost: 5408.851152608362\n",
      "Run: 10, iteration: 2/100, moves: 1308, ncost: 5034.35555574615\n",
      "Run: 10, iteration: 3/100, moves: 718, ncost: 4985.082149193016\n",
      "Run: 10, iteration: 4/100, moves: 343, ncost: 4973.150365354411\n",
      "Run: 10, iteration: 5/100, moves: 165, ncost: 4970.6396041347325\n",
      "Run: 10, iteration: 6/100, moves: 48, ncost: 4970.384313169704\n",
      "Run: 10, iteration: 7/100, moves: 20, ncost: 4970.3437617318905\n",
      "Run: 10, iteration: 8/100, moves: 7, ncost: 4970.336464786565\n",
      "Run: 10, iteration: 9/100, moves: 4, ncost: 4970.334203032992\n",
      "Run: 10, iteration: 10/100, moves: 0, ncost: 4970.334203032992\n",
      "Best run was number 7\n",
      "For n_clusters = 3, the average silhouette_score is : 0.24779459062010994\n",
      "Initialization method and algorithm are deterministic. Setting n_init to 1.\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 1, iteration: 1/100, moves: 3161, ncost: 4699.093212059224\n",
      "Run: 1, iteration: 2/100, moves: 2763, ncost: 3953.195629564178\n",
      "Run: 1, iteration: 3/100, moves: 1357, ncost: 3912.485039104879\n",
      "Run: 1, iteration: 4/100, moves: 259, ncost: 3906.4102885398606\n",
      "Run: 1, iteration: 5/100, moves: 101, ncost: 3905.001121926946\n",
      "Run: 1, iteration: 6/100, moves: 63, ncost: 3904.470665238728\n",
      "Run: 1, iteration: 7/100, moves: 31, ncost: 3904.320407110073\n",
      "Run: 1, iteration: 8/100, moves: 8, ncost: 3904.313358821848\n",
      "Run: 1, iteration: 9/100, moves: 3, ncost: 3904.3120753371827\n",
      "Run: 1, iteration: 10/100, moves: 1, ncost: 3904.3118424516533\n",
      "Run: 1, iteration: 11/100, moves: 0, ncost: 3904.3118424516533\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 2, iteration: 1/100, moves: 3068, ncost: 4579.565523992381\n",
      "Run: 2, iteration: 2/100, moves: 1751, ncost: 4109.677283498647\n",
      "Run: 2, iteration: 3/100, moves: 592, ncost: 4096.938750873614\n",
      "Run: 2, iteration: 4/100, moves: 823, ncost: 3915.7319164352\n",
      "Run: 2, iteration: 5/100, moves: 277, ncost: 3906.0815901610313\n",
      "Run: 2, iteration: 6/100, moves: 102, ncost: 3904.7561292919636\n",
      "Run: 2, iteration: 7/100, moves: 59, ncost: 3904.3767957120403\n",
      "Run: 2, iteration: 8/100, moves: 24, ncost: 3904.319706192948\n",
      "Run: 2, iteration: 9/100, moves: 7, ncost: 3904.3172950988705\n",
      "Run: 2, iteration: 10/100, moves: 6, ncost: 3904.3128139600226\n",
      "Run: 2, iteration: 11/100, moves: 2, ncost: 3904.312156866654\n",
      "Run: 2, iteration: 12/100, moves: 2, ncost: 3904.310895332728\n",
      "Run: 2, iteration: 13/100, moves: 1, ncost: 3904.310661093024\n",
      "Run: 2, iteration: 14/100, moves: 0, ncost: 3904.310661093024\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 3, iteration: 1/100, moves: 3370, ncost: 3912.559939197314\n",
      "Run: 3, iteration: 2/100, moves: 899, ncost: 3904.759393009534\n",
      "Run: 3, iteration: 3/100, moves: 66, ncost: 3904.4042109910465\n",
      "Run: 3, iteration: 4/100, moves: 22, ncost: 3904.3289134313877\n",
      "Run: 3, iteration: 5/100, moves: 10, ncost: 3904.3180621937777\n",
      "Run: 3, iteration: 6/100, moves: 7, ncost: 3904.312813960017\n",
      "Run: 3, iteration: 7/100, moves: 2, ncost: 3904.312156866655\n",
      "Run: 3, iteration: 8/100, moves: 2, ncost: 3904.3108953327273\n",
      "Run: 3, iteration: 9/100, moves: 1, ncost: 3904.3106610930226\n",
      "Run: 3, iteration: 10/100, moves: 0, ncost: 3904.3106610930226\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 4, iteration: 1/100, moves: 4450, ncost: 4143.903324301157\n",
      "Run: 4, iteration: 2/100, moves: 1214, ncost: 4099.347314631431\n",
      "Run: 4, iteration: 3/100, moves: 773, ncost: 3923.466313349219\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run: 4, iteration: 4/100, moves: 432, ncost: 3907.220485237014\n",
      "Run: 4, iteration: 5/100, moves: 136, ncost: 3905.009024210594\n",
      "Run: 4, iteration: 6/100, moves: 68, ncost: 3904.467321903645\n",
      "Run: 4, iteration: 7/100, moves: 34, ncost: 3904.3389591433847\n",
      "Run: 4, iteration: 8/100, moves: 16, ncost: 3904.3185163835624\n",
      "Run: 4, iteration: 9/100, moves: 2, ncost: 3904.317295098868\n",
      "Run: 4, iteration: 10/100, moves: 6, ncost: 3904.3128139600185\n",
      "Run: 4, iteration: 11/100, moves: 2, ncost: 3904.312156866656\n",
      "Run: 4, iteration: 12/100, moves: 2, ncost: 3904.3108953327264\n",
      "Run: 4, iteration: 13/100, moves: 1, ncost: 3904.3106610930254\n",
      "Run: 4, iteration: 14/100, moves: 0, ncost: 3904.3106610930254\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 5, iteration: 1/100, moves: 2686, ncost: 4064.6065186707783\n",
      "Run: 5, iteration: 2/100, moves: 1362, ncost: 3925.1955103637715\n",
      "Run: 5, iteration: 3/100, moves: 436, ncost: 3907.950758812508\n",
      "Run: 5, iteration: 4/100, moves: 163, ncost: 3905.2844377931647\n",
      "Run: 5, iteration: 5/100, moves: 80, ncost: 3904.5579285684425\n",
      "Run: 5, iteration: 6/100, moves: 40, ncost: 3904.375984462192\n",
      "Run: 5, iteration: 7/100, moves: 25, ncost: 3904.31974052531\n",
      "Run: 5, iteration: 8/100, moves: 4, ncost: 3904.3180621937713\n",
      "Run: 5, iteration: 9/100, moves: 7, ncost: 3904.312813960018\n",
      "Run: 5, iteration: 10/100, moves: 2, ncost: 3904.3121568666584\n",
      "Run: 5, iteration: 11/100, moves: 2, ncost: 3904.3108953327255\n",
      "Run: 5, iteration: 12/100, moves: 1, ncost: 3904.310661093026\n",
      "Run: 5, iteration: 13/100, moves: 0, ncost: 3904.310661093026\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 6, iteration: 1/100, moves: 2842, ncost: 3996.0423269429543\n",
      "Run: 6, iteration: 2/100, moves: 384, ncost: 3984.918261744051\n",
      "Run: 6, iteration: 3/100, moves: 91, ncost: 3983.70642316666\n",
      "Run: 6, iteration: 4/100, moves: 22, ncost: 3983.65355524827\n",
      "Run: 6, iteration: 5/100, moves: 12, ncost: 3983.6413029248265\n",
      "Run: 6, iteration: 6/100, moves: 3, ncost: 3983.6398836942435\n",
      "Run: 6, iteration: 7/100, moves: 0, ncost: 3983.6398836942435\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 7, iteration: 1/100, moves: 4060, ncost: 3936.787497795585\n",
      "Run: 7, iteration: 2/100, moves: 1014, ncost: 3908.1802747214606\n",
      "Run: 7, iteration: 3/100, moves: 174, ncost: 3905.0337704011863\n",
      "Run: 7, iteration: 4/100, moves: 67, ncost: 3904.4757951896154\n",
      "Run: 7, iteration: 5/100, moves: 34, ncost: 3904.344194414254\n",
      "Run: 7, iteration: 6/100, moves: 15, ncost: 3904.3185163835606\n",
      "Run: 7, iteration: 7/100, moves: 2, ncost: 3904.3172950988687\n",
      "Run: 7, iteration: 8/100, moves: 6, ncost: 3904.3128139600176\n",
      "Run: 7, iteration: 9/100, moves: 2, ncost: 3904.3121568666566\n",
      "Run: 7, iteration: 10/100, moves: 2, ncost: 3904.3108953327283\n",
      "Run: 7, iteration: 11/100, moves: 1, ncost: 3904.3106610930245\n",
      "Run: 7, iteration: 12/100, moves: 0, ncost: 3904.3106610930245\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 8, iteration: 1/100, moves: 4451, ncost: 4167.779276253412\n",
      "Run: 8, iteration: 2/100, moves: 1750, ncost: 3943.133120476213\n",
      "Run: 8, iteration: 3/100, moves: 552, ncost: 3912.0222002044047\n",
      "Run: 8, iteration: 4/100, moves: 248, ncost: 3905.8101511037385\n",
      "Run: 8, iteration: 5/100, moves: 95, ncost: 3904.770442933373\n",
      "Run: 8, iteration: 6/100, moves: 54, ncost: 3904.4281524435314\n",
      "Run: 8, iteration: 7/100, moves: 27, ncost: 3904.3356453310885\n",
      "Run: 8, iteration: 8/100, moves: 14, ncost: 3904.318516383562\n",
      "Run: 8, iteration: 9/100, moves: 2, ncost: 3904.3172950988683\n",
      "Run: 8, iteration: 10/100, moves: 6, ncost: 3904.3128139600203\n",
      "Run: 8, iteration: 11/100, moves: 2, ncost: 3904.3121568666547\n",
      "Run: 8, iteration: 12/100, moves: 2, ncost: 3904.310895332726\n",
      "Run: 8, iteration: 13/100, moves: 1, ncost: 3904.310661093025\n",
      "Run: 8, iteration: 14/100, moves: 0, ncost: 3904.310661093025\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 9, iteration: 1/100, moves: 3507, ncost: 4369.614878459624\n",
      "Run: 9, iteration: 2/100, moves: 710, ncost: 4323.226042179874\n",
      "Run: 9, iteration: 3/100, moves: 243, ncost: 4318.551961125623\n",
      "Run: 9, iteration: 4/100, moves: 80, ncost: 4317.977572054262\n",
      "Run: 9, iteration: 5/100, moves: 34, ncost: 4317.8665527702815\n",
      "Run: 9, iteration: 6/100, moves: 22, ncost: 4317.805681291243\n",
      "Run: 9, iteration: 7/100, moves: 6, ncost: 4317.799374807261\n",
      "Run: 9, iteration: 8/100, moves: 9, ncost: 4317.787533314032\n",
      "Run: 9, iteration: 9/100, moves: 7, ncost: 4317.777579201722\n",
      "Run: 9, iteration: 10/100, moves: 4, ncost: 4317.776145158845\n",
      "Run: 9, iteration: 11/100, moves: 0, ncost: 4317.776145158845\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 10, iteration: 1/100, moves: 2707, ncost: 3935.4747600620735\n",
      "Run: 10, iteration: 2/100, moves: 592, ncost: 3909.3787256413925\n",
      "Run: 10, iteration: 3/100, moves: 187, ncost: 3905.5640222539328\n",
      "Run: 10, iteration: 4/100, moves: 90, ncost: 3904.530134023701\n",
      "Run: 10, iteration: 5/100, moves: 47, ncost: 3904.320477135403\n",
      "Run: 10, iteration: 6/100, moves: 7, ncost: 3904.313358821854\n",
      "Run: 10, iteration: 7/100, moves: 3, ncost: 3904.312075337189\n",
      "Run: 10, iteration: 8/100, moves: 1, ncost: 3904.3118424516597\n",
      "Run: 10, iteration: 9/100, moves: 0, ncost: 3904.3118424516597\n",
      "Best run was number 3\n",
      "For n_clusters = 4, the average silhouette_score is : 0.28283672129525766\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1300x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1300x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1300x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "range_clusters = range(1, 5)\n",
    "\n",
    "# Adapted from:\n",
    "# https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html#sphx-glr-auto-examples-cluster-plot-kmeans-silhouette-analysis-py\n",
    "\n",
    "# Storing average silhouette metric\n",
    "avg_silhouette = []\n",
    "for nclus in range_clusters:\n",
    "    # Skip nclus == 1\n",
    "    if nclus == 1:\n",
    "        continue\n",
    "    \n",
    "    # Create a figure\n",
    "    fig = plt.figure(figsize=(13, 7))\n",
    "\n",
    "    # Initialize the KPrototypes object with n_clusters value and a random generator\n",
    "    # seed of 10 for reproducibility.\n",
    "    kproto = KPrototypes(n_clusters=nclus, init='Cao',verbose=2, random_state=1)\n",
    "    cluster_labels = kproto.fit_predict(dfDemographic,categorical=[1,2,3,4])\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 clusters\n",
    "    silhouette_avg = silhouette_score(dfDemographic, cluster_labels)\n",
    "    avg_silhouette.append(silhouette_avg)\n",
    "    print(f\"For n_clusters = {nclus}, the average silhouette_score is : {silhouette_avg}\")\n",
    "\n",
    "    # Compute the silhouette scores for each sample\n",
    "    sample_silhouette_values = silhouette_samples(dfDemographic, cluster_labels)\n",
    "\n",
    "    y_lower = 10\n",
    "    for i in range(nclus):\n",
    "        # Aggregate the silhouette scores for samples belonging to cluster i, and sort them\n",
    "        ith_cluster_silhouette_values = sample_silhouette_values[cluster_labels == i]\n",
    "        ith_cluster_silhouette_values.sort()\n",
    "        \n",
    "        # Get y_upper to demarcate silhouette y range size\n",
    "        size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
    "        y_upper = y_lower + size_cluster_i\n",
    "        \n",
    "        # Filling the silhouette\n",
    "        color = cm.nipy_spectral(float(i) / nclus)\n",
    "        plt.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",
    "        plt.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",
    "    plt.title(\"The silhouette plot for the various clusters.\")\n",
    "    plt.xlabel(\"The silhouette coefficient values\")\n",
    "    plt.ylabel(\"Cluster label\")\n",
    "\n",
    "    # The vertical line for average silhouette score of all the values\n",
    "    plt.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")    # The silhouette coefficient can range from -1, 1\n",
    "    xmin, xmax = np.round(sample_silhouette_values.min() -0.1, 2), np.round(sample_silhouette_values.max() + 0.1, 2)\n",
    "    plt.xlim([xmin, xmax])\n",
    "    \n",
    "    # The (nclus+1)*10 is for inserting blank space between silhouette\n",
    "    # plots of individual clusters, to demarcate them clearly.\n",
    "    plt.ylim([0, len(dfDemographic) + (nclus + 1) * 10])\n",
    "\n",
    "    plt.yticks([])  # Clear the yaxis labels / ticks\n",
    "    plt.xticks(np.arange(xmin, xmax, 0.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "ff4398b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialization method and algorithm are deterministic. Setting n_init to 1.\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 1, iteration: 1/100, moves: 3161, ncost: 4699.093212059224\n",
      "Run: 1, iteration: 2/100, moves: 2763, ncost: 3953.195629564178\n",
      "Run: 1, iteration: 3/100, moves: 1357, ncost: 3912.485039104879\n",
      "Run: 1, iteration: 4/100, moves: 259, ncost: 3906.4102885398606\n",
      "Run: 1, iteration: 5/100, moves: 101, ncost: 3905.001121926946\n",
      "Run: 1, iteration: 6/100, moves: 63, ncost: 3904.470665238728\n",
      "Run: 1, iteration: 7/100, moves: 31, ncost: 3904.320407110073\n",
      "Run: 1, iteration: 8/100, moves: 8, ncost: 3904.313358821848\n",
      "Run: 1, iteration: 9/100, moves: 3, ncost: 3904.3120753371827\n",
      "Run: 1, iteration: 10/100, moves: 1, ncost: 3904.3118424516533\n",
      "Run: 1, iteration: 11/100, moves: 0, ncost: 3904.3118424516533\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 2, iteration: 1/100, moves: 3068, ncost: 4579.565523992381\n",
      "Run: 2, iteration: 2/100, moves: 1751, ncost: 4109.677283498647\n",
      "Run: 2, iteration: 3/100, moves: 592, ncost: 4096.938750873614\n",
      "Run: 2, iteration: 4/100, moves: 823, ncost: 3915.7319164352\n",
      "Run: 2, iteration: 5/100, moves: 277, ncost: 3906.0815901610313\n",
      "Run: 2, iteration: 6/100, moves: 102, ncost: 3904.7561292919636\n",
      "Run: 2, iteration: 7/100, moves: 59, ncost: 3904.3767957120403\n",
      "Run: 2, iteration: 8/100, moves: 24, ncost: 3904.319706192948\n",
      "Run: 2, iteration: 9/100, moves: 7, ncost: 3904.3172950988705\n",
      "Run: 2, iteration: 10/100, moves: 6, ncost: 3904.3128139600226\n",
      "Run: 2, iteration: 11/100, moves: 2, ncost: 3904.312156866654\n",
      "Run: 2, iteration: 12/100, moves: 2, ncost: 3904.310895332728\n",
      "Run: 2, iteration: 13/100, moves: 1, ncost: 3904.310661093024\n",
      "Run: 2, iteration: 14/100, moves: 0, ncost: 3904.310661093024\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 3, iteration: 1/100, moves: 3370, ncost: 3912.559939197314\n",
      "Run: 3, iteration: 2/100, moves: 899, ncost: 3904.759393009534\n",
      "Run: 3, iteration: 3/100, moves: 66, ncost: 3904.4042109910465\n",
      "Run: 3, iteration: 4/100, moves: 22, ncost: 3904.3289134313877\n",
      "Run: 3, iteration: 5/100, moves: 10, ncost: 3904.3180621937777\n",
      "Run: 3, iteration: 6/100, moves: 7, ncost: 3904.312813960017\n",
      "Run: 3, iteration: 7/100, moves: 2, ncost: 3904.312156866655\n",
      "Run: 3, iteration: 8/100, moves: 2, ncost: 3904.3108953327273\n",
      "Run: 3, iteration: 9/100, moves: 1, ncost: 3904.3106610930226\n",
      "Run: 3, iteration: 10/100, moves: 0, ncost: 3904.3106610930226\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 4, iteration: 1/100, moves: 4450, ncost: 4143.903324301157\n",
      "Run: 4, iteration: 2/100, moves: 1214, ncost: 4099.347314631431\n",
      "Run: 4, iteration: 3/100, moves: 773, ncost: 3923.466313349219\n",
      "Run: 4, iteration: 4/100, moves: 432, ncost: 3907.220485237014\n",
      "Run: 4, iteration: 5/100, moves: 136, ncost: 3905.009024210594\n",
      "Run: 4, iteration: 6/100, moves: 68, ncost: 3904.467321903645\n",
      "Run: 4, iteration: 7/100, moves: 34, ncost: 3904.3389591433847\n",
      "Run: 4, iteration: 8/100, moves: 16, ncost: 3904.3185163835624\n",
      "Run: 4, iteration: 9/100, moves: 2, ncost: 3904.317295098868\n",
      "Run: 4, iteration: 10/100, moves: 6, ncost: 3904.3128139600185\n",
      "Run: 4, iteration: 11/100, moves: 2, ncost: 3904.312156866656\n",
      "Run: 4, iteration: 12/100, moves: 2, ncost: 3904.3108953327264\n",
      "Run: 4, iteration: 13/100, moves: 1, ncost: 3904.3106610930254\n",
      "Run: 4, iteration: 14/100, moves: 0, ncost: 3904.3106610930254\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 5, iteration: 1/100, moves: 2686, ncost: 4064.6065186707783\n",
      "Run: 5, iteration: 2/100, moves: 1362, ncost: 3925.1955103637715\n",
      "Run: 5, iteration: 3/100, moves: 436, ncost: 3907.950758812508\n",
      "Run: 5, iteration: 4/100, moves: 163, ncost: 3905.2844377931647\n",
      "Run: 5, iteration: 5/100, moves: 80, ncost: 3904.5579285684425\n",
      "Run: 5, iteration: 6/100, moves: 40, ncost: 3904.375984462192\n",
      "Run: 5, iteration: 7/100, moves: 25, ncost: 3904.31974052531\n",
      "Run: 5, iteration: 8/100, moves: 4, ncost: 3904.3180621937713\n",
      "Run: 5, iteration: 9/100, moves: 7, ncost: 3904.312813960018\n",
      "Run: 5, iteration: 10/100, moves: 2, ncost: 3904.3121568666584\n",
      "Run: 5, iteration: 11/100, moves: 2, ncost: 3904.3108953327255\n",
      "Run: 5, iteration: 12/100, moves: 1, ncost: 3904.310661093026\n",
      "Run: 5, iteration: 13/100, moves: 0, ncost: 3904.310661093026\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 6, iteration: 1/100, moves: 2842, ncost: 3996.0423269429543\n",
      "Run: 6, iteration: 2/100, moves: 384, ncost: 3984.918261744051\n",
      "Run: 6, iteration: 3/100, moves: 91, ncost: 3983.70642316666\n",
      "Run: 6, iteration: 4/100, moves: 22, ncost: 3983.65355524827\n",
      "Run: 6, iteration: 5/100, moves: 12, ncost: 3983.6413029248265\n",
      "Run: 6, iteration: 6/100, moves: 3, ncost: 3983.6398836942435\n",
      "Run: 6, iteration: 7/100, moves: 0, ncost: 3983.6398836942435\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 7, iteration: 1/100, moves: 4060, ncost: 3936.787497795585\n",
      "Run: 7, iteration: 2/100, moves: 1014, ncost: 3908.1802747214606\n",
      "Run: 7, iteration: 3/100, moves: 174, ncost: 3905.0337704011863\n",
      "Run: 7, iteration: 4/100, moves: 67, ncost: 3904.4757951896154\n",
      "Run: 7, iteration: 5/100, moves: 34, ncost: 3904.344194414254\n",
      "Run: 7, iteration: 6/100, moves: 15, ncost: 3904.3185163835606\n",
      "Run: 7, iteration: 7/100, moves: 2, ncost: 3904.3172950988687\n",
      "Run: 7, iteration: 8/100, moves: 6, ncost: 3904.3128139600176\n",
      "Run: 7, iteration: 9/100, moves: 2, ncost: 3904.3121568666566\n",
      "Run: 7, iteration: 10/100, moves: 2, ncost: 3904.3108953327283\n",
      "Run: 7, iteration: 11/100, moves: 1, ncost: 3904.3106610930245\n",
      "Run: 7, iteration: 12/100, moves: 0, ncost: 3904.3106610930245\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 8, iteration: 1/100, moves: 4451, ncost: 4167.779276253412\n",
      "Run: 8, iteration: 2/100, moves: 1750, ncost: 3943.133120476213\n",
      "Run: 8, iteration: 3/100, moves: 552, ncost: 3912.0222002044047\n",
      "Run: 8, iteration: 4/100, moves: 248, ncost: 3905.8101511037385\n",
      "Run: 8, iteration: 5/100, moves: 95, ncost: 3904.770442933373\n",
      "Run: 8, iteration: 6/100, moves: 54, ncost: 3904.4281524435314\n",
      "Run: 8, iteration: 7/100, moves: 27, ncost: 3904.3356453310885\n",
      "Run: 8, iteration: 8/100, moves: 14, ncost: 3904.318516383562\n",
      "Run: 8, iteration: 9/100, moves: 2, ncost: 3904.3172950988683\n",
      "Run: 8, iteration: 10/100, moves: 6, ncost: 3904.3128139600203\n",
      "Run: 8, iteration: 11/100, moves: 2, ncost: 3904.3121568666547\n",
      "Run: 8, iteration: 12/100, moves: 2, ncost: 3904.310895332726\n",
      "Run: 8, iteration: 13/100, moves: 1, ncost: 3904.310661093025\n",
      "Run: 8, iteration: 14/100, moves: 0, ncost: 3904.310661093025\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 9, iteration: 1/100, moves: 3507, ncost: 4369.614878459624\n",
      "Run: 9, iteration: 2/100, moves: 710, ncost: 4323.226042179874\n",
      "Run: 9, iteration: 3/100, moves: 243, ncost: 4318.551961125623\n",
      "Run: 9, iteration: 4/100, moves: 80, ncost: 4317.977572054262\n",
      "Run: 9, iteration: 5/100, moves: 34, ncost: 4317.8665527702815\n",
      "Run: 9, iteration: 6/100, moves: 22, ncost: 4317.805681291243\n",
      "Run: 9, iteration: 7/100, moves: 6, ncost: 4317.799374807261\n",
      "Run: 9, iteration: 8/100, moves: 9, ncost: 4317.787533314032\n",
      "Run: 9, iteration: 9/100, moves: 7, ncost: 4317.777579201722\n",
      "Run: 9, iteration: 10/100, moves: 4, ncost: 4317.776145158845\n",
      "Run: 9, iteration: 11/100, moves: 0, ncost: 4317.776145158845\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 10, iteration: 1/100, moves: 2707, ncost: 3935.4747600620735\n",
      "Run: 10, iteration: 2/100, moves: 592, ncost: 3909.3787256413925\n",
      "Run: 10, iteration: 3/100, moves: 187, ncost: 3905.5640222539328\n",
      "Run: 10, iteration: 4/100, moves: 90, ncost: 3904.530134023701\n",
      "Run: 10, iteration: 5/100, moves: 47, ncost: 3904.320477135403\n",
      "Run: 10, iteration: 6/100, moves: 7, ncost: 3904.313358821854\n",
      "Run: 10, iteration: 7/100, moves: 3, ncost: 3904.312075337189\n",
      "Run: 10, iteration: 8/100, moves: 1, ncost: 3904.3118424516597\n",
      "Run: 10, iteration: 9/100, moves: 0, ncost: 3904.3118424516597\n",
      "Best run was number 3\n",
      "For n_clusters = 4, the average silhouette_score is : 0.28283672129525766\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialization method and algorithm are deterministic. Setting n_init to 1.\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 1, iteration: 1/100, moves: 4069, ncost: 3783.812862733799\n",
      "Run: 1, iteration: 2/100, moves: 2711, ncost: 3325.88127851925\n",
      "Run: 1, iteration: 3/100, moves: 380, ncost: 3318.497338285602\n",
      "Run: 1, iteration: 4/100, moves: 117, ncost: 3317.3926696702074\n",
      "Run: 1, iteration: 5/100, moves: 45, ncost: 3317.210027633546\n",
      "Run: 1, iteration: 6/100, moves: 22, ncost: 3317.147848658429\n",
      "Run: 1, iteration: 7/100, moves: 21, ncost: 3317.101661943188\n",
      "Run: 1, iteration: 8/100, moves: 10, ncost: 3317.0875974053724\n",
      "Run: 1, iteration: 9/100, moves: 4, ncost: 3317.0860081015126\n",
      "Run: 1, iteration: 10/100, moves: 0, ncost: 3317.0860081015126\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 2, iteration: 1/100, moves: 2595, ncost: 3464.959358620569\n",
      "Run: 2, iteration: 2/100, moves: 366, ncost: 3454.3820097005405\n",
      "Run: 2, iteration: 3/100, moves: 104, ncost: 3453.446488252253\n",
      "Run: 2, iteration: 4/100, moves: 46, ncost: 3453.2183979959077\n",
      "Run: 2, iteration: 5/100, moves: 21, ncost: 3453.1581996352543\n",
      "Run: 2, iteration: 6/100, moves: 10, ncost: 3453.1450320321705\n",
      "Run: 2, iteration: 7/100, moves: 3, ncost: 3453.1419628487115\n",
      "Run: 2, iteration: 8/100, moves: 2, ncost: 3453.1410466374055\n",
      "Run: 2, iteration: 9/100, moves: 1, ncost: 3453.140737843383\n",
      "Run: 2, iteration: 10/100, moves: 0, ncost: 3453.140737843383\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 3, iteration: 1/100, moves: 3271, ncost: 3544.6913836978897\n",
      "Run: 3, iteration: 2/100, moves: 813, ncost: 3420.285100947882\n",
      "Run: 3, iteration: 3/100, moves: 599, ncost: 3396.9617069519672\n",
      "Run: 3, iteration: 4/100, moves: 187, ncost: 3393.5223634992285\n",
      "Run: 3, iteration: 5/100, moves: 61, ncost: 3393.124300532675\n",
      "Run: 3, iteration: 6/100, moves: 33, ncost: 3392.99213475602\n",
      "Run: 3, iteration: 7/100, moves: 18, ncost: 3392.9587590493556\n",
      "Run: 3, iteration: 8/100, moves: 7, ncost: 3392.950532514664\n",
      "Run: 3, iteration: 9/100, moves: 3, ncost: 3392.949515802583\n",
      "Run: 3, iteration: 10/100, moves: 0, ncost: 3392.949515802583\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 4, iteration: 1/100, moves: 4749, ncost: 3891.480820331082\n",
      "Run: 4, iteration: 2/100, moves: 1351, ncost: 3751.0452155229978\n",
      "Run: 4, iteration: 3/100, moves: 693, ncost: 3694.8578738643464\n",
      "Run: 4, iteration: 4/100, moves: 383, ncost: 3681.0967115283083\n",
      "Run: 4, iteration: 5/100, moves: 180, ncost: 3678.168500434776\n",
      "Run: 4, iteration: 6/100, moves: 95, ncost: 3677.3810457567347\n",
      "Run: 4, iteration: 7/100, moves: 42, ncost: 3677.1952730660405\n",
      "Run: 4, iteration: 8/100, moves: 27, ncost: 3677.127671494049\n",
      "Run: 4, iteration: 9/100, moves: 12, ncost: 3677.110386307243\n",
      "Run: 4, iteration: 10/100, moves: 8, ncost: 3677.0981348479795\n",
      "Run: 4, iteration: 11/100, moves: 6, ncost: 3677.0935358925594\n",
      "Run: 4, iteration: 12/100, moves: 1, ncost: 3677.0930243645807\n",
      "Run: 4, iteration: 13/100, moves: 0, ncost: 3677.0930243645807\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 5, iteration: 1/100, moves: 2470, ncost: 3869.1094736145424\n",
      "Run: 5, iteration: 2/100, moves: 1469, ncost: 3731.0464377992744\n",
      "Run: 5, iteration: 3/100, moves: 306, ncost: 3722.490626388449\n",
      "Run: 5, iteration: 4/100, moves: 158, ncost: 3720.1778491641953\n",
      "Run: 5, iteration: 5/100, moves: 92, ncost: 3719.3451648263276\n",
      "Run: 5, iteration: 6/100, moves: 68, ncost: 3718.909186900821\n",
      "Run: 5, iteration: 7/100, moves: 52, ncost: 3718.614328771284\n",
      "Run: 5, iteration: 8/100, moves: 26, ncost: 3718.558637027028\n",
      "Run: 5, iteration: 9/100, moves: 6, ncost: 3718.5555950043577\n",
      "Run: 5, iteration: 10/100, moves: 2, ncost: 3718.5547624196256\n",
      "Run: 5, iteration: 11/100, moves: 1, ncost: 3718.5545469780013\n",
      "Run: 5, iteration: 12/100, moves: 0, ncost: 3718.5545469780013\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 6, iteration: 1/100, moves: 2365, ncost: 3427.8797533216666\n",
      "Run: 6, iteration: 2/100, moves: 496, ncost: 3398.8587501959764\n",
      "Run: 6, iteration: 3/100, moves: 216, ncost: 3393.745525773834\n",
      "Run: 6, iteration: 4/100, moves: 77, ncost: 3393.139825011862\n",
      "Run: 6, iteration: 5/100, moves: 28, ncost: 3393.0164888847116\n",
      "Run: 6, iteration: 6/100, moves: 23, ncost: 3392.9601993948922\n",
      "Run: 6, iteration: 7/100, moves: 8, ncost: 3392.9505325146633\n",
      "Run: 6, iteration: 8/100, moves: 3, ncost: 3392.9495158025825\n",
      "Run: 6, iteration: 9/100, moves: 0, ncost: 3392.9495158025825\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 7, iteration: 1/100, moves: 2801, ncost: 3985.6277945872243\n",
      "Run: 7, iteration: 2/100, moves: 1943, ncost: 3585.7708580991584\n",
      "Run: 7, iteration: 3/100, moves: 417, ncost: 3572.4247438635066\n",
      "Run: 7, iteration: 4/100, moves: 143, ncost: 3570.9285498179247\n",
      "Run: 7, iteration: 5/100, moves: 48, ncost: 3570.7335085371674\n",
      "Run: 7, iteration: 6/100, moves: 9, ncost: 3570.7189259583306\n",
      "Run: 7, iteration: 7/100, moves: 6, ncost: 3570.711151170998\n",
      "Run: 7, iteration: 8/100, moves: 4, ncost: 3570.705711221851\n",
      "Run: 7, iteration: 9/100, moves: 5, ncost: 3570.7008284830235\n",
      "Run: 7, iteration: 10/100, moves: 2, ncost: 3570.7001288672063\n",
      "Run: 7, iteration: 11/100, moves: 0, ncost: 3570.7001288672063\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 8, iteration: 1/100, moves: 3980, ncost: 4037.9412382937426\n",
      "Run: 8, iteration: 2/100, moves: 1519, ncost: 3720.8691351409348\n",
      "Run: 8, iteration: 3/100, moves: 961, ncost: 3612.3049942553375\n",
      "Run: 8, iteration: 4/100, moves: 534, ncost: 3579.493530870112\n",
      "Run: 8, iteration: 5/100, moves: 296, ncost: 3572.4245044372465\n",
      "Run: 8, iteration: 6/100, moves: 113, ncost: 3571.2540239047757\n",
      "Run: 8, iteration: 7/100, moves: 71, ncost: 3570.746983457455\n",
      "Run: 8, iteration: 8/100, moves: 23, ncost: 3570.7093878540036\n",
      "Run: 8, iteration: 9/100, moves: 9, ncost: 3570.701621923093\n",
      "Run: 8, iteration: 10/100, moves: 3, ncost: 3570.7004555755148\n",
      "Run: 8, iteration: 11/100, moves: 0, ncost: 3570.7004555755148\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 9, iteration: 1/100, moves: 2389, ncost: 4312.428356253475\n",
      "Run: 9, iteration: 2/100, moves: 1201, ncost: 4177.141398422973\n",
      "Run: 9, iteration: 3/100, moves: 412, ncost: 4162.7862631278895\n",
      "Run: 9, iteration: 4/100, moves: 172, ncost: 4160.221115002682\n",
      "Run: 9, iteration: 5/100, moves: 634, ncost: 4044.4209636297483\n",
      "Run: 9, iteration: 6/100, moves: 346, ncost: 4031.1902205867614\n",
      "Run: 9, iteration: 7/100, moves: 101, ncost: 4030.144589619442\n",
      "Run: 9, iteration: 8/100, moves: 68, ncost: 4029.3609456798144\n",
      "Run: 9, iteration: 9/100, moves: 65, ncost: 4028.8120953571893\n",
      "Run: 9, iteration: 10/100, moves: 22, ncost: 4028.7543978993526\n",
      "Run: 9, iteration: 11/100, moves: 6, ncost: 4028.745116643095\n",
      "Run: 9, iteration: 12/100, moves: 1, ncost: 4028.7446422023104\n",
      "Run: 9, iteration: 13/100, moves: 0, ncost: 4028.7446422023104\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 10, iteration: 1/100, moves: 2469, ncost: 4035.313155320401\n",
      "Run: 10, iteration: 2/100, moves: 1084, ncost: 3891.8111883396946\n",
      "Run: 10, iteration: 3/100, moves: 686, ncost: 3751.3881915053034\n",
      "Run: 10, iteration: 4/100, moves: 714, ncost: 3701.759328726996\n",
      "Run: 10, iteration: 5/100, moves: 323, ncost: 3689.476496968838\n",
      "Run: 10, iteration: 6/100, moves: 119, ncost: 3687.5511368450734\n",
      "Run: 10, iteration: 7/100, moves: 41, ncost: 3687.2864551352077\n",
      "Run: 10, iteration: 8/100, moves: 28, ncost: 3686.975634381441\n",
      "Run: 10, iteration: 9/100, moves: 19, ncost: 3686.8712110489896\n",
      "Run: 10, iteration: 10/100, moves: 11, ncost: 3686.814549645977\n",
      "Run: 10, iteration: 11/100, moves: 9, ncost: 3686.7991449779583\n",
      "Run: 10, iteration: 12/100, moves: 3, ncost: 3686.7969940784005\n",
      "Run: 10, iteration: 13/100, moves: 4, ncost: 3686.7935494960225\n",
      "Run: 10, iteration: 14/100, moves: 3, ncost: 3686.7913636416424\n",
      "Run: 10, iteration: 15/100, moves: 2, ncost: 3686.7907307686432\n",
      "Run: 10, iteration: 16/100, moves: 0, ncost: 3686.7907307686432\n",
      "Best run was number 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 5, the average silhouette_score is : 0.29955419502420405\n",
      "Initialization method and algorithm are deterministic. Setting n_init to 1.\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 1, iteration: 1/100, moves: 3310, ncost: 3201.75585556072\n",
      "Run: 1, iteration: 2/100, moves: 1409, ncost: 3105.048277773142\n",
      "Run: 1, iteration: 3/100, moves: 215, ncost: 3102.270760675693\n",
      "Run: 1, iteration: 4/100, moves: 53, ncost: 3101.8740504179605\n",
      "Run: 1, iteration: 5/100, moves: 25, ncost: 3101.7888550999614\n",
      "Run: 1, iteration: 6/100, moves: 15, ncost: 3101.760931580335\n",
      "Run: 1, iteration: 7/100, moves: 15, ncost: 3101.7445179895853\n",
      "Run: 1, iteration: 8/100, moves: 2, ncost: 3101.743179796932\n",
      "Run: 1, iteration: 9/100, moves: 1, ncost: 3101.7428117870263\n",
      "Run: 1, iteration: 10/100, moves: 0, ncost: 3101.7428117870263\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 2, iteration: 1/100, moves: 2731, ncost: 3300.2925329717486\n",
      "Run: 2, iteration: 2/100, moves: 411, ncost: 3287.965951036052\n",
      "Run: 2, iteration: 3/100, moves: 157, ncost: 3284.4867284883912\n",
      "Run: 2, iteration: 4/100, moves: 116, ncost: 3280.9653787698603\n",
      "Run: 2, iteration: 5/100, moves: 76, ncost: 3278.062175897373\n",
      "Run: 2, iteration: 6/100, moves: 82, ncost: 3255.8641812950605\n",
      "Run: 2, iteration: 7/100, moves: 167, ncost: 3245.502963070293\n",
      "Run: 2, iteration: 8/100, moves: 131, ncost: 3234.8932475251527\n",
      "Run: 2, iteration: 9/100, moves: 96, ncost: 3229.977279504483\n",
      "Run: 2, iteration: 10/100, moves: 68, ncost: 3228.1460317468577\n",
      "Run: 2, iteration: 11/100, moves: 49, ncost: 3226.814010593712\n",
      "Run: 2, iteration: 12/100, moves: 42, ncost: 3225.971579361363\n",
      "Run: 2, iteration: 13/100, moves: 57, ncost: 3224.930987543057\n",
      "Run: 2, iteration: 14/100, moves: 53, ncost: 3224.5434505837807\n",
      "Run: 2, iteration: 15/100, moves: 22, ncost: 3224.5037219585747\n",
      "Run: 2, iteration: 16/100, moves: 3, ncost: 3224.5018316237065\n",
      "Run: 2, iteration: 17/100, moves: 0, ncost: 3224.5018316237065\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 3, iteration: 1/100, moves: 3075, ncost: 3353.4210884950835\n",
      "Run: 3, iteration: 2/100, moves: 955, ncost: 3214.550286727735\n",
      "Run: 3, iteration: 3/100, moves: 478, ncost: 3192.111495549438\n",
      "Run: 3, iteration: 4/100, moves: 169, ncost: 3188.722359976258\n",
      "Run: 3, iteration: 5/100, moves: 84, ncost: 3188.010885992323\n",
      "Run: 3, iteration: 6/100, moves: 30, ncost: 3187.8992934953403\n",
      "Run: 3, iteration: 7/100, moves: 15, ncost: 3187.8746239503616\n",
      "Run: 3, iteration: 8/100, moves: 3, ncost: 3187.8730947760673\n",
      "Run: 3, iteration: 9/100, moves: 0, ncost: 3187.8730947760673\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 4, iteration: 1/100, moves: 3317, ncost: 3300.6830473531236\n",
      "Run: 4, iteration: 2/100, moves: 1188, ncost: 3248.9702160535103\n",
      "Run: 4, iteration: 3/100, moves: 352, ncost: 3234.2801863368127\n",
      "Run: 4, iteration: 4/100, moves: 193, ncost: 3229.197639501539\n",
      "Run: 4, iteration: 5/100, moves: 153, ncost: 3226.861193730466\n",
      "Run: 4, iteration: 6/100, moves: 87, ncost: 3225.9556583453846\n",
      "Run: 4, iteration: 7/100, moves: 82, ncost: 3224.906072825694\n",
      "Run: 4, iteration: 8/100, moves: 51, ncost: 3224.5953102783706\n",
      "Run: 4, iteration: 9/100, moves: 25, ncost: 3224.5309837418713\n",
      "Run: 4, iteration: 10/100, moves: 12, ncost: 3224.512178695154\n",
      "Run: 4, iteration: 11/100, moves: 6, ncost: 3224.5090101812757\n",
      "Run: 4, iteration: 12/100, moves: 1, ncost: 3224.508665484127\n",
      "Run: 4, iteration: 13/100, moves: 0, ncost: 3224.508665484127\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 5, iteration: 1/100, moves: 2633, ncost: 4288.499945673374\n",
      "Run: 5, iteration: 2/100, moves: 761, ncost: 4166.417721297397\n",
      "Run: 5, iteration: 3/100, moves: 925, ncost: 4025.55101586557\n",
      "Run: 5, iteration: 4/100, moves: 1401, ncost: 3501.1485277131355\n",
      "Run: 5, iteration: 5/100, moves: 972, ncost: 3418.864862852724\n",
      "Run: 5, iteration: 6/100, moves: 398, ncost: 3404.227409454975\n",
      "Run: 5, iteration: 7/100, moves: 207, ncost: 3400.226135139919\n",
      "Run: 5, iteration: 8/100, moves: 152, ncost: 3397.2828231431963\n",
      "Run: 5, iteration: 9/100, moves: 164, ncost: 3394.3974897571147\n",
      "Run: 5, iteration: 10/100, moves: 149, ncost: 3390.834614670807\n",
      "Run: 5, iteration: 11/100, moves: 129, ncost: 3387.435611131272\n",
      "Run: 5, iteration: 12/100, moves: 167, ncost: 3381.8923667217987\n",
      "Run: 5, iteration: 13/100, moves: 289, ncost: 3251.4484148240103\n",
      "Run: 5, iteration: 14/100, moves: 977, ncost: 3154.479348120797\n",
      "Run: 5, iteration: 15/100, moves: 505, ncost: 3116.8706729885903\n",
      "Run: 5, iteration: 16/100, moves: 337, ncost: 3101.0293218164766\n",
      "Run: 5, iteration: 17/100, moves: 229, ncost: 3095.1689886898616\n",
      "Run: 5, iteration: 18/100, moves: 113, ncost: 3093.7045606185316\n",
      "Run: 5, iteration: 19/100, moves: 52, ncost: 3093.429104070839\n",
      "Run: 5, iteration: 20/100, moves: 19, ncost: 3093.3610649974157\n",
      "Run: 5, iteration: 21/100, moves: 15, ncost: 3093.3322357079105\n",
      "Run: 5, iteration: 22/100, moves: 6, ncost: 3093.328354138613\n",
      "Run: 5, iteration: 23/100, moves: 4, ncost: 3093.3263131803164\n",
      "Run: 5, iteration: 24/100, moves: 0, ncost: 3093.3263131803164\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 6, iteration: 1/100, moves: 3139, ncost: 3107.937344006837\n",
      "Run: 6, iteration: 2/100, moves: 541, ncost: 3098.080556753296\n",
      "Run: 6, iteration: 3/100, moves: 210, ncost: 3095.0527289903193\n",
      "Run: 6, iteration: 4/100, moves: 132, ncost: 3093.7411212961338\n",
      "Run: 6, iteration: 5/100, moves: 87, ncost: 3093.1656312139085\n",
      "Run: 6, iteration: 6/100, moves: 95, ncost: 3092.541883210996\n",
      "Run: 6, iteration: 7/100, moves: 72, ncost: 3092.1771009019863\n",
      "Run: 6, iteration: 8/100, moves: 50, ncost: 3091.9946590915433\n",
      "Run: 6, iteration: 9/100, moves: 30, ncost: 3091.8903207421313\n",
      "Run: 6, iteration: 10/100, moves: 45, ncost: 3091.5815847490985\n",
      "Run: 6, iteration: 11/100, moves: 39, ncost: 3091.3456742562926\n",
      "Run: 6, iteration: 12/100, moves: 33, ncost: 3091.1675676164023\n",
      "Run: 6, iteration: 13/100, moves: 29, ncost: 3091.0162399491824\n",
      "Run: 6, iteration: 14/100, moves: 18, ncost: 3090.9570999456314\n",
      "Run: 6, iteration: 15/100, moves: 8, ncost: 3090.9478009694044\n",
      "Run: 6, iteration: 16/100, moves: 0, ncost: 3090.9478009694044\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 7, iteration: 1/100, moves: 3145, ncost: 3566.3326780723746\n",
      "Run: 7, iteration: 2/100, moves: 1291, ncost: 3451.8986823972905\n",
      "Run: 7, iteration: 3/100, moves: 607, ncost: 3405.3344178905745\n",
      "Run: 7, iteration: 4/100, moves: 444, ncost: 3383.8966274994546\n",
      "Run: 7, iteration: 5/100, moves: 392, ncost: 3352.742681895343\n",
      "Run: 7, iteration: 6/100, moves: 351, ncost: 3316.497291955921\n",
      "Run: 7, iteration: 7/100, moves: 173, ncost: 3310.4936335873663\n",
      "Run: 7, iteration: 8/100, moves: 227, ncost: 3295.988841442779\n",
      "Run: 7, iteration: 9/100, moves: 224, ncost: 3288.797600573908\n",
      "Run: 7, iteration: 10/100, moves: 196, ncost: 3284.788222373117\n",
      "Run: 7, iteration: 11/100, moves: 131, ncost: 3283.411415711464\n",
      "Run: 7, iteration: 12/100, moves: 65, ncost: 3282.9887485861\n",
      "Run: 7, iteration: 13/100, moves: 21, ncost: 3282.9466383412246\n",
      "Run: 7, iteration: 14/100, moves: 8, ncost: 3282.9377505465663\n",
      "Run: 7, iteration: 15/100, moves: 7, ncost: 3282.9334435367427\n",
      "Run: 7, iteration: 16/100, moves: 5, ncost: 3282.9262970453547\n",
      "Run: 7, iteration: 17/100, moves: 4, ncost: 3282.923370736917\n",
      "Run: 7, iteration: 18/100, moves: 1, ncost: 3282.922996894089\n",
      "Run: 7, iteration: 19/100, moves: 1, ncost: 3282.92230935252\n",
      "Run: 7, iteration: 20/100, moves: 5, ncost: 3282.9203491581916\n",
      "Run: 7, iteration: 21/100, moves: 0, ncost: 3282.9203491581916\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 8, iteration: 1/100, moves: 4913, ncost: 3173.4579975522493\n",
      "Run: 8, iteration: 2/100, moves: 1288, ncost: 3085.305834784816\n",
      "Run: 8, iteration: 3/100, moves: 330, ncost: 3076.469541394749\n",
      "Run: 8, iteration: 4/100, moves: 147, ncost: 3074.1594069638936\n",
      "Run: 8, iteration: 5/100, moves: 60, ncost: 3073.695292632735\n",
      "Run: 8, iteration: 6/100, moves: 35, ncost: 3073.5823233526107\n",
      "Run: 8, iteration: 7/100, moves: 28, ncost: 3073.482874339063\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run: 8, iteration: 8/100, moves: 37, ncost: 3073.3808006258228\n",
      "Run: 8, iteration: 9/100, moves: 18, ncost: 3073.354918709884\n",
      "Run: 8, iteration: 10/100, moves: 13, ncost: 3073.3375630916794\n",
      "Run: 8, iteration: 11/100, moves: 4, ncost: 3073.335377382146\n",
      "Run: 8, iteration: 12/100, moves: 0, ncost: 3073.335377382146\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 9, iteration: 1/100, moves: 3160, ncost: 3249.104008825994\n",
      "Run: 9, iteration: 2/100, moves: 1039, ncost: 3116.3221530440987\n",
      "Run: 9, iteration: 3/100, moves: 394, ncost: 3099.080812050127\n",
      "Run: 9, iteration: 4/100, moves: 155, ncost: 3096.4801818279784\n",
      "Run: 9, iteration: 5/100, moves: 59, ncost: 3095.884933925842\n",
      "Run: 9, iteration: 6/100, moves: 18, ncost: 3095.8402015011247\n",
      "Run: 9, iteration: 7/100, moves: 3, ncost: 3095.839102511638\n",
      "Run: 9, iteration: 8/100, moves: 0, ncost: 3095.839102511638\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 10, iteration: 1/100, moves: 3148, ncost: 3579.2472567402356\n",
      "Run: 10, iteration: 2/100, moves: 2390, ncost: 3177.5051120828875\n",
      "Run: 10, iteration: 3/100, moves: 494, ncost: 3164.7948803158433\n",
      "Run: 10, iteration: 4/100, moves: 219, ncost: 3160.1229013499847\n",
      "Run: 10, iteration: 5/100, moves: 163, ncost: 3156.3416556355223\n",
      "Run: 10, iteration: 6/100, moves: 135, ncost: 3150.0753939737024\n",
      "Run: 10, iteration: 7/100, moves: 245, ncost: 3126.2006136575737\n",
      "Run: 10, iteration: 8/100, moves: 309, ncost: 3113.6148796454104\n",
      "Run: 10, iteration: 9/100, moves: 198, ncost: 3107.976616817771\n",
      "Run: 10, iteration: 10/100, moves: 187, ncost: 3102.9906829116017\n",
      "Run: 10, iteration: 11/100, moves: 188, ncost: 3098.432371546707\n",
      "Run: 10, iteration: 12/100, moves: 170, ncost: 3095.3349785095056\n",
      "Run: 10, iteration: 13/100, moves: 140, ncost: 3092.865228248468\n",
      "Run: 10, iteration: 14/100, moves: 124, ncost: 3091.1215771878283\n",
      "Run: 10, iteration: 15/100, moves: 92, ncost: 3090.214788174582\n",
      "Run: 10, iteration: 16/100, moves: 109, ncost: 3088.919276236062\n",
      "Run: 10, iteration: 17/100, moves: 91, ncost: 3088.0740437963914\n",
      "Run: 10, iteration: 18/100, moves: 84, ncost: 3087.3509191105218\n",
      "Run: 10, iteration: 19/100, moves: 53, ncost: 3086.977632358221\n",
      "Run: 10, iteration: 20/100, moves: 27, ncost: 3086.9194261595476\n",
      "Run: 10, iteration: 21/100, moves: 15, ncost: 3086.9044574208424\n",
      "Run: 10, iteration: 22/100, moves: 6, ncost: 3086.9011433171395\n",
      "Run: 10, iteration: 23/100, moves: 3, ncost: 3086.8999021190175\n",
      "Run: 10, iteration: 24/100, moves: 0, ncost: 3086.8999021190175\n",
      "Best run was number 8\n",
      "For n_clusters = 6, the average silhouette_score is : 0.29707041266173556\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1300x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1300x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1300x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "range_clusters = range(4, 7)\n",
    "\n",
    "# Adapted from:\n",
    "# https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html#sphx-glr-auto-examples-cluster-plot-kmeans-silhouette-analysis-py\n",
    "\n",
    "# Storing average silhouette metric\n",
    "avg_silhouette = []\n",
    "for nclus in range_clusters:\n",
    "    # Skip nclus == 1\n",
    "    if nclus == 1:\n",
    "        continue\n",
    "    \n",
    "    # Create a figure\n",
    "    fig = plt.figure(figsize=(13, 7))\n",
    "\n",
    "    # Initialize the KMeans object with n_clusters value and a random generator\n",
    "    # seed of 10 for reproducibility.\n",
    "    kproto = KPrototypes(n_clusters=nclus, init='Cao',verbose=2, random_state=1)\n",
    "    cluster_labels = kproto.fit_predict(dfDemographic,categorical=[1,2,3,4])\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 clusters\n",
    "    silhouette_avg = silhouette_score(dfDemographic, cluster_labels)\n",
    "    avg_silhouette.append(silhouette_avg)\n",
    "    print(f\"For n_clusters = {nclus}, the average silhouette_score is : {silhouette_avg}\")\n",
    "\n",
    "    # Compute the silhouette scores for each sample\n",
    "    sample_silhouette_values = silhouette_samples(dfDemographic, cluster_labels)\n",
    "\n",
    "    y_lower = 10\n",
    "    for i in range(nclus):\n",
    "        # Aggregate the silhouette scores for samples belonging to cluster i, and sort them\n",
    "        ith_cluster_silhouette_values = sample_silhouette_values[cluster_labels == i]\n",
    "        ith_cluster_silhouette_values.sort()\n",
    "        \n",
    "        # Get y_upper to demarcate silhouette y range size\n",
    "        size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
    "        y_upper = y_lower + size_cluster_i\n",
    "        \n",
    "        # Filling the silhouette\n",
    "        color = cm.nipy_spectral(float(i) / nclus)\n",
    "        plt.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",
    "        plt.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",
    "    plt.title(\"The silhouette plot for the various clusters.\")\n",
    "    plt.xlabel(\"The silhouette coefficient values\")\n",
    "    plt.ylabel(\"Cluster label\")\n",
    "\n",
    "    # The vertical line for average silhouette score of all the values\n",
    "    plt.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")    # The silhouette coefficient can range from -1, 1\n",
    "    xmin, xmax = np.round(sample_silhouette_values.min() -0.1, 2), np.round(sample_silhouette_values.max() + 0.1, 2)\n",
    "    plt.xlim([xmin, xmax])\n",
    "    \n",
    "    # The (nclus+1)*10 is for inserting blank space between silhouette\n",
    "    # plots of individual clusters, to demarcate them clearly.\n",
    "    plt.ylim([0, len(dfDemographic) + (nclus + 1) * 10])\n",
    "\n",
    "    plt.yticks([])  # Clear the yaxis labels / ticks\n",
    "    plt.xticks(np.arange(xmin, xmax, 0.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "73dccc65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# K-distance graph to find out the right eps value\n",
    "neigh = NearestNeighbors(n_neighbors=20)\n",
    "neigh.fit(dfDemographic)\n",
    "distances,_ = neigh.kneighbors(dfDemographic)\n",
    "distances = np.sort(distances[:, -1])\n",
    "plt.plot(distances)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "88d0ad06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of estimated clusters : 11\n"
     ]
    }
   ],
   "source": [
    "# Perform DBSCAN clustering\n",
    "dbscan = DBSCAN(eps=0.25, min_samples=12,n_jobs=-1)\n",
    "dbscan_labels = dbscan.fit_predict(dfDemographic)\n",
    "\n",
    "dbscan_n_clusters = len(np.unique(dbscan_labels))\n",
    "print(\"Number of estimated clusters : %d\" % dbscan_n_clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f129fc5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0: 2466,\n",
       "         9: 12,\n",
       "         1: 3397,\n",
       "         2: 1013,\n",
       "         3: 1394,\n",
       "         4: 864,\n",
       "         5: 481,\n",
       "         6: 169,\n",
       "         7: 201,\n",
       "         -1: 95,\n",
       "         8: 130})"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter(dbscan_labels)"
   ]
  }
 ],
 "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}