{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qiPp7b1nLfhj"
   },
   "source": [
    "# Cluster Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QRjjQ0FwLfhm"
   },
   "source": [
    "## Agenda"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZuMjLnnJLfhn"
   },
   "source": [
    "* Problem Statement\n",
    "* Understanding Data\n",
    "* Data Preparation\n",
    "    * Choose relevant features\n",
    "    * Missing value analysis\n",
    "    * Data standardization\n",
    "* Hierarchical(Agglomerative) Clustering\n",
    "* K-Means Clustering\n",
    "* Clustering in practice\n",
    "    * Silhouette value of clusters\n",
    "    * Stability check of the clusters\n",
    "    * Categorize new samples into predefined clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZwhaOZvkLfhn"
   },
   "source": [
    "### Problem Statement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_YQbHPqzLfho"
   },
   "source": [
    "The data set (Cereals.csv) given to you has nutritional information of various cereals available in the market. Based on the information, the elementary public schools would like to choose a set of cereals to include in their daily cafeterias. Every day a different cereal is offered, but all cereals should support a healthy diet."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6ZOZGGJJLfho",
    "tags": []
   },
   "source": [
    "##### Attributes Description\n",
    "* name: Name of cereal\n",
    "* calories: calories per serving\n",
    "* protein: grams of protein\n",
    "* fat: grams of fat\n",
    "* sodium: milligrams of sodium\n",
    "* fiber: grams of dietary fiber\n",
    "* carbo: grams of complex carbohydrates\n",
    "* sugars: grams of sugars\n",
    "* potass: milligrams of potassium\n",
    "* vitamins: vitamins and minerals - 0, 25, or 100, indicating the typical percentage of FDA recommended\n",
    "* shelf: display shelf (1, 2, or 3, counting from the floor)\n",
    "* weight: weight in ounces of one serving\n",
    "* cups: number of cups in one serving\n",
    "* rating: rating of the cereals (Possibly from Consumer Reports)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "1KuDwL4KLfhp"
   },
   "outputs": [],
   "source": [
    "#Import required packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.cluster import KMeans, AgglomerativeClustering, DBSCAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "GUwpGSI-Lfhr"
   },
   "outputs": [],
   "source": [
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact, interact_manual"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zcoMhW3_Lfhr",
    "tags": []
   },
   "source": [
    "### Understanding Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "PpsMLO__Lfhs",
    "outputId": "f96f8689-53a1-4bd3-c53d-fff711606593"
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>calories</th>\n",
       "      <th>protein</th>\n",
       "      <th>fat</th>\n",
       "      <th>sodium</th>\n",
       "      <th>fiber</th>\n",
       "      <th>carbo</th>\n",
       "      <th>sugars</th>\n",
       "      <th>potass</th>\n",
       "      <th>vitamins</th>\n",
       "      <th>shelf</th>\n",
       "      <th>weight</th>\n",
       "      <th>cups</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100%_Bran</td>\n",
       "      <td>70</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.33</td>\n",
       "      <td>68.402973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100%_Natural_Bran</td>\n",
       "      <td>120</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>33.983679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>All-Bran</td>\n",
       "      <td>70</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>260</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.33</td>\n",
       "      <td>59.425505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>All-Bran_with_Extra_Fiber</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>93.704912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Almond_Delight</td>\n",
       "      <td>110</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>34.384843</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        name  calories  protein  fat  sodium  fiber  carbo  \\\n",
       "0                  100%_Bran        70        4    1     130   10.0    5.0   \n",
       "1          100%_Natural_Bran       120        3    5      15    2.0    8.0   \n",
       "2                   All-Bran        70        4    1     260    9.0    7.0   \n",
       "3  All-Bran_with_Extra_Fiber        50        4    0     140   14.0    8.0   \n",
       "4             Almond_Delight       110        2    2     200    1.0   14.0   \n",
       "\n",
       "   sugars  potass  vitamins  shelf  weight  cups     rating  \n",
       "0     6.0   280.0        25      3     1.0  0.33  68.402973  \n",
       "1     8.0   135.0         0      3     1.0  1.00  33.983679  \n",
       "2     5.0   320.0        25      3     1.0  0.33  59.425505  \n",
       "3     0.0   330.0        25      3     1.0  0.50  93.704912  \n",
       "4     8.0     NaN        25      3     1.0  0.75  34.384843  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cereals = pd.read_csv(\"Cereals.csv\")\n",
    "cereals.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238,
     "referenced_widgets": [
      "19d05550a99e4aeb88958892aefb106e",
      "1f13ab24103948799791b3ace33b6980",
      "c628dc94bd5a40e0a7bc6080c7a6e187",
      "b9cbbfa7541e4dc2aa8b916d18a8c0c9",
      "0802e1eaa0684211a33d3d63e810d6c4",
      "09ec34c62b104c0b887685e601f9b1e3",
      "617bd7cc14164adeacd78b95c35501c5"
     ]
    },
    "id": "EZgmQh9vLfht",
    "outputId": "3815786c-1188-4c16-9655-60c91053e200",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6cd66e3f309a44edbaa48fcbbe720b4d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=5, description='rows', max=15, min=-5), Output()), _dom_classes=('widget…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@interact\n",
    "def First_Rows(rows=5):\n",
    "    return cereals.head(rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 207,
     "referenced_widgets": [
      "6672d1098694475a85fd989d99b0469f",
      "78afdea1e0bc4eb38f01dd6bc05a00fc",
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      "56515092c3764e1ebad1dcf5a65cf322"
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    },
    "id": "xF0r_J2jLfht",
    "outputId": "fe6b4812-5dfc-4a3c-9369-131e766f908e"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "087bda1afbb54091a9d6e0fe23826732",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='column', options=('name', 'calories', 'protein', 'fat', 'sodium', …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data type of each attribute\n",
    "@interact\n",
    "def describe(column=cereals.columns):\n",
    "    return cereals[[column]].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 77 entries, 0 to 76\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   name      77 non-null     object \n",
      " 1   calories  77 non-null     int64  \n",
      " 2   protein   77 non-null     int64  \n",
      " 3   fat       77 non-null     int64  \n",
      " 4   sodium    77 non-null     int64  \n",
      " 5   fiber     77 non-null     float64\n",
      " 6   carbo     76 non-null     float64\n",
      " 7   sugars    76 non-null     float64\n",
      " 8   potass    75 non-null     float64\n",
      " 9   vitamins  77 non-null     int64  \n",
      " 10  shelf     77 non-null     int64  \n",
      " 11  weight    77 non-null     float64\n",
      " 12  cups      77 non-null     float64\n",
      " 13  rating    77 non-null     float64\n",
      "dtypes: float64(7), int64(6), object(1)\n",
      "memory usage: 8.6+ KB\n"
     ]
    }
   ],
   "source": [
    "cereals.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 320
    },
    "id": "APqpVq_3Lfht",
    "outputId": "d456db91-ef13-4092-a901-eea88f81f98e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calories</th>\n",
       "      <th>protein</th>\n",
       "      <th>fat</th>\n",
       "      <th>sodium</th>\n",
       "      <th>fiber</th>\n",
       "      <th>carbo</th>\n",
       "      <th>sugars</th>\n",
       "      <th>potass</th>\n",
       "      <th>vitamins</th>\n",
       "      <th>shelf</th>\n",
       "      <th>weight</th>\n",
       "      <th>cups</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>106.883117</td>\n",
       "      <td>2.545455</td>\n",
       "      <td>1.012987</td>\n",
       "      <td>159.675325</td>\n",
       "      <td>2.151948</td>\n",
       "      <td>14.802632</td>\n",
       "      <td>7.026316</td>\n",
       "      <td>98.666667</td>\n",
       "      <td>28.246753</td>\n",
       "      <td>2.207792</td>\n",
       "      <td>1.029610</td>\n",
       "      <td>0.821039</td>\n",
       "      <td>42.665705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>19.484119</td>\n",
       "      <td>1.094790</td>\n",
       "      <td>1.006473</td>\n",
       "      <td>83.832295</td>\n",
       "      <td>2.383364</td>\n",
       "      <td>3.907326</td>\n",
       "      <td>4.378656</td>\n",
       "      <td>70.410636</td>\n",
       "      <td>22.342523</td>\n",
       "      <td>0.832524</td>\n",
       "      <td>0.150477</td>\n",
       "      <td>0.232716</td>\n",
       "      <td>14.047289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>18.042851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>42.500000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.670000</td>\n",
       "      <td>33.174094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>110.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>40.400208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>110.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>210.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50.828392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>160.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>93.704912</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         calories    protein        fat      sodium      fiber      carbo  \\\n",
       "count   77.000000  77.000000  77.000000   77.000000  77.000000  76.000000   \n",
       "mean   106.883117   2.545455   1.012987  159.675325   2.151948  14.802632   \n",
       "std     19.484119   1.094790   1.006473   83.832295   2.383364   3.907326   \n",
       "min     50.000000   1.000000   0.000000    0.000000   0.000000   5.000000   \n",
       "25%    100.000000   2.000000   0.000000  130.000000   1.000000  12.000000   \n",
       "50%    110.000000   3.000000   1.000000  180.000000   2.000000  14.500000   \n",
       "75%    110.000000   3.000000   2.000000  210.000000   3.000000  17.000000   \n",
       "max    160.000000   6.000000   5.000000  320.000000  14.000000  23.000000   \n",
       "\n",
       "          sugars      potass    vitamins      shelf     weight       cups  \\\n",
       "count  76.000000   75.000000   77.000000  77.000000  77.000000  77.000000   \n",
       "mean    7.026316   98.666667   28.246753   2.207792   1.029610   0.821039   \n",
       "std     4.378656   70.410636   22.342523   0.832524   0.150477   0.232716   \n",
       "min     0.000000   15.000000    0.000000   1.000000   0.500000   0.250000   \n",
       "25%     3.000000   42.500000   25.000000   1.000000   1.000000   0.670000   \n",
       "50%     7.000000   90.000000   25.000000   2.000000   1.000000   0.750000   \n",
       "75%    11.000000  120.000000   25.000000   3.000000   1.000000   1.000000   \n",
       "max    15.000000  330.000000  100.000000   3.000000   1.500000   1.500000   \n",
       "\n",
       "          rating  \n",
       "count  77.000000  \n",
       "mean   42.665705  \n",
       "std    14.047289  \n",
       "min    18.042851  \n",
       "25%    33.174094  \n",
       "50%    40.400208  \n",
       "75%    50.828392  \n",
       "max    93.704912  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Summary statistics\n",
    "cereals.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tA0pNEq1Lfhu"
   },
   "source": [
    "### Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ArZe3cfCLfhu"
   },
   "source": [
    "#### Choose required features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 198
    },
    "id": "eycUeV7OLfhu",
    "outputId": "a82e74cf-3153-4be8-cc0a-3cc281f98688"
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>calories</th>\n",
       "      <th>protein</th>\n",
       "      <th>fat</th>\n",
       "      <th>sodium</th>\n",
       "      <th>fiber</th>\n",
       "      <th>carbo</th>\n",
       "      <th>sugars</th>\n",
       "      <th>potass</th>\n",
       "      <th>vitamins</th>\n",
       "      <th>shelf</th>\n",
       "      <th>weight</th>\n",
       "      <th>cups</th>\n",
       "      <th>rating</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100%_Bran</td>\n",
       "      <td>70</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>280.0</td>\n",
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       "      <td>68.402973</td>\n",
       "      <td>100%_Bran (3 - 68.4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100%_Natural_Bran</td>\n",
       "      <td>120</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>33.983679</td>\n",
       "      <td>100%_Natural_Bran (3 - 33.98)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>All-Bran</td>\n",
       "      <td>70</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>260</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.33</td>\n",
       "      <td>59.425505</td>\n",
       "      <td>All-Bran (3 - 59.43)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                name  calories  protein  fat  sodium  fiber  carbo  sugars  \\\n",
       "0          100%_Bran        70        4    1     130   10.0    5.0     6.0   \n",
       "1  100%_Natural_Bran       120        3    5      15    2.0    8.0     8.0   \n",
       "2           All-Bran        70        4    1     260    9.0    7.0     5.0   \n",
       "\n",
       "   potass  vitamins  shelf  weight  cups     rating  \\\n",
       "0   280.0        25      3     1.0  0.33  68.402973   \n",
       "1   135.0         0      3     1.0  1.00  33.983679   \n",
       "2   320.0        25      3     1.0  0.33  59.425505   \n",
       "\n",
       "                           label  \n",
       "0           100%_Bran (3 - 68.4)  \n",
       "1  100%_Natural_Bran (3 - 33.98)  \n",
       "2           All-Bran (3 - 59.43)  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cereals['label'] = cereals['name']+ ' (' + cereals['shelf'].astype(str) + \" - \" + \\\n",
    "                    round(cereals['rating'],2).astype(str) + ')'\n",
    "cereals.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "id": "y_o1gD95Lfhu",
    "outputId": "181a76bd-ae4e-4da6-fd4f-4c1a011d675a"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calories</th>\n",
       "      <th>protein</th>\n",
       "      <th>fat</th>\n",
       "      <th>sodium</th>\n",
       "      <th>fiber</th>\n",
       "      <th>carbo</th>\n",
       "      <th>sugars</th>\n",
       "      <th>potass</th>\n",
       "      <th>vitamins</th>\n",
       "      <th>weight</th>\n",
       "      <th>cups</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>70</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.33</td>\n",
       "      <td>100%_Bran (3 - 68.4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>120</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>100%_Natural_Bran (3 - 33.98)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>260</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.33</td>\n",
       "      <td>All-Bran (3 - 59.43)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   calories  protein  fat  sodium  fiber  carbo  sugars  potass  vitamins  \\\n",
       "0        70        4    1     130   10.0    5.0     6.0   280.0        25   \n",
       "1       120        3    5      15    2.0    8.0     8.0   135.0         0   \n",
       "2        70        4    1     260    9.0    7.0     5.0   320.0        25   \n",
       "\n",
       "   weight  cups                          label  \n",
       "0     1.0  0.33           100%_Bran (3 - 68.4)  \n",
       "1     1.0  1.00  100%_Natural_Bran (3 - 33.98)  \n",
       "2     1.0  0.33           All-Bran (3 - 59.43)  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cereals.drop(['name','shelf','rating'], axis=1, inplace=True)\n",
    "cereals.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "id": "MIqml5L3Lfhv",
    "outputId": "a362b4a2-b8dc-44d4-b7be-5e1d7273181a"
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calories</th>\n",
       "      <th>protein</th>\n",
       "      <th>fat</th>\n",
       "      <th>sodium</th>\n",
       "      <th>fiber</th>\n",
       "      <th>carbo</th>\n",
       "      <th>sugars</th>\n",
       "      <th>potass</th>\n",
       "      <th>vitamins</th>\n",
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       "      <th>cups</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>70</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>120</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>260</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   calories  protein  fat  sodium  fiber  carbo  sugars  potass  vitamins  \\\n",
       "0        70        4    1     130   10.0    5.0     6.0   280.0        25   \n",
       "1       120        3    5      15    2.0    8.0     8.0   135.0         0   \n",
       "2        70        4    1     260    9.0    7.0     5.0   320.0        25   \n",
       "\n",
       "   weight  cups  \n",
       "0     1.0  0.33  \n",
       "1     1.0  1.00  \n",
       "2     1.0  0.33  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cereal_label = cereals['label']\n",
    "## Select all columns except \"label\"\n",
    "cereals.drop('label', axis=1, inplace=True)\n",
    "cereals.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 458
    },
    "id": "8-kSmZx8Lfhv",
    "outputId": "e038471c-503e-48a2-afff-06434ef63cff"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                     100%_Bran (3 - 68.4)\n",
       "1            100%_Natural_Bran (3 - 33.98)\n",
       "2                     All-Bran (3 - 59.43)\n",
       "3     All-Bran_with_Extra_Fiber (3 - 93.7)\n",
       "4               Almond_Delight (3 - 34.38)\n",
       "                      ...                 \n",
       "72                     Triples (3 - 39.11)\n",
       "73                        Trix (2 - 27.75)\n",
       "74                  Wheat_Chex (1 - 49.79)\n",
       "75                    Wheaties (1 - 51.59)\n",
       "76         Wheaties_Honey_Gold (1 - 36.19)\n",
       "Name: label, Length: 77, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cereal_label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jTW7bQA2Lfhw"
   },
   "source": [
    "#### Missing value analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 429
    },
    "id": "wKyJEOALLfhw",
    "outputId": "9c13a8db-303d-4b37-9ca0-c479d728d6c5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "calories    0\n",
       "protein     0\n",
       "fat         0\n",
       "sodium      0\n",
       "fiber       0\n",
       "carbo       1\n",
       "sugars      1\n",
       "potass      2\n",
       "vitamins    0\n",
       "weight      0\n",
       "cups        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cereals.isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 80
    },
    "id": "iujbxJF0Lfhx",
    "outputId": "e412b331-f865-4c41-c551-369aeae66115"
   },
   "outputs": [
    {
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       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
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       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
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       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
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       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
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       "\n",
       "#sk-container-id-1 pre {\n",
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       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
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       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
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       "\n",
       "#sk-container-id-1 div.sk-container {\n",
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       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SimpleImputer()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SimpleImputer()"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "mean_imputer = SimpleImputer(strategy='mean')\n",
    "mean_imputer.fit(cereals)\n",
    "mean_imputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "b1nskkmnLfhx"
   },
   "outputs": [],
   "source": [
    "cereals = pd.DataFrame(mean_imputer.transform(cereals),columns=cereals.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 429
    },
    "id": "E5TCUpawLfhx",
    "outputId": "fa5cebd0-5caa-481f-a64b-6877c16eb7f1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "calories    0\n",
       "protein     0\n",
       "fat         0\n",
       "sodium      0\n",
       "fiber       0\n",
       "carbo       0\n",
       "sugars      0\n",
       "potass      0\n",
       "vitamins    0\n",
       "weight      0\n",
       "cups        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cereals.isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HT6_UIzVLfhx"
   },
   "source": [
    "#### Data Standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "5h5e1Yn5Lfhx",
    "outputId": "da002d03-ea99-4371-84e7-892652e60ded"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calories</th>\n",
       "      <th>protein</th>\n",
       "      <th>fat</th>\n",
       "      <th>sodium</th>\n",
       "      <th>fiber</th>\n",
       "      <th>carbo</th>\n",
       "      <th>sugars</th>\n",
       "      <th>potass</th>\n",
       "      <th>vitamins</th>\n",
       "      <th>weight</th>\n",
       "      <th>cups</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.905397</td>\n",
       "      <td>1.337319</td>\n",
       "      <td>-0.012988</td>\n",
       "      <td>-0.356306</td>\n",
       "      <td>3.314439</td>\n",
       "      <td>-2.542013</td>\n",
       "      <td>-0.237495</td>\n",
       "      <td>2.627053</td>\n",
       "      <td>-0.14627</td>\n",
       "      <td>-0.198067</td>\n",
       "      <td>-2.123870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.677623</td>\n",
       "      <td>0.417912</td>\n",
       "      <td>3.987349</td>\n",
       "      <td>-1.737087</td>\n",
       "      <td>-0.064172</td>\n",
       "      <td>-1.764055</td>\n",
       "      <td>0.225316</td>\n",
       "      <td>0.526376</td>\n",
       "      <td>-1.27255</td>\n",
       "      <td>-0.198067</td>\n",
       "      <td>0.774053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.905397</td>\n",
       "      <td>1.337319</td>\n",
       "      <td>-0.012988</td>\n",
       "      <td>1.204578</td>\n",
       "      <td>2.892113</td>\n",
       "      <td>-2.023374</td>\n",
       "      <td>-0.468901</td>\n",
       "      <td>3.206550</td>\n",
       "      <td>-0.14627</td>\n",
       "      <td>-0.198067</td>\n",
       "      <td>-2.123870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.938605</td>\n",
       "      <td>1.337319</td>\n",
       "      <td>-1.013072</td>\n",
       "      <td>-0.236238</td>\n",
       "      <td>5.003745</td>\n",
       "      <td>-1.764055</td>\n",
       "      <td>-1.625929</td>\n",
       "      <td>3.351425</td>\n",
       "      <td>-0.14627</td>\n",
       "      <td>-0.198067</td>\n",
       "      <td>-1.388576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.161019</td>\n",
       "      <td>-0.501495</td>\n",
       "      <td>0.987096</td>\n",
       "      <td>0.484170</td>\n",
       "      <td>-0.486498</td>\n",
       "      <td>-0.208138</td>\n",
       "      <td>0.225316</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.14627</td>\n",
       "      <td>-0.198067</td>\n",
       "      <td>-0.307262</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   calories   protein       fat    sodium     fiber     carbo    sugars  \\\n",
       "0 -1.905397  1.337319 -0.012988 -0.356306  3.314439 -2.542013 -0.237495   \n",
       "1  0.677623  0.417912  3.987349 -1.737087 -0.064172 -1.764055  0.225316   \n",
       "2 -1.905397  1.337319 -0.012988  1.204578  2.892113 -2.023374 -0.468901   \n",
       "3 -2.938605  1.337319 -1.013072 -0.236238  5.003745 -1.764055 -1.625929   \n",
       "4  0.161019 -0.501495  0.987096  0.484170 -0.486498 -0.208138  0.225316   \n",
       "\n",
       "     potass  vitamins    weight      cups  \n",
       "0  2.627053  -0.14627 -0.198067 -2.123870  \n",
       "1  0.526376  -1.27255 -0.198067  0.774053  \n",
       "2  3.206550  -0.14627 -0.198067 -2.123870  \n",
       "3  3.351425  -0.14627 -0.198067 -1.388576  \n",
       "4  0.000000  -0.14627 -0.198067 -0.307262  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "standardizer = StandardScaler()\n",
    "standardizer.fit(cereals)\n",
    "cereals_std = pd.DataFrame(standardizer.transform(cereals), columns = cereals.columns)\n",
    "cereals_std.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 372
    },
    "id": "cfIvVXp4Lfhy",
    "outputId": "8ec41dda-d544-440d-ee78-12cc9cbcf469",
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calories</th>\n",
       "      <th>protein</th>\n",
       "      <th>fat</th>\n",
       "      <th>sodium</th>\n",
       "      <th>fiber</th>\n",
       "      <th>carbo</th>\n",
       "      <th>sugars</th>\n",
       "      <th>potass</th>\n",
       "      <th>vitamins</th>\n",
       "      <th>weight</th>\n",
       "      <th>cups</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7.700000e+01</td>\n",
       "      <td>7.700000e+01</td>\n",
       "      <td>7.700000e+01</td>\n",
       "      <td>7.700000e+01</td>\n",
       "      <td>7.700000e+01</td>\n",
       "      <td>7.700000e+01</td>\n",
       "      <td>7.700000e+01</td>\n",
       "      <td>7.700000e+01</td>\n",
       "      <td>7.700000e+01</td>\n",
       "      <td>7.700000e+01</td>\n",
       "      <td>7.700000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-8.398765e-17</td>\n",
       "      <td>2.018587e-17</td>\n",
       "      <td>8.583502e-17</td>\n",
       "      <td>5.767392e-18</td>\n",
       "      <td>9.155735e-17</td>\n",
       "      <td>-4.066012e-16</td>\n",
       "      <td>-2.000564e-16</td>\n",
       "      <td>-8.506904e-17</td>\n",
       "      <td>2.883696e-17</td>\n",
       "      <td>-1.881612e-16</td>\n",
       "      <td>1.492313e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.006557e+00</td>\n",
       "      <td>1.006557e+00</td>\n",
       "      <td>1.006557e+00</td>\n",
       "      <td>1.006557e+00</td>\n",
       "      <td>1.006557e+00</td>\n",
       "      <td>1.006557e+00</td>\n",
       "      <td>1.006557e+00</td>\n",
       "      <td>1.006557e+00</td>\n",
       "      <td>1.006557e+00</td>\n",
       "      <td>1.006557e+00</td>\n",
       "      <td>1.006557e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.938605e+00</td>\n",
       "      <td>-1.420902e+00</td>\n",
       "      <td>-1.013072e+00</td>\n",
       "      <td>-1.917189e+00</td>\n",
       "      <td>-9.088244e-01</td>\n",
       "      <td>-2.542013e+00</td>\n",
       "      <td>-1.625929e+00</td>\n",
       "      <td>-1.212115e+00</td>\n",
       "      <td>-1.272550e+00</td>\n",
       "      <td>-3.542628e+00</td>\n",
       "      <td>-2.469891e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-3.555846e-01</td>\n",
       "      <td>-5.014948e-01</td>\n",
       "      <td>-1.013072e+00</td>\n",
       "      <td>-3.563056e-01</td>\n",
       "      <td>-4.864980e-01</td>\n",
       "      <td>-7.267769e-01</td>\n",
       "      <td>-9.317120e-01</td>\n",
       "      <td>-7.774919e-01</td>\n",
       "      <td>-1.462701e-01</td>\n",
       "      <td>-1.980675e-01</td>\n",
       "      <td>-6.532825e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.610194e-01</td>\n",
       "      <td>4.179123e-01</td>\n",
       "      <td>-1.298811e-02</td>\n",
       "      <td>2.440343e-01</td>\n",
       "      <td>-6.417167e-02</td>\n",
       "      <td>-4.606439e-16</td>\n",
       "      <td>-6.089621e-03</td>\n",
       "      <td>-1.255577e-01</td>\n",
       "      <td>-1.462701e-01</td>\n",
       "      <td>-1.980675e-01</td>\n",
       "      <td>-3.072619e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.610194e-01</td>\n",
       "      <td>4.179123e-01</td>\n",
       "      <td>9.870962e-01</td>\n",
       "      <td>6.042382e-01</td>\n",
       "      <td>3.581547e-01</td>\n",
       "      <td>5.698204e-01</td>\n",
       "      <td>9.195328e-01</td>\n",
       "      <td>3.090651e-01</td>\n",
       "      <td>-1.462701e-01</td>\n",
       "      <td>-1.980675e-01</td>\n",
       "      <td>7.740527e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.744040e+00</td>\n",
       "      <td>3.176134e+00</td>\n",
       "      <td>3.987349e+00</td>\n",
       "      <td>1.924986e+00</td>\n",
       "      <td>5.003745e+00</td>\n",
       "      <td>2.125737e+00</td>\n",
       "      <td>1.845155e+00</td>\n",
       "      <td>3.351425e+00</td>\n",
       "      <td>3.232570e+00</td>\n",
       "      <td>3.146493e+00</td>\n",
       "      <td>2.936682e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           calories       protein           fat        sodium         fiber  \\\n",
       "count  7.700000e+01  7.700000e+01  7.700000e+01  7.700000e+01  7.700000e+01   \n",
       "mean  -8.398765e-17  2.018587e-17  8.583502e-17  5.767392e-18  9.155735e-17   \n",
       "std    1.006557e+00  1.006557e+00  1.006557e+00  1.006557e+00  1.006557e+00   \n",
       "min   -2.938605e+00 -1.420902e+00 -1.013072e+00 -1.917189e+00 -9.088244e-01   \n",
       "25%   -3.555846e-01 -5.014948e-01 -1.013072e+00 -3.563056e-01 -4.864980e-01   \n",
       "50%    1.610194e-01  4.179123e-01 -1.298811e-02  2.440343e-01 -6.417167e-02   \n",
       "75%    1.610194e-01  4.179123e-01  9.870962e-01  6.042382e-01  3.581547e-01   \n",
       "max    2.744040e+00  3.176134e+00  3.987349e+00  1.924986e+00  5.003745e+00   \n",
       "\n",
       "              carbo        sugars        potass      vitamins        weight  \\\n",
       "count  7.700000e+01  7.700000e+01  7.700000e+01  7.700000e+01  7.700000e+01   \n",
       "mean  -4.066012e-16 -2.000564e-16 -8.506904e-17  2.883696e-17 -1.881612e-16   \n",
       "std    1.006557e+00  1.006557e+00  1.006557e+00  1.006557e+00  1.006557e+00   \n",
       "min   -2.542013e+00 -1.625929e+00 -1.212115e+00 -1.272550e+00 -3.542628e+00   \n",
       "25%   -7.267769e-01 -9.317120e-01 -7.774919e-01 -1.462701e-01 -1.980675e-01   \n",
       "50%   -4.606439e-16 -6.089621e-03 -1.255577e-01 -1.462701e-01 -1.980675e-01   \n",
       "75%    5.698204e-01  9.195328e-01  3.090651e-01 -1.462701e-01 -1.980675e-01   \n",
       "max    2.125737e+00  1.845155e+00  3.351425e+00  3.232570e+00  3.146493e+00   \n",
       "\n",
       "               cups  \n",
       "count  7.700000e+01  \n",
       "mean   1.492313e-16  \n",
       "std    1.006557e+00  \n",
       "min   -2.469891e+00  \n",
       "25%   -6.532825e-01  \n",
       "50%   -3.072619e-01  \n",
       "75%    7.740527e-01  \n",
       "max    2.936682e+00  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cereals_std.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MIwkwr9iLfhy"
   },
   "source": [
    "### Agglomerative Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wZs-0m58Lfhz"
   },
   "source": [
    "**Parameter description**\n",
    "\n",
    "n_clusters : The number of clusters to find.\n",
    "\n",
    "linkage : {“ward”, “complete”, “average”}\n",
    "- ward minimizes the variance of the clusters being merged.\n",
    "- complete uses the maximum distances between all observations of the two sets.\n",
    "- average uses the average of the distances of each observation of the two sets.\n",
    "\n",
    "affinity : {“euclidean”, “l1”, “l2”, “manhattan”, “cosine”}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "id": "CEPyHjBbLfhz",
    "outputId": "99baa7bb-a3b5-416e-df20-e56a852995a7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calories</th>\n",
       "      <th>protein</th>\n",
       "      <th>fat</th>\n",
       "      <th>sodium</th>\n",
       "      <th>fiber</th>\n",
       "      <th>carbo</th>\n",
       "      <th>sugars</th>\n",
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       "      <th>cups</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>70.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>120.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   calories  protein  fat  sodium  fiber  carbo  sugars  potass  vitamins  \\\n",
       "0      70.0      4.0  1.0   130.0   10.0    5.0     6.0   280.0      25.0   \n",
       "1     120.0      3.0  5.0    15.0    2.0    8.0     8.0   135.0       0.0   \n",
       "2      70.0      4.0  1.0   260.0    9.0    7.0     5.0   320.0      25.0   \n",
       "\n",
       "   weight  cups  \n",
       "0     1.0  0.33  \n",
       "1     1.0  1.00  \n",
       "2     1.0  0.33  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cereals.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "id": "LCGMH2ktLfh0",
    "outputId": "dbad1fb4-a0a7-4afc-a4de-e559f022b9b0"
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calories</th>\n",
       "      <th>protein</th>\n",
       "      <th>fat</th>\n",
       "      <th>sodium</th>\n",
       "      <th>fiber</th>\n",
       "      <th>carbo</th>\n",
       "      <th>sugars</th>\n",
       "      <th>potass</th>\n",
       "      <th>vitamins</th>\n",
       "      <th>weight</th>\n",
       "      <th>cups</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.905397</td>\n",
       "      <td>1.337319</td>\n",
       "      <td>-0.012988</td>\n",
       "      <td>-0.356306</td>\n",
       "      <td>3.314439</td>\n",
       "      <td>-2.542013</td>\n",
       "      <td>-0.237495</td>\n",
       "      <td>2.627053</td>\n",
       "      <td>-0.14627</td>\n",
       "      <td>-0.198067</td>\n",
       "      <td>-2.123870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.677623</td>\n",
       "      <td>0.417912</td>\n",
       "      <td>3.987349</td>\n",
       "      <td>-1.737087</td>\n",
       "      <td>-0.064172</td>\n",
       "      <td>-1.764055</td>\n",
       "      <td>0.225316</td>\n",
       "      <td>0.526376</td>\n",
       "      <td>-1.27255</td>\n",
       "      <td>-0.198067</td>\n",
       "      <td>0.774053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.905397</td>\n",
       "      <td>1.337319</td>\n",
       "      <td>-0.012988</td>\n",
       "      <td>1.204578</td>\n",
       "      <td>2.892113</td>\n",
       "      <td>-2.023374</td>\n",
       "      <td>-0.468901</td>\n",
       "      <td>3.206550</td>\n",
       "      <td>-0.14627</td>\n",
       "      <td>-0.198067</td>\n",
       "      <td>-2.123870</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   calories   protein       fat    sodium     fiber     carbo    sugars  \\\n",
       "0 -1.905397  1.337319 -0.012988 -0.356306  3.314439 -2.542013 -0.237495   \n",
       "1  0.677623  0.417912  3.987349 -1.737087 -0.064172 -1.764055  0.225316   \n",
       "2 -1.905397  1.337319 -0.012988  1.204578  2.892113 -2.023374 -0.468901   \n",
       "\n",
       "     potass  vitamins    weight      cups  \n",
       "0  2.627053  -0.14627 -0.198067 -2.123870  \n",
       "1  0.526376  -1.27255 -0.198067  0.774053  \n",
       "2  3.206550  -0.14627 -0.198067 -2.123870  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cereals_std.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-23T11:11:37.246329Z",
     "start_time": "2019-02-23T11:11:36.976785Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 487
    },
    "id": "Nuue1DeuLfh1",
    "outputId": "71131c0e-ece8-4cf9-f2cb-161c4d001b3e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wDMMwmZQEHzHHMAyTVIgIFBaWpHvERsb87//QMMRGZ0nS/YzMzWFkZJSkezAMw3xvsCL4HUJECItO2iCaUmjKlV5lBABzEx700woiwuvOXRB282aS7hOeJSvgPBsA8LSOHbLFRCbpfuY1auDHrVu4XjAMk6lgRfA7g4jQ/Vh33Pp0K61FUcVhl0Nai6BI9YLVsbHJRh700wAKC0uyEggA2WIicezAqGSQKI6wGzdAYWEwyp492e7JMAyT3mFF8DsjLDrsu1AC0zs3/7uJsOgwZDflQT8tKXPhPIzNzdNUhtiwMDytY5emMjAMw6QVrAh+x3i194K5SdoOot8bYdFh6XqmMrNhbG4OY56BYxgmHkSE6IiIZLlXlMZ+4qjwCETFRif5niZmZhlmRYkVwe8YcxNzntFiGIZhMhREhB2Tx+DDk4fJcr8oIxPgpz4AgFV9u8CUkq4IFi1bAR2nzc0QyiArggzDMAzDpBuiIyKSTQkEAFOKxpCXq5LtfgDw4fEDREdEwDRbtmS9b1rAiiDDMAzDMOmSAWu3wNQs/ShbURHhWNW3a1qLkaywIsgwDMMwTLrE1Cxbhph1S8/wySIMwzAMwzCZFFYEGYZhGIZhMimsCDIMwzAMw2RSkl0R/Omnn2BkZKTzGTRokN7rvby89F7/6NGj5BaNYRiGYRiG0SDZjUWuXr2KmJj/OW+8d+8eGjVqhHbt2gnjPX78GLly5ZK/FyhQILlFYxiGYRiGYTRIdkUwvgL3xx9/oHTp0qhXr54wXsGCBZEnT57kFodhGIZhGIZRIEX3CEZGRmLLli3o3bu3qvft6tWro0iRInB0dMSZM2dU7x0REYHAwECtD8MwDMMwDGM4KaoIHjhwAAEBAejZs6fiNUWKFMHatWuxd+9e7Nu3D2XLloWjoyPOnj0rvPecOXOQO3du+VOiRIlklp5hGIZhGCZjk6IOpf/++280bdoURYsWVbymbNmyKFu2rPzd1tYWb9++xfz582Fvb68Yz93dHW5ubvL3wMBAVgYZhmEYhmESQIopgq9fv8apU6ewb9++BMe1sbHBli1bhNeYmZnBzMwsseIx6RAiQlh0WIo+Q/P+Kf0scxPzDHEgOcMwDJNxSTFFcP369ShYsCCaN2+e4Lg3b95EkSJFUkAqJr1CROh+rDtufbqVas902OWQovevXrA6NjbZyMogwzAMk25JEUUwNjYW69evR48ePWBiov0Id3d3vH//Hps2bQIALF68GD/99BMqVqwoG5fs3bsXe/fuTQnRmHRKWHRYqiqBqcHN/24iLDoM2U2zp7UoDMMwDKOXFFEET506hTdv3qB37946Yb6+vnjz5o38PTIyEqNGjcL79+9hbm6OihUr4siRI2jWrFlKiMZ8B3i194K5iXlai5FowqLDUny2kWEYhmGSgxRRBJ2cnEBEesM2bNig9X3MmDEYM2ZMSojBfKeYm5jzLBrDMAzDpAJ81jDDMAzDMEwmhRVBhmEYhmGYTAorggzDMAzDMJmUFHUozTAMkxoQESgscX4hYzXixSbyHgBgZM5+IxmG+f5gRZDJNKSGw2ogdZ1WS2Rm59VEhNeduyDs5s0k3+tpHbtExzWvUQM/bt2SacuBYZjvE1YEmUxBWjisBlLeabVEZnZeTWFhyaIEJpWwGzdAYWEwys4W7wzDfD+wIshkCjKiw2pN2Hl1HGUunIexeer6oIwNC0vSTCLDMExawoogk+n43h1Wa8LOq7UxNjeHMc/IMQzDGAwrgkymgx1WMwzDMEwc7D6GYRiGYRgmk8KKIMMwDMMwTCaFFUGGYRiGYZhMCu8RZBiGYZINIkJ0ZGxaiyETFRnzv/8jYhBF6cvFkklW40zp9olJP7AiyDAMwyQLRIR9f96A34tvaS2KTCQIyBP3/z+jzyMr0pfSVaR0brQeVYOVQSbNYEWQYRiGSRaiI2PTlRIIAFlhhNEB6dddlO/zb4iOjIWpWZa0FoXJpLAiyDAMwyQ7vebZsXIjICoiBuvHnE9rMRiGFUGGYRgm+TE1y8KKIMN8B7DVMMMwDMMwTCaFFUGGYRiGYZhMCiuCDMMwDMMwmRRWBBmGYRiGYTIpya4ITp06FUZGRlqfwoULC+N4e3ujZs2ayJYtG0qVKoXVq1cnt1gMwzAMwzBMPFLEarhixYo4deqU/D1LFmXLsZcvX6JZs2bo06cPtmzZggsXLmDgwIEoUKAAXF1dU0I8hmEYhmEYBimkCJqYmKjOAkqsXr0aJUuWxOLFiwEA5cuXx7Vr1zB//nxWBBmGYRiGYVKQFFEEnz59iqJFi8LMzAy1atXC7NmzUapUKb3X+vj4wMnJSeu3xo0b4++//0ZUVBRMTU1TQkSGYRgtiAgUFpbgeLEacWITER8AjMzN+YgxhmHShGRXBGvVqoVNmzbh119/xcePHzFz5kzUrl0b9+/fR/78+XWu9/PzQ6FChbR+K1SoEKKjo/H582cUKVJE73MiIiIQEREhfw8MDEzehDAMk2kgIrzu3AVhN28m6T5P69glKp55jRr4cesWVgYZhkl1kt1YpGnTpnB1dUXlypXRsGFDHDlyBACwceNGxTjxOz8i0vu7JnPmzEHu3LnlT4kSJZJBeoZhMiMUFpZkJTAphN24kajZSIZhmKSS4kfM5ciRA5UrV8bTp0/1hhcuXBh+fn5av/33338wMTHRO4Mo4e7uDjc3N/l7YGAgK4MMwySZMhfOw9jcPFWeFRsWluhZRIZhmOQgxRXBiIgIPHz4EHXr1tUbbmtri3///Vfrt5MnT8LS0lK4P9DMzAxmZmbJKivDMIyxuTmMs2dPazEYhmFShWRfGh41ahS8vb3x8uVLXL58GW3btkVgYCB69OgBIG4mr3v37vL1/fv3x+vXr+Hm5oaHDx/in3/+wd9//41Ro0Ylt2gMwzAMwzCMBsk+I/ju3Tt06tQJnz9/RoECBWBjY4NLly7hxx9/BAD4+vrizZs38vU///wzjh49ihEjRmDFihUoWrQoli5dmilcxxARwqITti9I8/qExpUwN2ELRSZ10GeJK7KyZetZhmGY1CXZFcEdO3YIwzds2KDzW7169XDjxo3kFiVdQ0Tofqw7bn26leh7OOxySFS86gWrY2OTjTzgMimKIZa48ffHsfUswzBM6sJnDacRYdFhSVICk8LN/24mejaRYQwlMZa4bD3LMAyTuqS4sQijjld7L5ibpLyVYlh0WKJnERkmKahZ4rL1LMMwTNrAimA6wNzEHNlN2UqRybiwJS7DMEz6hBXB75yEGJwk1tCEjUsYhmEYJmPCiuB3TFIMThKyRMzGJQzDMAyTMWFjke+Y1DI4YeMShmEYhsmY8IxgBiElDE7YuIRhGIZhMjasCGYQ2ODk+yYxzsUBdjDOMAzDJA1WBBkmjUkO5+IAOxhnGIZhEg7vEWSYNCYtnYsDvAeUYRgmM8MzggyTjkgt5+IA7wFlGIZhWBFkmHQF7/VkmPQJESE6MjbZ7hcVEaP3/+TAJKsxb/VgDCZjKoJEQFSo9m+RGg0tMhRAFu1w0+wANxyGYRgmHkSEfX/egN+Lbyly//Vjzifr/YqUzo3Wo2qwMsgYRMZTBImAfxoDby/H+90MwPq4///8BTCK0A4vYQP0Ps7KIMMwyQYRgcKU91/GaoTFCq4zMmfL7rQkOjI2xZTAlMD3+TeEBUXB1CyL+sUCMurMIhEhOiJC/UI9REWE6/0/oZiYmaWbvM14imBUqK4SCCC7UQReZeusHO/tpbi4WXOkoHAMwyQXkpKlpEyltfJERHjduQvCbt406PqndewUw8xr1MCPW7ekSnqSsgSaHMud6V356DXPLskKVnyICAcX38J/rwKT7Z7JMcuYEWcWiQg7Jo/BhycPk3yvVX27Jjpu0bIV0HHa3HSRtxlPEdRk1DMgq8p+q8hQYP4vqSMPwzDJgpKSpalMpabypA8KCzNYCVQj7MYNUFgYjLKn7P7R5FwCTawikt6VD1OzLMmuCEZFxCSrEphc+D7/hujI2GRPb1oSHRGRLEpgUvnw+AGiIyJgmi1bWouSwRXBrNl5ho9hMiCGKFmppTwZQpkL52FsnnBr8NiwMOFMYXKTHpZAM6LykRBSYsYxoURFxCT7vsX0yIC1W2BqlrqKWFREeJJmElOCjK0IMgyT4YmvZKW28mQIxubmME4HCmlCSG2FJLMoH2qkxIwjox9Ts2zpYkYurWFFkGEyAfqOsDPkeLrv4fi571HJ+h5ghYRhMgesCDJMBseQI+yUHEvz8XMMwzAZG1YEGSaDk5Qj7KTj59jJdcqjz9WMmnuZtLaMZpjMRlJczwDJ534GSD4XNKwIpiKay3NKy3Lfw1JcekTf0qcmhiyDSmTkMjD0CDs+fi51McTVjL59j2ltGc0wmYnkdD0DJM39DJB8LmiSXRGcM2cO9u3bh0ePHsHc3By1a9fG3LlzUbZsWcU4Xl5eqF+/vs7vDx8+RLly5ZJbxDRBtDynOeCmxVKckhKlpjylF4XJkKVPTdQUnIy8HMpH2KVPEutqJj1ZRjNMRie9uJ6RSC4XNMmuCHp7e2PQoEGwsrJCdHQ0JkyYACcnJzx48AA5cohduTx+/Bi5cuWSvxcoUCC5xUszDF2eS+2lOEOVKH3KU1IVpuSaIU3K0qc+eDmUSUsMcTWTHi2jGSahKC2zqi2fpodTOdLC9YxEcrugSXZF8Pjx41rf169fj4IFC+L69euwt7cXxi1YsCDy5MmT3CKlO/Qtz6XVUlxS9499Df+qkxZDlLeUmiE1dOlTH7wcyqQH2AqayQwYusyqT+FJD6dyZCTXMym+R/DbtzjnpPny5VO9tnr16ggPD0eFChUwceJEvcvFEhEREYjQeJMIDEx/XtmVSK/Lc/qUKCJCH48+uPv5rt44iZ0pTKkZ0vSatwyTGUnIcXWJPZ4uvR9Jx+gnKcus6elUjoxAiiqCRAQ3NzfY2dmhUqVKitcVKVIEa9euRc2aNREREYHNmzfD0dERXl5eirOIc+bMwbRp01JK9EyJPiUqNCpUUQlUIqHKW3qaIWUYJnlIynF1CXEsnd6PpGPUMXSZNT2eypERSFFFcPDgwbhz5w7Onxc36rJly2oZk9ja2uLt27eYP3++oiLo7u4ONzc3+XtgYCBKlCiRPIIzelFbdk2s8sazeAyT8Uit4+oy+5F0GQGlZVaRq5b0unfweyTFFMEhQ4bg0KFDOHv2LIoXL57g+DY2NtiyZYtiuJmZGczMzJIiIpNAMorCllgraSD9WEozzPdEShxXx0fSZWzU9hCm172D3yPJrggSEYYMGYL9+/fDy8sLP//8c6Luc/PmTRQpUiSZpft+ScwRYay06JIUK2kgY7qW4bqVfGg6hVZyBp0ZnUDzcXXpC7W9m4bu10zJ/ZmJ2UPIewcTR7IrgoMGDcK2bdtw8OBBWFhYwM/PDwCQO3dumP+/SwR3d3e8f/8emzZtAgAsXrwYP/30EypWrIjIyEhs2bIFe/fuxd69e5NbvO+SxB4RlhGVlqSSVFczGc21DNet5EPkFFrT1Ut6cAKtpAikBwWASVkSundTNOuaWvsz1fYQ8t7BpJHsiuCqVasAAA4ODlq/r1+/Hj179gQA+Pr64s2bN3JYZGQkRo0ahffv38Pc3BwVK1bEkSNH0KxZs+QW77skscpLRlNakpuEuJrJqMYrXLeSD0OdQqe1E2hDFYH0oAAwyU9y7t1Mrf2ZGclVS3okRZaG1diwYYPW9zFjxmDMmDHJLUqGxBDlJaMqLclNRtnzmFxw3Uo+9DmFTi9OoJNDEWADjYxBYvdu8v7MjEXmPGuYCIgKjfs/MvR/v2v+b5odSIdvu6y8fN8kZj8ekDp78rhuJR/fi1PohCoCrAAkjcQuyafUUjzv3WSAzKgIEgH/NAbeXtYNm//L//4vYQP0Pp4ulUHm+ySx+/EA3pOX3GgadQDKhh0SGdXAgxWB1CMpS/K8FM+kJJlPEYwK1a8Exuftpbhrs4rPR2YyHgmdtTN0ti6px/nxnrzkQWTUAUDv8m16MPBgvm+SsiTv+/wbwoKidJT21DLaiT+TmVYzmJkdya+i0lnMifWjmPkUQU1GPQOyxhtYI0O1ZwaZTEViZu0SM1tnqKEK78lLfgw16tAkrQ08mIyFviV5IsLBxbfw3yv9x6Wm1Uyh2kxmRpzBVHJkraSAaZJSTq2V/CpqWksn1o9i5lYEs2bPMDN+mrNY+mav2O+bYSRm1i4xs3X69uMpObrWlC3+PVJrNiCj1i19Rh2apBcDj8yEoecTJ/Rs4oTMUqX0DJi+JfmoiBhFJVCJ1DDaScxM5vdsTKTmyFpCyV1NSjm1NsSvYmL9KGZuRTCDIJrFkmaTeI9ZwkmpI/X0kVozkckpV0aoW9+LUUdaoqkUKSlEybUUmNjziQ0xYDF0lio9zICpGfGkldFOepJLc9YuOZdKE+PIWpPUcGod369iUv0osiKYQNRmR4DUnyExZBYro+0xS41ySE0r2tSaiUwombFuMf9DpBRpDvjJpQil5PnEhs5SpYcZsOQ24kkuZT69GBeJZu2SY6lUQs2RtSap6dQ6uf0qsiKYAAyZHQHSdoYk/ixWRtxj9j2UQ1JIzZnIhJAZ6hajjaFKUUosBSbX+cRJmaVKTzNgiSW1lfnUwNBZu6TOzmUWR9asCCYAQ2dt0nKGJDP4gvseyiEpKJWhmjVzSs9EZ4a6lZ5JyjnGyTEjpE8pSklFKD3MPumTwRBfgOnJajYtlfnUQN+sHR85lzBYEUwk+mZteIYk9cks5aA0C5oRZkDTG0lRuFJSpsSeY5xcM0LpQTFLa9Jydu17VOZTg8wya5eSsCKYSHh2JH2Q2uWQVntEea9e6pAUhStF5UrCOcYZfUYoNUmrvGRlPv2QHl3LJBVWBBnGQNLL3kTeq5dyJEXhSi2Sco5xRpwRSitSMy9ZmU8fpFfXMkmFFUGGMZD0sjeRZ6NTh6QoXClJUlzepLcZoZTy15caLm/SKi/TmzKfmu6F0prkcC0TFvhNa09jepglZEWQYRJBZtmbmJlhH4MpS0r568uIVrKapCdlPqPntQh9RipEhN0zJ8Lv2WPFePFnC9PDLCErggyTCHhWjmGSRkr56+Nl1NQjM+e1PiOVqPBwoRKoj9RwQK0GK4IMw6QZkoVuerHOZdKGlPLXl96WUTMynNfaqDmjTk8ublgRZJJEejxphfk+ULLQTUvrXCZtSKnlzvS0jJrR4bzW5ntya8OKIJNo0osVLfN9YoiFblpa5zIMw2QGWBFkEk16saJlvn/iW+imB+tchmGYzAArgkyywFa0TFJgC12GYZi0gRVBJllgK1qGYRiG+f4wTqkbr1y5Ej///DOyZcuGmjVr4ty5c8Lrvb29UbNmTWTLlg2lSpXC6tWrU0o0hmEYhmGYVIeIEBUernMkXVR43IeIUl2mFJkR3LlzJ4YPH46VK1eiTp06WLNmDZo2bYoHDx6gZMmSOte/fPkSzZo1Q58+fbBlyxZcuHABAwcORIECBeDq6poSIjIMwzDpmMx0YgWTOVA6ok7TjUxaOJhOEUVw4cKF+O233/D7778DABYvXowTJ05g1apVmDNnjs71q1evRsmSJbF48WIAQPny5XHt2jXMnz+fFUGGYZhMRmY+sYLJuBhyRF1aOJhOdkUwMjIS169fx7hx47R+d3JywsWLF/XG8fHxgZOTk9ZvjRs3xt9//42oqCiYmpomt5gMwzBMOiUzn1jBZA7iO5xOSwfTya4Ifv78GTExMShUqJDW74UKFYKfn5/eOH5+fnqvj46OxufPn1GkSBGdOBEREYiIiJC/f/sW12kEBgUBEf+/xh4YCGSNd3B5ZEiiw0OjQhETFvP/QYGINo3WiioK/x7jple5vse46VWulIwbGxqK4Jj/hRtHGx7OcZMvL6MiYhAWGSKH6Tv9QSk8PcTtOt1Wb9wtk32S/bnfQ36kZtwUlSs8HOFRUf8Li4zUjisI/x7jxg8Pi4hEtNH/zDSiIiKTVa7AwEAAMGzPISUz79+/JwB08eJFrd9nzpxJZcuW1RunTJkyNHv2bK3fzp8/TwDI19dXb5wpU6YQAP7whz/84Q9/+MMf/uj5vH37VlVvS/YZwR9++AFZsmTRmf3777//dGb9JAoXLqz3ehMTE+TPn19vHHd3d7i5ucnfY2Nj8fXrV+TPn5/3izAMwzAMk2khIgQFBaFo0aKq1ya7Ipg1a1bUrFkTHh4eaN26tfy7h4cHXFxc9MaxtbXFv//+q/XbyZMnYWlpqbg/0MzMDGZmZlq/5cmTJ2nCMwzDMAzDZABy585t0HUp4kfQzc0Nf/31F/755x88fPgQI0aMwJs3b9C/f38AcbN53bt3l6/v378/Xr9+DTc3Nzx8+BD//PMP/v77b4waNSolxGMYhmEYhmGQQu5jOnTogC9fvmD69Onw9fVFpUqVcPToUfz4448AAF9fX7x580a+/ueff8bRo0cxYsQIrFixAkWLFsXSpUvZdQzDMAzDMEwKYkSUBm6sGYZhGIZhmDQnxY6YYxiGYRiGYdI3rAgyDMMwDMNkUlgRZBiGYRiGyaSwIsgwDMMwDJNJSRGrYUabT58+4cGDBwCA8uXLo2DBgjrXSDY7+pxhi8LSCkPSxHwfJLZ+EZFs/V+iRAkYG/N7ZWJQy0dD8lmpDNXaqVq/k1Llq1bnRM8WpSmp/VJGrNOiPEmPY4saaSmzIfVWFJ5eyXBWw/oqfVhYGDZv3oxDhw7h8+fPAIACBQqgRYsW6NatG6Kjo3HixAmteI0bN9ZyxhgYGKh4zcmTJ3HgwAE8ePAARkZGKF++PFxcXBAUFIRNmzYhe/bs+Pnnn0FEePnyJcLCwtC9e3fUrl0by5cvx8WLFxEdHQ0igqmpKWxtbdG2bVvs3btXb9jgwYNRrFgxxfSqyWtIOABcvXpVK9zKygp79+4Vpqlt27bCjufJkyc4cOAAHj58KIe7uLigbNmyqjIp5XPjxo0V5dVskErPLlmypLB+vHv3TlFmtbxMSjkkJT9E+QwAHz58UKx7gwcPRkhIiGL8a9euYenSpfjw4QMKFCgAIsKnT59QrFgxDB06FJaWlqploRSe2DLKnj27sD2o9QHZs2dPUn6K4iqlVS0fAQjDixYtqliGP/30E44cOaK3nbZo0QKvXr1SLHtfX1/F5/bt2xcPHjwQ5qNSXllYWAjrXLFixYR5UrFiRVy4cEFvmsqUKYOnT58q9kvNmzcXlv+DBw8SlWapn1a6r5GRUZL6FlGdVusP9+zZo7ev9vf3R86cOfH161fh2KLW/4j6eVH7T8x4XKVKFWzatEl1PFRrp4kZm9TqrZGRkWq9FsklKsfk0F0MIcMogkqVPiwsDIGBgRg5ciTq16+PnDlzAgBCQkJw+vRpuLu7o1q1arCzs0OpUqXkeGfPnkWRIkWwcOFCuLm54ePHj3qvuXHjBgYPHoyGDRuiVKlSAIAXL17Aw8MDO3fuxNmzZ5Eli/ZB3DExMdi7dy98fHzQq1cvVKlSRSv8zp076NmzJzZs2KA3bMOGDahdu7ZiemNjY5E7d27FNAFQTE+RIkVQrFgxeHt7o0KFClr3vn//PvLmzYsNGzboTdPo0aPx7Nkzxc748OHDyJ8/v5xXUriHhwdOnDghLIcPHz6gTp06evN5/fr1KFGihI689+7dQ4MGDTBy5Ej07NlT8dmHDh3CokWL9NYPNzc3uLi46I339etX5MuXTzEvnzx5gnLlyiWqHNTiivJj0aJFaNOmjaLMGzZswIgRIxTrXqdOndCkSRPF+BUqVMDvv/+OfPnyacX9+vUrunfvDiMjI1SsWFGn7tSvXx8AFOvWx48fYW9vn+Ay2r9/P5o3by58QTl8+DBcXV0V44eFhSU6P8PDw/XGdXd3x9WrV9GtWze9eRETE6OYj3///TeISBj+4cMHxTJ0d3fHoUOH9LbTli1bYs6cOYp9S+HChRWf6+TkhClTpijmIxEptrPjx49jx44dis9duHAh5s2bp/jsIUOGYNOmTXrTNHLkSCxYsECxrz169Kiw/MuXL5+oNLu5uQnrJREpPletb2nRooVinf7y5Qs6duyot776+Phg+/bt2LlzJ9q2bauTJ8OGDUPx4sUxevRoxXIQjXlBQUEwNjbWK1fevHnx9etXxfYv9cVKeaI0Hi9evBjW1tbYvHmzosyiPv7GjRvIlStXosYmtXpLRIrtcMOGDfLRt/ruvX//fri5uSmWo5mZWYLzSlN3MRjV04i/E3bs2EHR0dE6v0dHR9POnTsV44kOZJbCDLnGEGJjYw2+Vg1ReleuXKkY7+3bt6rpuXTpkmL4qVOnEiXTzp07KSQkRDHukydPhDKJEMl7+fJlIiLhs5MSJpLtypUrimFq5aAWV0Ri05OY+PHLW60sROHe3t6Jkoko8e3fEBKbn4bUy4Ty/v17g67z9/dP1P2TSlLrnj5Eab5x40ai7pkeUMurlKjThuaXqI9ZsWKFolwzZ85UjGdInU/KWCvKz40bNyZ6bEpovY2JiTE4fmLbBFHy6SVERBlGERR1fCEhITqfDh06UGhoKBERvXv3jsLCwogorrLevXtXK76UqbGxsbRz506aMmUKbdy4kaKiohSfuXHjRpo/fz4REd2+fZtsbGzIxsaGatSoQWfOnCEiovv379Pvv/9OS5YsoZs3b1KTJk2obdu29PjxY2GYPjSVzMTIu2HDBsX7+fr6UkBAABERmZubk6OjI61atYo+fvyoc59v375RREQEERE9evRIq/FfvXqViOLK448//qAePXrQtGnT5LITlUNISAht2bKFrl27Rv7+/jR37lxavny5fL0+fH19FcMkjh8/Tvfu3SMiooiICFq4cCH16NGDZsyYQYGBgXrjzJ07V/7/0KFDQhniEz+f9XHr1i2KioqiQ4cOkY+PD8XGxtKaNWto7ty59PnzZyKK68B27dpFQ4YMoW7dutHo0aPpwoULRES0dOlSev36tcEyaeLm5kZERNeuXaNbt25phZ09e5aIiB48eEAtW7akcuXKkampKdWtW5cGDRpEX79+JSKiO3fu0Lt374iI6MSJE7Rz5065Tujj2LFjimFSXrdp04Y2bdpE3759E8r/5csXev78OQUHB2v9/u3bN5o/fz4tXrxYrstEcXmlhJQXavU2Ojqa9uzZQwcOHNAabHbt2iX/HxUVRS9fvqTw8HCh/EREnTt3JiKiI0eOaH0OHz5MlpaWdOTIESKKq98DBgygypUrU4kSJahhw4Y0e/ZsCg8PF7bTw4cPk62tLTk7O5OHhwdZWlpSlSpVaO3atUREtGXLFiIiev36Nbm4uJClpSU5OTnR3bt3ydfXl2bOnEkHDhyg169fU//+/WnUqFF6+wIJqQyll0h/f38aOnQo2dvb02+//SbXFbU0r1ixQuuzfPlyqlixIq1YsULx2Y0aNZLzSiT3jRs3qHnz5tSqVSu5HRER9e7dWzh+qNVLUbi/vz8tXbqUtm/fTmFhYTRu3DgaMGAAPX36VL5G1J/qY+PGjfL/Svk1ceJE1bFFrf8RERERQdevX6fTp0/T/fv3dcJF/a3S2PX8+XMaNGgQDR8+nF68eCHfa/z48UJZNPvq+MRXin18fGjBggU0YcIEWrBggfwiJ2pnRETTp08nIqKLFy+StbU11a5dm6ysrOjEiRN6n7l+/Xo6d+4cEcWNH0REHz9+pP79+5O9vT316tWLXr58KT87oe0tfr9tCBlmaTh79uyoXbs22rZtizZt2mit/RsbG8PS0hI5c+aUN3PeuXMHVatWhZWVFa5du4Y8efIgf/78+PjxI3LkyIFy5cph8uTJAAAHBwd4eXlh6NChyJ49Oxo0aICbN2/ixo0bmDJlio4sRIS+ffvCzMwMnp6eaNy4MZYuXYqyZcviv//+Q+vWrXHhwgXY2dlhzpw5+PbtGwYNGoS9e/fCwsICgwcPRlhYmGKYh4cHFixYgJEjR+LOnTvo168fACAyMhILFizA1KlTFeXduXMn2rdvryOvJM+uXbuwZMkSrFu3DqVLl4a1tTUOHDiAHDlyoEuXLti2bRs2b96MPXv2YN++fTA1NUXbtm3h6uqKNWvW4OTJkzAxMYGDgwNu374NCwsLZM2aFevWrZPzsUePHrC1tZXl2rRpEypVqiQsB2dnZ1hZWSE0NBReXl5wdXVF7ty54enpiR07duitE05OTjh58iQAIDQ0VG85tWzZEkQET09PDBw4EEWLFkXbtm1x9uxZHDt2DO/evZP3tkh15+nTp/j1119x5coVFC5cGEWKFEHZsmXRrl07NGvWDObm5gCgms9KODk5wcLCAuXLl0dAQAAeP36MZs2aIU+ePNi2bRs8PDzQu3dvVKhQAdWrV8epU6cQGhqKfPnywdzcHCtXrkTZsmUREhKC1q1bo23btvLxjhIrV67Umx8rVqxAw4YN8eXLF5iamuLLly/YuHEj8uXLhwYNGsDT0xP169fH1q1bUbRoUdy/fx/z58/H+PHjMXHiROTJkwchISEICQlBnjx5kCtXLuTJkwd3797Fvn37MGbMGJ1n7tu3D66urjhz5oxiXkdGRqJdu3Y4ePAgChUqhHbt2sHFxUXeC3P27FlMnjwZMTExuHfvHqpUqYJixYphzpw5+PHHH9G8eXO0bt0aJiYmWLt2LRYsWCDXQU9PTxQsWBAlS5aEsbGxzrOzZ8+uWG+PHDmCjh07onTp0jA1NcXp06exYcMGlC5dGiVLlsSbN29w5swZDB8+HOXLl8fjx4/h7u6O9u3b6+SFlO49e/bg5cuXKFiwICwtLWFjYyPLtGXLFnTr1g2TJ09GkyZNMGPGDNSoUUPeXtGqVSts3rwZz58/V2ynLi4uOHnyJAIDA1GrVi08evQI2bJlQ506dXD58mU5T1q1agU3NzfY29vj4cOH6N+/P0xMTNCjRw8EBgZixYoVmDFjBnLlyoVly5bh33//1doLFj8fc+bMCU9PT3Tt2hVNmjRB27Zt4e3tjYULF+LEiRMAIEzzzp078eOPP6J9+/byMxYsWIBRo0bh8OHDevPy/Pnz8PX1haOjo1DuunXr4p9//oGpqSlGjx4NW1tbuLm5oX79+vD29lYcP75+/Sqsl9WqVVMMb9y4Mdq1a4eAgABs3rwZ06ZNQ65cuTB16lScPXsWM2bMUOxPR4wYoTe9ffv2xYULFwAAFStW1Jtf4eHh+PvvvxXHFgBwdXVV7H/GjBmDiRMnIkuWLBg6dCg6duwIAGjevDm6d++OVatWoUSJErh06RIqVKiALFmyYPLkyahWrRoAyPVLX3/r7++vd+xauHAhtm7dChMTE0yfPh0DBw5E27ZtUb9+fZw5cwYAhHWvZ8+eOnm1atUqDBw4EAMHDsSIESMQEhKCJk2aIHfu3AgICMCJEyeQPXt2PHr0SLGd/fXXX3J6GjZsiK1bt6JQoUIICQlBo0aNcPHiRTRq1AgeHh5YunQpjh8/jpYtW+LcuXMoXrw4rly5gjNnzsDV1RV9+vRBgwYNcOPGDYwfPx6enp6q9VYfmuOewSRYdUynODg40Nu3b2nRokVUt25datCgAa1cuZI+fvxIFy9epE6dOtGcOXPkmYImTZoQEZGtrS0Rxb0BlSpVSp4Jq1u3rnxvR0dHIiJq0KCBzjMtLCyoV69e1LNnT61PsWLFyNramu7fvy8/Q8LOzk6OL1GnTh35/wYNGgjDiIjq169PREROTk706NEjIop7q6hdu7ZQXiKivn37UsuWLens2bP0+vVrevnyJdWrV49evXpFRETW1tYUGxtLwcHBVKxYMYqIiKDY2Fiys7OTnyvx7t07Wrx4Mdnb21Pt2rWJiCgyMpJ++eUX+RopLyV5GjZsqCOXWjlo5kf16tW18sPKykrnY2lpSblz55avMzc3p/r165ODgwM5ODjI/+fLl09OU7169bTkqlevHs2ePZu6dOlCPj4+8u9S3dGU6+bNmzR+/HiqVq0atW/fnnbv3q2az0WKFFGUWzOfK1eurPO8+OUg5W2jRo3kaz5//kxr164lJycnql27Nv3555/y9QUKFKCNGzfShg0btD6VKlXSqvvXrl2junXr0v379+Vn2tjYyMsfgYGB8vMaNGgg52FsbCyVL19eR+7GjRtT06ZN6fDhw+Tl5UVeXl5kbW1N3t7ewrzWTO+TJ09o1qxZZG1tTc2bNyciInt7e3mZxc/Pj9q1a0efP38mZ2dnrecTEQUEBFDz5s1p06ZN8n23bdtGrq6utGPHDp1ni+pt/Hu/fv2a6tWrR56enpQnTx45XHqDDwkJIRsbGyIiKlq0qJwHmp+aNWsSEVFwcDDNnj2bOnXqRBcvXiQioqZNm8rPktobUdzsiiSHo6OjsJ1K7TsoKIhKlChBkZGRRESyXFJe2tvba93D3t5eq43UqlVL/l/KI0PKUDO/iLTbnSjNsbGxtHXrVmrVqhVt27aNYmJi5LCffvqJXrx4Qa9evZI/L1++lPtaNbk16zwR0ZQpU+j3338nOzs74fihVi9F4ZoyVapUSf5fyh9RfyoadySU8kttbIkvd/z+x9bWlvz9/SkkJIRGjBhBgwcPppiYGHJwcCB7e3u5bwgODqYWLVpQaGgoOTk56dxbX3+rNHZp9uWRkZHUq1cvmjFjhpacorpXoUIFatq0Ka1fv17u6ypXrizPoMav6xKa4xqRbjsjImrZsiV9+PCBOnbsSB8+fCCiuH5RqlOSjPb29lqrcnXq1CFHR0eKioqixo0ba4VJ8ojqrWj8SCgZxn2MkZERihcvjuHDh2P48OF4//499uzZg3bt2sHb2xu2tra4cOECevfujapVqyIyMhIAYGJiIv8dMGCA/EahucHW2dkZo0aNQrly5TB8+HA4Ojri1q1bKFOmDMLCwvDnn38if/78WvI0b94cBQsWxJ9//oly5crB398fefPmRVBQkLwhOTY2FjExMciSJQsOHDgAIO5NJSYmRv6rLwyI2zD64MEDBAUFyRZRBQsWhLGxsVBeAFizZg1ev36NuXPnwtjYGKNHj0b27NnlWSNzc3MYGRkhR44c6NixI7JmzSrnUfny5bXSWaxYMQwbNgzDhg2DjY0Nnj59Cn9/f4SHh+PNmzfIlSsXoqKiAAB9+/ZF586dUbx4cbRp0wb169fHrVu3ULt2bZw7d05YDtI9AGDevHny/zExMQgKCsLdu3flspRo1KiR/H+5cuVw8OBBWFhY6Fxz+fJlWFtb49mzZ3I5xcbGIigoCO7u7ggODsaSJUuwZMkS2UosPtWqVUO1atUwa9Ys3L59G7t371bN5/z58+Ps2bPIli2bjkwxMTFYu3Ytvn37BmNjY2zZsgX58uWT3VkULVoUU6ZMQbVq1eDp6Sm/bWvmU/78+dGnTx/06dMHX758wf79++WwFi1aoHHjxihUqJDWsx8/fgxvb2/ExsbC2NgYNWvWxLZt29ClSxe8ePECAGTL4BIlSuDNmzf4448/AABVqlTB1atX4eHhAX9/f0RGRuLChQvImzcvoqOjAQDHjx+Hj48PlixZgmrVqmHw4MHInz8/7O3tYW9vr5jXpLFwUaZMGYwfPx7jx4/Hs2fPAMTNhkttIyoqSt6gHRwcLMcLCAhAnjx5kDt3bhw4cAADBgzA1atXAQCdOnVCx44dsW3bNrRq1Qrt27eXnymqt0BcHQwPD0e2bNlQsmRJ/Pvvv+jSpQuCgoJw9OhRBAYGyisU2bNnl8uwd+/eel2cdO/eHQCQI0cOuLu7IygoCEuWLMGiRYsQEBAgXycZsFSsWBFXr15F//79AcRZFcY3etBsp8uXL0elSpVQpkwZuLu7w9raGubm5mjXrh2AuBWU5s2bw8TERKvfCg0NhZmZGYgIRkZG+Ouvv+T7S+Urai/v3r2DlZUVgoOD5ftGRkZqlZEozUZGRujcuTM6duyIrVu3omXLlrIl5ejRo2FhYYEffvhBK92DBw+W5RPJXbBgQbx69Qo//fQTAGDq1KnYsmULNm/eDFtbW8XxQ61eisJHjBiB8ePHIzQ0FAULFsTMmTORL18+5MiRQ65XSv1phQoVFMcdCaX8Mjc3F44tEkr9T5YsWZAnTx4AwMKFC7Fhwwa4uLggODgY2bJlw+vXr/Hzzz/j4cOHiI2Nhbm5uVa/dO3aNcX+tnv37jpj182bN5EzZ074+vqiSJEiMDU1xT///IOZM2fKs59qde/evXvYvn07du/ejfbt26NDhw7YuXOn3NZKlSqFyZMnw8nJCbly5UJgYCBOnjyJn3/+GVWqVFFsZ0Dc6srIkSPx+vVr/Prrr/j111+RJ08ezJ07FwDw7NkzjBkzBm/fvtXK36ioKMyaNQtt2rRB7ty5UadOHdjZ2eHBgwfyLKuo3orGjwSTYNUxnTJw4ECDr/X29pb3BunbRBoREUEzZszQ+u369es0efJk6tevH40dO5YOHTpEMTExFBQUpLM5NCnExMQobo7WDJPeAHv16iXvzQoMDKSWLVsK5Y3PixcvqG/fvtS4cWP5tz/++ENvngwaNEivXNK1586do5YtW5K7uztdunSJ6tSpQ/b29loGJh8+fKB//vmH5syZQ6tWraI7d+4QkXo53Lt3T2/44cOHad++fXIeaOLl5SX///z5c717+ZT2loaGhurstfj27RtNmzaNunXrJv8m7atSQ18+X7x4Ue8+xIcPH9LHjx9p4cKFdOzYMfr06RONHTuWxo4dK++hiYmJod27d9PcuXPp0KFD8gyqn5+fvO8ksRw9elRns3FwcDDNmTNH/h4dHU0fP37UMYB6+PAhDR06lNasWUPPnj2jDh06UMeOHen27ds6zzl37hy1adOGqlWrphMWP6/fvHkjlPn06dNkY2NDVlZWZGdnJ+/r++OPP+T4+vZpac4eSMTExNDGjRtp3Lhx8m9K9ZYorq3F37MTExNDrq6uNHXqVJo6dapczwIDA6lfv37CtCjx7ds3nT1ifn5+dPnyZYP2bikRGBios6dSic+fP+uUeWRkJF2/fl2vvPHbS3wiIyOF+530pVlC1FcmRW41NMcPtXopCg8LC6N9+/bR3bt3KTQ0lFatWkWrVq2S+wRRf5qYcScmJkZx73D8vNTX/4wbN47evn1LgwcPlvewSXh6elLZsmXp7t271KJFC7KysiJXV1d5P5/a3uiQkBC5v9Ucu8aNG0eHDh2ioKAgvfuMpRm4+Eh1r3v37jrp3LRpEzVv3pysrKzk36Ojo2n//v00ZswY6tu3L40dO5b2798vjzmGtLOoqCjy9fXVGWc0Z/ulVYvAwED666+/iIgoPDycPD09afv27XTs2DH68uWLHFdUb0XjR0LJMHsEE8Pr16919k4BwM2bN1G9enX5uzSToMSHDx9w6dIl2Z2Ira0tihQpgiNHjmDWrFn44YcfMHToULi7uyMyMhKDBw9Gnz598PLlSyxbtgwFCxZEz549MWLECHz79g2TJ09GlSpVsH//fpQrVw6lS5fG2rVrkSNHDvz22286bwCpieSvSIKIMGDAAKxevRoVKlQQxr179y4qV66c6Ge/f/8e+fPnR7Zs2XDlyhVkz54dlSpVMjj+3bt3kS9fPhQrVgwnT55EQEAAWrVqJc94ajJv3jy9e7gSy4cPH/D48WP8/PPP8qyDIXz9+lWuV/FneSQ+fvyIhw8folSpUihZsiQAyG+RAODn5wdzc3Mt31KhoaEJrl9+fn4oXLgwXF1d0apVK7i4uCBXrlyKskuzDklFem58ElpGly5dwsWLF+X8rFOnDmrVqgUg7g372LFjKFCgAGrVqiXPhvz22286sy4SQUFB8gyzr68vfHx8EBAQgMKFC8PBwUH2bajJhw8fULRoUTldf//9NypVqoTq1atjzpw5yJkzJ0aPHo2CBQsiKChI9k/m7+8vyzxw4EBYWFjg33//hbOzMz5+/IipU6fKdWDy5MmKdWzkyJEAgNatW8POzk7vNYGBgVi3bh1MTEzQs2dPud4sW7YMQ4YMwfXr12FiYoKqVavKcc6dO4e6desK2/jIkSOFzwVgcN3SZM2aNWjatCnmzZsHU1NTDB06FD///DMAYMKECZg1axaWLVsm+6PUx9WrVzFnzhwULFgQgwcPxpAhQxASEoJJkybB2dlZse5I+/sKFCiAVq1aYdq0afj27Rvc3Nzwyy+/ABDXO30o1Xcl9LV/QLevBoD+/ftjxYoVuH//vo5Mrq6uOqsqqUn88fjDhw948uQJfvrpJ1hYWMjt8MCBA7h16xZ++eUXdO7cWcvpt2Yff+LECXz79k1vHx8bGws/Pz+5LSaGy5cvo1atWnj58iWWL1+OAgUK6IzjNjY2euMeP34cTZo00fpNqR+/ePEifv75ZxQoUAD79+9Hjhw5tGZ+k4MMrwiuWbNGNqaIz9y5c3WWCineJlJAbIgyd+5ceHl5oVGjRvIm01OnTqF+/frYv3+/cFO2g4MDJk6cCH9/f4wbNw579+5Frly50LVrV+TPn19oHPHgwQMsWrQIlStXhr29Pdzd3ZEzZ07MmjULt27dwpw5c1CxYkU0atQIs2fPRq5cuTBp0iS0bNkSHh4eipt9jxw5gq1bt6JLly548+YNhg4divfv3yNfvnxYuHAhatasCTs7O5QsWVJe9vDw8ICTkxP++ecfYRnkyZMHP/30E5o0aYJ27dqhZs2aquUnbXwdO3asojFJ6dKl9cq7YMECWVHs16+fohHD27dvhQYh+hgwYABWrVqlKPeAAQPw8eNH7Nu3D2vXrsW2bdtQu3Zt3L59G7Vr18aECROEaR47diwmT56MH374Ably5UJAQAD8/f0xbdo0ODo6olu3bti8eTN27tyJRYsWwcHBAZcvX0br1q0BxC3r6DP26dOnDwAkyfhGtAH+4cOHGDduHJ48eYLnz5/DxsYGVapUwYwZM5A3b17FNHfp0gVbt25VfK6/v7+wjE6fPg1HR0cEBARg6tSp8kAxbdo0FCtWTLgZfPHixTqb45s3b47cuXNj27Zt6NatG/7880+Ympqiffv2GDduHID/bXpftGgRvLy8UKVKFXh5eaFkyZKIiopC1apVtV4oiQhTp07F9OnT0bRpU9WN4C1btkSnTp1kB7GSzDt27MChQ4fkjfL6Npq3bdtWJx/p/42BYmNjUbduXVy6dAkODg5o164d7O3t5euaN2+OVq1awdTUVMewplKlSkJDIlEbL1eunPC5gNi4Qonbt2/Dzc0N7u7uisYEP/30k9CAqk6dOti4cSP8/f3Rpk0bXLlyBTly5ECjRo1Qu3Ztxbrz8OFDocGHWr1Tqu8nT54U9vEzZsyQ2//ChQtRv359uf1LzsizZcumt6/Oli0bevfurSPTw4cPdfz0xWfAgAGYMmUKpk+fjvPnzyMgIABly5ZFgwYN4ObmBjMzM73xRO1bYu7cubh8+bLcZ27fvh22tra4ffs2Hj58iBcvXmDChAn49OkT2rRpg7Nnz+LDhw/YsGEDAHEfv3jxYuGLghL9+/fH6tWr9YZt3LgRPXr0EI7jFy9eFBrI+fr6KvbjQ4cORY8ePZAlSxYQET5//owff/wRuXLlgr+/v+L4k6mNRZRQM6VW20RKJDZEib/JWMLOzk51U7Zm3HLlysn/16tXT2gcQRS30dTb25v+/fdfKlmyJF29epUePXpEDRs2JGtrawoJCSE/Pz8qXLgwBQQEUHh4uLxxWrTZl+h/m1tdXFxk324PHjwge3t7ev36NQ0aNIgGDx4sLzNoGk/oQyoDBwcHCg8Pp/3791Pnzp2patWqNGbMGLp69Sq1a9dO59O2bVsqXLiwLDORfmMSkbyaeUqk34hBzSAksUibiR0cHLSWcaRyEKW5du3aOtP+3759kzcua25ADgoKIqK45Q0bGxuhsY9muiUSanwj2gDv4OAgLzHdu3ePevbsSU+ePKH27dsTEdHo0aN1PqNGjaKffvpJ+Fy1MpJk6tKlC23evJnCwsLo+PHj8iZ10Wbw+GmKvznexsZGbrtLliwhV1dXCgkJ0WssEh0dTY0bN6aYmBgyNTWlpk2b0rRp0+Ql4l9++YWmTZtGROoGDJob+TWRfhdtNBcZA0nyRkdH08mTJ6lPnz5UqVIleeuHyLBGzZBI1MbVnhu/HPQZX+gjMDBQq3z1GROoGVDF718k7OzshHVHzeBDFFetnYn6eFH7l1Dqq5XGLKXf49O4cWO6evUqRUdH07Fjx2j48OHk5eVFv/32m7B9G4JSnynliT4DJglRH9+gQQPy8PCgM2fOUP369Wn37t1EpGt0p0ZkZCS9fPlSa/lXNI4TiQ3k1MpRundsbCxVqFBBK01qY2ZCyDDGIoD+5bCqVavi8OHDmD17tt4lWrVNpIDYECVfvnz4559/dDaZSp7E9W3Klt7WS5Uqhc6dOwMAatSogT59+iBv3rwoVqwYXr9+LT8/vnEEAJiamspv03/88QcsLS0BQN6cmz17dmTPnh2Ojo7y27Q07S/a7Cs9IzQ0FP7+/vIzJCORkiVLYvny5Xj16hXmzJkDIyMjva5ZNJE8pgOAmZkZWrVqhVatWiEiIgLHjx/HkiVLcPXqVXh6empN8xMRunXrpiW7PmMSkbwS0dHRikYMok3G0dHR2LNnj3AZ5erVqzh37py8LNiiRQuULFkSv/zyCw4fPgwrKyv8+++/sisEaRZaLc2fPn3SmrH+9OmTfO27d++wcuVKfP78WfY4Ly3Diox9JJJifEOCDfDdunWTl7VKliyJV69eoUyZMvKm/q1bt2Lbtm2Iz5kzZ4TPNdRo5/379+jatSsAoHHjxpgzZw4A8WZwCaXN8cbGxjA1NQUQZyhToUIFNG3aFF+/fpXz3cvLC1WrVoWnpycsLCxgbGwMW1tbebl0yJAhsLW1xeXLl2W3VGoGDG3btkXDhg3h4OCAXLly4du3b/D29paNOiZNmqS40fzq1auKxkA+Pj6y3I0aNZKNkyQ3HICyYU2VKlWEhkSAchuXED1XVLc2bdqkd2bWxcUF2bJl02tMcP78ea30KxlQOTg4oE6dOsifPz/atWuHJk2aIFeuXLCysoK/v79i3fny5YvQ4ENU7y5fvixsZ6I+XtT+JZT66lq1aqF79+46MmkuV5NgSTIoKEiWpUGDBpg7dy4WLVqEGTNm4NixY4rtW42RI0cq9plhYWFo3749nj59irCwMNlFV0hIiBxf1McbGxujYcOGAOJmf/v164dHjx7JcWNiYnDgwAGYmJigRYsWcl7u3r0bZ86cwcqVK3Hw4EFMnz4dZcuWxYsXLzBgwAD06NFDOI4DYgM5tXKU2oORkRHc3d218ktt/EgQCVYd0ymLFy+mihUrUsuWLWnmzJlkaWlJ9erVo7Vr15KNjQ0FBgbSu3fvqFixYhQUFERRUVFkbW0tx1faREokNkQJDg6mRYsWUfv27cnJyYk6duxIixcvlrV7TeJvyo6NjaUbN27Qf//9R7GxsXTs2DE6duwYxcTECI0jiOLeIKTwT58+yferV68eDRs2TG9cV1dXIiLhZl8ikl2sNGjQQMsQxdLSUidNz58/p3379hFRnMFHpUqVqHr16lqGBdJbj+R4Ux8rVqyQ06GJ5M5DZExiiLyGGjHE3+DetWtXmjVrFl2/fp2ePXtG165do1mzZlHXrl2JiGjUqFE0aNAg+uuvv6hjx47Up08f6tOnD23atIkiIiJo/vz51LBhQypbtizZ2NjQyJEj5c3xojQ/ePCAWrVqRTY2NlSrVi2ysbGh1q1b04MHD4iItGZ5JCOIwMBAmjZtmkHGPkkxvhFtgN+2bRtVr16dWrZsSdWqVaPjx48TEdHw4cOJiGjixIl6jQOWLFlikNEPkX4jhDJlypCVlRWVK1dOvkdERITsikVtM7hoc/ysWbN02su9e/dkw593797RoEGDqGnTpjRy5Ei5TKW0BwYG0owZM6hdu3ZarqQMMWD49OkTHT16lLZt2ybLpoloo7kSolNriMSGNWqGRKI2rvZc6dlKiGZmQ0JChMYEhhhQff36VZ5ZffjwobzpXlR31Aw+RHHV6ruojxe1fyU0++qbN2/SypUrafbs2bRq1Sq6efOm1rXdu3eX3dO0aNGCBg0aRO7u7tS/f39asGABOTo60tChQ8nW1lZePevYsaOwfUvEd3QtObsuX768Yp957do12S2QVAeCgoK0nNGL+ngnJycdw5IZM2aQqakpERF16NCBxo8fT1OmTCE7Ozt69uwZEcWNXZou26TxOzo6Wm7LonE8PvEN5NTK0cPDQ+cgiIiICFq3bp3qmJkQMowiKFoOU1ui1SQhlmiJRXT0FBEJTx8QhUVHR6e47EpcunRJdQnte0RtGSW+vyvpJIP4Pue+Fww5+UIJyT+ikkVxaqNmkZraiCxgNZHyMbHhojIU9T1qZa/23MRiSJ179eqVlj83orhB0t7eXmtJNj5qfa0hz01PJEcfr5QnmuUgWpIkSpq1umjbQkIxtL9Se1FQ8gVav3596tSpE926dYv69Okjb3N69eqV6rJyUvrS1MZYfc7w+0DfcpiRkRFMTEzQrVs3VKpUCV27dpWXaCXjj/gYGxujaNGiWLNmjeozRdcMGDBAMUxzSlof0pKHPuJPD2uSJUsWHV9FmqilSS384MGDimGPHj3SWULr378/mjZtKi8LKjFt2rREyyTKZ5G8hjx7wIAB8jLKli1bcOjQIWzZsgXdu3eXl1Fy5syJ9evX4+bNm1iwYAGKFy8O4H/Le/pISjkkJT9EaZUQ1S+1+JKRSZYsWVCwYEGtpVu1shCFq5WREqampnj58qXwuZJPsMTcXy0/4sfNlSsXrK2tVfNCyVjH0HBR/yHqe0Tx1J4ryidAnFdqz5We3bx5c7x69Ur+rWHDhli5cqW8BKcPtb5WTW5RmkV1R61eicJFeaXWx6ulB1DOE81yIMGSJAAUKlQI1tbWWhb1ly9fVnymZp2XfJj26NFD6+Ps7KwYX6nfE/VXEtOmTUP27Nn1eod48+YNgP/5AgUg+wJdtGgR7t69i1WrVmH9+vV4/PgxbGxsUKFCBYwePVrRiERCrV4ntl9TCzdEd9EhrTXR5CIhvu8M8ZtlyHl9iTnTL6Vn7ET+mtTkVQuXlrriI6VJbQlNCT8/v0TLJEJJXkOfLSFaRgkMDKQ///yTBgwYQEuXLpXPrxbJnZRySEp+GJLWlIqvVhai8KQ815CznRNLYuUypF5mNJJa9zITanmVknVaQrQkqYRIrqTW+ZTq9ySZlXyBbt++PdHPTYpcSSExeZVhFEFN4iuE8Q92rlevntbBzmqHZBPFTfNev36dPDw86Pr16/I0c2xsLJ0/f57ev39PkZGRtHPnTnkfn9IB6kePHiWiOCVi9uzZ1KJFC6pTpw45OzvTH3/8QYGBgcKDufUREBCgVbHU0nTnzh35sPcTJ07Qzp079U6dS0gHdKulSY3379/T3r17ad26dbR3716tfRuaS4m+vr4UEBCgKIvmwd0SX758oefPn6sq+X5+fnTmzBnZki4qKoq2b99OQ4YMoS5dutCQIUNox44dOh2hRPwOTyS3Wjko1SsllMpfQupw3717J1u2Xb58me7evat1ndph5h8+fKC9e/fS33//TUeOHJEdoUpxRYewi1By4K2ElNdqbTg+8duDEtL+pqVLlyo62yUS5weR8oH1RHH18unTpzr79969e0d9+/YlW1tbqlWrFtWuXZv69+8vt8saNWrQ9OnTFR3EivoPLy8vcnR0pBEjRtCJEyeoWrVqVK9ePfLx8VHtWyQH8P7+/jR06FCqV68e/fbbb7JconyMioqiQ4cOkY+PD8XGxtLq1atp7ty59PnzZ2rTpg1t2rRJ7/5DCcnSdfjw4bIzYiKi8ePH040bN6h58+bUqlUrunDhghzWu3dvYRiRev0R1ekXL17QiBEjaM6cOeTr60sdO3akpk2bCpedNb1OKIVLey1jY2Np586dNGXKFNq4caNiv0OkPZFw5coVWrBgAU2aNIlWrFghrL+aNG7cmFq3bk39+vWju3fvkoODA1lZWRm0jzJ+/xN/rI0v65kzZxTbqCZubm4697t8+TKtX7+evL29Zcf9jx49ombNmpG1tTU1atRIHpeIxHXPUJk1kfqekJAQnU+HDh3kl/6EjtUSksIm6jv0IeWV9Ew13cUQMowfQZH/sjZt2ij62+rbty9Wr14tPCR78+bNWLVqFaysrGSfbjdu3MDAgQNx8uRJRT8/e/fuFR4aL/ITFhwcrHgw9/79+/HXX39h+fLlyJMnD3r27Il//vkHWbJkgYODA8qWLStMk8jf0r59+7By5UqtvCUN34pTp05VTNOSJUvQqlUrtGvXDg0bNtSxhps3bx7OnDmj1+eimZkZ1q1bp+j7TnRwd7NmzTBlyhTExMTg3r17qFKlCooVK4Y5c+bIvsI0/e7F97t19epVlC9fXq9frYiICJ28uHDhAuzs7LBr1y4sWbJEUW4LCwthOYjqVUxMjNB3nT5LbSJCy5YtYWlpqehzUbJYFfmwa9CggV6/eFI5NGnSBDNnzkT16tV1DmF3cnLS68Ny8uTJcHZ2FvrkbN++vWJef/r0SXg4u6g9TJkyRa9zXSJC3759ceHCBaGfOSU/gVJ+KPmK+/z5Mz5+/KjjC3L69Olo0KABGjZsiOnTp8tH1QHAhQsXMHnyZJw+fRo2NjYYMmQIdu/ejXfv3qFly5Zo166dbBEv6j/+++8/7N69GwEBAXBycsLVq1eRPXt2tGnTBsbGxsK+RapjXbt2RZMmTdC2bVt4e3tj4cKFWLRokTAf4/tjbNasGfLkyYNt27bh06dPqj4CHR0dFf0BRkdH459//oGpqSlGjx4NW1tbuLm5yWHr16+HiYmJTtiZM2eEPhc9PT2FdfrZs2eKfuI0rb315Yeo3pmamsLLywvDhg2Dubm5bCF748YN7Ny5E0ePHtWJJ/mh9PT0RFhYGKpXr45Tp07J3gXq1q0rW43qc7ZORFi2bBnu3bun12eitLy7efNm2e9e/P5nxYoVimPtb7/9ptd3ap06dTB+/HgAccf5lSxZEsbGxjo+QbNnzw4vLy9Mnz4dDx48kPPk0KFDeP/+PZo2bYr58+ejYsWKePfuHTp27Chbhovq3tKlS4X+TUV9z549e2BpaYmcOXPK8t65c0f2EiC1F6X2pISTkxMqVqwo9DMpyis3NzdV3cVgEq1CpjNE/stE/rYMOSS7Tp06OhZAUVFRVKdOHeGmWrVD40V+wkQHcxPFGbpER0dTYGAglSxZUk6Xra2tappE/paIxL4VRWmqV68enT9/noYNG0aVKlWiXr160dGjR2XZDPG5qOT7TnRwt729vTxD4+fnR+3ataPPnz+Ts7OzfJ3IX5PIIKRv377UsmVLOnv2LL1+/ZpevnxJ9erVkzeRi+RWKwdRvVIzvDE3N6f69evLFtPS//ny5RP6XJQQ+bBT8osnGcCIDmFX82Ep8skpymu1w9lF7YGIyMLCQraC1PwUK1ZMlotIv585tfxQ8hWXK1cuoS9IW1tbndmJyMhIWWbNzehBQUG0detWatWqlWwJLeo/NMtX8z4NGjRQ7Vvi+97TDFfLR5E/RkN8BIr8AcZvp1OmTKHff/+d7OzshGFEYp+LROI6LfITp5YfonDJZ158gzPp2QUKFFD0Q2mIkVrRokW1jjeTPjly5CAi/T4TJUT9j9pYK6VBn+9UojjPAq6urlrWrZJPUEn++HUvb968FBQURC1btpRXfKKiorTuK6p7av5NRX3PxYsXqVOnTjRnzhz52fp8mCq1pyJFiij6i1TzbyrKK0N0F0PJMH4Ew8PDFf2XTZ48WdHf1rZt21QPyTY3N4e3tzfq168PIO5twdvbG9myZZNnjPRtqlU7NF7kJ2zSpEmKB3MD/9vcGhQUhOjoaISEhCBHjhyIjY1FbGysME0if0uA+gHdSmkyNjZGnTp1UKdOHQDAxYsXsXv3bowdOxZ37twR+lwMCAgQ+r4THdwdExMj+1eMiorC169fkT9/fq3D7EX+mkR+tf7880+8fv0ac+fOhbGxMUaPHo3s2bPLs0Uin33R0dHCchDVq7CwMKHvunLlyuHgwYM6J+M0atRIrpP6fC5KiHzYSTMV8f3iSXns6uqqeAh7dHS00IelyCent7e3Yl6LfOaptQcAqFChAv7880+d4+LiH9Wkz8+ckp9AKT/0+Yo7ceIEsmXLJvQFOXz4cNSpUweVKlWS2//Dhw8xfPhwuT5I5MyZE507d0bnzp3lei3qP3x8fOQj/jw9PeWyjY2NxfXr12FlZYXnz5/r7VvevXsHKysrBAcHy+GRkZEIDg42KB+V/DFKZQHo+giUEPkDbNmyJV69eiUfnzd16lRs2bIFmzdvRosWLRTDALHPRbU6bWZmpugnLjw8XJgfovxycnLCqFGjUK5cOQwfPhyOjo64desWypQpAwB4+fIlli5dqtcPpYuLC9avX49q1arB09NTr5Fa7969Ub58ea1ZdwCwtbVF7dq18cMPP+j4TJQQ+c7MmTOn4lhbpkwZoe9UAOjUqZM89rZq1Qrt27eX67qVlRWWL1+OmjVrYvHixXB0dMTNmzdhZWWFzp07IyoqChUqVIClpSU+ffokH5moVvdCQ0OF/k3XrFmj2Pf8+OOPsLW1xYULF9C7d29UrVoVkZGR8jOvXbsmHKvz58+Ps2fP6hzd2ahRIxQvXlxv3yH5NxXlldo4nyASrDqmU9T8lyn524p/SPbz58+JSHsfmK+vLw0bNoxq165N1tbWZGdnR8OHDydfX98EbarV5z5C00/Y8ePH9foFktA8mHvPnj1kaWlJ3bp1o4MHD1LFihWpRo0atH37dtU0GepTT+RbUV+a2rRpoyg7ka7PxQ4dOtCiRYsoKChI1dhHdHC3p6cn2djYkJWVFdnZ2dG1a9eIKM6ASELNX5OaXy0iohcvXlDfvn21jF9EcquVg6heqRnePH/+XOdwc6K4fV0in4sSIh92an7xiJTdR6j5sBT55NREymvNt1uRzzyl9rBt2zYiiptR0+fXS0K0P0otP5R8xd29e5datWpFtWrVImtrax1fkERxsxoPHjygixcv0sOHD7X6EmlGRoSan8H4KO0/0+xblOJ9/PhRNR9F/hhFPgI15dCX7vg+4BKDms9FpTot8hOnlh9q4devX6fJkydTv379aOzYsXTo0CGd6/X5oUyMkZomSj4TJUT9jzTWOjs764y18f0A1qpVS8t3anxiYmJo48aNNHbsWPm3PXv2UPfu3alx48bUoUMHWrZsmdznv3jxgnx8fOjhw4c6/Yy+ujd27Fh6+/atUOb46OvnNfH29qalS5cqZS0REYWGhsplcfHiRb37BaU0iPybivLKEN3FUDKMIkiUOv7LIiIi6MWLF/IgrLS5lUh7E+miRYuEm0iVDCDUwpJKZGSkVnr0ERERIXfEhmz4NpSklpOmM9H4GLIhWHOjcXxSwzJPH4mxKo+KiqKXL18qGmuo5bMh9UuUV0TaG5hTCjUDmISSEKOfhCKqmxIhISG0ZcsWunbtGvn7+9PcuXNp+fLlchrVwvUxd+5cWX4lYyCRgZshz01KOVy7dk1HWTl79qz8f3R0NO3Zs4cOHDig1YZ37dolvK+a8RNRnNIc34HzkydPhPcVGX1I6VC7b3LVW0P8UGqOLaI8USsHNVLTV+j79+9V66whpKbMUjtUQtSfqvUdKTE2ZRhjkcSydetWdOnSBW/fvsWQIUPw/v175MuXDwsWLEClSpUAAAMHDsTKlStx6NAh+YiZ58+fo3///tiwYYPeza1GRkZ49OiRcBOppgHEiRMn4OzsLBtA3LhxQ9E4Yu7cubh//z4qVqyIyMhIrFy5Erdu3cIvv/yCYcOG4dChQ8I0SemRjswpV64cnj9/Lh+ZM2PGDEyaNAk+Pj4YPnw4TE1NERkZiRkzZmDs2LEJPhReYsGCBRg5ciTu3r2Lvn37wsjICBEREViwYAHev3+PLl264M2bNxg6dKgs88KFC1GxYkXhwd09e/bUeVb//v2xZs0aeWO9yADGy8tLJ0zTICSxqNWt+JvBAWDKlCmYNm0a/P39hfkxevRo/Pnnnzhz5gyGDx+O8uXL4/Hjxxg/fjzevHmDkSNH4s6dO+jXrx8AIDIyEgsXLkS9evUAQGh8Iy1768urgQMHCjcwb9iwAYsWLULlypVhb28Pd3d35MyZE7NmzcKvv/6KBw8eKIbfunVLr6HJpEmTcOHCBaEBTGBgINatWwcTExP07NlTro/Lli3DkCFDhJvBd+3ahaVLl2Lt2rV6jX5y584tNIDRtyl/7969qFevHrJkyaKYF87OzrCyskJoaCi8vLzg6uqK3Llzw9PTEzt27FANt7Ky0vLVSERyOVhYWCgaA+XLl094kL3ouT/++KOwHE6ePIlJkyYhS5YsGDp0qLz02rx5c5QuXRpfvnyBqakpvnz5go0bNyJfvnzyZnsA6NixI0qXLg1TU1OcPn0aGzZsQOnSpbWu0YfI+Onff//FvHnz8O+//yJr1qwoU6YMli5diqxZs8r3VTMm0oeTkxMcHR1x+PBhxfuOHTtWMb/KlSunWN9btmwJPz8//P3336hUqRKqV6+OOXPmIGfOnBg9ejSOHz8uNCYT5Um/fv1Qu3ZtYTmIELU1R0dHYftXQ6lPzJ07N0qWLKlYZwFg165divlpbW2tmJfxl84TimY7jN8fXrlyRTj2aPrFlNi7dy9cXV0xb948Yb81aNAgzJw5E5UrV0aTJk0wduxYua+ytbVNWCKSXbX8zpA2erq4uMgzeQ8ePNDaxCltzJUMQIj+d8SM0uZWzc3Rapuy7e3ttZZDNI1F9BlHaMYdMGAAzZgxgx4+fEhr1qyhVq1aqaZJlB7Nezs6OsouOIKDg8nW1jZRh8JLSHGdnJzo0aNHRBQ3nV+7dm1VmUUHd5uZmZGjo6PWpuxixYpRr1695GeLDGDUDEISi1qaRJvB1eJqbuiXZj1CQkLIxsZGmM/xZdNXv0R5RSTewFynTh06e/Ys/fvvv1SyZEm6evUqPXr0SG4nonCRoYmaAUyzZs1o7dq1tH79erK1tZUNmaR0JsXoR80ARqluVq5cmby9vRXzQrPPqF69uvy/1D41+w194bNnz6YuXbqQj4+PTjmIjIHUTo0QyaVWDra2tuTv708hISE0YsQIGjx4MMXExJCDg4OW0cW1a9eobt26dP/+fa0+RXTCQ3zOnDkj/y9KL5G2McjBgwfJ0dGRfH195fuKjDpEm/3V7ivKL7V61aBBA9q4cSMtW7aMypUrR7t376YTJ05QixYtDDrFSSlPcufOrVoOIkRtrU6dOsI6r4ZSn/jjjz8SkXKdJSJhforyMqHEd4ElaodE4rFHNK4RifutWrVq0Zs3b+jOnTtUuHBhevv2LX358kVH3zCEDKsIanYS8dFc+5csTuNb72h+Fx0x4+7uTsuWLaORI0fSokWL6M6dO7Rx40Zq164dWVhYkJWVFeXNm1deOoiJiaEaNWoQEVGJEiVo9OjR9PPPP2sNxtbW1sIwIrGlklqa1I7MadmyJX348IE6duwoLwkHBgZS3bp1FY+Le/r0qc5v8cvA2tqa7t+/r3XeKhHJFrZq5XDx4kXq0KEDzZkzh4KCgmRrZcn32ODBg2VfWpoNkSiuA5GsLrdt20YxMTFaFtyvXr2iAQMG0KBBg+jVq1daYZpo7mXRx6JFi7RkF6VJZIGtFrdy5cp05MgRuS5JSPsNlfJZQlS/1PKKKC4/t2zZQi4uLrR161a5TWnWD02rVs2XD6VwzbrcpUsX+f/4SsSff/6pFRb/vgEBAdS8eXPatGmT1uAmKmPNZ48cOVLr/iK5JPTVTbW80PzNw8NDRxa1cKK4PWgzZ86kjh070vnz5+U01alTR1Y6NJci7e3tterB5s2bddIjeq5aOWjem4ho/fr11KJFC7K0tKTatWtr7X97+/Yt2dvbU/HixeXf6tatq7X0HRgYSM7OzvTDDz9QfDTLVpReItI5nu727dtka2tLv/zyCxHFKUr6jktr1qwZVapUSe9yfMOGDVXvK8ovtXolUm4NOW5PKU9y5cqlWg7x0ezXRG1Nrc7rQ3M8VuoT1eosEQnzU+1FIT6ifl6fwqzUDonUxx6lcU1Cqd8SeQZIKBlWERS93WhWHsm0vEGDBrKyFhgYSJaWlvI1AQEBNGzYMLK3t6ds2bJR+fLlqV27dvT48WMiEm9ujY/mpmyRAYQojIiESqZamtTS8+7dO+rUqRPZ2tpSzpw5qUaNGtSgQQO6ePGiQRu+lcpAesvu1auXllwtW7Y0qBwk4h/cLfHy5Uvq378/DRgwQNEsX80ARm2jsNq5yfFnNwxJk77N4GpxpbflqVOnym+ogYGB1K9fP2E+S6jVL0PySrpGcwOzvb29vLdLMlyIjY2VOy1RuMjQRM0AxsHBQetNPSoqin7//XfKmTOnjswJNfpRM4DRRLNuquXFvXv39N5X2vukFq7Jt2/faNq0adStWzciEhsDqRm4iZ6rVg6DBw/WMTLw9PSksmXL0tGjR2UnyhLBwcE0Z84c+XtCTnjo3r27/L8ovURxCmn8Gf4PHz5Qv379iEhs1CHa7K92X1F+qdUrkXJryClOSnmydOlS1XKIT/x+TamtqdV5fejrU+P3iYYYZYryU+1FwRCZDAmL3w41UetPlcY1ifj9VocOHXTSGxUVpTpG6SPDKoKanUR8NK32MhJqln8JJSoqinx9fYWb00WIyiAlef78Oe3bt094TUxMTKIMMyQrVCUWLlyY4HtKGLIZPC1IbF5JREdHC+OrhRvCmzdv9BowaS7XpAeSI61M5kBNuU1NNPu1hLY1tTovGo+Tq09MaF6K+vn4hkEJJan9qRqik2mUyPTGIgzDMAzDMJkV47QWIDOh6Uw6o5AR08QwGY3M1k4zW3oNgfOEUYIVwXjEdyWS0GsWL16sGNamTRvhffW5QTEkDBA3crU0qYU/evRIMUwtTSJ27NihGKYmkyifRfIa8mzJFUNyk5RySEp+iNIqIapfavGbNGmiGKZWFqLwpJSR2qCnFj8p+akUVy0vRPloSLioDEXtVK1vET1XLR9FeaX2XH3Pvn79Om7fvq3127lz53TiqfVLanInNs1q9xWFq9UrUZ0W1VcJpTwxpBxEiOQypC8WodTvGSKzKD8TqhRrLpz6+fnh27dviteqySaSS60cRfXHEN1FiQyjCNL/+9f58OEDoqKisGvXLhw5cgRAnH+h6tWro2vXrti4cSPKli0LKysrHDp0SOc+06dPV32W6Bp999SUUcTr168TFQaIOz61NKmFDxw4UDHMkJ0FJ06c0Pv72rVrEy2TKJ9F8hrybOngdQB48OAB+vTpg6VLl+LWrVto2rQp2rVrhydPngCI6xRmzZqFgwcP4s2bNxgwYABGjx6N//77T+e+SSmHpOSHKK0SovqlFl860k4famUhCje0jPShpgioxU9KfirFVcsLUT4aEi4qQ1E7VetbRM9Vy0dRXqk9N/6zhw4dioULF2LRokVwdnaWj1ucMmWKTjy1fklN7sSmWe2+onC1eiWq06L6KqGUJ/rKITAwEAsWLMCSJUu0lJ5ly5YlSC59dT45xmND6o4oPw2dwBg5ciSWLFmCypUrw8XFBbNmzYKzszNcXFywbt26RMkmkkutHEX1xxDdRYkMc9Zwz5499TpJPXz4MG7cuIELFy4gKCgI1apVw6NHj5AtWzY0bNgQLVu21LpPiRIlVJ8lusbZ2VkxbN++fcL7Tp48OVFhgLjjU0uTWrh05qQ+NNMUGhqqV6558+ahcePGOmGaZ1smVCZRPovkNeTZffv21fp/zpw5+PbtG1xcXLB3715YWFhg0KBB8PDwQJcuXdCjRw+8ffsW48aNk522/vbbb/j3338TlCZReFLyQ5RWCVH9Uosf32GqJmplIQo3tIz0oaYIqMVPSn4qxVXLC1E+GhIuKkNR36PWt4ieq5aPorxSe278Z9+6dQtnz54FEDcz2KpVK6xevVpvPLW+Vk3uxKZZ7b6icLV6JarTovoqoZQn+sqhU6dOaN26NUxMTNC0aVMsWLAAtra22L9/P4YMGWKwXPrq/IIFC5I8HhtSd0T5GV9mfeVNRDh27BgsLCxw9+5dhIaGomzZsnjx4gVMTU1hb2+PPn36JFg2kVxq5SiqP4boLook3UYlfSBykmqIH7DvnaRaMiUH5ubmsi8pyaG2g4MD5cuXL61FSzRqfrES6p+KSR3SQ3tgkhdD/BBmZFKzThvilzOxcqWH8Ti+zAUKFKCNGzdqnUm/YcMGqlSpktDHaEYhw8wI0v9r+EZGRnB3d9cKq1atGmJiYpAlSxZs2bIFQNyRW/nz5091OVOKvHnzprUIKFeuHA4ePAgLCwut3xs1apRGEiWd2NhYue4cOHAAQFxdi4mJAQBER0eDiGBkZIS//vpLjhcdHZ0W4jL/T3poD0zyMnHiRHz48AHFixcHABQvXhxHjhzB8uXL01iy1CG163RAQADy5MmD3Llz48CBAxgwYACuXr2aZLnSw3gcX+YWLVqgcePGKFSokNbvjx8/Ru7cuWV558+fL8tbsWLFVJM3pckw7mNOnToFBwcHmJj8T7eNjIzEpk2b8Pvvv6ehZLoEBQXpKEvfO35+fggNDUXRokWRLVs2rTCpQ8koxMTE4OPHjyhatCi+fPmCvHnzwtj4f9tto6KicPfuXdSoUSMNpWQYXb7nvic4OBg5c+bUG6aUru85vSmFn58fChcuLLzm7du3yJUrl84Z8pcuXYKNjU1KisekARnGWMTGxkZLCQSArFmz4vfff1d8k7l06RKGDBmiaGQiQQJDFJGxwKZNm1C5cmXUqFEDf/zxh3w/FxcXYVq6dOkCb29vNGzYEG5ubjh58iSqV68OBwcH+Pj4COMGBQUBMNy6ThNNo47Pnz/D399fK/zp06eKcbt3746CBQvqKIEAkCdPHlkufWzatElVJrXNy+/fv0d4eDgA4MqVK7h3757WfUJDQ7F161Zcv34dAQEBmDdvHlasWIHw8HChQciWLVt0ZveyZMmCH374AZs2bcLDhw+1lEAgbl9MjRo14O3trVq3lNIsqnMi5s2bJ0yrRGLql6icAMDJyQmAelkohSe2jCRIYNmXFKOfmJgY7N27FwcPHpRnggFg9+7dqnFFeaG2aV4tXFSGor7n5s2baNGiBVq3bo2LFy/KYb/99ptq+b558waDBw/GiBEj8PLlSzlswoQJwribNm0y6LkeHh6oVasWateurWVd2bx5c4wYMQJDhgzB9u3b4ePjg5s3b2LPnj0YMGAAnJ2dhX2tmtyi54riJjY/Ro4caVA7FdVpfcybN08YDgCtWrVSLYfXr1/rKIFA3Dgr7dOMj6iP1xyLhg0bluDx2JD+ypD8VJJZXz8PxE0m1a1bF7t374avr6/8++fPn7F9+3b06NFD8d5dunRRDAPE/alUjqJ+K7FjhD4yzIxgnz59kC1bNtSuXRs//fQTsmXLhufPn+P06dMIDg5GmTJlcOzYMYSHhyNbtmwwMzODo6Mj7t27hxw5cugYmfj7+2PVqlUAgB49eug1RPH398eTJ0/Qo0cPBAYGYsWKFbKxwLJly/D582ecPXsWpqamWLp0Kc6ePYtNmzahefPmOHPmDMaMGaOTDiLCnj17UKhQIezevRsBAQFwcnLC1atXkT17drRp0wZeXl7YtGkT/vzzT5iamqJ9+/ayWXmDBg1QqVIlfPnyBaampvjy5Qs2btyIfPnyoUGDBvD09FQ06mjZsiVOnz6NefPm4d9//0XWrFlRpkwZLF26FFmzZkWDBg0QHBysN+7Tp0/Rrl07xTKIiYnB8OHD9cbt27cvPDw8hDI1b95c3ry8du1aefNygwYNYGVlhWvXriFPnjzInz8/Pn78iBw5cqBcuXLyxl1nZ2dYWVkhNDQUXl5ecHV1Re7cueHp6Yl3797JBiGDBg2SDUIGDx6M4cOH46+//kJ4eDhKliyJbNmy4cWLF8iSJQv69euHFy9e4NixYyhWrJic5mfPnuHly5f4+vUrKlSooFi3ROVQvHhxxTq3atUqHD16VG/cSZMmoVixYopplQY5GxsbxfqltHG6b9++uHDhAtq3b683/Pz58+jevbuwLMaOHasYfvXq1USVkYeHB5YsWYJ169ahdOnSsLa2xoEDB5AjRw506dIFffr0gZ2dnTC+o6OjYjvOkSMHSpcuDVNTU5w+fRobNmxA6dKl5fakFHfgwIH48ccfFfOiVq1aOHPmjN5N8+fOnVMNF5VhRESEYt8THR2N9evXw8TEBKNHj4atrS3c3NxQv359nDlzRli+FSpUgLu7O0xMTDB9+nQMHDgQbdu2lfPiwYMHinXH2NhY+FwAqF27No4ePYqsWbNi4sSJiIqKwpIlS+Do6IgzZ87g1atX+Pfff2W3JOXKlYOzszM6deok7GsdHR2Fcouea2xsrBhX2oqkdF+ltrRixQqULl1a2E5FdbpYsWKK7f/GjRsAAGtra73X3Lx5E48ePRKWw4oVKxT7tQIFCuDGjRt6xx5PT0+dZwJxLxEnT54EAHz69AmrVq1K0Hj8999/49q1a4rjISDu45s1a6Y4Xnp6euLIkSOK/Xzv3r0REBCAgwcP4sWLFzAzM0Px4sXh4uKCDh06YOLEiXrzec+ePXj58qWwTeh7YdAsR1G/VbRoUeEYkSBSeA9iqvLy5UtaunQpDRw4kAYOHEhLly7VOY8xPiIjE0OuScrB4EWLFtU681X61KxZU/VAaRsbG4qMjCQioiVLlpCrqyuFhISQg4OD1kHn165do7p169L9+/fl+6gZdWjKffDgQXJ0dCRfX1+qX78+lStXTu8RNg0bNiQicRlYWFhQr1695LNwpU+xYsVUZRJtXpbO542KiqJSpUrJRwlp5oNm/OrVq2vlpyEHpQcEBNCVK1foypUrFBAQoJP+O3fu0K5du2jXrl10+/Ztrecr1S1RmtXi5s2bl6ZNm6Z15vDUqVOpdOnSwrRKiOqXqJyIiH766Sd68eIFvXr1Sv68fPmS7OzsVMtCFJ6UMrK2tqbY2FgKDg6mYsWKUUREBMXGxsqH1SfF6Ecz7uvXr6levXrk6ekp55tS3Fy5cgnzQm3TfELC45ehqO/RbBdERFOmTKHff/9dzitR+WqezRoZGUm9evWiGTNmyM8X1R215xKR1v9EcWcEt2jRQu+Z45qo9bVqcoueK4qrdl+REYJaOxXVaVH7l1Dqq/PkyaP1XV85SOjr10RjT5EiRcjKykrrY2lpSblz59a5tz6U+j3N+PrGQyJxHy+SWRO1fl4fonGcSNwm1MpR1G8ZorsYSoZSBBODZuXfvHmz/L9mZoquScrB4BMnTtQ5YJ0orqKqHSgt6vjUrOuqV6+u9xB1SZmLf+/bt2+Tra0t/fLLL7Rv3z69VmJeXl46v8WnVq1a9PnzZ53fmzVrpiqT6KBzzQHmzz//lP9XakQeHh7y//Xq1UvUQemGoFa3RGlWi9uoUSPy8/PTidu7d29hWiVE9UtUTkREK1askPNJkx07dqiWhSg8KWWkZtmnFl/UjuvWrat13nZgYCA5OzvTDz/8IIwrKYJKeTFs2DCdMoiIiCBXV1eDwkVlKOp7XF1ddcI2b95MZmZmRCQuXycnJ/rw4YPW7zNmzKCsWbMSkbiNqz2XiGjw4ME613h6elLZsmV17qmJWl+rJrfouaK4avft1auX3nbq7u6u2k5FdVrU/iWU+mp7e3vVchAhGnsqVaqk92x6qR9XQ6nfK1CggHA8JBL38WovCklBNI4TiduEWjmK+i1DdBdDyfSKoIeHh85bU0REBK1bt86gaz5//qyldBGl7MHgkhyiju/o0aP09u1brbCgoCCaM2cOERE9f/5cb2OVFK3169fTq1evtMI+fPhA/fr1S5LsQUFBOnkloSbTmzdv9L6h+fj40MaNG/UOmDNmzJC/37t3T+81hw8f1iuP2kHphqBWt0RpNqReKpHQtGoSFRUlLCc11MpCFJ6UMvrjjz/0xh00aJCirJrxRe34+vXrOh19TEwMbd++XRh32rRpqvUyJUjMofOGEhISQhERETq/S8pQUupOSqImd2LjJuW+avU9MXU6NRCNPRcvXtT7cvvw4UOD7p3Qfk/zWlF+qr0opCTJ3SakfispY0R8Mr0iGBQUpBgmVWjRNSdOnFAMa9KkCe3atUurU/j06RNt27aNunfvTrNmzdL7NvD+/Xtq2LChYpjaQKKWJrVwaXZDHz169BCmSYRomV5zJkWfTOfOnVMMF81GSmkRPXvhwoV6B8+IiAjauHGjYjw1klIOooFELT927NihGPbixQsiImHdc3NzE8YfMmQIXblyRSfs4sWLNHjwYMW4sbGxwrolyaaPpJbR5s2bhfGTkp9KcWNjY8nb21sxbOjQoXrz0cfHh4YMGaIaLirD6tWrK7bT6tWrC/sWUfk2atRImI+idjZy5EjVPk0tzRLPnj2js2fPkre3N3l7e6v2S2rlL3quKM19+/YV3lf03Pnz5+v8LiFqC0QkrK9SnVPKk/bt21O7du104hkytqghat/6wuIrbkp94qxZs+jZs2c6v2vKLKp7avmphKYC9/TpU62wa9euybKJ6rVIrp07dyqGeXt7C+vPmjVrFOPqU8ZFZBhjESBuk2WzZs1w7NgxveFbt27FihUr5GuNjIxQsWJFoYHD2rVrhYYod+7cQd68efVuqm3SpAny5MmjuMn0yZMnWLx4MR48eAALCwtky5YN/v7++PXXX9G8eXMcOXJEb9jw4cMxf/58NG/eHHZ2dihSpAiAOEsmDw8PTJ48GU2aNFFMExEJ0xwSEoJq1arBzs5OK9zDwwO3b99Gs2bNFNOUNWtWEBH2798PPz8/9OvXD7dv30aNGjXg7u6OFy9eoFq1alr39fHxwYsXL2BnZ6coU9WqVRU3L3/+/Bnt2rXTK++9e/ewdetW4bNNTEwQGRmpaBDStGlTTJ06FVOnTsWxY8cwduxY9O7dW8v4xc/PD3v27IG/v79s6ff27VthPovK4cyZM2jUqFGi8iMmJgZ58uTRm9Zy5cph1qxZuHfvnmLdAyAbKOiLP3z4cMXN3rdu3YKNjY1iWQBQrFtbtmxBpUqVElVGO3fuVGwPx48fR/v27RNt9KMvP589e4ZLly6hXLlyKFq0qN64+/fvh42NDUaPHq03LxYvXqyYj/379wcAYfjHjx8Vy3DgwIG4d++e3nZaqVIlrFixQive169fUbZsWQwfPhyFChVSfG7p0qWxe/duxXw8e/asYjvLly8fiEjxuZUqVRIaEvTv3x8FChTA0KFD8f79e1y/fh01a9ZETEwMdu7ciZ07dyr2Sx4eHsLyt7S0TFSaq1evjlu3bineNzY2VvG5WbNmhZGRkWI7e//+vWKdXrx4MQoUKKC3vjo7O2PAgAGIiIjQmyc1a9bE06dP8ejRI8VykFizZg369euHy5cvY/z48ejbty+OHTumKNeECRMwYMAAYV/88OFDjBs3Dk+ePMHz589hY2ODKlWqYMaMGRgzZozePnH37t24ffs28uXLp3c8rFSpkrCPf/r0KSZNmqTYP2zcuBFAnMHJqlWrULx4cTx48ADDhg2TDRkbN26MFi1aoH///pgxYwaePHmCHTt2CPvS4cOHY+vWrYpyBQcHw9TUVLEcS5YsqVh/wsPDUaZMGaHuYigZShEEgFGjRqF58+awsrKS3Xpkz54dAFCzZk2cPXsWOXLk0IqjZIn2008/GXzN3bt35bCyZcuiSpUqCZL706dPAIACBQoYFKbUyKWO78OHD0J51dJz9uxZHDx4EI8ePYKRkRHKli0LFxcX2Nvbq6ale/fuqFatGnbv3g0fHx80bNgQp06dAhBn4n/69GmtvHJ0dESOHDkMKgelfDZEXtGzAeDbt2+yO5Fff/1Vy32CZFHXs2dPrFu3DrVr19ZygVCrVi307dtXdnYLxHUcamkShSclP9TSqom++pWQ+PFRKwtReGLLSK09ZM2aVRg/OfJTX9yktKOEIOo/UiKeKB8NyavEPhcA7OzscP78eTg4OMDLywtt27bFnj17kix3UuKq3VcpXJRXhtTplBx3gP9Z1fbs2ROzZ89Gy5YtceHCBaFcly5dEtb5+vXrY+vWrShatCju37+P+fPnY/z48Zg4cSJ27typ2u+JZFbKTxMTE4P6h3v37mHYsGGyIr58+XK5Tyci9OvXD3v27MHkyZP1esFQkk2tTaiVo1L9MWSMMIgEzR9+B0gWmJqWmBJdu3al4ODgNJQuc+Do6EhE/7PuygjHrdWqVYuOHDlCw4YNIyJtK0QioubNm6eBVAyT+ZD6laZNm9KxY8eoYsWKaSxRxsXa2pru379PAwYMICJtA5bEYmNjIy+5BgYGysYN6WGcePLkCTVs2JA6dOhAPXv2pG/fvslhM2bMIBcXFzp27BjVq1eP9uzZk4aSJi8Z5og5CckPkj6uX7+OkiVLolSpUgDijqO7cuVKaomWabCwsICXlxdiYmJw4cKFDHGqyOLFi3H06FGMHz8e4eHhaNOmjVa4mZkZfv/9d1SvXh1GRkYAgIEDB6aFqAyToVm6dCkiIiKwcOFCrFy5EosWLUprkTIs7u7umDt3LiZNmoTw8HDY2tom+Z5Dhw6FpaUlSpQogTdv3sgOwBM6m5kSDBkyBOvWrcNPP/0Eb29vtGjRQnagXbhwYfmYUQcHB0ycOBGurq5pKG3ykeGWhr28vDBz5kz8999/uHnzJkaOHInFixen+HPjH2UUEhKitRTy5s0bPH/+HKVLl0bJkiVTXJ605MuXL/jjjz/w4MEDlC9fHhMmTEj0OZmxsbE6J3ekNqSy9xSAvMdEE5HX+YzA169fkS9fPvn727dvUaJEiRR95tOnT1GmTBn5+6tXrxK+DPId8+HDBxQtWtTg6yMjI+V9uxcuXECNGjXkrTKGQP/vrPaHH36QX3CSSlRUFExNTeXvoqPjAODdu3coXry4XgfsElKakprejEhS8+Tp06cICAiQ9z7rc1SdUGJiYvDlyxcUKFAg2epVciCl8dOnTyhYsKC8h8/MzExv/UurupXs42KazUWmEHXq1KHQ0FB5ullzaTgoKIj++usvmj59Ok2bNo2mTZumFffu3btalmjxiY2Npb1799KKFSsoOjpay0WM5nOISMsqa+bMmeTk5ETjxo0jJycnmj17NhERtW3bltq1a6f1kX6TkFy2bNq0iSpUqECjRo3Seo7kuiA2NpbOnTtHISEhctjly5eJiMjPz092z6GJps8pIm1fVdI9JYey8c3f//jjD6patSrZ2tqSjY2N7CiYiGj58uVa1l979+7VinvgwAEiInr8+DH99ttvdObMGZo5cybFJygoSPZdJ/H+/XsaPHgwdevWjaKjo+mff/6Rw16/fk1Ece4eNm/eTL6+vjr3DAsLo3///Zc2bdpEGzdu1LI4vXfvHrm5uVHv3r2pV69e1KtXL628OXPmDAUFBVFISIhWPkuEh4fTmzdv6PXr17IsROr5LContTJcvXo1ERFdunSJGjRooGXhqi+f4yOqX7NmzZLLPTw8nEaMGKEV187Ojv79918iIvr777+pUaNGWuGBgYF684NIXFYiuUXtzBBEZUxE5OvrS8uWLdPbR4jqDpG4LG7cuEG7du2iGzduyL9J7cba2ppKly5NNWrUoFKlSpG1tTURER05ckTrc/jwYbK0tKQjR45oPff06dPUpk0b2fm1Zh5JS3kTJkygvn37arUntb5ly5YtVLNmTWrdujVZWVnRtm3b5LApU6YQEdHRo0epcuXKtGjRIq24QUFBtGDBAho4cCAtXLhQqz+wt7enmzdvEhHRqVOn9C43uri4EBHR/PnzycnJibp06aLldF1p+48ovYbIvWXLFrK1tdXbr4nqjtp9ReFq7XTp0qVa3zX7PFGdU8sTtfInInJ1daU2bdrQ4MGDafDgwVqW2/qQ2rq+jxpz587V+q5vPDZEZlF+Kln+SuzYsYPq1atHlStXpujoaOrQoYPc74nqnSGyifpTtXJs0aKF7BLu/v37Wn4Z1cYIQ8hwiqDksFYqJM29XE5OTrR06VKqWrUqLV26lH7//Xc5rFWrVtS1a1caN24cjRs3jtzd3XXu3a1bN1qwYAHZ2NgQUdxeuJMnT9KoUaOoRIkSNHr0aBo9ejSNGDFC67maji5jY2Pl75qe++N/JKR09OjRg4i0Ty4gEnd8Ulj//v1p27ZtstxPnz6lw4cPU5UqVeRB5uDBg7LMV69epW7dupGjoyN17NiROnToQA0aNKBu3brR1atXZTmUXAX89NNP5OjoKDf++A1GUtJ79+5NFy9eJEtLSxo6dCgNHz5cvubdu3dkbW1NW7du1Yrr5ORE169f17uvRErviBEjaPbs2XoHmHr16tHEiRNp9erV8keievXq5OHhQY8ePZI/mjKLOoG5c+eSjY0NFSpUiCwtLcnBwUE1n+PLHb+c1MI087ZHjx70/v172Zu9Uj7HR1S/Vq9eTY6OjrRr1y5ycHCgQ4cOacUNDQ2lzp07U4UKFWjSpElaLwuDBg0iOzs76tSpE3Xs2JE6deqkN836ykqf3Lt27aK2bdtSgQIF5Jem1q1bU5MmTbTuK3pBIRKXMVHcnqi//vqLjh8/Ln80ZVaqO/HzUrMshg4dSl27dqUlS5ZQt27daOjQoVrxBg8eTE+ePCGiuLYp+YkrUKAANW3aVOvkgV9++UXnBbZq1ar09OlTCg8Plz/x87Jnz55EpN0XqfUtVlZW8otmREQEWVlZ6dy3R48eFBkZqVO3mjRpQn/99Rfdvn2b/v77b61y8vPzo5YtW1KzZs2ob9++el2GSLJJcscvRyVE6TVE7ho1aijuI1frH0T3FYUrtdOoqCgKDg4me3t7Cg0NpZCQEPr27ZtWXorav1qeqJU/EQn97Olrax07dqSOHTtSgwYNqHTp0tS6dWsqXbq0Vn8Z/wVHesnRPA1EaTw2RGZRvyfpAJGRkTRp0iTq0KGDVlzJOby+iSQ11GQT9adq5Xj37l1q0KABDRw4kFxcXLT8BKuNEYaQ4fYI/vbbb2jSpAmePXsGZ2dn9OnTRw6LjIzEkCFDsGfPHgwZMgQtWrSQw4KDg7F//37hvT98+IBNmzbh8OHDAOKmkX/99VdkzZoVb9++RfPmzQEApqammDZtmlbcJ0+e4Ndff5UtfwDgxx9/lO/j6emJDx8+yFPT3bt3BxBnDbly5UrZ5F2ybpKQptXfv3+P9evXw87OTg6LiopCbGwsoqKi0KlTJ6xevVq+9tq1awgMDJQtX01NTTF37lwAgKenJxYvXqy17AfELQX+/fffsLS0RPXq1fH+/XstK1mJn3/+Gf/88w969+6NqVOn6oSHhoYiKCgIWbJkga2tLXLkyIElS5ZgxowZ6NmzJ4YOHYpevXph5cqVqFOnjlbc6Oho1KhRQ043aexskP4PCAjAwoUL9S7lmpubY8aMGTq/A0CJEiXQsGFDvWGivacAsHfvXly+fBkODg44c+YMOnfurJrPEkrlpBYGxG1BePDgAbJnz46iRYtqLbHpy+f4iOpXr169cOHCBYwYMQIjRoyAs7OzTpo/fPiAIUOGYPv27bh27Zq8bHTnzh2cO3dOMb9EZaVPbicnJ1hbW2PVqlUYMGCAnJeS3BL79+/HzZs3FZebRGUMxFn7/fbbb3rDRHUHUC6L27dvy+ehAkC9evW04t24cUNe7v7ll1/ks2JfvnyJpUuX4u7duxgyZAhsbW1x+fJl+fxsiV9++QWlS5fWm2YLCwu0b98e9erVAxFpLSep9S0VK1ZEWFgYsmbNirCwMJQvX14OCwsLw9GjR5EnTx6YmprqXSKT8rFKlSrYtWuX/PuTJ0/w9etX1KlTB3fu3MGXL190loaJCGPGjMEvv/wCADAx+d9QVb9+fZ20SmfcitJriNwVKlTQSYeEqO6o3VcUrtROt27dig0bNuDOnTto3rw5iAhZs2bVaoei9i+hlCdq5Q8ALVu2xLJly1ClShU5zyXrX1Fba9euHY4dO4asWbMiMjISnTt3lsO6du2K4cOHa/XdABAYGCj/rzQeGyKzqN87fvw4+vXrh0KFCmHy5Mny2esSxsbGiImJgZGREWJiYhAbGyuHnTlzBpMmTZLzcPr06ahfv77Bson6U7VyNDMzg7GxMb58+YK8efMiV65ccpjaGGEQCVYdvwM+f/5MV65c0TnWxcnJicLDw6lr1640duxYqlGjhhzWp08f2rx5M927d4/u379P9+/f17lvq1at6MyZM2Rvb0/nz5+nNm3aaIXHd3IqcefOHWrRogVZWVmRs7OzfGajRLt27cjNzY3Kli1Lbm5u1LlzZ617/vPPPxQcHExhYWE6DiidnZ2pXbt2tHz5coqNjdU6wmvixIlkbW1NJ0+epLCwMJ23O32nWmjy7t07re+aU87SMlbNmjXJ0tJS72xBUFAQtWvXjgoUKKB1n5UrV1L9+vXp6tWrFBYWRj169JDzfNCgQZQvXz7av3+/3nIYMmQITZkyhSpXrkwzZ87UmkXs2bMn2dra0vbt2ykqKkrvjGCXLl1oxowZdPjwYfltVKJJkybUsGFDGjVqlDy7K3HmzBlydHSUlwwk62EJ6Y2uYcOG9PDhQ6pcubIcppbPEyZMUCwnURgR0f79+6l79+709OlTCgsLo3Hjxslhq1at0snn+Ijql729Pe3atYuio6Np+vTp1Lp1ax25pfM7P336pPV2PXz4cDp//jwFBwfrXUoXlZW++iGxYcMGeVlW3/Js//79dU7V0URUxkREbdq0od9++42WL19OK1asoBUrVshhorpDFLck1a1bN52yGD16NJ06dYq+fv1Kp0+fplGjRmnlyaxZs8jBwYGGDh1K9evXl7eOSAQGBtKMGTOoXbt2emfGateuTeXKlaO2bdvqbC2JjIyk58+fE1HcrJ6m43a1vqVChQpkYWFBlStXJgsLC6pQoYLc1n18fGjSpEn08eNHCgsLo8WLFxPR/2Z7evbsSRMmTKB9+/bRpEmT5NkooriVFenEoEePHsleBohIXsZ7//49nT59mqKjoyksLIzOnj0rXyPNeoaFhdG5c+e0tlpopjcyMlKeaZXw8fGhyZMn68gtpat8+fKUL18+srS0lH+T6oqo7ijlh9pzicT1XYqrhKj968uTiIgIOU/Uyp+IqGXLltStWze9K2Witla9enX5JKiAgACtsdaQ4/GUxuNnz57R33//TUFBQYoyi/JTzfL3+PHjVLt2bSpQoADVq1dP68CImjVrykf2ff36VWfWTi0/Rf2pWjk2btxYdkzt5eWlNc6rjRGGkOGMRT58+IA//vgDz58/xy+//IKxY8fKm6v9/f2RN29ehIaG4vjx47CxsZHDevXqpXUfIyMj/PPPP1q/xTeCGD9+vDxrps/JqWRhBMRt7vz8+bPezbGSPyzpb+vWreW3oeDgYKxZswZfvnzBjBkzcPLkSTRt2lSO++TJE5iYmKBUqVKIjIzE69ev5dmF3bt3o127dvK1R44ckWctgbg3ziVLluDp06fImTMn8ubNizt37sjhTZs2xcaNG1GwYEFcu3YNEyZMwIkTJwwqAylf6f83KGvOVALA69ev8eLFC9l4plevXjAyMtJ5S5TKISAgQLY+lhxtSz6TNImOjoaJiQliY2MRFBSk488r/kwtAEyZMgUA4O3trRMmzd7Y2dnBw8MDzZo1w5kzZ2T/WhKnTp2CnZ0dbty4gT/++AOurq6ysYhaPgNAeHg4fH19UbRoUZiZmWmFSXWnYMGCOvIBcdbw79+/R4sWLfDhwwd5lnbZsmUYMmSIfN369et16rmofn38+BGFChWSr7116xaqVaumFf/GjRt49+4dWrRogffv38vGIppvykBcOWrmF/C/siIiBAUFab3lKrFmzRoAcfXq+vXr+Pbtm9ZsU82aNREQEIC8efPCyMhIxzOAqIwBsdGPqO4AwIQJEzBr1iz5+7x58zBmzBidvJDQzJOPHz/Khi+aea5JYGAgHj16JM+6btq0Cd27d8fr1691rpVWG7p06YLNmzfD2NgYX79+RefOnXH8+HEAcWW/du1aua/s06eP0GhDk+3bt6NTp07y9z/++APjxo3Tm0cSmnmlCf2/c38AsLW1hY+Pj077EmFvby9bdt67dw8TJ06UN/vPmDFDy0FyVFQUvLy8tIwf2rdvL7y/vjojoVl37t69q3VfTV+R0dHR2LZtG54/f44yZcqgY8eOWrOcIjw9PbFy5UotZ/WaeXP27Fn5vvH7WQC4f/8+Jk2ahODgYBw7dgx//vknxo0bpzq2AECTJk3k+iJhZWUlz5gFBATIY6BmWzt58iSmTJmCmJgYmJiYYNq0aWjUqJFB6b1z546OJbg0DkgHLUj06NFDb5v977//8O7dO9SoUQMRERFyn/rXX3/h999/BxDX506cOBHz58/XiksKBlLt27fHtm3bYGJigujoaHTq1Am7d++Ww9XyU60/VerHJZkAbSMWzbaqNkaokmDVMZ1Tt25dOnXqFH379o1OnTqlpTkbevC1Eg8fPpT3xcXGxmqdnyjtu5BmNqSD4YmItm7dSpaWltSmTRudTddEcXsNY2JiqFWrVrRq1Sqt2SQXFxfau3evfMC05tszkXjzfPwwaQO2RM2aNSkiIoLq1atH4eHhOkYNr1+/pmbNmtG+ffuoWbNmWsfWvH37liZOnKi1cVraM6X5xqxv1mXWrFnUuHFj2XhGOgNZxM8//0xERO3bt1e8Rm3WzhCUZnVFe09jY2P1blqWUMvnDRs2kKWlJXXs2JEsLS21Zrk0Ny9HRUXp7Glxc3OjIUOGyPtgpOOw1PYWSYjq171796h169bUqFEjio6O1iknfc9WQ9pfqe8j8ccff1C1atUU9/lp0qJFC9VnxkepjCWUjH6U+PTpE927d4+sra3pwYMHdP/+fbp9+7bBftFEBioipFkWfXuuJE6cOCHvO2rQoIHWaoRoHx+R9gZ2R0dHrQ3sAwYMoH379hERkbu7u15jr4iICPrvv//o48eP9PHjR+rfvz8R/W/2zcrKSmc1Yfr06WRtbU0WFhaK12ga2dna2tLAgQPlMGtra639lpLhjYSDgwO5ubnR/Pnzaf78+bRgwQKt8DFjxsj/x8bGas3MSGdLS2Ga39X2mHfo0IFmzpxJx48fpxkzZmi1Y7X6LtoDOnDgQPr9999p9erV9Ntvv2nlhUS9evXIz89Px6+r2thCRNS5c2eaPXu24ix4SqBvX96VK1do+fLl9Ouvv8oz9UuWLJFl12TBggXUunVrqlatGsXExFCTJk3kPJNm4jU/mty7d49Gjhyp1yDI3t6eSpUqRS1btqRSpUqRvb29XA+J1PNT1J+q9aX6jFj0hekbIwwhw+0RtLCwgKOjIwDA0dFRS9svU6YM1q9fr3XqiLQn5PTp05g1axZ8fX0BALlz58alS5e07j1gwAB5r5iRkREGDx4sn5gh7QfInj07jh8/Lnv6BuJ80F24cEHeL2FnZ6f1Nr1t2zbExsZi9erV2Lp1KzZv3iyHBQcHo02bNli+fDmA/70Z7N69G7t27cK9e/fkN9ro6GiEhYVh3bp1WLt2LR4/fgxra2t5T4ODg4NWenLnzi3LHRkZidu3bwMAjh49Kl/j5OSEoUOHYuHChTh37hyaNWsGAOjcuTNGjx6NWbNmYcKECTh9+jRsbGwAQGvvpT6OHj2K8+fPy+mpW7cuxo0bJ4wTGBiIVq1a4eLFizpv8NKM0MSJE+VZuyxZsujMugG6s3P58uWT061vVld6qxftPTUyMsKnT58QGBiod1ZLKZ8lVq9eLR+jFhUVBXt7e3mP6LJly3Du3Dk0aNAAJiYm+O+//7Ti3rx5E56envKsU0xMjEF7iySU6hcADBo0CDt37kSnTp2QJUsWeHh4aJWTvmdL3LlzB/PmzYOvr6/WLIY0uzBlyhQ0bNgQ1tbWuHr1Kk6ePCnH3b9/P27cuKF375Fm3Xzz5g38/Py0wt+9e4c1a9Zo7bfVnNkXlTEQN4u3f/9+vHz5EiVKlEDOnDnlNq9Udy5cuIADBw7gzZs3mDdvHoC4/YuDBw8GAPTv3x+rV6/G5s2bMXfuXDRt2hR//vmn/EwXFxf06dMnwW45JHcWmifcAHH1UWrrdnZ2+Pr1KxwcHLBz5055z52E0j4+ANi5cyf69euHVatWYdOmTWjZsiU6dOgAAFixYgX69OmDTZs2oX79+hg6dKhW3OHDh+PixYsoXLiwPON36NAhEBHWr1+vNUunSd26dTFp0iTMnj0b48eP13uN1KcbGRkhT548Wm2ucOHCWvst48+Q5MiRAwsWLNB7XwBas8dGRka4fPmy/H3NmjXo2LGjHLZu3Tr5u9oe848fP8r70Ro3bqzVF4vqu5QOpT2g9+7dk2cs+/Xrp7P/VEJzJkpqp6K2L/Hrr78iMjIS165dk3+TxoDu3btj06ZNctzevXsjODhYMR3x65c+BgwYACKSZx2le4eFhWH06NHImzcvcubMCSKCqamp/HxNDh48CG9vb9SvXx/GxsaIiIiAu7s7Fi5ciObNm2utPMVfqejWrRvmzZun1w2Wvmdpopafov5U1JcC2uNAlixZtMYBtTHCEDKMIrhy5UoAQK5cudC1a1fUrFkTN2/e1FoaDAsLw9mzZ+VlBM3l33HjxuH48eNwdXXFnj17MHPmTJ1nREZGan3X9CskcnIq2nQNxG1Q37dvHz5+/IihQ4dqKTBFihTBhg0bEBISgq1bt8rTxaLN80ZGRujTpw8OHDiAVq1aKeZZr169EB4ejhEjRsDBwUF2jhl/YPn999/x4MEDGBkZyZ2AiYkJnJ2dMX/+fDg7O2P58uWoWrUqgLhN9dbW1vj48SPWrVuH1q1b6zxbn/GMiCpVqmDVqlUYPny4PNjGx9jYGObm5nIHEr8xAcCiRYtw8eJFODk54cSJE1pOn2/cuCEfXbV37160bdtWDmvTpg1atGiBFy9eoFSpUsiWLZvWfa9cuYKSJUvil19+0VmSVMpnTYKCgpA3b14EBQVpdSCizctA3AvIy5cvYWRkhLdv38LMzAw9evRAjx49cOnSJVk5V0KpfknoG0BEz5bo27cv1q1bh8GDB2P58uXYtm0bAMjXXL58WXYk6+joqGWEITJEkuqmkZER8ubNi0OHDmmF63tB0URUxoB+ox8Jpbrj4uICFxcXRZ+GUh0/ffo07t27p1MmBQoUkJerEoK0Kd3d3R1Zs2ZFbGwsLl68iBo1augMeMWKFYObm5vWwFe4cGFMnDhR7iuLFCkiK9rNmjXTu4G9Xbt2cvuKjo7GlStXYGJigvPnz2sN9Ldu3dJx1i/5Wh07dqzW8p4m7u7u8PHxwalTpxQVwX379mHEiBEA4gbbxYsXy8d9ERHs7e3lM4Bz5cqFMWPGAIhT8jt27IiRI0dqGT9IL11AXL92584dVKlSRedFMiIiAmFhYTA3N0doaKicP0CcgdyWLVu0HMprGp5YWFhg7dq1sLKywpUrV7R8zirVdymvfX19UaFCBVl5NjIykvPaxMQEHh4esLS0lMsiPo0bN0bPnj3x7t079O3bV+7D1do+oLycD8T5DZUwMjLCq1evsGHDBsXrDWHVqlVo0KCBzhgk0aNHD3kJtXnz5vLEjSZZsmTBt2/fYGRkhKCgIBgbG2PUqFEAdI3+4n8XGQTdunULLi4uePLkCebNm4euXbtqKfSG5KdSfyrqSwHxOKA2RhhChlEEJcugJk2ayL9JM4MS69evV4xvYWGB/PnzIzY2Fvny5dNbEZs3bw5XV1fUqVMHFy5ckBsUAFSqVEneL7Vo0SKtCnr58mWUKFFCPj+2RIkS8hvPlStX0KNHD/ls3oEDB2LMmDHyTONff/2Fv/76C5aWlvD395cPks6dOzdy586N2bNn48yZM3otji0tLTFp0iTF2ZGuXbuCiGBra4vr16/Lv0uN/9GjRyhbtqw8oDx+/Fi+pmjRoggPD0fVqlXRsWNHhIWFyWFjxoyBl5cXpk6dCnt7e/z+++/w8fGRw1euXImRI0fi48ePKFy4sKzEiwgLC0ORIkUwefJkhISE6L1GNGsnIZqdE83quri4wNPTE/nz5wcQt19Ec+CTBgR9KOWzxMyZM9G0aVPExsYiS5YsmD17thw2YcIE1KtXD0+fPoWjoyMmTJigFXfVqlUYO3Ysvnz5glGjRmHFihX/x951RkWRbd3doGLGMMwYwTGCigGJIsmEgooJQUVRATNmRTEHzDiKAcScc5ZRYQQUHQVFFMUcUMAAIhIkw/1+8O6dqu6q6gb1m3m+2WuxbLu6q6or3Dr3nH32ZsvmzJkj2l1JIX99bdu2jS0Te4Cosu1KlSpBX18fxcXF0NfX52VWgJLJEZ2w3blzB61atWLLoqKiYGVlJcjzk3owAcITFC6kzjHw1zhSvnx5PHnyBHFxcWyZsszuixcvMGrUKHbvV69eHZGRkUq7CcvqSkODzu7duyM8PBwLFy5EcnIyVqxYobTLnfv92NhYqKuro1GjRrh16xab8M2ZMwcrV67EggULmKvE2LFjla4XKBl7z58/D319ffabRowYgdDQUGhpaWHhwoW8ygy9tnr27AkTExM8evSIVTMAPv/s7NmzLBCUyWQ4d+4cCwSnT5/O9kHe/Qcoyb537twZubm5gvsdEBCAmTNnIjExEdra2rz7Yd68eTAzM0PDhg2RlJSEZcuW8TK7ly9fZhMPeY75/v37ERgYiMDAQDRt2hQHDhxgy+j1Ls+1k+euCWHPnj1YuXIl1q1bh2bNmgny5WbOnInHjx/j0aNH0NXVxa+//gpA/NnChVQFpVq1ajhz5gwsLCxw7do1VKlShXFTP3/+DD8/P8ZdpNlxVUAIQVZWFo4cOcJ7di1YsADTp09HQUEBbty4gT59+mDUqFG8agIArFmzBgMGDEBcXBwGDhyINWvWoG/fvjh06BCaNGnCPrdv3z74+/vjzz//xMyZMyGTyZCfn49u3bqhXbt27LqliYf169fDwcEBq1atgru7OyZNmsSLE6TGUkB6PJUaSwHF5wB3kiS1rDQH/YeAFAcgPDycdOvWjQwYMIAEBwcTfX190qhRI7J79272/TVr1pCcnBwSGBhIWrRowbwV5XHv3j1y5MgRhc7fsvClKMS8eYuLiwV5XVxIdRxbWFiQs2fPEhMTE3L27FkFzpwU74CQv7p/5fezuLiYdRAXFRWR6OhoHteiY8eOpKioiLi5uRFCFH15ywKurhj3j3I4iouLSUhIiGjHOMW+fftITk4OOX36NDEwMCA+Pj5s2f3790lubi559OgR8fT0JMHBwUp1IrOzs8nBgwfJuXPnSE5ODlm2bBmZNm0a6/AiRPlxlkJiYiIpLi4mycnJvOOuCqS6KylWr17NXhcXFytc9/fv3ydHjx4lsbGxKm+XkBLR7JycHLJ06VJiYmKiwE8lpETM9fDhwwqirkKg3Cnun6GhIeOOUgwdOpTk5OQQT09P4uTkxOMI098jf465CAkJITk5OeT69eukd+/evDFC/tqR7+41NDQkHz9+JFZWViQlJYXdb7TTUaybcPfu3Qp/XEjx1giR1s7jjkNC48nr169JaGgojwupCp9q3bp1vPXKCyiPGzeOWFpaMl25wYMHs3t40aJFjAspxonkrl8eFhYWTID83bt3vN9bUFBA9u7dSxYsWED27dtHCgoKeN/9Wk/w4uJi8uHDBwUNVe49yoWq3DQpyHfJHzx4kERFRan8/SFDhjCNz9TUVNK1a1fB/RHaJyl+c0pKCpk6dSrp2bMnmT59OklJSWHL7O3tyf79+8njx4/Jvn37FIS9CREX2k9PTxfV/KXXkJCOLIX8GBkWFkYeP35MjIyMmJD5woULSe/evZlmZHh4uOgfhbGxMcnIyCAeHh6EEEXfZWVjKSFlH0/pOoWuMWXLVMEPkxGU5wAAf3WjffnyBSdOnEBGRgasra3x8OFDVKlSBVZWVnB1dQUhBL/88gsqVqwIDw8PhUxSZGQkTExMWNmkatWqSExMRGJiIovqpWr8HTt2RNeuXeHi4oLmzZsr7LuYN69MJkOrVq0QHh4OQ0NDNnvmalAlJyfj6NGjiI6Ohq+vL68Mqyw7IsU7AMRL4TKZDLNmzUJQUBDU1NRgYGDA+5yNjQ3MzMywbNky5ObmolKlSgBK+B/+/v48/geFlZWVKLdk9erVLJMln9Xl8oU2bNiAc+fOsaydEHr16oWKFSuycl5aWhqAkmtlzZo12LNnD3R1deHn5wegpLtZSidywIAB6NixIzIzM+Hl5YXx48ejUaNGcHV1ZdwdseO8ePFiLFy4kFduo6DZRnd3d9a5HR0dDW9vb1y6dEnyWNLMCbe80KlTJ8GZYn5+Pvz8/DBx4kSMHDmSl0XPzc1FfHw8cnNzERMTg5iYGF4Z7cCBAwoz1z///BOEEPTo0QMVK1bEvHnzMH78eAWLQdolnZ+fj7i4OMTFxbF1C/H8uBnlrKwsBAQE4MiRI7wMFSEEc+bMQcWKFbF+/XrcvXsXurq6vOVC55i7/NKlS+jatSs6duzIKzsTQvD+/XvetSMP+aoCvWafPHmCOnXqsOtBvjPX1dWVVROEyl1SvDW6XTHtPO49LJPJeHSW5cuX4+rVq2jfvj3u3LkDGxsbzJ49W3Ispb9JKisHlFQT5Lttq1evzsvycfdLXhsxIyMDS5Ys4b1HP+Pr64uBAwciPz8fFStW5NFwXFxcoK+vj44dO+LWrVtwcXHhacWVL18eY8aM4ZWGudlXsWsaKOm+3bVrF69797fffsPEiRNx8+ZN/Pzzz0hJSYGZmRn8/PygqakpeSydnJwwZswYlo3igkt/uXTpEtTU1GBkZITo6Gjk5OTg7t276Nq1K8vgctcrX5J3dXXFqFGjsHz5cgwbNgwZGRno1asXL9sqf34ppLLgP/30E9atWwchZGVlYejQoQCAFi1a8LJjz58/x5MnT3D79m32TC0sLGTZterVq4tq/ioroQIlNC9fX180b94cv//+O3bs2IETJ07g1KlTcHR0hJaWFho1aoQzZ86wY6empoaKFSvCyMiIrefWrVu8+2fEiBFwcHDA6tWrkZubq0ADkRpLAeHx9MaNGwrjuNB5FLr2ateuLfnMLA1+mECQprvDwsJw9epVHgG8c+fOrF7fsmVL/PTTTwD+GpBlMhlOnDiBYcOGCa778ePHMDExESRl00BQ6gK9du0aQkNDsWLFCiQkJMDBwQGDBw9m+7F9+3asXLkSVatWxalTp3g3TXR0NK+cKH+zUqmUmjVrIiAgAC9evGDLpMq3gHJugVQpXKq8s2zZMh7HkjYIVKpUSZQsLiXRIIXg4GDG/6hSpQpcXV15+yRfYuvXrx+vbObo6Ig//vgDMpkMmpqaePXqFbuWgBIZDh0dHQXeo6amJlq1aoXs7GzMmzcPQInP9YQJEwCUnFMKseNMgxgpL+atW7di5MiRcHd3x/bt23H8+HEAJaUEoKQcxSXLZ2Vl8X4bHeSTkpLQvn17hfXPnTsX8+bNQ6dOnTBjxgxeOa1Hjx6wsLAQ5LoAwLp163D16lUFoWoalFOujbwwubJ1i/H8UlJSsH79eoSGhsLd3Z01YHG3KzVBETvH3OViTT8ymQy3bt1CUVER1NXVBY+HnZ0dcnNzMWzYMLRs2ZJJKEmNGwCUlrukeGtACa8xISEBjRs3RkFBAXbs2MGW1atXDxs2bGDlO64At1jT1rp160AIwciRI3mBPxdFRUV4//496tSpg/fv3yMvL4+3vGnTpjh06BAv4OrQoYNKJWsAjEdJ/iMTxOUSGxkZ4dq1a4LnQqopA4AkZxoQv6YB4UaCcePGwc7Ojtfgd/DgQYwdOxaHDh2SPJY0qFLWXJeens5MDICScfnp06fo2LEjli9fLirLQoN++YahFi1aqOyRK8VvXrlyJQ4fPozKlSuz4IUGzfXr14e3tzfjRHLvc67Q/u3bt1njB1dov0KFCsjLy4O2tjZmz57NJkfKSqhAScnXxcUFXbp0wa1bt3DkyBE2FlaqVAlXr15FhQoVWOPT0aNHBaXRWrdujZ49ezIx+HHjxjE+PgAFPqTUWAoIj3l0HBfjRFIIXXvyjXJfgx9GR5CrOyWvQaWtrQ1nZ2cQQnDkyBH2+ujRo0yDq3fv3sjMzORl3qSi6sLCQpw8eZJ1sL569QpeXl548uQJdHV1sXLlSt7DprCwEEFBQdi1axdyc3OhoaEBXV1ddvHn5+cjPT2dRfuq6gElJyejVq1aSE1NxYEDB9ClSxfWtEFRXFzMsiPcAeDSpUtYsmQJnj17hpYtW8Lb2xvdu3fnfTc2NhaPHz+Grq4u2rRpw96X1wsTmtXLg54XeS0oebx48YKXDeIG9fLo3r07e2hKacBRcDXHgJIuReqAIaVBZ/0fjcdx48bB0tISfn5+uHHjBoyNjbFnzx4UFxdj1KhR2L17N3tNMzfKjnO3bt0QEhLC209ud+yzZ8+wdu1arFu3DlWqVOEFEfLXOpe7SK9toe5K+Rno69evoaOjw/vNPXv2FHRnoRg2bBgCAgIEH5rOzs7Q0NAQDcql1k1/k5WVFa5cuQJbW1s0adIE0dHRmDhxIvr168fLenGv6REjRkBHR0dwggIo1xls2bIl3r59K9j0Y2lpiXfv3qFt27ZsmbJOSPIfhwwul0zs99rY2CAsLAxdunThNbm8ePGCx1tbtWoVmjRpgq1btyrNKOXm5iIwMBCPHz9Gy5Yt4e7uzhqdOnXqhJ07d6J58+Z48uQJ3NzcWGAIlARNXC1ULm7duoWpU6eyrJyvry8vmyKky/rq1SuVA0F52NnZsXvi0qVLmDt3LjQ0NJCfnw8fHx92P/Xp0we9evViAcj58+dx7tw5ACXnws7OrszXtIODA86cOcN7j44L8pB/X+pYRkVFKTTXcTmznTp1wuLFi2FgYIA7d+5g4cKFuHbtGiwtLaGpqcl+nzyoA4tQBjY0NFSpU4YymJqa4saNG4JZqaKiIpw6dYpxBB0cHBSCdqrHy+Wg0wy+lOavGLhc8+TkZOzYsQOzZs2Curo6Tz9XHjo6OpLnUU9PTzJrp8pYCkiPeW/fvsWqVavw/PlzBf1jQPjaoyCE4PTp0+y7ffv2Fc0UiuF/IhBURRBUSmg2JiYGK1asAFBSnrhx4wYCAgLQo0cPnoCsGMaOHYsnT56gV69eGDp0KOrUqQMATDh68uTJuHHjhoLUAiAuw0FBCGEdx6NHj0ZsbCzLhLx9+xYrVqxARkYGdu7cib179yoM0ERAPFO+FM6FfMNAXl4e8vPzeV1wYqDnRephLSbMLVQ+JYTg2rVrvFJaXl4ekpOT2bHS1tbmfWfixIkoX748y5Dk5uaq1Kxibm6OiIgIjB49Gtu3b2dBivzx5IJbxhY6zhTjx4+HkZER73hwhUrlsXDhQoSEhCA4OJhNbICSyUZ0dLSkDZpUUE1Bz/vBgwehq6vLa2Kws7NjA19WVhY+fPiAxo0bA+AT+pUF5S4uLoLrpsu2b9+OWbNmITk5GW/fvuU9RMTKlUDZJiiqQky4OT8/H0uXLsWxY8dQWFiIChUqYNCgQfD29kaFChUwYsQI+Pn5iQpm9+jRA/7+/nBzc8OePXswZswYwXtPHvfu3UPbtm2VimSL3RP379+Ht7c3a9patmwZb7JX2smxPPLy8pi4vIaGBgoKClC+fHmVvrt582Z2nt+8eYMbN26w32lmZobg4GBUq1YNGRkZsLW1ZdSBjIwMBAYG4uXLl2jatCnc3d15x33GjBmwt7fn3WvciYSenh6Sk5MFr+mePXuisLCQ10gQFRWFs2fP8raRkZGBPn368AILqWMpNsmkSEhIwKpVq5jw94wZM9CgQQO8fv0a3t7ekhMuKRgaGiIkJAQ1a9ZEWloaunXrxpOJAaRL5ePGjcPcuXN5Ga6srCwUFhaiRo0aCA8PZ1lJQ0NDheQGnfhQdO3alTVJCk2OAcDY2BifPn2CpqYm0tPT8dNPP0FLSwuLFy/G/fv3RX+rfEJAHj179sT27dtRv3599l5CQgJGjx6NCxcugBCCP/74Q2VRbC6UjadAybi8YMECJqe1ePFiXL16lU3y7t+/r3Dt0evH2dkZzZo1g5GREW7fvo2nT58qWOcpww9TGk5MTGT8k/T0dPZaiDMhD3Nzc5w/fx41a9bE9evX8enTJ54i+IQJE+Dr64u0tDR07twZ8+fPR2RkJK9VX+qGGTt2rIIjAwCmO3Xv3j3RfRST4aCQ6jgeOXIkVqxYgenTp0NdXR379+/nBS5CvIOdO3eKlsKBkht91apVqFKlCkaOHAlfX18UFRXB1dVVqRYg3QbNlNKSN7dUJibvoUoHnZQGHMWmTZtw9uxZPH78GDY2Nujduzfev3+PXbt2oVq1anB0dMTcuXORlpaGuXPnsqBajPco1YlO98ne3l7wOFNIyRqJQcrjmtt1TGfbQUFBePr0qUK3pFB3Hv23WbNmKCoqYg8Hep6UlTEIIWzgTUhIwOfPn6Gvr8/7jNi6gZIOSwA8np+yUhZ12aBdxfITFGXnOCcnB6dPn0a1atXQtWtX+Pr64tOnT/D09ESjRo1w48YNVKtWDa1bt8aBAwfw6dMnRiWZMmUK6tevj3v37kFDQ4PJSE2ePBn+/v64deuWqLQQIF7uEuKBcsc0mvm3srJSyKJTCN0TJ06cwNatW5GYmIhu3brB3d1d8PhSyoUQlE1Q9+zZg02bNqFZs2Z49uwZPD09RcvMQqhatSr7zebm5rxucUIIu/8o3YSC8hBr1aqF5s2bKwTfyqg2jx49Et0nofHNzs4O3bt3x9ChQ9GgQQMkJibi8OHDLHFAIXUslXnFNmzYUIHfDQCNGzdWcAIRgti5aty4Mbs/qlWrJkiXkCqVC3X3N2/eHHPmzEGNGjUwbdo09O3bFwUFBTh06BCvfA5Iy7GJaf6amprC09MTzZo1w/Pnz5l0kIuLC0/3V37yowy+vr5wdHSEubk56tWrh8TERNy8eZNRfGQyGfz8/CQDQbFOZzpeSo15dNwB+PrHlDYgldFMTU1lgV+fPn0kvdRFUaYWk/9CcD0DuZgxYwapUaMGsbKyIsOHDyeOjo7E3d1d1KHDxMREcD0GBgasA0keQi4cXCxZsoScO3eOxMfHK7gZ0G1TtXJ51XWxjmPua7pM/rvt27cnISEh5PHjx+xPGYyMjEh2djZJSUkhderUITk5OaSoqEhBwV8IGzZsYK+Tk5NJdHQ0IYTwlPLpPvbs2ZNcuHCBtGrVSul6Keg+WFlZkeLiYuLs7KzwmeLiYnL8+HGyefNmUlBQQKKjo4m1tTXZt28f2bJlC2nYsCEJCgoiERERko4WqsLGxkal40y7EeWxf/9+YmRkRGrUqEEaNGhA2rRpo/AZMaeMwsJCsm/fPmJmZkamTZsm6Asq1J2XlJTE63omhJBXr16xTs0tW7YwT1iK8PBwEhAQQNatW0f09fVJhw4dyIIFC4i5uTnp2rUrmThxosK2hTpWCVHeJSuEVq1aEQMDA2JhYUF27txJWrVqRXR1dZl6v7Jz3LNnT7J06VIya9Ys0rJlS7Jp0yayf/9+YmlpSYYOHUrc3NyIk5MTsbGxITNmzCDLli0j3bt3J4SId8ULeV1/D3h6epL+/fsTHR0d0r9/f16HttA90bVrV+Lr60suXbpE5s2bp+Bte/XqVYWu1KioKHLt2jX2fxMTExIbG0ssLS1JbGyswjkyNTVlHbv5+fnE1NRUpd/y4sUL4uHhQXr27EmmTp1KkpOTFT5z4MAB0qZNG9K7d2/Stm1bnlOTKk4bUpDvZuZ2NNP7i/tHSInv7N69e8mqVavIvn37mB8tIYTd09xxp7CwkI19hIh7xarixEIIIdHR0eTMmTOksLBQ8B6XP1ctWrQgjo6OCk4ZQteri4uL6HNNCNx7gTv+Cqlf+Pj4kP79+xNfX1/Sv39/snTpUrZMTB2iY8eOvHXQ/3P3fdWqVcTU1JT88ssvxNDQUEH9Qgz5+fnk6tWr5MiRIyQiIoLXcX7v3j3i5OREhg8fTjZu3KjgQ06I8FhKCFE6nhJScqyGDh1K1q1bR4YNG0acnJzI5s2bydSpU5XeixMmTCDbtm0jd+7cITt27CDjx4/n+TOrgv+ZQFDItoaQkgvJxsaGFBYWkhYtWrD3ubITWlpaxNHRkQwcOJD3mhssSt0wymRchKQWKKgMx5IlS4iJiQnp06cP77t9+/YlYWFhxNLSkly7do3079+fLfP09CQLFy4k+vr6ZNmyZWTKlCm878qvSx5UssPU1JTUqlWLtGnThnccxV5fvnyZ9O/fn3Tu3JlZinFBLYDat2+vIGmhTN5DCnQ7Xbt2JY8ePeJZ9VEMGzaM+Pr6sgdTly5deAMFd6Dhvs89Fk2aNBGdEAjtk7LjLCUvo8yeTigIyMnJIRs3biTGxsbEx8eHpKWliW6b/kY6gNvb2xN7e3uFgSs+Pp707t2bHQshdOzYkRgbG5Pi4mKSk5NDGjVqxGQr5INqKZtB+YFb7N7lolq1apITFGXnmPsg4U5qrK2teWNB27Zt2Wv6HQsLCwWZkry8PNK0aVPRgJmQvx7wyh70UlZvhEjbWwrdE/LHV/7/FhYWJDs7m/dednY27xgpm6CampqygCg1NVXl+6VTp07kxIkT5MmTJ2T79u2838JFUVERef/+Pbu+KOSDcvn/37t3jwwdOpR07txZwdqQEEIuXrxILl68SC5cuECWLVtGhg8fzpZR+zgvLy/SrVs3laRo6PqFxp1vAVVky+TPlZmZGYmPjxf8o6DXoZ6eHqlZsyYxNDRUuDZpgMb9Ewu6xCZFYnJsYvDx8SHW1tZk0qRJxNramixbtowUFBQQb29v9hlVEgKlhY2NjVKZJ6GxlP4rNZ4SIiwhtXv3btK8eXOl96J80CwfPKuCH6Y0rAxEJEWsoaEBQgjU1dV55Exu2VeqFGZkZITCwkLk5eWhYcOGTLCSW/5RJuMiJLVAQVPE8+fPx4QJExQ6MKU6jv38/BAUFIQqVapAT0+PpZlVEc8E+JIdGRkZGD9+PIKDgzFo0CAQQpi9HSGEJ7w7bdo0HD9+XNCmB+BbAMlkMuTl5TFj8MaNG6OoqAja2trMeUJVeHt7Izc3F4sWLcKsWbN44rIUb9++xd69e1kXHiEEqamp+P3330EIQWZmJnv96dMnwWPx+vVrlfZt5syZeP78OVq1aiV5nKVkfJSJGAuV0hs2bIi6devC1dUVFStW5NEJ5PlDQt15VapUUZBG0NHRQXp6OgAouKpw16WhoQGZTIaKFSvi119/ZWUd+e9I2Qwq65IVgrq6OipVqoRKlSpBT0+PbY+WtJSd4+zsbDx69Ih1dNPX2dnZqFixInJyckAIgaamJnJyclBcXMzGFE9PT/To0QOTJ09m5cFNmzZBJpMpdK1aWVnB29sbY8aMUVpip5CyeqPHHRAWyRa6J+bPn8/cNoCSZhSu+wZ16OFC/v8GBgbIzc1F9+7dYWpqynNMAKRF0qVQvnx51m3ZvHlznvAyhRilBVDutKGMamNra8te9+jRg8eJli/3KutABv567giNO8pK/0JNQBR0/FBmTQYInysdHR1kZWVh27ZtrMmAK5umyrVJ71fyn87u69evo127dqyJicLf31+QGsW1bLt79y7u3r2L7OxstG7dGp06dYKpqSmKi4tRVFSE4cOHY/LkyfD29oabmxvi4+Ph7e3NrjsuV19KFL4sePjwIbKysng8wwcPHihcW2KdzhkZGZLjKVBCPWrYsCGys7Nx6tQpdO3aFXXq1MGuXbuU3ovK6Emq4H8mEOTeUM+ePUNWVhbatWuHxMREqKurw8jIiMctzMjIYJ+naulCuHXrlkJzijy4Mi5OTk4KMi5CUgvUnmjkyJEKgwGXP1a7dm3JjkQdHR1Ur14dhBAmq6MK70AeampqePjwoUoDhJQ3JiBsAaSKdpkyUG6Eubm5gvUYhZBmo6WlJftdAwYMYK+FnAmAErL9nTt3eO89e/aMeZwCQHx8PHr16oV3794JOpxwISXjo8yeTigIUIVPSXHx4kXIZDJs3boVFy9exKRJkzBkyBDk5+fzpFm43EJtbW0EBwfzOp8vXbqEhg0b4vTp0+we4npdC1kJitkMSrk7iCE3N1dygsI9r0LnuFWrVuzh2rJlS97r+Ph45tmspqbGggPawOLo6Ii2bdviyJEjuHDhAurXr4+NGzfyHoZc0OOqjJdIIWT1xoW8veX69evZMqF7Qv6hJD8OVKpUCUlJSQrEee4DSNkEtUuXLgo6aqqA653OPZfAX9qaUn6w8k4b8v6wlStXlnS8kW9S4boYcd2DEhISEB8fr/T30HUJjTvKpEOUycoAqunqyZ8rKlXl6OiIgQMHYsyYMbh9+zYcHR0VulrFeG9AiT4gha6uLnbv3o3z589j2bJlMDU1ZfdLt27deNIwFPQ5SAPJ9PR0vH//nnGlK1WqhLCwMBBCYGtri8mTJytoS5YvXx5NmzZFv379WGCmSkKgNJg0aZLCca1bty4GDx7Mk3kSGkuBkvFdajwFStzAwsPDMX/+fGhpacHZ2Rnh4eGS96IqWrKq4ofpGlYGKj+wZs0a5OXlsVmXfEs2bVtXFQYGBnj79i0WLlwIU1NTtG3blidtwYWYjIuQ1AIN9qitG3fWxe1yldJy6tevH6pWrcq6umQyGZYvX47Ro0fDzMwMpqamCr7HXNALjBCCcuXKYfz48SwoNjY2Fs0MmZub49OnT4LemEAJYdvLywv3799Hu3btsGLFCgXNt7LAzMwMMpkMxcXFeP78OSPwc5GamoqVK1cy6QJvb29BnTsupk2bhoiICHYs8vLyMGTIEB55XEjGpUaNGiodZ1VkfMTw4MEDNGvWDK9evcKWLVvQu3dvBUJzeHi4QmZKqis1Pz8fAQEB8PT0ZMTpzZs3Y+zYsejbty/S09MxceJEvHjxAlpaWkhOTkbz5s2xYcMGJoYuBprpU9axWlxcjJSUFIVME8W0adMwY8YMrFu3DmvXrhXs6KXQ0dFBcnKypCTTtGnTRMVxgZIMDDeLxgVtVJGHq6srhg4dipUrV7Jr49KlSzhw4AD27t0LOzs7JkZ+/vx5jB8/HjVq1EBgYCCvQnD69GmcPHkSCxYsQIMGDbB48WKF7BTFo0ePsHHjRp7QMBcymQzly5eHp6cnL6vIhYmJCdTV1QWJ8/Q6joiIQEBAAD5//swCBO41xZV1IirItlCoch49PDxEpTTkMXz4cF4wOGPGDGYNFxQUhF9++YW3LtrtLpOVeFl37dqVBcB0nJbJZKhVqxZcXV0VmqDkQTtjpcYdqWAL4CtDjBkzBvfu3WPjpZRsmbKMorycSY8ePZjmK4WtrS169eqFHTt2wM3NDbGxsWxixl3/mzdvkJubKyqRw92u2H3Uu3dvfPnyhd0rtCseAJNUcnd3R926dZm4dnx8PLS1tfH48WNJpYWvgZWVFdTU1BQaD2m3t9RYamdnh9OnT0uOp3QbV65cwahRo7Bz504mc/bw4UO4u7uL3ot0siuvy1ta/HCBICEEoaGhvJvq+vXr8PT0ROvWrTFr1iwMGjQIeXl5mDVrFq5fv877vrLsnjyKi4thamoKd3d3REZG4v79+6hatSqMjIwkM4ncEt2xY8fg6OjI/n/+/HnR2aB8W72UlpNYC35kZCQiIyNx8+ZNPH78GD///DNMTExgZmbG82oWwuLFiyGTyTBx4kTRAEpMZkMVlPUBIg9ayqYdqC9fvsTKlSuRlJSEFi1aYM6cOdDS0lJpXVLXxLFjx3D06FFcuXKFBVuFhYXIycnBokWLVD7ORE5eRqhcRCE/25N3pZAXaRbaf3mZFQoqt/LkyRMcPXoU7969Q/369eHo6KjgilNcXMz2mTv5EQuMxPZFHkeOHIG/vz8+ffqEmJgYDB06VEEO4eHDh7Czs8PIkSOV+g+rst2vWS62jAbMZ8+ehbW1tULAzNUuMzExYdkpeVkNQFgCZs+ePVi6dCkKCwuxdOlShISEICcnBx4eHpITiZycHFhZWYlmDWxsbBAcHIybN2/i3bt3qFevHkxNTVnGJTY2lgVY3EwF19FHXpeNPui+BjNnzsSRI0fQqlUrUSkNeYiNgQDw6dMn3hhGCMGIESMEpY8IIVi7di1mzpwpun/Pnz/nlauNjY1Z1hso0bZLTEyEgYEBo8IA0sEWUBLMUmWIGzdu8GRWAPFJk9jxvnnzJvT19XHs2DHUr18fHTp0QExMDBISEhTKjPRapOevV69erLxN10+DZmVBMcC/V7gB1Js3b7Bjxw78/PPPCAgI4GV7X79+jXHjxuH333/n6UkCf2nzWVhYIDc3V+UxUwyEEJw6dQrv379nQffMmTOxe/du3j6lpaVh4MCBuHz5stKxFIDS8XTkyJF48uQJJk2ahIEDB6Jr167s/ikoKBC8FzMzM/Hq1SvMmTNHUpdXFfxwpWEnJyc0bNgQQUFBsLe3x/v377Fz5074+flh165dGD16NK5evYqsrCwFfgggziUUg5qaGqpUqYKePXuiVq1azJT78ePH2LZtG5o3b45+/fqhbt26ouv29/fnBYLbt29X4PMBJTeLfBt/+/btkZSUxHv40xLGr7/+iv379/N0i1q2bAkTExOYmJhg0qRJ+PjxI86dOwc/Pz/4+fkxy7UdO3bg0KFDTAdsxIgRcHFxwYwZMwSlBLjQ1NSUNBwPDw/HsmXLkJycjJiYGEyfPp2Vs6QssUoDWsqmcHV1xdSpU9G6dWtERERg3LhxzKVDClu2bEFSUpKg1uD48ePRvXt3GBsbw9/fn6nOly9fHnXr1oVMJlN6nIESztP8+fORlZWFCxcuYM2aNSrzx1QxYRcqoQ0ZMoRXypZHixYtMH/+fMFldDBXU1MTzLLt3r1bNBCk94Cvry/27dvHK5fQwVqIM2ltbY2TJ0+iVq1ayM7OxqJFizBnzhzRh7zYdr8HxNatqamJffv2wcbGBtu2bVMImN+8eSPKS+RCTBZpy5YtiIuLQ2ZmJpo0aYIrV64IcrHkUalSJWRmZooup1lDCwsLweVTpkyBnp4e9PT0RHUBpRxNyopevXrhjz/+UCpTxYX8BDkuLg47d+7kZTJp9UXKeUYmk+H27duizjIDBw5EcXExC4xlMhmMjY3Zw37dunW4du0a4uPjER0djb59+7IJrpidGoUQv5BCatKUnJzMnitc6kp4eDhu3brFKAKxsbFQV1dXoAwA4rw3QghWrlxZpkk6BR3jaCB59uxZfP78GU5OTgoZMOqWU1RUhO3bt6NDhw64c+cOo9JQ55+vhZAc26JFi+Ds7IwhQ4awfTpy5AgrdysbSwHVxtPCwkKUK1cOhBAetUnsXqQ2l9ra2qIuX6rihwsEhbx3NTQ0MHPmTKSmpmLt2rWoXLkypk2bJhjQUG0/Lt68eSO6valTp0JDQwObNm2CsbExJk2axB68BQUF+OOPP3Dq1ClERkbC2toaAwcOZE0p27ZtQ2BgII9LJZPJeOrudFCQn3XRjFFRURGsrKzYzFYmk/FU6S9fvsxcCmjJeePGjYiJiUFOTg60tLRgZGSEQ4cOMVX3BQsW4N27d9i6dSsaNmyIhIQEFpxERUXhxo0bWLVqFQ4dOiRYknZxcYGzszOcnJxw69YtDB06lDdjmTdvHkJCQmBnZwd1dXVeQ8DXPEDkS9nUCxVQjYQuhCpVqrBgXwiamprQ1NSEnp6ewgw8PT1d8jhTTJgwAUeOHMHgwYOhrq6OkJAQZGZmMgK0fMaYC2Vk8ZSUFEyePBkAeFmIMWPGsJm5MqcXeSgLqqSW0wfziRMncOfOHUEahRBnMj8/H7Vq1cLLly8xdOhQLFiwAD179lTq7CG/3bLss7Llqqj4CwXMOTk5orxELk6cOMHGj7CwMAwZMgRAybWpoaEBDQ0NtG/fXqUgkIKK2gtBlWPh4OCARo0asUBHnsu7c+dOBAYGYseOHdDT01Ow4yoL1NTUoK6uzhPLpn6wQjwp2hjEhRS/ECipHgUFBQk6z7x79w66urqCzjJZWVkKZVUuhBrkKMSCLQoxL3pAutGMm2Dg3u/cDHpKSgoSEhJYllIehw8fhoaGhgLvjT5nwsLCRMW5hcC9toQy+XXr1sWVK1dYBszIyAgrV65kEw5q6BAYGIimTZvi6NGjkMlkuHjxItzc3FjAuHHjRnh6ekruixCEgm4LCwucO3cOQUFBePbsGerVq4dz584xCtnXjKWLFy8GIYRnmECfp6qMbYQQ/Prrr5DJZDze+v98ICjkvbtw4UI8ePAA6urqGDRoENq3b49Zs2ahTZs2zGeWC/kT4eXlBaAkyHz9+jXrZtTW1oa9vT3u3buHV69eITc3F3l5eTAxMUGTJk1Qvnx59OzZEz179sTVq1cxdepUvHz5kmW/PDw84OHhgdOnT4t2oHH3LS0tjRFpV69eLWoH9Oeff8LQ0JCXbeEiJCQEampq0NXVZdlBbsd0WFgYs10DgCZNmiAgIADNmjVjWdRTp04hJiZG8CGYlZUFFxcXAIqG4wBYVyL9Ljd4oQ+Q7du3o2XLlqV6gEjNCFUhoUdERPBmXtQ+yMHBQWnHGCX/conPubm5kseZC25Zp6ioiNelLJ8x5kKKLM7NQty+fZuXheBCvnlJGZQFPlLL6YPAyMgIcXFxguWkuXPnwsrKCs+ePUOXLl3g7e2N+/fvw8zMDImJibCxsUGzZs0QERGh4I+tbLti6Nevn+RyMzMz0WVCmRQuxEpHurq6kh1/lE8l1gX5NSL6UudIbH+5312+fDkiIiJ4pWEuKlasiEmTJuHw4cPM+eZrMXfuXIVAg/rBKssG0eamhg0bSgrucsWm5SEviMxFnz59sHHjRl7DH9fFR6hBjkK+yYBO3CiklCGU+cVLQSpLSTFo0CCEhISgcuXKCs1zUuLc2dnZgkGhmZkZ43J/+fIFr1+/RpMmTfD8+XP8+uuvuHv3rmQ2eubMmTyaSEZGBmrUqIHKlSvj5cuX7P1Tp06VKRAUC7pr1arFBOSlUNqxdOzYsbhy5Yqk37wYqDA1118eKJub0g8XCB48eBDFxcUICAjAgQMHsG/fPowZMwbXr19HQUEB3N3dMXDgQGzevBk3btxQyokDgEOHDgEA66qqUKEC8vPzMWTIEF5HUkpKCs6dO4eBAwciISEBJ06cwMmTJ/HmzRuYmZnh6NGjTF6GC0NDQ8yfP5/Ha6TlinXr1iEtLY2lwmvWrMkeIHp6ejA2NoapqSlMTU1Z9mzfvn2YNm0aypUrhw4dOrDltNxB087x8fGIiorCunXrEBcXh8qVK+PEiROizS6UpAsIl6QppAzHAcDNzQ09evTA8+fP0bt3b15XbcWKFWFubo5GjRqJct7EMGrUKNFlyh4UycnJWLhwIS+rsWbNGqSlpeHIkSO8zwp1jMl3iPbu3Zt5gIodZwpbW1uMGDECiYmJGD16NOzs7BAUFMRkSoqKiph8CcCfdUuZsHOzEGpqarwZ//PnzzFr1iwQQthrCmU2YnQ/MjMzedaCdGCvUaMGryxP0bJlS0bM/+WXX9CnTx9GmaAZ5Rs3biAjIwObN29G/fr1GWeye/fu7F5LS0uDl5cXMjMzBTuKMzMzeaU/bW1taGhosMCf+zvohI9mOsRKh8uXL8f79+9x/PhxHg9swYIFePToETp27Cj6YBOz9VMWUF+8eBGzZs0S7YIU6sRWFRUrVhTN6iuzISSEoG3btqhbt65glyoXgYGBZQ4E5XmRampqCAwM5H1GXkpDDO7u7nB2dhaVzFqwYAFsbGx4k+vw8HCEhYWhbdu2+Pnnn9GpUye27Nq1a/j48SPjPl+6dAmampp4+/YtgJJzyz2Oa9aswYABAxAXF4eBAweye+zRo0cghKBly5YoV64cPnz4gD59+vDGK9oRX69ePYXAW2jSRHH79m3BDn5uo6RYlpJCzOEDgKRvdK9evdCgQQO4uLigW7du7FhzZYRcXV0RHh7OLO7oPSiFFy9eYPbs2SguLkZsbCy0tLRYJebLly+MXpGVlcUbg+Sz7GKQCrrF8DVj6S+//AJCCGbOnKkyzYWiQ4cOAKTPg6r4oZpFxJoLgoKCsG/fPlSqVAlTp07ldScq6/jhwsDAAGFhYcznsHPnzggICGANAQ8fPkTNmjVZ9qdfv34wNDRkgwV34OemfS0tLTFz5kz4+Phg7ty5uHz5Mssacs2mCSHo06cPCzDS09NZ40dUVBTevXuHJk2asMAlJycHt2/fxs2bN3Hw4EG8ffsWHz58YJ1GN2/exM2bN5k1lpGREX777Tds3LgRN2/exOzZs9GgQQMkJSVh1apVMDQ0ZLPVDh06ID09nc1iuFkIajhO/T6FDMdTU1Px8uVLNG7cmEcy53Lebt26xWseUYZJkyahTZs2MDY2xu3btxETE8P4iVypA3ls27aNPVx1dXXZYNmiRQu8efNGkHQtT4YXIj5HRUVJHmcuHjx4gEePHkFXVxf6+voKhvH0dWnkdDp37oxTp06hX79+OHPmDPr168dI5qr4b4uByhAJdUqLldC5nfBASWaAm/UESjTe3r9/j9atW+PPP/+Era0t5syZo/R3AiU8nYMHD2LixIm4d+8eGjZsyI7XwYMHlXajUhgYGCiUDum1Y2JigtGjR/MmJlzdOVdXV6xfv573YJPKItEOQTHIH19lvt4jRoxQyKBnZWVh69atSE1NxdKlSxEcHMxsyaQazSgePHjAC3wtLS2RkZEBU1NTUZ9pLry8vASlQ5RBiBdZsWJFST9YKbRv354nq8OFlZUVOnbsyKgtXHTq1AnFxcWIiIjgjWGUkkO1MIU6bpVh4sSJTMpMT08P8fHx6Nu3L4YNGwZNTU0kJCTA2dmZcY7fvn2LoqIiHD58mHcNyjeaqQpjY2OEhIQIjg8UQooWampqGDp0qELQfPDgQV6g/vz5c+zfvx9hYWEwNDTE8OHDWRcwUFIVuHHjBsqVK4fCwkKYmZkpnbBz74nCwkIMHjyYdQuL+b7Ljz1SePz4MVq0aMHGW9qNLYWvGUuBkvH08OHDCn7z3OBVqIll+vTpOHPmjKiHeWnwQ2UExXgL9vb2sLe3R2pqKtLS0njdYmIXnkwmUwgEV65ciR49eqCoqAjlypXDypUrsXXrVpiamsLLywutW7fm3YivXr1iN6gUpASn3717xwQ/X7x4gQ8fPrBlGhoaTERXU1MTGhoa+OWXX5CRkYGoqCjcvHkTcXFxKCoqgq2tLYyNjQGUPOxat24NY2NjjBkzBu3ateORvj09PaGnp4f169ezTqVhw4bxOhGlSiiEEGRnZyMrKwvZ2dkKZTmxzAqgmkCqGB4/fgw/Pz8AQJs2bdCtWzfJAJDCw8MDBw8ehJOTE8aOHctb1qVLF6Snp0NTU5O9l5aWphDYChGflR1nitzcXMTHxyM3NxcxMTGIiYlROsujnblSHtdiWQhA+QDVuXNn2NraCmaM8vLymFg2nf0WFhbiw4cPKneGmpiYsO5Fes88evSIURKKiorQpUsXlQNByquKjY3l0RooaLAnrysHgNfYIlU61NLSgpubm+g+PHz4kOfdSoWdxa53ZQ+nDx8+oEOHDir7egsFuy4uLhg+fDhOnz4NdXV1+Pr6skBQKqsPCMtPWVpaonr16oIZXy5GjhyJYcOGlSkIBIR5kfPnz5f0g5WCTCZD5cqVWUUD+ItfCEA0s0nvV/n7XV1dnXcv165dGytWrOCVhrnPj9DQUGzZsoV3DRQUFCAiIgJFRUVo1KgR4uLieA/1WbNmYfHixbzr8fLly5g5cyarUkmJaysDIUR0fKAQoi506tRJgZZkbW2tUI7U1NREzZo1oaGhgS9fvmDr1q3IzMxkk6OJEyfCyMgIDRs2RFJSkkJJXAiFhYWsmSohIYHJq4ntKxdS0jUU48aNY2MvVceQD47locpYGhoaKpmB37Vrl6TfvFATS3BwsGizVmnxQwWCgDBvYd68eZg/fz5++uknVK9eHZ8/f0ZaWhoWL14sSFh98eKFYEdp9+7dFWQZpEyodXR0MHLkSKUZHCnB6W3btmHq1KlMb43OuDp06IBatWqhf//+sLGxwZQpUxhvrVy5crCwsICnpye8vb0VSr0xMTGS+zN48GAcOnRI8IFIBa5XrFghqvHm4uICfX19mJub49atW3BxceHxOhwcHODh4cECUy5UEUgVg6amJubNm8fK6Mo07biQyWRwcXHBjh07eCX6RYsWwc7OTrRjjELoOlJ2nGkWq0ePHrCwsFC5BA781ZkrZQrfoUMHpYOYGOgMVIgH2rx5c1SoUAEJCQlMjLh8+fI8btnly5fh4+PDAjRNTU2eKfy9e/d4Go8ymQzp6em8zOqnT5/Y/5WRn+k+dujQgTkc0Pe4pXQhEVtuICjltqOhoQF3d3deFz5XBkrswebg4IDRo0eLdhaK8anS0tLw4sULfPnyBfr6+nj16hUqVKgAMzMzwUBQiBeUlZWF/v37s8kld1IWFRUFa2trwaw+/a5881xQUBAsLCxQvXp1vHjxAvPnzwchBAsWLOBpZXp5eWH//v1YuHAhOnbsyMYEVSHEi2zZsqVCI8GqVat4sjbcag8X8fHxClprlF8YHh4OfX19bNu2jUdTCQwMhL6+PpKSkvDgwQPe92NjY3mcrubNmyM/P5/xtgD+NSvktmRhYcFKw3Xr1kVSUhISExMBlGSDkpOTFcbgLl268MqryppfhFBQUIDjx4/j06dPmD9/Pnr37q1wjwcEBIg6fMjzoym46+jduzcqVKiAIUOG4Pz584yvPmHCBPYZV1dXDBs2TFCCSigDRrUTqelArVq1FCorUqBUCylwVSsAlFm1ggt6zwmNp/S6kg9i5ZsZhZpYvlUQCPyAgeDixYt53IzIyEhMmzYNFy9e5JVUMjIy0LNnT6Z+//LlSxw9ehRBQUHQ1dUVVMWXkj0RgyqVd6p1t379eiY4TdG2bVucO3cOX758YbNaoEQ9PSoqCuHh4YiIiEDr1q1hYmICIyMjvH//Hrdu3UJkZCSOHz8OQgiaNm0KExMTldTqQ0NDsWbNGnTq1AmNGjVCxYoV8eLFC4SEhCAtLQ0bNmzglXO5iI2NxYcPH1jgZ2trC2s5MWMtLS24u7sLfl+K86YMhw8fxunTp/H06VO0b98eixYtUvm7dHbcq1cvnDhxgul5KesYo8RnLgoKCljpWwo0SKpUqRKWLl2q8r7S/QWkuS9hYWHsQa2mpoYlS5aINhjJQyaTiWaMdHR0oKOjAysrK7x48QJv375FYWEhYmJi2L03e/ZsXLx4EQMGDMDx48exbNkyhX2Tx+LFi3kZeuoAIpSdlwc9HtSqivs7uBMxIS4nF1LSJH369JHcB/pgS0lJgZaWFnuwKcskivGpdHV1JW3zqKYnFzQbSoPCunXrYvfu3fjy5QsOHDjAziUhBJMnTxaV+QGE5aeWLl3KAnpXV1f4+Pjgp59+woQJE3jHWVdXF8uWLWNd65aWlmjfvj0mTZqkkjUb5UUuXryYx4tUJmtTqVIlXtemq6sr9uzZgzZt2khada1ZswZLly6FiYkJADBHjDVr1uDjx48YMWIEatSowSaDWVlZvIe3Mi1LIbclrpsN93VpSpnKml+EMGjQIDRs2BDly5fHsWPHEBYWphBQHTx4UNTho3Pnzpg7dy7mzJmDqlWrIisrC8uXL+c9M3fu3Cmo08odz2NiYjBr1izW8ME1FhDKgP3xxx9sn74X7O3tMWDAAJibm+P69eul7r4VAj3nQuOpm5sbNmzYAFNTU/be0qVLERUVhaFDh7L3pDrHvwV+uEBw0aJFvAFp7dq1UFNTQ0pKCi8QTElJgZqaGlauXInLly+jSZMmcHZ2RlhYmChBVEr2RAzKOBuhoaE8vb4hQ4agcuXKGDt2LFatWgVNTU3s3LkTmzZtQqVKlTBkyBBMmDABAwYMYHZjubm5OHjwIGbMmIH79++jsLAQPXv2hI2NDeMIHjhwAP7+/vj48aPSfW7VqhVMTExw/PhxPH78mPHlHBwclJbqpkyZgmrVqiEwMJA1i9DjTrX4pDIrv/76Kw4fPoyPHz9CS0tLJc5LUlIStLS0UKFCBdjZ2WHfvn1ISUlBXl6eQqblzZs30NbWRnZ2Nk6ePMk8Hak/ppCel1THGJfnlpWVhYCAABw5ckShxCwEmvGqXbs2li1bxjseygag6OhoGBsbIysrS9TjmhKQKWetW7duvIyFFAghiIqKgpWVlaCUBlDCyUxKSkJ0dDQ6dOiAoqIiFghWq1YNtWvXRnFxMWrVqsUCPK5MgjyUySVMmzYNZmZmvA5qqhdKNRyVldTluZzv378HUFJW1dHREXx4Udkdsc5tCrEynbJMYmhoKONT+fj4MD4V1ypPyDaPPjyOHTuGOnXqMG5sUlISW/f27duxfft2GBoaIi0tjVUUZDIZTp48KRkIFhQUKMhP0eApOTkZX758ES2LHT58GEeOHIFMJoOzszN27doFQgi6d++uUiDYtWtXvHnzBnl5edi0aRO0tbUlP3/r1i0kJiZCJpOxcaawsJBNxpTZ5mloaGDZsmUKExagpPmtW7duGDx4MKPKyE+ODhw4gA0bNuDZs2eoWrUq05Ol1/u7d+/QsmVLUbclIcTGxipUTch/7E9V9YsXQlpaGk6dOgUbGxvs27dPYaIOgJf1o8kO2pk8f/587NixA3379kVmZiY0NTXh7OzM4+jFxcUpTZp4enriwIED0NHRQXx8PFxcXBjnUj4DFhcXJ9oMWJpSuDJ4e3sjNjYWjx8/xsKFC0UzzKUB3a5QBv78+fMYMGAA5s2bh65du8Ld3R2VKlVScM4pSxNLafDDBIJimnwtWrRAYGAgpk+fjnfv3rHsSN26dREYGAh3d3fo6OjAwcEBnTp1EhQLpZCSPRGD1MW3ceNGXLhwAdOnT0fDhg2RmJgIX19fxMXFIS4ujvHSVqxYgbt376Jy5cro1KkTJkyYgGPHjrEmkezsbLRp0wbjxo2DiYkJxo8fz8qSHTp0gLGxMY4cOaLgDCG1z5aWlqLdg1JcC0II9u/fj8DAQGzbtg1NmzZlaW6ayZDKrBw4cADr16+HtrY2EhISMHXqVAwePFhyfx0dHVkA4ObmhhYtWkBLSwsuLi44efIk77PU03HevHk8T0da7pTS8xJDSkoK1q9fj9DQULi7u+P69eui0j1cZGRk4NatW2jWrBmKiopYkKZKBmzgwIFKB8DGjRvzOGvyIrlSkM+sC+HOnTu4du0arK2tceLECQwcOJAts7OzQ25uLoYNG4aWLVuic+fOACDqg6xKwE+zfdyAbNu2bbC3t8ejR48ACAea3AeuEJcTKBHmnTVrloJvt0wmQ40aNVTywRYr0ynLJALCfCpzc3NJH3HaqLJhwwbGk+vTpw969uzJK2lxH9DcMauoqAjW1tYwNDRk2UtuEEGDt5SUFKaD2KdPH/j7+yMqKop1YRcUFCiU0N6/f4/AwECFwFrVh/by5ctx5coVGBgY4M6dO7CxsZHM1laoUAHq6urQ1NRE1apVWemMckJ9fX3LzC9cvHgxdu/eDXd3d0E6CwD89ttv+PPPP9G9e3dcunSJBfql8f2Wh76+viit6MqVK7h165bkMRHLnr148QJbtmxBr169sGXLFrx9+5YFz3S/K1WqhISEBDRs2JA1eLx+/RoaGhqQyWRwd3cXrepMmzYNUVFRvKTJ/fv3FT5XXFzMuLs6Ojo86Rv5DJi+vj6TcQNK7uPSlsSlJKAiIyNhYmLCJopVq1ZFYmIiEhMTvzorSLUCxTLwv//+OwYMGIAFCxbA2dmZp39LUbt2bcmx4GvxwwSCVJMvICBAMBsjJBQNlAiIJiYm4vjx40yp/8yZM7CyslJIvwrJnkiJTWtra2Pr1q2iy48cOYLw8HA2+2revDksLS1hbW3NBuy7d++iefPmLIiigeqTJ0/Qo0cPLFiwgPEe6f4OGTIE69atE/UClrIAA8CyK2KQ4lrIZDJUr14dnTt3hra2Npo1a8YI0EZGRiguLuZxbeQ1+TZs2MACqfz8fHTq1ElpIFihQgVoaGggOzsbt27dYlqHQjxPGph//vwZ69at43UbipmGS2H8+PGIjo7GxIkTMWfOHKipqaGwsBCFhYVKxVW9vLzQo0cPng5dfHw877zRTGVkZCS8vb0xevRoODk54e7du3B1dWXSQEIe1x8+fECLFi3QunVrPHjwAA0aNFDQTpQiMCcmJmLr1q2Cskb0uAMlHLyLFy+y5ghCCH755RdUrFiR3ZcU8laDDx48wNGjR3HhwgXJjsFt27axQJlO9NTU1Fg2g97zYg9e2lFboUIFwaw2vZ655b7ExETUr1+fBX/Kso3yZToqx6EskyjFp1LFmlHebaG4uJgFrVJd5zNmzJBcr5BrBZXksrKyYlnyd+/eYd68ebzvurm5ITAwEJ8+fcKSJUtYt3LTpk2V/h6g5OFIs0NU1Fcq6Gnbti3q1KmDsLAwXL16FS9evIC2tjab/HwNv9Da2hqnT5+WvJ81NTXZecvPz2f81+joaKXSM2WBmpoaDh8+zDuH3OYXAGxse/z4MVJTU2FoaIg7d+7g559/RpUqVdgzZe7cuQrrX7t2raTDhxTu3r2rkDQpLCxU+NyAAQNgbW2Ntm3b4u7du7yJpHwG7PDhw6hVqxZCQkLg6+sLLS0t7N+/X/CcSUlAieHx48cwMTFRGINUmZRTKJNjEsrAUyH0nJwcvHnzBgcPHsTBgwdZ9YVrkiA/CVXVNk8lkB8Mo0ePJllZWYQQQpKTk8mgQYNIYmIiGT16NOnYsSMxMTEhHTt2JGPHjiWJiYkK309MTCTr168nlpaW7L2wsDBCCCFv374lHz9+JFFRUeTjx4+EEEKcnZ2Js7Mz6dy5M2nSpAnp168fadKkCbGxsVG6r1ZWVqLv//bbb8TU1JQ0b96cXL58mRBCSEJCAunSpYvgd1TZHkX16tWJmZkZMTY2Jk2aNCEGBgakcePGxNjYWKXvS23L2tqaTJo0ibi4uJANGzaQYcOGkUmTJhFCCOnSpQs7bhQfP34k3bp1Y/8fMWIE+fz5MyGEkM+fP5Phw4cr3R9LS0vy8OFDsnnzZjJu3Dj2vtDvGTFiBDEzMyOHDh0iBQUF7Bw8ffqULFu2jIwfP54sW7aMPH36VOl26e+lfzY2NsTGxoa9pti/fz8xMzMjZmZmxNTUlJiZmRFCCLG3tyevXr3irS8+Pp707t2b/Z+ux9XVlSQlJZEOHToQQggpKioi9+7dI1u3biWjRo0iRkZGxMbGhsyaNYu3LrE/ChMTE1JcXCz42ywsLMjZs2eJiYkJOXv2LJk8eTJv+f3790lubi559OgR8fT0JMHBwWyZg4OD6DGLjY0l8+bNI7a2tqRJkybk2rVrpKCgQPTzFNbW1uTUqVOSnzl9+jQhhJAnT54QNzc3du8aGhqS9evXk+bNm5PNmzfz/rig++3r60u6d+9OXFxc2LIxY8YQQgjZu3cvadWqFZkxYwbvuz169CBdu3YlM2bMIDNnzmTnSuraIKRknPoafP78maxdu5aMHTuW+Pr6svtHGQoKCsjevXvJggULyL59+xTOgbm5OSkuLibW1taEkNKNMQ4ODuTEiROkU6dOhBAiOm6JwdzcnDx58oQQQsjjx4+Jubk5WzZq1CjeZ6dPn04IISQ9PZ2MHz+euLu7k4CAAOLm5kbGjx+v0vaU/TZ6DO7fv0+uXr1Krly5Qq5cucKW79u3j+Tk5JDTp08TAwMD4uPjQwghxMzMjBQWFvLWVVhYyPs9yrYpBAsLC2JhYcF7Lzs7W/CZ0qdPH/a6uLiY9OrVS3K7gwcPJoQQkp+fT65evUqOHDlCIiIiSH5+PvvMvXv3RL9vY2NDdu/eTWxtbUnDhg1Jr169yL59+3ifKS4uJiEhIeT9+/fk5s2b5P3795L7dPToUWJpaUkmTZpEXr9+LfnZ9u3bk5CQEPL48WP2VxYUFBSQI0eOqPx5qbGUEEJ69epFrKysyPTp08nMmTPJzJkzla5T6jh/S/xwgeDFixeJhYUFWb16NbGxsSHR0dGkS5cu5Pr167zPXbt2jXTu3FmldZqamhJCpAeLgQMHkry8PEIIIXl5eWTAgAFK1ztv3jwye/Zskp6eTgghJCMjg3h7e5O5c+cSQgjJzMwkX758YZ/Pzc0lmZmZhBBC9uzZw1uX1KAhD/rZiRMnsoDn2bNnZMKECSp/X2owlh+MaFDNDa654H6+ZcuWpFq1akRfX59Uq1aNtGzZkhgaGhIjIyPR/YmNjSWOjo5kxIgRJCUlhf2epUuXCn6ePvCKi4tJeno6OXbsGDE0NCTbt28nly5dIjt27CDGxsbk+PHj4gdBRezZs4cYGBiwyQkX8gM5Bfc4GRsbk7i4OBbgyh/bN2/ekGPHjpEZM2aQbt26KQz6f/zxB9m7dy/Zs2ePwjVDCCFjx44lCQkJgvtBr3e6P927d1f4THR0NDlz5gwpLCzkrUds0GvRogUZOXIkuXHjBiGkJHhShry8PBIQEEA2bNjAgizuHxf02h41ahT5888/iaGhISGEkKSkJHLo0CGiq6tLdu/ezf7kjwn9zSNGjCCEEBa0c5e5uroSQkoGfi7Cw8MV/lRBaGgoMTc3Jx07diSdOnViE7/SICMjg7x584a8fv2a96CUX3doaChb5uTkRJYtW0YuXrxIli5dSpycnHjrtLCwIAUFBcTGxoYUFhaKTlyFQAM/esxUHWspYmNjSa9evYiRkRHp3bs3uXfvHnn27Bk5f/48adOmDQkKCiJBQUHkzJkzvPtFfowRG3PkQa8b+QkgnajNmTOH9O3bl7i4uJDZs2eT2bNnkzlz5qi8XlXf50Jq/VZWVoLPI6FzZGRkRJ49e0YIIeT58+eSY6mq+6YsGUAIUUiayEMoIKVjPf2XvgZAGjdurPC+0G/hjoGq4s6dO8TR0ZE4OjqS+/fvk8DAQGJgYEC8vb1VXofUWEpI2cYHepzv379PHBwcSMeOHUnfvn3JgwcPVN4vVfDDlIYpjIyM8NNPP+HEiROwsbFB27ZtkZ2dzbrBKIyNjVW2g7Gzs4OJiQkePXrEOCJELj374sUL5OTkoEKFCsjJycGrV6+UrnfJkiXYsWMH+vfvj6ysLFSrVg2DBw9mnJ6qVavyPk99RYG/5EMoxErfQqAp5jt37jBJi6ZNm/K8CgG+UTlQUrYsLCyElpYWbt++zfgUhYWFLKVevXp1GBsb4/LlyzAwMEBMTAyMjY2RnZ3NJEKkNPkoEb400NfXVyBeN23alJWrqEwLwNfzombleXl5CAsL4x3vQYMGoUePHqwhp6zYvXu3aGevmpoa8vPzeXxCalVH4e3tjZUrV2LBggXIzc1lPJcBAwZAJpOhSZMmCh7XFE5OTmjYsCFrqHj//r1CaUKqIYQra+Ts7Kxwv3DFv/v06YNRo0Yx8W+xsuPUqVMRFBSEjRs3MoFcMdjZ2WHUqFHYtm0bVq9ejbZt2yrVKczOzkZmZibU1dVhZmbGyl/16tWDs7Mznj59yrMLlJdtIIRg1qxZrITJpS3k5eVhy5YtzMFHngfK7aImHG7w2LFjERAQgH379mHVqlXo2bMnj+/zNU09QIlsTWxsLBo0aMAT0Va2bmXd/VKuFcog1q2sKho2bIizZ8+ysSotLQ2xsbG4ffs249YCJV3EXCmncuXKISQkBIaGhoiKihKVOpEH3Q7XNxYooQwcPXoUy5cvR7du3UTHWTE9z1q1aimVnqFwdHSEl5cXfH19cejQIclSZqVKlRScQLjNL1yISZCJQRW+LiEE8fHxgvaK/fr1k9SKpahSpQpcXV15mr/KBKXFwC3tl6WBZsKECfD19UVaWho6d+6M+fPnIzIyUuXrB1Aux2Rubo5Dhw7h+fPnaNasmUqOO/TYubm5Yf/+/WjWrBmeP3+OoUOHIjIyUuV9U4YfLhB0cHDA2rVrYWJign379qFbt26YMmUKzM3N0bp1a1SvXh3p6el49OiRSgKWQIne0/z587F8+XLRwVBIbFoZVCHdrlu3TnAZDTSlzKqFLMCqVKnCbl57e3vY2NigTZs2ePDggYKUhtCgOGHCBLRq1Qq3bt0SHYy5yyjs7e2RkZEhqsmnqj5ZWcBt+hDS8+rRo4dC0F21atVvotNE/qNXp62treDCMGXKFPTp0weenp7seGzevJlHFo6KiuKJINNBpmPHjqIe1xTJyck4evQooqOj4evrK+inKyYMTgjBnDlzULFiRUFZI0Ba/FvsYTJmzBiMGTMGqampOHnyJCpUqABHR0dYWFjA1tYW9evXZ+diz549MDExwbRp0xhhXdlDasSIEXBwcMDq1auRm5vLrvXCwkLk5+czQV5CCIqKinD06FFeM8XJkycRExMDa2tr5OXlwcfHhy3bu3cvrly5gnnz5iE3N5e51lCIdVFTK7jLly/jwYMHPKkI4OuaeoCSh6BYY4DUusW6+ylsbW3RvXv3MrlWiHUrq4p+/frxOJmOjo74448/YGVlBS8vL1H+8549e7By5Ur89ttvaNq0Kfbs2aPS9j58+ABHR0eeB3lhYSFv8iMkp0MneWJ6nn5+fqLSM58/f0aVKlXYOLNlyxa0b9+eSeUIiZ9T+Pr6wt3dHTNnzlTa/NK2bVucOXMGKSkpotqvXBCJBkcKmUyG2bNnIy0tDQMGDMCgQYMYR33SpEkwMTER1YqloMLmQpCXllm5ciXat28v+vkpU6awZ9WYMWNK7dtbsWJFNslu3LhxqX2KiQpyTFRLs2PHjoL6ukKg11mdOnV4SRvavPWt8MMFgsHBwWxWNGzYMHTu3Bn169dH//798ezZM3z+/Bk1a9ZE06ZNVY7258yZgxs3buCPP/4QDQSFxKa/Flw9NHlQGQSprjQHBwdeIDdy5EgcPXqUEf69vb3h5ubGZnZ0kDh27BiOHj0qOChaWVkpHYylSPWfPn0S1OQzNTVVSZ+sLOA+wIT0vPT09FhTBsW2bduUWgupum0xF4a+fftCT0+P6VfWr18ffn5+aN68OT5+/IgPHz7gjz/+gIuLCwghKCwsxKVLl3i6aoCixzWVCCpXrhyKi4tRs2ZNBAQE4MWLFwr7INYQIpPJMGvWLAQFBUFNTY3pe3EhJf5NieqEENy5cwcVKlTgdSHXrl2bNZKkpqbi1KlTGDlyJDw8PDBy5EjExcVh3LhxOH36NLy8vFjQpWy948aN4zU7Ucu1AwcOYPfu3YiNjYW9vT0IIahQoQKb/Jw5cwYODg7MsYE2vtDfaWJigidPnqBOnTosKyk/eRDrolaWSVSlqUcKUiLaUuum3f2BgYFo1qwZ6+6novFCULXrV0NDA8OHD2eE/Q8fPiiVgOFCPlPMzX5t2LBBkJQPAA0aNMDMmTPx4sULNG3aVCETSR1PaBc7xcWLF1FcXAx/f392/VBrNwohOR16PMSy/vXr10dISAgSEhIUpGfMzc3h5eWFPn364N27dxg+fDi2b9/OJoM0CL169SpkMhmTByosLMTw4cNVbn4Ravo5fPgwrly5Iij/o6xZECi5/w4fPozMzEycOHECgwcPRuXKlTF8+HD07t1bUiv25cuXWLlyJRITE6Grq4s5c+YodJdLScuI7Q/F5s2bS+3by5VnevnyJXutiswPoJock7IMvBC4PsZUizMmJuZfHUExjB49GoGBgahUqRIWLFiAJUuWACgpjRw8eBDlypVTyCwp656l6NmzJ680TC86buq3LGLTXwMqBfPLL7/gjz/+4KXg69ati+DgYEELMC7evn2LFStWID09Hbt27cKuXbswcuRIdO/eHcbGxpKD4okTJ3i6WTVr1mS6ilKlMDFNPlX1ycoC7iAhpOe1b98+LF26FMbGxqxE0b17968+f2fOnEGjRo2YNAMXVKahRYsWmD9/vsLy69evs05YWtYoX748C4ioYDjX45pepxQHDx5EcXExAgICWLenPIYMGaLgc02hpaWFhQsX8ko33A46KfHvFStW8LYjpR1Xu3ZtuLu7IzAwEDt37kTlypWxdu1anDhxAtra2vjy5YvS9Y4bNw7+/v68Ljvgr3vU1dUVrq6uuHnzpkJGDoCCWDMXqnYUinVRK8skSmV+VIGUiLbQup89ewZCCKpXr85K+A8ePMDbt29RvXp11p27efNmBe9uVSFVrlYFbdq0wdSpU2FhYYFr167xXEnEHG8AwMfHBxEREWjfvj1WrVoFa2trXpc4dTxZsGABzM3NWZaGVgjMzMygo6ODp0+fYunSpRg2bBgbi4QszOj1JqXnCZSUuuWpG4WFhfjtt9+gpaWFCRMmYPv27czrGvhL/PzcuXNMTw/46x5UJq5Nr4GNGzciIiICnTt3hrq6OpKTkwGUdGbPnTsXlpaWGDZsGHs+0okChZDfNNW5e/bsGe7du4f8/HxYWVnhyZMnCAgIQOXKlUW1M11dXTF16lS0bt0aERERGDdunILCg5S0jBC410KzZs2wa9cuUd9eIZS1JM2FMjkmqQw8EXFSCQgIQJUqVZiDEwD079//q/dVHj9MIPjs2TP2mjtzePfunWBGhhCCrVu3qhQILliwAAsWLMBvv/0mqPEDlE1sWhkIIUp9SoXsyVSxAANKZscrVqzA9OnToa6ujv3792PkyJHQ1NSEpqYmli9fjrCwMF62iB4vMd0sAEpLYUKoVq2aSvpkUjh27Jik2DAAwYeRlJgsoJpHpdB5MjExwc6dO1UuT3Hh4OCA9evXY9euXYI8HCmPa6Dk2nF1dcWFCxfwyy+/YNq0aYLbkfK5pmVEWj6WD3x+/fVX0dky955LSEhAfHy80t+8Zs0a9O/fH4cPH4aFhQWCg4Px/v173gREbL3+/v4AxAd0aifp6+srqDNINflcXV2Rl5eH5ORkdh5pJkuZe4Sfnx/y8vKwbt06bNmyBaNHjwYApZnEu3fvwsHBAU+fPsXq1avh4uKiUraAgmbgi4qKFHRQa9eujW3btjG/cg8PD7i5ueHIkSO8z9WtWxeDBw9GcHAw8+cW8u5WFVLlalWwadMmnD17Fk+ePEHnzp15jkhSHskXLlxQkJ3hBoLKHE/Wr18PBwcHrFq1Cu7u7pg0aRKqVq0KmUyGvXv3KmzzawKIqVOnYsyYMZgwYQKMjY3x6dMn7Nu3T6HSkp6ezjjXd+7cQWZmptJ1cye/ampqKCoqYoLQNKiidJ7w8HDMnz8fr169wvDhwzFq1CgWpEj5TVtYWMDAwADDhg3jOZNQLVYxlC9fngUzzZs3V7BTAxSlZZRxtakwPFBiIynl2yuEr5HyoVAmxySWgQfEnVToePu9HVV+mEBQrJQhk8lgamqKgQMHKnAfhEzapZCRkcEyjRSUAFsWsWngLwFmoSxZv379lPqUCtmTvX79GlZWVgq8noSEBN7MqLCwEAYGBjyOIRfOzs6izQZiulmA8lKYEFTVJ5OCv7+/oNjwoEGDmGCoUDOKskFAFY9KofMkFVyoAplMhsGDB7MM18ePH1GrVi1ERkYqFcKVyWRo1aoVwsLCeDNjeS00qYYQGvjk5eUhPz9fgUMmRpAHwDLA1BNUKBspDysrK7Rt25YR8g8ePIiffvqJBdLJyclK1ytW6lamM0ixevVqnDp1Cq9evULDhg1RtWpVFmiJuUdQ0Cyzrq4u/Pz82H5PmDABI0aMYJ+TD6iFgo/SBBjBwcHw9vaGhoYG8vPz4ePjw2gqjo6OGDhwIMaMGYPbt2/D0dERBQUFChaRtWvXVvBZ/RrvbmWez8pQWFiIz58/IzMzE58/f0ZhYSEreypzvHn69CmaN2/OJqRcKHM8EWo2UqYfCZS4Ch05coR33Ql5P3Ph7OyMwMBAhIaGIi8vD76+vsjMzFTI4h48eBCrV6/G2rVr0axZM5Uyq9xxX6zpJz8/H+fPn8ehQ4dQVFSEmTNnQk1NDfb29izwEPKbpggLC4O6ujpSUlJ479MKR0REBGuM4OoocilHhOOaA/xFhZg+fTpcXFwQHx8Pb29vpdxGbvC8a9cu5OXlMbeu0vjVfw3EmkEePnwIQghatWolmIEHhL2EuVBGifla/DCB4O3bt1nZlusu8vTpU7Rs2RJr1qxRGPy46VZVQDNbtAGAO9AIiU2rAplMhhMnTggGgpMmTUJwcLCkT6mQPdmDBw8wa9YshYee/MyoVatWWLRoET5+/AgfHx9GyKeQajYYOXIkcnNzMXXqVFhbW/NmbLQUNnfuXF4pTEq0tVq1arysIlCSiVGFVyTkKsMVGwbEy3uAcjs3VSDlJysVXEiBEMKzsHv9+rVKTUgU0dHRvGYQeScMsYaQo0ePYtWqVahSpQpGjhwJX19fFBUVwdXVlSfqK0aQB0roFNwSU1BQEK+8JwZu9nPIkCG8ZU5OTrCzs8PMmTNFvy9W6qYPEqGuY25V4MSJE4iMjIS1tTXCwsJ4+yCVBRcC7U5u1KiRZDZRrNNZVSxcuBBhYWGoVq0aMjIyWJMHBb0u27Rpg6NHj6JcuXJKu/cBvnd3u3btSuXdrczzWRloydbc3FyBWC/W4ASUTAanT5/OOmTlaRkJCQnYunUrI9tnZWWhatWqbFwUazYCSsbVefPmMaeVpUuXsuBfyKdcFdAARkNDQ4F/3rlzZ4SGhqJhw4bYuHGjSuuj4AYSjRs3ZiLWP/30E0uAdO/eHQ4ODti0aRMv0Pr8+TN7LdUgc+LECcY9vHPnDu8cTZgwAfn5+TA0NMTu3btx6NAhNmlUZZKjStcxF9xrec+ePdi0aROaNWuGZ8+ewdPTU6XK39dCrBlk0qRJkhl4QLmXcGmoNmXCN5Cg+ccjMzOTxMTEfPP19uzZU1JsWlVICU3279+fuLm5kU2bNgkK4C5atEjhrzQ4f/48Wb16NTl37pzCsi5dupCioiLSt29f4u/vT/T19XnLi4uLyYcPH1TeVmkEacsCZWLDZYUqulpS54kKW1tZWZHi4mLi7Oys0na5grWElBxvVUW/VYWdnZ3Ce0ZGRiQ7O5ukpKSQOnXqkJycHFJUVKSwbRcXF0F9REIUz7WYwPTUqVNJUlIS06GUgrW1NRk0aJCCQK/QdsW0DwMCAkhAQADx9/cn7u7uxNHRUfD7Xbt2JY8ePeJd81QLz8rKimRkZBADAwOl+0wIIXp6esTIyIjUqFGDNGjQgLRp04a3fMuWLcTGxobcunWL5OTkMJ1CVWFiYsK0MfPz83n6hiNGjCBz584lJ0+eJPPnzycjRowgq1atInp6emTTpk3k5MmTxM/Pj5ibm5Nr164RQgiJi4sT/SstpM6VFOTvOa4+3ogRI8jIkSN5f4SU3B/yIt/yGDx4MCkqKiKEEJKamkpsbW1V3idjY2Oe7ir3fqD7S687e3t73nfl7zNVhPLpOleuXEnatm2rIEhPISRyTbVpCVG8F+k1v2HDBt77O3fuVNiHESNG8P7osSZEWnC8rHqOFMbGxmTbtm3k4sWL7E8K3G2bmpry7geqA/y9IXbNqqKf+/HjRzJjxgxiZ2dHpk+fTlJTU3mf5d6DFy9eJG3btv2Wu/7j6QgKoWrVqpg2bdpXd59u3ryZzYrevHmDL1++sI7ioUOHIjQ0VCHrqAqkuAXKfEqlMg1mZma8smLt2rVx8+ZNHt+gWrVqrMHg6tWrvHSzVLOBWCeaFEgpyqJlgaGhIebPny9qiQYoL+8JQcqjkkLqPNEMT/ny5fHkyROF8rSUNRG3+SEvL08hSyaEU6dOoW7dujA1NUXfvn3x9u1bABD0bRZqCKlatSoqVaqESpUqQU9PT6GRQoog7+HhoTQ7y4W7uzs6duzIk3ARg0wmw7t376Crq4u2bduysiCXp8gtdTs5OSloH3I7wwEoSCZ5e3sjNzcXixYtwqxZs3j3pnwWnGuJJYWEhASkpqaKZhLFOp1VxaRJk9ChQwfo6OjgzZs3PE9WmtGKjY2Furo6GjVqhJycHPTp0wfVq1dX6N4HIOi3TK9LVbuGpcrVqkCKWE+z0uQ/lZnr16+z/UxJSUFGRgYrucljxIgRGDVqFJYvX67AbQP440OVKlVQu3ZtNj5ISXiI+ZTfunULUVFReP78OctOFhYW4uXLl0qPAX3WSDXHSHH4xNQfvnz5gi9fvuDEiRPw8PBgigTyUkqAsN80hRj3ECi7niOFVNexEOSfLZmZmahZsyYyMzO/+3OHQuyaVSUDn5KSgtWrV7N77cmTJ6hVqxZbzqXE1KxZUyWqTanwTcPKfzBUyeooA3Ui2LNnDzl79izJzs4mS5YsIcbGxqRatWpKFc/FUFxcTI4fP042b95MCgsLSXR0NMnNzSWEEPLlyxeFP0JKVMwJISoprRNSYjdGv0OV8fv27UssLCzI1KlTiZWVFenbt6/ofhUUFJDo6Gi2rCz2U987I6jMEo0QQjp06EDy8vKIlZUVyc3NJaNGjWIzXWU2R0JQdp4IISQkJITk5OSQ69evk969e5Pdu3fz1qHMmqi0sLKyYpkYen4KCgp4Vn4U8tnkxYsXEy0tLeLo6EgGDhzIe/3zzz+rvA9i2VkrKys22/3y5QtxdHQkAQEBSp14nj59SqytrSXt8rgoKioi0dHRJDs7m/c+daQICgoi/v7+zHlECKmpqWTVqlWCy0pjP0Uzh/KZxNLew1IoKioi79+/Z9kuITx//pysXLmyVOvNzc0lL1++ZNe5qjA1NSUZGRmEkJLsVGmzMunp6WTNmjXMNo+b4ZJH165d2euWLVsSTU1N0qFDB96x5N6Xhw4dIs2aNSN37tzh3aeECI8PFL179yYWFhZk0qRJxNLSkvTq1YtVcD59+sS2c+LECZKUlEQIIeTu3btk9+7dxMTEhLnZHDhwgLx8+VLpMaD3rpRjBfe3y+Pz588kPj6eeHl5sXslKSmJ7Nq1i1hbW5MaNWow28Pu3buTjRs3Kqzj8OHDxMrKiujr65PCwkKe+8zFixdJx44diZaWFrGysiKXLl1iyxISEsiECRNIjx49iKenp6TjBhe0miJWYVm0aBF5/vy5wveoHSEhhFy+fJmYmJgQIyMjYmpqWiannrJA7Jq9evUq6dixo2gGnhDF+ETIkrGs96Iq+J/ICAKqqaWLQUr3SBWxaWUQ6hhq06YN1q1bx8zjgb8Eo0NDQ5V2ScpDW1ubOYdQvoGDgwPLDhJCFLJaYp1MgPRsUAzkO8/MpDpgKYSaXE6cOAEAZcrmzpkzR/I8EUJw6dIldO3aFR07dsTZs2cV1iHUBSklKaKM76KmpsZmmzR7QjUF5SHUEMJ13hCCgYEB9PX1YWpqClNTU7Rt25ZlEynEsrP5+fmoVasWXr58iaFDh2LBggXo2bOnpFZXQUEBxowZg61bt0JHRwf29vYICgrifaaoqAjBwcEghMDW1hZpaWk4cuQILl26xOOq0fuFzqzp+bh06RIjua9atQp//PEHrl69iuHDhyMmJobdMwsWLMCNGzewdetW2NraKkhtUCQlJcHR0RFdu3bF1KlTBTOJ/v7+IIQw14qyIiIiAlu3buXxqajrz8uXL5lOpa6uLrp06aLyer+Ga0UIYZJQlSpVUvneV4VYP3PmTF5lpkqVKoiIiICFhQXi4uIEM4L0/qT7Ub9+fUybNk2BuyjVBMfV7pSX8Bg0aBBCQkJQuXJl3rK2bduibdu2sLa2Fn0Gbdy4kSdgTGW8qMqDVHOMFIePqj8ISeKEhYWJSinJ75uQ9AwgLTjeoEEDwfFXGWjVQazCYmxsjIULFyI5ORn9+vWDk5MTatWqxeTUgBJuJdWk/f+AsmvWwsIC586dE9TPpZBv1JJXy/juvMdvHlr+Q/E1GcFOnTqREydOkCdPnpDt27fzsheq+BArQ1l8OeX9VoW8V7m+jfr6+mTFihW8dSjzoJTaL6nZoJQP8ffE0KFDSU5ODvH09CSDBg0S9PKVN4dfvnw5W5aTk0POnTsn6c1bFri6ukr+dgMDA+ajSc8Z5bIFBASQrVu3kt9++43o6+sTLS0tpdvr0qULSUtL47338eNH3vk7cuQIMTAwIBYWFmTnzp2kVatWRFdXV+EaEUJRURG5d+8e2bp1Kxk1ahQxMjIiNjY2ZNasWewzYtnZtWvXElNTU9KgQQMybNgw8uzZM3L16lXR+zMpKYnY2NiQevXqsfeE7rWBAweS0aNHEycnJ9KjRw/Ss2dPcvbsWV6GTIpDZmxsTN6+fUvi4uJI9erVydmzZ9kyMzMz8ueff5KgoCCipaVF/Pz8GAdJGZKTk3n/F8okCnmulgZt27Yl9+7dIx8/fmR/K1asIF27diVjxowhYWFhgj7RyvA1XKsDBw6QNm3akN69e5O2bduSgwcPqvS9Ll26KHCsP378yMtmU5/WK1eukNjYWEII/5r4mrFYfnzw8fHhLX/+/LkCH48QQsaNG0d27txJ7t+/L8indHZ2JoMHDyZOTk5ET0+PWFpaTGynzAAAXSBJREFUkoKCApKVlUUsLS1JdnY2+fLlC0lPT1fJf5tCisNHYS3nv03vpYEDBzJ/XfonDym/6QcPHpDp06eTUaNGMa4m5TCampqSFi1aiPIaxeDj40NCQ0NFuccUnz9/JoMHDyaVKlUiAwcOJJGRkcTNzY3npU0IIWFhYcTDw0OlbZcVqlyzyuDj40P69+9PfH19Sf/+/cnSpUt5y7837/GHzAhKCWCWBVK6R6qITSuDVMeQmDjz77//jqpVq2LgwIEwMjJSWCchBLt27eJ5XMojMDBQ0oNSar+EZoPPnz/HkydPJH2Ivyf2798PAKKWaOQ/uowVK1aEg4MDHBwceMuFNBlVhZSIdlRUFLS1tdG0aVNBuQupLsjPnz8zTTUPDw+VeDOLFy+Gvb29gpUfV9x07dq1uHbtGr58+QJ9fX28evUKFSpUgJmZGa8zWAhqampo06YNatasiVq1ajGeJdeNQyw7O336dJZZSUtLg5eXFzIzM7Ft2zbBbdWrVw+hoaHQ1tZmCvtcoXSgpCv748ePOHbsGIAS/tbDhw8VZIukOGRVqlRB3bp1UbduXbRt25bHHVTVfoq6k1A9QFNTU9YVKJVJFPJcVdaRzIWenh709PR4lojnzp2Djo4OHBwc0KlTJ4WOYFVRVq7VkCFD4OzsjJSUFGhpaSlkjMWgirSNubk5Dh48iBcvXqBZs2alsqDs3r07OyeEENjZ2TFpDqCk8zMvLw9t2rTBn3/+yZMeEbMQBJRr11HHGqBkTBw8eLBStxsKKbs1VaRS5LvSqVyWMiklQNpvetiwYVi9ejVPKJtqUAKAjY2NSuoIXDRp0gTnzp3DggULkJ+fj3bt2sHU1BRmZmbQ1dVFZGQk9u/fj7i4OPTo0YP9hoEDB0JNTY3ZXVJYW1srlfH5WqgqxyQFb29vxMbG4vHjx1i4cKGgusb35D3+cIGgFHm2rFCme6RMbFoZtm/fjpUrV6Jq1ao4deoUTyNOTJz5wYMHePz4MY4dOwZfX180a9YMw4cP53myenl5KZTQuGjXrh3OnTun8n5xH9ZxcXHYtWsXL+B2dXVVagr/vRAaGopDhw6xAXHIkCEKumUymQy3bt0SFN4FhDUZVYWUiLaYxRy1teOWuiimTJmCdevW4ebNmxg/fjxu3Lih8sPc3NxcoRRx9uxZHvlYWUOIFAYMGACZTIYmTZrA2NgYkyZNUnBNUNa0AZT4JstPPoYMGQIrKyuMGTMGkZGRmDt3Ljw8PHjkaK64MEVqaiouXLgAQggqV67MKAwAXx7o1q1bgkG5mPyUTCZDfHy8SvZT8nqA3bp1Q0hICM/I/ubNmwrEeSnPVVXg4OCARo0asfKYTCbD9evXkZiYiOPHj2PLli2Ii4vDmTNnYGVlpbIeoI+PD3r27Ini4mKoq6tj+fLlSr+zePFi0RKoKg9kVYj1QjIdXIsw7mv5c8R9OMtkMsES3MaNG9GsWTM8f/6cV4ITsxAEhF1HuOBuJyEhAU+ePMGxY8ck3W4opOzWVCkZykvi0OtEmZQSIF3+bdiwIbp27Sq632WhYzk5OcHJyQlACV3l4MGD8PPzg7u7O4qKinDo0CGMHDlSwfJy7dq1otSsr6GFqQJV5ZiEQPVtaeKkatWqSExMRGJiIm/cWrZsWanvxVL9hm+6tn8ApAQwywpVeHhSYtPKEBwczOvUW7lyJcvKSIkz6+rqYsqUKTh//jw2bdqEWrVq8bQAxSzCaDdxUVERUlNToampifT0dNSuXRuRkZHs+7Vr11boIKQQmw0q8yH+Hti4cSMuXLiA6dOno2HDhkhMTISvry/i4uIU7Lykuk6FNBlV1RiUOk/y10X58uXRtGlTGBoaAhAObH799Ve0aNECtra2iImJ4fHcuJk9MYhZ+VGIPTiFBLfl0bFjR9y7dw+vXr1Cbm4u8vLyYGJiwrqHAeXZWTG8e/cOR44cwZgxY+Dv74+9e/eiT58+uH37tuT3MjMzWZZ1wIABPC6gnZ2dUg5ZRkaG6Lpfv36NR48eKc08yWdeypcvL5lJpNwiLifzwYMHpe6wXL58OSIiIpj/OEWDBg0wZcoUTJkyBUlJSTh+/DjWrVsnGAAIoSxcK25A4+XlhdWrV5cqe7Fo0SLY2dkpZLO5k0khz1ZVudL16tXDhg0bmHUd17UGAAICAliwXlBQAEtLSxYcCVkIBgQEoHXr1ujUqRNMTU1RXFyMoqIiDB8+HJMnT2brpZq1VAid261cuXJluLi44N27d+xYcXmLUnZrAQEBuHHjhuD+UowbNw52dnZ4+fIlkpOTWVc6nZyR/3Rfp6ens++q4jedn5+Pbt268UTDqdYkIQRFRUXIyclhv0kVQfFz584hMjKSdVXr6upixYoVMDY2BlAynjx48AARERG8il/Hjh3RuXNnzJ07F3PmzEHVqlWRlZWF5cuXl4oXWxaocs2KQVX7yi5dunzX3/HDBII06yJFni0rVLGfkRKbVoaIiAhUrFgR/fr1g7e3N++GEfMp3bNnDy5evIjy5cujV69eCA4OVsjmiFmEUZFiT09PTJo0ic1+5b11V65cicOHDwuau0vNBqV8iL8Hjhw5gvDwcPYAbd68OSwtLWFtba0QCEq13Tdr1gxFRUW8oEPVQFDKT/bNmzeoW7cujIyMEB0djRcvXiA7OxtHjx7FsWPHYGVlhRcvXvAaK2j56nvha6yxuKT5lJQUnDt3DgMHDkRCQgLWrVsn+J24uDjJwJRCJpPhy5cvePjwISpXrox69eopWLIJQZlo89ChQ/HmzRsAJWKspZGS0tHRwciRI5V+Z+TIkbzMS25urmQmURWhWVXQtm1b1K1bV9JBoX79+pg8eTIvOBGDmPzQlClTlMoXUTcdoGT8KI1kDACViPVCMh2qWoTt3LkTgYGB2L59O1q2bCko1SNWgpO3EPztt9+wdOlSVg6uVKkSwsLCWMMS91hLlUhHjx6Nbdu2YeLEidi0aZOCcwjXbi0mJkbBbk1ZyXD58uW4evUq2rdvjzt37sDGxgazZ8+WlFJSxW9aiELCbcqhLiX0mlflnps7dy60tLTg6OgIExMTtGnThpdZE6v4ASWOJjt27EDfvn2RmZkJTU1NODs7qyRN9TVQ5ZoVA50EcseuwsJCnDx5EkBJgmHhwoU4c+YM8vPzoaGhgX79+mHBggXf1DFFRr51sflvgtjJLo321beEnZ0dS/cqAyEEHh4eSE1NhY2NDSZNmqSQMpZft5qaGlq2bMk687haX/r6+tixYwcAxY40LszNzZkGF1CS6aGBHlAS3N64cYM3M6RlzPv376OwsJA3G6SZKkNDQwUHBro/3wPW1tYIDw+XfP/t27coKipCw4YNsX//fpYBsrW15WWySgtl54n+y13es2dPXLhwARYWFoiIiBDkHp0+fVpyu9R14O/ArVu3EBkZiZs3b+Lhw4eoWbMmTExMYGJiwvP8pKW3nTt34v3797yOQzHY2Nhg8uTJOHXqFObPn48GDRpg8eLFCsr68lB2PGrWrIm0tDSVPiu2X8r4TvL32po1a0S7inV0dBTuNwqx61kMLVu2xIcPH9C4cWMApeMnC8Ha2hqXL1+Guro6+92FhYWws7MrVYD6va7RjIwMBAYGMo6gu7u7ytQfOhngQltbm3WiX758GXPnzmUlOB8fH9SpU4d1hVLQzO348ePZb7x37x6ryHTp0gWXL19WKbNGjzEdD4SO24cPHxAfH49GjRrxXECE9rdz586873bq1EnBf/natWu8MenNmzfYsWOHwgSRy6kEwOgOQEnAIm+ppiybvXfvXqXdrl++fGH6i/fv30dBQQHq168PX19f3vZLi2nTpolOVP8uCCkSBAQEoEePHvDx8cHs2bNRoUIFzJs3DxUqVEB+fj5WrFiB7Ozsb0q5+mEygpSjcezYMZ7fLPXu+94QEptWBkdHR/adwsJCJrx57do12NvbS6aMpeRaOnfuzOzcTp06JRoI2tvbw8bGBm3atMGDBw8USMpCsia0jCllzyclwfA9QI3l58yZg+rVqyMzMxMrV67k+VtOnDgRGzZsAABs2LABnp6eKCgogJeXF7S0tODv78+EkoG/JGCUPVBVSe0XFRVh+/btzLOVyu3QbUlxj8Twd87ftm7dClNTU3h5eaF169aCD7qyNLkAJWWsvn37omHDhnj48CEaN26MCRMmKP2esuPx5csXlThkYpDiGRUWFiIvLw/Hjx+Hu7s7E+gNDQ2VtMN79OhRmblFXIhxUMuK0sgPyYOOaWU9zlJQRVpGGby8vCCTyVBcXIzY2FhoaWnhypUrrEwqVILr2rWraOa2UqVKSEhIQMOGDVkQ+Pr1a5atkc+a3bp1S4FS06FDB+Tm5qJ79+4wNTVV8NV98uQJ9u3bx+NjU3FqVUuGQv7LYlJKXEj5TYtZqklh9+7dSgPBoqIiFBYWIj8/H58/f8abN2/w8eNHPHz48Ksqflx6zT8FEyZMgK+vL49HHBkZyQLqyMhI3gS0QoUKWLhwoUJTzNfihwkEKfz9/XmB4Pbt2wU5WN8aVatWZRemubm5ZJmKQqpri5Y6VFmPPLKysuDh4YGdO3ciKyuL96Dg3jTe3t5wc3NTmGnSgKioqAhWVlasyUAmk6Fdu3YwMzODqampKGdKyof4e2DJkiXYsWMH+vfvj6ysLFSrVg2DBw/mZYk/ffrEBt9OnTqxwejYsWM4fvw4gLKVS4VS+/I4evQo80Nu2rQpjh49CplMhosXLwIQ5h4pw/cmQEuB28wkj7dv35a6ySUpKQmDBw+Gq6sr3NzcMH36dBQUFODGjRvo06cPRo0axbISyjTXxNChQwdRvqsqkAo0Ve3+lEejRo3KzC0CoOCjy0Vpuo7loaamhs+fP6NGjRqs1JuamqrS5EOVTtSy4luU0oW6d4G/vOqFUKlSJdGu0A0bNsDJyQnm5ubsHN68eZNVQGgXbUhICHx9faGlpYX9+/fzukLpMZs/fz4mTJigUFJ0cnLCkiVLFDigQAktZuPGjbwmGPnJq5D/MiEEWVlZSu8JKb9pIa6mMii7hjp06IAqVarA2NgYJiYmGD58OLS1tTFy5Ei2r5cvX2Ye4n9Xxe9bQZkigdjYWVYVADH8MIEgfdCqam31rSAlNq0MNNgjhCA0NJTHEaOBSlks0Vq1aoWLFy9i7dq1aNWqFc+ehnvTvH37FitWrEB6ejp27drFHqo3b94UvdAiIyMRGRmJpUuX4vHjx/j5559hYmICMzMz9OjRA0DJTJEQAjMzM0lplG8FmUwGd3d30azTtGnTeP/nErWzs7MFu3YpVGnMAKTPk6amJmbMmIHDhw/D2dmZfYdyQYW4R8rwT2V0lKXJpX79+ggLC2OC5zExMQgNDWWzXpohEMq6UVssrjWiEAYPHqwyj4yLvLw8aGhoSMpPubq6qtT9KY+aNWvi+PHjotyi5ORkBVsvLsQ6vL92kqCK/JAYynKMVcW3kOkQ6t4FSgIQsdJ/ly5dRDO3enp6uHLlCm7evIl3797ByMgIK1euZFI+a9euxblz59CuXTsEBgZCW1tbYf1xcXGYP38+srKycOHCBaxatYqXSWzSpImowPLq1asRGhrK2zcuCCHYu3evoDqElB0fN3lA5YmAkuwkTSZI2QCKQdm1yc2GcUErfuPHj+dV3z59+qR0mxT/xDGTmzUX4hHHxsYq0EsIIbh///433Y8fhiNIcfr0afTt2/f/bXsWFhaYOnUqWrdujYiICFy4cIFlmFTFoEGD0LBhQwQFBcHe3h7v379nWoVl5dtJ8XNWr16NWbNmwdbWFitWrMD06dMRFhbGeC0GBgbQ1NSEsbExc4+Q764DgI8fP+LcuXPw8/NDfHw842CVxYf4e6Jz587o3LkzsrOz4e3tzTrKli1bhqpVq8LCwkL0u1ZWViptQ5XzJHROCCEYMWIE9uzZI7rulJQUJCQkwMDAgAUm8r7Q/xRIdaRyj2VUVBSMjY3x4cMHbNu2Df369WMcrB49esDf3x9ubm7Ys2cPxowZAycnJ+zevRt3795F+/bteVm3iRMn8mgWFN+iJFkaXpGNjY3CPkhx5JTxDkvLsSOEICwsDMeOHWPOQ2XFp0+fEBQUhHfv3qFevXqws7PjyQ/9HejSpQtOnjypEJANHDiQZYiUgXuOatWqhfHjx6Nz586S5yIiIgKzZ88WzNyam5tLbk8mk6Fx48aoVauWKO3E2toaR44cweDBgxEaGsrGYYpTp05h+fLlPBoGndCPHDkSa9eulXRFGjFiBPz8/BQCvlatWiEpKUlQ31Secy/kN83lajZt2hQeHh5KS/SjRo36qgwel+sLlJTtuVJRUvDz88OkSZPKvO3vgdevX4su09HRUbr8W+GHyQhSTbbr168rELBVzeqUBVJi06oiOTkZR48eRXR0NHx9fdGvXz+2TJ5vpyrPQSq+v3jxImbNmoXCwkIYGBjwBiighLOWnp7Osn87d+7Eu3fv0KRJE3Tq1AkxMTHIycmBlpYWjIyMcOjQIZ48iJQt0d+FuXPnYufOnayjrHr16qx8TH//oUOHWKkIKOl6VDUQVOU8CQl/y2QyaGpq4tWrV6zLm4t169bh2rVrePXqFaKjo9G3b19cuHDhHxkEAsoDZxrczJo1C+Hh4Vi0aBEsLS3h7u7Outn9/f3h5eWF1NRUzJgxA5s3b8avv/4qmXX7ViXJZ8+eoVmzZuz/YvxaIdBSPyEEt2/fxrFjxxR06igqV66MrVu3Sq5PlTl6cXExwsPDcezYMYSGhmL48OHfxHpKmfzQ34GvkemgkA/2fH190blzZ0lLRwsLC6ipqaF69eql7gq1traWDPapmgKXF1hUVMT7zPz58+Hn5ydYGu7YsSOaNGnCMnZcZQcKee3MrKwsPHr0SFRKCeDrIooJVlevXp1xNeVBn8eRkZHw9vbG6NGj4eTk9NVlXPl7Ii8vj71WNhn8pwWBgPJg7ntm2Ln4YQJB+nD4/+ADcqFMbFoVUCJ2zZo1ERAQgBcvXrBl8nw72khAJQuEoKqTSqtWrbBo0SJ8/PgRPj4+PA1CDQ0NJjisqakJDQ0N/PLLLwgJCYGamhp0dXVZp2i9evV46y2LD/H3BB0M3Nzc4ObmJviZadOmITc3FxoaGujfv7+CjI8yCJ0nmr2jwcDChQvZa+66r1+/jvPnz7OsAXdmfubMGVy5cgU2NjZQU1PjDXz/jaADeUFBAYqLi1FQUIDBgwcjICCAfebXX38VvXeys7MxcOBAHnE+NDRUJZqFKhgzZgwvC+fl5aXyfcx9SHbq1Am9evXC/fv3FR5eVEqD648qBGVltDFjxuD58+fo1q0bZs2ahfj4eMydO1elff1vxNfIdIjh0qVLTH9UCuXKlStTYKzsHE6ZMgW2trYYMWIEEhMTMXr0aAXJKj09PYVOYAp/f3+8e/eOqUdwIaadyV2XMiklIcFqf39/liF89OgRWrZsqSAvxtUC3bdvH/r06cOEor8GVapUwdSpU1nns76+Plv2PfmpPzp+mECQBjE3btyApaUljIyMeJZL3wtfo8dGcfDgQRQXFyMgIAAHDhzgad25uLgAKHEPsLe3Z/pC1Bbp8ePHSE1NhaGhIe7cuYOaNWsqdVKhD6YlS5bg+vXrqFKlCnR1dRm5vUOHDqhVqxb69+8PGxsbTJkyRYG3ER8fj6ioKKxbtw5xcXGoXLkyTpw4AUDaluh7gRCC/fv3Cw7W3AyrGO7evYvLly8zFwsq46MqhM7TnDlzsG7dOqatRfdTXlNLikeprq6O9PR0yGQyZGZmqmzV9U8FPQ42NjYwMzPDsmXLkJuby3uQHThwAJs3b+Z9jz5gpk2bhuPHj4s+uJ2cnBRoFqoEgseOHcPRo0d5E7nCwkJBRxQxcDtmk5KSMHToUIXfURooywhmZWWhYsWK0NTU5DWr/cj4uzKVZT22ys4hIQRz5szBgwcP8OjRI+jq6vKCG6Ckmaply5aMOsGlO5iYmCAtLU0wEFy4cCEbZ0qrnUkhJFhNM/eAOL2hLFqgqsDLywuNGjXC48ePYWNjw2vI+laTwf9F/HAcweDgYERERLAAzdDQEMuWLfub90oaQtm9atWqKegLUZ9SHx8f9jkHBweW/SOEoE+fPowYnJ2dLZjV8vb2xvLly0U1mU6cOIGoqCjEx8dDXV0drVu3homJCYyMjFCtWjXExcXh5s2buHnzJu7duwcNDQ0YGRnxmhwIIYK2RN8Tffv2Vaq/JwRHR0dcuXIF1tbWKCwsRGRkJDp27KgSv0xIB4p7nggh2Ldvn+BAFBERgapVq6J9+/aYOHEiK6GPHj2aiXVHR0fDy8sL9+/fR7t27bBixQoFe6X/JnAfHMXFxfj48aNCQ0SHDh1w9epVwWaIgQMH4tixY6LXFNXgo//269dPJaeh9PR0fP78Gf7+/hg3bhyAEtpH3bp1Vb5+KZ9HJpOhRo0abDIWGxuL1atXizpHXLt2jSd1xPUsVpY1zMvLw4ULF3Dy5Elcu3YNkydPZpJQ/+IviJUNr127hnfv3in9vjI+58iRIzFs2DCFzJ1Y6ZWic+fO+P333/HHH3/wstzc8UKIJ0aDng4dOuDz58+oWbOmQjWByzHlvv75559hbW0NQggb98T4tGZmZvj9999Rs2ZNfPr0CXZ2djy3GTEe6+nTp0utBQooL+8CJVU47rGSp8lIce7/hTB+uEDw48ePuHr1KiIiIvDmzRvUrl1bwc/0n4Y5c+YAKLng79y5gwoVKuDTp09MX2jEiBGYP38+xo0bp5CZMzY2xsGDB9G0aVO8ePECgwcP5g0EDRo0gIuLC7p166Zwg40fPx5GRkY8Czp5Tabc3FwcPHgQGzduxP3796Gvr4/WrVuzRpJ27dopZF6FfIj/P1r8e/fujczMTBgaGrLfowo/9PXr13B2dhZsaFHG0ejYsaPS8yQWoHbt2hVnz55F5cqVYWFhgQMHDiA/Px+enp4s4/vfjmfPniErK4sJj9MmF25D0Z07d3gaZMOGDUNAQAAvEKQPiKSkJHz69AmtW7cGAIWHV9euXREcHIwBAwbA1tYWW7ZsKZWrTXFxMcLCwsqUTcjNzRV8oJuamio4R3Afii4uLqhTpw5mzJiB2bNn46effipTmSsvLw8XL17E8ePHsW/fPqYl+i+Uk/KVQVlz1uPHj7F//36Eh4fD3Nycaewpg42NDRN55uq1jhkzRtKQQBXHI7GALzs7W1R6SP5YhIaGwtvbW0FgGyh5Xo0aNYrHJ+Q+P6Kjo5GUlIRevXrh7du3vN8nBmXnSchZRN53t6yTwf9l/HCBoIaGBrp27Yq5c+eiY8eOf/fulAl9+/ZFRkYGm2mZmpqKen7eu3cP8+bNYxpRS5YsQbt27djy58+fY//+/QgLC4OhoSGGDx/OyuhCnWE7d+7EsWPHEBkZiaioKGRnZ6Nt27aMD8jlEQphyJAhePz4saAP8feGUMeqfPMC5c1Q0O5VPz8/FBUVYerUqQBKBrkNGzZgypQpktvkzojFzpNYgMrNMuzZs4dpEnIztWFhYZg/fz6TQ1qyZMk3FxP9XlizZg3y8vJYloLLW+3UqRNrKAoLC0Pnzp2RmZnJyOzyThnHjh0T3Q734ZWcnIxatWohNTUVBw4cQNeuXUsVDH1NNsHa2lrwgU6vESnniAkTJiAwMBBbtmyBh4eHyvsrhb/Tfea/FatWrcKhQ4cEbTVVQUpKCiZPnowLFy6gffv2mDRpEvr27SuZMXRychKc+IlpY8pkMuZjn5iYiK1bt/ImLnTS/b06TlVx8eJqgd66dUvBoUQZxMq7qjiLfO1k8H8RPwxHkCItLQ03btxAcHAwfHx8UK1atb9VukQVcDWbEhISEB8fj7dv30rqC1G0bdsWZ86cQUpKioIiPVDSzVqzZk1oaGjgy5cv2Lp1KzIzM7Fv3z7eTI6LJ0+eoEePHliwYIFgWUMq0/Du3TtJH+LvCUtLS5w8eRIfPnzAmDFjBPUWubwZoCRYOXbsGCZNmgQbGxsWCMpkMpw7d05pIKhMBwqAQmcdly9IG0poEJiTk8PTRZs5cyZCQkKYbEK3bt14Xsj/NIwZMwaenp5o3bo1UlJSMGjQIOTl5SlkN4QailTh28p3d5YvXx7JycmsI3vOnDnYsWMHfvnlF0ybNg0zZswoVXZNqoNfGSpVqoSlS5cqvG9gYCDpHOHh4QENDQ3ExsZi5syZSE9PF+3GLA1+sDn+/wtOnTqFmJiYUtNZDh8+jCNHjkAmk8HZ2Rm7du0CIQTdu3dH37594eXlhf3792PBggUsY5iQkACgRAtx2bJlPLcMOzs7QZH6Fy9e8OTJhgwZgpkzZ8LHxwdz587lyc58TbAn5Tct9tzgjltCWqClgRjXVxVnEXnO/f79+0u17f9F/HCB4O+//46rV68iLi4ONWrUEFWL/yeBK/hcs2ZN7Nu3T2XLJCnNvt69e6NChQoYMmQIzp8/z+RNqGWXGCl/3rx5ktucMmWKYKZh5syZePHiBVq1aoVu3boJ+hB/T7i6uqJdu3Y4duwYxo8fj1mzZjGNKSHBcZlMxstUFhUV4f3796hTpw7ev3+vUoeuqsELV1NwxowZsLS0xLRp0+Dg4ABPT08mh7Fp0yZMnz6dfbZx48ZMqLVatWqCEjP/JPj5+cHPzw+7du3C6NGjcfXqVWRlZeHgwYO8z0k1FHl5eTFJEEIIvL29WSn10qVLUFNTg5GREaKjo5GTkwMNDQ0UFxdjyJAhuH37Ngs6CwsLS93MJdXBrwxiD3RlzhGDBg1Ct27dAJRYYm7atKlU+yyG/4XmkW8NIVtNVfD+/XsEBgYqmAnQDJmuri6WLVvGMoaWlpaoUaMGTE1Noauri6KiIjbB49pTAiWmBUePHkVQUBB0dXV5lnLlypVD7969sXbtWvTu3fubXTsbNmxgQWV6ejqioqKY3/SQIUN4n7W3t8fIkSOxbds2XLp0CUCJW9KrV68gk8mQkJDA66hXBWITsoKCAqXOIl87GfxfxA8XCKakpGDMmDE8g3BAOov1d2Pnzp1ISUlBrVq1lJp2y0NMs48QAkdHR0F+Ew3+1q1bJ0rKl4JYpqFXr174448/FPw1/7/w9u1b7N27l/lLc/fTw8MDHh4eCAgIwNixYwW/7+vri4EDByI/Px8VK1ZUyeFDatb9/PlzPHnyRDQ46dOnD1q0aMEG+fr162Pjxo28BoEPHz6gRYsWaN26NR48eIAGDRqUWp7o/xMaGhqYOXMmUlNTsXbtWlSuXBnTpk1TuMYaN26Ma9eusYYibhmLa5Elk8kQGRnJ/p+ens7zD7e3t8fRo0fRunVrNG/eHBkZGbh9+zYIIShfvnypjdmlOviVoVmzZrwHOlASCIo5R7x+/Ro6OjqoX78+ryogJhVSWvybESw9oqKiYGVlJdh8IQU3NzcEBgbi06dPWLJkCYKDg9GzZ080bdoUgHDG8NChQ9iyZQvP9o6LlStX4vLly2jSpAmcnZ0RFhaGbdu28T5Tr1495Obmom3btnBycipVl7sUpPymnzx5gvr167NO4N27d8PExITn4CSkBVoaiE3IxLKRgPLx9l+I44cLBGnHnzzEslh/J9LT0zFhwgTcvHkTv/zyCz5+/IhOnTphw4YNiI+PZ4R4KYhp9slkMpw8eVKS6K6qWbc8xDINBw4cgKmpKX7++WdRH+LviWrVqiE8PBxFRUW4fv06zyCdwsXFBTt27OBxTyjfxsjICNeuXftm+5OUlITbt28jIyODDUbywUmLFi0wf/58ACWEc/kuUSmh238iFi5ciAcPHkBdXR2DBg1C+/btMWvWLLRp0wZjxoxhn6N6fTSDMmvWLBbYlitXjk3c5Lk9nz9/Zu43VPgcKJEVWbhwIby8vJCcnIwXL16gSZMmgpZeQnjz5g17TctgVLOzNL9dCBMmTGDOEerq6ggJCcHs2bNx+PBheHl5Kfi9lsU/VaiTUhUt0X/BR1ktMYcNG4bhw4fj9OnTUFdXh6+vL3r27MmWC2UMP3z4gBo1aqB9+/bQ1tZmLk7GxsaoUqUKzp07Bx0dHTg4OKBTp06Ctp+07Ll+/XrcvXuXJ+z/NZDymx45ciRTuoiLi8O4ceNw+vRpeHl5YeLEiQCktUBVgVh518zMjEk0ffz4EbVr12a8bO54+zWTwf9F/HDNImJQ1v7/d2DIkCGws7NjGnRAibtFQEAAcnJyVJqJXrp0CUuWLMGzZ8/QsmVLeHt7o3v37gDEmxSMjIwYKf/9+/do0qQJALDZLx0AhGBmZoaHDx9CV1cXqamp0NTUxOfPn/HTTz/Bz88PkZGRuHnzpqgP8fdEamoqVq5ciYcPH0JPTw9z585VKMPZ2tqiV69e2LFjB9zc3BAbG8tm2cpkPsoKygNUBjELuv8mTSxTU1Ncv34dBQUFcHd3ZwP4jRs3YGZmxvT6aBcj8JdeHyXMv3jxAjNnzkRiYiK0tbWxatUqdo0mJCRg1apVzNZqxowZaNCgAV6/fo3GjRtj+fLluHr1Ktq3b487d+7A2tqadeVLgesoQyc6Dx48QFxcnFJ+E72fuODaiNHuRXp+6f+/FVTppPwXqiEmJgazZs1CRkYGatSogZUrV6J9+/ZKv0etzug5lreJy8zMFMwYUiQkJCA4OBibNm3CgwcPUFBQAKCkGeT48eO4fPkyYmNj4efnBysrKzY+v3//HsePH+dNAujE9mtw/fp1zJo1S9BvesqUKbh06RKCg4Oxdu1anDhxAtra2rC0tESrVq3g7+8veE+o8jyjcHNzU6DTyJd3X79+jZUrVyrYKebm5pZpMvg/DfI/Ahsbm797FxRgZWUl+L6BgQH5+PGjSut4+vQpKS4uJsnJyaS4uJi8evWKLTt16hQJDw/n/akCS0tLQgghgwYNElyenp5OJk6cSJ4+fUoIIeTZs2dkwoQJvM+kpKSQnTt3knbt2pEaNWqotN2vxcGDB3n/X7FihcJnrK2tCSF//UZ7e3u2zMTEhMTGxhJLS0sSGxtLZs+e/U32y9TUlJiZmRFTU1NSq1Yt0qZNG8HPde/eXeE9R0dHMm3aNNKiRQsybdo0MmTIkG+yT98L58+fJ05OTmTEiBHk3r17Css/f/5M4uPjiZeXF4mPjyfx8fEkKSmJFBcXf9V2MzIyCCGEmJubs/eKi4t5/1cVERERpE+fPmTAgAEkMjLyq/aLEEKWL19OXF1dSbNmzYiHhwdZtWoVIYQQQ0NDYmRkxPuj75UGXbt2/ep9/BclMDc3J/Hx8YQQQl69eqXy9ePi4kJ27dpFjI2Nyf79+8nw4cN5yx0cHMiJEydIp06dCCGEdOnShcTGxpJt27aR0aNHEycnJ+Lm5kb8/f1JdHS04DYSExPJ+vXr2dhFCCHGxsZk27Zt5OLFi+zvWyE1NZXs3buXrFq1iuzbt4+kpqYSQggJDw8n9vb2pG/fvmTq1Klk27ZtZOnSpbznRXp6Om9dmZmZKm3z2bNn5Pz586RNmzYkKCiIBAUFkTNnzvB+M0VxcTExNjZWeN/Hx4fY2tqS2bNnk+7du5Ply5eX5mf/T+KHKw2LgfxDE5/ygqMZGRmoWrWqpIk4F1Ilts2bNwu22gcFBcHCwgLVq1fHixcvmDzJggULoKenB01NTfTt2xd//vknzz6PdsJWr14dd+7cYZ6sTZs2xZ07d7Bx40alPsTfExEREahYsSL69esnahFXoUIF5OXlQVtbG7Nnz+aJyVaqVAn6+vooLi6Gvr4+j5v2NeAq8WdkZGD8+PGCn6NEay6+pov174C9vT3s7e2RmpqKtLQ0fPr0CbVq1WLLNTU1oampieXLl4vq9V2+fBk+Pj7s3FSvXh0GBgYKmQbCybo5ODiwbCoVYn7y5Emp9v38+fP47bff0KRJE6xdu5bnOawKxKQ8uM4Renp6jPLxrbhLqnRS/gvVUFxczHi/Ojo6Kttjbt++Hdu3b4ehoSHS0tIUtGuzsrLQv39/1sxB/qMd2L59e0yYMAFdunRRapVXv359TJ48GZMnT2bvaWlpwd3dvTQ/UWWIubhYWVnxZLkOHjyIn376iUdjkXcyGTVqlEqlYmXlXXr/k/8oLnAbVzIzM1GtWjX8/vvvjOJDj7MqVYH/ZfyQgeB/C19m0aJF6N69O4YOHYoGDRogMTERBw8eVKmso4olVrNmzbBr1y4FweilS5cyXoWrqyt8fHzw008/YcKECQgNDcXZs2fx7t07TJkyBWvWrBEMou3t7ZmDwYMHD9C7d2+VfIi/JzZv3gwPDw/s3btX1CLu4sWLkMlk2Lp1Ky5evMj7TIcOHZCbmwtbW1uYmpoy4dRvCTU1NdYYMG7cOMngBvi6Lta/A6GhoZg/fz5++uknVK9eHZ8/f0ZaWhoWL17M63Z0dnYWtYKbPXs2Ll68iAEDBuD48eNYtmwZ1q9fD0AxeJInx/v7+2PGjBn48OEDfvnlF4WykRhat24NNTU1TJ48GXXq1MGzZ8/w7NkzAKqJ9wLiUh4fP37EzZs3kZaWhlevXuH333/HrFmz2Pe2bt2KMWPGIDIyEnPnzoWHh0epfFlV6aT8F6phwIABsLa2Rtu2bRETE4MBAwao9D0NDQ0MHz4cnz9/BiEEHz584JUk69ati927d+PLly84cOAAGjRogMuXL+PRo0eIjIzEvHnzkJqaisqVK8PAwIBx7VTZrru7O28SIDbR/Nag5VsajM2YMQO2trYIDg7G8+fP2TVeWFiIDx8+qLROGmQKcX0JIdi1a5cod/5bTAb/V/HDcQT/2/gyaWlpOH/+PN69e4e6devC3t6el0ERgyqWWGKC0ZQvmZycDFtbW8TExABQ5KgVFBTg8OHDeP78OZo1awZnZ2deV/OHDx8QHx+PRo0a8bTRqA9xVFSUgg/x9wDXlkiZRVxWVha2bt2K1NRULFmyBCEhIejZsycIIbh8+TLTP/z06RPrHPxacGex5cqVw/jx43kzbfmscFZWFuvIkxdI7tKli1JR778T5ubmuHjxIpO8AUp+X8+ePXH9+nX2npT6P70OLS0tER4eDgsLC1y9ehXBwcEghMDW1hZpaWlYs2YNLl26hLt376J69erQ1dVVmLSo2vWpinivMtD9trKywpUrV2Bra4tLly7BxMQELi4uvEkRN8Cg3xsxYgSWL1+OPn36/KO1In90iI1rUpg4cSJiY2PRoEEDNpnjSibl5eVh+/btjL9MtSOBkm7XmzdvIjIyEhEREVBTU8OdO3dU2u6ePXsU3qOapN8LtDuXK+tUVFSEtWvXYu/evYiPj1d4Lunr6/PGBGUQ4/ra29sjKChI8Dv0Prp//z7mzp3LJoM+Pj4qubz8L+OHywhmZWX9V9nJ1KxZs0wm6rTEZmZmBh0dHTx9+hRLly7FsGHDWNqeipqmpKTw/FyrVasGf39/REVFsWxiQUEBsrOzedsYNmwY9PX10bFjR9y6dYtnA/b27VusWLEC6enp2LVrF3bt2oURI0YI+hB/79JwaTSiXFxcWHdfuXLlWHefTCbDhg0bWCCoSjCuKpSVAMXKKIQQuLq64sKFC0wT658ONTU1pKSk8Ab9lJQUlpGmkMp02tnZITc3F8OGDUPLli1hY2MDZ2dn1KpVC+np6di4cSNkMhnGjRvHHkQdOnT4qmYwsY5fimnTpmHdunWSnxGT8qhTpw48PT1Fv/flyxc8fPgQlStXRr169dgkQBlUySj/i9LhyZMn2LdvH6+iJGbHxkVsbKygZzyFUMZw7NixSEpKQrNmzWBqagpHR0esWrVKkNIiBldXV2RmZrL1/n9ASg1BR0cHOjo6sLKywosXL/D27VsUFhYiJiZG0qKPQll5V0tLCwsXLuRVuWjG/vbt20wjluLdu3dwc3P7935Qgh8uIzh69GhYWlr+z/BlaHbPzc0N7u7umDRpErs5xcSmqbNI5cqVMWzYMKirq+PNmzeIjY1Fr1692Lrluxu5/7e1tcWKFSswffp0hIWFoUuXLswDVsqH+HuCqNBhK9Xd5+zsDA0NDd4g8zVllsWLF4tmFBcsWICQkBAEBwfjyJEjcHZ2BlCS0YyOjmZ2eTNmzIC9vT1vn0rzoPj/xqNHj+Dt7c06r9XU1FC3bl34+PjwJIVKm+nkdv03bdoUDx8+ZALp8su/B6Ts2g4dOgRTU1Mm9l1cXMykPGg2/ODBg2jTpg27HrhZxtOnT+PUqVOYP38+GjRogMWLF/O8iP/F/x/atWuHJUuWoH79+uy9Dh06KP3e1KlTMXDgQJ6IPvc+FcoYrl69WlK4evXq1TwKgRCUZSK/J6TUECZNmoSkpCRER0ejQ4cOKCoqEvRblwe9zzp16oSdO3eiefPmePz4Mdzd3XHt2jWFzD03Y/9PVAb5b8EPlxH8X+PLZGdnIzMzE+rq6jAzM+MJ94qJTVerVk0hwNHW1lZos69evToCAwNhZGSEqKgoXpansLAQBgYGvEwELTGLYciQId91kBKzJeJCiKtDwZVz+BYwNTVlr2UyGRITE7F+/XoUFxdjwYIFaN68OSpUqICEhATY29sDKJlZcwe76OhonraZTCb7x+lhcqGnp6c0I5+SkoKQkBC8ffsW9erVw7Bhw3j6auHh4Vi2bBmSk5MRExOD6dOnIzU1FRcuXAAhBJUrV2aOMUBJRqA0PqbfGm/evMGxY8fw6tUrnh4cnYwsW7YMnp6evOCCi759+6Jhw4Z4+PAhGjduzJx/VEVWVhaOHDkiqI35L0qHJk2aoE+fPqX+3t27d3H37l32f/n7VFnGUAgXL15UGgiWZb3fCtbW1ozy8vTpUzRo0IDZet65cwfXrl2DtbU1Tpw4UWpNTnmu75IlS/D69Wte5j4+Ph4VK1b8pr/pfxU/XCAopTz+I2LEiBFwcHDA6tWrkZubi0aNGrFlYmLTqmL//v0IDAxEYGAgmjVrhgMHDrBlrVq1wqJFi/Dx40f4+PioxFvjduh+D6jSYSvU3ffw4UNWhqV48OBBqV1e5EGFWJ88eYI1a9bg1atX8PHxQe/evQGUlAS1tLQUHtoJCQksi/3fNsNNSkrCkiVL8ODBAxQVFUFdXR1t2rTBvHnzUL9+fVy/fh0eHh4YNGgQ6tati6dPn2LlypXYunUrOnbsCACYN28eQkJCYGdnB3V1dcTGxmLAgAGsvDNgwACW9aZ2XN878yxVOPHy8mKvqR7ctGnTmB6cjo4ORo0aJfr96dOno6CgADdu3ECfPn0watSoUgW2AwYMQK9evXDixAmmjfkvygYXFxcYGRmhdevWbJKrSiKB3qf0mpdHhw4dcP36ddGMYVnxvdarCqTUEGi2vnLlyrh48SIeP36s0jrFyrv9+/fnBdpAyb0/evRonD17FgD+1sngfzt+uEBQXnpCU1OTdcj+iBg3bhzs7Ozw8uVLJCcnY/fu3WyZlJ+rKqhevTpmzJih8D5N3wcFBaFKlSrQ09PjlZTF8L29T5V12BJC0LdvXyZcTDFp0iQcOXKE917dunUxePDgrxpcbt26hdWrV6OwsBAzZ85kgQ6FvKMEhUwmQ+/evQVN36dOncoTP/6nwdXVFUuWLOH91uvXr2P48OG4fPkyli5diosXL/Kyz+7u7vDw8MDFixcBlExgKlWqxK6XoqKib8LhUwURERGwsLBg/4+KioKxsbGkbM/9+/cRGRmJW7duIT09HVWrVsWYMWOYz3lOTg5sbW15pWGu93ZMTAxCQ0NhY2MDAEoFrOWRn58PT09PHD9+HJ6enirdi/9CGPPnz4efn59o9lYMwcHB8Pb2hoaGBvLz8+Hj48OE/QHlGUMhqMLaKst6vwe4aghAied4Xl4e1q1bhy1btqhk1wmIc32trKx4SQ6gRN6HOgsB+H+lIf1o+OECQSHpiR8Z8t1VNjY2zBvS1tYW3bt3Z36u3yIQu3r1KtLS0nD16lVUq1YNJiYm7H1lZODvTUdV5hMrk8nQqlUrhIeH89xWCgoKFHQba9eujfz8/K/aHxMTE7Rs2RJ6enpYv349k0ABSnyCpbLX1tbWoqbv/+RAMDs7m10TFMbGxqxxoqioSIGC0LBhQ17w4+bmhh49euD58+fo3bs3PDw8lG5XPltQVixcuJD3IF2zZg2OHTsmKEVEoUwPTn4CJn8fVqhQAa9evYJMJkNCQoJKLjTy3xfTxvwXpYOenl6ZvJ4XLlyIsLAwVKtWDRkZGWzspZDKGGZnZwtm8czMzJRu9++sGMirIUydOpUta926Ne7cuYPExET89ttvX31NymQy5Ofn83jBubm5X7XOf/EXfrhAsFq1aqhduzaKi4tRq1atH95wWqi7igaCDx48wLx581jX8NKlS1XyL5bChQsX8OnTJ/z2229ITU2FoaEh7ty5g5o1ayoNBMV8oL8VuOUHQ0ND3myRQohzV65cOaSnp0NTU5O9n5aWJljiKQ1evXql0ueE/DMrV64savr+T8aUKVNgbm6O1q1bo3r16khPT8ejR4+YCG56ejovcwCUXLcZGRkASrrRW7ZsiQMHDuDly5do3LixyuLqX4Nt27YhMDAQT548YaUpmUyGFi1aKP3u58+fJfXg9u7dq2CXxb1X/P394eXlhdTUVMyYMQObN28u1b4fPnwYGhoagtqY/6J0SEpKQsuWLdGqVSsAEJSgEgIhBJUqVQJQIkwvP+mVyhj26tULDRo0gIuLC7p168YmClKyZ4sXL8bChQt50lkUX+PxWxpIPVvLSncQ+8yUKVPQp08feHp6Msu7zZs384LPf1F2/HCBoLz0RFlmd/9toOKZT58+5b3v5uaG/fv3o1mzZnj+/DmGDh361W4ZK1aswM2bN1FcXMxIyoQQ9OnThwU0RUVFCj7EkZGRTKrme4GWfAkhuHPnDipUqKAQnArNoCMiImBnZ6fgq/m1ZuXUoUAZuFwb6p/57NkzUdP3fzIGDRqE/v37s/2vWbMmmjZtyviWrVq1EiyJt2zZEosWLcLZs2ehq6uL+/fvY+rUqTAyMlJpu197XDw8PODh4YGAgACMHTu21N/X09ND+fLlUa5cOaYHFxkZiV9//RW3b9/G77//DqCkyUr+Afrrr79+1cN70KBBCAkJQeXKldG/f/8yr+dflHSAlwWTJk1Chw4doKOjgzdv3vB4o4B0xjA0NBTPnz/H/v374ePjA0NDQwwfPlySd02v0dJIZ30rKFNDAMpOdxAr7/bt2xd6eno4evQogoKCUL9+ffj5+aF58+Zl+AX/Qh4/VCBICMEvv/yCihUrsoH9R4e/vz+mT5+ODx8+oE6dOjzNqzp16vBs4LhagqoiPz8f6enp7EH7888/Y/HixZgxYwaeP3+Opk2b4uXLl/jw4QMj83t6emLSpEksAOWWRL8n5CU3+vbty16fOnVKknN37tw5BAUF4dmzZ6hXrx7OnTun1PLpe0BbWxt37tzBunXrYG9vL2j6/k9HuXLleFIxALB3714MHz5cshxuZmYGJycneHl5IScnBz179lRosigLh680oA+40rh82NnZCerB3bp1C+Hh4YJ6a1zIZ4Rr1apVqgmbmIPQvyg9tLW1lUpQCWHIkCH4v/buPCrKeo0D+PcFlxRQIZeDihSSEJqmsYoKHFCvkoIXUTEFzcjMU4kouJFXSxNIK7uFC0dNJRVJMzBjC4VLsqYSonhTYpMQFMFRQcC5f3De987AMMwwyzvMPJ9zOmfkhXl/pjM+8/s9y6JFi1BTU4MhQ4Z06JvZ1Y7hwIEDYWxsjL59++Lx48fYv38/1+ZLkszMTAwdOhRTpkzhvvaf//wHtbW1Mn8A7a6uuiEAiqc7SGJlZYWwsDCJ16S1dyJd07o+gt7e3jL1K9IGQqEQISEhHXZY1q9fD4ZhcPPmTTx8+BATJ07ElStXMGjQIK7CShZr1qxBZmYmTE1NuaMy9uevXr2KsLAwLgDdvn07Xn/9dQBt0yVEp0hMnjwZv/32m+K/4S6IHjmWl5cjNDSUyx1jc+709fW5flNszh3f1WaS5mdu2LABDx48wPnz51FVVYXhw4dj9uzZSm10rQrtj32Btr+n7777rtjfCUns7e3BMAwXLC5btowrfmIDm/Zv+L6+vjh9+rSSVt+9KR8VFRVS+8GxI+dkUVpaivDwcJmaGLM6myBE5LdgwYIOLahEuyW0J8vuGNCWv8w2XGZ3DNlc3zlz5qBPnz5YvHgx9xgAVq9e3WmawOTJk5GRkSGWvtLa2goXFxcuVUjVRLshrFmzhuuGALSlxYSGhqK4uBjW1tbYtWsX12dTFaiHoGK0akcQaHsxuLq6ihUD9IRdlO5gGAY1NTUdRpSxVYNsbzoA3Toyunr1aqd5IK+//jri4+MlXpM0h1gd2ICYYRgYGxuLfZrW09PT2Jy7zv4fdzb0XZM5Ojpi/vz5HXY8SktLu/zZsWPH4pdffuH+HNljZIZh4OTk1O0cPnl0Z8qHtCAQAGJiYnDu3Dn897//haGhIYyNjTtt8TJq1CixHFZZ6FrLLFWSpQWVKNHdsdDQUEREREhMU5C2Y3jo0CGxPposabmiffv27ZDDrK+vr5bK2a66IQCKpzvIS9UdKbSd1gWCktqdaLPc3FyMGjUKlpaWYBhGbLzUrl27MG3aNNjZ2XXrDcLd3R0JCQl47bXXuBfawoULO+QB1tfX48UXX+SOszZt2oQVK1bIPa9TUYcOHUJNTQ1MTEw69ADU09PTuJw7abs+6hocr2w2NjaIjIzsUOAh+qGkM4cPH4abm1ungU1dXR0GDBjQrRw+WW3cuBHh4eEICwtDY2OjTJWbXamoqEBtbS1mzJiBxMTEDn+2knaEZbFv3z6MGzcOU6ZMgaOjI54/f47W1lb4+/tzxTlEPl21oGqPfS8B2t5vRSuFAdl2DAsLCxEWFsZN4tm2bVuXue0mJiYoLCwUK/4rKChQSzpLV90QgLYPP+0DWVWeCmnZwabaaVUgWFVVhfHjx8PY2BiZmZl48OCB0qdFaAo2V+r69esddgRZkyZNwoULF7B9+3YAbZW08rTTqaqqQkpKCoYPHw6g7VMXW9ggLQ9Q0hzi9sdXylRfX4/Vq1cjKysLw4YNQ21tLaZMmYKvvvoKf/31F8aNG4dt27ZpXM7dli1bMGbMGMybN487fu/pUlJSJLbC6GxQfHvSAq9ffvmFy9eTJ4dPHt7e3jAxMUFGRgZeeeUVpYx609fX5477nj17xk1fYHW3s8H333/PFWz169cPaWlpEAqFmDlzJgWC3dRVCyppJAV8suwYrl+/HsnJyTA2NkZdXR2mT5/eZTrC3r17sWzZMgwaNIh7PxMIBGrZHZalG8KePXuQnp4uNulKGb7++muxud3svy3tR88R+WhNILh+/XpcuXIFLS0tMDc3x9OnTzFw4EAcO3ZMrVvU6iLa78zb21tiouykSZMgEAggEAhQVlbGjZiT1c2bN7mZt+39/vvvYoUov//+O3dt+fLl3BxifX19HD9+XKWBINtU+/jx49zXTpw4AU9PTzx9+hQ5OTlwdnbuUBDy008/8ZpzV11djZSUFJw9exbZ2dlwdXXF/PnzucC7J5LlKLW9Bw8e4KuvvsKSJUuktswA2uZnr1y5ElFRUTh69Cjmzp2r1EBw9erVePbsGWxtbXHkyBGcOHFC7nYu7U2dOhWNjY0ICgri/owBxXeERXe92Q9i7G496Z6hQ4eisLAQdXV1nbagEsW2bxEKhSgsLMSCBQu4tIXY2NgudwwBwMLCghvfaWRkJFMu3YgRI5CcnIzy8nIuh1g0RSEpKUnivZRBlmIUZRcrtbS0oKmpCXFxcXjnnXcgFArR0tKC2NhYLF++vMvWZUQ6rQkEf/vtN2RmZqK1tRVjx47lesrp8l+QESNGwMPDA5s3b5aYx9EVS0tLnDhxQmwiAvsCl5YHKGkOsSrdvXsXS5YsEfuan58fPv/8c7FCEE3LuevduzdmzZqFWbNmIT09HUFBQbhz547aqqw1Rf/+/TFt2jTEx8dj7dq1nX6fUCjEkydP5M7hk0dhYSH34WflypVwcXHp1vOIBrfnzp1DbW0t5s6dCy8vL+57FN0R7tevH8rLy2FmZsa1GiktLVVKhaaumjdvHgwNDbmgimEYqf+GyNO+pbMj4urqalhZWWHcuHEoLCzEyJEjuVZbXW1imJmZwczMrMPXOws6VY1NcxAIBDAzM8Po0aMBQCxlqTtiYmJw5MgRFBQUwNPTE0KhEH369FFb/rm205pAkH3z09fXF9tRUXRerKYS/fQp6ZMo0JZTdfnyZSQlJWHHjh0wMjLCyZMnZb5Hc3MzkpKSuGBKtBpRWh5gd+YQK6r98XhDQwMMDQ3V0oy4uy5duoQzZ86grKwMTk5OiI2N5d44tR07JUAoFCIvLw9OTk5wd3eX+jNOTk5wcHBQeg6fqF69eiE5ORm2trbIycnp9vsH21Daz88P9fX1GDJkCGpra+Hk5IS9e/di4MCBCu8If/7551i4cCGcnZ2548GsrCyx5tVEPgKBAGfPnpX5+7vaHetqxxBoa62kbHylmahqgENAQAACAgKQlZUldtxOlENr2seMGTMGgwYNglAoRH19Pfe4oaEBxcXFfC9P6aRVYbJvTnFxcUhPT8f169cxaNAgODs7S91xkUf7PMCjR4+KHf+eP38eRUVFMs8hVsTFixexYcMGvPXWWxg5ciQqKirw/fffY+fOnVxDU02kp6cHW1tb7s9LdMdAG9MZRLm6uuLixYvc5JuKigqJeYSiO2uvvPIKysrKOnxP+5F1iqioqMCuXbtw+/ZtvPLKKwgJCemyKpglGtxmZmZi7969mDt3rthu9ffff4/4+PgOjYvZHeGpU6fKtSPc3NyMrKws7njQwcGBZq4q4N1338W0adMwceLEDqcg3SHL+/S5c+fg5eWFW7duISIiAkuWLIGrq2u37wnw11dv0qRJeO211+Do6AhHR0dMmDChQ09FRfz666/49ttvUVdXxwW71D9QcVoTCLZXV1fHS0NgTRIVFQUXF5duv5GlpqZix44d3JzIgQMHIisrC0BbtRybB5iWlgZ3d3ds3bq10+dS9RF9XV0dEhISUFVVBVNTU3h6emp8zz1Z/pHQVmzfr+XLl+Pw4cOYMmWKxP5njY2NyMzMxLVr17B27Vr4+fmBYRg8f/4cBQUFGDJkSKd5rPK4c+cOdu3ahcrKSlhZWWHjxo0SW3pI0z64jYuLw/379zv9vvY7wj4+PgrtCJ88eRKLFi3q9s8Tfnoysq+FFStW4J133sGHH36o8M7a7NmzuWk26vT8+XMUFhYiKysL2dnZ+OOPP2BoaAg7OzuFJzUBbW3L4uLixI7DKRVCcdp5bgrAx8dH5z8prFy5EmlpacjPz5erSz5rw4YN+OWXX+Dj44O4uDiximNJeYDsiLebN2/KPYdYUcbGxhqV/ycLbQ/2pDEyMsKCBQvg4uLCtc1gSTs2Ft1Ja2lp4ZryKiogIABBQUEYN24cMjIysGrVKsTFxcn1HOxrobKyEocPH8bJkyclpiyw3NzcuB3h3NxcsUrR7uwIHzhwgAJBBfHRk/HJkyd49OgR9PX14eTkJFelrUAgwP79+3H//n188sknSEpKwqxZs3gJAoG2Uw62c4eJiQlMTExw7do1sTnwirC0tMTo0aOpb6CSaW0gqKUbnXJZtGhRhy758gSCRkZGePHFF/H8+XOYmJiIfUqVlAfIttrw8vLqMIeYEFE//PADysvLYWFhgebmZrG8thkzZuDixYsICwtDTU0NPvvsM+7Y+MmTJ9z3lZWVKS3to3fv3lzT9TFjxkidJtGZ9sGtubk5ZsyYIZaycPLkSe51IksbDnnIOpeZdE7d/e+Atgk6Xl5eiIiIQGNjI1566SWZf3bJkiXw9/fHjz/+CH19fezevZvXlmk+Pj5gGAajR4+Gvb09PvzwQ4nFLPJicy2rqqpgY2PD9U8UzbUk3ae1gaA8Cb/aSt4u+e3Nnj0bjY2NWLp0KWxsbMTy7bZv347MzEwYGBjA2tparHqrqqqqwxxiQkTdunULhw4dwsOHD7kPbewRXPudNdF5qp6enlzjdGNjY6VVWLOJ/ADEEvsB2XfnRIPbZ8+e4YcffsDgwYORkJDAtSxKSEjgUlYU3RFuampC3759ueB469at3GNJvRxJ11TV/06aVatWYdWqVdyv2bGKshAIBPjnP/+Jf//73wD43wCZPHkyrl27hpKSEjQ2NqKpqQkODg4KF8HJU51N5Kc1gSD7iUFU++osXSNvl/z22CktgYGBCAwMFLvm6+uL5ORkiRMjDhw4gKCgIG4O8YEDB7r/myBaaenSpYiIiJC4WyDp2Jh9fQ8ePFjsew8ePIiEhAQEBgbi1Vdf7fZ6lFHteOvWLYSFhUEgEODChQv44YcfsGHDBpWlLGzcuBF79uzhgmPg/+95up4W013K7n8nzapVqxAVFcW1XAH+/+cna6sVU1NTHDlyBI8fP0ZMTIzMhU2qEhwczD2uqalBfHw85s+fj/LyctTW1nb7edkPTe0rrHv37o179+7RbriCtKZYRJcT7ztz7949mJiY4P79+4iJiYG7u7tcrVwuXryITz/9FPfu3cOVK1cQHBzM7cC8//77sLOzg52dHZffpc43UdKzeXl54dy5cxKvNTc3ix0b//XXX9xkjvaEQiFKS0sRGhrKFTLxxdXVFadOnYKfnx9+/fVXuLu7IzU1VaX3FAqFOHbsmFwpH6RzNjY2qK6uhoWFBQDF+991RSgUIiUlBdOnT+/Wzzc1NSE6Oprr0BAYGMhr8URubi6ys7ORlZWFoqIiGBsbw8HBAQ4ODmI9NLvrrbfegp6eHuzs7JCfn4+nT5+ib9++MDIyktqgnUinNTuC169f7/SaLgaCQqEQAQEBuHDhAoYNG9attjFbtmxBcnIyZs+eDX19fRQUFHDXnj59ivT0dC4XkGEY3Lhxo8s5xIQAbQUh06dPx+uvv87thrDj/qQdG0vy0ksvacyIKdF+muqY8MEwDM6cOUOBoJIUFRWp9X4Mw2Dv3r1yB4KiubKilc58T5XZv38/HB0dERoainHjxim9qKO+vh4JCQncrz09PREbGwtnZ2el3kfXaE0g2NnRDsMwmD17tppXwz+GYTB27FikpaWJ7drJkzukp6eHfv36cS9m0TcZadV10uYQEwK0VaR3RtqxcWdER3nxZebMmVi2bBkqKirw7rvvqu19p7W1Fa6urrC1teVe53zO0O6J2F5+knaVZBn3pwgDAwMEBASIvU93dU82HYD9oMQ+5jstIDo6WqXP//DhQ6SmpmLSpEn4/fffuRGA+vr6Kr2vttOaQFBSD7vbt2/L3QJCm+Tn5yM/P5/7tbxvEitWrMA//vEP/Pnnn5gzZ45YnqC06jppc4iJbistLYW5ubnUHn1mZmbw8PBQ46qUY+PGjSgsLMSNGzdgbW2N1157TS33ZXN5WdRaQ34vvPACAKi1SITVnSrftLQ0FaxE8504cQLh4eH4/PPPYWlpiZiYGLS2tspVYEM60pocQdadO3cQGxuL8+fPw9raGu7u7jrbWys9PV2sf192djYcHBxk+lmhUIjU1FRMnDgRd+7cgYWFhdi4tjfeeKPT6rqdO3ciOTmZm0Ps4eGBjRs3Kv4bIj1eREQEQkJCpDbunTVrFlpaWiQeG2syT09PsekoAQEB+O6771R+3xUrVoi131m3bh1VWcqpqKgIQqEQY8eO5b5WWFiIXr16wdraWmPvmZaWhrCwMK6oavv27Ro9TYloJq0JBHft2oXU1FSMHj0aixYtwmeffYbExES+l8Wr9mOGfH19cfr0aZl/fs6cOYiPj5d4benSpdi3b1+nn6Crq6slziEmpL2KigqMGDGCC/okTQpxcXFR97Jklpubi5ycHOzduxcfffQRgLZm16dPn0ZGRobK7vvnn3+iuLgYmzZt4noTtrS0YPfu3UqZtqJLPDw8cOrUKbEPu/fv34efnx83a10T72lra4vk5GQYGxujrq4O06dPF2tMri2UUWFNOqc1R8Px8fEwNzeHl5cXpkyZotM5AwcPHsSBAwdQXFwMe3t77gVjZWUl1/NIyl05fPgwGIaBQCCAmZkZ1x9K9AXZfg7x4cOHO+wAEd3m7e2NH3/8EXv27EFiYiKGDh2KTz/9tMtjY03Up08fGBoawtjYmPtg1Lt37w6tLpStsrISeXl5aGho4HKke/furZRRXrqmublZLCADgBdffBHPnj3T6HtaWFjAyMgIQFvbpZdfflmpa9QUUVFRADrWAjx9+pSP5WgdrQkEMzMzUVFRgbi4OHz77be4fv06zp07BxcXFwwaNIjv5akV2/dv3759eO+997r9PJJyV2Tpt7Z8+XJuDrG+vj6OHz9OgSARw45a++OPP5CYmIjJkyfj1KlTCAkJQWRkpNj3qmPeqyImTJiACRMmICAgAE1NTbh3755aGvu6uLjAxcUFGzZsoHmrCurVqxfq6+sxcOBA7mt1dXUq3VBQxj2rq6thZWWFcePGobCwECNHjpS7EXpP0NraiqSkJAiFQsycORN1dXWIjIxEYmIirl69yvfyejytORpur7KyEnFxcThz5ozOHZOcP38eU6dOxYABA3D79m0uhyQsLEymXn/Scldu374t8bk//vhjrqEv2z+NPZpuf0RNiJubG+zs7DBw4EBs3rwZ06ZN41oRiWp/bKzJIiIicPbsWZSUlMDMzAyGhoZqSep3cnLiqkZv3bqFkSNH4tq1ayq/rzbJyMjAhg0bsHjxYgwfPhwVFRU4deoUwsPDVdaaRBn31JX+ub6+vjAxMUF9fT3q6+vBMAxWrVoFT09PsTnlpHu0NhDUZY6Ojlxz3SlTpmDHjh0YPHgwPvjgA5kCMmm5Kw0NDV0+94cffggTExOcOXMGCxcuRG1tLb744gsV/E5JT1VXV4crV67A1dUVzc3NyMnJwdSpUwFIPjY+duwYzyvumoODA7Kzs+Hq6oq0tDQsXrwYJ06cUOsaGhoa8P777+P48eNqva82ePDgAc6fP4+qqioMHz4cnp6e3DhATb2nQCDAwYMHuZGegYGBMDQ0VOGK+eHm5sZ9qLK0tERRUVGnTeaJ/LTmaJj8X79+/QC0TRZ5/Pix3In20nJXZHluaXOIiW5j+7WxAdLNmze5a2wgKOnYuCcQzQ8sLi6W2uReVfT09NTeFFlbmJiYqGwcoKru6evri/nz52PlypXIy8uDr68vLly4oMQVaob79+/jwoULEAqF6N+/P1JSUrhrutgnWNkoENRCRkZGiIqKQk5ODpcv0tzcLNaNXhppuSsGBgZdPre0OcREt8nSr00oFCIkJASWlpYA2v4+9gSbNm1CY2Mjtm3bhpCQELG5q6rEVlIKhUL06tULQUFBarkv0QwrVqwAAIwfP16r8gJF+fj4cMWIPj4+XK66rg6MUDY6GtZCjx49wrFjx9C/f38sXboU+vr6KCsrQ0FBAd58880uf15a7sr48eO7fG6aQ0xk0b6wYtSoUQCkHxtrssrKSty9exfDhw/HiBEj+F4O0QHLly/HiBEj8MYbb+DKlSsoLy+Hr68vAN3aKVu7di327NnD9zJ6LAoEiUSK5K5IaxZMCCC5sGLNmjW8jflSRHl5ORYtWoTevXvD1NQUd+/eRWtrK06ePImRI0eq7L7btm3rtIjm448/Vtl9iebobMY2wzA69XeAChIVQ4EgIUTtJBVWLFu2DDNnzpQ4jSMgIICHVcrGz88PK1asEBuLl5qaiujoaJUWi4g2zGcYBhUVFfjyyy/x/PlzFBYWquy+RDOxI1VDQ0P5XoraUSComJ6RfEN6FGlziAkBJBdWzJw5EwDU3o9PUffu3eswG9nd3R07d+5U6X3Z/1/FxcWIjIxESUkJduzYQcVZOkTSSFVd1BPeJzQZBYJE6fbs2dPpHGJCgP8XVvzrX/9CSEgI1q1bx13jqx9fT5Obm4uIiAi0tLRg/fr1Paa6miiu/UjVtLQ0HDx4kO9lqVxGRoZYvnBOTg7s7e0xb948HlfV89HRMFG6ruYQEyLqwYMHiI6ORkhICADN6McnjzFjxnSYXiQUCtHQ0IDi4mKV3VdPTw82NjZ49dVXO+QKamv1KGnj7OwMc3NzLF26FNOnT8fcuXPx888/870slWt/BOzr64vTp0/zuCLtQDuCRGnYNhbS5hAT3ZaYmIiwsDAAQHh4OFJSUpCeng5/f3/uezShH588bt26JfV6UlISZsyYofT7lpSUKP05Sc+gayNVDx48iAMHDqC4uBj29vYQCoVgGAZWVlZ8L00r0I4gIURtHBwc8OOPP6Kurg5OTk44fvx4h5y2lJQUTJkyBfn5+QgPD8f8+fPFAsWehhLZiarpykjVffv24b333uN7GVqHhvQRpTl//jw3FeL27dtYvHgx/Pz8cOPGDZ5XRjSFgYEBTE1NYWNjgwkTJkgsbPDw8MALL7wAZ2dnHDlyBH///TcPK1Ue+qxNVG3EiBH46KOPtDoIBMClQGRnZ3OjUIniKBAkSvPJJ59gwIABANoqP1euXIktW7Zg9erVPK+MaIq8vDzY29vDzs4OV69e5R7b29sjMTER9vb2sLe3R1paGjZv3gwvLy+Vz3tVtc56/RFC5MMGflFRUTh69CgiIyN5XpF2oBxBojSKzjgm2o/dMZbEwcEBn332GUxNTblj4x07dqhxdarBjtUjhCjm8ePHKCoqQv/+/TF8+HAYGhryvSStQDuCRGnYGcehoaHdmnFMdJuBgQF27Ngh9dhYkwkEAuzevRubNm1Ca2srLly4AAA6Uc1JiDps2rQJ4eHhWLt2LRobG+Hk5MT3krQCFYsQpVF0xjHRbWxagZWVFYqLi2Ftbc1VB/aEqnNvb2/4+/vjiy++QEZGBjw8PJCSksL3sgjRGmVlZR2+xs4oJ91HgSAhRGNIq7AtKCjA+PHj1bwi2bGBH/t7cHd3R2pqKt/LIkRr+Pn5gWEYPH/+HAUFBRgyZIjWF8ioA+UIEkI0hrTPpWvWrNHoNiympqY4cuQIHj9+jJiYGIwcOZLvJRGiVUQby7e0tMDPz4/H1WgPCgQJIWpTWloKc3NzFBUVdbhmY2ODc+fOdfqzmn54ER0djejoaNja2qKurg4HDhzge0mEaBXRfPOysjKVTu7RJRQIEkLU5uTJkwgNDe3Q9oFhGBw6dIjLE5REk9uwCIVCeHt7cwUihBDl8/T0BMMwYBgGxsbG+PLLL/leklagHEFCSI/g5uaGtLQ0vpfRqXXr1uHNN9+Era0t9PTaGjL079+f51UR0vP5+vqCYZgOpwK9evWCqakpAgMD8eqrr/K0up6PAkFCiNqw86hFsZXBp0+flnps3NDQIHXHkG9ubm5iv2YYRqNzGgnpKUpLSyV+XSgUorS0FKGhocjKylLzqrQHBYKEEI0QHh6O0NBQLF++XOzr7LExIYRIkpiYiJkzZ/K9jB6LAkFCiNrt378fK1euRHZ2NjZv3ozAwEAsXLiQ72UpJC0tDWFhYRAKhdDT08P27ds77BISQoimoUCQEKJ2bK+9ZcuWYefOnZg7dy6XBC6qJzWUtrW1RXJyMoyNjVFXV4fp06cjLy+P72URQohUVDVMCFE7STNDL168yPeyFGJhYQEjIyMAbeMWX375ZZ5XRAghXaNZw4QQtdu4cWOnM0P3798PAMjOzoaHhwdOnTrF1zJl4uvriwULFqC6uhpWVlbw8vKClZUVampq+F4aIYR0iY6GCSG8yM/PR2VlJd58803cvXuXm8Qh6dhYk49YO6toBABzc3M1roQQQuRHR8OEELULDg5Gc3MzLl++jLlz5+Ltt99GUlISAMnHxprs77//hoODA37++ecO1ygQJIRoOgoECSFqd+XKFfz6669cVW1rayt3jT02DgsL63BsrImKi4vh4OCA3Nxcsaa3DMNg9uzZPK+OEEKko0CQEKJ2ffr0QUlJCRiGQXl5Ofr27ctd8/b2hpmZGYqKimBhYYHVq1fzuNKuXbp0Cf7+/ti6dSu+/vprfPDBB3wviRBCZEbFIoQQtYuKikJoaCju37+PdevW4ZtvvuGuBQcH47vvvsMnn3wCPT09vP322zyutGslJSXc47Nnz/K4EkIIkR/tCBJC1O7ll19GbGysxGvSjo01kUAgwI0bN/D8+XMIBAKxEXk2NjY8rowQQrpGgSAhRO2cnJy4fLra2lqYmJggOzsbgPRjY000duxYREREcI8jIyMB0Gg8QkjPQO1jCCG8Ki0tRXh4OL799lsAbUetoaGhKC4uhrW1NXbt2tWjmzNHREQgJCSE72UQQohEFAgSQnglFArh6OjI7QhqG7YvIiGEaCI6GiaEqJ2dnR13NNzU1ITFixdz16QdGxNCCFEuCgQJIWqXm5vb6bXLly9zj9lj456MDl0IIZqMAkFCiNqweYCSvP/++x2+NmrUKOTn56tySSqn6Q2xCSG6jfoIEkLUZsuWLTh69CgePXoEQ0NDGBgYcP+x7OzsYG9vDzs7O0yYMAHz5s3jccXy8/X1RV5eHvz8/AAAO3fu5HlFhBDSOSoWIYSoTXNzM1JSUnD27FnU1tbC1dUV8+fPx/Dhw/leWrc9fPgQBgYG6N27NwCgpqYGEydORHBwMIKCgnheHSGESEeBICGEF+np6QgKCsLUqVPx5Zdfyn1srCmcnZ2RnJyM/v37o6qqCv7+/ggODsY333yD+Ph4vpdHCCFSUY4gIURtLl26hDNnzqCsrAxOTk6IjY3F6NGjAbQdG48ZMwbz5s2DqalpjymyaGlpQb9+/XD58mWsXr0a0dHRmDRpEtdkmhBCNBkFgoQQtXFzc4OtrS3Mzc2Rm5uLvLw87lp1dTV3bJydnd1jjo3ZXc0nT57A3t4eDx48wLFjx/DCCy/wvTRCCOkSHQ0TQtSmtLS002vm5ubc4/bHxj1FU1MTdu/ejUePHiE4OBiDBw/me0mEECIVBYKEEI3Q/tjYx8eHOzbuyWiyCCFEk1EgSAjRCHp6etyxMQAwDMNdi42N5WtZCnNzc0NaWhrfyyCEEIkoR5AQohFKSkr4XoJKiAa0hBCiaSgQJIRoBNEcQW1Chy6EEE1Gk0UIIUQJvv76a7FfHz58GACwbds2PpZDCCEyoUCQEEIU0NLSgsePHyMuLg5Pnz7FkydP0NDQwOU1Tps2jecVEkJI5+homBBCFBATE4MjR46goKAAnp6eEAqF6NOnD+bMmcP30gghpEtUNUwIIUqQlZUFR0dHvpdBCCFyoUCQEEIUsG3bNmzduhW+vr4dKoR7ctsbQohuoECQEEIUUF1djWHDhkmcmqKtldCEEO1BOYKEEKKAYcOGAWibjNKev7+/updDCCFyoaphQghRgqdPn3JVwxkZGUhISOB7SYQQ0iU6GiaEEBWYM2cO4uPj+V4GIYRIRUfDhBCiBD///DP3uKysDH///TePqyGEENlQIEgIIUqQm5sLoG22sLGxMX766SeeV0QIIV2jQJAQQhQkFAohEAgQGRnJ91IIIUQuVCxCCCEKYhgGNTU1aGho4HsphBAiFyoWIYQQJRg7diwqKythaWkJhmHAMAxycnL4XhYhhEhFgSAhhCggIyMDU6dOBQA0NDRgwIABPK+IEEJkR0fDhBCigK1bt3KPvb29+VsIIYR0AwWChBBCCCE6io6GCSFEAUOHDoWrqyuEQiEuXbrEPWYYBrGxsXwvjxBCpKL2MYQQogC2fyAhhPREtCNICCGEEKKjKEeQEEIIIURHUSBICCGEEKKjKBAkhBBCCNFRFAgSQgghhOgoCgQJIYQQQnQUBYKEEEIIITqKAkFCCCGEEB1FgSAhhBBCiI76H620yAerAYL2AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.ion()\n",
    "\n",
    "from scipy.cluster.hierarchy import linkage, dendrogram\n",
    "\n",
    "# Preparing linkage matrix\n",
    "linkage_matrix = linkage(cereals_std, method='ward',metric='euclidean')\n",
    "\n",
    "dendrogram(linkage_matrix,labels=cereal_label.values)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WU_5LHvzLfh1",
    "outputId": "053ca2b8-2d76-420e-b1c7-fddce6281ad4",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.40000000e+01, 1.80000000e+01, 1.44874262e-01, 2.00000000e+00],\n",
       "       [1.50000000e+01, 6.20000000e+01, 1.88161824e-01, 2.00000000e+00],\n",
       "       [6.40000000e+01, 6.50000000e+01, 5.74073478e-01, 2.00000000e+00],\n",
       "       [4.40000000e+01, 4.50000000e+01, 6.60373898e-01, 2.00000000e+00],\n",
       "       [1.60000000e+01, 7.80000000e+01, 8.75507965e-01, 3.00000000e+00],\n",
       "       [4.80000000e+01, 7.60000000e+01, 8.85514125e-01, 2.00000000e+00],\n",
       "       [7.30000000e+01, 7.70000000e+01, 9.00789301e-01, 3.00000000e+00],\n",
       "       [2.40000000e+01, 4.20000000e+01, 9.30820518e-01, 2.00000000e+00],\n",
       "       [1.30000000e+01, 5.90000000e+01, 1.02098928e+00, 2.00000000e+00],\n",
       "       [2.10000000e+01, 8.10000000e+01, 1.08586587e+00, 4.00000000e+00],\n",
       "       [5.20000000e+01, 5.80000000e+01, 1.10266673e+00, 2.00000000e+00],\n",
       "       [4.00000000e+00, 5.00000000e+00, 1.14573093e+00, 2.00000000e+00],\n",
       "       [3.20000000e+01, 7.50000000e+01, 1.18437314e+00, 2.00000000e+00],\n",
       "       [1.00000000e+01, 3.50000000e+01, 1.19253426e+00, 2.00000000e+00],\n",
       "       [7.00000000e+00, 4.90000000e+01, 1.19459229e+00, 2.00000000e+00],\n",
       "       [6.00000000e+00, 1.70000000e+01, 1.23534547e+00, 2.00000000e+00],\n",
       "       [5.40000000e+01, 5.50000000e+01, 1.37331643e+00, 2.00000000e+00],\n",
       "       [4.10000000e+01, 5.60000000e+01, 1.37458110e+00, 2.00000000e+00],\n",
       "       [2.50000000e+01, 2.90000000e+01, 1.38145319e+00, 2.00000000e+00],\n",
       "       [3.80000000e+01, 6.90000000e+01, 1.41321170e+00, 2.00000000e+00],\n",
       "       [3.00000000e+01, 6.60000000e+01, 1.45624833e+00, 2.00000000e+00],\n",
       "       [8.00000000e+00, 7.40000000e+01, 1.48061049e+00, 2.00000000e+00],\n",
       "       [2.60000000e+01, 6.80000000e+01, 1.48167797e+00, 2.00000000e+00],\n",
       "       [4.70000000e+01, 8.90000000e+01, 1.52519818e+00, 3.00000000e+00],\n",
       "       [8.30000000e+01, 8.40000000e+01, 1.52904711e+00, 5.00000000e+00],\n",
       "       [1.20000000e+01, 9.00000000e+01, 1.53129897e+00, 3.00000000e+00],\n",
       "       [6.10000000e+01, 8.60000000e+01, 1.56677647e+00, 5.00000000e+00],\n",
       "       [3.10000000e+01, 8.20000000e+01, 1.65874524e+00, 3.00000000e+00],\n",
       "       [1.90000000e+01, 8.50000000e+01, 1.68427872e+00, 3.00000000e+00],\n",
       "       [2.30000000e+01, 7.20000000e+01, 1.69344714e+00, 2.00000000e+00],\n",
       "       [2.20000000e+01, 8.80000000e+01, 1.69446264e+00, 3.00000000e+00],\n",
       "       [2.80000000e+01, 8.70000000e+01, 1.71725840e+00, 3.00000000e+00],\n",
       "       [0.00000000e+00, 2.00000000e+00, 1.80916263e+00, 2.00000000e+00],\n",
       "       [5.00000000e+01, 1.00000000e+02, 1.81779722e+00, 4.00000000e+00],\n",
       "       [3.60000000e+01, 1.07000000e+02, 1.81916403e+00, 4.00000000e+00],\n",
       "       [9.20000000e+01, 9.70000000e+01, 2.05122826e+00, 4.00000000e+00],\n",
       "       [9.00000000e+00, 9.80000000e+01, 2.10718597e+00, 3.00000000e+00],\n",
       "       [2.70000000e+01, 5.10000000e+01, 2.12165737e+00, 2.00000000e+00],\n",
       "       [9.50000000e+01, 1.04000000e+02, 2.13160516e+00, 5.00000000e+00],\n",
       "       [6.00000000e+01, 9.90000000e+01, 2.16696705e+00, 3.00000000e+00],\n",
       "       [3.40000000e+01, 1.05000000e+02, 2.17432415e+00, 4.00000000e+00],\n",
       "       [7.10000000e+01, 9.60000000e+01, 2.25238894e+00, 3.00000000e+00],\n",
       "       [3.70000000e+01, 1.01000000e+02, 2.47094730e+00, 6.00000000e+00],\n",
       "       [2.00000000e+01, 7.90000000e+01, 2.50304435e+00, 3.00000000e+00],\n",
       "       [5.30000000e+01, 1.18000000e+02, 2.56538205e+00, 4.00000000e+00],\n",
       "       [1.10000000e+01, 6.70000000e+01, 2.58329591e+00, 2.00000000e+00],\n",
       "       [6.30000000e+01, 1.20000000e+02, 2.69532065e+00, 4.00000000e+00],\n",
       "       [4.30000000e+01, 5.70000000e+01, 2.73189208e+00, 2.00000000e+00],\n",
       "       [9.40000000e+01, 1.17000000e+02, 2.76291158e+00, 6.00000000e+00],\n",
       "       [1.03000000e+02, 1.06000000e+02, 2.82528015e+00, 7.00000000e+00],\n",
       "       [4.60000000e+01, 9.10000000e+01, 2.85420732e+00, 3.00000000e+00],\n",
       "       [3.30000000e+01, 1.13000000e+02, 3.01089221e+00, 4.00000000e+00],\n",
       "       [3.90000000e+01, 7.00000000e+01, 3.25194715e+00, 2.00000000e+00],\n",
       "       [1.12000000e+02, 1.19000000e+02, 3.27156964e+00, 1.00000000e+01],\n",
       "       [1.14000000e+02, 1.27000000e+02, 3.36186753e+00, 5.00000000e+00],\n",
       "       [4.00000000e+01, 1.26000000e+02, 3.38243021e+00, 8.00000000e+00],\n",
       "       [1.11000000e+02, 1.15000000e+02, 3.39103411e+00, 9.00000000e+00],\n",
       "       [3.00000000e+00, 1.09000000e+02, 3.41251552e+00, 3.00000000e+00],\n",
       "       [1.02000000e+02, 1.33000000e+02, 3.52678578e+00, 1.20000000e+01],\n",
       "       [1.10000000e+02, 1.28000000e+02, 3.83878519e+00, 8.00000000e+00],\n",
       "       [1.16000000e+02, 1.23000000e+02, 4.08832570e+00, 7.00000000e+00],\n",
       "       [1.24000000e+02, 1.37000000e+02, 4.50615338e+00, 9.00000000e+00],\n",
       "       [1.00000000e+00, 8.00000000e+01, 4.53189914e+00, 3.00000000e+00],\n",
       "       [1.08000000e+02, 1.31000000e+02, 4.97093871e+00, 8.00000000e+00],\n",
       "       [1.30000000e+02, 1.35000000e+02, 5.85700572e+00, 2.20000000e+01],\n",
       "       [1.25000000e+02, 1.36000000e+02, 6.47109754e+00, 1.40000000e+01],\n",
       "       [1.29000000e+02, 1.40000000e+02, 6.58566205e+00, 1.00000000e+01],\n",
       "       [1.22000000e+02, 1.42000000e+02, 7.20143518e+00, 1.60000000e+01],\n",
       "       [1.21000000e+02, 1.32000000e+02, 8.28412127e+00, 1.20000000e+01],\n",
       "       [1.38000000e+02, 1.44000000e+02, 8.51247236e+00, 2.50000000e+01],\n",
       "       [1.39000000e+02, 1.43000000e+02, 8.56030214e+00, 1.30000000e+01],\n",
       "       [9.30000000e+01, 1.46000000e+02, 1.03069891e+01, 2.70000000e+01],\n",
       "       [1.41000000e+02, 1.45000000e+02, 1.28384235e+01, 3.40000000e+01],\n",
       "       [1.34000000e+02, 1.48000000e+02, 1.34747680e+01, 3.00000000e+01],\n",
       "       [1.47000000e+02, 1.49000000e+02, 1.55367286e+01, 4.70000000e+01],\n",
       "       [1.50000000e+02, 1.51000000e+02, 1.64264620e+01, 7.70000000e+01]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linkage_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "vj9RmUcLLfh2"
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "\n",
    "## Instantiating object\n",
    "agg_clust = AgglomerativeClustering(n_clusters=6, metric = 'euclidean', linkage='ward')\n",
    "\n",
    "## Training model and return class labels\n",
    "agg_clusters = agg_clust.fit_predict(cereals_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EO5uUBszLfh2",
    "outputId": "84cc5315-3b94-4937-cbed-889142979af3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 0, 3, 3, 4, 4, 4, 0, 2, 2, 4, 2, 4, 2, 4, 1, 1, 4, 4, 2, 2, 1,\n",
       "       4, 1, 4, 4, 2, 0, 0, 4, 4, 4, 2, 2, 2, 4, 4, 4, 1, 0, 1, 2, 4, 2,\n",
       "       0, 0, 0, 2, 4, 0, 2, 0, 0, 1, 5, 5, 2, 2, 0, 2, 2, 1, 1, 2, 2, 2,\n",
       "       4, 2, 2, 1, 0, 1, 1, 4, 2, 2, 4])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "so9U2IA_Lfh2",
    "outputId": "7461b5a0-1f1e-414d-df17-7713edf00a7a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5]), array([13, 12, 25,  3, 22,  2]))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(agg_clusters, return_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "1s2YKJhILfh3",
    "outputId": "eb006764-ae5a-47a9-d51f-adb991c3e371"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>agg_cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100%_Bran (3 - 68.4)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100%_Natural_Bran (3 - 33.98)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>All-Bran (3 - 59.43)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>All-Bran_with_Extra_Fiber (3 - 93.7)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Almond_Delight (3 - 34.38)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  label  agg_cluster\n",
       "0                  100%_Bran (3 - 68.4)            3\n",
       "1         100%_Natural_Bran (3 - 33.98)            0\n",
       "2                  All-Bran (3 - 59.43)            3\n",
       "3  All-Bran_with_Extra_Fiber (3 - 93.7)            3\n",
       "4            Almond_Delight (3 - 34.38)            4"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Label - Cluster\n",
    "agg_result = pd.DataFrame({\"label\":cereal_label,\"agg_cluster\":agg_clusters})\n",
    "agg_result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "id": "t722WyGCLfh3",
    "outputId": "82a6bc85-55e8-43ec-b7c0-6e26948aa6d5"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>agg_cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100%_Bran (3 - 68.4)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100%_Natural_Bran (3 - 33.98)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>All-Bran (3 - 59.43)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>All-Bran_with_Extra_Fiber (3 - 93.7)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Almond_Delight (3 - 34.38)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Triples (3 - 39.11)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Trix (2 - 27.75)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Wheat_Chex (1 - 49.79)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Wheaties (1 - 51.59)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Wheaties_Honey_Gold (1 - 36.19)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   label  agg_cluster\n",
       "0                   100%_Bran (3 - 68.4)            3\n",
       "1          100%_Natural_Bran (3 - 33.98)            0\n",
       "2                   All-Bran (3 - 59.43)            3\n",
       "3   All-Bran_with_Extra_Fiber (3 - 93.7)            3\n",
       "4             Almond_Delight (3 - 34.38)            4\n",
       "..                                   ...          ...\n",
       "72                   Triples (3 - 39.11)            1\n",
       "73                      Trix (2 - 27.75)            4\n",
       "74                Wheat_Chex (1 - 49.79)            2\n",
       "75                  Wheaties (1 - 51.59)            2\n",
       "76       Wheaties_Honey_Gold (1 - 36.19)            4\n",
       "\n",
       "[77 rows x 2 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qZzOrtuALfh4"
   },
   "source": [
    "### K-Means Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PFEY6nT6Lfh4"
   },
   "source": [
    "- Kmeans is a distance based iterative technique, where the instances that are \"closer\" are \"grouped\" together forming a \"cluster\".\n",
    "- This \"closeness\" is computed by distances,by default, Euclidean distances\n",
    "- We need to specify prior, how many clusters we want to get.\n",
    "- What is iterative in this case?\n",
    "    - We specify a number of clusters we need, so in the first iteration, centroids(centre) of the cluster are randomly picked in the data (this centroid need not be a data point but could be any other point as well). For eg: if we need 3 clusters, 3 centroids are randomly picked.\n",
    "    - Now with respect to each of these centroids, distance is computed for each of the points in the data and the data point is assigned to that cluster for which the point's distance is closest to its centroid. This is \"Assignment phase\".\n",
    "    - Once all points are assigned to the clusters, new centroids are computed from the points of each cluster (in 2d it is (x1+x2)/2, (y1+y2)/2)..remember this formula :)\n",
    "    - Once, this new centroids are computed, the assignment phase starts-- compute the distance between each of the data points with each of the new centroids and assign the point to the closest cluster. After assignment, the new cluster centroids are computed. This process continues until there is no change in cluster centroids from previous iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "FiIzRutYLfh4"
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "kmeans_object = KMeans(n_clusters=5, random_state=1240)\n",
    "kmeans_object.fit(cereals_std)\n",
    "kmeans_clusters = kmeans_object.predict(cereals_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yQwn14n2Lfh4",
    "outputId": "3e66a9c3-e6aa-4d74-afbf-55cf5e8c81f8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 2, 2, 2, 4, 0, 0, 2, 1, 2, 0, 2, 1, 1, 2, 2, 0, 1, 1,\n",
       "       2, 1, 2, 2, 0, 0, 0, 2, 2, 2, 0, 0, 0, 2, 2, 2, 4, 4, 1, 0, 2, 0,\n",
       "       0, 0, 4, 1, 2, 4, 1, 0, 0, 1, 3, 3, 0, 0, 0, 0, 0, 1, 1, 3, 0, 0,\n",
       "       2, 1, 2, 4, 4, 4, 1, 2, 0, 1, 2], dtype=int32)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NYlO_TjiLfh5",
    "outputId": "363fd4f3-0dc9-4dad-fc54-4810aef6668f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4], dtype=int32), array([28, 15, 23,  3,  8]))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(kmeans_clusters, return_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "edJxJgvJLfh5",
    "outputId": "82a6eb70-9849-4fba-a7b0-665c9fea3a58"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>kmeans_cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100%_Bran (3 - 68.4)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100%_Natural_Bran (3 - 33.98)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>All-Bran (3 - 59.43)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>All-Bran_with_Extra_Fiber (3 - 93.7)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Almond_Delight (3 - 34.38)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  label  kmeans_cluster\n",
       "0                  100%_Bran (3 - 68.4)               0\n",
       "1         100%_Natural_Bran (3 - 33.98)               0\n",
       "2                  All-Bran (3 - 59.43)               0\n",
       "3  All-Bran_with_Extra_Fiber (3 - 93.7)               0\n",
       "4            Almond_Delight (3 - 34.38)               2"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans_results = pd.DataFrame({\"label\":cereal_label,\"kmeans_cluster\":kmeans_clusters})\n",
    "kmeans_results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RUoqzXzKLfh5"
   },
   "source": [
    "#### Inspecting cluster centroids to understand average statistics of each cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "husnivD-Lfh5",
    "outputId": "a6e43085-0845-45d0-ae35-23a5e8f440ef"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calories</th>\n",
       "      <th>protein</th>\n",
       "      <th>fat</th>\n",
       "      <th>sodium</th>\n",
       "      <th>fiber</th>\n",
       "      <th>carbo</th>\n",
       "      <th>sugars</th>\n",
       "      <th>potass</th>\n",
       "      <th>vitamins</th>\n",
       "      <th>weight</th>\n",
       "      <th>cups</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>104.285714</td>\n",
       "      <td>3.285714</td>\n",
       "      <td>1.357143</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>3.989286</td>\n",
       "      <td>13.314380</td>\n",
       "      <td>6.500940</td>\n",
       "      <td>157.321429</td>\n",
       "      <td>21.428571</td>\n",
       "      <td>1.053214</td>\n",
       "      <td>0.638214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>104.666667</td>\n",
       "      <td>2.733333</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>235.333333</td>\n",
       "      <td>1.066667</td>\n",
       "      <td>19.533333</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>61.911111</td>\n",
       "      <td>28.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.025333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>110.000000</td>\n",
       "      <td>1.565217</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>163.260870</td>\n",
       "      <td>0.695652</td>\n",
       "      <td>12.608696</td>\n",
       "      <td>11.043478</td>\n",
       "      <td>51.463768</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.875217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>53.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.610000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>128.750000</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>1.375000</td>\n",
       "      <td>188.750000</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>18.125000</td>\n",
       "      <td>7.875000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>71.875000</td>\n",
       "      <td>1.245000</td>\n",
       "      <td>0.855000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     calories   protein       fat      sodium     fiber      carbo     sugars  \\\n",
       "0  104.285714  3.285714  1.357143  125.000000  3.989286  13.314380   6.500940   \n",
       "1  104.666667  2.733333  0.400000  235.333333  1.066667  19.533333   2.800000   \n",
       "2  110.000000  1.565217  1.000000  163.260870  0.695652  12.608696  11.043478   \n",
       "3   60.000000  1.666667  0.000000    0.000000  1.333333  13.000000   0.000000   \n",
       "4  128.750000  2.750000  1.375000  188.750000  2.250000  18.125000   7.875000   \n",
       "\n",
       "       potass   vitamins    weight      cups  \n",
       "0  157.321429  21.428571  1.053214  0.638214  \n",
       "1   61.911111  28.333333  1.000000  1.025333  \n",
       "2   51.463768  25.000000  1.000000  0.875217  \n",
       "3   53.333333   0.000000  0.610000  1.000000  \n",
       "4  115.000000  71.875000  1.245000  0.855000  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Using inverse_transform to retrive actual values from standardized data\n",
    "cluster_centroids = pd.DataFrame(standardizer.inverse_transform(kmeans_object.cluster_centers_),\n",
    "                                 columns=cereals.columns)\n",
    "cluster_centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "s98kQqmkLfh5"
   },
   "outputs": [],
   "source": [
    "cluster_centroids.to_csv(\"cereals_best_kmeans_cluster_centroids.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MBn7AVOuLfh6"
   },
   "source": [
    "#### In the above case, we have randomly given clusters number. But how would we know the optimal clusters\n",
    "- The clustering is said to be good, if the points in the cluster are closer to each other and the clusters themselves are far apart. The two quantities which describe the above said factors are \"Within Sum of Squares (wss)\" and \"Between Sum of Squares(bss)\" respectively. In kmeans function in python, wss is defined as inertia.\n",
    "- Ideally, if wss is minimum, we have a better clustering.\n",
    "- Now the question is.. how do we choose k"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NKOiMh0OLfh6"
   },
   "source": [
    "**Parameter description**\n",
    "\n",
    "n_clusters : The number of clusters to find.\n",
    "\n",
    "n_init : Number of time the k-means algorithm will be run with different centroid seeds. The final results will be the best output of n_init consecutive runs in terms of inertia.\n",
    "\n",
    "max_iter : max iterations of recomputing new cluster centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "xzvMQN4-Lfh6"
   },
   "outputs": [],
   "source": [
    "wss= {}\n",
    "for k in range(1, 21):\n",
    "    kmeans_loop = KMeans(n_clusters=k,n_init=30,max_iter=300,random_state=1000).fit(cereals_std)\n",
    "    clusters = kmeans_loop.labels_\n",
    "    wss[k] = kmeans_loop.inertia_ # Inertia: Sum of squared distances of samples to their closest cluster center\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "id": "PIIu_ManLfh6",
    "outputId": "b3ca5f2d-03bd-487c-a19f-0d09f030bb4d"
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(list(wss.keys()),list(wss.values()), 'bx--')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('Total within sum of squares')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M8ZfLxl6Lfh7"
   },
   "source": [
    "### Silhouette Analysis\n",
    "**Higher the silhouette score better the clustering**\n",
    "\n",
    "*The silhouette value is a measure of how similar an object is to its own cluster (cohesion) compared to other clusters (separation). The silhouette ranges from −1 to +1, where a high value indicates that the object is well matched to its own cluster and poorly matched to neighboring clusters. If most objects have a high value, then the clustering configuration is appropriate. If many points have a low or negative value, then the clustering configuration may have too many or too few clusters.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "rBRpH-eBLfh7",
    "outputId": "34fdc960-be67-4a4d-c2cd-d9e373f03d60"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Standard plotting code copied from sklearn documentation.\n",
    "## Just change \"X_matrix\" to the data of your choice\n",
    "\n",
    "from sklearn.metrics import silhouette_samples, silhouette_score\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "### Just change this to your dataframe\n",
    "X_matrix = cereals_std.values\n",
    "\n",
    "range_n_clusters = [3, 4, 5, 6, 7, 8, 9, 10, 11]\n",
    "\n",
    "for n_clusters in range_n_clusters:\n",
    "    fig, (ax1) = plt.subplots()\n",
    "\n",
    "\n",
    "    # Initialize the clusterer with n_clusters value and a random generator\n",
    "    # seed of 10 for reproducibility.\n",
    "    clusterer = KMeans(n_clusters=n_clusters, random_state=10)\n",
    "    cluster_labels = clusterer.fit_predict(X_matrix)\n",
    "\n",
    "    # The silhouette_score gives the average value for all the samples.\n",
    "    # This gives a perspective into the density and separation of the formed\n",
    "    # clusters\n",
    "    silhouette_avg = silhouette_score(X_matrix, cluster_labels)\n",
    "\n",
    "    # Compute the silhouette scores for each sample\n",
    "    sample_silhouette_values = silhouette_samples(X_matrix, cluster_labels)\n",
    "\n",
    "    y_lower = 0\n",
    "    for i in range(n_clusters):\n",
    "        # Aggregate the silhouette scores for samples belonging to\n",
    "        # cluster i, and sort them\n",
    "        ith_cluster_silhouette_values = \\\n",
    "            sample_silhouette_values[cluster_labels == i]\n",
    "\n",
    "        ith_cluster_silhouette_values.sort()\n",
    "\n",
    "        size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
    "        y_upper = y_lower + size_cluster_i\n",
    "\n",
    "        color = cm.nipy_spectral(float(i) / n_clusters)\n",
    "        ax1.fill_betweenx(np.arange(y_lower, y_upper),\n",
    "                          0, ith_cluster_silhouette_values,\n",
    "                          facecolor=color, edgecolor=color, alpha=0.7)\n",
    "\n",
    "        # Label the silhouette plots with their cluster numbers at the middle\n",
    "        ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n",
    "\n",
    "        # Compute the new y_lower for next plot\n",
    "        y_lower = y_upper + 10  # 10 for the 0 samples\n",
    "\n",
    "    ax1.set_title(\"The silhouette plot for the various clusters\")\n",
    "    ax1.set_xlabel(\"The silhouette coefficient values\")\n",
    "    ax1.set_ylabel(\"Cluster label\")\n",
    "\n",
    "    # The vertical line for average silhouette score of all the values\n",
    "    ax1.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n",
    "\n",
    "    ax1.set_yticks([])  # Clear the yaxis labels / ticks\n",
    "    ax1.set_xticks([-0.2, 0, 0.2, 0.4, 0.6, 0.8, 1])\n",
    "\n",
    "    plt.suptitle((\"For %d clusters, silhouette avg coeff = %f \" % (n_clusters,silhouette_avg)),\n",
    "                 fontsize=14, fontweight='bold')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aW3uJU4XLfh7"
   },
   "source": [
    "#### Note: Higher the silhouette score better the clustering.\n",
    "Hence best K value for this dataset is 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "XFtCkW4lLfh8"
   },
   "outputs": [],
   "source": [
    "best_kmeans = KMeans(n_clusters=8, random_state=1240)\n",
    "best_kmeans.fit(cereals_std)\n",
    "best_kmeans_labels = best_kmeans.predict(cereals_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "AGOXXzGSLfh8",
    "outputId": "c36cac79-467f-49b4-e22e-f83e84ef76fb"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>kmeans_cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100%_Bran (3 - 68.4)</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100%_Natural_Bran (3 - 33.98)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>All-Bran (3 - 59.43)</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>All-Bran_with_Extra_Fiber (3 - 93.7)</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Almond_Delight (3 - 34.38)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  label  kmeans_cluster\n",
       "0                  100%_Bran (3 - 68.4)               6\n",
       "1         100%_Natural_Bran (3 - 33.98)               0\n",
       "2                  All-Bran (3 - 59.43)               6\n",
       "3  All-Bran_with_Extra_Fiber (3 - 93.7)               6\n",
       "4            Almond_Delight (3 - 34.38)               2"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans_results = pd.DataFrame({\"label\":cereal_label,\"kmeans_cluster\":best_kmeans_labels})\n",
    "kmeans_results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rh2QgNrhLfh8"
   },
   "source": [
    "### Add Cluster Labels to Original Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "id": "vwNXi6ulLfh8",
    "outputId": "a2cbd84c-b853-4916-f052-a950d182ba4c"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calories</th>\n",
       "      <th>protein</th>\n",
       "      <th>fat</th>\n",
       "      <th>sodium</th>\n",
       "      <th>fiber</th>\n",
       "      <th>carbo</th>\n",
       "      <th>sugars</th>\n",
       "      <th>potass</th>\n",
       "      <th>vitamins</th>\n",
       "      <th>weight</th>\n",
       "      <th>cups</th>\n",
       "      <th>label</th>\n",
       "      <th>kmeans_cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>70</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.33</td>\n",
       "      <td>100%_Bran (3 - 68.4)</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>120</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>100%_Natural_Bran (3 - 33.98)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>260</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.33</td>\n",
       "      <td>All-Bran (3 - 59.43)</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>All-Bran_with_Extra_Fiber (3 - 93.7)</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>110</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>Almond_Delight (3 - 34.38)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>110</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>180</td>\n",
       "      <td>1.5</td>\n",
       "      <td>10.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>Apple_Cinnamon_Cheerios (1 - 29.51)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>110</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Apple_Jacks (2 - 33.17)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>130</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>210</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1.33</td>\n",
       "      <td>0.75</td>\n",
       "      <td>Basic_4 (3 - 37.04)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>90</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>200</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.67</td>\n",
       "      <td>Bran_Chex (1 - 49.12)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>90</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>210</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.67</td>\n",
       "      <td>Bran_Flakes (3 - 53.31)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   calories  protein  fat  sodium  fiber  carbo  sugars  potass  vitamins  \\\n",
       "0        70        4    1     130   10.0    5.0     6.0   280.0        25   \n",
       "1       120        3    5      15    2.0    8.0     8.0   135.0         0   \n",
       "2        70        4    1     260    9.0    7.0     5.0   320.0        25   \n",
       "3        50        4    0     140   14.0    8.0     0.0   330.0        25   \n",
       "4       110        2    2     200    1.0   14.0     8.0     NaN        25   \n",
       "5       110        2    2     180    1.5   10.5    10.0    70.0        25   \n",
       "6       110        2    0     125    1.0   11.0    14.0    30.0        25   \n",
       "7       130        3    2     210    2.0   18.0     8.0   100.0        25   \n",
       "8        90        2    1     200    4.0   15.0     6.0   125.0        25   \n",
       "9        90        3    0     210    5.0   13.0     5.0   190.0        25   \n",
       "\n",
       "   weight  cups                                 label  kmeans_cluster  \n",
       "0    1.00  0.33                  100%_Bran (3 - 68.4)               6  \n",
       "1    1.00  1.00         100%_Natural_Bran (3 - 33.98)               0  \n",
       "2    1.00  0.33                  All-Bran (3 - 59.43)               6  \n",
       "3    1.00  0.50  All-Bran_with_Extra_Fiber (3 - 93.7)               6  \n",
       "4    1.00  0.75            Almond_Delight (3 - 34.38)               2  \n",
       "5    1.00  0.75   Apple_Cinnamon_Cheerios (1 - 29.51)               2  \n",
       "6    1.00  1.00               Apple_Jacks (2 - 33.17)               2  \n",
       "7    1.33  0.75                   Basic_4 (3 - 37.04)               4  \n",
       "8    1.00  0.67                 Bran_Chex (1 - 49.12)               5  \n",
       "9    1.00  0.67               Bran_Flakes (3 - 53.31)               5  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cereals = pd.read_csv(\"Cereals.csv\")\n",
    "cereals['label'] = cereals['name']+ ' (' + cereals['shelf'].astype(str) + \" - \" + round(cereals['rating'],2).astype(str) + ')'\n",
    "cereals.drop(['name','shelf','rating'], axis=1, inplace=True)\n",
    "\n",
    "final_cluster_data = pd.merge(cereals, kmeans_results, on='label')\n",
    "final_cluster_data.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "usRJpugoLfh9",
    "outputId": "667492f2-fc43-4c75-d5b4-fb3dc0cfbd94"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(77, 13)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_cluster_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 366
    },
    "id": "x-opXF3_Lfh9",
    "outputId": "bf782e90-7d53-43cd-e6bc-ee173287ac30"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "kmeans_cluster\n",
       "2    23\n",
       "5    18\n",
       "4    11\n",
       "0     8\n",
       "1     7\n",
       "7     5\n",
       "6     3\n",
       "3     2\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_cluster_data.kmeans_cluster.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "Omc57WZNFzwB"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calories</th>\n",
       "      <th>protein</th>\n",
       "      <th>fat</th>\n",
       "      <th>sodium</th>\n",
       "      <th>fiber</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>70</td>\n",
       "      <td>4</td>\n",
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       "      <td>130</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>100%_Bran (3 - 68.4)</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>120</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>100%_Natural_Bran (3 - 33.98)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>260</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.33</td>\n",
       "      <td>All-Bran (3 - 59.43)</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>All-Bran_with_Extra_Fiber (3 - 93.7)</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>110</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>Almond_Delight (3 - 34.38)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>110</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>Triples (3 - 39.11)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>110</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>140</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Trix (2 - 27.75)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>230</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.67</td>\n",
       "      <td>Wheat_Chex (1 - 49.79)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>200</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Wheaties (1 - 51.59)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>110</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>Wheaties_Honey_Gold (1 - 36.19)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    calories  protein  fat  sodium  fiber  carbo  sugars  potass  vitamins  \\\n",
       "0         70        4    1     130   10.0    5.0     6.0   280.0        25   \n",
       "1        120        3    5      15    2.0    8.0     8.0   135.0         0   \n",
       "2         70        4    1     260    9.0    7.0     5.0   320.0        25   \n",
       "3         50        4    0     140   14.0    8.0     0.0   330.0        25   \n",
       "4        110        2    2     200    1.0   14.0     8.0     NaN        25   \n",
       "..       ...      ...  ...     ...    ...    ...     ...     ...       ...   \n",
       "72       110        2    1     250    0.0   21.0     3.0    60.0        25   \n",
       "73       110        1    1     140    0.0   13.0    12.0    25.0        25   \n",
       "74       100        3    1     230    3.0   17.0     3.0   115.0        25   \n",
       "75       100        3    1     200    3.0   17.0     3.0   110.0        25   \n",
       "76       110        2    1     200    1.0   16.0     8.0    60.0        25   \n",
       "\n",
       "    weight  cups                                 label  kmeans_cluster  \n",
       "0      1.0  0.33                  100%_Bran (3 - 68.4)               6  \n",
       "1      1.0  1.00         100%_Natural_Bran (3 - 33.98)               0  \n",
       "2      1.0  0.33                  All-Bran (3 - 59.43)               6  \n",
       "3      1.0  0.50  All-Bran_with_Extra_Fiber (3 - 93.7)               6  \n",
       "4      1.0  0.75            Almond_Delight (3 - 34.38)               2  \n",
       "..     ...   ...                                   ...             ...  \n",
       "72     1.0  0.75                   Triples (3 - 39.11)               1  \n",
       "73     1.0  1.00                      Trix (2 - 27.75)               2  \n",
       "74     1.0  0.67                Wheat_Chex (1 - 49.79)               5  \n",
       "75     1.0  1.00                  Wheaties (1 - 51.59)               5  \n",
       "76     1.0  0.75       Wheaties_Honey_Gold (1 - 36.19)               2  \n",
       "\n",
       "[77 rows x 13 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_cluster_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xAt6Gh6vLfh9"
   },
   "source": [
    "### Analyzing clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "ywUt993bLfh-",
    "outputId": "676f92ca-c9d2-4706-a712-452f56e271b1"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>kmeans_cluster</th>\n",
       "      <th>calories</th>\n",
       "      <th>protein</th>\n",
       "      <th>fat</th>\n",
       "      <th>sodium</th>\n",
       "      <th>fiber</th>\n",
       "      <th>carbo</th>\n",
       "      <th>sugars</th>\n",
       "      <th>potass</th>\n",
       "      <th>vitamins</th>\n",
       "      <th>weight</th>\n",
       "      <th>cups</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>107.500000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>99.375000</td>\n",
       "      <td>2.525000</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>6.571429</td>\n",
       "      <td>119.375000</td>\n",
       "      <td>18.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.583750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>108.571429</td>\n",
       "      <td>1.857143</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>261.428571</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>21.571429</td>\n",
       "      <td>2.714286</td>\n",
       "      <td>36.428571</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.054286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>110.434783</td>\n",
       "      <td>1.565217</td>\n",
       "      <td>1.043478</td>\n",
       "      <td>172.173913</td>\n",
       "      <td>0.652174</td>\n",
       "      <td>12.608696</td>\n",
       "      <td>11.086957</td>\n",
       "      <td>49.318182</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.875217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>32.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>134.545455</td>\n",
       "      <td>3.181818</td>\n",
       "      <td>1.727273</td>\n",
       "      <td>181.363636</td>\n",
       "      <td>3.681818</td>\n",
       "      <td>15.227273</td>\n",
       "      <td>11.090909</td>\n",
       "      <td>179.090909</td>\n",
       "      <td>31.818182</td>\n",
       "      <td>1.286364</td>\n",
       "      <td>0.759091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>96.666667</td>\n",
       "      <td>3.111111</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>118.055556</td>\n",
       "      <td>2.611111</td>\n",
       "      <td>16.611111</td>\n",
       "      <td>3.166667</td>\n",
       "      <td>105.588235</td>\n",
       "      <td>19.444444</td>\n",
       "      <td>0.990556</td>\n",
       "      <td>0.821111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>63.333333</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>176.666667</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>310.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.386667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>18.800000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.060000</td>\n",
       "      <td>0.950000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   kmeans_cluster    calories   protein       fat      sodium      fiber  \\\n",
       "0               0  107.500000  3.500000  2.500000   99.375000   2.525000   \n",
       "1               1  108.571429  1.857143  0.285714  261.428571   0.285714   \n",
       "2               2  110.434783  1.565217  1.043478  172.173913   0.652174   \n",
       "3               3   50.000000  1.500000  0.000000    0.000000   0.500000   \n",
       "4               4  134.545455  3.181818  1.727273  181.363636   3.681818   \n",
       "5               5   96.666667  3.111111  0.388889  118.055556   2.611111   \n",
       "6               6   63.333333  4.000000  0.666667  176.666667  11.000000   \n",
       "7               7  112.000000  2.600000  0.800000  212.000000   1.400000   \n",
       "\n",
       "       carbo     sugars      potass    vitamins    weight      cups  \n",
       "0  11.500000   6.571429  119.375000   18.750000  1.000000  0.583750  \n",
       "1  21.571429   2.714286   36.428571   25.000000  1.000000  1.054286  \n",
       "2  12.608696  11.086957   49.318182   25.000000  1.000000  0.875217  \n",
       "3  11.500000   0.000000   32.500000    0.000000  0.500000  1.000000  \n",
       "4  15.227273  11.090909  179.090909   31.818182  1.286364  0.759091  \n",
       "5  16.611111   3.166667  105.588235   19.444444  0.990556  0.821111  \n",
       "6   6.666667   3.666667  310.000000   25.000000  1.000000  0.386667  \n",
       "7  18.800000   4.800000   69.000000  100.000000  1.060000  0.950000  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ClusterLevelMean = final_cluster_data.groupby(['kmeans_cluster']).mean(numeric_only=True).reset_index()\n",
    "ClusterLevelMean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iumCowCALfh-"
   },
   "source": [
    "#### Checking cluster stability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "dmZTfy8qLfh_"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import adjusted_rand_score\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7fzBtVGNLfh_",
    "outputId": "7ef23170-da79-4c9a-b4bc-c03c2a2a1d35"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([53, 64, 70,  4, 60, 23, 29, 61,  8, 74,  9, 75, 44, 24, 76, 63, 71, 43,\n",
       "       51,  1, 37, 45, 58, 31, 14, 72, 16, 26, 19, 69,  6, 62, 50, 67, 15, 18,\n",
       "       13, 22, 10, 59, 11,  0,  5, 42,  3, 40, 12, 54, 34, 27, 30, 21, 33, 38,\n",
       "       52, 28, 35, 41,  7, 48, 56, 73, 20, 65, 39,  2, 55, 49, 68],\n",
       "      dtype='int64')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indices=cereals_std.sample(frac=0.9,random_state=123).index\n",
    "indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "id": "ZRbfMpoQLfh_"
   },
   "outputs": [],
   "source": [
    "cereals_std_subset=cereals_std.iloc[indices,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "O-BX2S46Lfh_",
    "outputId": "3d52ee6c-78c5-4740-8d92-235f52dc4f1c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(69, 11)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cereals_std_subset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "id": "fYxMfSmeLfiA"
   },
   "outputs": [],
   "source": [
    "kmeans_object = KMeans(n_clusters=5,n_init=30,max_iter=300,random_state=1000)\n",
    "kmeans_object.fit(cereals_std)\n",
    "clus1= kmeans_object.predict(cereals_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "id": "Ve8aiDp8LfiA"
   },
   "outputs": [],
   "source": [
    "kmeans_object = KMeans(n_clusters=5,n_init=30,max_iter=300,random_state=1000)\n",
    "kmeans_object.fit(cereals_std_subset)\n",
    "clus2= kmeans_object.predict(cereals_std_subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "u0C1LtyaLfiA",
    "outputId": "a7a5535a-aad4-4cc6-aaff-e760fa47fcf1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "77\n",
      "69\n"
     ]
    }
   ],
   "source": [
    "print(len(clus1))\n",
    "print(len(clus2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oOKqYEW5LfiA",
    "outputId": "7c339eac-cc58-4b5f-ab76-b8559d475ea2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "69\n",
      "69\n"
     ]
    }
   ],
   "source": [
    "clus1=clus1[indices]\n",
    "print(len(clus1))\n",
    "print(len(clus2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qpWymvRSLfiB",
    "outputId": "e27c8b35-17aa-4dfa-b914-6d5fea1ffb4a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6530732963343134"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adjusted_rand_score(clus1,clus2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-5jnuK5lLfiB",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Categorize new samples into predefined clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "id": "r9f9olq5LfiB"
   },
   "outputs": [],
   "source": [
    "newdata=pd.read_csv(\"Cereals.csv\",nrows=5) ## Assume this part is a newly added samples for demonstration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "id": "c11os2zNLfiB"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>calories</th>\n",
       "      <th>protein</th>\n",
       "      <th>fat</th>\n",
       "      <th>sodium</th>\n",
       "      <th>fiber</th>\n",
       "      <th>carbo</th>\n",
       "      <th>sugars</th>\n",
       "      <th>potass</th>\n",
       "      <th>vitamins</th>\n",
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       "      <th>cups</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100%_Bran</td>\n",
       "      <td>70</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>280.0</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.33</td>\n",
       "      <td>68.402973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100%_Natural_Bran</td>\n",
       "      <td>120</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>135.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>33.983679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>All-Bran</td>\n",
       "      <td>70</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>260</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>320.0</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.33</td>\n",
       "      <td>59.425505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>All-Bran_with_Extra_Fiber</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.50</td>\n",
       "      <td>93.704912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Almond_Delight</td>\n",
       "      <td>110</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.75</td>\n",
       "      <td>34.384843</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        name  calories  protein  fat  sodium  fiber  carbo  \\\n",
       "0                  100%_Bran        70        4    1     130     10      5   \n",
       "1          100%_Natural_Bran       120        3    5      15      2      8   \n",
       "2                   All-Bran        70        4    1     260      9      7   \n",
       "3  All-Bran_with_Extra_Fiber        50        4    0     140     14      8   \n",
       "4             Almond_Delight       110        2    2     200      1     14   \n",
       "\n",
       "   sugars  potass  vitamins  shelf  weight  cups     rating  \n",
       "0       6   280.0        25      3       1  0.33  68.402973  \n",
       "1       8   135.0         0      3       1  1.00  33.983679  \n",
       "2       5   320.0        25      3       1  0.33  59.425505  \n",
       "3       0   330.0        25      3       1  0.50  93.704912  \n",
       "4       8     NaN        25      3       1  0.75  34.384843  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "piAzWvS2LfiC",
    "outputId": "2a1210c0-acba-4cfe-ef6f-000180ed5b2b"
   },
   "outputs": [
    {
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       "      <th>calories</th>\n",
       "      <th>protein</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50</td>\n",
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       "      <td>140</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>0.50</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>200</td>\n",
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       "      <td>NaN</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   calories  protein  fat  sodium  fiber  carbo  sugars  potass  vitamins  \\\n",
       "0        70        4    1     130     10      5       6   280.0        25   \n",
       "1       120        3    5      15      2      8       8   135.0         0   \n",
       "2        70        4    1     260      9      7       5   320.0        25   \n",
       "3        50        4    0     140     14      8       0   330.0        25   \n",
       "4       110        2    2     200      1     14       8     NaN        25   \n",
       "\n",
       "   weight  cups  \n",
       "0       1  0.33  \n",
       "1       1  1.00  \n",
       "2       1  0.33  \n",
       "3       1  0.50  \n",
       "4       1  0.75  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdata['label'] = newdata['name']+ ' (' + newdata['shelf'].astype(str) + \" - \" + \\\n",
    "                    round(newdata['rating'],2).astype(str) + ')'\n",
    "\n",
    "newdata.drop(['name','shelf','rating'], axis=1, inplace=True)\n",
    "\n",
    "\n",
    "newdata_label = newdata['label']\n",
    "## Select all columns except \"label\"\n",
    "newdata.drop('label', axis=1, inplace=True)\n",
    "newdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "ysK4l2SfLfiC",
    "outputId": "9639b29c-4b2d-4cbc-91c7-5d48c459c6d2"
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   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>70.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>120.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>135.000000</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>70.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>110.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>98.666667</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   calories  protein  fat  sodium  fiber  carbo  sugars      potass  vitamins  \\\n",
       "0      70.0      4.0  1.0   130.0   10.0    5.0     6.0  280.000000      25.0   \n",
       "1     120.0      3.0  5.0    15.0    2.0    8.0     8.0  135.000000       0.0   \n",
       "2      70.0      4.0  1.0   260.0    9.0    7.0     5.0  320.000000      25.0   \n",
       "3      50.0      4.0  0.0   140.0   14.0    8.0     0.0  330.000000      25.0   \n",
       "4     110.0      2.0  2.0   200.0    1.0   14.0     8.0   98.666667      25.0   \n",
       "\n",
       "   weight  cups  \n",
       "0     1.0  0.33  \n",
       "1     1.0  1.00  \n",
       "2     1.0  0.33  \n",
       "3     1.0  0.50  \n",
       "4     1.0  0.75  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdata = pd.DataFrame(mean_imputer.transform(newdata),columns=newdata.columns)\n",
    "newdata\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "id": "HgkBrtkMLfiC",
    "outputId": "9d47a761-c346-4b3b-ae04-9b44ed17319d"
   },
   "outputs": [
    {
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       "      <td>1.337319</td>\n",
       "      <td>-0.012988</td>\n",
       "      <td>-0.356306</td>\n",
       "      <td>3.314439</td>\n",
       "      <td>-2.542013</td>\n",
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       "      <td>3.987349</td>\n",
       "      <td>-1.737087</td>\n",
       "      <td>-0.064172</td>\n",
       "      <td>-1.764055</td>\n",
       "      <td>0.225316</td>\n",
       "      <td>0.526376</td>\n",
       "      <td>-1.27255</td>\n",
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       "      <td>1.204578</td>\n",
       "      <td>2.892113</td>\n",
       "      <td>-2.023374</td>\n",
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       "      <td>-0.198067</td>\n",
       "      <td>-2.123870</td>\n",
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       "      <th>3</th>\n",
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       "      <td>-0.14627</td>\n",
       "      <td>-0.198067</td>\n",
       "      <td>-1.388576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.161019</td>\n",
       "      <td>-0.501495</td>\n",
       "      <td>0.987096</td>\n",
       "      <td>0.484170</td>\n",
       "      <td>-0.486498</td>\n",
       "      <td>-0.208138</td>\n",
       "      <td>0.225316</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.14627</td>\n",
       "      <td>-0.198067</td>\n",
       "      <td>-0.307262</td>\n",
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      ],
      "text/plain": [
       "         0         1         2         3         4         5         6   \\\n",
       "0 -1.905397  1.337319 -0.012988 -0.356306  3.314439 -2.542013 -0.237495   \n",
       "1  0.677623  0.417912  3.987349 -1.737087 -0.064172 -1.764055  0.225316   \n",
       "2 -1.905397  1.337319 -0.012988  1.204578  2.892113 -2.023374 -0.468901   \n",
       "3 -2.938605  1.337319 -1.013072 -0.236238  5.003745 -1.764055 -1.625929   \n",
       "4  0.161019 -0.501495  0.987096  0.484170 -0.486498 -0.208138  0.225316   \n",
       "\n",
       "         7        8         9         10  \n",
       "0  2.627053 -0.14627 -0.198067 -2.123870  \n",
       "1  0.526376 -1.27255 -0.198067  0.774053  \n",
       "2  3.206550 -0.14627 -0.198067 -2.123870  \n",
       "3  3.351425 -0.14627 -0.198067 -1.388576  \n",
       "4  0.000000 -0.14627 -0.198067 -0.307262  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdata_std = pd.DataFrame(standardizer.transform(newdata))\n",
    "newdata_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PcXqc-UgLfiD",
    "outputId": "d15fe64c-6130-4bad-da6a-e3de952d4bf7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniconda/base/envs/accelai/lib/python3.12/site-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but KMeans was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([6, 0, 6, 6, 2], dtype=int32)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_kmeans_labels = best_kmeans.predict(newdata_std)\n",
    "best_kmeans_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "M68G6dxaLfiD",
    "outputId": "8031daaa-267e-4dfc-bf0d-6bd79b739cd1"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>kmeans_cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100%_Bran (3 - 68.4)</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100%_Natural_Bran (3 - 33.98)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>All-Bran (3 - 59.43)</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>All-Bran_with_Extra_Fiber (3 - 93.7)</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Almond_Delight (3 - 34.38)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  label  kmeans_cluster\n",
       "0                  100%_Bran (3 - 68.4)               6\n",
       "1         100%_Natural_Bran (3 - 33.98)               0\n",
       "2                  All-Bran (3 - 59.43)               6\n",
       "3  All-Bran_with_Extra_Fiber (3 - 93.7)               6\n",
       "4            Almond_Delight (3 - 34.38)               2"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans_results = pd.DataFrame({\"label\":newdata_label,\"kmeans_cluster\":best_kmeans_labels})\n",
    "kmeans_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "id": "PP0nsSLTQsaM"
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import DBSCAN\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "id": "fsU4A_DHQtjl"
   },
   "outputs": [],
   "source": [
    "# Apply DBSCAN\n",
    "dbscan = DBSCAN(eps=0.25, min_samples=3)\n",
    "clusters = dbscan.fit_predict(cereals_std)\n",
    "\n",
    "# Add the cluster labels to the original dataframe\n",
    "cereals_std['Cluster'] = clusters\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iklMyCslS_fZ",
    "outputId": "fbb07207-7e60-402b-ea54-2d69b289969b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1]\n"
     ]
    }
   ],
   "source": [
    "# Check the cluster labels\n",
    "print(np.unique(clusters))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "id": "L8ymdODMRQEd"
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calories</th>\n",
       "      <th>protein</th>\n",
       "      <th>fat</th>\n",
       "      <th>sodium</th>\n",
       "      <th>fiber</th>\n",
       "      <th>carbo</th>\n",
       "      <th>sugars</th>\n",
       "      <th>potass</th>\n",
       "      <th>vitamins</th>\n",
       "      <th>weight</th>\n",
       "      <th>cups</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.905397</td>\n",
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       "      <td>0.677623</td>\n",
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       "      <td>-1.27255</td>\n",
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       "      <td>0.774053</td>\n",
       "      <td>-1</td>\n",
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       "      <th>2</th>\n",
       "      <td>-1.905397</td>\n",
       "      <td>1.337319</td>\n",
       "      <td>-0.012988</td>\n",
       "      <td>1.204578</td>\n",
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       "      <td>-2.123870</td>\n",
       "      <td>-1</td>\n",
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       "      <th>3</th>\n",
       "      <td>-2.938605</td>\n",
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       "      <td>-1.388576</td>\n",
       "      <td>-1</td>\n",
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       "      <th>4</th>\n",
       "      <td>0.161019</td>\n",
       "      <td>-0.501495</td>\n",
       "      <td>0.987096</td>\n",
       "      <td>0.484170</td>\n",
       "      <td>-0.486498</td>\n",
       "      <td>-0.208138</td>\n",
       "      <td>0.225316</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.14627</td>\n",
       "      <td>-0.198067</td>\n",
       "      <td>-0.307262</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>72</th>\n",
       "      <td>0.161019</td>\n",
       "      <td>-0.501495</td>\n",
       "      <td>-0.012988</td>\n",
       "      <td>1.084510</td>\n",
       "      <td>-0.908824</td>\n",
       "      <td>1.607098</td>\n",
       "      <td>-0.931712</td>\n",
       "      <td>-0.560180</td>\n",
       "      <td>-0.14627</td>\n",
       "      <td>-0.198067</td>\n",
       "      <td>-0.307262</td>\n",
       "      <td>-1</td>\n",
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       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>0.161019</td>\n",
       "      <td>-1.420902</td>\n",
       "      <td>-0.012988</td>\n",
       "      <td>-0.236238</td>\n",
       "      <td>-0.908824</td>\n",
       "      <td>-0.467457</td>\n",
       "      <td>1.150938</td>\n",
       "      <td>-1.067240</td>\n",
       "      <td>-0.14627</td>\n",
       "      <td>-0.198067</td>\n",
       "      <td>0.774053</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>-0.355585</td>\n",
       "      <td>0.417912</td>\n",
       "      <td>-0.012988</td>\n",
       "      <td>0.844374</td>\n",
       "      <td>0.358155</td>\n",
       "      <td>0.569820</td>\n",
       "      <td>-0.931712</td>\n",
       "      <td>0.236628</td>\n",
       "      <td>-0.14627</td>\n",
       "      <td>-0.198067</td>\n",
       "      <td>-0.653283</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>-0.355585</td>\n",
       "      <td>0.417912</td>\n",
       "      <td>-0.012988</td>\n",
       "      <td>0.484170</td>\n",
       "      <td>0.358155</td>\n",
       "      <td>0.569820</td>\n",
       "      <td>-0.931712</td>\n",
       "      <td>0.164191</td>\n",
       "      <td>-0.14627</td>\n",
       "      <td>-0.198067</td>\n",
       "      <td>0.774053</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>0.161019</td>\n",
       "      <td>-0.501495</td>\n",
       "      <td>-0.012988</td>\n",
       "      <td>0.484170</td>\n",
       "      <td>-0.486498</td>\n",
       "      <td>0.310501</td>\n",
       "      <td>0.225316</td>\n",
       "      <td>-0.560180</td>\n",
       "      <td>-0.14627</td>\n",
       "      <td>-0.198067</td>\n",
       "      <td>-0.307262</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    calories   protein       fat    sodium     fiber     carbo    sugars  \\\n",
       "0  -1.905397  1.337319 -0.012988 -0.356306  3.314439 -2.542013 -0.237495   \n",
       "1   0.677623  0.417912  3.987349 -1.737087 -0.064172 -1.764055  0.225316   \n",
       "2  -1.905397  1.337319 -0.012988  1.204578  2.892113 -2.023374 -0.468901   \n",
       "3  -2.938605  1.337319 -1.013072 -0.236238  5.003745 -1.764055 -1.625929   \n",
       "4   0.161019 -0.501495  0.987096  0.484170 -0.486498 -0.208138  0.225316   \n",
       "..       ...       ...       ...       ...       ...       ...       ...   \n",
       "72  0.161019 -0.501495 -0.012988  1.084510 -0.908824  1.607098 -0.931712   \n",
       "73  0.161019 -1.420902 -0.012988 -0.236238 -0.908824 -0.467457  1.150938   \n",
       "74 -0.355585  0.417912 -0.012988  0.844374  0.358155  0.569820 -0.931712   \n",
       "75 -0.355585  0.417912 -0.012988  0.484170  0.358155  0.569820 -0.931712   \n",
       "76  0.161019 -0.501495 -0.012988  0.484170 -0.486498  0.310501  0.225316   \n",
       "\n",
       "      potass  vitamins    weight      cups  Cluster  \n",
       "0   2.627053  -0.14627 -0.198067 -2.123870       -1  \n",
       "1   0.526376  -1.27255 -0.198067  0.774053       -1  \n",
       "2   3.206550  -0.14627 -0.198067 -2.123870       -1  \n",
       "3   3.351425  -0.14627 -0.198067 -1.388576       -1  \n",
       "4   0.000000  -0.14627 -0.198067 -0.307262       -1  \n",
       "..       ...       ...       ...       ...      ...  \n",
       "72 -0.560180  -0.14627 -0.198067 -0.307262       -1  \n",
       "73 -1.067240  -0.14627 -0.198067  0.774053       -1  \n",
       "74  0.236628  -0.14627 -0.198067 -0.653283       -1  \n",
       "75  0.164191  -0.14627 -0.198067  0.774053       -1  \n",
       "76 -0.560180  -0.14627 -0.198067 -0.307262       -1  \n",
       "\n",
       "[77 rows x 12 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cereals_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 565
    },
    "id": "f0hMa3zLRNgm",
    "outputId": "2d7faf4e-23d1-4610-94e0-b06e7fa83142"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the clusters\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x='calories', y='sugars', hue='Cluster', data=cereals_std, palette='Set1', s=100, edgecolor='k')\n",
    "\n",
    "# Set plot titles and labels\n",
    "plt.title('DBSCAN Clusters on Cereal Dataset', fontsize=14)\n",
    "plt.xlabel('Calories')\n",
    "plt.ylabel('Sugar')\n",
    "plt.legend(title='Cluster')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 536
    },
    "id": "46hDBnhLTZs2",
    "outputId": "343bfb7d-78d5-4836-c040-5cb23a8dfcce"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Silhouette Score: 0.13959959858399767\n",
      "Best Parameters: eps=1.9000000000000001, min_samples=5\n"
     ]
    }
   ],
   "source": [
    "import itertools\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "# Step 2: Define a range of values for eps and min_samples\n",
    "eps_values = np.arange(0.1, 2.0, 0.1)  # Change these values as per your dataset\n",
    "min_samples_values = range(2, 10)  # Minimum number of points required to form a cluster\n",
    "\n",
    "# Step 3: Iterate over the grid of eps and min_samples values\n",
    "best_score = -1\n",
    "best_params = {'eps': None, 'min_samples': None}\n",
    "best_labels = None\n",
    "\n",
    "for eps, min_samples in itertools.product(eps_values, min_samples_values):\n",
    "    dbscan = DBSCAN(eps=eps, min_samples=min_samples)\n",
    "    labels = dbscan.fit_predict(cereals_std)\n",
    "\n",
    "    # Check if there are at least two clusters formed\n",
    "    if len(set(labels)) > 1:\n",
    "        # Step 4: Calculate silhouette score (higher is better)\n",
    "        score = silhouette_score(cereals_std, labels)\n",
    "\n",
    "        # Step 5: Store the best parameters based on silhouette score\n",
    "        if score > best_score:\n",
    "            best_score = score\n",
    "            best_params['eps'] = eps\n",
    "            best_params['min_samples'] = min_samples\n",
    "            best_labels = labels\n",
    "\n",
    "# Step 6: Output the best parameters and silhouette score\n",
    "print(f\"Best Silhouette Score: {best_score}\")\n",
    "print(f\"Best Parameters: eps={best_params['eps']}, min_samples={best_params['min_samples']}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "ename": "InvalidIndexError",
     "evalue": "(slice(None, None, None), 0)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/accelai/lib/python3.12/site-packages/pandas/core/indexes/base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3805\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m   3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[0;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: (slice(None, None, None), 0)",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mInvalidIndexError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[93], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Step 7: If needed, visualize the best clustering result\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m cereals_std[:, \u001b[38;5;241m0\u001b[39m]\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/accelai/lib/python3.12/site-packages/pandas/core/frame.py:4102\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   4101\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 4102\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mget_loc(key)\n\u001b[1;32m   4103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   4104\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
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      "\u001b[0;31mInvalidIndexError\u001b[0m: (slice(None, None, None), 0)"
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    }
   ],
   "source": [
    "# Step 7: If needed, visualize the best clustering result\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "cereals_std[:, 0]\n",
    "\n",
    "#plt.scatter(cereals_std[:, 0], cereals_std[:, 1], c=best_labels, cmap='viridis')\n",
    "#plt.title(f\"DBSCAN with eps={best_params['eps']}, min_samples={best_params['min_samples']}\")\n",
    "#plt.show()"
   ]
  },
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   "source": [
    "### Learning Outcomes"
   ]
  },
  {
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    "id": "Yx-eFgQALfiD"
   },
   "source": [
    "* Be able implement cluster analysis in Python\n",
    "    * Be able to pre-process data\n",
    "    * Be able to implement hierarchical clustering\n",
    "    * Be able to visualise dendrogram and variants of linkage methods\n",
    "    * Be able to implement the cut-off value to obtain clusters\n",
    "    * Be able to implement k-means clustering algorithm\n",
    "    * Be able to experiment with different values of ‘k’ and display the elbow plot of errors\n",
    "    * Be able to compute Silhouette value of clusters\n",
    "* Be able to perform cluster stability check\n",
    "    * Be able to implement the stability check of clusters in multiple ways such as: random sampling of data, changing the value of ‘k’, etc.\n",
    "* Be able to extract cluster results to interpret cluster profiles\n",
    "    * Be able to extract the data with annotated cluster id\n",
    "    * Be able to derive cluster properties by analysing data with cluster statistics\n",
    "    * Be able to predict the clusters for new samples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J_sUEDErLfiE"
   },
   "source": [
    "#### References:\n",
    "\n",
    "* https://kapilddatascience.wordpress.com/2015/11/10/using-silhouette-analysis-for-selecting-the-number-of-cluster-for-k-means-clustering/\n",
    "* https://scikit-learn.org/stable/modules/clustering.html#hierarchical-clustering\n",
    "* https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html"
   ]
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         "text/plain": "                        name  calories  protein  fat  sodium  fiber  carbo  \\\n0                  100%_Bran        70        4    1     130   10.0    5.0   \n1          100%_Natural_Bran       120        3    5      15    2.0    8.0   \n2                   All-Bran        70        4    1     260    9.0    7.0   \n3  All-Bran_with_Extra_Fiber        50        4    0     140   14.0    8.0   \n4             Almond_Delight       110        2    2     200    1.0   14.0   \n\n   sugars  potass  vitamins  shelf  weight  cups     rating  \n0     6.0   280.0        25      3     1.0  0.33  68.402973  \n1     8.0   135.0         0      3     1.0  1.00  33.983679  \n2     5.0   320.0        25      3     1.0  0.33  59.425505  \n3     0.0   330.0        25      3     1.0  0.50  93.704912  \n4     8.0     NaN        25      3     1.0  0.75  34.384843  "
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         "text/plain": "             name\ncount          77\nunique         77\ntop     100%_Bran\nfreq            1"
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